Reviewerly/exHarmony huggingface dataset:
Enhancing Transformer with Horizontal and Vertical Guiding Mechanisms for Neural Language Modeling

Language modeling is an important problem in Natural Language Processing (NLP), and the multi-layer Transformer network is currently the most advanced and effective model for this task. However, there exist two inherent defects in its multi-head self-attention structure: (1) attention information loss: the lower-level attention weights cannot be explicitly passed through upper layers, which may lead the network lose some pivotal attention information captured by lower-level layers; (2) multi-head bottleneck: the dimension of each head in vanilla Transformer is relatively small and the process of each head is independent, which introduces an expressive bottleneck and makes subspace learning inadequate constitutionally. To overcome these two weaknesses, a novel neural architecture named Guide-Transformer is proposed in this paper. The Guide-Transformer utilizes horizontal and vertical attention information to guide the original process of the multi-head self-attention sublayer without introducing excessive complexity. The experimental results on three authoritative language modeling benchmarks demonstrate the effectiveness of Guide-Transformer. For the popular perplexity (ppl) and bits-per-character (bpc) evaluation metrics, Guide-Transformer achieves moderate improvements over the powerful baseline model.
Attribute Guided Sparse Tensor-Based Model for Person Re-Identification

Visual perception of a person is easily influenced by many factors such as camera parameters, pose and viewpoint variations. These variations make person Re-Identification (ReID) a challenging problem. Nevertheless, human attributes usually stand as robust visual properties to such variations. In this paper, we propose a new method to leverage features from human attributes for person ReID. Our model uses a tensor to non-linearly fuse identity and attribute features, and then forces the parameters of the tensor in the loss function to generate discriminative fused features for ReID. Since tensor-based methods usually contain a large number of parameters, training all of these parameters becomes very slow, and the chance of overfitting increases as well. To address this issue, we propose two new techniques based on Structural Sparsity Learning (SSL) and Tensor Decomposition (TD) methods to create an accurate and stable learning problem. We conducted experiments on several standard pedestrian datasets, and experimental results indicate that our tensor-based approach significantly improves person ReID baselines and also outperforms state of the art methods.
XIN Enterprise – MyVenders / Nursyahirah Najmy Mohd Rosly

MyVenders is a product that brings benefit to both of its customers and users. This newly developed vending machine consist of new features that is expected to give an opportunity to customers who wanted to have an energy-saving vending machine and a product that pays more attention towards users’ concern. The target market of this product is shopping malls and offices building owners since this product is more suitable to be placed at an open space with high traffic. Since there are users who are concerned about their safety and lack of cash on hand, MyVenders come out with the solution where this product provided an automated hand sanitizer and cashless payment. This product offers service and features that no other sanitary pad vending machine could give to customers. Presenting themselves as a product that is rare and different from others, MyVenders enable customers and users to experience the ability to pay through their smart phone by scanning a QR code. Although some may say that MyVenders is overprice, the price offered is still reasonable since it include features that are favourable to customers. Other than that, the market analysis proved that Xin Enterprise is able to expand their business with the production of MyVenders since this product is something that has been anticipated and hoped by the public. This means that the population of target market might increases and Xin Enterprise are able to expand their coverage to the whole West Malaysia. Management team of Xin Enterprise consist of the CEO, General Manager, Human Resource Executive, Operation Executive and Marketing Executive where each employee have their own roles and responsibility. The total equity invested by the team members is RM 400,000 and every person contributed different percentage of personal financing. In conclusion, Xin Enterprise are able to captivate the market with MyVenders as long as they plan the strategy thoroughly and carry out the task attentively.
Memory Optimization Techniques in Neural Networks: A Review

Deep neural networks have been continuously evolving towards larger and more complex models to solve challenging problems in the field of AI. The primary bottleneck that restricts new network architectures is memory consumption. Running or training DNNs heavily relies on the hardware (CPUs, GPUs, or FPGA) which are either inadequate in terms of memory or hard-to-extend. This would further make it difficult to scale. In this paper, we review some of the latest memory footprint reduction techniques which would enable faster low model complexity. Additionally, it improves accuracy by increasing the batch size and developing wider and deeper neural networks with the same set of hardware resources. The paper emphasizes on memory optimization methods specific to CNN and RNN training.
The Implementation of Augmented Reality and First Person Method of Promotional Media in Kemang Pratama Housing Bekasi

The promotional media used in the Kemang Pratama Bekasi housing complex uses brochures with 2D images, causing a lack of promotion of the housing. And if you want to see the condition or appearance of the house, you have to come to the location of the house in the Kemang Pratama Bekasi housing complex as a clear picture. But by using the implementation model of Augmented Reality and First Person Mode as a media for promoting housing in Kemang Pratama, Bekasi, it makes it easier for buyers to see the condition and appearance of the house in terms of home specifications, because it can display a 3D-shaped house using a smartphone camera as an initial image before seeing the house to the location. So with this technology it becomes more effective and efficient in terms of time. Augmented Reality is used to view the exterior with a brochure (as a marker) which is scanned using a camera and First person mode to see the interior of the house.
Complex Scene Understanding and Recognition Based on the Explainable Machine Learning in AI Models

Abstract Information fusion is an important part of numerous neural network systems and other machine learning models. However, there exist some problems about fusion in scene understanding and recognition of complex environment, such as difficulty in feature extraction, small sample size and interpretability of the model. Deep reinforcement learning can combine the perception ability of deep learning with the decision-making ability of reinforcement learning to learn control strategies directly from high-dimensional original data. However, It faces these challenges, such as low optimization efficiency, poor generality of network model, small labeled samples, explainable decisions for users without a strong background on Artificial Intelligence (AI). Therefore, at the level of application and theoretical research, this paper aims to solve the above problems,the main contributions include: (1)optimize the feature representation methods based on spatial-temporal feature of the behavior characteristics in the scene, deep metric learning between adjacent layers and cross-layer learning theory, and then propose a lightweight reinforcement learning network model to solve these problems of the complexity of the model to be explained, the difficulty of extracting feature and the difficulty of tuning parameter; (2)construct the self-paced learning strategy of the deep reinforcement learning model, introduce transfer learning mechanism in the optimization process, and solve the problem of low optimization efficiency and small labeled samples; (3)design the behavior recognition framework of the multi-perspective deep knowledge transfer learning model, construct a explainable behavior descriptor, and solve the problems of poor network generality and weak 1explanation of network. Our research is of great theoretical and practical significance in the fields of artificial intelligence and public security.
Batch Normalization Preconditioning for Neural Network Training

Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks. Despite its success, BN is not theoretically well understood. It is not suitable for use with very small mini-batch sizes or online learning. In this paper, we propose a new method called Batch Normalization Preconditioning (BNP). Instead of applying normalization explicitly through a batch normalization layer as is done in BN, BNP applies normalization by conditioning the parameter gradients directly during training. This is designed to improve the Hessian matrix of the loss function and hence convergence during training. One benefit is that BNP is not constrained on the mini-batch size and works in the online learning setting. Furthermore, its connection to BN provides theoretical insights on how BN improves training and how BN is applied to special architectures such as convolutional neural networks. For a theoretical foundation, we also present a novel Hessian condition number based convergence theory for a locally convex but not strong-convex loss, which is applicable to networks with a scale-invariant property.
Prosody and Corpora

This paper focuses on the experience of spoken corpora compilation and discusses the relevance of prosody in this type of endeavor, as well as in the study of spoken language in its several possibilities. Through the voices of scholars associated with four different projects (CorpAfroAs, Mohawk Corpus, LABLITA, C-ORAL-BRASIL), the steps considered of utmost relevance in both the compilation and research potential of spoken corpora are presented; additionally, perspectives for the field in the future are pointed out.
Product Recommendation System using Scalable Alternating Least Square Algorithm and Collaborative Filtering using Apache Spark in E-Commerce

Recommender System is tremendously used in numerous spaces, such as e-commerce and entertainment to enhance businesses by increasing the chance of sales. Earlier researches have focused more on traditional Machine Learning (ML) and Artificial Intelligence (AI)-based approaches.  Developing a scalable recommender system has been challenging concerning high availability and fault tolerance. The traditional collaborative filtering approach used with the recommender system also faces challenges due to the absence of explicit product ratings by the customer and the cold start problem. We have proposed a scalable Alternating least square (ALS) and collaborative filtering-based approach for the recommender system. The experimental results of the proposed hybrid approach show improved performance as compared with the traditional approach.
METHODOLOGICAL DEVELOPMENT OF THE QUEST SCENARIO

The article presents the methodological development of the scenario of the chemical quest as a form of conducting career guidance activities in order to improve knowledge, skills, and the formation of independent work skills. The expediency of the development of this direction in the modern educational process is proved. The quest technology allows you to diversify the career guidance process, activate the cognitive processes of students, and help students actively apply their knowledge and skills in practice.
The effect of managers' ethical leadership on organizational health mediated by organizational justice In the schools of Qorveh city

Purpose: The aim of this study was to investigate the effect of principals' ethical leadership on organizational health mediated by organizational justice in schools.  Methodology: The study method was descriptive and correlational studies. The study population was all 339 secondary school teachers in Qorveh city in 2020. From this population, based on Morgan table, a sample of 181 people was selected by simple random sampling method. Data were collected using the Ethics Leadership Questionnaire by Brown et al (2005), the Morman and Niehoff (1993) Organizational Justice Questionnaire, and the Hui and Fieldman (1996) Organizational Health Questionnaire. In the present study, Cronbach's alpha values were Ethical Leadership Questionnaire (0.89), Organizational Justice (0.92) and Organizational Health (0.90). GFI values were confirmatory factor analysis of Ethical Leadership Questionnaire (0.92), Organizational Justice (0.93) and Organizational Health (0.92). Structural equation modeling test using LISREL10.30 software was used to analyze the data.  Findings: Ethical leadership, principals had an effect (0.61) with a significant amount (7.61) on the organizational health of schools and an effect (0.62) with a significant amount (6.79) on organizational justice. Organizational justice had an effect (0.27) with a significant amount (3.63) on the organizational health of schools. Ethical leadership Principals due to organizational justice had an indirect effect (0.167) with a significant amount (3.22) on the organizational health of schools.  Conclusion: According to the research findings, the moral leadership of principals and organizational justice has a positive and significant relationship with the organizational health of schools.
Accountability and Forensics in Blockchains: XDC Consensus Engine DPoS 2.0

This document introduces XinFin DPoS 2.0, the proposed next generation decentralized consensus engine for the XinFin XDC Network. Built upon the most advanced BFT consensus protocol, this upgrade will empower the XDC Network with military-grade security and performance while consuming extremely low resources, and will be fully backwards-compatible in terms of APIs. It will also pave the road to the future evolution of the XDC Network. The core invention is the holistic integration of accountability and forensics in blockchains: the ability to identify malicious actors with cryptographic integrity directly from the blockchain records, incorporating the latest peer-reviewed academic research with state of the art engineering designs and implementation plans.
Understanding Character Recognition using Visual Explanations Derived from the Human Visual System and Deep Networks

Human observers engage in selective information uptake when classifying visual patterns. The same is true of deep neural networks, which currently constitute the best performing artificial vision systems. Our goal is to examine the congruence, or lack thereof, in the information-gathering strategies of the two systems. We have operationalized our investigation as a character recognition task. We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps and an activation mapping technique for obtaining analogous distributions for deep networks through visualization maps. Qualitative comparison between visualization maps and fixation maps reveals an interesting correlate of congruence. The deep learning model considered similar regions in character, which humans have fixated in the case of correctly classified characters. On the other hand, when the focused regions are different for humans and deep nets, the characters are typically misclassified by the latter. Hence, we propose to use the visual fixation maps obtained from the eye-tracking experiment as a supervisory input to align the model's focus on relevant character regions. We find that such supervision improves the model's performance significantly and does not require any additional parameters. This approach has the potential to find applications in diverse domains such as medical analysis and surveillance in which explainability helps to determine system fidelity.
Henna@Printer / Nurul Atikah Badrol Hisham

GreeN@ company is a small running company which mainly focus producing a high quality of printer which base in organic henna as the ink which is HENN@PRINTER. By the name of the company, we want to show that, the company is producing a safe product which combine the natural organic product and electronic product at the same time. Our company is targeting to supply a unique organic henna printer as nowadays, people are love to have a unique and exclusive design of henna for any occasion such as wedding ceremony, engagement and even in Eid, Thaipusam and Deepavali celebration. Even though the process looks easy to make, but there must be some difficulty to produce a beautiful and perfect henna details. Therefore, only professional henna artist who has high experience in henna design could produce a well-designed henna and somehow to rent them require a lot of money and takes a lot of time to be done. Therefore, our company has produced a product which not just can help the henna artist but any people who wish to having an exclusive henna design for any occasion and at any time. Moreover, our marketing strategy would involve business strategy, marketing philosophy, the channel of distribution and the value chain of target market. The company main target market is on printer company at an urban area in Petaling Jaya Plus, we also target henna artist. Finally, the product that we produce can reduce time usage for producing a nice henna design. Furthermore, the price of the product is affordable. The price of our product is RM 471.48per/unit. Our company has targeting RM 1, 806, 239 of production by the second year of business.
Decision Making with Ahp Approach (Analytical Hierarchy Process)

According to J. Salusu (1996) that the decision-making role in an organization, including organizational principles will ensure that decisions are made correctly, and must include principles for effective action. Based on the opinion above, decision making is one of the most decisive factors in the success of achieving a goal. The ability to make decisions is one of the competencies that must be possessed by a manager. The higher a person's position in the organization, the greater the impact of the decisions he makes on the organization. This study investigates on the decision making using the AHP (Analytical Hierarchy Process) approach to ensure the organization is being controlled and monitored with better decision making by the higher level of management.
Various Techniques of Image Segmentation

Image segmentation techniques are essential for any kind of digital image or picture analysis. This paper suggests different methods of image segmentation like threshold, clustering, matching, and edge. Additionally, one method is proposed whose goal is to make the features of image usable to create segments for efficient processing techniques like recognition or compression. This method is used to find the objects and the images edges. Images are segmented or divided into components on the basis of similar characteristics of the pixels. Further, results obtained from the proposed method are a set of outlines that are taken out from the image by setting precise value of threshold.
Blockchain-based registration and transfer of shares

Distributed ledger technology is a convenient mechanism supporting the trade of shares, and in certain jurisdictions it is also considered a valuable tool to maintain stock ledgers. Among the most notable examples are Delaware and Vermont. Nevertheless, similar legal provisions have also been enacted in Europe, in particular in Poland, where the national legislator explicitly allowed the use of distributed ledgers for the purposes of registering transactions regarding shares, inter alia by allowing the companies to conduct blockchain-based stock ledgers. This, however, leaves the door open to further problems, such as: (i) the application of smart contracts to transactions concerning shares, (ii) numerous questions arising in the area of the law of obligations (in particular concerning liability for losses incurred as a result of bugs in the code underlying the ledger). The aim of this chapter is to provide comparative legal analysis of the current state of affairs with respect to blockchain technology, as well as to present possible solutions of the aforementioned problems, with particular emphasis on the questions arising in the field of the law of obligations.
A Fast and Efficient Face Pose Estimation Algorithm for Mobile Device

This paper proposes a method for face pose estimation from a single image. Face pose estimation is mainly used to estimate the 3D Euler Angle of the input face region image and is widely applied in human-computer interaction, video surveillance and other fields. For the purpose of improving the speed and accuracy of face pose estimation, multifarious operators or modules are utilized to build an efficient convolution neural network. Moreover, a variety of methods for data enhancement are used while training the CNN. Experiments show that our proposed method has higher speed and accuracy compared with previous face pose estimation methods.
Supervised Learning Based On Filtered Information

In this paper, we propose a framework to identify relevant information embedded in the training samples from a multi-class sample space for a supervised learning task. In a nutshell, we assume that the information contained in each individual sample can be decomposed into three categories: the first component is affected by certain latent factors, named as factors'', that impact all points in the sample space; the second component is affected by some class-specific factors, named as factors'', whose impacts are limited to the members of a certain class in the sample space; and the last component is unique to an individual sample and has no impact to any other samples. For a given training set of supervised learning, it is clear that one should focus on the information represented by class components of the training samples. We propose a framework for such information decomposition in a general setting and apply it to supervised learning. The proposed algorithm has several advantages: it applies to a general multiple-class supervised learning task where distances are defined between samples; the model fitting process converges fast to a unique solution such that the model performance is expected to be stable; it is scalable such that the model can fit to a large training set effectively; it take integer type model parameters, i.e. the dimensions of common factors and class factors, that are general small and can be learned easily from a given training set; it is robust in the sense that it performs well with redundant common and class factors. A case study based on simulated data is provided to demonstrate the model performances. We shall extend framework for unsupervised learning in a separate paper.