Reviewerly/exHarmony huggingface dataset:
Classifications

The use of standard classifications is of paramount importance in the collection, compilation and dissemination of statistics. Standard classifications facilitate data collection, as they provide a clear definition, with a unique structure, of the objects being measured and collected. They facilitate the compilation of data, as the classifications define relationships between concepts and objects. Finally, they allow for better integration of data collected across different statistical domains, such as energy, environment and economic statistics.
Optical computing powers graph neural networks

Graph-based neural networks have promising perspectives but are limited by electronic bottlenecks. Our work explores the advantages of optical neural networks in the graph domain. We propose an optical graph neural network (OGNN) based on inverse-designed optical processing units (OPUs) to classify graphs with optics. The OPUs, combined with two types of optical components, can perform multiply–accumulate, matrix–vector multiplication, and matrix–matrix multiplication operations. The proposed OGNN can classify typical non-Euclidean MiniGCDataset graphs and successfully predict 1000 test graphs with 100% accuracy. The OPU-formed optical–electrical graph attention network is also scalable to handle more complex graph data, such as the Cora dataset, with 89.0% accuracy.
Construction Progress Management System of Building Group Project Based on CIM Technology

The construction process of building complex projects is complex, and the construction progress is often delayed. The current construction progress management system can only manage a single link separately, ignoring the connectivity of each link in the construction of the complex project, resulting in low management efficiency. In view of the above defects, this study designed a construction schedule management system for building group projects based on CIM technology. Based on the embedded hardware architecture, the three-dimensional construction model of the building complex project is established by using BIM. After analyzing the factors affecting the construction progress, the CIM technology is used to predict the construction progress. According to the prediction results, a construction schedule scheduling model is established to manage the project schedule. In the system test, the management system using CIM technology can basically control the construction progress to meet the design requirements. The management efficiency is as high as 90% and the performance is better.
Poster: Selective Reduce for Heterogeneous Distributed Training

To improve the performance of partial reduce on synchronizing models for heterogeneous data-parallel distributed training, we explore the idea of selective waiting and worker selection to propose the flexible solution of selective reduce. Our preliminary study shows that with progress- and bandwidth-aware decisions, the proposed partial reduce outperforms the original partial reduce significantly, in terms of both the average synchronization scales and completion times.
Using cognitive modeling to examine the effects of competition on strategy and effort in races and tournaments

Abstract We investigated the effects of two types of competition, races and tournaments (as well as an individual challenge and a do-your-best condition), on two different aspects of performance: effort and strategy. In our experiment, 100 undergraduate participants completed a simple cognitive task under four experimental conditions (in a repeated-measures design) based on different types of competitions and challenges. We used the Linear Ballistic Accumulator to quantify the effects of competition on strategy and effort. The results reveal that competition produced changes in strategy rather than effort, and that trait competitiveness had minimal impact on how people responded to competition. This suggests individuals are more likely to adjust their strategy in competitions, and the uncertainty created by different competition types influences the direction of these strategy adjustments.
Failures

We have covered several aspects of identity management relevant to an application development project. Writing an application with perfect identity management will be for naught, however, if flaws in your environment or processes introduce a vulnerability, especially one that compromises sensitive identity data. It is often said that one can learn a lot from failure. When it comes to identity management, however, we think it is preferable to learn from others’ failures rather than your own. Inspired by George Santayana’s advice about the need to learn from the past, we’ve collected stories of past security breaches and their causes to help you avoid making such mistakes in your environment.
Trained Artificial Intelligence (AI) for Predicting Therapy Discontinuation Based on Patient Observations in Advanced Breast Cancer

Background: In various fields outside of medicine, AI-supported systems have been established that can predict an undesirable event. The purpose of such systems is to detect events earlier and, if necessary, to be able to prevent them. In medicine, it would be particularly interesting to be able to make such predictions based solely on patient observations.
BLOCKCHAIN TECHNOLOGY: CRYPTOCURRENCIES, APPLICATIONS AND SECURITY ANALYSIS

From a business perspective, it's helpful to think of blockchain technology as a type of next-generation business process improvement software.Collaborative technology, such as blockchain, promises the ability to improve the business processes that occur between companies, radically lowering the "cost of trust."For this reason, it may offer significantly higher returns for each investment dollar spent than most traditional internal investments.Financial institutions are exploring how they could also use blockchain technology to upend everything from clearing and settlement to insurance.These articles will help you understand these changesand what you should do about them.We will also look at how cryptocurrency market participants, such as investors, technology providers, and financial institutions, will be affected as the market matures.
Factorial Design and Optimization of Date Stone as A Natural Coagulant for Organic and Heavy Metals Removal from Industrial Wastewater

&lt;p&gt;Several chemical coagulants previously utilize for wastewater treatment with significant performance in removing heavy metals and other parameters. However, their cost effective and the residual of toxic chemical precipitates that pose problems to human health and the environment. Therefore, using plant-based natural coagulants is considered as an alternative technique which is non-toxic, biodegradable and environmentally friendly. This research aims to explore the performance of date stones as a natural coagulant in iron and steel plant wastewater treatment and to optimize the operating parameters to assess the feasibility of using date stones in the wastewater treatment sector. Response Surface Methodology (RSM), a statistical experimental design, was used to improve the COD, TSS, NH3-N, and heavy metals removal efficiency using 1.4 g/200 mL date stones as control parameters, as well as pH and settling time. At pH 8, the most significant removal efficiency for COD, NH3-N, Fe, Mn, Al, Cu, and Ni was 61, 63, 93, 93, 96.5, 51, and 86 %, respectively. The quadratic models for the parameters chosen were found to be significant with low probability (&amp;lt;0.001), except for NH3-N (0.004), Fe (0.0042), and Ni (0.0025). The study's results revealed the potential of utilizing date stones as a natural coagulant for the treatment of iron and steel industrial wastewater&lt;/p&gt;&#x0D;
DIGITAL MENU FOR BARS AND RESTAURANTS USING AUGMENTED REALITY WITH THE AIM OF CREATING AN INTERACTIVE EXPERIENCE WITH CUSTOMER

AR offers immense marketing and advertising opportunities. In the right hands, AR can boost your salespeople. AR can be used in flyers, print ads, billboards, newspapers, etc. By providing a potential customer with just one QR code, you can attract multiple customers to your business. AR was used to create a digital menu for restaurants and cafeterias, so the AR application allows your customers to view the menu in a projected 3d way. Choose your favorite dish from the menu in no time with such applications. Upon entering the restaurant, the customer can open the application and check the entire menu in 3d.
KEMA++: A Full Representative Knowledge-Graph Embedding Model (036)

Nowadays, representing entities and relations in a machine understandable way through Knowledge graph embedding (KGE) has been proven as an effective approach for predicting missing links in knowledge graphs (KGs). Mainly, the success of such approach depends on the model ability to infer the patterns of the relations. Indeed, most of the existing KG models highly focus on modeling simple relation patterns such as symmetry, anti-symmetry, inversion, and composition. However, there are few models in the literature that take into consideration the modeling of complex relation patterns like 1-[Formula: see text], [Formula: see text]-1 and [Formula: see text]-[Formula: see text], which are common in real-world applications. To overcome this challenge, this paper presents a new KGE model called KEMA[Formula: see text], i.e. KGE using Modular Arithmetic, that relies on the combination of projection and modular arithmetic. The main idea behind KEMA[Formula: see text] is to project the entities of a relation to represent the relations of a KG, before applying a modular arithmetic over it. Thus, KEMA[Formula: see text] will be able to infer all simple and complex relation patterns for any KGE applications. Through extensive experiments on several datasets, we demonstrated the relevance of KEMA[Formula: see text] in terms of effectively representing all model relations in a KG. Simulations on several tested datasets show that KEMA[Formula: see text] obtains the good scores for Mean Rank (MR) and Hits@1 tests. Moreover, KEMA[Formula: see text] obtains the good Hits@1 score compared to the existing models.
Adversarial joint attacks on legged robots

We address adversarial attacks on the actuators at the joints of legged robots trained by deep reinforcement learning. The vulnerability to the joint attacks can significantly impact the safety and robustness of legged robots. In this study, we demonstrate that the adversarial perturbations to the torque control signals of the actuators can significantly reduce the rewards and cause walking instability in robots. To find the adversarial torque perturbations, we develop black-box adversarial attacks, where the adversary cannot access the neural networks trained by deep reinforcement learning. The black box attack can be applied to legged robots regardless of the architecture and algorithms of deep reinforcement learning. We employ three search methods for the black-box adversarial attacks: random search, differential evolution, and numerical gradient descent methods. In experiments with the quadruped robot Ant-v2 and the bipedal robot Humanoid-v2, in OpenAI Gym environments, we find that differential evolution can efficiently find the strongest torque perturbations among the three methods. In addition, we realize that the quadruped robot Ant-v2 is vulnerable to the adversarial perturbations, whereas the bipedal robot Humanoid-v2 is robust to the perturbations. Consequently, the joint attacks can be used for proactive diagnosis of robot walking instability.
Performance Analysis of Spatio-temporal Human Detected Keyframe Extraction

Closed circuit television (CCTV) surveillance for detecting the humans involves an expanded research analysis especially for crime scene detection due to various restraints such as crowded annotation, night footages, and rainy (noisy) clips. The main visualization of the crime scene is to recognize the person in particular obtained in all frames is a challenging task. For this occurrence, Content-Based Video Retrieval (CBVR) method refines the collection of these video frames resulting keyframes to reduce the burden of huge storage. Here, Spatio-Temporal classifiers method as an added advantage with frame differencing and edge detection method reports the human detected keyframes without the termination of background regions in order to negotiate the crime scene more efficiently. The main objective of this paper is to analyze the obtained keyframes with Human detection pointing a distinctive between Spatio-Temporal HOG-SVM and HAAR-like classifier to survey the optimum. Finally, the resulting keyframes mutated with the canny edge detection method by HOG-SVM sequel with greater accuracy level of 98.21% compared to HAAR-like classifier.
End-To-End Gender Determination by Images of an Human Eye

For immediate gender classification based on face eye images, a convolutional neural network (CNN) is presented. The suggested architecture has much less design complexity than previous CNN systems used for pattern recognition. With the use of computer vision, we can train a computer to recognize and classify things in the physical world. In this view, computer vision entails developing mathematical models that can simulate how a human's visual system and brain work. The goal is to teach computers how to recognize objects in pictures and movies. The science of computer vision and imaging makes extensive use of CNNs, a type of deep neural network. Object recognition, picture labeling, and similarity grouping are all possible with the help of convolutional neural networks (CNNs). The Sequential model will be our starting point. Last but not least, CNN is constructed using a number of layers, including an input layer, an output layer, and multiple hidden layers. Additionally, fully connected layers, convolutional, an activation function layer (typically ReLU or Softmax), normalisation layers, and pooling layers are all included in a CNN's hidden layers. This network was trained with a Sequential model, binary cross volatility as the loss function, and RMSProp as the operator. Dphi data set, which contains around 9000 pictures, is used to evaluate the suggested CNN solution. As a result of our efforts, we are able to attain a 95.78% success rate in In less than 0.27 milliseconds, a neural network can analyze and label a 32 x 32 pixel facial image, giving it the ability to scan more than 3700 images per second. A training converges within 5 epochs. These findings demonstrate that the suggested CNN is a viable method for instantaneous gender recognition.
Distributed random vector functional link network with subspace-based local connections

In order to solve the problem of poor generalization ability and high computational complexity of random vector functional link (RVFL) network when dealing with large-scale data classification, we design and implement a distributed RVFL network with subspace-based local connections in Spark framework (DRVFL-SLC). Firstly, in order to take advantage of the partition parallelism of resilient distributed dataset (RDD), the large-scale dataset stored in the Hadoop distributed file system HDFS is randomly divided into random sample partition (RSP) data blocks and each RSP data block corresponds to a partition of the RDD, where the RSP data block is a subset of data that maintains probability distribution consistency with the big data at a given significance level. After that, the mapPartitions transformation is invoked on the RDD containing multiple partitions in a distributed environment and this operation trains the corresponding optimal RVFL-SLC efficiently in parallel. Then, the collect execution operator is used to efficiently fuse the optimal RVFL-SLC corresponding to each partition of the RDD to obtain DRVFL-SLC for realizing the classification of big data. Finally, the feasibility and effectiveness of DRVFL-SLC are verified based on several large-scale data set with at least million records on a Spark cluster deployed with 6 computing nodes. The results show that DRVFL-SLC has a good speedup ratio, scalability and scale growth, and can achieve better generalization performance than RVFL-SLC trained on a single machine with full data.
Registry Troubleshooting

The Registry is what has always set Windows apart from other operating systems; in fact, it’s only Windows that does things the Registry way. Whereas Unix-based operating systems such as Linux, Android, and Mac OS use a series of folders such as /etc that contain configuration files for the operating system and installed applications, the Windows Registry consists of a series of binary database files.
Emerging Areas and Techniques in Recommender Systems

This book has shown you multiple implementations of recommender systems (also known as recommendation systems) using various techniques. You have gained a holistic view of all these methods. Topics like deep learning and graph-based approaches are still improving. Recommender systems have been a major research interest for a long time. Newer, more complex, and more interesting avenues have been discovered, and research continues in the same direction.
Meta-analysis of AI Research in Journalism: Challenges, Opportunities and Future Research Agenda for Arab Journalism

The use of artificial intelligence tools in newsrooms is revolutionary and controversial as well. Despite the promising opportunities provided by AI to enhance digital journalism practice, it also raises several legal, professional, and ethical considerations. Research about AI in Arab media is a promising area of interest that increasingly attracts Arab scholars. However, there is a need for systematic and purposive growth in future research about AI and Arab media, that considers the socio-cultural and economic contexts of Arab countries and meets the priorities and needs of Arab media organizations. Accordingly, this paper provides researchers with an overview of the main challenges and debates in the field of AI and journalism studies. This study applies a systematic review of a sample of English and Arabic-written studies from 2014 to 2022 about the implications, challenges and considerations of using AI in newsrooms. Based on the analysis, the study proposed a future research agenda about AI and Arab journalism.
Devtools Pages

Modern browsers offer developers a rich suite off developer tools for debugging, profiling, and inspecting web pages. Browser extensions are able to supplement these tools by providing custom interfaces that live inside the developer tools interface and can access special developer tools APIs.
Tooling and Frameworks

Modern browser extensions are rarely written from scratch. There is a wealth of developer tools at your disposal that allow you to efficiently build and test extensions in your chosen JavaScript framework and seamlessly publish them to multiple extension marketplaces.
Browser Extension Architecture

Understanding how the various elements of browser extensions work is important, but even more important is understanding how they work together. The architecture of browser extensions is unusual and multifaceted:
Networking

As you wade deeper into browser extension development, you will quickly realize that there are some interesting idiosyncrasies when it comes to sending network requests. Although browser extensions and web pages use the same APIs and network layers to send requests, there are fundamental differences that you will need to surmount when building a well-formed browser extension. This chapter will cover some core areas of extension networking: dispatching network requests, authentication, and browser extension networking APIs.
Permissions

Browser extension permissions are conceptually identical to mobile app permissions. Both software platforms have the ability to access very powerful APIs, but to protect the end user permission to access these APIs must be explicitly requested on a piecewise basis. For both apps and extensions, permissions are declared by the developer in the codebase – for extensions, this is the extension manifest. In both cases, requesting access to trivial permissions will not require explicit user permission, but very powerful permissions will require the user to explicitly grant access. Furthermore, adding additional permissions in a subsequent update will typically require the user to explicitly accept this change in scope.
A contextualized study of blockchain technology adoption as a digital currency platform under sanctions

Purpose The blockchain technology is based on distributed ledger. Many stakeholders, such as developers, entrepreneurs and technology lovers, consider the blockchain as an economic and business re-creation that is faced with numerous challenges in its application. Blockchain technology is the backbone of many digital currencies, i.e. Bitcoin and Ethereum. Although presently digital currencies are recognized as payment and exchange instruments in many countries, the economic sanctions imposed on some countries have restricted the possibility of the trading. So, this study seeks to evaluate the adoption of blockchain for digital currency use in Iran in the shadow of economic sanctions. Design/methodology/approach The developed value-based technology adoption model and smart PLS software have been used in this research. The statistical population of the study was people active in the Iranian stock market; the purpose for this selection was their familiarity with financial and digital currency issues. Findings The results show that the terms of sanctions, usage and facilitating conditions are directly related to users' perceived value of digital currency and its use; As a result, in a sanctioned country like Iran, the use of digital currencies is being accepted as a way of rolling out economic sanctions and thus making commercial payments. Originality/value As well as the fact that evaluating the adoption of the blockchain technology in a sanctioned country like Iran is considered as the originality aspect of the research, applying an extended model in the technology adoption is also the research innovation.
Addressing the cybersecurity issues in cloud computing

Nobody can deny the importance of security in cloud computing. It has been found that secure data on any means of storage platform is a necessity of modern computing. With wide users of multiple social media platforms or the entertainment industry, cloud computing plays a vital role for each of the organizations in terms of storage and accessibility. A network that allows such companies to ease their work at a major level cannot be feeble toward the security of data. Cloud computing follows the most effective encryption-decryption approach that acts as a level of security for users to access or modify data on cloud servers. At the same time, it is difficult but not impossible to access data for intruders which is why they can't cope with it easily. In addition to all the security provided, a high-level systematic security procedure is essential to achieve a better cloud computing experience for the users.