Reviewerly/exHarmony huggingface dataset:
Exploring Context’s Diversity to Improve Neural Language Model

The neural language models (NLMs), such as long short term memery networks (LSTMs), have achieved great success over the years. However the NLMs usually only minimize a loss between the prediction results and the target words. In fact, the context has natural diversity, i.e. there are few words that could occur more than once in a certain length of word sequence. We report the natural diversity as context's diversity in this paper. The context's diversity, in our model, means there is a high probability that the target words predicted by any two contexts are different given a fixed input sequence. Namely the softmax results of any two contexts should be diverse. Based on this observation, we propose a new cross-entropy loss function which is used to calculate the cross-entropy loss of the softmax outputs for any two different given contexts. Adding the new cross-entropy loss, our approach could explicitly consider the context's diversity, therefore improving the model's sensitivity of prediction for every context. Based on two typical LSTM models, one is regularized by dropout while the other is not, the results of our experiment show its effectiveness on the benchmark dataset.
Human Fall Detection Using Temporal Templates and Convolutional Neural Networks

The emerging areas of smart homes and smart cities need efficient approaches to monitor human behavior in order to assist them in their activities of daily living. The rapid growth in technology led to the use of depth information for privacy-preserving video surveillance, thereby making depth-based automatic video surveillance, a major area of research in academic and industrial communities. In this work, a temporal template representation of depth video is used for human action recognition using convolutional neural networks. A new temporal template representation capturing the spatial occupancy of the subject for a given time-period is proposed to recognize human actions. The ConvNet features extracted from these temporal templates that capture the local discriminative features of these actions are used for action detection. The efficacy of the proposed approach is demonstrated on SDUFall dataset.
Genetic algorithm attack on Enigma’s plugboard

The history, operating principles, strengths, and weaknesses, of the German cipher machine Enigma, have been widely studied for almost 50 years. Even though Bletchley Park regularly broke Enigma encrypted traffic during World War II, new pieces of information and fresh analysis are still aggregated to the remarkable “puzzle” called Enigma. This paper shows that Enigma’s plugboard is vulnerable to Genetic Algorithm (GA) attacks, which solves Enigma’s plugboard faster than earlier published ciphertext-only techniques. The Genetic Algorithm does this using the counting measure Index of Coincidence (IC). Independently of the GA, but related to the analysis, we introduce a new measure Progress Index of Coincidence (PIC). PIC is a measure of the relative progress in decryption between the ciphertext and plaintext measured by IC.
Preparing, Not Repairing

LibGuides are widely used in academic libraries to organize information and resources to better serve their users, yet the maintenance of LibGuides across many different academic libraries ranges from neglectful to fastidious, making it difficult to determine the accuracy and helpfulness of information on LibGuides (Randtke & Burrell, 2012). A Scrapy-based Python script developed by the SIS Programming Team was used to crawl the LibGuides of each academic library member of the ARL, and a second script was used to test the HTTP status of each link.  Maintaining link validity in LibGuides is a proactive and strong first step toward patron satisfaction, and using a combination of scripts and manual processes can be a proactive way to monitor link validity and fight link rot.
Tracking of Spline Modeled Extended Targets Using a Gaussian Mixture PHD Filter

This paper presents the integration of a spline based extension model into a probability hypothesis density (PHD) filter for extended targets. Using this filter the position and extension of each object as well as the number of present objects can jointly be estimated. Therefore, the spline extension model and the PHD filter are addressed and merged in a Gaussian mixture (GM) implementation. Simulation results using artificial laser measurements are used to evaluate the performance of the presented filter. Finally, the results are illustrated and discussed.
Formation of Future Mathematics Teachers’ Professional Competences Using Error Correction Tasks

The article presents results of studying the techniques to form future Mathematics teachers’ professional competences. The authors describe the process of forming pedagogical students’ professional competences using error correction tasks within the framework of the discipline “Methodology of Teaching Mathematics”. Stage-by stage methodology of using error correction tasks to form the key professional competences is proposed, which determines originality of the study. Special attention is paid to justifying the efficiency of error correction tasks when forming future Mathematics teachers’ professional competences.
Deep Learning Approach: Detection of Replay Attack in ASV Systems

Saranya, S. Rupesh Kumar, Suvidha Bharathi, B.Automatic speaker verification (ASV) system is a bio-metric authentication system, which accepts or rejects speech utterance depending on the voiceprint of speakers. Vulnerability of such system to spoofing attacks is the biggest challenge at hand. Proposed system is an initiative toward improvising the countermeasure for replay attacks on ASV systems. It is developed and tested on ASVspoof2017 corpus. Constant Q cepstral coefficients (CQCCs) feature is extracted to build the models using simple neural network and convolutional neural network. CQCC feature is chosen, as it a feature proved to be robust for ASV spoof detection when tested on GMM classifier. A significant improvement of 4.9% EER is achieved using convolution neural network (CNN).
Domain Neural Chinese Word Segmentation with Mutual Information and Entropy

Chinese word segmentation (CWS) is an important basic task for NLP. However, the word segmentation model trained by the generic domain corpus has a significant decline in performance in the word segmentation task oriented to the specific domain. Aiming at the features of domain segmentation, this paper using domain corpus as the training samples, and proposed combined with the terminology dictionary, new word detection and Bi-LSTM-CRF segmentation method to improve the problem of out-of-vocabulary (OOV). The word segmentation experiment was carried out on the corpus of the automotive domain. The results show that the precision and recall of the word segmentation have reached 0.95, and the value of F1 also achieved 0.95, and they are better than state-of-the-art method. This method can also be combined with N-gram and chi-square statistic to further improve the recognition accuracy of OOV.
Performance Analytics of a Virtual Reality Streaming Model

This work focuses on post-analysis of performance results by means of Performance Analytics. The results to be post-analysed are provided by a Stochastic Fluid Flow Model (SFFM) of Virtual Reality (VR) streaming. Performance Analytics implies using the Machine Learning (ML) algorithm M5P for constructing model trees, which we examine amongst others for asymptotic behaviours and parameter impacts in both uni- and multivariate settings. We gain valuable insights into key parameters and related thresholds of importance for good VR streaming performance.
Security Management Techniques and Tools for IS Auditing

Every organization needs to constitute a strong information system (IS) and security management in order to improve its business processes with the best practices. Information system processes must be reviewed and audited by IS auditors. IS auditors use a set of tools and techniques to perform the auditing process on the organizations. In this paper, we evaluate a set of tools and techniques which perform the security management of the organizations. Also, we classify the security management into five domains such as processes of auditing, governance and management, IS acquisitions and development, IS operations and support, and protection of information assets. Each domain has a set of auditing tasks. These tasks need inputs from various knowledge sources such as planning, risk assessment, evidence collection, laws and regulations, etc. Moreover, these tasks are performed through a number of tools and techniques in order to automate the security management process for IS auditors. The evaluation of tools and techniques target the security areas such as management, risk management and internal auditing controls with auditing tasks. Our main contribution is to determine the processes, tasks, and the suitable tools/techniques for each information system area from cybersecurity perspective. Moreover, each organization can analyze the security gaps in order to find a suitable solution for bridging these gaps.
Multivariate Prediction in Photovoltaic Power Plants by a Stacked Deep Neural Network

In this paper, a new approach on energy time series prediction is carried out. We propose a deep learning technique with the employment of specific neural network architectures: Convolutional Neural Network and Long Short-Term Memory network. The goal is to exploit the correlation between several time series, joining and filtering them together as to bring out the long-term dependencies among all the observations. We superpose many different functional layers, thus providing a stacked scheme that can result in a greater approximation capability. The novel architecture is assessed in a real-world prediction problem, in order to evaluate the performance regarding prediction accuracy.
The modified method for position estimation of human body joints in video sequences

Reliable recognition of human movements has a wide range of applications, including games, human-computer interaction, security and healthcare. In recent years, computer graphics and computer vision researchers have developed plenty of new motion-capture algorithms that operate on simpler hardware and with far fewer limitations than before. The objective of this paper is to improve the accuracy of estimation of human skeleton joints positions in video sequences. Particularly the proposed method in this paper consists of five blocks. The input sequence of images is fed to the tracking and motion compensation unit where the tracking algorithm determines the object displacement and centers it within the frame. The motion information is also propagated to the additional unit of point-of-view estimation. This unit calculates the motion angles in the frame and monitors the object size, thus determining whether the object is approaching or moving away from the camera, and then feeds these data to the neural network. The network consists of three convolutional layers. Each convolutional layer is followed by a pooling layer. The last pooling layer connects to the cascade of three fully connected layers. All activation functions in these layers are the ReLU ones, except the last layer, where the linear activation is used. The HOG3D features treated as the input of the first convolutional layer. The data from the point-of-view, tracking and motion compensation unit goes directly to the input of fully connected layers. To cope with inaccurate or undetected joints positions, the method uses the additional procedure, which determines unreliable joints and extrapolates their new positions from the previous ones using the additional neural network. It is assumed that this structure of the method improves the position prediction accuracy due to the following reasons: taking into account the information about motion angles and zooming allows to distinguish movements that are similar in centered frames but different in displacement; using of adaptive window size for HOG3D features; using the neural network to extrapolate the positions of joints in case of absence of the prediction or in case of its low accuracy. Experiments on the HumanEva-1 dataset confirmed that the suggested modifications permit achieving higher accuracies, and thus the prospect of the use of proposed modified method to predict the body position in motion recognition systems.
Differential Privacy in Online Dating Recommendation Systems

By their very nature, recommendation systems that are based on the analysis of personal data are prone to leak information about personal preferences. In online dating, that data might be highly personal. The goal of this work is to analyse, for different online dating recommendation systems from the literature, if differential privacy can be used to hide individual connections (for example, an expression of interest) in the data set from any other user on the platform - or an adversary that has access to the information of one or multiple users. We investigate two recommendation systems from the literature on their potential to be modified to satisfy differential privacy, in the sense that individual connections are hidden from anyone else on the platform. For Social Collab by Cai et al. we show that this is impossible, while for RECON by Pizzato et al. we give an algorithm that theoretically promises a good trade-off between accuracy and privacy. Further, we consider the problem of stochastic matching, which is used as the basis for some other recommendation systems. Here we show the possibility of a good accuracy and privacy trade-off under edge-differential privacy.
Multi-Objective Optimization Techniques for Software Refactoring: A Systematic Literature Review

Software Refactoring is an essential activity of software maintenance. It aims at improving the internal structure of the program without affecting its external functionalities which not only aids in improving maintainability and readability but also helps in reducing overall software complexity. Many different manuals and automated software refactoring tools are available but most of these tools focus single objective refactoring i.e. improving the quality or reducing the code lines. Software refactoring involves many factors so different authors have proposed different multi-objective software refactoring approaches. We have performed systematic literature to classify and analyzed the studies published in the field of multi-objective software refactoring. The main objectives of our research are to categorize the studies on multi-objective software refactoring according to 4 criteria. We have considered studies from electronics databases from 2014 to 2019. A total of 19 studies were finalized based on our inclusion-exclusion and quality assessment criteria. The results of our research show that NSGA-II is a widely popular technique in the domain of multi-objective software refactoring whereas NSGA-III is popular when many objectives were considered. Furthermore, 11 most widely uses open source and industrial projects are identified which are used to evaluate the multi-objective software refactoring approaches. It was also observed that Precision, Recall and Inverse Generation Distance are commonly used evaluation metrics. The chronological distribution of studies shows that 2016 was the most productive research year in this field. Our results show that 76% of studies are ranked high based on our predefined quality assessment criteria. Based on our results we have concluded that multiobjective software refactoring is still an emerging field and there is a need to apply the latest state-of-the-art multi-objective approaches to get better results.
Augmenting and Automating Corpus Enrichment

An agent in pursuit of a task may work with a reference library containing documents with linked subjective content descriptions. Faced with a new document, an agent has to decide whether to include the new document in its reference library. Basing the decision on only words, topics, or entities has shown not to lead to a balanced performance for varying documents. Even a combination of words and descriptions does not lead to a single indicator, requiring manual post-processing. Therefore, in this paper, we build a single indicator by detecting the type of a new document using sequential information about descriptions, thus automating the decision. Specifically, an ensemble of hidden Markov models for the document types detects the type of a document. The agent then bases its decision on the detected type. Using hidden Markov models also allows for identifying positions of interest within a new document. A case study shows the effectiveness of our approach.
Analysis of the Visual Analytics Techniques for Monitoring Heterogeneous Data Streams

The efficiency of complex object monitoring as well as detection of anomalies in its behavior strongly depends on automatic models used and the way how this information is presented to the analysts in order to maintain their situational awareness. The paper analyzes the existing visualization-driven approaches to the monitoring different complex objects targeted to form its normal behavior as well as highlight possible anomalous deviations in its functioning. The most commonly used visualization and interaction techniques for monitoring streaming heterogeneous data are presented, authors discuss their advantages and disadvantages. The analytical dashboard for monitoring and correlation heterogeneous data from distributed sensors of the heating conditioning and ventilation system is presented.
Development of a Filipino Speaker Diarization in Meeting Room Conversations

Speaker diarization pertains to the process of determining speaker identity at a given time in an audio stream. It was first used for speech recognition and over time became useful in other applications such as video captioning and speech transcription. Recently, deep learning techniques have been applied to speaker diarization with considerable success, however, deep learning are conventionally data intensive and collecting large training samples can be difficult and expensive to collect especially for resource scarce languages. This study focused on investigating a speaker diarization approach for meeting room conversations in the Filipino language. To compensate for lack of resources, a one shot learning strategy was explored using Siamese neural network. Among the experiments conducted, the lowest diarization error rate yielded to 46%. There are, however, more parameters that can be tuned to improve the diarization results. To the best of our knowledge, no work in speaker diarization dedicated for Filipino language has yet been done.
Towards A Practical Approach to Improve the Interdisciplinary Teaching and Learning Process through M-learning Innovative Projects

This research to practice work in progress has two main objectives: i) the improvement of interdisciplinary teaching and learning processes in an integrated course of informatics in high school course and; ii) turning students into learners by the designing and development of innovative features for m-learning applications. Such objectives are integrated each other, since the improvement in the teaching and learning processes will not be restricted in the proposal and use of a practical framework for the improvement of students' skills in the computational thinking, but also in the use of the m-learning features and applications to improve educational issues faced by both students and teachers. In this perspective, active-learning and problem-based learning theories were considered to support the proposal of a series of activities composing a framework to be adopted to achieve the target objectives. The interdisciplinary teaching and learning processes improvement has its roots at the proposal of solutions for problems into disciplines of high school curricula through the innovative m-learning features to be designed and developed by the students. In this paper it is discussed the motivations about the proposal of this project, the teaching and learning implications in the context of a integrated course of informatics in high school course, its planning and a pilot conduction of mainly initial activities with the volunteers students. Such activities include: (a) the identification of problems and the planning of solutions based on m-learning features; (b) the design and development phases of an initial set features; and (c) a preliminary evaluation of usability of some mockups of m-learning application. At the end, the identified evidence are discussed, mainly those related with the implications for the interdisciplinary scenario for the teaching and learning processes, the initial identification of benefits in the perspective of the participation of students as learners and the future work, as well.
Japanese grammatical simplification with simplified corpus

We construct a Japanese grammatical simplification corpus and established automatic simplification methods. We compare the conventional machine translation approach, our proposed method, and a hybrid method by automatic and manual evaluation. The results of the automatic evaluation show that the proposed method exhibits a lower score than the machine translation approach; however, the hybrid method garners the highest score. According to those results, the machine translation approach and proposed method present different sentences that can be simplified, while the hybrid version is effective in grammatical simplification.
Recent Advances in Extreme Learning-Based Approaches for Breast Cancer Diagnosis

This paper reviews some of the notable recent findings in breast cancer detection for Computer-Aided Diagnosis (CADx) systems using Extreme Learning Machine (ELM) approaches. Digital mammogram classification using machine learning is a popular technique used for minimizing the risk of beast cancer. In this paper, a brief survey on the usage and appreciability of ELMs for breast cancer detection in CADx systems has been studied. The databases mostly preferred for training and testing of the ELM-based systems include the Mini-MIAS and DDSM databases among a select group of synthetic mammographic databases; they may also include actual medical images procured from supportive hospitals. In most of the reported results, it has been observed that the ELM-based approaches for breast cancer detection show superior results in terms of accurate classification as well as accurate segmentation. The articles studied in this review show how ELMs have been utilized in both a basic form as well as various modified versions.
Prostate Segmentation and Tumor Detection from MR Images Using Latent Features

A latent feature learning technique is used to segment prostate from Magnetic Resonance Imaging. An automatic segmentation of prostate is very important and challenging step in prostate cancer investigation. Accurate prostate segmentation becomes difficult due to unclear prostate boundaries as well as shape deviation among patients. Hence to tackle these difficulties latent features were extracted from MR images by deploying deep learning technique. Auto encoders are stacked together to refine the derived features. Prostate likelihood map is derived for new prostate MR image by processing these refined features. The final prostate segmentation is achieved by deploying active contour model. We used K-nearest neighbour classifier to locate cancerous region voxel-wise .The achievement of presented technique is massively tested on the dataset that includes 218 T2-weighted prostate MR images with tumors. The precision and mean absolute surface distance (MASD) are calculated to assess the performance of our method by considering manual groundtruth delineated by experienced radiologist. The average precision obtained in our study is 88.7% ±4.6%, and MASD is 1.93 mm. The accuracy and AUC obtained by K-nearest neighbour classifier of 88.2% and 0.89. The primary outcome reveals that the latent features are more proficient in prostate segmentation from MRI.