Miracle-dz/UniFAR-FTUs huggingface dataset:
On Benign Features in Malware Detection

This paper investigates the problem of classifying Android applications into malicious and benign. We analyze the performance of a popular malware detection tool, Drebin, and show that its correct classification decisions often stem from using benign rather than malicious features for making predictions. That, effectively, turns the classifier into a benign app detector rather than a malware detector. While such behavior allows the classifier to achieve a high detection accuracy, it also makes it vulnerable to attacks, e.g., by a malicious app pretending to be benign by using features similar to those of benign apps. In this paper, we propose an approach for deprioritizing benign features in malware detection, focusing the detection on truly malicious portions of the apps. We show that our proposed approach makes a classifier more resilient to attacks while still allowing it to maintain a high detection accuracy.
Comparison of Several Machine Learning Classifiers for Arousal Classification: A Preliminary study

Detection of arousal intervals, especially stress detection via a human-machine interface is a trending topic. Stress detection algorithms with high accuracy can be used in many fields such as criminal interrogations or a variety of stress-related experiments. There are many indicators of the stress on the human body, especially on the face area, such as galvanic skin response (GSR), pupil diameter, heart rate (HR), and electromyography (EMG). Hereby, the measurement of such physiological data in stressful, joyful and non-stressful cases can reveal the effects of the stress on the body signals.This preliminary study aims to compare several machine learning approaches, namely, linear discriminant analysis (LDA), k-nearest neighbour (k-NN), Naive Bayes, support vector machines (SVM) and coarse tree algorithms in a classification study. To perform the analyses, the pupil data are collected from a total of 9 subjects while the subject was watching three types of movies, independently. The classifications are performed among the labelled data with multivariate features such as mean, median, maximum to minimum difference and variance, and their univariate versions in order to observe their independent discrimination performances. Moreover, the preprocessed raw data are also used in classification, independently. Here, the movies are selected such that they include either annotated positive, negative or neutral scenes, which may indicate the stressful, joyful and non-stressful intervals, respectively. Therefore, the classification results of these algorithms for the annotated labels in each channel separately are found to observe their effectiveness in detection of arousal intervals. Hence, the main aim is to contribute to the stress detection literature by providing a comparison between both the classification algorithms, features and raw data classification.
Miniaturized Sixteenth-mode Substrate Integrated Waveguide Bandpass Filter with Slot Line Array

A sixteenth-mode substrate integrated waveguide (SIW) bandpass filter with slot line array is proposed in this paper. As the slow wave performance of the slot line array, the circuit size of the proposed bandpass filter is further reduced about 23% comparing with the traditional sixteen-mode one. The simulated and measured results illustrate that the operating frequency is from 1.92 GHz to 2.10 GHz with 9% bandwidth. The return loss at the central frequency is lower than 20 dB while the minimal insertion loss is 0.55 dB.
What do Affect Customers to Use Mobile Payment Continually? A Systematic Literature Review

The intention to use mobile payment (M-Payment) has been growing, in line with digital economy growth. This phenomenon contributes to the emergence of M-Payment research lately. Several theories can be applied to those researches. In general, the research tends to adopt any particular theory to explore the factors that affect the use of M-Payment. A comprehensive and succinct review is needed to continually help scholars and practitioners understand what affects customers' use of M-Payment, the marketing strategy applied in the M - Payment business, and the theories implemented in current M-Payment research. This study aims to categorize the findings and assess M-Payment research's state of the art to facilitate future research. Data retrieved from IEEE, ACM, Science Direct, Scopus, EBSCOhost, and SpringerLink databases were published between 2017 and 2020. The data is analyzed by following the Kitchenham systematic literature review approach. As a result, this study establishes a table of theoretical used in M-Payment research, the marketing strategy applied in the M - Payment business, and factors affecting M-Payment's use classified into three main groups: technological factors, behavioral factors, and personal factors.
A Dynamic Self-Clamping Caused by Dynamic Avalanche During the Turn-Off of PT-IGBT

A dynamic self-clamping is found during overstress turn-off of the PT-insulated gate bipolar transistor (IGBT) with a higher carrier lifetime. Theoretical analysis and numerical device simulation reveal that it is caused by the remaining plasma that cannot be completely removed due to dynamic avalanche injection. A 1-D physics-based analytical model for the self-clamping is established, and analytical results indicate that clamped voltage is inversely proportional to the initial turn-off current density and the carrier lifetime. When the dc-link voltage is larger than the clamped voltage of the device at a certain turn-off current, both terminal voltage and terminal current will be clamped, causing the device to lose control of the gate. Simulations of the multicell structure demonstrate that the avalanche-generated current filament under the self-clamping is stationary and may exist for a longer time, which is extremely likely to lead to local overheat and device destruction.
ImpAPTr: A Tool For Identifying The Clues To Online Service Anomalies

As a common IT infrastructure, APM (Application Performance Management) systems have been widely adopted to monitor call requests to an on-line service. Usually, each request may contain multi-dimensional attributes (e.g., City, ISP, Platform, etc.), which may become the reason for a certain anomaly regarding DSR (De-clining Success Rate) of service calls either solely or as a combination. Moreover, each attribute may also have multiple values (e.g., ISP could be T-Mobile, Vodafone, CMCC, etc.), rendering intricate root causes and huge challenges to identify the root causes. In this paper, we propose a prototype tool, ImpAPTr (Impact Analysis based on Pruning Tree), to identify the combination of dimensional attributes as the clues to dig out the root causes of anomalies regarding DSR of a service call in a timely manner. ImpAPTr has been evaluated in MeiTuan, one of the biggest on-line service providers. Performance regarding the accuracy outperforms several previous tools in the same field.
Time-Course Sensitive Collision Probability Model for Risk Estimation

Avoiding critical situations is a prerequisite for Advanced Driver Assistant Systems and Autonomous Driving to decrease the number of total hazards and fatal collisions. As a guide for safe motion behavior and for avoiding critical situations in complex scenarios with several interacting traffic participants, an appropriate risk measurement is necessary. It should incorporate system-inherent uncertainties like present in environment recognition, behavior predictions and physical model assumptions. In this paper, we introduce a time-course-aware incremental risk model for motion planning which predicts state distributions along forecasted trajectories and regards their magnitude evolution by the Survival Theory and their shape adaptation by removing collided distribution parts while preserving statistical moments. Our approach is able to reproduce motion risk probability costs as found by particle-based Monte-Carlo (MC) simulations in a range of scenarios, at much lower computational costs.
Learning Locomotion For Legged Robots Based on Reinforcement Learning: A Survey

The legged robot can adapt to almost any kind of complex terrain and overcome all kinds of obstacles. So to this day, many people are working on using leg-based robots for complex locomotion tasks. It is tractable and difficult to achieve the agile locomotion of quadruped robots. Conventional controllers always need a lot of professional experience and lots of time to debug and tune the parameters. Deep reinforcement learning(DRL) can learn the effective skills from trails directly in practice, which holds the promising to overcome the limitation of the conventional controllers. Therefore, we have surveyed the current research working on learning locomotion skills via DRL techniques; and compare two commonly used DRL algorithms to learn the locomotion skills on a constructed simulation task.
Machine Learning In Bioinformatics: Gene Expression And Microarray Studies

Machine learning methods used in the field of bioinformatics are a frequently used solution method in diagnosing, treating and investigating the underlying causes of diseases. In addition, it is an important field of study that allows for the ease of processing, the provision of computational power and the diversity of computational tools specific to the subject, especially in processes that require processing on gene expression and microarray data sets. In this study, an introduction has been made on the use of machine learning methods in the field of bioinformatics gene expression, and the use of machine learning methods has been exemplified by recent studies.
An Effective QRS Selection Based on the Level-Crossing Sampling and Activity Selection

The Internet of Things (IoT) healthcare framework is a new trend. In this context, biomedical wearable devices are linked to the cloud. This work contributes to the development of efficient Electrocardiogram (ECG) wearables by redesigning their signal processing chain. The emphasis is on developing a system for effective and precise QRS selection. QRS complexes of heartbeats contain most important arrhythmia related information. The proposed system uses an analog band-pass filter to reduce the impact of Baseline Wander (BW) and aliasing. The band-limited signal is then digitized by using a 5-Bit resolution level-crossing A/D converter. Onward, an original activity selection algorithm is used for an effective selection of the QRS complexes. Results show that the proposed solution attains an average QRS detection sensitivity of 98.4% and positive predictive value of 100% while securing on average 4.77-fold and 2.1-fold compression gains respectively in terms of count of data samples and bits over the classical counterparts.
Multisampling wave union time-to-digital converter

A novel time-to-digital converter principle is presented that introduces multisampling capability to the wave union method. The design employs two wave union launchers for both start and stop signals. For test purposes the converter was implemented in Kintex-7 FPGA device (Xilinx). During the initial experimental verification a mean resolution (LSB) of up to 0.7 ps and precision even below 4.7 ps (RMS resolution better than 3.3 ps) were achieved. The design is FPGA logic resource saving comparing to similar solutions but requires much effort during implementation and data encoding.
Mode Errors and Intentional Violations in Visual Monitoring of Level 2 Driving Automation

Due to technical advancements, the driving task is changing from a manual task to system supervision. Research suggests that drivers do not always comply with safety requirements and neglect their monitoring task. On one hand, neglections in system monitoring could originate from mode confusion, with the driver assuming that the system is in a different mode than it actually is. In this case, insufficient system monitoring is the result of an unintentional mode error. On the other hand, drivers might deliberately omit system monitoring while knowing that the system is operating in a mode that requires constant surveillance. Such behaviour is considered an intentional violation. This study (30 participants) investigates the nature of neglections in visual system monitoring during a 60-minute simulator drive using eye-tracking data. The results reveal a substantial decline in system monitoring during Level 2, which further deteriorates with increasing time during which the system operates reliably. A systematic post-experiment interview reveals that more than half of the participants were aware of the system’s mode (Level 2), and knew that the system actually required monitoring but still neglected their monitoring task.
A Linear Extendable Phase-shift Controlled Multi-coil Transmitter Architecture for Wireless Power Transfer with 3D Magnetic Field Shaping

Wireless power transmission (WPT) systems operating at MHz have advantages of small size and a high tolerance for coils misalignment. There is an urgent need for existing MHz WPT systems to improve coupling coils efficiency and positional freedom of the receiver simultaneously. There exist some ways for improving coils efficiency and positional freedom of the receiver, such as introducing repeater coils and employing special coils structures. However, lack of magnetic field strength can limit the freedom of the receiver, and the excessive coupling may cause inefficiency in coils. This paper proposes a phase-shift controlled multi-coil transmitter architecture. The architecture is linear extendable and it enables the 3D magnetic field shaping, which helps to improve the coupling coils efficiency and the spatial freedom of the receiver. The design of the compensation capacitors of the transmitting coils is discussed to achieve the optimal operation for the high-frequency power amplifier (PA) when considering the effect of the cross-coupling among transmitting coils. The mathematic model for the 3D magnetic field shaping is derived to prove the concept of the approach. The calculation results show the capability of 3D magnetic field shaping when using a two-coil transmitter as an example. The experimental results match well with the theoretical analysis and the simulation results. Three WPT systems are studied in the paper. Compared with the single transmitter system (STS) and the two-coil transmitter system (TTS) without phase-shift control (PSC), the TTS with PSC can maintain higher system efficiency in three degrees of freedom (vertical movement, horizontal movement, flip with fixed axis). In particular, only the TTS with PSC can work when the receiving coil is perpendicular to the transmitting coils. Therefore, due to the ability to shape the 3D magnetic field, the linear extendable multi-coil transmitter architecture with PSC has advantages in terms of the receiver freedom and the coupling coils efficiency.
Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining

With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models. One of the major challenges is the data gap between the training data to construct the models and the testing data to evaluate them. To bridge the gap, testers aim to collect an effective subset of inputs from the testing contexts, with limited labeling effort, for retraining DL models.To assist the subset selection, we propose Multiple-Boundary Clustering and Prioritization (MCP), a technique to cluster test samples into the boundary areas of multiple boundaries for DL models and specify the priority to select samples evenly from all boundary areas, to make sure enough useful samples for each boundary reconstruction. To evaluate MCP, we conduct an extensive empirical study with three popular DL models and 33 simulated testing contexts. The experiment results show that, compared with state-of-the-art baseline methods, on effectiveness, our approach MCP has a significantly better performance by evaluating the improved quality of retrained DL models; on efficiency, MCP also has the advantages in time costs.
Digital Map Based on Unmanned Aerial Vehicle

Institut Teknologi Sepuluh Nopember (ITS) is one of the state universities in Surabaya, East Java. The main campus of ITS occupies an area of 180 hectares of land where there are scattered buildings of service offices, lecture buildings, laboratories, and other supporting facilities. From this, it is felt necessary to make an accurate digital map to help plan and introduce ITS. The development of photogrammetry by utilizing Unmanned Aerial Vehicle (UAV) as the data maker, makes digital map-making activities faster and more accurate than before. The activity of processing map images produced by UAV currently still requires software with a relatively high price. So it is necessary to make another alternative to making the overall ITS map by adding accurate coordinates to the result. The error hole in the orthophoto map produced was 2.0690%, while the orthophoto map produced by Agisoft PhotoScan was 24.5370%.
AltCC: Alternating Clustering and Classification for Batch Analysis of Malware Behavior

The most common goal of malware analysis is to determine if a given binary is malware or benign. Another objective is similarity analysis of malware binaries to understand how new samples differ from known ones. Similarity analysis helps to analyze the malware with respect to those already analyzed and guides the discovery of novel aspects that should be analyzed more in depth. In this work, we are concerned with similarities and differences detection of malware binaries. Thousands of malware are created every day and machine learning is an indispensable tool for its analysis. Previous work has studied clustering and classification as competing paradigms. However, in this work, a malware similarity analysis technique (AltCC) is proposed that alternates the use of clustering and classification. In addition it assumes the malware are not available all at once but processed in batches. Initially, clustering is applied to the first batch to group similar binaries into novel malware classes. Then, the discovered classes are used to train a classifier. For the following batches, the classifier is used to decide if a new binary classifies to a known class or otherwise unclassified. The unclassified binaries are clustered and the process repeats. Malware clustering (i.e. labeling) may entail further human expert analysis but dramatically reduces the effort. The effectiveness of AltCC is studied using a dataset of 29,661 malware binaries that represent malware received in six consecutive days/batches. When KMeans is used to label the dataset all at once and its labeling is compared to AltCC’s, the adjusted-rand-index scores 0.71.
Deep Learning-Based Generic Automatic Surface Defect Inspection (ASDI) With Pixelwise Segmentation

Automatic surface defect inspection (ASDI) is a crucial and challenging problem in industry because it affects the quality and efficiency of production greatly. Deep learning-based methods achieve promising improvements for surface detection, but they rely on a massive training data set that is impractical in industry. In this study, we propose a generic method that works effectively even on the small size training data set. Especially, we introduce a ResMask generative adversarial network (GAN) framework that is a residual GAN to expand the insufficient defect data sets. Meanwhile, the existing inspection data sets are so much easier to detect than the data sets in the real industrial scenarios that new industrial surface defect data sets containing more diverse and challenging images are established. Then, a coarse-to-fine module (CFM) that consists of a coarse detection subnetwork and a fine segmentation subnetwork is proposed for the needs of fast detection for high-resolution images. In the coarse detection stage, spatial pyramid pooling (SPP) is utilized to increase the receptive field of the network, reduce the false detection rate, and determine the approximate location of defects. In the fine segmentation stage, the receptive field of the network is enlarged by atrous SPP (ASPP), and skip links that incorporate low-level with high-level features achieve pixelwise precision on defect segmentation. Finally, our algorithm has achieved state-of-the-art results in DAGM, HR, and WB data sets (0.859, 0.761, and 0.805, respectively) according to MIoU. It achieves an average processing time of 44.4 ms on the test images with a resolution of $512 \,\,\times \,\, 512$ and 131.6 ms for $2048 \times 2048$ images. On the DAGM data set, the detection accuracy of mean intersection over union (MIoU) reaches 0.859.
Leg Structure Design for Special Hexapod Robot with Climbing Vertical Walls Ability

In order to solve the climbing problem of hexapod robot system, we mainly innovate its leg structure. First of all, shape of feet of the hexapod robot is designed to be a crescent. Compared with the traditional bouncing jump method, the designed hexapod robot can avoid obstacles and climb steps by turning the front foot onto the obstacle and then driving the rear foot. Secondly, the bottom of the foot is equipped with six symmetrically distributed suckers in the outer ring and randomly distributed spikes in the middle. The sucker helps the hexapod robot stabilize on the smooth wall and achieve vacuum adsorption by emptying the gas. Moreover, the retractable spikes are applied to the fixation of the hexapod robot on rough walls, and the internal electromagnet controls the rise and fall of the spikes plate. Simulation results show that our design is successful.
Probabilistic Decision Modeling in Social Networks

Bayesian approaches have been successfully applied in social network analysis to study group behaviors such as online information dissemination and voting pattern. The focus has been on estimating the structure and strength of peer influence and its impact on the decisions of an individual. Less attention has been given to incorporating contextual information and individuals' hidden characteristics (or bias). In this work, we examine the social dynamics where social influence and contextual information play pivotal roles in driving one's decision. We design a probabilistic graphical model called CLAP to understand users' decision behavior in a social network, with an emphasis on both social-level and individual-level factors. To this end, the proposed model introduces hidden bias states associated with each actor and jointly estimates each actor's hidden bias state together with the social influence network. We demonstrate the effectiveness of CLAP on two types of social networks, a real-world US Congress network where senators vote on new bills, and online Twitter networks where users debate on the effectiveness of vaccine and lockdown policy during COVID-19. The experiment results show that CLAP outperforms state-of-the-art game theoretic approaches in predicting user decision. Further, the estimated social influence networks by CLAP has high edge homogeniety ratios.
Characterization of Objects in Indoor Spaces of Human Occupation Using Knowledge Graphs

This paper proposes a knowledge graph to describe the semantic relationships of objects present in an indoor space, such as a bedroom in a house. We base our work in the representation model Object1–Predicate–Object2. In the predicate, we consider spatial relationships such as above, below, under, on top, next to, in, has, in front, and behind. To fulfill our purpose, we used the Grakn NoSQL database, which allows defining a knowledge graph schema of type entity-relation. The knowledge graph obtained needs a previously identified object in order to start looking for the correct relationships. That object is considered as input to the algorithm. The information on the semantic relationship has proved effectiveness in characterizing objects using Grakn, through which it was possible to charac-terize objects and their relationships based on the principles of knowledge representation.