The Topics over Time (TOT) model allows users to be aware of changes in certain topics over time. The proposed method inputs the divided dataset of security blog posts based on a fixed period using an overlap period to the TOT. The results suggest the extraction of topics that include malware and attack campaign names that are appropriate for the multi-labeling of cyber threat intelligence reports.

In this paper, the effective Long Range (LoRa) based wireless sensor network is designed and implemented to provide the remote data sensing functions for the planned smart agricultural recycling rapid processing factory. The proposed wireless sensor network transmits the sensing data from various sensors, which measure the values of moisture, viscosity, pH, and electrical conductivity of agricultural organic wastes for the production and circulation of organic fertilizers. In the proposed wireless sensor network design, the LoRa transceiver module is used to provide data transmission functions at the sensor node, and the embedded platform by Raspberry Pi module is applied to support the gateway function. To design the cloud data server, the MySQL methodology is applied for the database management system with Apache software. The proposed wireless sensor network for data communication between the sensor node and the gateway supports a simple one-way data transmission scheme and three half-duplex two-way data communication schemes. By experiments, for the one-way data transmission scheme under the condition of sending one packet data every five seconds, the packet data loss rate approaches 0% when 1000 packet data is transmitted. For the proposed two-way data communication schemes, under the condition of sending one packet data every thirty seconds, the average packet data loss rates without and with the data-received confirmation at the gateway side can be 3.7% and 0%, respectively.

In Internet applications, when users search for information, the search engines invariably return some invalid webpages that do not contain valid information. These invalid webpages interfere with the users' access to useful information, affect the efficiency of users' information query and occupy Internet resources. Accurate and fast filtering of invalid webpages can purify the Internet environment and provide convenience for netizens. This paper proposes an invalid webpage filtering model (HAIF) based on deep learning and hierarchical attention mechanism. HAIF improves the semantic and sequence information representation of webpage text by concatenating lexical-level embeddings and paragraph-level embeddings. HAIF introduces hierarchical attention mechanism to optimize the extraction of text sequence features and webpage tag features. Among them, the local-level attention layer optimizes the local information in the plain text. By concatenating the input embeddings and the feature matrix after local-level attention calculation, it enriches the representation of information. The tag-level attention layer introduces webpage structural feature information on the attention calculation of different HTML tags, so that HAIF is better applicable to the Internet resource field. In order to evaluate the effectiveness of HAIF in filtering invalid pages, we conducted various experiments. Experimental results demonstrate that, compared with other baseline models, HAIF has improved to various degrees on various evaluation criteria.

Computer networks are facing serious threats from the emergence of sophisticated new DGA bots. These DGA bots have their own dictionary, from which they concatenate words to dynamically generate domain names that are difficult to distinguish from human-generated domain names. In this letter, we propose an approach for identifying the callback communications of DGA bots based on relations among the words that constitute the character string of each domain name. Our evaluation indicates high performance, with a recall of 0.9977 and a precision of 0.9869.

Thanks to the excellent learning capability of deep convolutional neural networks (CNNs), CNN-based methods have achieved great success in computer vision and image recognition tasks. However, it has turned out that these methods often have inherent vulnerabilities, which makes us cautious of the potential risks of using them for real-world applications such as autonomous driving. To reveal such vulnerabilities, we propose a method of simultaneously attacking monocular depth estimation and optical flow estimation, both of which are common artificial-intelligence-based tasks that are intensively investigated for autonomous driving scenarios. Our method can generate an adversarial patch that can fool CNN-based monocular depth estimation and optical flow estimation methods simultaneously by simply placing the patch in the input images. To the best of our knowledge, this is the first work to achieve simultaneous patch attacks on two or more CNNs developed for different tasks.

Electromagnetic scattering of an electromagnetic plane wave from a rectangular hole in a thick conducting screen is solved using the Kirchhoff approximation (KA). The scattering fields can be derived as field radiations from equivalent magnetic current sources on the aperture of the hole. Some numerical results are compared with those by the Kobayashi potential (KP) method. The proposed method can be found to be efficient to solve the diffraction problem for high frequency regime.

In this paper, we present a hardware implementation of a colorectal cancer diagnosis support system using a colorectal endoscopic video image on customizable embedded DSP. In an endoscopic video image, color shift, blurring or reflection of light occurs in a lesion area, which affects the discrimination result by a computer. Therefore, in order to identify lesions with high robustness and stable classification to these images specific to video frame, we implement a computer-aided diagnosis (CAD) system for colorectal endoscopic images with Narrow Band Imaging (NBI) magnification with the Convolutional Neural Network (CNN) feature and Support Vector Machine (SVM) classification. Since CNN and SVM need to perform many multiplication and accumulation (MAC) operations, we implement the proposed hardware system on a customizable embedded DSP, which can realize at high speed MAC operations and parallel processing with Very Long Instruction Word (VLIW). Before implementing to the customizable embedded DSP, we profile and analyze processing cycles of the CAD system and optimize the bottlenecks. We show the effectiveness of the real-time diagnosis support system on the embedded system for endoscopic video images. The prototyped system demonstrated real-time processing on video frame rate (over 30fps @ 200MHz) and more than 90% accuracy.

Delay Tolerant Networks (DTNs) are vulnerable to message flooding attacks in which a very large number of malicious messages are sent so that network resources are depleted. To address this problem, previous studies mainly focused on constraining the number of messages that nodes can generate per time slot by allowing nodes to monitor the other nodes' communication history. Since the adversaries may hide their attacks by claiming a false history, nodes exchange their communication histories and detect an attacker who has presented an inconsistent communication history. However, this approach increases node energy consumption since the number of communication histories increases every time a node communicates with another node. To deal with this problem, in this paper, we propose an energy-efficient defense against such message flooding attacks. The main idea of the proposed scheme is to time limit the communication history exchange so as to reduce the volume while ensuring the effective detection of inconsistencies. The advantage of this approach is that, by removing communication histories after they have revealed such inconsistencies, the energy consumption is reduced. To estimate such expiration time, analytical expressions based upon a Markov chain based message propagation model, are derived for the probability that a communication history reveals such inconsistency in an arbitrary time. Extensive performance evaluation results obtained by means of computer simulations and several performance criteria verify that the proposed scheme successfully improves the overall energy efficiency. For example, these performance results have shown that, as compared to other previously known defenses against message flooding attacks, the proposed scheme extends by at least 22% the battery lifetime of DTN nodes, while maintaining the same levels of protection.

A switching-based Li-ion battery charger without any additional compensation circuit is proposed. The proposed charger adopts a dual-current sensor and a current window control to ensure system stability in different charge modes: trickle current, constant current, and constant voltage. The proposed Li-ion battery charger has less chip area and a simpler structure to design than a conventional Li-ion battery charger with pulse width modulation. Simulation with a 1000µF capacitor as the battery equivalent, a 5V input, and a 1A charge current resulted in a charging time of 1.47ms and a 91% power efficiency.

The pervasive application of Small Private Online Course (SPOC) provides a powerful impetus for the reform of higher education. During the teaching process, a teacher needs to understand the difficulty of SPOC videos for students in real time to be more focused on the difficulties and key points of the course in a flipped classroom. However, existing educational data mining techniques pay little attention to the SPOC video difficulty clustering or classification. In this paper, we propose an approach to cluster SPOC videos based on the difficulty using video-watching data in a SPOC. Specifically, a bipartite graph that expresses the learning relationship between students and videos is constructed based on the number of video-watching times. Then, the SimRank++ algorithm is used to measure the similarity of the difficulty between any two videos. Finally, the spectral clustering algorithm is used to implement the video clustering based on the obtained similarity of difficulty. Experiments on a real data set in a SPOC show that the proposed approach has better clustering accuracy than other existing ones. This approach facilitates teachers learn about the overall difficulty of a SPOC video for students in real time, and therefore knowledge points can be explained more effectively in a flipped classroom.

The circuit satisfiability problem has been intensively studied since Ryan Williams showed a connection between the problem and lower bounds for circuit complexity. In this letter, we present a #SAT algorithm for synchronous Boolean circuits of n inputs and s gates in time $2^{nleft(1 - rac{1}{2^{O(s/n)}} ight)}$ if s=o(n log n).

Sound event detection (SED) and acoustic scene classification (ASC) are important research topics in environmental sound analysis. Many research groups have addressed SED and ASC using neural-network-based methods, such as the convolutional neural network (CNN), recurrent neural network (RNN), and convolutional recurrent neural network (CRNN). The conventional methods address SED and ASC separately even though sound events and acoustic scenes are closely related to each other. For example, in the acoustic scene “office,” the sound events “mouse clicking” and “keyboard typing” are likely to occur. Therefore, it is expected that information on sound events and acoustic scenes will be of mutual aid for SED and ASC. In this paper, we propose multitask learning for joint analysis of sound events and acoustic scenes, in which the parts of the networks holding information on sound events and acoustic scenes in common are shared. Experimental results obtained using the TUT Sound Events 2016/2017 and TUT Acoustic Scenes 2016 datasets indicate that the proposed method improves the performance of SED and ASC by 1.31 and 1.80 percentage points in terms of the F-score, respectively, compared with the conventional CRNN-based method.

3D ultrasound imagers require low-noise amplifier (LNA) with much lower power consumption and smaller chip area than conventional 2D imagers because of the huge amount of transducer channels. This paper presents a low-power small-size LNA with a novel current-reuse circuitry for 3D ultrasound imaging systems. The proposed LNA is composed of a differential common source amplifier and a source-follower driver which share the current without using inductors. The LNA was fabricated in a 0.18-μm CMOS process with only 0.0056mm2. The measured results show a gain of 21dB and a bandwidth of 9MHz. The proposed LNA achieves an average noise density of 11.3nV/√Hz, and the 2nd harmonic distortion below -40dBc with 0.1-Vpp input. The supply current is 85μA with a 1.8-V power supply, which is competitive with conventional LNAs by finer CMOS process.

Side-channel (SC) leakage from a cryptographic device chip is simulated as the dynamic current flowing out of the chip. When evaluating the simulated current, an evaluation by comparison with an actual measurement is essential; however, it is difficult to compare them directly. This is because a measured waveform is typically the output voltage of probe placed at the observation position outside the chip, and the actual dynamic current is modified by several transfer impedances. Therefore, in this paper, the probe voltage is converted into the dynamic current by using an EMC macro-model of a cryptographic device being evaluated. This paper shows that both the amplitude and the SC analysis (correlation power analysis and measurements to disclosure) results of the simulated dynamic current were evaluated appropriately by using the EMC macro-model. An evaluation confirms that the shape of the simulated current matches the measured one; moreover, the SC analysis results agreed with the measured ones well. On the basis of the results, it is confirmed that a register-transfer level (RTL) simulation of the dynamic current gives a reasonable estimation of SC traces.

This paper proposes a route calculation method for a bicycle navigation system that complies with traffic regulations. The extension of the node map and three kinds of route calculation methods are constructed and evaluated on the basis of travel times and system acceptability survey results. Our findings reveal the effectiveness of the proposed route calculation method and the acceptability of the bicycle navigation system that included the method.

For exploration of the functional use of dielectric anisotropy of liquid crystals (LCs), we investigated the dynamic response of molecular alignment in a nematic-phase LC cell with compressive force-induced flow behavior. The results showed that the initial alignment and thickness of the LC layer affect the capacitance of the cell when mechanical pressure is applied.

A method for the calibration of S11 at the front surface of a material for a coaxial-feed type cut-off circular waveguide with three reference materials inserted and no short termination condition was proposed as a preliminary step for dielectric measurement in liquids. The equations for jig calibration of S11 with these reference materials were first defined, and the electrostatic capacitance for the analytical model unique to the jig was quantified by substituting the reflection constant (calculated at frequencies of 0.50, 1.5 and 3.0 GHz using the mode-matching (MM) technique) into the equivalent circuit, assuming the sample liquid in the jig. The accuracy of S11 measured using the proposed method was then verified. S11 for the front surface of the sample material was also measured with various liquids in the jig after calibration, and the dielectric constants of the liquids were estimated as an inverse problem based on comparison of S11 calculated from an analytical model using EM analysis via the MM technique with the measured S11 values described above. The effectiveness of the proposed S11 calibration method was verified by comparison with dielectric constants estimated after S11 SOM (short, open and reference material) calibration and similar, with results showing favorable agreement with each method.

Dynamic spectrum access (DSA) exploits vacant frequency resources via distributed wireless access. The two nodes of DSA, master and slave, access different channels, and thus, cannot communicate with each other. To compensate for the access channel mismatch between the two nodes, a rendezvous channel, which exchanges control signals between two nodes, has been considered. The rendezvous channel based on channel-occupancy ratio (COR) adaptively constructs the channel in accordance with the channel occupancy of other systems, and both a high-speed rendezvous channel and high usage efficiency of the frequency resource are accomplished owing to exploitation of the vacant channel. In the rendezvous channel based on COR, the master and slave recognize the channel with minimum measured COR as the superior channel. As the master sends the control signals through the superior channel recognized by the master, the slave accesses to the superior channel recognized by the slave with higher access rate than to the other channels. As a result, the slave can receive the control signals with highly probability and thus high speed rendezvous channel is achieved. If the master and the slave recognize the different channel as the superior channel, the access rate to the other channel should be larger. This is because the slave obtains the opportunity of receiving the control signals through the different channel from the superior channel recognized by slave and thus the high probability that the slave can receive the control signals is maintained. Therefore, the access rate of slave should be constructed in accordance with the recognition of superior channel by master and slave. In this paper, the access rate of slave to the superior channel is optimally constructed using the analyzed probability of completion of rendezvous channel. The analysis of the probability of completion of rendezvous channel includes the recognition of superior channel by master and slave. Even if the master and the slave recognize the different channel, the constructed access rate of slave can maintain the high speed rendezvous channel. From the theoretical analysis and computer simulation, the rendezvous channel based on COR with the optimal access rate to the channel with the lowest COR achieves reduced time for the rendezvous channel.

With the spread of high-performance sensors and social network services (SNS) and the remarkable advances in machine learning technologies, fake media such as fake videos, spoofed voices, and fake reviews that are generated using high-quality learning data and are very close to the real thing are causing serious social problems. We launched a research project, the Media Clone (MC) project, to protect receivers of replicas of real media called media clones (MCs) skillfully fabricated by means of media processing technologies. Our aim is to achieve a communication system that can defend against MC attacks and help ensure safe and reliable communication. This paper describes the results of research in two of the five themes in the MC project: 1) verification of the capability of generating various types of media clones such as audio, visual, and text derived from fake information and 2) realization of a protection shield for media clones' attacks by recognizing them.

In this short note, we formally show that Keyed-Homomorphic Public Key Encryption (KH-PKE) is secure against key recovery attacks and ciphertext validity attacks that have been introduced as chosen-ciphertext attacks for homomorphic encryption.