Reviewerly/exHarmony huggingface dataset:
BLOCKCHAIN BASED FRAMEWORK FOR EDUCATIONAL CERTIFICATES VERIFICATION

Document verification is a complex domain that involves various challenging and tedious processes to authenticate. Moreover various types of documents for instance banking documents, government documents, transaction documents, educational certificates etc. might involve customized verification and authentication practices. The content for each type vary significantly, hence requires to be dealt in a distinct manner. For students, educational certificates are the most important documents issued by their universities. However, as the issuing process is not that transparent and verifiable, fake certificates can be easily created. A skilfully generated fake certificate is always hard to detect and can be treated as the original. With the increase of forged documents, credibility of both the document holder and the issuing authority is jeopardized. Blockchain technology has recently emerged as a potential mean for authenticating the document verification process and a significant tool to combat document fraud and misuse. This research aimed to enhance the document verification process using blockchain technology. In this research, authors have identified the security themes required for document verification in the blockchain. This research also identifies the gaps and loopholes in the current blockchain based educational certificate verification solutions. At the end, a blockchain based framework for verifying educational certificates focusing on themes including authentication, authorization, confidentiality, privacy and ownership is proposed using the Hyperledger Fabric Framework.
End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things

This work is the first attempt to evaluate and compare felderated learning (FL) and split neural networks (SplitNN) in real-world IoT settings in terms of learning performance and device implementation overhead. We consider a variety of datasets, different model architectures, multiple clients, and various performance metrics. For learning performance, which is specified by the model accuracy and convergence speed metrics, we empirically evaluate both FL and SplitNN under different types of data distributions such as imbalanced and non-independent and identically distributed (non-IID) data. We show that the learning performance of SplitNN is better than FL under an imbalanced data distribution, but worse than FL under an extreme non-IID data distribution. For implementation overhead, we end-to-end mount both FL and SplitNN on Raspberry Pis, and comprehensively evaluate overheads including training time, communication overhead under the real LAN setting, power consumption and memory usage. Our key observations are that under IoT scenario where the communication traffic is the main concern, the FL appears to perform better over SplitNN because FL has the significantly lower communication overhead compared with SplitNN, which empirically corroborate previous statistical analysis. In addition, we reveal several unrecognized limitations about SplitNN, forming the basis for future research.
Comparison of the Effects of Mel Coefficients and Spectrogram Images via Deep Learning in Emotion Classification

In the present paper, an approach was developed for emotion recognition from speech data using deep learning algorithms, a problem that has gained importance in recent years.Feature extraction manually and feature selection steps were more important in traditional methods for speech emotion recognition.In spite of this, deep learning algorithms were applied to data without any data reduction.The study implemented the triple emotion groups of EmoDB emotion data: Boredom, Neutral, and Sadness-BNS; and Anger, Happiness, and Fear-AHF.Firstly, the spectrogram images resulting from the signal data after preprocessing were classified using AlexNET.Secondly, the results formed from the Mel-Frequency Cepstrum Coefficients (MFCC) extracted by feature extraction methods to Deep Neural Networks (DNN) were compared.The importance and necessity of using manual feature extraction in deep learning was investigated, which remains a very important part of emotion recognition.The experimental results show that emotion recognition through the implementation of the AlexNet architecture to the spectrogram images was more discriminative than that through the implementation of DNN to manually extracted features.
Effect of Annotation Errors on Drone Detection with YOLOv3

Following the recent advances in deep networks, object detection and tracking algorithms with deep learning backbones have been improved significantly; however, this rapid development resulted in the necessity of large amounts of annotated labels.Even if the details of such semi-automatic annotation processes for most of these datasets are not known precisely, especially for the video annotations, some automated labeling processes are usually employed.Unfortunately, such approaches might result with erroneous annotations.In this work, different types of annotation errors for object detection problem are simulated and the performance of a popular state-of-the-art object detector, YOLOv3, with erroneous annotations during training and testing stages is examined.Moreover, some inevitable annotation errors in Anti-UAV Challenge dataset is also examined in this manner, while proposing a solution to correct such annotation errors of this valuable data set.
Anonymous Collocation Discovery: Harnessing Privacy to Tame the Coronavirus

Successful containment of the Coronavirus pandemic rests on the ability to quickly and reliably identify those who have been in close proximity to a contagious individual. Existing tools for doing so rely on the collection of exact location information of individuals over lengthy time periods, and combining this information with other personal information. This unprecedented encroachment on individual privacy at national scales has created an outcry and risks rejection of these tools. We propose an alternative: an extremely simple scheme for providing fine-grained and timely alerts to users who have been in the close vicinity of an infected individual. Crucially, this is done while preserving the anonymity of all individuals, and without collecting or storing any personal information or location history. Our approach is based on using short-range communication mechanisms, like Bluetooth, that are available in all modern cell phones. It can be deployed with very little infrastructure, and incurs a relatively low false-positive rate compared to other collocation methods. We also describe a number of extensions and tradeoffs. We believe that the privacy guarantees provided by the scheme will encourage quick and broad voluntary adoption. When combined with sufficient testing capacity and existing best practices from healthcare professionals, we hope that this may significantly reduce the infection rate.
Similarity Metric Method for Binary Basic Blocks of Cross-Instruction Set Architecture

Basic block similarity analysis is a fundamental technique in many machine learning-based binary program analysis methods.The key to basic block similarity analysis is mapping the semantic information of the basic block to a fixeddimension vector, which is the so-called basic block embedding.However, existing solutions to basic block embedding suffer from two major limitations.1) The basic block embedding contains limited semantic information; 2) they are only applicable to a single instruction set architecture (ISA).To overcome these limitations, we propose a cross-ISA oriented solution for basic block embedding which utilizes an NMT (Neural Machine Translation) model to establish the connection between two ISAs.The proposed embedding model can powerfully map rich semantics of basic blocks from arbitrary ISAs into fixed-dimension vectors.Several measures have been taken to further improve the embedding model.To guide the embedding model to a better state, we creatively use the pretrained model to generate hard negative samples.To promote the effectiveness of the proposed embedding model, we propose a reasonable assembly instruction normalization method in the data preprocessing phase, which is shown to outperform the previous methods.A similarity metric method is then derived and a million-scale dataset is presented to train and evaluate this method.To the best of our knowledge, this is the first million-scale dataset in this field.We implement a prototype system MIRROR.The experimental results show that MIRROR significantly outperforms the representative baseline in the respect that the basic block embeddings, i.e., the vectors, are more distinguishable to discriminate between similar basic blocks and dissimilar ones, and as a result, MIRROR can obtain obviously more accurate evaluation results.The significance of pre-training, the effectiveness of the proposed negative sampling method, and the instruction normalization method have also been justified in experiments.
A Video-Based DT–SVM School Violence Detecting Algorithm

School bullying is a serious problem among teenagers. School violence is one type of school bullying and considered to be the most harmful. As AI (Artificial Intelligence) techniques develop, there are now new methods to detect school violence. This paper proposes a video-based school violence detecting algorithm. This algorithm first detects foreground moving targets via the KNN (K-Nearest Neighbor) method and then preprocesses the detected targets via morphological processing methods. Then, this paper proposes a circumscribed rectangular frame integrating method to optimize the circumscribed rectangular frame of moving targets. Rectangular frame features and optical-flow features were extracted to describe the differences between school violence and daily-life activities. We used the Relief-F and Wrapper algorithms to reduce the feature dimension. SVM (Support Vector Machine) was applied as the classifier, and 5-fold cross validation was performed. The accuracy was 89.6%, and the precision was 94.4%. To further improve the recognition performance, we developed a DT–SVM (Decision Tree–SVM) two-layer classifier. We used boxplots to determine some features of the DT layer that are able to distinguish between typical physical violence and daily-life activities and between typical daily-life activities and physical violence. For the remainder of activities, the SVM layer performed a classification. For this DT–SVM classifier, the accuracy reached 97.6%, and the precision reached 97.2%, thus showing a significant improvement.
Modal Equivalence and Bisimilarity in Many-valued Modal Logics with Many-valued Accessibility Relations

In this paper we investigate the Hennessy-Milner property for models of many-valued modal logics defined based on complete MTL-chains having many-valued accessibility relations. Our main result gives a necessary and sufficient algebraic condition for the class of image-finite models for such modal logics to admit the Hennessy-Milner property.
Structured Citation Trend Prediction Using Graph Neural Networks

Academic citation graphs represent citation relationships between publications across the full range of academic fields. Top cited papers typically reveal future trends in their corresponding domains which is of importance to both researchers and practitioners. Prior citation prediction methods often require initial citation trends to be established and do not take advantage of the recent advancements in graph neural networks (GNNs). We present GNN-based architecture that predicts the top set of papers at the time of publication. For experiments, we curate a set of academic citation graphs for a variety of conferences and show that the proposed model outperforms other classic machine learning models in terms of the F1-score.
Motion Words: A Text-Like Representation of 3D Skeleton Sequences

There is a growing amount of human motion data captured as a continuous 3D skeleton sequence without any information about its semantic partitioning. To make such unsegmented and unlabeled data efficiently accessible, we propose to transform them into a text-like representation and employ well-known text retrieval models. Specifically, we partition each motion synthetically into a sequence of short segments and quantize the segments into motion words, i.e. compact features with similar characteristics as words in text documents. We introduce several quantization techniques for building motion-word vocabularies and propose application-independent criteria for assessing the vocabulary quality. We verify these criteria on two real-life application scenarios.
Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties
Unsupervised Commonsense Question Answering with Self-Talk

Natural language understanding involves reading between the lines with implicit background knowledge. Current systems either rely on pre-trained language models as the sole implicit source of world knowledge, or resort to external knowledge bases (KBs) to incorporate additional relevant knowledge. We propose an unsupervised framework based on self-talk as a novel alternative to multiple-choice commonsense tasks. Inspired by inquiry-based discovery learning (Bruner, 1961), our approach inquires language models with a number of information seeking questions such as $\textit{what is the definition of ...}$ to discover additional background knowledge. Empirical results demonstrate that the self-talk procedure substantially improves the performance of zero-shot language model baselines on four out of six commonsense benchmarks, and competes with models that obtain knowledge from external KBs. While our approach improves performance on several benchmarks, the self-talk induced knowledge even when leading to correct answers is not always seen as useful by human judges, raising interesting questions about the inner-workings of pre-trained language models for commonsense reasoning.
3D Modeling and Computer Graphics in Virtual Reality

In the era of digital information technologies, 3D modeling and computer graphics techniques not only apply to the development of virtual models for computer simulation, artificial intelligence (AI), big data analytics, etc., but also they can be applied in many different applications in virtual reality (VR).However, the computer graphics effect and visual realism are usually the trade-offs with the real-time and realistic interaction in VR.In this book chapter, we would like to review the general flow of the VR program development process, and the recent 3D modeling and texture painting techniques used in VR.On the other hand, we would introduce some of the key 3D modeling and computer graphics techniques that can be applied in VR in order to enhance the speed of interaction.The key techniques including smoothing techniques and mesh editing modifiers are not only useful for the designers to learn the 3D modeling process, but it also helps to create less complex mesh models maintaining good visual effects.The techniques are particularly important in the development of 3D models to satisfy the demanding computation requirement of real-time interaction in VR program.
Secure and efficient multiparty private set intersection cardinality

<p style='text-indent:20px;'>In the field of privacy preserving protocols, Private Set Intersection (PSI) plays an important role. In most of the cases, PSI allows two parties to securely determine the intersection of their private input sets, and no other information. In this paper, employing a Bloom filter, we propose a Multiparty Private Set Intersection Cardinality (MPSI-CA), where the number of participants in PSI is not limited to two. The security of our scheme is achieved in the standard model under the Decisional Diffie-Hellman (DDH) assumption against semi-honest adversaries. Our scheme is flexible in the sense that set size of one participant is independent from that of the others. We consider the number of modular exponentiations in order to determine computational complexity. In our construction, communication and computation overheads of each participant is <inline-formula><tex-math id="M1">\begin{document}$ O(v_{\sf max}k) $\end{document}</tex-math></inline-formula> except that the complexity of the designated party is <inline-formula><tex-math id="M2">\begin{document}$ O(v_1) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M3">\begin{document}$ v_{\sf max} $\end{document}</tex-math></inline-formula> is the maximum set size, <inline-formula><tex-math id="M4">\begin{document}$ v_1 $\end{document}</tex-math></inline-formula> denotes the set size of the designated party and <inline-formula><tex-math id="M5">\begin{document}$ k $\end{document}</tex-math></inline-formula> is a security parameter. Particularly, our MSPI-CA is the <i>first</i> that incurs <i>linear</i> complexity in terms of set size, namely <inline-formula><tex-math id="M6">\begin{document}$ O(nv_{\sf max}k) $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M7">\begin{document}$ n $\end{document}</tex-math></inline-formula> is the number of participants. Further, we extend our MPSI-CA to MPSI retaining all the security attributes and other properties. As far as we are aware of, there is no other MPSI so far where individual computational cost of each participant is independent of the number of participants. Unlike MPSI-CA, our MPSI does not require any kind of broadcast channel as it uses star network topology in the sense that a designated party communicates with everyone else.
Quantum correlations of two qubits beyond entanglement in two lossy cavities linked by a waveguide

We explore the quantum correlations (QCs) of two non-interacting two-level systems (qubits). Each qubit is embedded in an open cavity, the cavities are linked by an optical fiber and leak photons to the external environment. The quantum correlations are investigated via three different quantifiers (measurement-induced nonlocality, geometric quantum discord and negativity) under the effects of the coupling of the qubit-cavity and the fiber-cavity interactions as well as the cavity dissipations. It is found that the generation of QCs and their sudden birth and death phenomena, depend not only on the qubit-cavity and fiber-cavity couplings, but also on the initial states. The robustness of the QCs against the cavity dissipations can be enhanced in the regime of the ultra-strong cavity-fiber coupling. We convey that it is possible to control the quantum correlations, as well as to reduce the effect of cavity dissipation.
Emergency Vehicle Recognition and Classification Method Using HSV Color Segmentation

Classification and recognition of emergency vehicles in traffic surveillance cameras offer an early warning to ensure the prompt reaction to the traffic about emergency stating. Much existing traffic surveillance camera detection monitoring based on computer vision technology has achieved high detection rates. Some of them are very innovative and have a higher novelty index. One of the innovative ideas is detecting an emergency vehicle in the moving traffic routes from the input of a video surveillance camera. The Classification and recognition of emergency vehicles in traffic surveillance cameras offer an early warning to ensure the prompt reaction to the traffic about emergency stating. This paper proposes an algorithm which addresses a novel visual analysis technique for Detecting a moving emergency vehicle in traffic surveillance camera using a combination of Hue Saturation and Value (HSV) color segmentation and support vector machine (SVM). Experiments conducted on various benchmarking databases show that the proposed algorithm successfully distinguishes the emergency vehicles from the traffic in a surveillance camera. The results show that the proposed algorithm has an innovative classification method, a higher accuracy rate, as well as comparable recall and specificity, compared with other methods.
Leveraging Schema Labels to Enhance Dataset Search

A search engine’s ability to retrieve desirable datasets is important for data sharing and reuse. Existing dataset search engines typically rely on matching queries to dataset descriptions. However, a user may not have enough prior knowledge to write a query using terms that match with description text. We propose a novel schema label generation model which generates possible schema labels based on dataset table content. We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels. To evaluate our method on real-world datasets, we create a new benchmark specifically for the dataset retrieval task. Experiments show that our approach can effectively improve the precision and NDCG scores of the dataset retrieval task compared with baseline methods. We also test on a collection of Wikipedia tables to show that the features generated from schema labels can improve the unsupervised and supervised web table retrieval task as well.
Impact of foreign direct investment and inflation on economic growth of five randomly selected Countries in Africa

Foreign Direct Investment (FDI) has been viewed as a major source of finance for developing countries and it has gained significant momentum since the sweep of globalization in the early 1990.This is because most of the developing countries see FDI as important in their strategy for growth.In this study, the impact of FDI and inflation on economic growth was examined.Five countries in Africa were selected randomly for the study.The variables used for this study are inflation rate, GDP per capita (economic growth), and FDI inflows.The study relied on IMF Data Mapper and UNCTAD stat for a period of 23-year time series 1996 to 2018 as the source of data for the study.Lastly unit root test and regression analysis were employed to estimate the objective of the study and output showed that FDI has a positive impact on economic growth in all the five countries under review.Except Egypt, Inflation has a negative impact on economic growth in four out of the five countries reviewed.This study recommended that Government should endeavor to create conducive environment that will enable FDI to thrive and also look into policies that will regulate money supply to encourage low and stable inflation rate, in order to absorb the maximum benefits of FDI inflows.