Reviewerly/exHarmony huggingface dataset:
Remote Video Surveillance

We present a video surveillance solution that requires minimal installation and configuration effort. Video feeds are encrypted and accessible to anyone with the appropriate authentication credentials. The presented solution does not require any router nor firewall reconfiguration and the video feed is accessible even if the camera is behind a firewall and in a private network. Normally, port-forwarding needs to be enabled on the firewall, at minimum, to enable internet clients to access a camera located in a private network. Our solution enables a camera to be placed behind firewalls, in private networks, and still be accessible from the internet or other private networks without reconfiguring any of the existing network infrastructure. We achieve this by initiating a connection from the camera to a public proxy server which serves the role of an intermediary between clients/users and the camera. Clients connect to the proxy server. The proxy server then hands off the video feed of the camera to the client. This way, there is never a connection initiation from the internet to the camera. We provide three different methods of accessing the video feeds: via: a mobile app, a conventional browser, and a Linux command-line interface.
SAI: Self-Adjusting Incremental Quantile Estimation for Sparse Training of Neural Networks on Hardware Accelerators

Supporting sparse training of neural networks has become a trend for hardware accelerators. Existing sparse training algorithms generally rely on sorting to compress neural network models. The accelerated sparse training of hardware accelerators is inseparable from the hardware implementation of sorting, which should have the characteristics of one-pass, storage-friendly, and non-dependent hyperparameters. In this paper, we propose a hardware friendly and one-pass Self-Adjusting Incremental Quantile Estimation method, shorten to SAI, to replace the sorting operations commonly used in sparse training algorithms. After applying SAI to sparse training of neural network models, our method reduces the time cost of determining to clip and activate connections to <tex>$O(n)$</tex>, and avoids auxiliary storage and frequent I/O operations, which is unattainable by sorting. SAI-based sparse training algorithms show the performance advantages under the condition of smaller sparsity, and can effectively develop parallelism. We design the quantile estimation module of SAI, and test its effect on the image classification combined with sparse training algorithm on CIFAR-10 dataset. Experimental results show that SAI-based sparse training algorithms can achieve high accuracy, and has obvious parallel optimization ability.
QHash: An efficient hashing algorithm for low-variance image deduplication

Attributed to the widespread use of general artificial intelligence (AI), large-scale datasets have become a critical component for the success of AI-powered applications. While collecting a larger dataset is desirable in general, studies have revealed that many databases include duplicated images. The accumulation of redundant images will result in excessive resource usage and inefficient cloud storage utilization. To get rid of the duplicates, hashing-based methods have been developed for the problem of image deduplication. However, as demonstrated in our experiments, current approaches fail in dataset with small visual difference, such as medical images. To this end, we propose QHash, which achieves effective image deduplication on the low-variance dataset. QHash leverages Vector Quantized Variational AutoEncoder (VQ-VAE) to learn the data distribution in an unsupervised manner. In addition, hash sequences are implemented using integer-based tensor, enabling distance calculation and deduplication being processed in parallel on either CPU or GPU. Extensive experiments show that QHash outperforms other baseline approaches by at least 50&#x0025;, while is about 23&#x0025; memory efficient and 18&#x0025; deduplication time speedup.
PAL-FD: an Efficient and Accurate Multi-Scene Human Face Detection Tool for Windows

Face detection serves as a fundamental component for facial analytics such as face recognition at edge devices, which has been extensively studied in recent years. However, previous studies focus on the architecture design of face detection models based on deep convolutional neural networks (DCNN), ignoring the rapid deployment and intuitive use of human face detection models. In this paper, we develop an easy-to-use but accurate face detection tool called PAL-FD under Windows for public use. First, we release a toy graphical user interface (GUI) library called PAL written in C++ for rapid visual software development. Second, we simplify Faster R-CNN based on benchmark datasets to obtain an efficient face detector. Third, we build the user-friendly tool PAL-FD for human face detection based on effective fusion of the face detector and PAL GUI library. Quantitative and qualitative evaluations on real world images show that PAL-FD can efficiently detect human faces from multiple types of images with large variations in scale, pose, blurry, occlusion, illumination and makeup of human faces.
High Speed True Random Number Generator Controlled by Logistic Map

The integrated circuit technology has occupied a dominant position in electronic equipment. Traditional True Random Number Generators (TRNGs) are mostly designed based on analog circuits, which cannot satisfy current needs. The ring oscillators are easy to produce phase jitter and D flip-flop can easily enter the metastable state at higher input signal frequency. In this paper, a TRNG is designed by using the ring oscillator and D flip-flop as a source of randomness. Moreover, the Logistic map is used to realize the frequency conversion between the high-frequency output of the system and the low-frequency sampling of the random source to make the random source accumulate more jitter. The design has been implemented on the field-programmable gate array device, and the generated random sequence can successfully pass NIST SP800-22 and TestU01 tests. The TRNG designed in this paper first introduces the Logistic map as the control module and has a high output frequency.
Danger Warning System for Blinds using Computer Vision (Sixth Sense) and OCR

Danger warning system for blinds is a unique system which admonishes blinds, if there is any danger area having a sign board like &#x201C;Men At Work&#x201D;, &#x201C;Maintenance Going On&#x201D;, by playing a sound in their ears through a headphone. In this paper, the camera is acting as an input device which takes continues snapshot and then indicates the blind person, if it detects any warning sign board. First, it eliminates the background (preprocessing), then it crop the ROI, segregate the alphabets, recognize the alphabets, makes the word again and if that word would be related to the danger it will indicate the person by making a sound.
Abstractive Text Summarization Using Generative Adversarial Network and Relation Extraction

In this paper, we propose a new adversarial model to solve some common problems existing in Generative Adversarial Network for text summarization. we simultaneously train a generative model G, a discriminative model D, and a model which auxiliary generator to generate a high quality summary .This module is like a teacher to guide G to quickly optimize himself and then against D, so we call it Teacher model. Teacher is an entity relationship extraction model to extract the triples of the real summary. The words in the triples are defined as keywords, and the keywords are revealed to the generator like the teacher points out the key points, and the summary generated by the guiding generator contains more Keywords. Model achieves competitive ROUGE scores with the baseline on CNN/Daily Mail dataset.
DCLPP: A Distributed and Cooperative approach based on Local Path Planning for Multi-sensors Patrolling - Application to Rapid Bushfire Detection

This study aims to show that rapid detection of fire risk areas in a forest can be done by keeping the forest under constant and regular surveillance by a small number of mobile wireless sensors. The proposed surveillance approach is based on continuous local path planning. We propose a genetic algorithm suitable for planning a longer path, allowing the sensor to visit a cluster of cells while traversing as many cells as possible in the cluster. The evaluation of the proposed approach shows its effectiveness in reducing environmental idleness and in quickly detecting emerging events.
MetaProfiling: Inferring User Profiles with Few-Shot Data

Inferring user profiles plays a pivotal role in various real-world scenarios. The rapid development of the online social media enables user profiling based on their interacted data. However, some user profiles are diverse and constantly changing. On the one hand, a number of new occupations are emerging, but only with few labeled users. This often leads to the class imbalance problem. On the other hand, there are some similar occupation classes which are difficult to distinguish. These issues bring new challenges to build the user profiles. In this paper, we propose a novel model, named MetaProfiling, to solve the few-shot user profiling problem, i.e., distinguishing occupation classes with few labeled users. Firstly, we extract multi-field features from multi-faceted user information, including user personal attributes and behavior attributes, to characterize users from different aspects. Then, we design a user embedding generator to learn an effective user embedding space by using the feature attention layer and the user embedding fusion layer. Finally, we design a margin-based user profiling classifier to effectively distinguish users from different classes, especially for the similar classes. Specifically, the classifier can enlarge the margin between the similar classes. Experiments on a real-world dataset from a question answering community validate that our approach can consistly outperform many baseline methods under both standard and generalized few-shot learning settings.
Sentiment Analysis of Code-Mixed Social Media Text (SA-CMSMT) in Indian-Languages

The arrival of web 2.0 platforms and increasing usage of social networking sites have proliferated social media content on the web. These platforms also provides multilingual interface to allow people to write freely in their native language. Over the past few decades, a new phenomenon called code-mixing has been observed in social media data which has attracted attention of researchers in sociolinguists and Natural Language Processing domains. However, due to informal nature of the text present in code-mixing phenomenon, there are a number of challenges ranging from data extraction to summarization. Sentiment Analysis of code-mixed data is a key research field which has emerged in the recent past. NLP researchers aim to provide natural language processing (NLP) tools that can collect, analyzed, evaluate, and summarize code-mixed data. The researchers had to deal with dataset construction, preprocessing, annotation, language identification, feature extraction, feature selection, and sentiment classification when it came to sentiment analysis of Code-mixed Social Media Text. This paper provides an overview of work carried out in sentiment analysis of code-mixed Indian languages textual data.
Service Recommendation based on DIKW Pyramid

In the initial establishment of wisdom, data, information, knowledge and wisdom are a progressive relationship. When we test whether the current wisdom system is fully developed, our judgment is to verify whether the knowledge, information and data generated by the wisdom system are correct. However, the growth process of the wisdom system is not smooth sailing. It will generate wrong knowledge, information and data, and these mistakes can also be used as the raw materials for the growth of the wisdom system, that is, a more perfect wisdom system can be formed through reflection. By analyzing the process of Service Recommendation based on deep learning, we found that a Service Recommendation model based on the data, information, knowledge and wisdom has excellent performance in accuracy, reliability and other aspects. We proposed a Service Recommendation model based on DIKW Pyramid. By comparing the recommendation by the model with the service that is users&#x0027; real like, the model reflects on the differences between the recommendation results and the real results, making the recommendation results closer to the real, so as to improve the accuracy of Service recommendation.
A Study on Deep Learning Approach to Optimize Solving Construction Problems

In this study, we focus on a problem domain, construction problems, for reinforcement learning systems to optimize. We relate our approach to existing research in the field of automated theorem proving and other related techniques to optimize the solutions in this domain. We expect this study can inspire more interest in the adoption of and improve the efficiency of existing production systems.
Arbitrage Behavior amongst Multiple Cryptocurrency Exchange Markets

Compared with fiat currencies, cryptocurrencies are usually more vulnerable to speculation and thus lead to massive price fluctuations, which makes exchanging cryptocurrencies a potentially profitable but risky endeavor. We aim to contribute to the understanding of the arbitrage behavior involving multiple cryptocurrency exchange markets. Specifically, we applied a Bellman-Ford based algorithm to detect possible arbitrage opportunities. By investigating historical data from three cryptocurrency exchange markets, i.e., Gemini, Coinbase, and Kraken, we designed experiments to identify how often arbitrage was possible in the past as well as the factors that contribute to the existence of arbitrage. We believe this may bring insights into strategies to stabilize the cryptocurrency exchange markets.
A Pre-processing Model for Feature Extraction Based on K-mean, PSO and ABC

To achieve efficient rule mining feature selection or preprocessing is need to be handled before the implementing the optimization technique. For these different methods are available. In the proposed model <tex>$K$</tex> means clustering is used to generate the clusters. Then PSO-ABC hybrid approach is for feature optimization. For the obtained result, PSO-ABC represent more normalized features as compared to using PSO only.
Safe Social Internet of Thing for Urban Mobility Services

Smart cities are characterized by smart heterogeneous devices that can interact and cooperate with each other by exchanging regularly big amounts of data with the big issue to treat sensitive data in a properly respectful manner, avoiding exposure to the risks that new technologies inevitably bring to the fore. This objective can only be pursued with adequate knowledge of the risks and methods of protection, for this reason in addition to producing materially functional results, it has been studied in depth the techniques of protection of personal data including the anonymization and pseud-anonymization of sensitive data. We provide the analysis of the state of the art that starts from the concepts of security and privacy and comes to an analysis of anonymization algorithms. This analysis tries to give an overview of the two fundamental issues in the field of data security: privacy, according to the European Regulation 2016 (GDPR) and the practical techniques with which it is preserved, with particular attention to anonymization algorithms: we analyze the advantages and disadvantages of the latter. The performance of the proposed solution is compared against that of a TraffictYpe-based DifferEntiated Reputation (TYDER) algorithm. This performance was evaluated in terms of QoS parameters such as delay, latency, packet loss and prediction error. The results show how MISSION outperforms TYDER in urban mobility scenario.
An Algorithm to Enhance Data Integrity in Cloud Computing

Cloud computing storage services allow people to easily maintain and manage large amounts of data at a low cost. However, it cannot guarantee data integrity. Data is sent to remote cloud servers, which is insecure and untrustworthy. Meaning data might be altered intentionally by unauthorized users or accidentally by the service provider causing data loss or modification. Therefore, it's become critical to protect data from hackers to maintain its integrity. This paper proposes EDIE algorithm to enhance data integrity in cloud computing. The proposed algorithm is designed by incorporating Moving Target Defense and New Lightweight Cryptographic Algorithm. The EDIE algorithm will use MTD to dynamically change network configurations and frequently confuse attackers therefore minimizing the level of attacks and leading to enhanced data integrity. In comparison to other commonly used integrity schemes in cloud, the proposed algorithm is expected to have lower attack level and improved execution time resulting in enhanced data integrity in cloud computing.
Parametric Analysis for Fake Reviews Identification

Online reviews are one of the most important aspects in a buyer&#x0027;s choice to buy a new product or use a service. As a result, it serves as a helpful source of data for determining public opinion regarding these products and services. It also provides companies with an indication of what kind of changes they need to make in their products to improve further. Thus, reviews also give competitors and product-based organizations a possible option to create fake reviews in order to advertise or degrade a product based on their interest. Hence, it is vital that the correct reviews are reached to the customers, and for this, the detection of fake ones is to be done effectively. In order to reduce the time for fake review detection, automated techniques are being used in the current scenario. Another concern is how to differentiate between the original and fake reviews. This paper discusses the various factors that can help in the identification of the same. They are broadly classified into two types: behavioral and feature-based. Also, the challenges that are still there in fake the review identification methods are depicted, and the open research areas where further work can be carried out are also being highlighted. The factors mentioned in the paper can prove useful for improvising the performance of any fake review detection system once applied to any real data set.
Engineering Cloud Applications for the Workplace

This paper will cover the planning and development of cloud applications that are used in the workplace. In addition to researching the feature needs of these applications, we will examine the risks and rewards of using these cloud applications for businesses. For instance, we go into how cloud software could be used in education as well as for small business and dive into the benefits that can be obtained from both. On the other hand, when conducting business on the cloud, there is always a risk that your data can be intercepted by an unwanted party through scripting or injection attacks. With the cloud becoming larger every year, we also dive into the European Union (EU) and the United States (US) privacy regulators and how they act in favor of the consumer, so that their data and rights are protected. Finally, we examine the many different scaling techniques and infrastructure providers can elect to deploy a combination of them, so that service quality does not degrade with an increase of traffic.
Modeling Data, Information and Knowledge for Security Protection of Tasks Scheduling Algorithms in Cloud Computing

As a key element in cloud computing, with the increasing uncertainty and complexity, task scheduling becomes more and more import. How to perform cloud scheduling effectively while safeguarding the data and information meets its bottleneck that prevents cloud computing from moving forward. In order to protect the data under being processed, this essay raises an attribute-based outsourced encryption. which enables users to pass the data on shared equipment while making the data more invisible for hackers. Meanwhile, based on the task scheduling method of deep reinforcement learning (DRL), through intention analysis, consider the user&#x0027;s optimization preferences in task execution, and realize the interaction with the user. reasonable scheduling is performed according to the sensitivity of the data in the task and the credibility of the cloud server. It can be seen from the test results that the algorithm has significant superiority and versatility, can improve the efficiency and security of task scheduling, and has better performance and load balance.
Model-fusion-based sustainable trust assessment method

Power mobile Internet plays an important role in the development of smartcity. There is a large amount of interactive information in power mobile applications, and the trust in individuals can be judged by analyzing these behaviors and texts. Traditional trust evaluation methods cannot achieve accurate and reliable trust evaluation. This paper proposes a sustainable trust evaluation method under the zero-trust architecture. First, according to the data generated by the user&#x0027;s operation in the mobile application, the sentiment analysis of the user&#x0027;s text interaction information is carried out to establish a more reasonable trust evaluation model. Secondly, classic clustering algorithms and residual networks are used to learn the collected data. When the learning accuracy is stable, the algorithm can predict the trustworthiness of subsequent behaviors. Finally, when predicting future data, loading the previously trained model can achieve sustainability. Experimental simulation shows that the trust evaluation model and algorithm can effectively judge whether the user&#x0027;s operation is trustworthy within a period, and achieve a more reasonable and efficient sustainable trust evaluation.
Game Engine Based 2D Emotion Segmentation Generation Method

This paper proposes a low-cost production and utilization technique for labeling emotion data in game engines, which can be used to support rapidly developing deep learning technologies. The proposed system extracts realistic images from game environments and automatically creates quantified two-dimensional (2D) emotion segmentation images linked to the extracted images. The segmentation data are learned through an image-to-image translation network. This 2D emotion segmentation mapping technique is trained using many training data, which allows stable learning. Industries that require spatial emotion interpretation can utilize the results of this study.
A New Code-based Blind Signature in Rank Metric

Blind signatures are widely used in Internet banking, e-voting and blockchains. In this paper, we propose the first code-based blind signature scheme under rank metric which is based on a variation of Durandal signature algorithm. We prove that our scheme can satisfy blindness and provide the security proof of our scheme in the random oracle model. Our scheme enjoys the advantages of short signature size. We give the instantiation of our scheme and the experimental results show that our scheme is feasible in practical applications.