Reviewerly/exHarmony huggingface dataset:
Self-Supervised Depth Completion for Active Stereo

Active stereo systems are used in many robotic applications that require 3D information. These depth sensors, however, suffer from stereo artefacts and do not provide dense depth estimates. In this work, we present the first self-supervised depth completion method for active stereo systems that predicts accurate dense depth maps. Our system leverages a feature-based visual inertial SLAM system to produce motion estimates and accurate (but sparse) 3D landmarks. The 3D landmarks are used both as model input and as supervision during training. The motion estimates are used in our novel reconstruction loss that relies on a combination of passive and active stereo frames, resulting in significant improvements in textureless areas that are common in indoor environments. Due to the non-existence of publicly available active stereo datasets, we release a real dataset together with additional information for a publicly available synthetic dataset (TartanAir [30] needed for active depth completion and prediction. Through rigorous evaluations we show that our method outperforms state of the art on both datasets. Additionally we show how our method obtains more complete, and therefore safer, 3D maps when used in a robotic platform.
Blockchain and Its Implementation for Charitable Organizations

Trust and transparency are the basics of any nonprofit or non-financial organizations. It is believed that charities that are accountable and transparent are more likely to act with integrity because they want donors to know that they&#x2019;re trustworthy. Keeping in mind the fact of transparency and integrity, block chain is the state-of-the-art technology which has given solution to the problem mentioned before. In this paper the use of proof of authority as a consensus protocol in Ethereum blockchain platform is proposed. A detailed tutorial of blockchain mechanism along with the detailed comparison of three main platforms bitcoin, Ethereum and Hyperledger in which blockchain has been explored a lot. Furthermore, there is a detail overview of how blockchain has flourished itself in various fields of non-profit and non-financial organizations. Finally, we have proposed an economical model along with the framework which uses less computational power.
The Quality of Training Staff for the Digital Economy of Russia within the Framework of STEAM Education: Problems and Solutions in the Context of Distance Learning

The purpose of this study is to analyze the problems of quality training of personnel for the digital economy of Russia during the pandemic within the framework of STEAM education and to identify their solutions with the help of the most effective distance learning tools. The research is based on a survey of 138 respondents (students of the Department of Mathematics and Natural Sciences of the Yelabuga Institute of the Kazan Federal University), who have mastered professional competencies in a distance learning format during the pandemic. The results of the survey confirmed the conclusion that distance learning tools such as Moodle, Google Classroom, and Microsoft Teams have shown their effectiveness and can be successfully used not only in the context of extreme situations, for example, a pandemic, but also as elements of blended learning while bringing variety to the forms of learning, and thereby strengthening the process of training STEAM education specialists. The scientific novelty of the study lies in a deep analysis of the distance learning tools used for training specialists for the digital economy of Russia, namely STEAM education specialists, based on the collection of information from students during the period of transformational changes in the pedagogical education system.
A Homogeneous Ensemble Classifier for Breast Cancer Detection Using Parameters Tuning of MLP Neural Network

Breast cancer is one of the most common cancers among worldwide, and its detection is recognized as a significant public health problem in today’s society. Extensive studies have been conducted to classify patients into malignant or benign groups, but given the importance of the problems, efforts are still ongoing. This paper aims are to parameters tuning of Multi-Layer Perceptron (MLP) neural network for the breast cancer detection. This work presents an MLP-based homogeneous ensemble approach for classifying breast cancer samples. Basically, ensemble learning is used to improve the classification process. This technique is a method of combining different basic classifiers from which a new classifier is derived. In this regard, several optimization algorithms including GA, PSO, and ODMA have been used to determine which algorithm provides the most suitable parameters for MLP. These parameters include effective features, number of hidden layers, number of nodes in layers, and weight values. The proposed algorithm is applied to three datasets of the Wisconsin Breast Cancer Database (i.e., WBCD, WDBC, and WPBC) and then comparison is made between different algorithms to achieve the highest accuracy. Experiments show that the proposed classifier has promising results in breast cancer detection than other state-of-the-art classifiers with 98.79% in the WBCD. Data analysis and its results can confirm the superiority of ensemble classifiers over state-of-the-art methods for breast cancer detection.
Attention Based Model for Segmental Pronunciation Error Detection

The Goodness of Pronunciation (GOP) algorithm is one well-established method of pronunciation assessment dependent on precise phoneme segment boundaries. The alignment process of canonical pronunciations to obtain these boundaries is prone to errors. To overcome this issue, the present paper proposes a combination of Bidirectional Long-Short Memory with saliency region selection with attention weights to provide estimates of mispronunciations without the need of phoneme boundaries. Three output and annotation configurations were used to train the model which was then assessed against a GOP baseline in the task of detecting segments with mispronunciations. The experiments were conducted using data from young Dutch learners of English, annotated by three trained phoneticians. The proposed model outperformed the GOP baseline. It was also found that the attention weights aligned themselves with the phoneme labels, helping detect mispronunciations and allowing further interpretation of the internal representation of the model.
I Know Your Social Network Accounts: A Novel Attack Architecture for Device-Identity Association

Online social networks revolutionize the way people interact with each other. When various social network information aggregates over time, a rich online profile of the user is formed. Owing to the various features provided by mobile devices, a user's online social activities are tightly tied to his phone, and are conveniently, sometimes unnecessarily, available to social networks. In this article, we propose a novel attack architecture to show that attackers can infer a user's social network identities behind a mobile device through new dimensions. Specifically, we first developed a correlation between a user's device system states and the social network events, which leverage multiple mechanisms such as the learning-based memory regression model, to infer the possible accounts of the user in the social network app. Then we exploited the social network to social network correlation, via which we correlated information across different social networks, to identify the accounts of the target user. We implemented and evaluated these attacks on three popular social networks, and the results corroborate the effectiveness of our design.
Low Light Image Contrast Enhancement using Blending of Histogram Equalization Based Methods BBHE and BPHEME

Image contrast is the difference between the brightness and colors of a part of an image compared to its objects around. The contrast enhancement means increasing the original input brightness values. Images captured in low-light environments suffer from inferior visibility caused by low contrast. It is said that Histogram equalization is the foundation of image contrast enhancement and is used even in new contrast enhancement methods. Even though Histogram Equalization (HE) is primitive, it is effective. HE increases the brightness of the output image significantly, which is often undesirable. There are various enhanced versions of histogram equalization methods to improve image contrast are proposed to overcome the brightness preservation and image details preservation challenge. This paper focuses on studying different popular and approved HE methods and experimental studies based on the image, PSNR - peak signal to noise ratio, BRISQUE - Blind / Reference less Image Spatial Quality Evaluator, and Entropy. Results from the above study direct the goal towards the Image fusion of the two selected methods, which gives improved results on the preservation of brightness and contrast enhancement of the original image.
A Data-driven Affective Text Classification Analysis

Affective texts play a key role in sentiment classification/prediction and decision making. They are being increasingly used to form and/or share sentiments in financial, economic and/or political applications. However, the processing time is exponentially increased for large affective textual datasets. Moreover, casual expressions such as emoji, slang, abbreviation and misspelling words usually make data analysis (i.e., text classification) complicated. This paper proposes a pipeline model consisting of data pre-processing, feature extraction and classification model training to classify affective text datasets. It offers three contributions including Emoji recovery, misspelling word correction and abbreviation translation that results in maximised classification accuracy. A rigorous experimental plan is designed to evaluate the performance of the proposed approach according to three factors including dataset size (i.e., small, medium and large), NLP feature extraction technique (i.e., TF-IDF, word2vec and BERT) and classification model (i.e., MLP, Logistic Regression, Naive Bayes and SVM). In addition, the proposed approach is compared with a well-known Deep Learning sentiment analysis approach, named sentimentDLmodel, which addresses a pre-trained sentiment analysis. According to the results, the proposed approach significantly outperforms benchmarks in terms of classification model accuracy for most cases.
Person Fall Detection System Based on Video Stream Analysis

The purpose of this paper is to develop an information system that would detect a fall based on video stream analysis. This work contains research and a description of the subject environment and the structure of the system. An analysis of current scientific works in this area was conducted, which allowed defining the purpose and primary objectives of the thesis. In addition, this analysis showed that this system would be in demand in the market of software that uses computer vision. The practical significance of the results can be determined by the fact that the proposed application performs on the level of other research works in this field and can be used by a broad market of customers. We applied pre-trained TensorFlow Lite CNN real-time model PoseNet to detect keypoints of a subject. Keypoints sequence is analyzed by LSTM as it works best when processing long sequences of data. The system was trained using a combination of the two biggest fall detection datasets. The system extracts the locations of 17 joint points of the human body and recognizes human movement by detecting the joint point location changes. By using joint points location as input to LSTM, the training and inference time was improved drastically. It also helped minimize the adverse effects of light sources and shadows
Uncovering Smart Contract VM Bugs Via Differential Fuzzing

The ongoing public interest in blockchains and smart contracts has brought a rise to a magnitude of different blockchain implementations. The rate at which new concepts are envisioned and implemented makes it hard to vet their impact on security. Especially smart contract platforms, executing untrusted code, are very complex by design. Still, people put their trust and money into chains that may lack proper testing. A behavior deviation for edge cases of single op-codes is a critical bug class in this brave new world. It can be abused for Denial of Service against the blockchain, chain splits, double-spending, or direct attacks on applications operating on the blockchain. In this paper, we propose an automated methodology to uncover such differences. Through coverage-guided and state-guided fuzzing, we explore smart contract virtual machine behavior against multiple VMs in parallel. We develop NeoDiff, the first framework for feedback-guided differential fuzzing of smart contract VMs. We discuss real, monetary consequences our tool prevents. NeoDiff can be ported to new smart contract platforms with ease. Apart from fuzzing Ethereum VMs, NeoDiff found a range of critical differentials in VMs for the Neo blockchain. Moreover, through a higher-layer semantics mutator, we uncovered semantic discrepancies between Neo smart contracts written in Python when executed on the blockchain vs. classic CPython. Along the way, NeoDiff uncovered memory corruptions in the C# Neo VM.
Scientific Theories as Bayesian Nets: Structure and Evidence Sensitivity

Abstract We model scientific theories as Bayesian networks. Nodes carry credences and function as abstract representations of propositions within the structure. Directed links carry conditional probabilities and represent connections between those propositions. Updating is Bayesian across the network as a whole. The impact of evidence at one point within a scientific theory can have a very different impact on the network than does evidence of the same strength at a different point. A Bayesian model allows us to envisage and analyze the differential impact of evidence and credence change at different points within a single network and across different theoretical structures.
Fast, exact, and parallel-friendly outlier detection algorithms with proximity graph in metric spaces

Abstract In many fields, e.g., data mining and machine learning, distance-based outlier detection (DOD) is widely employed to remove noises and find abnormal phenomena, because DOD is unsupervised, can be employed in any metric spaces, and does not have any assumptions of data distributions. Nowadays, data mining and machine learning applications face the challenge of dealing with large datasets, which requires efficient DOD algorithms. We address the DOD problem with two different definitions. Our new idea, which solves the problems, is to exploit an in-memory proximity graph. For each problem, we propose a new algorithm that exploits a proximity graph and analyze an appropriate type of proximity graph for the algorithm. Our empirical study using real datasets confirms that our DOD algorithms are significantly faster than state-of-the-art ones.
Vulnerability analysis of Qualcomm Secure Execution Environment (QSEE)

TrustZone technology is used to implement Trusted Execution Environment (TEE) in mobile devices. TEE is responsible for isolating and protecting the Trusted Computing Base (TCB) of the device. There are several TrustZone-based TEE solutions utilized in devices, among which Qualcomm Secure Execution Environment (QSEE) is the most well-known TEE utilized in many Android devices. Since the underlying QSEE is entailed to ensure the security of sensitive data on the user device, it is essential to analyze its vulnerability state; however, most of the prior work on QSEE targets older devices or firmware versions. In this work, we carry out a comprehensive review of QSEE vulnerabilities and validate the current security state of the target TEE on a physical device. Our approach has been based on mining of CVEs targeting QSEE or its components spanning about seven years, so as to derive the security trend for QSEE as well as a heat-map to narrow down the more targeted areas of the TEE. As the heat-map indicated a major problem area to be vulnerabilities in Trusted Apps (TAs), we validated the integrity issues of TAs on a physical Android device. Our review highlights that while QSEE has considerably improved its security over the years; yet, still vulnerabilities may arise due to variations in vendor-level configurations on physical devices.
The Impact of Chatbot Linguistic Register on User Perceptions: A Replication Study

Chatbots often perform social roles associated with human interlocutors; hence, designing chatbot language to conform with the stereotypes of its social category is critical to the success of this technology. In a previous study, Chaves et al. performed a corpus analysis to evaluate how language variation in an interactional situation, namely the linguistic register, influences the user’s perceptions of a chatbot. In this paper, we present a replication study with a different corpus to understand the effect of corpus selection in the original study’s findings. Our results confirm the findings in the previous study and demonstrate the reproducibility of the research methodology; we also reveal new insights about language design for tourist assistant chatbots.
Secure learning-based MPC via garbled circuit

Encrypted control seeks confidential controller evaluation in cloud-based or networked systems. Many existing approaches build on homomorphic encryption (HE) that allow simple mathematical operations to be carried out on encrypted data. Unfortunately, HE is computationally demanding and many control laws (in particular non-polynomial ones) cannot be efficiently implemented with this technology.We show in this paper that secure two-party computation using garbled circuits provides a powerful alternative to HE for encrypted control. More precisely, we present a novel scheme that allows to efficiently implement (non-polynomial) max-out neural networks with one hidden layer in a secure fashion. These networks are of special interest for control since they allow, in principle, to exactly describe piecewise affine control laws resulting from, e.g., linear model predictive control (MPC). However, exact fits require high-dimensional preac-tivations of the neurons. Fortunately, we illustrate that even low-dimensional learning-based approximations are sufficiently accurate for linear MPC. In addition, these approximations can be securely evaluated using garbled circuit in less than 100 ms for our numerical example. Hence, our approach opens new opportunities for applying encrypted control.
Two-Way Neural Network Chinese-English Machine Translation Model Fused with Attention Mechanism

This study uses an end-to-end encoder-decoder structure to build a machine translation model, allowing the machine to automatically learn features and transform the corpus data into distributed representations. The word vector uses a neural network to achieve direct mapping. Research on constructing neural machine translation models for different neural network structures. Based on the translation model of the LSTM network, the gate valve mechanism reduces the gradient attenuation and improves the ability to process long-distance sequences. Based on the GRU network structure, the simplified processing is performed on the basis of it, which reduces the training complexity and achieves good performance. Aiming at the problem that some source language sequences of any length in the encoder are encoded into fixed-dimensional background vectors, the attention mechanism is introduced to dynamically adjust the degree of influence of the source language context on the target language sequence, and the translation model’s ability to deal with long-distance dependencies is improved. In order to better reflect the context information, this study further proposes a machine translation model based on two-way GRU and compares and analyzes multiple translation models to verify the effectiveness of the model’s performance improvement.
Understanding Blockchain Technology

The increasing worldwide demands for innovation have augmented the adoption of disruptive technologies such as blockchain for industrial and organizational developments. Blockchain technology has been gaining much interest and has developed to be a fundamental aspect of organizations and one of the most dominant topics in recent academic research. This study attempts to identify the underpinnings of blockchain technologies and the reasoning behind this disruptive innovation. This study uses centering resonance analysis to develop an empirically grounded understanding of the logic for blockchain technologies beyond the obvious, through secondary archived data. Through the content analysis of 172 peer-reviewed published articles, the authors decode seven themes (i.e., politics, performance, added-value, datafication, digital revolution, robotics, and security) through three theoretical frameworks: economic, innovative, and societal perspectives.
SABDM: A self‐attention based bidirectional‐RNN deep model for requirements classification

Abstract The success of software depends upon functional and non‐functional requirements as both requirements are equally important in software development. However, the requirements engineering community still lacks in comprehensive understanding of functional and non‐functional requirements. In addition, the requirements in software documents are expressed in natural language and also intertwined with each other. Requirements classification is a crucial task that correctly extracts functional and non‐functional requirements and organizes them in specified categories. Automated classification of software requirements leads to reduced ambiguity, misunderstanding, and development cost. Most of the recent studies have used machine learning and deep learning techniques for automatic classification of requirements. However, there is one drawback of such techniques, that is, poor generalization. To address these problems, this research work proposes self‐attention based bidirectional LSTM deep model. This automated approach has used recurrent neural network, which handle long sequential natural language requirements statements and classify them into five classes such as capability, maintainability, performance, security, and usability. The proposed approach train and evaluate over pre‐labeled dataset comprised of 34 industrial requirements specifications and PROMISE dataset. Over this dataset, the proposed approach yields 95% of precision, 96% of recall, 96% of F‐measure, and 96% of accuracy. The proposed approach can be applied to wide variety of datasets with different domain. Furthermore, this paper applies pre‐processing techniques to improve the performance of the requirements classification model. The results of the proposed model are compared with existing baseline state‐of‐art techniques, and it is shown that the proposed model outperforms the baseline models in requirements classification.
Deep Learning for Text Matching: A Survey

Text matching is one of the crucial technology in the field of Natural Language Processing (NLP), and it has been applied in many tasks, such as textual similarity, information retrieval and question answering. The target of text matching is to model the relationship between two input texts. In this paper, we aim to give a survey on recent advance techniques of deep-learning based text matching methods. Specifically, depending on whether a model will first encode a sentence into a fixed-length vector without any incorporating from the other sentence, the existing studies can be categorized into two major categories: representation-based models and interaction-based models. The latter can be divided into two groups according to the interaction methods. In addition, we summarize the strengths and weaknesses of these methods to help beginners in this area to choose the appropriate model for their application. Finally, we make a conclusion by highlighting several directions and open problems which need to be further explored by the community in the future.