Reviewerly/exHarmony huggingface dataset:
A game-theoretic defensive approach for forensic investigators against rootkits

Forensic science aims to present evidence in the courtroom, in a forensically sound manner. Therefore, forensic procedures must guarantee the provability, admissibility, accuracy, and authenticity of the case's evidence. However, anti-forensics threaten forensic procedures by forging, hiding, and even modifying remaining evidence in a crime scene. For instance, rootkits hide traces of attacks in a compromised system. To prevent anti-forensics, forensic investigators use de-anti-forensic methods (e.g. anti-rootkits). The necessity of more research on anti-forensics motivated us to propose a game-theoretic approach to model the interactions between an attacker and an investigator (players) who use rootkits and anti-rootkits, respectively. We assume these players act competitively, and each does not know his/her opponent's pay-off. We identify sets of characteristics for rootkits and anti-rootkits to profile them and define each player's actions. We examine the existence of the Nash Equilibrium for a two-player game. Experimental results show the simulated game is convergent. Thus, we identify the investigators' most desirable and stable defensive strategies against the attacker's most desirable and stable offensive strategies. We also formulate a relationship between characteristics of rootkits and anti-rootkits using the Nash Equilibrium of the game. Finally, we propose some general features to help investigators to evaluate anti-rootkits and design more efficient defensive tools.
Document-Level Sentiment Classification in Japanese by Stem-Based Segmentation with Category and Data-Source Information

Existing studies focus on text information while ignoring category and data source information, both of which are verified to be important in interpreting sentiments in travel comments in this paper. Furthermore, the unique linguistic characteristics of Japanese cause difficulty in applying the conventional token-based word segmentation methods to Japanese comments directly. In this paper, we propose a method of stem-based segmentation based on Japanese linguistic characteristics and incorporate it with category and data source information into a hierarchical network model for document-level sentiment classification. Empirical results of our proposed model outperform existing models on a real-world dataset.
A Scheme for Latency Analysis of Different Cryptography Methods for Security in 5G Era

In this paper, we present a scheme for performance analysis of different cryptography methods namely symmetric ciphers, and asymmetric ciphers to encrypt and decrypt the text, and audio data for online secure data access in the browser window using LabVIEW approach on myRIO hardware module for 5G systems. In this work, the text message with the different payloads is encrypted and decrypted. Similarly, the same process repeated for audio data. First, the text, and audio data are converted into string format; then the data format is encrypted using different cryptography methods from the sender side. On the receiver side, same cryptography method is used to decrypt the data with a generated key associated among the parties. In the case of symmetric ciphers, AES, Blowfish, DES, and IDEA are used to encrypt and decrypt the data. Further, the asymmetric ciphers, RSA, ECC, and DSA are used to encrypt and decrypt the data. LabVIEW programming tools are used to develop a scheme of cryptography methods. Finally, latency analysis is made on text, and audio data with symmetric and asymmetric ciphers.
Analysis of Flood Discharges of Serayu River at Serayu Barrage using HSS Gama I and Nakayasu Models

Abstract Serayu River, located in the central of the Java island, is one of the largest rivers in the island. The length of the river to the control point of the Serayu barrage is 118.276 km and the basin areas is 3001.73 km 2 . The aim of the research was to analysis flood discharge in the Serayu river at Serayu Barrage. The analysis of flood discharges on the Serayu River would use the HSS Gama I and Nakayasu models. The result of research shown that f flood discharges using the Nakayasu model Qp = 18.833 m3/sec and HSS Gama I model Qp = 59.953 m3/sec. by using rainfall intensity of 7.431 mm / hour, the peak discharge with the Nakayasu model Qp = 139.156 m3/sec and HSS Gama I model Qp = 445.509 m3/sec.
Robust Face Authentication Based on Dynamic Quality-weighted Comparison of Visible and Thermal-to-visible images to Visible Enrollments

We introduce, in this paper, a new scheme of score level fusion for face authentication from visible and thermal face data. This proposed scheme provides a fast and straightforward integration into existing face recognition systems and does not require recollection of enrollment data in thermal spectrum. In addition to be used as a possible countermeasure against spoofing, this paper investigates the potential role of thermal spectrum in improving face recognition performances when employed under adversarial acquisition conditions. We consider a context where individuals have been enrolled solely in visible spectrum, and their identity will be verified using 2 sets of probes: visible and thermal. We show that the optimal way to proceed is to synthesis a visible image from the thermal face in order to create a synthetic-visible probe; and then to fuse scores resulting from comparisons between visible gallery with both visible probe and synthetic-visible probe. The thermal-to-visible face synthesis is performed using a Cascaded Refinement Network (CRN) and face features were extracted and matched using LightCNN and Local Binary Patterns (LBP). The fusion procedure is performed based on several quality measures computed on both visible and thermal-to-visible generated probes and compared to the visible gallery images.
Multi-sensor Box Particle Filter with Iterated Measurement Contraction

Ahstract-The box particle filter is a time-saving alternative to the particle filter for nonlinear multi-target tracking in the presence of interval measurements. Box particle implementations of single-sensor multi-target tracking algorithms have been developed in the literature, which involve the contraction of box particles under the constraints imposed by the set of measurements from the sensor. However, tracking with multiple sensors all returning interval measurements still remain an unanswered issue for box particle filtering. Namely, there is no available mechanism for the contraction of box particles given multiple sets of measurements from multiple sensors. In this paper, a novel mechanism is proposed that features iterated contraction of box particles under the constraints imposed by the sets of measurements from the sensors. Based on the proposed mechanism a multi-sensor box particle implementation is developed. Simulation results show that the proposed mechanism is effective for the considered issue.
Effects of Horizontal Field of View Extension on Spatial Judgments in Virtual Reality

It is known that observers tend to misperceive distances during spatial judgment tasks in virtual reality. Virtual environments restrict field of view as compared to real environments. We explore whether horizontal field of view restriction affects real or perceived ocular convergence. We also explore effects that the size and symmetry of field of view may have. We find that convergence is not impacted by altering field of view, but the subjective median plane is affected. We also find that distance is better estimated in wider fields of view, and that lateral bias in estimation is correlated to the symmetry of the field.
Doppler Bearing Tracking with Fusion from Heterogeneous Passive Sensors: ESM/EO and Acoustic

A heterogeneous acoustic and Electronic Support Measures (ESM)/Electro-Optic (EO) measurement fusion approach has recently been developed to address the Bearings-Only Tracking (BOT) problem, where target range can be inferred from the acoustic propagation delay. The target state is therefore observable even when the sensor platform is stationary. In this paper, we seek to extend this existing approach to the Doppler-Bearing Tracking (DBT) problem, in which the acoustic measurements include Doppler shifted frequencies on top of bearing measurements. Another recent study shows that the unknown acoustic propagation delay poses an additional challenge in the DBT. The Doppler shifted frequencies cannot be expressed analytically in the measurement model, and so a numerical approach should be employed. Thus, to address the DBT, we present the Out-Of-Sequence Measurement fusion from heterogeneous Acoustic and ESM/EO sensors (OOSM-AE-DBT) algorithm, which considers the effects of propagation delay in both time and Doppler shifted frequencies. Simulation tests are conducted for targets of various speeds to assess the performance of the algorithm and results are presented.
Normalization of GOP for Chinese Mispronunciation Detection

Goodness of Pronunciation (GOP) is a kind of Computer-Assisted Pronunciation Training (CAPT) technique that can provide language learners with scoring feedback, and its accuracy easily suffers from the performance of model alignment and phone classification. In order to reduce the influence of those aspects, this paper proposes two ways to normalize GOP scores. The first is to separate the GOP calculation of Chinese Initials and those of Chinese Finals. The second is to use the corresponding native pronunciation score as a template to scale the non-native one. In 2-hours test set of Japanese speaking Chinese corpus, the experiment results show the average relative improvement of Diagnose Accuracy (DA) in the approach one is 16.9%, and 28.7% in scaling approach comparing to the traditional scoring method. The combination of those two methods achieves the best performance. The result is 35.9% of average relative improvement. Experimental results demonstrate the effectiveness of the two methods.
Chapter 9. Parts of speech categories in the lexicon of Modern Hebrew

Parts of speech in Modern Hebrew are analyzed in relation to three categories: Open Class items (Nouns, Verbs, Adjectives), Closed Class paradigmatically organized grammatical items (e.g., Pronouns, Case-Markers, Conjunctions), and Intermediate elements lying between the two (e.g., Prepositions, Adverbs, Floating Operators). The chapter considers what is meant by “a word” in Hebrew, taking into account the contrast between older and more current items as well as between conventional dictionaries and the mental lexicon, and the critical role of morphology in the Hebrew lexicon. The verb lexicon is characterized by types of consonantal roots (full versus defective) and the binyan conjugation patterns or prosodic templates. Nouns and adjectives are described as morphologically less restricted than verbs, including loan words that are partially integrated into the Hebrew phonological system, generally not constructed by the nominal morphological patterns (miškalim ‘weights’). So-called “function words”, traditionally grouped together under the label particles and analyzed here as members of either closed or intermediate classes of items, are also typically morphologically non-derived. The chapter concludes by summarizing current trends in lexical innovation in relation to productivity in the MH lexicon.
A Comparative Analysis of Quantum Inspired Evolutionary Algorithm with Differential Evolution, Evolutionary Strategy and Particle Swarm Optimization

QIEA-ℝ (Quantum Inspired Evolutionary Algorithm with Real Codification) inspired by the concept of quantum superposition was proposed for solving numerical optimization problems. QIEA-ℝ was compared with Differential Evolution (DE) and Particle Swarm Optimization (PSO) on the dimension of 30 input variables. This study analyzes the performance of QIEA-ℝ with Differential Evolution (DE), Evolutionary Strategy (ES) and Particle Swarm Optimization (PSO) on the dimension of 250, 500 and 1000 input variables. The experimental results show that QIEA-ℝ is significantly better than DE, ES, and PSO.
Control and Understanding in Malware and Legitimate Software

This paper presents a study examining mental models of malware and regular software, in search of deep misunderstandings about malware and software which can be used in the design of new software and educational material. The study involved both a questionnaire, and two diagramming exercises. We decided to use a diagramming exercise because it is an effective medium for expressing spatial information which is important to mental models, and can get lost in verbal reports. Ours is the first study to examine mental models of malware using this technique. For the diagramming tasks, participants were asked to draw their understanding of how a word processor and malware work, respectively. Several key patterns emerged. General knowledge about malware, shown in the questionnaire responses was reasonable, but the deeper understanding of how malware functions, shown in the drawings, was concerning. Participants showed lesser knowledge of malware compared to regular software, and they seemed to regard malware as a fundamentally different kind of entity than regular software. They made black-and-white distinctions between malware and regular software in terms of whether the software is helpful or harmful, who the software serves, and who controls it. We discuss how these findings relate to decision-making online, and suggest that it might be beneficial to increase support for the control users have over their software. We speculate this might better equip users to make safe decisions surrounding software, thereby decreasing the effectiveness of malware.
A Novel Method for Multiple Power Quality Disturbances Classification Using a Multi-Task Convolution Neural Network

In this paper, we proposed a multi-task convolution neural network (MT-CNN) to realize the multi-label classification of multiple power quality disturbances (MPQDs). According to the characteristics of PQD signals, the multiple labels of MPQDs are assigned to three groups corresponding three learning tasks in MT-CNN. In the process of training, these tasks can help each other and the label correlations among various PQDs are utilized in the joint learning of interrelated tasks. Further, since each group can be designed different network structure, the MT-CNN can extract more discriminative features and obtain better recognition rate compared with traditional CNN. In addition, due to the special network structure, the MT-CNN has very strong ability to resist over-fitting. Extensive experiments have demonstrated that our network had better performance and it can greatly improve the accuracy rate for identifying MPQDs under different SNR conditions.
Feature-Aided Multitarget Tracking for Optical Belt Sorters

Industrial optical belt sorters are highly versatile in sorting bulk material or food, especially if mechanical properties are not sufficient for an adequate sorting quality. In previous works, we could show that the sorting quality can be enhanced by replacing the line scan camera, which is normally used, with an area scan camera. By performing multitarget tracking within the field of view, the precision of the utilized separation mechanism can be enhanced. The employed kinematics-based multitarget tracking crucially depends on the ability to associate detection hypotheses of the same particle across multiple frames. In this work, we propose a procedure to incorporate the visual similarity of the detected particles into the kinematics-based multitarget tracking that is generic and evaluates the visual similarity independent of the kinematics. For evaluating the visual similarity, we use the Kernelized Correlation Filter, the Large Margin Nearest Neighbor method and the Normalized Cross Correlation. Although no clear superiority for any of the visual similarity measures mentioned above could be determined, an improvement of all considered error metrics was attained.
A Scalable Approach to Time Series Anomaly Detection &amp; Failure Analysis for Industrial Systems

Modern industrial systems are complex and require continuous monitoring for smooth operation. Even a small anomaly in an important variable could lead to suboptimal performance, or worse, a system failure. In critical systems, anomalies that go unaccounted can lead to increased maintenance and operating costs. For this reason, industrial systems opt for algorithms that can predict these anomalies. Modern industrial systems have tens or hundreds of variables with potential correlation with an anomaly. For this reason, a method of detecting anomaly and key failure is developed. By finding the key factors for failure, we can get a better insight about that anomaly, avoiding it in the future. Creating a scalable anomaly detection and key factor analysis framework for different industrial systems is difficult as the systems are very dynamic and varying. In our work, we have proposed a scalable stochastic anomaly detection and key factor analysis framework that is scalable across industries reducing downtime costs, maintenance overheads and increasing system efficacy. We have used a combination of Bayes' theorem and Bitmap detection to detect anomalies in time series data. Then, we have aggregated the anomalies and built a mapping tree to find key factors of the anomalies. We have successfully scaled our work achieving high accuracy anomaly detection and precise key factor analysis for different industries.
Service Oriented Reference Architecture for the Development of Context-awareLearning Environments

This Research-to-Practice Full Paper is concerned with the development of u-learning environments, which is a very complicated task due to, among other factors, the complexity in managing large amounts of context information and adapting content to different users and contexts. Possible solutions to overcome the challenges and provide support for the development of context-aware systems is the use of reference architectures (RA) and service-oriented architectures (SOA). RA is a software architecture that can be applied to build architectures of specific domain systems, supporting the development of system families. SOA is an architectural style that provides functionalities encapsulated in Web services, which make it easier the integration of dynamic adaptive behavior into the applications. Therefore, this paper presents oriented RA to assist the development of context-aware ubiquitous learning environments, characterized by their ability to obtain and use information from the users' context. This enables the software to adapt the behavior, services or didactic resources, in accordance with the context where learning is taking place (e.g., location, time, student preference, etc.). The presented RA, named MOA, can be instantiated to develop specific context-aware learning environments. MOA was built based on ProSA-RA, which is a RA development process, and evaluated using the checklist technique. Also, a prototype was built to illustrate MOA instantiation. The evaluation served to demonstrate the validity of MOA, both at a practical level (applicability) and in meeting the quality requirements stipulated for RA.
Evaluation of neural networks and feed forward neural network models on to content-based image retrieval

The advanced technological developments in the machine learning models are being to develop new methodologies for content based image retrieval (CBIR). Since the ML models has the capability of learning global visual features for any given query enables them a better solutions for the models deal with massive amount of different image dataset. At the same time, the application of ML models like neural networks (NN) has some difficulties like the search goal has to be fixed or the computation complexity become too expensive for an online setting. In this study, a performance evaluation is carried out between NN and feed forward neural network (FNN) for CBIR. A set of benchmark images is employed to study the performance of the two ML models interms of different measures. The attained results exhibit that the FNN model is found to be better than the NN on all applied test images.
Stability of Optimal Filter Higher-Order Derivatives

In many scenarios, a state-space model depends on a parameter which needs to be inferred from data. Using stochastic gradient search and the optimal filter (first-order) derivative, the parameter can be estimated online. To analyze the asymptotic behavior of online methods for parameter estimation in non-linear state-space models, it is necessary to establish results on the existence and stability of the optimal filter higher-order derivatives. The existence and stability properties of these derivatives are studied here. We show that the optimal filter higher-order derivatives exist and forget initial conditions exponentially fast. We also show that the optimal filter higher-order derivatives are geometrically ergodic. The obtained results hold under (relatively) mild conditions and apply to state-space models met in practice.
Wavelet-SVD based Zero-Bit Watermarking for Securing Biometric Images

The Biometric incorporates pictures, for example, iris filters, fingerprints, palm prints which have fine highlights of uniqueness for every person. Since, these pictures are functional in numerous applications, for example, get to control, military, CBI examination, ID, and so forth, its security is a significant concern. A methodology of verifying biometric pictures can be Digital Watermarking. Customary watermarking can mutilate the picture that causes data loss. Indeed, a little contortion can leave biometric of an individual unrecognizable. So here is an urgent requirement for a watermarking plan that doesn't misshape the original picture by any means. This paper presents a watermarking of zero bit strategy in which one of a kind distinguishing proof codes are produced from biometric pictures by applying Discrete Wavelet Transform (DWT) trailed by Singular Value Decomposition (SVD). Exploratory outcomes delineate that exceptional highlights separated are profoundly steady even by various picture preparing attacks and henceforth declare its value for classified biometric pictures.
Myanmar Dialogue Act Recognition (MDAR)

This research aim to make the very first machine learning based Myanmar Dialog Act Recognition (MDAR) for Myanmar Dialogue System. As we know, Dialog Act (DA) recognition is the early level of dialogue understanding which can capture aspects of the user, and they are sentence-level units that represent states of a dialogue, such as greeting, question, inform, and so on. We focus on the current works about DA recognition, especially for Myanmar Dialogue. In this work, we used two machine learning approaches, which are Naïve Bayes classifier and Support Vector Machine (SVM), for dialogue act tagging in the MmTravel (Myanmar Travel) corpus, and the results of two approaches are slightly different but the result of SVM approach attained in the term of average F-measure scores of 0.79; showed that these approach has moderately good accuracy for Myanmar dialogue.
Nonlinear State Estimation Using Skew-Symmetric Representation of Distributions

Knowledge about higher moments, such as skewness and kurtosis, of the state of a stochastic system has potential benefits for state estimation. In order to model more complex nonlinear problems involving higher moments, a skew-symmetric representation of distributions is employed in this work. Based on a first-order skew-Gaussian representation, a novel method for nonlinear point estimation is developed. The proposed skew-Gaussian (SG) filter is more general than traditional Gaussian filters and LMMSE-based nonlinear filters, which propagate only the first two moments. Numerical results illustrate that our SG filter can outperform conventional nonlinear filtering methods.