Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait analysis system based on slippers with flexible piezoelectric sensors

This study introduces a gait analysis system using a pair of slippers with flexible piezoelectric sensors. We install only one flexible sensor based on polyvinylidene fluoride, which is a flexible piezoelectric material, in each slipper. The flexible sensor is positioned in the slipper sole area under the inner arch of the foot. In this position, the sensor undergoes bending for walking, and generates electrical signals through the piezoelectric characteristic. We focus on the detection of the heel strike and toe-off moments from the sensor output. We then acquire the floor contract information of the slippers from the detection and extract eight gait parameters. We develop a gait analysis system including the sensing slippers, a sensing module for data collection and wireless communication, and an external computer with the data processing program. Moreover, we demonstrate that the proposed total gait analysis system based on the slippers with the flexible piezoelectric sensors works efficiently.
A survey on terahertz communications

With the exponential growth of the data traffic in wireless communication systems, terahertz (THz) frequency band is envisioned as a promising candidate to support ultra-broadband for future beyond fifth generation (5G), bridging the gap between millimeter wave (mmWave) and optical frequency ranges. The purpose of this paper is to provide a comprehensive literature review on the development towards THz communications and presents some key technologies faced in THz wireless communication systems. Firstly, despite the substantial hardware problems that have to be developed in terms of the THz solid state superheterodyne receiver, high speed THz modulators and THz antennas, the practical THz channel model and the efficient THz beamforming are also described to compensate for the severe path attenuation. Moreover, two different kinds of lab-level THz communication systems are introduced minutely, named a solid state THz communication system and a spatial direct modulation THz communication system, respectively. The solid state THz system converts intermediate frequency (IF) modulated signal to THz frequency while the direct modulation THz system allows the high power THz sources to input for approving the relatively long distance communications. Finally, we discuss several potential application scenarios as well as some vital technical challenges that will be encountered in the future THz communications.
A Diffusion-Based Method for Entity Search

Entity search has become an import task in the Web of Data recently. Most solutions developed so far have focused on modelling entity search using standard information retrieval model and adapting graph-based objects to multi-fielded pseudo-documents. Among the models proposed to this regard, we can found bag-of-words, multi-gram, and mixtures of language models. While these works have produced interesting findings, little attention has been put on the graph structure of the Web of data. In this work, we aim to fill this gap by introducing a two-stage method based on a standard information retrieval model combined with a diffusion-based approach. We implemented and tested several diffusion models finding that heat kernel diffusion processes have a competitive performance with state-of-the-art models.
An Efficient Image-ELM-Based Chip Classification Algorithm

The algorithm of classification is one of the important problems to be solved in the field of chip manufacturing, which has a great impact on the efficiency of process such as subsequent chip packaging. According to the requirement of intelligent control of chip production system, chip classification algorithm based on extreme learning machine (ELM) is studied. In this paper, we use image edge gradient information as feature vector and use ELM to classify the chip. In order to improve the speed of the algorithm, we use image pyramid to down-sample the image first. The final experimental results show that, in small-scale testing, our algorithm can achieve 100% accuracy and it is insensitive to illumination changes. When the image rotates, our method can achieve more than 93.3% accuracy.
Développement et évaluation d'obsfucations de protocoles basées sur la spécification. (Development and evaluation of protocol obsfucations based on the specification)

Il existe de plus en plus de protocoles de communications differents. La specification de beaucoup d’entre eux est disponible. Cependant, quand il s’agit de moyens de communication proprietaires, cette specification est gardee secrete : un attaquant qui aurait acces a cette specification pourrait compromettre un systeme utilisant ce protocole. Meme s’il n’a pas acces a cette specification, l’attaquant peut l’obtenir par retro-conception. Ainsi, il est interessant de creer des protocoles qui sont difficiles a retro-concevoir. Dans cette these, nous proposons une nouvelle approche specifiquement developpee pour rendre complexe la retro-conception de protocole. Nous appliquons pour cela des obfuscations au format du message et ceci de facon automatique a partir de la specification du protocole. Pour cela, nous avons dans un premier temps etudie plus de 30 contributions differentes concernant des outils de retro-conception de protocole et en avons tire des conclusions suivantes : 1) les outils de retro-conception de protocole pratiquent l’inference de modeles reguliers ; 2) ils supposent que le parsing d’un message s’effectue de gauche a droite ; 3) ils delimitent le message en champs d’apres des delimiteurs bien connus ou via des algorithmes ad-hoc ; 4) ils regroupent les messages d’apres des mesures de similarite sur des patterns. Ainsi, pour creer un protocole difficile a retro-concevoir, une solution est de s’assurer que le protocole ne respecte pas ces conditions. Dans un second temps, nous avons donc propose un modele de format de messages qui permet l’application d’obfuscations. Nous avons defini des obfuscations atomiques qui peuvent etre composees. Chacune de ces obfuscations cible une ou plusieurs des hypotheses des outils de retro-conception. La composition des obfuscations assure l’efficacite de notre solution et rend la retro-conception de protocole complexe. Ce modele est utilise pour generer automatiquement le code du parseur, du serialiseur et des accesseurs. Cette solution est implementee dans un prototype nomme ProtoObf grâce auquel nous avons pu evalue les performances des obfuscations. Les resultats montrent une nette augmentation de la complexite de la retro-conception avec le nombre de compositions d’obfuscation tandis que les couts induits (particulierement la taille du buffer serialise) restent bas.
Semantics-Constrained Advantageous Information Selection of Multimodal Spatiotemporal Data for Landslide Disaster Assessment

Although abundant spatiotemporal data are collected before and after landslides, the volume, variety, intercorrelation, and heterogeneity of multimodal data complicates disaster assessments, so it is challenging to select information from multimodal spatiotemporal data that is advantageous for credible and comprehensive disaster assessment. In disaster scenarios, multimodal data exhibit intrinsic relationships, and their interactions can greatly influence selection results. Previous data retrieval methods have mainly focused on candidate ranking while ignoring the generation and evaluation of candidate subsets. In this paper, a semantic-constrained data selection approach is proposed. First, multitype relationships are defined and reasoned through the heterogeneous information network. Then, relevance, redundancy, and complementarity are redefined to evaluate data sets in terms of semantic proximity and similarity. Finally, the approach is tested using Mao County (China) landslide data. The proposed method can automatically and effectively generate suitable datasets for certain tasks rather than simply ranking by similarity, and the selection results are compared with manual results to verify their effectiveness.
Wireless Laser Power Transmission: A Review of Recent Progress

Laser power transmission (LPT) is one of the most promising technologies in the long-range wireless power transfer field. LPT research has been driven by the desire to remotely power unmanned aerial vehicles, satellites, and other mobile electric facilities. However, the low overall efficiency is the main issue that limits the implement of high-intensity laser power beam (HILPB) system. As seen from the contemporary understanding of efficiency of laser power transmission channel, the efficiencies of laser and PV array are the main limiting factors to the HILPB system from the perspective of power conversion. Thus, a comprehensive overview of LPT technology is presented from the point of efficiency optimization view in this paper. First, the basic principles of laser power transmission are briefly summarized. Then, a survey of the efficiency optimization methods for HILPB system with regard to the laser and PV technologies is provided in detail. Additionally, the open issues and challenges in implementing the LPT technology are discussed.
Wideband mm-Wave CMOS Slow Wave Coupler

In this letter, we have presented a design of on-chip millimeter-wave 3-dB quadrature coupler that utilizes the coupled slow wave coplanar waveguide. The designed CMOS coupler covers the whole <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band and occupies a silicon area of only 0.0115 mm<sup>2</sup>, which is significantly smaller compared to the conventional microstrip-line-based Lange couplers. Measurement of the quadrature coupler shows a −3.5 dB through and a −4.4-dB coupling at 90 GHz. A less than ±1-dB amplitude and a ±4° phase errors from 55 to 110 GHz are recorded.
Decision Feedback Scheme with Criterion LR+Th for the Ensemble of Linear Block Codes*

A decision criterion called LR+Th for decision feedback scheme was proposed by Hashimoto. Though Forney’s decision criterion (FR) is optimal and meets the Neyman-Pearson’s lemma, LR+Th is suboptimal. However, the error exponent of LR+Th is shown to be asymptotically equivalent to that of FR by random coding arguments for block codes. In this paper, applying the technique of DS2 bound, we derive an upper bound for the error probability of LR+Th with the ensemble of linear block codes. It elucidates the relation between the random coding exponents of block codes and those of linear block codes.
Deep Hierarchical Attention Networks for Text Matching in Information Retrieval

Text Matching is the task of examining two pieces of texts, such as query and documents, and determining whether they have the same meaning. Text Matching is very important in many NLP tasks, such as document retrieval, question answering, automatic conversation, machine translation, etc. In recent years, there existed some representation-based and interaction-based neural networks which have achieved some improvements. However powerful attention mechanism is rarely used in these models. Inspired by the success of attention in machine translation and document classification, in this paper, we propose a Deep Hierarchical Attention Networks for Text Matching, namely Deep-HAN-Matching. Specifically, Deep-HAN-Matching extracts meaningful matching patterns and rich contextual features hierarchically from words to total document at the query term level using the recurrent neural network and attention mechanism, and finally rank the matching score produced by the fully connected neural network. Experimental results on WikiQA, a popular benchmark dataset for answer sentence selection in question answering, show that our model can significantly outperform traditional retrieval baseline models and some recent deep neural network based matching models.
Applying K-Means Clustering on Questionnaires Item Bank to Improve Students’ Academic Performance

The use of learning management systems where students can take online examinations is growing in acceptance and being adopted in the field of education. One of the major setbacks of having a web-based application in assessing the students’ performance is that, it only assesses them based on the number of correct and wrong answers. It does not provide an intelligent way of assessing the knowledge of the students. The system should provide a tool where the teacher can assess and categorize questions in the item bank and do modifications according to the individual performance of the students. The items that are deemed to be very hard or to be very easy should be transformed suited to the students’ performance. To determine the difficulty level of the questions in the item bank, K-Means clustering algorithm is suggested. The study aimed to develop a prototype that will help the students to improve their academic performance. The research was able to provide an item analysis using the examination results of the students. The level of difficulty, discrimination index, and response pattern were generated. The level of difficulty of each item were used as the observation value used in k-means clustering algorithm. There were five clusters generated that correspond to the five different difficulty levels namely: Very Easy, Easy, Average, Hard, and Very Hard. The items that belonged to the Easy, Average, and Hard clusters remained in the primary item bank; whereas, the items included in the Very Hard and Very Easy cluster were put in a secondary item bank. These items were analyzed and modified. Practice exam or reinforcement exam was created to help the teacher in creating an assessment test. This enables the students to improve on their performance in that course.
Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features

Gliomas have the highest mortality rate and prevalence among the primary brain tumors. In this study, we proposed a supervised brain tumor segmentation method which detects diverse tumoral structures of both high grade gliomas and low grade gliomas in magnetic resonance imaging (MRI) images based on two types of features, the gradient features and the context-sensitive features. Two-dimensional gradient and three-dimensional gradient information was fully utilized to capture the gradient change. Furthermore, we proposed a circular context-sensitive feature which captures context information effectively. These features, totally 62, were compressed and optimized based on an mRMR algorithm, and random forest was used to classify voxels based on the compact feature set. To overcome the class-imbalanced problem of MRI data, our model was trained on a class-balanced region of interest dataset. We evaluated the proposed method based on the 2015 Brain Tumor Segmentation Challenge database, and the experimental results show a competitive performance.
An Instruction Blocking Methods for Disk Data Protection

Disk data is frequently subject to malicious tampering attacks, causing huge losses to computer users. The existing disk data protection technology mainly protects data at the file system layer, the general block layer, the driver layer, and the hardware layer, and cannot protect against attacks at the disk controller layer. Aiming at this phenomenon, this paper proposes an instruction blocking method for disk data protection based on disk controller. By acquiring and analyzing computer runtime instructions, the method combines disk controller and binary cartridge techniques to modify and block malicious instructions that write to protected disk sectors, making them non-malicious. Experimental results show that this method can effectively protect the data in the specified area of the disk from malicious tampering.
Big data in building design: a review

Compared with other industries, the use of big data is substantially less developed in the architecture, construction, engineering, and operation (AECO) industry. Available examples are mostly experimental, limited in scope, and without an obvious transfer to practice. Yet, a substantial number of academics and practitioners envision that the use of big data will have a significant impact on how decisions are made in the building design process, providing designers with an unprecedented amount of exceptionally detailed data on buildings and their occupants that is redefining what can be observed, modeled, simulated, predicted and measured in the industry. We first review the main concepts and technologies that shape this technological phenomenon, and then, based on the survey of around 100 cases of applications of big data and machine learning throughout all phases of building design we identify 12 clusters of applications where the technology seems to be more disruptive. The discussion then focuses on the transformative impacts of the technology on three niches where further research is more urgently needed: 1) understanding the design problem; 2) simulating and predicting the holistic performance of the design solution; 3) evaluating integrally and continuously the design product during its complete lifecycle.
Towards Fortifying the Safety and Security of IoT Systems

Author(s): Nguyen, Dang Tu | Advisor(s): V. Krishnamurthy, Srikanth | Abstract: Today's IoT systems include event-driven smart applications (apps) that interactwith sensors and actuators. A problem specific to IoT systems is that buggy apps, unforeseen bad app interactions, or device/communication failures, can cause unsafe and dangerous physical states. Detecting flaws that lead to such states, requires a holistic view of installed apps, component devices, their configurations, and more importantly, how they interact. In this paper, we design IoTSan, a novel practical system that uses model checking as a building block to reveal "interaction-level" flaws by identifying events that can lead the system to unsafe states. In building IoTSan, we design novel techniques tailored to IoT systems, to alleviate the state explosion associated with model checking. IoTSan also automatically translates IoT apps into a format amenable to model checking. Finally, to understand the root cause of a detected vulnerability, we design an attribution mechanism to identify problematic and potentially malicious apps. We evaluate IoTSan on the Samsung SmartThings platform. From 76 manually configured systems, IoTSan detects 147 vulnerabilities. We also evaluate IoTSan with malicious SmartThings apps from a previous effort. IoTSan detects the potential safety violations and also effectively attributes these apps as malicious.
Algorithmic Transparency of Conversational Agents

A lack of algorithmic transparency is a major barrier to the adoption of artificial intelligence technologies within contexts which require high risk and high consequence decision making. In this paper we present a framework for providing transparency of algorithmic processes. We include important considerations not identified in research to date for the high risk and high consequence context of defence intelligence analysis. To demonstrate the core concepts of our framework we explore an example application (a conversational agent for knowledge exploration) which demonstrates shared human-machine reasoning in a critical decision making scenario. We include new findings from interviews with a small number of analysts and recommendations for future research.
Simulations massives de Dynamique des Dislocations: fiabilité et performances sur architectures parallèles et distribuées. (Large scale Dislocation Dynamics simulations: performance and reliability on parallel and distributed architectures)

La Dynamique des Dislocations modelise le comportement de defauts lineiques - les dislocations - presents dans la structure des materiaux cristallins. Il s'agit d'un maillon essentiel de la modelisation multi-echelles des materiaux utilise par exemple dans l’industrie du nucleaire pour caracteriser le comportement mecanique et le vieillissement des materiaux sous irradiation. La capacite des dislocations a se multiplier, s’annihiler et interagir pose de nombreux defis informatiques, notamment sur la maniere de stocker et traiter de maniere efficace les donnees de la simulation. L'objectif de cette these est de repondre a ces defis que posent les simulations massives de Dynamique des Dislocations dans un environnement parallele et distribue au travers du logiciel Optidis. Dans cette these, je propose des ameliorations au simulateur Optidis afin de permettre des simulations plus complexes en utilisant la puissance des super-calculateurs. Mes contributions sont axees sur l'amelioration de la fiabilite et de la performance d'Optidis. La mise en place d'une nouvelle interface d'acces aux donnees a permis de dissocier l'implementation des algorithmes de l'optimisation des performances. Cette structure de donnees permet de meilleures performances tout en ameliorant la maintenabilite du code, meme lorsque les donnees sont distribuees. Un nouvel algorithme de gestion des collisions entre dislocations et de formation des jonctions fiable et performant a ete mis en place. Des techniques de detection de collision empruntees aux application en temps reel et a la dynamique moleculaire sont utilisees pour accelerer le calcul. S’appuyant sur l’utilisation de la nouvelle structure de donnees et un traitement des collisions plus elabore, il permet une gestion de collisions fiable et autorise l'utilisation de pas de temps plus grands. La precision du resultat a ete etudiee en se comparant au code NUMODIS, et la performance d'Optidis a ete mesuree sur des simulations massives contenant plusieurs millions de segments de dislocations en utilisant plusieurs centaines de cœurs de calcul, demontrant que de telles simulations sont realisables en un temps raisonnable.
Progress and Challenges on Entity Alignment of Geographic Knowledge Bases

Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.