Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Keystroke Dynamics Biometrics: Approaches, Advances, and Evaluations

In this review paper we present a comprehensive survey of research efforts in the past couple of decades on keystroke dynamics biometrics. We review the literature in light of various feature extraction, feature matching and classification methods for keystroke dynamics. We also discuss recent trends in keystroke dynamics research, including its use in mobile environments, as a soft biometrics, and its fusion with other biometric modalities. We further address the evaluation of keystroke biometric systems, including traditional and new performance metrics, and list publicly available keystroke datasets for performance benchmarks to promote synergy in the research community.
Personalized scheduling algorithm based on user profile for meta search engine

Scheduling algorithm is to help users choose proper search engines to get more accurate result with less resource. A novel algorithm of database representation is proposed. It probes the usefulness of search engines by query-based classification sampling. And a personalized scheduling strategy incorporating user's interests is designed to select most latently useful search engines for user query. In order to prove that the strategy promotes the effectiveness and efficiency of retrieval by computing the similarity between search engines and user query, a system personalized meta search engine, PMSE was built, and researches and experiments were carried out on PMSE. The results show that the strategy can promote the effectiveness and efficiency of retrieval.
Research on Virtual Navigation System Based on Panoramic Images

This paper proposes a virtual navigation system based on panoramic images.In order to stitch the images automatically from different perspectives,first extracts feature points from the scale space of the images,and assigns the main orientation.Feature vector can be calculated based on neighborhood information,and initial feature points matching can be calculated by using the distance ratio of the nearest neighbor feature point and the next nearest neighbor feature point.Feature matching refinement can be accomplished by using the RANSAC(Random Sample Consensus) algorithm,while calculating transformation parameters between the two images.After completion of image stitching,different perspective views of the scene can be obtained by re-projection algorithm,and gives user an immersive experience.
Machine Learning Coalgebraic Proofs

This position paper argues for a novel method to machine learn patterns in formal proofs using statistical machine learning meth-ods. The method exploits coalgebraic approach to proofs. The success of the method is demonstrated on three applications allowing to distinguish well-formed proofs from ill-formed proofs, identify families of proofs and even families of potentially provable goals.
Compact Models for Asymmetric Double Gate MOSFETs

Double-gate MOSFET's are one possible option to further extend CMOS scaling when planar MOSFET's have reached their scaling limit. This paper presents an analytic potential model for long-channel asymmetric double-gate (ADG) MOSFETs. The asymmetry is due to a difference in the work functions of the two gates. Taur has derived equations from the exact solution to Poisson’s and current continuity equation without the charge-sheet approximation. In previous work by the authors it was shown that, by means of the Lambert function, compact formulae could be derived from Taur’s equations for the symmetric double-gate (SDG) case. In this paper we show that these results of can be extended to the asymmetric case and we construct generalized compact formulae for an ADG device that are suitable for use in SPICE type simulators.
Content-based image retrieval of integrated color texture and shape feature

Image feature extraction and the use of the features as query terms are crucial problems in content-based image retrieval(CBIR) systems.The main focus is on integrated color,texture and shape extraction methods for CBIR.The image is devided into blocks according to regular,then feature is extracted vector in every block.Color features extraction is based on color histograms in YUV space,texture feature based on Gabor filters and shape features based on Zernike moments are calculated.The experimental result indicates that the arithmetic of the extraction based on the feature of color,texture and feature improve image retrieval accuracy.
Assessing asthma quality of life: its role in clinical practice

Key points Asthma patients want help to improve their ability to function in their daily lives (physical, social, occupational and emotional). Awareness of patients’ problems and a willingness to treat them may improve both asthma control and quality of life. The clinician and the patient should negotiate a treatment plan that addresses both asthma control and the patient's needs, and that the patient is willing to follow. Valid, easy-to-use, self-administered asthma quality-of-life questionnaires can be used in the clinic to identify quickly the patient's specific problems and treatment goals. Educational aims To define HRQL and emphasise its importance in patients with asthma. To describe the HRQL impairments experienced by adults and children with asthma. To discuss the role of HRQL in clinical practice and the concept of shared decision making. To provide information on the selection, methods of administration, analysis, interpretation and cultural adaptation of HRQL questionnaires Summary An important contributor to poor patient compliance with treatment instructions may be a discrepancy between the goals of the clinician and those of the patient. Improved clinician awareness of patients’ asthma-related quality-of-life goals and a readiness to address them may enhance patients’ willingness to take medications and, thus, improve both their asthma control and their quality of life. The aim of this article is to explain how quick, valid, easy-to-use, self-administered and clinic-friendly HRQL can be used to identify patients’ greatest needs, ascertain how troublesome they are and ensure that they are included in the treatment plan. HRQL questionnaires can also be used to monitor patient progress over time.
Integration Research of Multi-domain Collaborative Simulation Model Based on HLA

This paper studies the problem of integrated simulation model in the process of collaborative simulation development of complex product.Buffer pool is put forward in the combined use of loose coupling and close coupling.With software bus of High Level Architecture(HLA),the definition criterion of software simulation model in multi disciplinary is unified by using distributed database,program simulative database and XML technology,transformed model data between different software of the collaborative simulation are interacted.Database and file storage provide a moderating effects in the collaborative simulation processes.Service oriented Architecture(SOA) is used to construct the sub module,and simulation software in various fields is loosely coupled to collaborative simulation platform.An example of electromechanical simulation of thermal control mechanism is compared between the centralized simulation and the collaborative simulation.Result shows that the method improves the simulation speed and accuracy.
Why do enterprise transformations fail? HINT: IT’S not what you think

Abstract Enterprise transformation has a serious impact on a firm’s survivability. Even though 70% of firms fail to transform (e.g., ToysRUs, Blockbuster, MySpace, Blackberry, etc.), not enough has been researched on factors that could improve transformation success. Our research explores four new factors—timing, leadership involvement, psychological ownership, and transparency—that influence transformation success. A survey was administered to a sample of 190 transformation experts that shared their transformation experiences at multiple firms. This is one of the few empirical studies that uncover key factors that significantly influenced transformation success at not just one firm but at multiple firms. In addition to contributing to theory, the findings provide practitioners data-driven knowledge that can be applied to their ongoing transformation efforts to increase their odds of beating the 70% failure rate.
Automatic speech recognition, with large vocabulary, robustness, independence of speaker and multilingual processing

This work aims to provide automatic cognitive assistance via speech interface, to the elderly who live alone, at risk situation. Distress expressions and voice commands are part of the target vocabulary for speech recognition. Throughout the work, the large vocabulary continuous speech recognition system Julius is used in conjunction with the Hidden Markov Model Toolkit(HTK). The system Julius has its main features described, including its modification. This modification is part of the contribution which is in this work, including the detection of distress expressions ( situations of speech which suggest emergency). Four different languages were provided as target for recognition: French, Dutch, Spanish and English. In this same sequence of languages (determined by data availability and the local of scenarios for the integration of systems) theoretical studies and experiments were conducted to solve the need of working with each new configuration. This work includes studies of the French and Dutch languages. Initial experiments (in French) were made with adaptation of hidden Markov models and were analyzed by cross validation. In order to perform a new demonstration in Dutch, acoustic and language models were built and the system was integrated with other auxiliary modules (such as voice activity detector and the dialogue system). Results of speech recognition after acoustic adaptation to a specific speaker (and the creation of language models for a specific scenario to demonstrate the system) showed 86.39 % accuracy rate of sentence for the Dutch acoustic models. The same data shows 94.44 % semantical accuracy rate of sentence.
Highly-Efficient Low-Voltage-Operation Charge Pump Circuits Using Bootstrapped Gate Transfer Switches

This paper describes highly efficient on-chip high-voltage multipliers using charge pump circuits. The proposed charge pump circuits use bootstrapped gate transfer switches to avoid the threshold voltage drop in conventional Dickson charge pump circuits and enables them to generate a given voltage with a smaller number of pumping stages, which results in higher efficiency. The SPICE simulation results show that the proposed circuits have high pumping gain, are suitable for low-voltage operation, and have ample current drive capability.
Laser Communications Prototyping for the Group of Mobile Robots

Laser communications in case of a group of mobile robots communication in extreme conditions can be considered as an alternative or supplement to the radio. Thus, this work aims to develop a laser communication terminal for a mobile robot and with the particular focus on building the pointing, acquisition and tracking (PAT) system. During the present work, the idea of the laser terminal design was proposed based on the analysis of requirements and the model of data transmission between two mobile robots. The mathematical model of the PAT control system and stability analysis showed the feasibility of the design. A separate part of the work was also dedicated to the development of the robot orientation determination system and PAT stabilization system. The prototype and all necessary algorithms were implemented and the hardware and software were created. At this point, the laser terminal has been tested and has achieved acceptable performance of 5 degrees of accuracy under existing constraints. The paper was partially supported by the Russian Foundation for Basic Research, project No. 16-07-01264а, 16-07-00935а, 16-07-00811а.
THE KINEMATIC MODEL OF THE TRTR MODULAR SERIAL ROBOT

Abstract: The paper has three parts in its structure: theoretical approach, the kinematic modeling of the TRTR modular serial robot and conclusions, followed by a reference list. After a brief resentation of the iterative method for the kinematic modeling, the mechanical structure of the TRTR robot is mentioned, according to [4]. The iterative method is then applied to the mechanical structure of TRTR robot, based on certain geometrical expressions previously determined. The kinematic parameters are thus obtained, which are the operational angular and linear velocities and accelerations of the gripper with respect to the mobile frame (T5). Applying some transformation equations, these kinematic parameters are eventually expressed with respect to the fixed frame (T0) from the robot base. Key words: modular serial robot, kinematic model, iterative method.
A Rewriting-based, Parameterized Exploration Scheme for the Dynamic Analysis of Complex Software Systems

Today? s software systems are complex artifacts whose behavior is often extremely difficult to understand. This fact has led to the development of sophisticated formal methodologies for program analysis, comprehension, and debugging. Trace analysis is concerned with techniques that allow execution traces to be dynamically searched for specific contents. The search can be carried out forward or backward. While forward analysis results in a form of impact analysis that identifies the scope and potential consequences of changing the program input, backward analysis allows provenance analysis to be performed; i.e., it shows how (parts of) a program output depends on (parts of) its input and helps estimate which input data need to be modified to accomplish a change in the outcome. In this thesis, we investigate a number of trace analysis methodologies that are suitable for analyzing complex, textually-large execution traces in rewriting logic (RWL), which is a logical and semantic framework particularly suitable for formalizing highly concurrent, complex systems. The first part of the thesis is devoted to develop an incremental, slicing-based backward trace analysis technique that achieves huge reductions in the size of the trace. This methodology favors better analysis and debugging since most tedious and irrelevant inspections that are routinely performed during diagnosis and bug localization can be eliminated automatically. This technique is illustrated by means of several examples that we execute by using the iJulienne system, an interactive trace slicer that we developed which implements the backward trace analysis technique. The second part of the thesis formalizes a rich and highly dynamic, parameterized scheme for exploring rewriting logic computations. The scheme implements a generic animation algorithm that allows the nondeterministic execution of a given conditional rewrite theory to be followed up by using different modalities, including incremental stepping and automated forward/backward slicing, which drastically reduce the size and complexity of the traces under examination and allow users to evaluate the effects of a given statement or instruction in isolation, track input change impact, and gain insight into program behavior (or misbehavior). Moreover, cutting down the execution trace can expose opportunities for program optimizations. With this methodology, an analyst can browse, slice, filter, or search the traces as they come to life during the program execution. The generic trace analysis framework has been implemented into the Anima system and we report a thorough experimental evaluation that we conducted which assesses the usefulness of the proposed approach.
DEUCON: Distributed End-to-End Utilization Control for Real-Time Systems

This paper presents the Distributed End-to-end Utiization CONtrol (DEUCON) algorithm. DEUCON can dynamically enforce desired CPU utilizations on all processors in a distributed real-time system despite uncertainties in the system workload. In contrast to earlier centralized control schemes, DEUCON is a distributed control algorithm that is systematically designed based on the Distributed Model Predictive Control theory. We decompose the global multi-processor utilization control problem into a set of localized subproblems, and design a peer-to-peer control structure where each local controller only needs to coordinate with a small number of neighbor processors. DEUCON can provide utilization guarantees similar to a centralized control algorithm, while significantly reducing the per-controller run-time overhead in terms of both computation and communication. Furthermore, it can tolerate considerable network delay and individual processor failures. Consequently, DEUCON can provide scalable and robust utilization control services for large distributed real-time systems that operate in unpredictable environments.
Detection, Tracking of the Dynamic Foreground in Complex Videos and Background Subtraction using Low Rank

: In the modern computer vision applications many different techniques have been proposed for the enhancement of the performance of compression methods of video. One of the approach is for a given scenario contents motion models are used. Then compensated coder of the classical motion is made use which skips or the some parts of the sequential frames of the videos are not coded. The decoder is used for reconstructing the parts of the video frames which are not coded. This paper demonstrates the method of video coding by making use of special surface or texture models. We build the gesture models by making use of motion insights of the humans. Foreground objects moves differently from background hence we construct a new model known as motion classification model.
The User Action Event Generator Design for Leading Malicious Behaviors from Malware in Sandbox

The number of malwares has been consistently growing for several years and the working platform of them was diversified. To analyze these malwares, an analyst uses automated investigation tools as sandbox. However, current malwares apply the various techniques to avoid the detection of the sandbox. Especially, it is hard to be analyzed when the malicious behavior is triggered by user events. In this paper, we propose methods to enter malicious behavior routine in the sample malware codes, which is happened during the virtual execution in the sandbox in order to perform the analysis of malware. We design the methods as the user action event generator using fuzzing. The malicious behaviors triggered by the generator are exported to the sandbox report as API list. We show the result of the event generator.
Face Detection Based on the Matching of Multiple Related Templates

In this paper, a face detection algorithm based on the matching of multiple related templates is presented. The templates are a series of related types: eyes-in-whole and face itself, which are produced by affine transforms varying in stretch and pose from an average frontal face. The eyes-in-whole templates are used as the first search step for face candidates and then the face templates are matched. Finally some heuristic rules are used to verify face detection results. Experimental results obtained from images containing frontal and in-plane rotated faces demonstrate the feasibility of this approach.
Face detection based on skin color with complex scenes

The image color bias produced is corrected by the illuminant color.A skin-color model ofCb-Cr chroma lookup table combined with the brightness is constructed.Pre-processing techniques are adopted to remove some non-face regions and reduce the searching re- gions for face location.Finally,template-matching method is used in the face candidate regions to detect face.Experiments demonstrate that the proposed method can detect faces of rotated angle in 45 degrees from the color images with complex scenes effectively,and is invariant to face emotion,scale and number,with lower error rates.
Research on single image defogging algorithm based on dark primary colors

Dark channel prior can achieve better results when dealing with single image,but this algorithm which is slow takes a long time. This paper brought out a better method that replaced the soft matting algorithm in dark channel prior with guiding filter. Furthermore,it also employed box filter to get high speed. At the same time,in order to improve the output quality of images,it used block processing and 3 × 3 template to solve the dark color of the image,and obtained the better transmittance t values. According to the experiments,the above algorithm achieves not only high speed processing but also better image quality. The processed images are much natural and realistic.
Generating a variety of backchannel forms based on linguistic and prosodic features for attentive listening agents

There is a growing interest in conversation agents and robots which conduct attentive listening. However, the current systems always generate the same or limited forms of backchannels every time, giving a monotonous impression. This study investigates the generation of a variety of backchannel forms appropriate for the dialogue context, using the corpus of counseling dialogue. At first, we annotate all acceptable backchannel form categories considering the permissible variation in backchannels. Second, we analyze how the morphological form of backchannels relates to linguistic features of the preceding utterance such as the utterance boundary type and the linguistic complexity. Based on this analysis, we conduct machine learning to predict backchannel form from the linguistic and prosodic features of the preceding context. This model outperformed a baseline which always outputs the same form of backchannels and another baseline which randomly generates backchannels. Finally, subjective evaluations by human listeners show that the proposed method generates backchannels more naturally and gives a feeling of understanding and empathy.
A Web-Based Medical Diagnostic System using Data Mining Technique

The purpose of this paper is to apply data mining technique for medical diagnostic system on web application supporting Thai language. This web would help users to reduce expense and time of visiting doctors. It is capable of giving preliminary diagnosis. The proposed system will discover the implication knowledge with association rules derived from formal concept analysis (FCA) to advice co-symptom of diagnosing to achieve more correctly. The association rules are built from its subconcept and superconcept relation from concept lattice. In addition, the proposed system supporting Thai language is challenged because this language is streaming string without the boundary delimiters. The proposed system is developed based on online web application to demonstrate real situation. The result show that the proposed system can suggest co-symptom to achieve more correctness for medical diagnostic
Text classification based on the TAN model

This paper proposes a text classification method based on TAN model. Naive Bayesian classifier is the most effective and popular text classification method, but its attribute independence assumption makes it unable to express the dependence among text terms. TAN (Tree Augmented Naive Bayes) combines the simplicity of Naive Bayesian with the ability to express the dependence among attributes in Bayesian network. This paper reviews some existing text methods, introduces TAN model, and applies TAN model to text classification. Naive Bayesian and TAN classifiers are also compared by our experiments. Experimental results show TAN classifier has better performance.