Reviewerly/exHarmony huggingface dataset:
Fast Multi Object Detection and Counting by YOLO V3

Object detection (OD) has been a deep and vital field in different industries such as security, medical and automobile industry. Counting identified objects has been proved as a crucial research field. You Only Look Once (YOLO) algorithm is a very powerful algorithm for this aim with a quick performance compared to other existing algorithms. In this paper, an algorithm to detect and count objects simultaneously from a single photo is proposed based on YOLO V3 algorithm. The YOLO V3 algorithm has been modified to be able to classify 82 types of objects. After this algorithm succeeds in detecting objects, it draws bounding boxes around each object in the image and displays the exact type of them, and counts the bounding boxes drawn around the object, determines the number of each specific object. The photos are taken regularly from the environment and a kind of auto-shooting algorithm is employed. The results show that the algorithm presented in this paper succeeds in counting several objects in a single image with reduced calculation time and a very light process. It will be clearly visible that the approach of this article can help many industries that need to count different objects fast and cheaply.
Swarm Intelligent Optimization Algorithms and Its Application in Mobile Robot Path Planning

Mobile robot path planning is generally a kind of optimal problems, which is to find a best path of a track between a starting point to a goal point in the constraint conditions. Mobile robot path planning can be divided into two categories according to different environment planning awareness information: one is the global path planning and the other is the local path planning. We employed ACO, PSO, FA, FOA, FWA and ABC swarm intelligent optimization algorithms to optimize the global and local path planning of mobile robot, and gave the detailed implement steps and the comparing results to show the feasibility of using swarm intelligence optimization algorithms to solve the robot path planning problems.
Real-time 3D neural facial animation from binocular video

We present a method for performing real-time facial animation of a 3D avatar from binocular video. Existing facial animation methods fail to automatically capture precise and subtle facial motions for driving a photo-realistic 3D avatar "in-the-wild" (i.e., variability in illumination, camera noise). The novelty of our approach lies in a light-weight process for specializing a personalized face model to new environments that enables extremely accurate real-time face tracking anywhere. Our method uses a pre-trained high-fidelity personalized model of the face that we complement with a novel illumination model to account for variations due to lighting and other factors often encountered in-the-wild (e.g., facial hair growth, makeup, skin blemishes). Our approach comprises two steps. First, we solve for our illumination model's parameters by applying analysis-by-synthesis on a short video recording. Using the pairs of model parameters (rigid, non-rigid) and the original images, we learn a regression for real-time inference from the image space to the 3D shape and texture of the avatar. Second, given a new video, we fine-tune the real-time regression model with a few-shot learning strategy to adapt the regression model to the new environment. We demonstrate our system's ability to precisely capture subtle facial motions in unconstrained scenarios, in comparison to competing methods, on a diverse collection of identities, expressions, and real-world environments.
Research on Productization and Development Trend of Data Desensitization Technology

At present, data security has become a key area of attention from all aspects. The proliferation of data leakage incidents, the acceleration of the introduction of data security laws and regulations, and the rising cost of enterprise compliance punishment jointly urge all enterprises to step up to improve their own data security system. In the construction of data security, data desensitization is always a core technical ability. In view of the current situation and future development trend of data desensitization technology and products, this paper introduces the development of data desensitization technology in detail from desensitization algorithm, anonymization, static data desensitization and dynamic data desensitization. At the same time, from the core function, product form, supply side status and other aspects of the data desensitization technology are described. On this basis, the standardization direction of data desensitization products in terms of core functions, concept definition, performance and stability is proposed. Finally, the development trend of data desensitization technology and products in four aspects of regularization, anonymization, intellectualization and contextualization are put forward.
Regression

Regression is a procedure to (Paige and Saunders, ACM Trans Math Soft 8:43–71, 1982; Abramowitz and Stegun, Handbook of mathematical functions. In National bureau of standards series, vol. 55, Dover Publications, USA, 1964) induce the statistical correlation among two or more variables where a change in a dependent variable is linked with and relies on a change in 1 or more (Hocking, Methods and applications of linear models. In Wiley series in probability and statistics, Wiley-Interscience, New York, 1996) independent variables. Coefficients describe these changes.
Depression Detection Using Asynchronous Federated Optimization

With the rapid growth of population, the life pressure, and the various intensive pressures every day deepen the competition among people more intensive. Tens of millions of people have been suffering from depression every year and only a fraction receives adequate treatment. The development of social networks such as Facebook, Twitter, Weibo, and QQ provides more convenient communication and provides a new emotional release window. People communicate with their friends and share their opinions to express their feelings. It provides an opportunity to detect depression in social networks. Although using social networks to detect depression has picked an established position on a global scale, few researchers consider the data security and privacy-preserving schemes. Therefore, we propose the federated learning technique as an efficient and scalable method. With the larger number of social networks data from diverse edge devices, federated learning can process a large number of edge devices in parallel. For the study, we aim to analyze depression on Weibo collected from an online social network. We propose a novel algorithm Text-CNN Asynchronous Federated optimization (CAFed) based on federated learning to improve the communication cost and convergence rate. We have shown our proposed method can effectively protect users' privacy under the premise of ensuring the accuracy of prediction. We prove that our proposed method's convergence rate is faster than the Federated Averaging (FedAvg) for non-convex problems. Federated learning techniques identify high-quality solutions to mental health issues among Weibo users.
W8-Scope: Fine-grained, practical monitoring of weight stack-based exercises

Fine-grained, unobtrusive monitoring of gym exercises can help users track their own exercise routines and also provide corrective feedback. We propose W8-Scope, a system that uses a simple magnetic-cum-accelerometer sensor, mounted on the weight stack of gym exercise machines, to infer various attributes of gym exercise behavior. More specifically, using multiple machine learning models, W8-Scope helps identify who is exercising, what exercise she is doing, how much weight she is lifting, and whether she is committing any common mistakes. Real world studies, conducted with 50 subjects performing 14 different exercises over 103 distinct sessions in two gyms, show that W8-Scope can, at the granularity of individual exercise sets, achieve high accuracy—e.g., identify the weight used with an accuracy of 97.5%, detect commonplace mistakes with 96.7% accuracy and identify the user with 98.7% accuracy. By incorporating an additional, simple IR sensor on the weight stack, the exercise classification accuracy (across the 14 exercises) further increases from 96.93% to 97.51%. Moreover, by adopting incremental learning techniques, W8-Scope can also accurately track these various facets of exercise over longitudinal periods, in spite of the inherent natural changes in a user’s exercising behavior. Our comprehensive analysis also reveals open challenges, such as adapting to the expertise level of individuals or providing in-situ, early feedback, that remain to be addressed.
Prospect of Low Power Sensor Network Technology in Disaster Management for Sustainable Future

Countries with better disaster management plans can better signify their existence. The earthquake is one such hazardous catastrophe that leads to heavy damage. Disaster management does not avert or eliminate the threats fully; instead, it focuses on use of technology, creating plans to lessening the effect of disasters up to significant level. Depending on the magnitude and type of waves, earthquakes crush everything, from buildings to small houses, from towers to trees, and from roads to bridges. Tsunamis and landslides follow its way as well. After the earthquake comes, the settling of things back to normal involves a lot of time, money, and manpower as well. Hence, the only solution of this is leading-edge technology support. Sensor network technology is one of the proven techniques for not only varieties of early warning, prediction, and detection systems but also post-phenomenon rescue and support. This chapter will help researchers and policy makers to think fresh on disaster management with sensor network technology.
A five-layer architecture for big data processing and analytics

Big data technologies have attracted much attention in recent years. The academia and industry have reached a consensus, that is, the ultimate goal of big data is about transforming 'big data' to 'real value'. In this article, we discuss how to achieve this goal and propose five-layer architecture for big data processing and analytics (BDPA), including a collection layer, a storage layer, a processing layer, an analytics layer, and an application layer. The five-layer architecture targets to set up a de facto standard for current BDPA solutions, to collect, manage, process, and analyse the vast volume of both static data and online data streams, and make valuable decisions for all types of industries. Functionalities and challenges of the five-layers are illustrated, with the most recent technologies and solutions discussed accordingly. We conclude with the requirements for the future BDPA solutions, which may serve as a foundation for the future big data ecosystem.
COMMENTS ON THE EU PROPOSAL FOR THE ARTIFICIAL INTELLIGENCE ACT

In April 2021, the European Commission issued its first legislative proposal to regulate AI: EC Proposal for the AI Act. The provided feedback described in the paper was submitted during the official procedure of public consultations. I generally support the architecture and the ideas of the proposed AI Act. It is an impressive work and provides a great start for the legislative process. Highly welcome things are the risk-based and data-focusing approaches, level of specifications for the core requirements (transparency, human-oversight, data governance, risk management, record- keeping, accuracy, robustness, and security), the introduction of different roles for subjects involved in the AI life cycle with their relevant rights and obligations. The great achievement of the AI Act is the implementation of the proposed requirements into the existing conformity assessment procedures (or when there are no applicable existing procedures – the suggestion of similar ones).Yet, I have discovered some areas of improval: - the roles of the subjects who are affected by the decision made with the use of AI Systems shall be defined; - the concept of AI user shall be clarified (especially in multi-stakeholder environment); - 'transparency' terminology shall be used consistently;- 'transparency-interpretability' requirement in article 13 shall be clarified; - the hierarchy between transparency and the related concepts shall be defined - transparency as a legal principle and the other concepts as the measures supporting the principle; - clarification on the concept of 'bias' is needed. I explain each of the point in the text.
Effective Communication among Globally Distributed Software Development Teams

Global software development, an emerging software development methodology within a technology-enabled environment has recently received increased attention from scholars and practitioners. Despite the fact that the lack of effective communication is a major challenge of globally distributed teams, communication research related to the scale development is still very limited in existing literature. Considering the communication challenges and increasing need to measure the effectiveness of communication, this research is focused to develop a scale for “Effective Communication” for globally distributed software development teams. Systematic literature review is performed to identify the best practices of effective communication for the development of “Effective Communication” scale. A questionnaire survey consisting of 29 items is administered in globally distributed software development teams. Confirmatory factor analysis and statistical results depicted that the four-factor model (e.g. stakeholders' involvement, acculturation, usage of appropriate tools and technology, and information availability) is more parsimonious and an optimal model fit.
Hand pose estimation system based on combined features for mobile devices

Today's mobile devices or smartphones have a revolutionary impact on how we communicate, especially after the advent of devices like smart watches and Google glasses that require new ways to interact with them. To optimise the use of mobile devices, special input and output peripherals have been designed over the years to facilitate communication with them. The well known peripherals are the multi-touch screens. Smartphones are too small to work freely using their input screens. To solve this problem, recent research has focused on contactless and natural gestural interfaces. In this context, we propose a hand gesture recognition system for mobile devices as a simple way of communication with smartphones. In this work, we describe a hand gesture recognition system for Android devices based on a combination of HOG and LBP features and the SVM classifier. An accuracy rate of about 95% is obtained on the improved 'NUS database I'. In addition, we conduct experiments on different Android devices to know the impact of using such a recognition task on embedded systems like smartphones.
An innovative and efficient method for Twitter sentiment analysis

The research in sentiment analysis is one of the most accomplished fields in data mining area. Specifically, sentiment analysis centres on analysing attitudes and opinions relating a particular topic of interest using machine learning approaches, lexicon-based approaches or hybrid approaches. Users are purposive to develop an automated system that could identify and classify sentiments in the related text. An efficient approach for predicting sentiments would allow us to bring out opinions from the web contents and to predict online public choices, which could prove valuable for ameliorating changes in the sentiment of Twitter users. This paper presents a proposed model to analyse the brand impact using the real data gathered from the micro blog, Twitter collected over a period of 14 months and also discusses the review covering the existing methods and approaches in sentiment analysis. Twitter-based information gathering techniques enable collecting direct responses from the target audience; it provides valuable understanding into public sentiments in the prediction of an opinion of a particular product. The experimental result shows that the proposed method for Twitter sentiment analysis is the best, with an unrivalled accuracy of 86.8%.
Assessing the linguistic quality of REST APIs for IoT applications

Internet of Things (IoT) is a growing technology that relies on connected 'things' that gather data from peer devices and send data to servers via APIs (Application Programming Interfaces). The design quality of those APIs has a direct impact on their understandability and reusability. This study focuses on the linguistic design quality of REST APIs for IoT applications and assesses their linguistic quality by performing the detection of linguistic patterns and antipatterns in REST APIs for IoT applications. Linguistic antipatterns are considered poor practices in the naming, documentation, and choice of identifiers. In contrast, linguistic patterns represent best practices to APIs design. The linguistic patterns and their corresponding antipatterns are hence contrasting pairs. We propose the SARAv2 (Semantic Analysis of REST APIs version two) approach to perform syntactic and semantic analyses of REST APIs for IoT applications. Based on the SARAv2 approach, we develop the REST-Ling tool and empirically validate the detection results of nine linguistic antipatterns. We analyse 19 REST APIs for IoT applications. Our detection results show that the linguistic antipatterns are prevalent and the REST-Ling tool can detect linguistic patterns and antipatterns in REST APIs for IoT applications with an average accuracy of over 80%. Moreover, the tool performs the detection of linguistic antipatterns on average in the order of seconds, i.e., 8.396 s. We found that APIs generally follow good linguistic practices, although the prevalence of poor practices exists.
Framing data science, analytics and statistics around the digital earth concept

Abstract Environmental data science can be shaped around the concepts of data streams (or the data deluge), and both data driven and process models. Together they lead to the concept of a digital earth. In this short opinion piece, I reflect on some of the challenges in truly realizing the digital earth concept.
Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.
Video Quality Model of Compression, Resolution and Frame Rate Adaptation Based on Space-Time Regularities

Being able to accurately predict the visual quality of videos subjected to various combinations of dimension reduction protocols is of high interest to the streaming video industry, given rapid increases in frame resolutions and frame rates. In this direction, we have developed a video quality predictor that is sensitive to spatial, temporal, or space-time subsampling combined with compression. Our predictor is based on new models of space-time natural video statistics (NVS). Specifically, we model the statistics of divisively normalized difference between neighboring frames that are relatively displaced. In an extensive empirical study, we found that those paths of space-time displaced frame differences that provide maximal regularity against our NVS model generally align best with motion trajectories. Motivated by this, we built a new video quality prediction engine that extracts NVS features that represent how space-time directional regularities are disturbed by space-time distortions. Based on parametric models of these regularities, we compute features that are used to train a regressor that can accurately predict perceptual quality. As a stringent test of the new model, we apply it to the difficult problem of predicting the quality of videos subjected not only to compression, but also to downsampling in space and/or time. We show that the new quality model achieves state-of-the-art (SOTA) prediction performance on the new ETRI-LIVE Space-Time Subsampled Video Quality (STSVQ) and also on the AVT-VQDB-UHD-1 database.
Leveraging the Automated Machine Learning for Arabic Opinion Mining: A Preliminary Study on AutoML Tools and Comparison to Human Performance

Despite the broad range of Machine Learning (ML) algorithms, there are no clear guidelines on how to identify the optimal algorithm and corresponding hyperparameters configurations given an Opinion Mining (OM) problem. In ML, this is known as the Algorithm Selection Problem (ASP). Although Automatic Algorithm Selection or AutoML has proven to be successful in many areas of ASP, it has hardly been explored in OM. This paper explores the benefits of using AutoML in this field. To this end, this work examines to what extent AutoML can be competitive against ad hoc methods (manually select and tune ML pipelines) on Arabic opinion mining modeled from a supervised learning perspective. We compare four state-of-the-art AutoML tools on 10 different popular datasets to human performance. Experimental results show that the AutoML technology can be considered as a powerful approach to support the ML algorithm selection problem in opinion mining.