Multiparty computation (MPC) is a cryptographic method that enables a set of parties to compute an arbitrary joint function of the private inputs of all parties and does not reveal any information other than the output. MPC based on a secret sharing scheme (SS-MPC) and garbled circuit (GC) is known as the most common MPC schemes. Another cryptographic method, homomorphic encryption (HE), computes an arbitrary function represented as a circuit by using ciphertexts without decrypting them. These technologies are in a trade-off relationship for the communication/round complexities, and the computation cost. The private decision tree evaluation (PDTE) is one of the key applications of these technologies. There exist several constant-round PDTE protocols based on GC, HE, or the hybrid schemes that are secure even if a malicious adversary who can deviate from protocol specifications corrupts some parties. There also exist other protocols based only on SS-MPC that are secure only if a semi-honest adversary who follows the protocol specification corrupts some parties. However, to the best of our knowledge, there are currently no constant-round PDTE protocols based only on SS-MPC that are secure against a malicious adversary. In this work, we propose a constant-round four-party PDTE protocol that achieves malicious security. Our protocol provides the PDTE securely and efficiently even when the communication environment has a large latency.

Real-time applications are becoming more and more popular, and due to the demand for more compact and portable user devices, offloading terminal processes to edge servers is being considered. Moreover, it is necessary to process packets with low latency on edge servers, which are often virtualized for operability. When trying to achieve low-latency networking, the increase in server power consumption due to performance tuning and busy polling for fast packet receiving becomes a problem. Thus, we design and implement a low-latency and energy-efficient networking system, energy-efficient kernel busy poll (EE-KBP), which meets four requirements: (A) low latency in the order of microseconds for packet forwarding in a virtual server, (B) lower power consumption than existing solutions, (C) no need for application modification, and (D) no need for software redevelopment with each kernel security update. EE-KBP sets a polling thread in a Linux kernel that receives packets with low latency in polling mode while packets are arriving, and when no packets are arriving, it sleeps and lowers the CPU operating frequency. Evaluations indicate that EE-KBP achieves microsecond-order low-latency networking under most traffic conditions, and 1.4× to 3.1× higher throughput with lower power consumption than NAPI used in a Linux kernel.

We consider network security exercises where students construct virtual networks with User-mode Linux (UML) virtual machines and then execute attack and defense activities on these networks. In an older version of the exercise system, the students accessed the desktop screens of the remote servers running UMLs with Windows applications and then built networks by executing UML commands. However, performing the exercises remotely (e.g., due to the COVID-19 pandemic) resulted in difficulties due to factors such as the dependency of the work environment on specific operating systems, narrow-band networks, as well as issues in providing support for configuring UMLs. In this paper, a novel web-based hands-on system with intuitive and seamless operability and lightweight responsiveness is proposed in order to allow performing the considered exercises while avoiding the mentioned shortcomings. The system provides web pages for editing device layouts and cable connections by mouse operations intuitively, web pages connecting to UML terminals, and web pages for operating X clients running on UMLs. We carried out experiments for evaluating the proposed system on the usability, system performance, and quality of experience. The subjects offered positive assessments on the operability and no negative assessments on the responsiveness. As for command inputs in terminals, the response time was shorter and the traffic was much smaller in comparison with the older system. Furthermore, the exercises using nano required at least 16 kbps bandwidth and ones using wireshark required at least 2048 kbps bandwidth.

Predicting the grasping point accurately and quickly is crucial for successful robotic manipulation. However, to commercially deploy a robot, such as a dishwasher robot in a commercial kitchen, we also need to consider the constraints of limited usable resources. We present a deep learning method to predict the grasp position when using a single suction gripper for picking up objects. The proposed method is based on a shallow network to enable lower training costs and efficient inference on limited resources. Costs are further reduced by collecting data in a custom-built synthetic environment. For evaluating the proposed method, we developed a system that models a commercial kitchen for a dishwasher robot to manipulate symmetric objects. We tested our method against a model-fitting method and an algorithm-based method in our developed commercial kitchen environment and found that a shallow network trained with only the synthetic data achieves high accuracy. We also demonstrate the practicality of using a shallow network in sequence with an object detector for ease of training, prediction speed, low computation cost, and easier debugging.

Deployment of machine-type communications (MTCs) over the current cellular network could lead to severe overloading of the radio access network of Long Term Evolution (LTE)-based systems. This paper proposes a slotted access-based solution, called the Slotted Access For Group Paging (SAFGP), to cope with the paging-induced MTC traffic. The proposed SAFGP splits paged devices into multiple access groups, and each group is then allocated separate radio resources on the LTE's Physical Random Access Channel (PRACH) in a periodic manner during the paging interval. To support the proposed scheme, a new adaptive barring algorithm is proposed to stabilize the number of successful devices in each dedicated access slot. The objective is to let as few devices transmitting preambles in an access slot as possible while ensuring that the number of preambles selected by exactly one device approximates the maximum number of uplink grants that can be allocated by the eNB for an access slot. Analysis and simulation results demonstrate that, given the same amount of time-frequency resources, the proposed method significantly improves the access success and resource utilization rates at the cost of slightly increasing the access delay compared to state-of-the-art methods.

In recent years, there has been an increasing trend of applying artificial intelligence in many different fields, which has a profound and direct impact on human life. Consequently, this raises the need to understand the principles of model making predictions. Since most current high-precision models are black boxes, neither the AI scientist nor the end-user profoundly understands what is happening inside these models. Therefore, many algorithms are studied to explain AI models, especially those in the image classification problem in computer vision such as LIME, CAM, GradCAM. However, these algorithms still have limitations, such as LIME's long execution time and CAM's confusing interpretation of concreteness and clarity. Therefore, in this paper, we will propose a new method called Segmentation - Class Activation Mapping (SeCAM)/ This method combines the advantages of these algorithms above while at simultaneously overcoming their disadvantages. We tested this algorithm with various models, including ResNet50, InceptionV3, and VGG16 from ImageNet Large Scale Visual Recognition Challenge (ILSVRC) data set. Outstanding results were achieved when the algorithm has met all the requirements for a specific explanation in a remarkably short space of time.

This paper presents robust optimization models for minimizing the required backup capacity while providing probabilistic protection against multiple simultaneous failures of physical machines under uncertain virtual machine capacities in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the dedicated backup physical machines, which are determined in advance. We consider two uncertainties: failure event and virtual machine capacity. By adopting a robust optimization technique, we formulate six mixed integer linear programming problems. Numerical results show that for a small size problem, our presented models are applicable to the case that virtual machine capacities are uncertain, and by using these models, we can obtain the optimal solution of the allocation of virtual machines under the uncertainty. A simulated annealing heuristic is presented to solve large size problems. By using this heuristic, an approximate solution is obtained for a large size problem.

Recently, with the spread of Internet of Things (IoT) devices, embedded hardware devices have been used in a variety of everyday electrical items. Due to the increased demand for embedded hardware devices, some of the IC design and manufacturing steps have been outsourced to third-party vendors. Since malicious third-party vendors may insert malicious circuits, called hardware Trojans, into their products, developing an effective hardware-Trojan detection method is strongly required. In this paper, we propose 25 hardware-Trojan features focusing on the structure of trigger circuits for machine-learning-based hardware-Trojan detection. Combining the proposed features into 11 existing hardware-Trojan features, we totally utilize 36 hardware-Trojan features for classification. Then we classify the nets in an unknown netlist into a set of normal nets and Trojan nets based on a random-forest classifier. The experimental results demonstrate that the average true positive rate (TPR) becomes 64.2% and the average true negative rate (TNR) becomes 100.0%. They improve the average TPR by 14.8 points while keeping the average TNR compared to existing state-of-the-art methods. In particular, the proposed method successfully finds out Trojan nets in several benchmark circuits, which are not found by the existing method.

Incremental Learning, a machine learning methodology, trains the continuously arriving input data and extends the model's knowledge. When it comes to unlabeled data streams, incremental learning task becomes more challenging. Our newly proposed incremental learning methodology, Data Augmented Incremental Learning (DAIL), learns the ever-increasing real-time streams with reduced memory resources and time. Initially, the unlabeled batches of data streams are clustered using the proposed clustering algorithm, Clustering based on Autoencoder and Gaussian Model (CLAG). Later, DAIL creates an updated incremental model for the labelled clusters using data augmentation. DAIL avoids the retraining of old samples and retains only the most recently updated incremental model holding all old class information. The use of data augmentation in DAIL combines the similar clusters generated with different data batches. A series of experiments verified the significant performance of CLAG and DAIL, producing scalable and efficient incremental model.

Basic characteristics for relating design and base station layout design in land mobile communications are provided through a propagation model for path loss prediction. Owing to the rapid annual increase in traffic data, the number of base stations has increased accordingly. Therefore, propagation models for various scenarios and frequency bands are necessitated. To solve problems optimization and creation methods using the propagation model, a path loss prediction method that merges multiple models in machine learning is proposed herein. The method is discussed based on measurement values from Kitakyushu-shi. In machine learning, the selection of input parameters and suppression of overlearning are important for achieving highly accurate predictions. Therefore, the acquisition of conventional models based on the propagation environment and the use of input parameters of high importance are proposed. The prediction accuracy for Kitakyushu-shi using the proposed method indicates a root mean square error (RMSE) of 3.68dB. In addition, predictions are performed in Narashino-shi to confirm the effectiveness of the method in other urban scenarios. Results confirm the effectiveness of the proposed method for the urban scenario in Narashino-shi, and an RMSE of 4.39dB is obtained for the accuracy.

Magnetic fields are often utilized for position sensing of mobile devices. In typical sensing systems, multiple sensors are used to detect magnetic fields generated by target devices. To determine the positions of the devices, magnetic-field data detected by the sensors must be converted to device-position data. The data conversion is not trivial because it is a nonlinear inverse problem. In this study, we propose a machine-learning approach suitable for data conversion required in the magnetic-field-based position sensing of target devices. In our approach, two different sets of training data are used. One of the training datasets is composed of raw data of magnetic fields to be detected by sensors. The other set is composed of logarithmically represented data of the fields. We can obtain two different predictor functions by learning with these training datasets. Results show that the prediction accuracy of the target position improves when the two different predictor functions are used. Based on our simulation, the error of the target position estimated with the predictor functions is within 10cm in a 2m × 2m × 2m cubic space for 87% of all the cases of the target device states. The computational time required for predicting the positions of the target device is 4ms. As the prediction method is accurate and rapid, it can be utilized for the real-time tracking of moving objects and people.

Packet processing on commodity hardware is a cost-efficient and flexible alternative to specialized networking hardware. However, virtualizing dedicated networking hardware as a virtual machine (VM) or a container on a commodity server results in performance problems, such as longer latency and lower throughput. This paper focuses on obtaining a low-latency networking system in a VM and a container. We reveal mechanisms that cause millisecond-scale networking delays in a VM through a series of experiments. To eliminate such delays, we design and implement a low-latency networking system, kernel busy poll (KBP), which achieves three goals: (1) microsecond-scale tail delays and higher throughput than conventional solutions are achieved in a VM and a container; (2) application customization is not required, so applications can use the POSIX sockets application program interface; and (3) KBP software does not need to be developed for every Linux kernel security update. KBP can be applied to both a VM configuration and a container configuration. Evaluation results indicate that KBP achieves microsecond-scale tail delays in both a VM and a container. In the VM configuration, KBP reduces maximum round-trip latency by more than 98% and increases the throughput by up to three times compared with existing NAPI and Open vSwitch with the Data Plane Development Kit (OvS-DPDK). In the container configuration, KBP reduces maximum round-trip latency by 21% to 96% and increases the throughput by up to 1.28 times compared with NAPI.

Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.

This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.

As the mainstream approach in the field of machine translation, neural machine translation (NMT) has achieved great improvements on many rich-source languages, but performance of NMT for low-resource languages ae not very good yet. This paper uses data enhancement technology to construct Mongolian-Chinese pseudo parallel corpus, so as to improve the translation ability of Mongolian-Chinese translation model. Experiments show that the above methods can improve the translation ability of the translation model. Finally, a translation model trained with large-scale pseudo parallel corpus and integrated with soft context data enhancement technology is obtained, and its BLEU value is 39.3.

Direction of arrival (DOA) estimation of wireless signals is demanded in many applications. In addition to classical methods such as MUSIC and ESPRIT, non-linear algorithms such as compressed sensing have become common subjects of study recently. Deep learning or machine learning is also known as a non-linear algorithm and has been applied in various fields. Generally, DOA estimation using deep learning is classified as on-grid estimation. A major problem of on-grid estimation is that the accuracy may be degraded when the DOA is near the boundary. To reduce such estimation errors, we propose a method of combining two DNNs whose grids are offset by one half of the grid size. Simulation results show that our proposal outperforms MUSIC which is a typical off-grid estimation method. Furthermore, it is shown that the DNN specially trained for a close DOA case achieves very high accuracy for that case compared with MUSIC.

Translation of long and complex sentence has always been a challenge for machine translation. In recent years, neural machine translation (NMT) has achieved substantial progress in modeling the semantic connection between words in a sentence, but it is still insufficient in capturing discourse structure information between clauses within complex sentences, which often leads to poor discourse coherence when translating long and complex sentences. On the other hand, the hypotactic structure, a main component of the discourse structure, plays an important role in the coherence of discourse translation, but it is not specifically studied. To tackle this problem, we propose a novel Chinese-English NMT approach that incorporates the hypotactic structure knowledge of complex sentences. Specifically, we first annotate and build a hypotactic structure aligned parallel corpus to provide explicit hypotactic structure knowledge of complex sentences for NMT. Then we propose three hypotactic structure-aware NMT models with three different fusion strategies, including source-side fusion, target-side fusion, and both-side fusion, to integrate the annotated structure knowledge into NMT. Experimental results on WMT17, WMT18 and WMT19 Chinese-English translation tasks demonstrate that the proposed method can significantly improve the translation performance and enhance the discourse coherence of machine translation.

Binary code similarity comparison methods are mainly used to find bugs in software, to detect software plagiarism, and to reduce the workload during malware analysis. In this paper, we propose a method to compare the binary code similarity of each function by using a combination of Control Flow Graphs (CFGs) and disassembled instruction sequences contained in each function, and to detect a function with high similarity to a specified function. One of the challenges in performing similarity comparisons is that different compile-time optimizations and different architectures produce different binary code. The main units for comparing code are instructions, basic blocks and functions. The challenge of functions is that they have a graph structure in which basic blocks are combined, making it relatively difficult to derive similarity. However, analysis tools such as IDA, display the disassembled instruction sequence in function units. Detecting similarity on a function basis has the advantage of facilitating simplified understanding by analysts. To solve the aforementioned challenges, we use machine learning methods in the field of natural language processing. In this field, there is a Transformer model, as of 2017, that updates each record for various language processing tasks, and as of 2021, Transformer is the basis for BERT, which updates each record for language processing tasks. There is also a method called node2vec, which uses machine learning techniques to capture the features of each node from the graph structure. In this paper, we propose SIBYL, a combination of Transformer and node2vec. In SIBYL, a method called Triplet-Loss is used during learning so that similar items are brought closer and dissimilar items are moved away. To evaluate SIBYL, we created a new dataset using open-source software widely used in the real world, and conducted training and evaluation experiments using the dataset. In the evaluation experiments, we evaluated the similarity of binary codes across different architectures using evaluation indices such as Rank1 and MRR. The experimental results showed that SIBYL outperforms existing research. We believe that this is due to the fact that machine learning has been able to capture the features of the graph structure and the order of instructions on a function-by-function basis. The results of these experiments are presented in detail, followed by a discussion and conclusion.

In a wireless ad hoc network (MANET), nodes can form a centerless, self-organizing, multi-hop dynamic network without any centralized control function, while hidden and exposed terminals seriously affect the network performance. Meanwhile, the wireless network node is evolving from single communication function to jointly providing a self precise positioning function, especially in indoor environments where GPS cannot work well. However, the existing medium access control (MAC) protocols only deal with collision control for data transmission without positioning function. In this paper, we propose a MAC protocol based on 802.11 standard to enable a node with self-positioning function, which is further used to solve hidden and exposed terminal problems. The location of a network node is obtained through exchanging of MAC frames jointly using a time of arrival (TOA) algorithm. Then, nodes use the location information to calculate the interference range, and judge whether they can transmit concurrently. Simulation shows that the positioning function of the proposed MAC protocol works well, and the corresponding MAC protocol can achieve higher throughput, lower average end-to-end delay and lower packet loss rate than that without self-localization function.

Machine reading comprehension with multi-hop reasoning always suffers from reasoning path breaking due to the lack of world knowledge, which always results in wrong answer detection. In this paper, we analyze what knowledge the previous work lacks, e.g., dependency relations and commonsense. Based on our analysis, we propose a Multi-dimensional Knowledge enhanced Graph Network, named MKGN, which exploits specific knowledge to repair the knowledge gap in reasoning process. Specifically, our approach incorporates not only entities and dependency relations through various graph neural networks, but also commonsense knowledge by a bidirectional attention mechanism, which aims to enhance representations of both question and contexts. Besides, to make the most of multi-dimensional knowledge, we investigate two kinds of fusion architectures, i.e., in the sequential and parallel manner. Experimental results on HotpotQA dataset demonstrate the effectiveness of our approach and verify that using multi-dimensional knowledge, especially dependency relations and commonsense, can indeed improve the reasoning process and contribute to correct answer detection.