Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Foreign Language Accent

Automatic accent classification using a database developed with both L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> and L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> language data has been proposed. Speech samples were collected from native Indian speakers speaking in their mother tongue namely Kannada, Tamil, or Telugu, and from non-native English speakers with one of the above as the first language. The vocal tract characteristics were used in the present study. The MFCC features extracted from both native speech and non-native speech were extensively analyzed. Performance validation in Regional Nativity Identification has been investigated using both native South Indian speech, and non-native English speech by the compatriots of the linguistic groups. Detecting regional identity using MFCC features with GMMUBM / i-vector modeling has been proposed. The challenges of second language speech recognition have been addressed by leveraging native, and non-native speech, which produced an SVM classification score of 86.1%, and the Area Under Curve (AUC) is found to be well above 90% for all three languages.
Conversational recommendation: Theoretical model and complexity analysis

Recommender systems help users find items of interest in situations of information overload in a personalized way, using needs and preferences of individual users. In conversational recommendation approaches, the system acquires needs and preferences in an interactive, multi-turn dialog. This is usually driven by incrementally asking users about their preferences about item features or individual items. A central research goal in this context is efficiency, evaluated concerning the number of required interactions until a satisfying item is found. Today, research on dialog efficiency is almost entirely empirical, aiming to demonstrate, for example, that one strategy for selecting questions to ask the user is better than another one in a given application. This work complements empirical research with a theoretical, domain-independent model of conversational recommendation. This model, designed to cover a range of application scenarios, allows us to investigate the efficiency of conversational approaches in a formal way, particularly concerning the computational complexity of devising optimal interaction strategies. An experimental evaluation empirically confirms our findings.
Target Tracking Using Kalman Filter Based Algorithms

Abstract Kalman filter and its families have played an important role in information gathering, such as target tracking. Data association techniques have also been developed to allow the Kalman filter to track multiple targets simultaneously. This paper revisits the principle and applications of the Kalman filter for single target tracking and multiple hypothesis tracking (MHT) for multiple target tracking. We present the brief review of the Bayes filter family and introduce a brief derivation of the Kalman filter and MHT. We show examples for both single and multiple targets tracking in simulation to illustrate the efficacy of Kalman filter-based algorithms in target tracking scenarios.
Exploiting Sub-page Write Protection for VM Live Migration

Virtual machine (VM) live migration is an essential feature for cloud vendors. Since VM live migration involves transferring a large amount of memory between VMs, reducing memory transfer is critical to successful and efficient migration. However, software-based approaches consume considerable CPU and memory resources, resulting in degraded performance under heavy load. We propose to exploit a new CPU feature, sub-page write protection, to reduce memory transfer in VM live migration. To mitigate the overhead in naive implementations, we identified its leading cause and introduced an optimization. Emulator-based experiments demonstrated that our approach reduced memory transfer as much as software-based approaches while consuming less CPU and memory. Our optimization reduced the maximum CPU overhead by 25.5 percentage points.
Comparison of Optical Flow Methods: Study About Left Ventricular Tracking in Multi View Echocardiographic Images

Tracking of the left ventricle on multi view echocardiographic images aims to obtain an overview on the heart condition; it is conducted by assessing the size of the cavity from systole to diastole. The manual measurement methods in the current assessment of echocardiographic images requires an immensely high level of concentration. Tracking the left ventricle in multi view echocardiographic images involves two methods: Farneback optical flow and Lucas-Kanade optical flow. It is significant to compare between the performance of Farneback optical flow and Lucas-Kanade optical flow in determining which method results in more accurate tracking. This paper uses 40 datasets with 10 picture frames in each data. The experiment results indicated that the accuracy of Farneback optical flow is at 79.17% with a sensitivity of 87.23%. In comparison, the Lucas-Kanade optical flow has 83.27% accuracy with a sensitivity of 91.51%. All in all, it was concluded that the Lucas-Kanade optical flow has a higher level of accuracy compared to the Farneback optical flow. The Lucas-Kanade optical flow method has an accuracy rate of 83.48% with a sensitivity of 89.21%.
Black-Box Impossibilities of Obtaining 2-Round Weak ZK and Strong WI from Polynomial Hardness

We study the problem of obtaining 2-round interactive arguments for NP with weak zero-knowledge (weak ZK) [Dwork et al., 2003] or with strong witness indistinguishability (strong WI) [Goldreich, 2001] under polynomially hard falsifiable assumptions. We consider both the delayed-input setting [Jain et al., 2017] and the standard non-delayed-input setting, where in the delayed-input setting, (i) prover privacy is only required to hold against delayed-input verifiers (which learn statements in the last round of the protocol) and (ii) soundness is required to hold even against adaptive provers (which choose statements in the last round of the protocol).
On the logic-game approach to the problems of working with big data

When working with big data, the problems of search, presentation, systematization, updating, analysis of information, application of artificial intelligence, communication, verification and understanding of conclusions are considered. Examples of solutions to some aspects of these problems, shown in the organization of work with information in chess, are given. The so-called logic-game approach is proposed, which provides ways to adapt and formalize solutions for a fairly wide range of problems with big data.
An Analytical Prediction of Breast Cancer Using Machine Learning

Breast Cancer is one of the most occurring cancer among women affecting about 2 million people. There is 98% chance of 5 years survival rate if detected at early stage. The data about Breast cancer used in this paper is the Wisconsin dataset which is taken from Kaggle. This is a classification problem, there are two classes (0 representing a non-malignant tumor, 1 representing malignancy). Min Max scalar is used for preprocessing of data to limit data within certain range (known as scaling). The algorithms used for classification are Support Vector Classifier, Random Forest, Naïve Bayes, Decision Tree, K-Nearest Neighbours. Support Vector Classifier and Random forest gave the highest accuracy, Evaluation metrics such are Area Under Curve-Rectified Operational Characteristics curve, confusion matrix, Recall score, accuracy. To avoid overfitting cross validation is used where k fold value is 3.
Turn-Based Communication Channels

We introduce the concept of turn-based communication channel between two mutually distrustful parties with communication consistency, i.e. both parties have the same message history, and happens in sets of exchanged messages across a limited number of turns. Our construction leverages on timed primitives. Namely, we consider a \(\mathsf{\Delta }\)-delay hash function definition and use it to establish turns in the channel. Concretely, we introduce the one-way turn-based communication scheme and the two-way turn-based communication protocol and provide a concrete instantiation that achieves communication consistency.
A corpus-based examination of reflexive metadiscourse in majority and dissent opinions of the U.S. Supreme Court

This article examines the use of reflexive metadiscourse in majority and dissent opinions of the U.S Supreme Court (SCOTUS). In doing so, it deals with two largely overlooked phenomena in studies on judicial discourse and reflexive metadiscourse respectively. In relation to the former, it examines the phenomenon of separate opinions. In relation to the latter, it examines the discursive foci of reflexive metadiscourse in interactive written genres. Using two corpora, one containing 60 majority decisions (302,921 words in total); one containing 60 dissent opinions (223,149 words in total), the results show that judicial opinion writers used a similar overall amount of reflexive metadiscourse. The results also identify a number of significant differences in the frequency use of reflexive metadiscourse. In their use of reflexive metadiscourse, majority writers display a pronounced self-focus (i.e., focus on their own discourse); dissent writers display a pronounced other-focus (i.e., focus on the majority's discourse). Reflecting the overall trend identified, highly significant differences were also identified in the use of communicating, clarifying, and enumerating devices.
Multifeature Fusion Human Motion Behavior Recognition Algorithm Using Deep Reinforcement Learning

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.
Efficiently Supporting Online Privacy-Preserving Data Publishing in a Distributed Computing Environment

There has recently been an increasing need for the collection and sharing of microdata containing information regarding an individual entity. Because microdata typically contain sensitive information on an individual, releasing it directly for public use may violate existing privacy requirements. Thus, extensive studies have been conducted on privacy-preserving data publishing (PPDP), which ensures that any microdata released satisfy the privacy policy requirements. Most existing privacy-preserving data publishing algorithms consider a scenario in which a data publisher, receiving a request for the release of data containing personal information, anonymizes the data prior to publishing—a process that is usually conducted offline. However, with the increasing demand for the sharing of data among various parties, it is more desirable to integrate the data anonymization functionality into existing systems that are capable of supporting online query processing. Thus, we developed a novel scheme that is able to efficiently anonymize the query results on the fly, and thus support efficient online privacy-preserving data publishing. In particular, given a user’s query, the proposed approach effectively estimates the generalization level of each quasi-identifier attribute, thereby achieving the k-anonymity property in the query result datasets based on the statistical information without applying k-anonymity on all actual datasets, which is a costly procedure. The experiment results show that, through the proposed method, significant gains in processing time can be achieved.
Automate Allocation of Secure Slice in Future Mobile Networks using Machine Learning

Flexibility is one of the primary aims of 5G Networks to support various use-case applications in mobile networks. In this work we have given the concept of fine-grain network slices which means giving a slice for network traffic of specific application. The fine-grain network slices make network more flexible to analyze, update and detect application failures. In this work we proposed a 2-tier approach for allocating a secure slice using Random Forest method. The datasets used in this work are application traffic dataset which has 50 plus different application labels like google, youtube, facebook etc and DDoS attack dataset. Random Forest model is showing promising results to allocate the secure slice for the network traffic.
Learning from YouTube Videos using Drona Extension

There has been a boom in the use of online resources for education since the onset of the COVID-19 pandemic. Educational videos are an important part of these online resources and are very helpful to students. However, there are various issues related to the use of educational videos such as losing focus, lack of feedback to students, and taking notes manually while watching. The primary objective of this work is to design and develop an intelligent browser action chrome extension that generates smart notes and quizzes from educational YouTube videos. The extension will serve anyone learning from YouTube by providing a dynamic platform to grasp the content and assess themselves.
A Copula-Driven Unsupervised Learning Framework for Anomaly Detection with Multivariate Heterogeneous Data

We consider the problem of anomaly detection with heterogeneous and correlated multivariate data, with no assumption on the knowledge of statistical correlation. First, we use a copula-based approach to measure the statistical correlation among the modalities. Next, we employ an unsupervised learning (UL) framework for anomaly detection, using data points sampled from the copula-based joint distribution. In particular, we consider Gaussian, R-Vine, D-Vine and C-Vine copula techniques, with isolation forest, one-class SVM, local outlier factor, elliptic envelope and autoencoder UL algorithms, for our extensive study. Through Monte Carlo simulations and an experimental study on the IEEE signal processing cup – 2020 dataset, we show that the proposed framework significantly outperforms the direct training method, in terms of detection accuracy. Furthermore, we show that the C-Vine-based autoencoder technique yields the best performance in comparison with other techniques, in terms of area under the receiver operating characteristics curve, and accuracy of detecting anomalies.
A Novel Proof of Shuffle: Exponentially Secure Cut-and-Choose

Shuffling is one of the most important techniques for privacy-preserving protocols. Its applications are manifold, including, for example, e-voting, anonymous broadcast, or privacy-preserving machine-learning. For many applications, such as secure e-voting, it is crucial that the correctness of the shuffling operation be (publicly) verifiable. To this end, numerous proofs of shuffle have been proposed in the literature. Several of these proofs are actually employed in the real world.In this work, we propose a generic compiler which can transform any “shuffle-compatible” \(\varSigma \)-protocol (including, among others, \(\varSigma \)-protocols for re-randomization, decryption, or key shifting) into a \(\varSigma \)-protocol for permutations of the underlying relation. The resulting proof of shuffle is black-box, easily implementable, simple to explain, and comes with an acceptable computational overhead over the state-of-the-art. Because we machine-checked our compiler in Coq, the new proof of shuffle is particularly suitable for applications that require a superior level of security assurance (e.g., high-stake elections).
Review and perspective on 3D Gesture Recognition Technologies for Automotive Industry

The paper reviews 3D Gesture Recognition technologies, that can be potentially used in future automotive applications. The purpose of the article is to find the best 3D gesture recognition technology for mass scale adaptation in future vehicles. Described gesture recognition technologies are divided into six categories: Vision Systems, Wearable sensors, Infrared, Electric field, Radar and Ultrasonic Waves. The article identifies principles of operation for each of chosen methods, compares advantages and disadvantages of the technologies.
Labeling the Phrases of a Conversational Agent with a Unique Personalized Vocabulary

Mapping spoken text to gestures is an important research topic for robots with conversation capabilities. According to studies on human co-speech gestures, a reasonable solution for mapping is using a concept-based approach in which a text is first mapped to a semantic cluster (i.e., a concept) containing texts with similar meanings. Subsequently, each concept is mapped to a predefined gesture. By using a concept-based approach, this paper discusses the practical issue of obtaining concepts for a unique vocabulary personalized for a conversational agent. Using Microsoft Rinna as an agent, we qualitatively compare concepts obtained automatically through a natural language processing (NLP) approach to those obtained manually through a sociological approach. We then identify three limitations of the NLP approach: at the semantic level with emojis and symbols; at the semantic level with slang, new words, and buzzwords; and at the pragmatic level. We attribute these limitations to the personalized vocabulary of Rinna. A follow-up experiment demonstrates that robot gestures selected using a concept-based approach leave a better impression than randomly selected gestures for the Rinna vocabulary, suggesting the usefulness of a concept-based gesture generation system for personalized vocabularies. This study provides insights into the development of gesture generation systems for conversational agents with personalized vocabularies.
Gaze-Adaptive Subtitles Considering the Balance among Vertical/Horizontal and Depth of Eye Movement

Subtitles (captions displayed on the screen) are important in 3D content, such as virtual reality (VR) and 3D movies, to help users understand the content. However, an optimal displaying method and framework for subtitles have not been established for 3D content because 3D has a depth factor. To determine how to place text in 3D content, we propose four methods of moving subtitles dynamically considering the balance between the vertical/horizontal and depth of gaze shift. These methods are used to reduce the difference in depth or distance between the gaze position and subtitles. Additionally, we evaluate the readability of the text and participants’ fatigue. The results show that aligning the text horizontally and vertically to eye movements improves visibility and readability. It is also shown that the eyestrain is related to the distance between the object and subtitles. This evaluation provides basic knowledge for presenting text in 3D content.
Software Fault-Proneness Analysis based on Composite Developer-Module Networks

Existing software fault-proneness analysis and prediction models can be categorized into software metrics and visualized approaches. However, the studies of the software metrics solely rely on the quantified data, while the latter fails to reflect the human aspect, which is proven to be a main cause of many failures in various domains. In this paper, we proposed a new analysis model with an improved software network called Composite Developer-Module Network. The network is composed of the linkage of both developers to software modules and software modules to modules to reflect the characteristics and interaction between developers. After the networks of the research objects are built, several different sub-graphs in the networks are derived from analyzing the structures of the sub-graphs that are more fault-prone and further determine whether the software development is in a bad structure, thus predicting the fault-proneness. Our research shows that the different sub-structures are not only a factor in fault-proneness, but also that the complexity of the sub-structure can affect the production of bugs.
Ignorance is Bliss: Exploring Defenses Against Invariance-Based Attacks on Neural Machine Translation Systems

This article addresses an invariance-based attack on the transformer, a state-of-the-art neural machine translation (NMT) system. Such attacks make multiple changes to the source sentence with the goal of keeping the predicted translation unchanged. Since the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">gold translation</i> is not available for the adversarial sentences, tackling invariance-based attacks is a challenging task. We propose two contrasting defense strategies for the same, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn to deal</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn to ignore</i> . In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn to deal</i> , NMT system is trained not to predict the same translation for a clean text and its noisy counterpart, whereas in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn to ignore</i> , NMT system is trained to output a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dummy sentence</i> in the target language whenever it encounters a noisy text. The experiments on two language pairs, English–German (en–de) and English–French (en–fr), show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn to deal</i> strategy reduces the attack success rate from 84.0% to 62.2% for en–de and from 84.6% to 73.8% for en–fr, whereas <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learn to ignore</i> strategy reduces the attack success rate from 84.0% to 27.2% for en–de and from 84.6% to 37.0% for en–fr.