Reviewerly/exHarmony huggingface dataset:
Leaky Controller: Cross-VM Memory Controller Covert Channel on Multi-core Systems

Data confidentiality is put at risk on cloud platforms where multiple tenants share the underlying hardware. As multiple workloads are executed concurrently, conflicts in memory resource occur, resulting in observable timing variations during execution. Malicious tenants can intentionally manipulate the hardware platform to devise a covert channel, enabling them to steal the data of co-residing tenants. This paper presents two new microarchitectural covert channel attacks using the memory controller. The first attack allows a privileged adversary (i.e. process) to leak information in a native environment. The second attack is an extension to cross-VM scenarios for unprivileged adversaries. This work is the first instance of leakage channel based on the memory controller. As opposed to previous denial-of-service attacks, we manage to modulate the load on the channel scheduler with accuracy. Both attacks are implemented on cross-core configurations. Furthermore, the cross-VM covert channel is successfully tested across three different Intel microarchitectures. Finally, a comparison against state-of-the-art covert channel attacks is provided, along with a discussion on potential mitigation techniques.
SmartAmb: An Integrated Platform for Ambulance Routing and Patient Monitoring

This paper presents an integrated platform for smart ambulance routing and patient status monitoring during the ride. The main target of the platform is to increase the likelihood of the patient's survival by having the ambulance arriving to the hospital as soon as possible while allowing the responsible doctor to monitor the patient's biomedical data. Hence, the doctor can provide the paramedics riding with the patient with helpful instructions or prepare the needed medical services to be received by the patient upon arrival. Furthermore, the platform applies machine learning techniques on the collected data to help the doctor identify possible medical threats. We adopt a layered approach in the design of the system. A prototype of the integrated system is implemented, and its performance is evaluated.
FPGA Acceleration of Approximate KNN Indexing on High- Dimensional Vectors

Accurate and efficient Machine Learning algorithms are of vital importance to many problems, especially on classification or clustering tasks. One the most important algorithms used for similarity search is known as K-Nearest Neighbor algorithm (KNN) which is widely adopted for predictive analysis, text categorization, image recognition etc. but comes at the cost of high computation. Large companies that process big data on modern data centers adopt this technique combined with approximations on algorithm level in order to compute critical workloads every second. However, a significant computation and energy overhead is formed further with the high dimensional nearest neighbor queries. In this paper, we deploy a hardware accelerated approximate KNN algorithm built upon FAISS framework (Facebook Artificial Intelligence Similarity Search) using FPGA-OpenCL platforms. The FPGA architecture on this framework addresses the problem of vector indexing on training and adding large-scale high-dimensional data. The proposed solution uses an in-memory FPGA format that outperforms other high performance systems in terms of speed and energy efficiency. The experiments were done on Xilinx Alveo U200 FPGA achieving up to 115× accelerator-only speed-up over single-core CPU and 2.4× end-to-end system speed-up over a 36-thread Xeon CPU. Also, the performance/watt of the design was 4.1 × from the same CPU and 1.4× from a Kepler-class GPU.
A Novel Approach to Classify Bangla Sign Digits using Capsule Network

Communication between hearing impaired and general people is one of the most immense problem nowadays. The main medium of conversation with deaf people is through sign language. But it is an arduous task to learn and communicate with the sign language for any class of people. Over the years, researchers from diverse backgrounds tried to establish a model to automate the process of the detection of sign language. Traditional machine learning technique - k Nearest Neighbor (kNN), Support Vector Machine (SVM) etc. and Neural Network architectures-Back-propagation method, Convolutional Neural Network (CNN), Ensemble Neural Network etc. are existing methods by which the researchers proposed their work. In this paper, we worked on the detection of Bangla sign digits. We adopted and modified Capsule Network by the means of detecting Bangla sign digits. In a capsule network architecture, a capsule is a group of neurons that constitutes the feature vector and spatial properties of the object. A seven-layer of capsule network architecture along with some pre-processing is proposed for the detection. The Ishara-Lipi dataset is used for the training of the model and we get 98.84% accuracy for the classification.
Clothing Recommendation System based on Visual Information Analytics

Due to the short fashion style life circle, much more different clothing designs show up. It is hard for consumers to find the suitable clothes effectively. To solve this problem, an automatic and reliable recommendation system is in great demand. In this paper, the clothing attributes recognition, gender recognition, and body height are considered to design the recommendation system. Based on the clothing style, gender and body height, the system can recommend the proper clothes with suitable size. On-line texture modeling is proposed to produce the variation of the clothing texture so that the recommendation system can give reasonable and diversified choices for the consumers. Besides, the data of consumers' wearing style is also useful to make the better marketing strategy. According to the reasons above, the clothing recognition and recommendation system can create a win-win situation between the consumers and the fashion industry.
Unsupervised Image-to-Video Clothing Transfer

We present a system to photo-realistically transfer the clothing of a person in a reference image into another person in an unconstrained image or video. Our architecture is based on a GAN equipped with a physical memory that updates an initially incomplete texture map of the clothes that is progressively completed with the new inferred occluded parts. The system is trained in an unsupervised manner. The results are visually appealing and open the possibility to be used in the future as a quick virtual try-on clothing system.
A Complete Bangla Optical Character Recognition System: An Effective Approach

Bangla character recognition is a significant field of research because Bangla is the most widely used language in the Indian subcontinent and eighth most used language for the written document. Research on Bangla character recognition has been started since the mid-1980s. This report describes a complete Optical Character Recognition (OCR) system for scripted Bangla characters. This paper proposes a new recognition system for scripted Bangla characters. We used a Convolutional Neural Network to build the model which identifies individual Bangla characters. From an image, the lines, words, and characters are segmented based on empty space. We also used binarization, noise reduction and other techniques to improve accuracy. Finally, we have been able to achieve a very impressive accuracy rate in the image to text conversion and by far the highest accuracy rate in scripted character detection.
USE OF ALTMETRICS IN BIBLIOGRAPHIC SERVICES

Potentiality of using altmetrics in the academic library bibliographic services is examined. Free altmetric services such as Altmetric bookmar klet and tracking online-news about scientific articles (Paperbuzz, PLOS Article Level Metrics) and others are reviewed. 90 foreign and 30 Russian publications for the period of 2012 – 2019, which are dedicated to altmetric researches of works in library science or using altmetric tools in library activities, have been submitted to content analyses. Information and analytical systems such as Scopus, Web of Science and Russian Index of Scientific Citing (RISC) are its sources. The main directions elicited in foreign publica tions are as follows: altmetric analysis of publications, comparison of altmetric indica tors with traditional ones, investigation of cases concerning provision of scientific activi ties with ailtmetric data, altmetrical analysis of online libraries and repositories, use of altmet ric facts for acquisition needs. The main directions elicited in Russian publications are as follows: altmetrics as an indicator of scientific influence, provision of librarians and researchers with information about altmetric opportunities, provision with altmetric infor mation from licensed databases. Libraries have an opportunity to generate altmetric informa tion, possibility of generating altmetric infor mation by libraries. The language barrier, paucity evidence about Russian publications, provoke doubts about favors of using altmetric information reduce Russian users’ interest to alternative metrics. The described possible scripts of Russian scientific libraries`work with altmetrics are as follows: provision of users with specialized altmetric services by analogy with services on defining traditional indicators of publication activities, generation of own altmetric informa tion. Informing about possibility of using altmetrics is important for referencebibliographic servicing practice. For lack of direct inquiries for alternative metrics, they could be one of information criteria for thematic search or preparation of reviews.
Pedagogical Considerations for Successful Implementation of Virtual Reality in the Language Classroom

Virtual reality (VR) offers unique opportunities for immersive activities previously not possible in the language classroom. However, as with any new instructional medium, it is difficult to employ these technologies effectively. There is often a gap between teachers' understanding of how to use the technology and the pedagogical needs of the classroom. This chapter introduces the VR Application Analysis Framework. The framework, supported by established theory, assists with the analysis and implementation of commercial off-the-shelf VR applications into language classroom tasks. This chapter explores the history of VR and the background for its potential use in the classroom. The four key aspects of the framework are presented: immersive capacity, cognitive load, purpose, and communicative capability. Four existing VR applications and their accompanying activities are presented as examples of using the framework.
Artificial Intelligence in Medical Diagnosis: Methods, Algorithms and Applications

Artificial intelligence (AI) has evolved rapidly since the late 1980s. Increasing of healthcare datasets and its performance, the past two decades have seen an exponential progress in publications on AI. However, with the advent of increased computational power, availability of AI devices was increased. There are two main devices in AI, machine learning, where structured data (i.e. images, EP and genetic data) are analyzed and natural language processing, where unstructured data are analyzed. Both AI devices have been improved in great detail over the past two decades for its methods, algorithms, and applications. However, various attempts and new methods of AI have been used in recent years and few diseases such as cancer, nervous system disease, cardiovascular disease, liver disease, congenital cataract disease, etc. were potentially analyzed using AI. Now a day an advanced method called deep learning has initiated a boom of AI and great modifications of diagnostic medical imaging systems like endoscopic diagnosis, pathology and dermatology will be expected in the near future. Herein, the authors give a basic technical knowledge about popular methods, algorithms and applications in medical diagnosis which emerged in the past years.
Imbalanced Data Classification Using Improved Clustering Algorithm and Under-Sampling Method

Imbalanced classification problem is a hot issue in data mining and machine learning. Traditional classification algorithms are proposed based on some form of symmetry hypothesis of class distribution, whose main purpose is to improve the overall classification performance. It is difficult to obtain ideal classification result when handling imbalanced datasets. In order to improve the classification performance of imbalanced datasets, this paper proposes a cluster-based under-sampling algorithm (CUS) according to the important characteristic of support vector machines (SVM) classification relying on support vector. Firstly, majority class is divided into different clusters using improved clustering by fast search and find of density peaks (CFSFDP) algorithm. The improved clustering algorithm can realize automatic selection of clustering centers, which overcomes the limitation of the original algorithm. Then the minority class and each cluster of the majority class are used to construct training set to get the support vector of each cluster by support vector machine. Retaining support vectors for each cluster and deleting non-support vectors are to construct a new majority class sample points to obtain relatively balanced datasets. Finally, the new datasets are classified by support vector machines and the performance is evaluated by cross validation sets. The experimental results show that CUS algorithm is effective.
Indic TIMIT and Indic English lexicon: A speech database of Indian speakers using TIMIT stimuli and a lexicon from their mispronunciations

With the advancements in the speech technology, demand for larger speech corpora is increasing particularly those from non-native English speakers. In order to cater to this demand under Indian context, we acquire a database named Indic TIMIT, a phonetically rich Indian English speech corpus. It contains ~240 hours of speech recordings from 80 subjects, in which, each subject has spoken a set of 2342 stimuli available in the TIMIT corpus. Further, the corpus also contains phoneme transcriptions for a sub-set of recordings, which are manually annotated by two linguists reflecting speaker's pronunciation. Considering these, Indic TIMIT is unique with respect to the existing corpora that are available in Indian context. Along with Indic TIMIT, a lexicon named Indic English lexicon is provided, which is constructed by incorporating pronunciation variations specific to Indians obtained from their errors to the existing word pronunciations in a native English lexicon. In this paper, the effectiveness of Indic TIMIT and Indic English lexicon is shown respectively in comparison with the data from TIMIT and a lexicon augmented with all the word pronunciations from CMU, Beep and the lexicon available in the TIMIT corpus. Indic TIMIT and Indic English lexicon could be useful for a number of potential applications in Indian context including automatic speech recognition, mispronunciation detection & diagnosis, native language identification, accent adaptation, accent conversion, voice conversion, speech synthesis, grapheme-to-phoneme conversion, automatic phoneme unit discovery and pronunciation error analysis.
A novel principle to validate digital forensic models

Digital forensic models (DFMs) form the base for any digital investigation because they guide the investigators with necessary steps and procedures to be taken during the investigation. State-of-the-art DFMs assume that it is safe to proceed from one stage of the investigation to the next without taking into account the anti-forensic techniques that could be used to defeat the investigation process. However, the findings in the literature shows that common phases in the digital forensic process such as acquisition, examination, analysis, and reporting are affected by various anti-forensic (AF) methods. To fill this gap, we propose an abstract digital forensic framework and validate DFMs by factoring in AF techniques affecting various phases in a digital forensic process. This validation principle can be used to enhance state-of-the-art DFMs to enable principled detection and countering of AF techniques before being applied to a real-time investigation case.
Comparison of Algorithms for Dynamic Hand Gesture Recognition

The aim of this work is to compare different algorithms for dynamic hand gesture recognition using the Kinect v2 sensor. We have developed an application that has implemented algorithms such as dynamic time warping algorithm, hidden Markov models, long short-term memory network and convolutional neural networks. To test and compare the mentioned algorithms, a simple database was created. Using our own database we have gained an objective way of comparing these algorithms.
Low-Light Image Enhancement Based on Deep Symmetric Encoder–Decoder Convolutional Networks

A low-light image enhancement method based on a deep symmetric encoder–decoder convolutional network (LLED-Net) is proposed in the paper. In surveillance and tactical reconnaissance, collecting visual information from a dynamic environment and accurately processing that data is critical to making the right decisions and ensuring mission success. However, due to the cost and technical limitations of camera sensors, it is difficult to capture clear images or videos in low-light conditions. In this paper, a special encoder–decoder convolution network is designed to utilize multi-scale feature maps and join jump connections to avoid gradient disappearance. In order to preserve the image texture as much as possible, by using structural similarity (SSIM) loss to train the model on the data sets with different brightness level, the model can adaptively enhance low-light images in low-light environments. The results show that the proposed algorithm provides significant improvements in quantitative comparison with RED-Net and several other representative image enhancement algorithms.
Adversarial text generation with context adapted global knowledge and a self-attentive discriminator

Text generation is a challenging task for intelligent agents. Numerous research attempts have investigated the use of adversarial networks with word sequence-based generators. However, these approaches suffer from an unbalance between generator and discriminator causing overfitting due to the strength that the discriminator acquires by getting too precise in distinguishing what the generator is producing and what instead comes from the real dataset. In this paper, we investigate how to balance both generator and discriminator of a sequence-based text adversarial network exploiting: i) the contribution of global knowledge in the input of the adversarial network encoded by global word embeddings that are adapted to the context of the datasets in which they are utilized, and ii) the use of a self-attentive discriminator that slowly minimizes its loss function and thus enables the generator to get valuable feedback during the training process. Through an extensive evaluation on three datasets of short-, medium- and long-length text documents, the results computed using word-overlapping metrics show that our model outperforms four baselines. We also discuss the results of our model using readability metrics and the human perceived quality of the generated documents.
A Study on Application of Soft Computing Techniques for Software Effort Estimation

Software is everywhere. Now-a-day' s software plays an indispensable role in all the fields like Education, Medical, Insurance, Marketing, Stock Exchange etc. The major goal of software organization is to achieve the Win-Win condition. As per the Standish Group Chaos Survey, only 30–40% of the software projects are successful. One of the main reasons for failure of the software projects is inaccurate estimations of the cost and schedule. In the conventional software development Algorithmic and Expert Based techniques are used to predict the effort, duration and cost of the software project. But they are not providing accurate estimations of the software effort. Most recently, the industries and researchers are adopting Soft Computing techniques to estimate the software effort accurately in the early stage of software development. In this chapter, the Soft Computing techniques for effort estimation using Fuzzy Logic, Neural Networks (NN), Adaptive Neuro Fuzzy Inference System (ANFIS), Random Forest and Support Vector Machines (SVM) are introduced. Soft Computing models are developed using Fuzzy Logic, Neural Networks and ANFIS using the NASA93 and Desharnais Datasets. Comparing these models using different evaluation criteria, it is observed that the Adaptive Neuro Fuzzy Inference System produced better effort estimates.
Would You Fix This Code for Me? Effects of Repair Source and Commenting on Trust in Code Repair

Automation and autonomous systems are quickly becoming a more engrained aspect of modern society. The need for effective, secure computer code in a timely manner has led to the creation of automated code repair techniques to resolve issues quickly. However, the research to date has largely ignored the human factors aspects of automated code repair. The current study explored trust perceptions, reuse intentions, and trust intentions in code repair with human generated patches versus automated code repair patches. In addition, comments in the headers were manipulated to determine the effect of the presence or absence of comments in the header of the code. Participants were 51 programmers with at least 3 years’ experience and knowledge of the C programming language. Results indicated only repair source (human vs. automated code repair) had a significant influence on trust perceptions and trust intentions. Specifically, participants consistently reported higher levels of perceived trustworthiness, intentions to reuse, and trust intentions for human referents compared to automated code repair. No significant effects were found for comments in the headers.
Age Estimation From Facial Image Using Convolutional Neural Network(CNN)

Automatic age estimation of facial images is a challenging problem in computer vision and image analysis as the process of aging is affected by different issues like gender, ethnicity, environment etc. Also to estimate near accurate age from facial images requires a large amount of data and tedious training period. In this paper, we have proposed an age estimator based on the Convolutional Neural Network (CNN) that can predict age from facial images almost accurately. Our approach requires less training data than the related works yet keeping a low Mean Absolute Error (MAE). We built a model on top of the 50 layer Residual Network named ResNet 50 that implements age estimation as a regression problem. Our experimental results are compared with other age estimation techniques. The comparison exhibits that the performance of our age estimation system is close to other related works even after training with a relatively smaller dataset.