While the introduction of softwarelization technologies such as software-defined networking and network function virtualization transfers the main focus of network management from hardware to software, network operators still have to deal with various and numerous network and computing equipment located in network centers. Toward fully automated network management, we believe that a robotic approach will be essential, meaning that physical robots will handle network-facility management works on behalf of humans. This paper focuses on robotic assistance for on-site network maintenance works. Currently, for many network operators, some network maintenance works (e.g., hardware check, hardware installation/replacement, high-impact update of software, etc.) are outsourced to computing and network vendors. Attendance (witness work) at the on-site vendor's works is one of the major tasks of network operators. Network operators confirm the work progress for human error prevention and safety improvement. In order to reduce the burden of this, we propose three essential works of robots, namely delegated attendance at on-site meetings, progress check by periodical patrol, and remote monitoring, which support the various forms of attendance. The paper presents our implementation of enabling these forms of support, and reports the results of experiments conducted in a commercial network center.

In the Artificial Intelligence for IT Operations scenarios, KPI (Key Performance Indicator) is a very important operation and maintenance monitoring indicator, and research on KPI anomaly detection has also become a hot spot in recent years. Aiming at the problems of low detection efficiency and insufficient representation learning of existing methods, this paper proposes a fast clustering-based KPI anomaly detection method HCE-DWL. This paper firstly adopts the combination of hierarchical agglomerative clustering (HAC) and deep assignment based on CNN-Embedding (CE) to perform cluster analysis (that is HCE) on KPI data, so as to improve the clustering efficiency of KPI data, and then separately the centroid of each KPI cluster and its Transformed Outlier Scores (TOS) are given weights, and finally they are put into the LightGBM model for detection (the Double Weight LightGBM model, referred to as DWL). Through comparative experimental analysis, it is proved that the algorithm can effectively improve the efficiency and accuracy of KPI anomaly detection.

Service chaining is attracting attention as a promising technology for providing a variety of network services by applying virtual network functions (VNFs) that can be instantiated on commercial off-the-shelf servers. The data transmission for each service chain has to satisfy the quality of service (QoS) requirements in terms of the loss probability and transmission delay, and hence the amount of resources for each VNF is expected to be sufficient for satisfying the QoS. However, the increase in the amount of VNF resources results in a high cost for improving the QoS. To reduce the cost of utilizing a VNF, sharing VNF instances through multiple service chains is an effective approach. However, the number of packets arriving at the VNF instance is increased, resulting in a degradation of the QoS. It is therefore important to select VNF instances shared by multiple service chains and to determine the amount of resources for the selected VNFs. In this paper, we propose a cost-effective service chain construction with a VNF sharing model. In the proposed method, each VNF is modeled as an M/M/1/K queueing model to evaluate the relationship between the amount of resources and the loss probability. The proposed method determines the VNF sharing, the VNF placement, the amount of resources for each VNF, and the transmission route of each service chain. For the optimization problem, these are applied according to our proposed heuristic algorithm. We evaluate the performance of the proposed method through a simulation. From the numerical examples, we show the effectiveness of the proposed method under certain network topologies.

Modular multilevel converters (MMCs) are an emerging and promising option for medium voltage direct current (MVDC) of all- electric ships. In order to improve the stability of the MVDC transmission system for ships, this paper presents a new control inputs-based Lyapunov strategy based on feedback linearization. Firstly, a set of dynamics equations is proposed based on separating the dynamics of AC-part currents and MMCs circulating currents. The new control inputs can be obtained by the use of feedback linearization theory applied to the dynamic equations. To complete the dynamic parts of the new control inputs from the viewpoint of MVDC system stability, the Lyapunov theory is designed some compensators to demonstrate the effects of the new control inputs on the MMCs state variable errors and its dynamic. In addition, the carrier phase shifted modulation strategy is used because of applying the few number of converter modules to the MVDC system for ships. Moreover, relying on the proposed control strategy, a simulation model is built in MATLAB/SIMULINK software, where simulation results are utilized to verify the validity of proposed control strategy in the MMC-based MVDC system for ships.

This paper considers an emergency communication system controlling multiple unmanned aerial vehicles (UAVs) in the sky over a large-scale disaster-affected area. This system is based on delay-tolerant networking, and information from ground users is relayed by the UAVs through wireless transmission and the movement of UAVs in a store-and-forward manner. Each UAV moves autonomously according to a predetermined flight method, which uses the positions of other UAVs through communication. In this paper, we propose a new method for UAV flight considering the non-uniformity of user distributions. The method is based on the Voronoi cell using the predicted locations of other UAVs. We evaluate the performance of the proposed method through computer simulations with a non-uniform user distribution generated by a general cluster point process. The simulation results demonstrate the effectiveness of the proposed method.

Global Navigation Satellite System (GNSS) receivers often realize anti-jamming capabilities by combining array antennas with space-time adaptive processing (STAP). Unfortunately, in suppressing the interference, basic STAP degrades the GNSS signal. For one thing, additional carrier phase errors and code phase errors to the GNSS signal are introduced; for another, the shape of the cross-correlation function (CCF) will be distorted by STAP, introducing tracking errors when the receiver is in tracking mode. Both of them will eventually cause additional Pseudo-Range (PR) bias, and these problems prevent STAP from being directly applied to high-precision satellite navigation receivers. The paper proposes a novel anti-jamming method based on STAP that solves the above problems. First, the proposed method constructs a symmetric STAP by constraining the STAP coefficients. Subsequently, with the information of the steering vector, a compensation FIR filter is cascaded after the symmetric STAP. This approach ensures that the proposed method introduces only a fixed offset to the code phase and carrier phase, and the order of the STAP completely determines the offset, which can be compensated during PR measurements. Meanwhile, the proposed method maintains the symmetry of the CCF, and the receiver can accurately track the carrier phase and code phase in tracking mode. The effectiveness of the proposed method is validated through simulations, which suggest that, in the worst case, our method does not increase carrier and code phase errors and tracking error at the expense of only a 2.86dB drop in interference suppression performance.

In the conventional single-phase PWM rectifier, the sinusoidal fluctuating current and voltage on the grid side will generate power ripple with a doubled grid frequency which leads to a secondary ripple in the DC output voltage, and the switching frequency of the conventional model predictive control strategy is not fixed. In order to solve the above two problems, a control strategy for suppressing the secondary ripple based on the three-vector fixed-frequency model predictive current control is proposed. Taking the capacitive energy storage type single-phase PWM rectifier as the research object, the principle of its active filtering is analyzed and a model predictive control strategy is proposed. Simulation and experimental results show that the proposed strategy can significantly reduce the secondary ripple of the DC output voltage, reduce the harmonic content of the input current, and achieve a constant switching frequency.

This paper presents a channel operating margin (COM) based high-speed serial link optimization using machine learning (ML). COM that is proposed for evaluating serial link is calculated at first and during the calculation several important equalization parameters corresponding to the best configuration are extracted which can be used for the ML modeling of serial link. Then a deep neural network containing hidden layers are investigated to model a whole serial link equalization including transmitter feed forward equalizer (FFE), receiver continuous time linear equalizer (CTLE) and decision feedback equalizer (DFE). By training, validating and testing a lot of samples that meet the COM specification of 400GAUI-8 C2C, an effective ML model is generated and the maximum relative error is only 0.1 compared with computation results. At last 3 link configurations are discussed from the view of tradeoff between the link performance and cost, illustrating that our COM based ML modeling method can be applied to advanced serial link design for NRZ, PAM4 or even other higher level pulse amplitude modulation signal.

In this paper, the author performed an electromagnetic field simulation of a typical bonding wire structure that connects a chip and a package, and evaluated the signal transmission characteristics (S-parameters). In addition, the inductance per unit length was extracted by comparing with the equivalent circuit of the distributed constant line. It turns out that the distributed constant line model is not sufficient because there are frequencies where chip-package resonance occurs. Below the resonance frequency, the conventional low-frequency approximation model was effective, and it was found that the inductance was about 1nH/mm.

In this study, a computational method is proposed for acoustic field analysis tasks that require lengthy observation times. The acoustic fields at a given observation time are obtained using a fast inverse Laplace transform with a finite-difference complex-frequency-domain. The transient acoustic field can be evaluated at arbitrary sampling intervals by obtaining the instantaneous acoustic field at the desired observation time using the proposed method.

Deep neural networks (DNNs) provide excellent services in machine learning tasks such as image recognition, speech recognition, pattern recognition, and intrusion detection. However, an adversarial example created by adding a little noise to the original data can result in misclassification by the DNN and the human eye cannot tell the difference from the original data. For example, if an attacker creates a modified right-turn traffic sign that is incorrectly categorized by a DNN, an autonomous vehicle with the DNN will incorrectly classify the modified right-turn traffic sign as a U-Turn sign, while a human will correctly classify that changed sign as right turn sign. Such an adversarial example is a serious threat to a DNN. Recently, an adversarial example with multiple targets was introduced that causes misclassification by multiple models within each target class using a single modified image. However, it has the weakness that as the number of target models increases, the overall attack success rate decreases. Therefore, if there are multiple models that the attacker wishes to attack, the attacker must control the attack success rate for each model by considering the attack priority for each model. In this paper, we propose a priority adversarial example that considers the attack priority for each model in cases targeting multiple models. The proposed method controls the attack success rate for each model by adjusting the weight of the attack function in the generation process while maintaining minimal distortion. We used MNIST and CIFAR10 as data sets and Tensorflow as machine learning library. Experimental results show that the proposed method can control the attack success rate for each model by considering each model's attack priority while maintaining minimal distortion (average 3.95 and 2.45 with MNIST for targeted and untargeted attacks, respectively, and average 51.95 and 44.45 with CIFAR10 for targeted and untargeted attacks, respectively).

CPU flush instruction-based cache side-channel attacks (cache instruction attacks) target a wide range of machines. For instance, Meltdown / Spectre combined with FLUSH+RELOAD gain read access to arbitrary data in operating system kernel and user processes, which work on cloud virtual machines, laptops, desktops, and mobile devices. Additionally, fault injection attacks use a CPU cache. For instance, Rowhammer, is a cache instruction attack that attempts to obtain write access to arbitrary data in physical memory, and affects machines that have DDR3. To protect against existing cache instruction attacks, various existing mechanisms have been proposed to modify hardware and software aspects; however, when latest cache instruction attacks are disclosed, these mechanisms cannot prevent these. Moreover, additional countermeasure requires long time for the designing and developing process. This paper proposes a novel mechanism termed FlushBlocker to protect against all types of cache instruction attacks and mitigate against cache instruction attacks employ latest side-channel vulnerability until the releasing of additional countermeasures. FlushBlocker employs an approach that restricts the issuing of cache flush instructions and the attacks that lead to failure by limiting control of the CPU cache. To demonstrate the effectiveness of this study, FlushBlocker was implemented in the latest Linux kernel, and its security and performance were evaluated. Results show that FlushBlocker successfully prevents existing cache instruction attacks (e.g., Meltdown, Spectre, and Rowhammer), the performance overhead was zero, and it was transparent in real-world applications.

With the development of in-vehicle network technologies, Automotive Ethernet is being applied to modern vehicles. Scalable service-Oriented MiddlewarE over IP (SOME/IP) is an automotive middleware solution that is used for communications of the infotainment domain as well as that of other domains in the vehicle. However, since SOME/IP lacks security, it is vulnerable to a variety of network-based attacks. In this paper, we introduce a new type of intrusion detection system (IDS) leveraging on SOME/IP packet's header information and packet reception time to deal with SOME/IP related network attacks.

Multiple human tracking is a fundamental problem in understanding the context of a visual scene. Although both accuracy and speed are required in real-world applications, recent tracking methods based on deep learning focus on accuracy and require a substantial amount of running time. We aim to improve tracking running speeds by performing human detections at certain frame intervals because it accounts for most of the running time. The question is how to maintain accuracy while skipping human detection. In this paper, we propose a method that interpolates the detection results by using an optical flow, which is based on the fact that someone's appearance does not change much between adjacent frames. To maintain the tracking accuracy, we introduce robust interest point detection within the human regions and a tracking termination metric defined by the distribution of the interest points. On the MOT17 and MOT20 datasets in the MOTChallenge, the proposed SDOF-Tracker achieved the best performance in terms of total running time while maintaining the MOTA metric. Our code is available at https://github.com/hitottiez/sdof-tracker.

Considering the rapidly increasing number of academic papers, searching for and citing appropriate references has become a nontrivial task during manuscript composition. Recommending a handful of candidate papers to a working draft could ease the burden of the authors. Conventional approaches to citation recommendation generally consider recommending one ground-truth citation from an input manuscript for a query context. However, it is common for a given context to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modelling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4REC), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective in recommending multiple positive candidates. Second, we adopt noise distributions on the basis of historical co-citation frequencies; thus, MP-BERT4REC is not only effective in recommending high-frequency co-citation pairs, but it also significantly improves the performance of retrieving low-frequency ones. Third, the proposed dynamic context sampling strategy captures macroscopic citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allows the algorithm to further improve performance. Single and multiple positive recommendation experiments confirmed that MP-BERT4REC delivers significant improvements over current methods. It also effectively retrieves the full list of co-citations and historically low-frequency pairs better than prior works.

Codebooks with small maximal cross-correlation amplitudes have important applications in code division multiple access (CDMA) communication, coding theory and compressed sensing. In this letter, we design a new codebook based on a construction of Ramanujan graphs over finite abelian groups. We prove that the new codebook with length K=q+1 and size N=q2+2q+2 is asymptotically optimal with nearly achieving the Levenshtein bound when n=3, where q is a prime power. The parameters of the new codebook are new.

New services can use fog nodes to distribute Internet of Things (IoT) data. To distribute IoT data, we apply the publish/subscribe messaging model to a fog computing system. A service provider assigns a unique identifier, called a Tag ID, to a player who owes data. A Tag ID matches multiple IDs and resolves the naming rule for data acquisition. However, when users configure their fog node and distribute IoT data to multiple players, the distributed data may contain private information. We propose a table-based access control list (ACL) to manage data transmission permissions to address this issue. It is possible to avoid unnecessary transmission of private data by using a table-based ACL. Furthermore, because there are fewer data transmissions, table-based ACL reduces traffic. Consequently, the overall system's average processing delay time can be reduced. The proposed method's performance was confirmed by simulation results. Table-based ACL, particularly, could reduce processing delay time by approximately 25% under certain conditions. We also concentrated on system security. The proposed method was used, and a qualitative evaluation was performed to demonstrate that security is guaranteed.

In this paper, we propose an online probabilistic activation/deactivation control method for base stations (BSs) in heterogeneous networks based on the temporal system throughput and activation states of neighbor BSs (cells). The conventional method iteratively updates the activation/deactivation states in a probabilistic manner at each BS based on the change in the observed system throughput and activation/deactivation states of that BS between past multiple consecutive discrete times. Since BS activation control increases the system throughput by improving the tradeoff between the reduction in inter-cell interference and the traffic off-loading effect, the activation of a BS whose neighbor BSs are deactivated is likely to result in improved system performance and vice versa. The proposed method newly introduces a metric, which represents the effective ratio of the activated neighbor BSs considering their transmission power and distance to the BS of interest, to the update control of the activation probability. This improves both the convergence rate of the iterative algorithm and throughput performance after convergence. Computer simulation results, in which the mobility of the user terminals is taken into account, show the effectiveness of the proposed method.

Design of distributed energy management systems composed of several agents such as factories and buildings is important for realizing smart cities. In addition, demand response for saving the power consumption is also important. In this paper, we propose a design method of distributed energy management systems with real-time demand response, in which both electrical energy and thermal energy are considered. Here, we use ADMM (Alternating Direction Method of Multipliers), which is well known as one of the powerful methods in distributed optimization. In the proposed method, demand response is performed in real-time, based on the difference between the planned demand and the actual value. Furthermore, utilizing a blockchain is also discussed. The effectiveness of the proposed method is presented by a numerical example. The importance of introducing a blockchain is pointed out by presenting the adverse effect of tampering the actual value.

Anonymous attackers have been targeting the Android ecosystem for performing severe malicious activities. Despite the complement of various vulnerabilities by security researchers, new vulnerabilities are continuously emerging. In this paper, we introduce a new type of vulnerability that can be exploited to hide data in an application file, bypassing the Android's signing policy. Specifically, we exploit padding areas that can be created by using the alignment option when applications are packaged. We present a proof-of-concept implementation for exploiting the vulnerability. Finally, we demonstrate the effectiveness of VeileDroid by using a synthetic application that hides data in the padding area and updates the data without re-signing and updating the application on an Android device.