Reviewerly/exHarmony huggingface dataset:
Summarizing Finite Mixture Model with Overlapping Quantification

Finite mixture models are widely used for modeling and clustering data. When they are used for clustering, they are often interpreted by regarding each component as one cluster. However, this assumption may be invalid when the components overlap. It leads to the issue of analyzing such overlaps to correctly understand the models. The primary purpose of this paper is to establish a theoretical framework for interpreting the overlapping mixture models by estimating how they overlap, using measures of information such as entropy and mutual information. This is achieved by merging components to regard multiple components as one cluster and summarizing the merging results. First, we propose three conditions that any merging criterion should satisfy. Then, we investigate whether several existing merging criteria satisfy the conditions and modify them to fulfill more conditions. Second, we propose a novel concept named clustering summarization to evaluate the merging results. In it, we can quantify how overlapped and biased the clusters are, using mutual information-based criteria. Using artificial and real datasets, we empirically demonstrate that our methods of modifying criteria and summarizing results are effective for understanding the cluster structures. We therefore give a new view of interpretability/explainability for model-based clustering.
Dark Web: A Facilitator of Crime

Anonymity is easy to accomplish on the internet through VPNs and onion routing. Anonymity can be good if you are trying to view pro-democracy content or western news in China, however, not all people are gaining internet anonymity for altruistic reasons. Internet crime has been on the rise and web browsers like TOR are helping to facilitate internet crime. The dark web is helping create online marketplaces for illicit goods, and these web criminals are harder to catch and with globalization even harder to prosecute. Law enforcement has been struggling to curtail internet crime due to a knowledge barrier and further jurisdictional problems once perpetrators are apprehended. The goal of this paper is to analyze how the dark web works, secures anonymity, and how it helps protect criminals and allow illegal marketplaces. It also aims to discuss different strategies law enforcement has been adapting to crack down on internet crime.
Object Detection Using YOLO Framework for Intelligent Traffic Monitoring

This paper presents a new real-time intelligent traffic monitoring system. To perform the vehicle detection, a filtered You Only Look Once (YOLO) is used. The pre-trained YOLO framework can detect 80 objects. The proposed system is tested for three classes of vehicles such as bus, truck, and car. After extracting the three categories, to obtain the count of that vehicle in each lane, checkpoint is assigned. The count is used to control the real-time road traffic signal. The system is tested with three different publicly available traffic videos. In the present work, we have used Kernel Correlation Filter (KCF) tracker and the object retrieval accuracy is obtained. Experimental results show that YOLO and KCF outperform Scale Invariant Feature Transform (SIFT) and Region-based Convolutional Neural Network (RCNN) with KCF tracker, and Maximally Stable Extremal Regions (MSER) and faster RCNN with KCF tracker.
Development of Security Performance and Comparative Analyses Process for Big Data in Cloud

Big data is accumulate and development with the facilitate of disseminated encoding structures like Hadoop which handles the new encoding prototype. Big data can handle huge quantity of information with thousands of computers in clusters. Through the condition of compute of different possessions in public cloud, people and institute are clever to outsource their statistics storage and services. Conversely, the information owners are disturbed with security of their outsourced statistics. The Cloud Security Alliance has recognized and announced different security confronts with reverence to big data security and safe estimation in disseminated encoding structure veracity and immediate security. There are important investigate assistance establish in the survey. Conversely, a complete approach that provides to the security wants of the cloud-big data environment to stimulate of the different afore reveals dispute is lost. Consequently the plan of this proposal is to examine, design, and execute method for safe computation, big data privacy, big data security, and efficient statistics administration. Individual challenges are big data privacy, infrastructure security, big data security, and data management. Authentication using username and password which contains the user data such as identity number, data provider name, date and time of uploading the data is also used to recognize the person who uploaded the data into the big data cloud. It identifies the origin of data and someone who downloads the data could know the person who created it. A variety of cryptographic methods used for encryption are Attribute-Based Encryption as Scheme 1 , Advanced Encryption Standard as Scheme 2 , hybridized Attribute-Based Encryption and Advanced Encryption Standard as Scheme 3 , and the proposed algorithm named Secure Dynamic Bit Standard as Scheme 4 which offer high security for the data stored and access by the end user. The Secure Dynamic Bit Standard algorithm provides the security during two dissimilar keys such as master key and session key produced by cloud service provider. The Secure Dynamic Bit Standard algorithms enclose the three disparate key lengths such as 128 bits, 256 bits, and 512 bits. The key length of the master key and session key randomly creates through the encryption process. This method will minimize the maximum number of unauthorized and unauthenticated users in the big data cloud. It also minimizes the maximum number of assault in the big data cloud environment such as collusion, brute force attack, and Structured Query Language.
Sustainable Development of African Countries: Minding Public Life, Education, and Welfare

Sustainable development (SD) has increasingly played a key background role in government policymaking across the world, especially for the least developed countries in Africa. Therefore, the purpose of our research is to study the SD of African countries in public life, education, and welfare, and then to help policy makers better monitor the status of sustainable development and formulate development policies in these aspects. We firstly propose a new method to assess the SD in public life, education, and welfare. Then we assess the SD status in 51 African countries as well as other countries in the world. After that, we also make a comparison between African countries and the countries in other continents.
Cyclic Einstein-Podolsky-Rosen steering

Einstein-Podolsky-Rosen (EPR) steering is a form of quantum correlation that exhibits a fundamental asymmetry in the properties of quantum systems. Given two observers, Alice and Bob, there are known to exist bipartite entangled states which are one-way steerable in the sense that Alice can steer Bob's state, but Bob cannot steer Alice's state. Here, we generalize this phenomenon to three parties and find a cyclic property of tripartite EPR steering. In particular, we identify a three-qubit state whose reduced bipartite states are one-way steerable for arbitrary projective measurements. Moreover, the three-qubit state has a cyclic steering property in the sense that by arranging the system in a triangular configuration the neighboring parties can steer each other's states only in the same (e.g., clockwise) direction. That is, Alice can steer Bob's state, Bob can steer Charlie's state, and Charlie can steer Alice's state, but not the other way around.
An authorization model for query execution in the cloud

We present a novel approach for the specification and enforcement of authorizations that enables controlled data sharing for collaborative queries in the cloud. Data authorities can establish authorizations regulating access to their data distinguishing three visibility levels (no visibility, encrypted visibility, and plaintext visibility). Authorizations are enforced accounting for the information content carried in the computation to ensure no information is improperly leaked and adjusting visibility of data on-the-fly. Assignment of operations to subjects takes into consideration the cost of operation execution as well as of the encryption/decryption operations needed to make the assignment authorized. Our approach enables users and data authorities to fully enjoy the benefits and economic savings of the competitive open cloud market, while maintaining control over data.
Deep Learning based Eye gaze estimation and prediction

Eye gaze understanding plays a crucial role in human social interactions. The gaze has emerged as a powerful tool for many applications including health assessment, disease diagnosis, human behavior, communication analysis. Eye gaze estimation and prediction has become a very hot topic in the field of computer vision during the last decades, with a surprising and continuously growing number of application fields. In the last decade deep neural networks have revolutionized the whole machine learning area, and gaze tracking. Appearance-based models use deep convolutional networks (CNNs) to directly estimate the direction of gaze in the camera's frame of reference. This paper focuses on analyzing and investigating different CNN architectures for gaze estimation and prediction. Two tasks have been developed in this work: the gaze estimation and the gaze prediction based on previously estimated gaze-points. In the first task, several CNNs were used for finding the most accurate gaze estimation. In the second task, we predict gaze locations basing on the previously estimated gaze vectors while leveraging spatio-temporal information encoded in a previously recorded eye-images sequences. We used a Long Short Term Memory (LSTM) and Transformers based on self-attention approach and use of positional encoding, in order to predict next gaze locations. Different architectures has been trained on the OPENEDS2020 public dataset which has been revisited in order to improve overall performance. We, also, propose a novel architecture composed by ResNext50 and two fully connected layer. We demonstrate that proposed architectures obtain results that in terms of angular error outperforms the state of art.
Identifying Mislabeled Images in Supervised Learning Utilizing Autoencoder

Supervised learning is based on the assumption that the ground truth in the training data is accurate. However, this may not be guaranteed in real-world settings. Inaccurate training data will result in some unexpected predictions. In image classification, incorrect labels may cause the classification model to be inaccurate as well. In this paper, I am going to apply unsupervised techniques to the training data before training the classification network. A convolutional autoencoder is applied to encode and reconstruct images. The encoder will project the image data on to latent space. In the latent space, image features are preserved in a lower dimension. The assumption is that data samples with similar features are likely to have the same label. Noised samples can be classified in the latent space by the Density-Base Scan (DBSCAN) clustering algorithm. These incorrectly labeled data are visualized as outliers in the latent space. Therefore, the outliers identified by the DBSCAN algorithm can be classified as incorrectly labeled samples. After the outliers are detected, all the outliers are treated as mislabeled data samples and removed from the dataset. Thus the training data can be directly used in training the supervised learning network. The algorithm can detect and remove above 67\% of mislabeled data in the experimental dataset.
A Scalable Log Differencing Visualisation Applied to COBOL Refactoring

Large code refactoring projects can consist of hundreds of refactoring rules that are applied iteratively to make code easier to maintain.Visualising the refactoring process can help engineers and stakeholders understand how chains of refactorings were applied and to gain more confidence in the produced result.An apparently suitable existing visualisation using log-based behavioural differencing suffers from scalability issues when applied to industrial-size cases.We propose an adapted visualisation tool that highlights those parts that really changed in-between iterations of a large refactoring process and collapses those parts that remain stable.We show that our alternative visualisation scales well on large logs of a process with many possible refactoring chains, of which significant parts are shared.Consequently, it allows engineers and stakeholders to quickly answer relevant questions about what happened during the refactoring process.
WITHDRAWN: Core competence-based English major practical teaching system

There isa widespread pattern in the current college teaching and learning environment where colleges and universities emphasize introducing experience, conventional teaching approaches, measuring expertise, non-practical skills, innovation, and market-orientated skills. English is becoming ever more important as an instrument for acquiring scholarly knowledge and information with the difficulties posed by expertise and an intensely dynamic human resources environment. Furthermore, the Core Competence-based English Practical Teaching System (CCEPTS) has been proposed to improve student curriculum development and enhance students' performance in the carrier. The overall implementation method of the college of English supported an artificial intelligence teaching system. AI provides a high-level multi-collaboration expert system that helps students enhance their educational performance and content. The programming technology incorporates the latest conventional programming technology. This suggests CCEMPTS method restructuring of the university English teaching the issues of inadequate teachers and a lack of commitment to the underlying knowledge of students. Finally, our goals are to turn test-oriented English into market-oriented English and enhance the practical skills of university students.
What Helps Transformers Recognize Conversational Structure? Importance of Context, Punctuation, and Labels in Dialog Act Recognition

Abstract Dialog acts can be interpreted as the atomic units of a conversation, more fine-grained than utterances, characterized by a specific communicative function. The ability to structure a conversational transcript as a sequence of dialog acts—dialog act recognition, including the segmentation—is critical for understanding dialog. We apply two pre-trained transformer models, XLNet and Longformer, to this task in English and achieve strong results on Switchboard Dialog Act and Meeting Recorder Dialog Act corpora with dialog act segmentation error rates (DSER) of 8.4% and 14.2%. To understand the key factors affecting dialog act recognition, we perform a comparative analysis of models trained under different conditions. We find that the inclusion of a broader conversational context helps disambiguate many dialog act classes, especially those infrequent in the training data. The presence of punctuation in the transcripts has a massive effect on the models’ performance, and a detailed analysis reveals specific segmentation patterns observed in its absence. Finally, we find that the label set specificity does not affect dialog act segmentation performance. These findings have significant practical implications for spoken language understanding applications that depend heavily on a good-quality segmentation being available.
TWEETSUMM - A Dialog Summarization Dataset for Customer Service

In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues.In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.The goal of the present article is advancing the automation of this task.We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries.The data is based on realworld customer support dialogs and includes both extractive and abstractive summaries.We also introduce a new unsupervised, extractive summarization method specific to dialogs.
Transformer-S2A: Robust and Efficient Speech-to-Animation

We propose a novel robust and efficient Speech-to-Animation (S2A) approach for synchronized facial animation generation in human-computer interaction. Compared with conventional approaches, the proposed approach utilizes phonetic posteriorgrams (PPGs) of spoken phonemes as input to ensure the cross-language and cross-speaker ability, and introduces corresponding prosody features (i.e. pitch and energy) to further enhance the expression of generated animation. Mixture-of-experts (MOE)-based Transformer is employed to better model contextual information while provide significant optimization on computation efficiency. Experiments demonstrate the effectiveness of the proposed approach on both objective and subjective evaluation with 17× inference speedup compared with the state-of-the-art approach.
Video Streaming Analysis in Multi-tier Edge-Cloud Networks

Video streaming services represent most internet traffic, and according to Cisco forecasts, in 2022, 82% of all internet traffic will be dominated by video streaming. This includes current video services as well as innovative services such as Real-Time video Streaming and future cloud gaming, whereas, for mobile devices, this estimate represents 78% of all mobile data traffic. A good cloud architecture partially solves some issues related to the live stream and Video on Demand (VoD) services to accommodate video traffic. However, a centralized cloud service introduces some issues such as higher latency and core network congestion. Therefore, to improve video services, it is paramount to distribute video streams according to their requirements properly: a real-time video streaming infrastructure is an interactive service that needs reduced delays (a few milliseconds). At the same time, a non-interactive VoD delivery can tolerate higher delays without impairing the quality of experience. This work discusses and gives evidence for the need for proper management and orchestration of video delivery over the Internet as it is core to the smooth coexistence of video services in multi-tier edge/cloud environments. The results assessment corroborate that well-defined video management can considerably increase the end-user QoE.
An extensive study of user identification via eye movements across multiple datasets

Several studies have reported that biometric identification based on eye movement characteristics can be used for authentication.This paper provides an extensive study of user identification via eye movements across multiple datasets based on an improved version of method originally proposed by George and Routray.We analyzed our method with respect to several factors that affect the identification accuracy, such as the type of stimulus, the IVT parameters (used for segmenting the trajectories into fixation and saccades), adding new features such as higher-order derivatives of eye movements, the inclusion of blink information, template aging, age and gender.We find that three methods namely selecting optimal IVT parameters, adding higher-order derivatives features and including an additional blink classifier have a positive impact on the identification accuracy.The improvements range from a few percentage points, up to an impressive 9 % increase on one of the datasets.
BiLSTM-CNN Hyperparameter Optimization for Speech Emotion and Stress Recognition

The most automated speech recognition (ASR) systems are extremely complicated, integrating many approaches and requiring a high variety of tuning parameters. Deep understanding and experience of each component are required to achieve optimal performance in ASR, confining the development of ASR systems to the experts. Hyperparameters are crucial for machine learning algorithms because they directly regulate the behavior of training algorithms and have a major impact on model performance. As a result, developing an effective hyperparameter optimization technique to optimize any given machine learning method would considerably increase machine learning efficiency. This work investigates the use of Random Forest and Bayesian to automatically optimize BiLSTM-CNN systems. We built the ASR based on the BiLSTM-CNN model and customized its hyperparameters value to heed our low-hardware specification during optimization. Furthermore, we gathered 1,000 clips of speech data from various movies, classifying them according to emotion and stress classes. In pursuit of contextual-level understanding in our ASR, we transcribed our speech data and used the bigram textual feature. Our Random Forest-optimized BiLSTM-CNN model ultimately reaches 84% of accuracy result and learning runtime in under 17 seconds.
A Social Recommendation Based on Metric Learning and Users’ Co-Occurrence Pattern

For personalized recommender systems, matrix factorization and its variants have become mainstream in collaborative filtering. However, the dot product in matrix factorization does not satisfy the triangle inequality and therefore fails to capture fine-grained information. Metric learning-based models have been shown to be better at capturing fine-grained information than matrix factorization. Nevertheless, most of these models only focus on rating data and social information, which are not sufficient for dealing with the challenges of data sparsity. In this paper, we propose a metric learning-based social recommendation model called SRMC. SRMC exploits users’ co-occurrence patterns to discover their potentially similar or dissimilar users with symmetric relationships and change their relative positions to achieve better recommendations. Experiments on three public datasets show that our model is more effective than the compared models.
IOT based Smart Traffic Signal Violation Monitoring System using Edge Computing

Monitoring the traffic signal surveillance camera by a group of police administrators can be a daunting task due to the sheer volume of data(videos) that must be collected and analyzed. In developing countries such as India, traffic police department need a easy way to catch the vehicle owners who have violated the traffic signal rules. Currently, they are collecting the video near the traffic signal for 24 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> 7 and the administrators are manually analyzing to find if anyone has violated the traffic signal rules. They need to capture the license plate and then send a report. The proposed system addresses this problem. IOT based Smart traffic signal violation system make use of the features provided by edge computing. It shall surveil the signal only when it turns red and captures the video. Later it analyses the video using suitable image processing algorithm and captures the traffic rule’s violator’s license plate and send it to the administrators. In this way it reduces the manual work done by the police administrators and analyzes the video at the edge device itself.