Miracle-dz/UniFAR-FTUs huggingface dataset:
A High-efficient Duty-controlled Synchronous Rectifier for Uniformly Powering of Multiple Receivers

A high-efficient synchronous rectifier in the receivers, which controls the duty cycle of the rectifier to modulate the load voltage, is proposed in this paper. The proposed rectifier can be utilized to reduce the conduction loss of the switching devices, and to control the load voltage with a simple control circuit. Thus, the load voltage of multiple rectifiers, which are connected in parallel each other, can be regulated for uniformly powering of the receivers under multiple wireless charging condition. In this paper, the new control method of two AC/DC rectifiers installed in the railway vehicle was introduced and verified by simulation. As a result, the uniformly powering of the two rectifier was achieved for 500kW load power delivery by the proposed rectifier with 97.0% of power efficiency, although each receiver has different magnetic field in the railroad track.
Melting of free copper clusters

Extensive molecular-dynamics simulations of melting processes of copper clusters, Cu55, Cu147 and Cu309 with closed-shell icosahedral structure, were performed using an environment-dependent classical tight-binding potential. The results reveal that the cluster may transit completely from pure solid to pure liquid phase with temperature via a dynamics-coexistence (DC) process, depending upon the latent heat and vibrational entropy. In the DC regime, no other than the first-order solid-liquid phase transition with time is related to the specific structure of the cluster.
An Analytical Latency Model for AVB Traffic in TSN Considering Time-Triggered Traffic

Time-Sensitive Networking (TSN) is an emerging network technique used in real-time communication. There are three typical traffic types in TSN: Time-Triggered (TT) traffic with strict time stability requirements, Audio-Video-Bridging (AVB) traffic with large bandwidth and bounded latency requirements, and Best-Effort (BE) traffic. This paper presents an analytical latency model for AVB traffic in TSN considering TT traffic. According to the time-slots of TT traffic, an accurate single-hop service curve for AVB traffic is established based on Network Calculus, from which a more precise queueing delay through one hop can be calculated. Moreover, a strict worst-case end-to-end latency of AVB traffic can be obtained by the calculated queueing delays. We evaluate the proposed model in a real network environment, and the results show that the proposed model is more accurate and more accordance with actual circumstance compared with other methods.
Feel Vibration: Challenge-Response Mobile Authentication with Covert Channel

PIN is widely used as an authentication method on the mobile phone in various public places. However a malicious person nearby may peek into the screen and remember the password. This kind of shoulder surfing attack is more difficult to prevent in modern society. A stronger adversary may use his camera or video surveillance system to record the entry process. In this paper, we present three approaches to solve this problem. Those approaches utilize the phone’s vibration as the covert channel to deliver challenge- response secretly from camera. The experimental results show that those approaches can effectively prevent the shoulder surfing attack, even if the password entry process is completely exposed to the adversary's camera.
Driver State Regulation via Real-Time Neurofeedback in Partially Automated Driving

Improving driver performance in critical driving situations is the key to increased road safety. In SAE Level 3 of automation, the driver is retained as a fallback in critical situations. Once the system reaches its limits, the distracted driver must overtake the driving task and is responsible for the present and upcoming situations. This contribution proposes a new method to impact driver performance by regulating the driver’s arousal level. The arousal level of the driver is determined by detecting the driver’s brain activity, and proper auditory feedback is issued to reduce the arousal towards the optimal value. The suggested approach utilizes neurofeedback to downregulate the driver’s arousal via self-regulation in critical driving situations. The results show that the proposed neurofeedback supports the drivers to self-regulate their arousal during simple and difficult driving tasks and reach the optimal arousal level. The proposed approach is especially helpful in driving intervals with high criticality where the driver has to consecutively make decisions and react under demanding conditions.
Novel Long Period Gratings in Channeled Optical Fibers

Here we report about the development of a fiber optic based microfluidic sensor. It is based on a single-mode fiber having two micro-channels with a diameter of 28 μm, usually referred to as side-hole fiber (SHF). The setup for providing the fiber with both optical connection and access to the micro-channels for input/output of liquid solutions was developed. Moreover, to probe the refractive index of the substance inside the micro-channels, a long period grating (LPG) transducer was written in the SHF by electric arc discharge technique. Testing was conducted by using distilled water inside the channels, demonstrating the possibility to detect the presence of a liquid rapidly and consuming low sample volumes through the LPG response. This device can find application for bio-chemical sensing.
An enhanced fault detection method for railway turnouts incorporating prior faulty information

Railway turnouts monitoring is critical for ensuring the safety of railway systems. Owing to the scarcity of faulty samples, many approaches focus on the fault detection of railway turnouts only using normal samples. However, although the faulty samples are insufficient, they can still provide useful information for fault detection. To improve the fault detection performance of railway turnouts, this paper proposes an enhanced fault detection method incorporating prior faulty information, which deals with the limitation of existing fault detection methods of turnouts that they fail to take insufficient faulty samples into account. In our method, a novel model which shares the architecture of deep autoencoders but has a different training objective is presented. By minimizing the difference between the averaged reconstruction error of normal samples and that of faulty samples, the proposed model enlarges the gap between the normal and faulty classes and making them more separable. The field data collected from a real high-speed railway in China is used to evaluate the proposed method, and a comparative study with several existing approaches is conducted. Experimental results show the effectiveness of the proposed method.
Dilated LSTM Networks for Short-Term Traffic Forecasting using Network-Wide Vehicle Trajectory Data

Short-term traffic forecasting is anticipated as an always evolving research topic, boosted by the tremendous recent advances of Machine Learning and Deep Learning, as well as computational power of modern PCs. In this paper, the Dilated Recurrent Neural Networks are introduced in traffic forecasting. Their architecture promotes the deployment of long-term relations and prevents common issues of RNNs, such as exploding and vanishing gradients. The Dilated LSTM Network is exploited to perform traffic conditions forecasting using network-wide data. The data consist of GPS trajectories of ride-hailing company DiDi’s vehicles from November of 2016. After preprocessing the data and organizing them into section’s travel speed of five-minute time resolution timeseries for each one of the 498 road sections of the road network of Xi’an, China, we fed them to the Dilated LSTM Network. The model consists of four hidden layers, each of them implementing an LSTM Network with one, two and four-step dilation correspondingly. The model achieves 85% accuracy, which is improved over a classic LSTM structure, trained on the same data.
Foreign Exchange Prediction using CEEMDAN and Improved FA-LSTM

In Foreign Exchange (Forex) Prediction with high accuracy it becomes a challenge because time series data has chaotic characteristics, uncertainty, and complexity. To improve the accuracy of the forex prices prediction, prediction models are proposed which Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Improved Firefly Algorithm-Long Short Term Memory (IFA-LSTM). In this model the preprocessing data using the CEEMDAN to decomposed into IMF sequence and residual sequence. LSTM prediction models are established for all each characteristic series from CEEMDAN deposition. IFA is applied to optimize neural network structure to improve the performance of the model prediction accuracy. We compare our proposed models with LSTM and CEEMDAN-LSTM models, the experimental results show that the proposed models performs better in the prediction of forex time series.
Urban Traffic Flow Forecasting Based on Memory Time-Series Network

Predicting urban traffic flow is significant to intelligent transportation systems. Urban traffic flow data is a type of time-series data, which collects the traffic flow of a road section or area. So in this paper, we treat urban traffic flow prediction as a time series problem. The traditional method to tackle traffic flow prediction is difficult, because of the complex influence factors and nonlinear dependencies. Recently, LSTM based network has been widely used to model long-term series, but the memory of LSTM is typically too small and is not enough to accurately remember facts from the past. In this paper, we using memory time-series network with additional memory mechanisms to address urban traffic prediction problems. Historical data were divided into longterm and short-term two parts, long-term historical data models the overall trend and short-term historical data takes into account recent changes. The experiment results on two urban traffic flow datasets demonstrate the model is effective and outperforms baselines.
Analysis of Fileless Malware and its Evasive Behavior

Malware is any software that causes harm to the user information, computer systems or network. Modern computing and internet systems are facing increase in malware threats from the internet. It is observed that different malware follows the same patterns in their structure with minimal alterations. The type of threats has evolved, from file-based malware to fileless malware, such kind of threats are also known as Advance Volatile Threat (AVT). Fileless malware is complex and evasive, exploiting pre-installed trusted programs to infiltrate information with its malicious intent. Fileless malware is designed to run in system memory with a very small footprint, leaving no artifacts on physical hard drives. Traditional antivirus signatures and heuristic analysis are unable to detect this kind of malware due to its sophisticated and evasive nature. This paper provides information relating to detection, mitigation and analysis for such kind of threat.
Piracy on the Internet: Publisher-side Analysis on File Hosting Services

In the file sharing ecosystem, One-Click File Hosting Services (FHS) such as Rapidgator and Uploaded, the previously Rapidshare and Megaupload, provide a platform for users to share copyrighted content. We present a publisher-side analysis of FHS file sharing dynamics through data collected from active measurement by crawling Warez-BB. The website is essentially a forum where publishers can share links to content they have uploaded on file hosting services. Consumers can use the website to gain access to content shared on the website, often free of charge. We primarily analyse various characteristics of file sharing with respect to view count as the evaluation metric.
Classical Machine Learning Classification for Javanese Traditional Food Image

Indonesia is a culturally rich nation with more than three hundred ethnic groups. This sheer number of ethnic groups reflects the country’s diverse culture. One of the identities that could be associated with a group of people is its cuisine. As with the high number of ethnic groups, the diversity of Indonesian traditional food is also very high. However, the diversity of food is threatened by the current food systems, which could endanger food security of a population. To prevent this issue, a traditional food database system is created to monitor the food systems of each area in Indonesia. In this research, automatic traditional food classification is developed as one of the main features of this system. There were 17 Indonesian traditional foods from the Java area that were acquired and used as a dataset for this research. Several key features of the food dataset were extracted using various methods. The data were then classified using various machine learning algorithms. From the experiment, Random Forest classifier achieved the highest accuracy compared to other classical machine learning methods.
Design of 2.4 GHz Slotted SIW Array Antenna for WLAN Application

This paper deals with the design of 2.4 GHz slotted substrate integrated waveguide (SIW) array antenna for wireless local area network (WLAN) application. The use of SIW structure is addressed to achieve a compact shape of waveguide-based array antenna especially at low frequency region. The proposed array antenna which is intended to work at the frequency of 2.4 GHz applies several configurations of longitudinal slot array on an SIW surface. A 1.6 mm thick FR4 epoxy dielectric substrate is used to deploy the design of SIW-based slotted waveguide array antenna. The performances of array configuration as well as the effect of slots number are investigated to attain the specified requirement. A 4.8 dBi gain could be achieved by a 1 × 2 slotted waveguide array antenna which has 8 slots in each array configuration.
Vulnerabilities and Digital Violations in Software Products: Logistic Regression Analysis

Human being are living in an era where network communication over 5G cellular infrastructure is gaining in fashion. In an environment of integrated technologies along with distributed architectures, there are variety of software spaces which need to be managed. Software, whether proprietor one or open source, may have variety of vulnerabilities which an attacker can exploit and which may compromise the CIA triad of a system. This article performs an analysis of various software vulnerabilities in diverse software products of major OS software vendors by using the logistic regression technique on Common Vulnerabilities and Exposures (CVE) details data which is derived from National Vulnerability Database. The logistic regressions have been estimated, with the help of STATA, for software vulnerability types of various kinds as dependent binary variables and several independent variables like vulnerability scores, a continuous variable and CIA triad, gained access level, access, authenticity and complexity as categorical variables.
MultiMix: A Multi-Task Deep Learning Approach for Travel Mode Identification with Few GPS Data

Understanding how people choose to travel is essential for intelligent transportation planning and related smart services. Recent advances in deep learning, coupled with the increasing market penetration of GPS devices, have paved the way for novel travel mode identification methods based on GPS data mining. While many have shown promising results, most methods have often relied heavily on the few available labeled data, leaving large amounts of unlabeled ones unused. To address this issue, we propose MultiMix, a semi-supervised multi-task learning framework for travel mode identification. Our framework trains a deep autoencoder using batches of labeled, unlabeled, and synthetic data by simultaneously optimizing three corresponding objective functions. We show that MultiMix outperforms several fully-and semi-supervised baselines, achieving a classification accuracy of 66.2% on Geolife using just 1% of labeled data, with accuracy reaching 84.8% when incorporating all available labels. We also verify the necessity of its components through an ablation study designed to provide insights into the proposed approach.
A Mapping Method for Convolutional Neural Networks on Network-on-Chip

Convolutional Neural Network (CNN) is the most widely used deep neural network model which involves massive computations, so we implement the CNN with Network on Chip (NoC). In this paper, an allocation method and a mapping strategy are proposed. We allocate computation operations to processing elements (PEs) first and then map the PEs to the nodes on NoC by genetic algorithm for CNN. The evaluation results show that the proposed method can achieve lower power consumption and latency.
Smart Hub based on Brain Computer Interface by Low Cost Devices

This work designed a portable Brain Computer Interface (BCI) Smart Hub system using low cost BCI headset. The aim was to introduce a new and evaluate existing BCI control methodologies. Smart Hub is part of the smart home system. In this work, we focus on development and comparison BCI control methodologies based on smart hub system. From survey similar works, we found several works had used attention and Eye Blink to control BCI. From the literature survey we conclude that our system used WiFi network for communication and the BCI control methods chosen were attention and Eye Blink. Five BCI control methodologies, all based on eye blinks and attention, were evaluated by 10 subjects. As a result we conclude that the attention accuracy is more than double blink but the attention required more activation time than doubleblink. As the result the control methodology should use 1 methodology with 1 command and shouldn't complexity for good performance.
Feature Extraction of Load Curve Based on Autoencoder Network

Feature extraction of load curve is the basis for load forecasting and user profile, but existing extraction methods generally require artificial construction. Autoencoder is an important unsupervised learning method in deep learning. It can automatically learn from a large amount of unlabeled data and obtain the features contained in data. This paper uses a stacked autoencoder with L21-norm regularization to performs dimensionality reduction and hidden variable analysis on the electricity load data, so as to achieve the effective extraction of features. The experimental results show that L21-norm regularization can effectively improve the performance of the autoencoder.
A Semi-supervised Classification Algorithm for Encrypted Discrete Sequential Protocol Data Based on GAN

To solve the problem of classification for the sparsely labeled encrypted Discrete Sequence Protocol Data (DSPD), a two-stage semi-supervised classification method based on Generative Adversarial Networks (GAN) is proposed. As data annotation for DSPD is difficult, the semi-supervised GAN is used for classification by mining the data distribution hidden in unlabeled data. Meanwhile, a data filtering method based on traditional GAN is put forward to avoid the adverse effects caused by the irrelevant data. In addition, to directly process the raw data, the discriminator in the semi-supervised GAN is constructed by a Long Short-Term Memory (LSTM) network. Experiments results show that when the labeling rate in the class-balanced mixed data is as low as 0.48%, the average accuracy and F1 can reach 96%. Compared with the supervised classification based on LSTM, the accuracy and F1 of the proposed method are improved by more than 10 percentage points on average. For the class-imbalanced data, when the proportion of smallest class is not less than 10%, the accuracy and F1 can be maintained above 90%.
A Hybrid Analysis to Detect Java Serialisation Vulnerabilities

Serialisation related security vulnerabilities have recently been reported for numerous Java applications. Since serialisation presents both soundness and precision challenges for static analysis, it can be difficult for analyses to precisely pinpoint serialisation vulnerabilities in a Java library. In this paper, we propose a hybrid approach that extends a static analysis with fuzzing to detect serialisation vulnerabilities. The novelty of our approach is in its use of a heap abstraction to direct fuzzing for vulnerabilities in Java libraries. This guides fuzzing to produce results quickly and effectively, and it validates static analysis reports automatically. Our approach shows potential as it can detect known serialisation vulnerabilities in the Apache Commons Collections library.
Barley root hydraulic conductivity and aquaporins expression in relation to salt tolerance

Abstract Root hydraulic conductivity (Lpr) and aquaporins were investigated in barley seedlings under salt stress of 100 mmol L−1 NaCl in hydroponic culture for 24 and 48 h. No reduction in root elongation was observed in stressed seedlings, and more that 75% of root and shoot growth was maintained under salt stress of 100 mmol L−1 NaCl. A slight increase in HvPIP2;1 transcript, which was the most abundant of the three aquaporins investigated, was observed. No significant changes in Lpr and HvPIP2;1 protein were detected. The possible reason for these complicated results and the function of aquaporins in Lpr under salt stress are discussed.
Scalable Visual Representation of Sensor-Based, Nested Robot Programs

An easy to understand graphical representation for nested robot programs is important to allow also non-experts, who are usually unable to read source code, to understand, what a robot program is meant to do. In this paper we present a Backus-Naur form for describing sensor-based nested robot programs with a corresponding visual representation of these robot programs in the form of control flow along a timeline. To achieve this, we utilize a scalable layered representation to visualize the components of the Backus-Naur form. In addition to that, we add temporal information to the visual representation, so that the users are able to see the execution time of different parts of the program and of the program as a whole. The result of this work are two equivalent, equally powerful representations of robot programs, namely the textual representation in form of the Backus-Naur form and the scalable layered visual representation. Both can be converted into each other without loss. The evaluation shows, that the visual representation is easy to understand for non-experts and therefore suitable to use within the context of intuitive robot programming.
SAT-Based Arithmetic Support for Alloy

Formal specifications in Alloy are organized around user-defined data domains, associated with signatures, with almost no support for built-in datatypes. This minimality in the built-in datatypes provided by the language is one of its main features, as it contributes to the automated analyzability of models. One of the few built-in datatypes available in Alloy specifications are integers, whose SAT-based treatment allows only for small bit-widths. In many contexts, where relational datatypes dominate, the use of integers may be auxiliary, e.g., in the use of cardinality constraints and other features. However, as the applications of Alloy are increased, e.g., with the use of the language and its tool support as backend engine for different analysis tasks, the provision of efficient support for numerical datatypes becomes a need. In this work, we present our current preliminary approach to providing an efficient, scalable and user-friendly extension to Alloy, with arithmetic support for numerical datatypes. Our implementation allows for arithmetic with varying precisions, and is implemented via standard Alloy constructions, thus resorting to SAT solving for resolving arithmetic constraints in models.