Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF HUMAN RESOURCE POLICIES IN PRIMARY SCHOOLS

Every enterprise, organization or institution that aims to achieve its goals must be committed to providing not only the technical and financial resources, but also the human resources that are vital to the success of the organization. Although the beginnings of human activities have appeared since the organization of people centuries ago, only in the last century has a body of knowledge been developed that incorporates theories and practices of personnel management called human resource management. This is explained by the fact that in many scientific fields, systematic and scientific research was conducted for the first time in the twentieth century.Human resource management has gone through a long process of development until it has achieved the status it has today, one of the most important management functions of an organization. Human resource policy is a set of systematic, codified and widely disseminated rules and regulations that do not specify how human resources should be treated in an organization, what rights and responsibilities they have and what rights and responsibilities managers have. In the past, human resource or personnel policy, as it was often talked about, was indefinable, depending on the mood, attitude or desire of the director, or in some cases it was not a policy at all, but merely an individual desire.Studies show that such a method leads to divisions, dissatisfaction and squabbles between teachers and principals. The way out of this situation is a set of policies that cover all issues that are likely to be of concern to the workplace.In this paper we analyze the situation in the primary schools in the region of Skopje, Tetovo, Struga and Kicevo in terms of decision making for systematization of the working relations, planning of the teaching staff, the way of selection of the teaching and non-teaching staff, the professional development program of the teachers. staff, the provision of seminars and workshops for professional development, the way school teams work.
An efficient image to column algorithm for convolutional neural networks

Convolutional Neural Networks (CNNs) are a class of deep neural networks. The image to column (im2col) procedure is an important step for CNN and consumes about 28.8% of the whole inference time. In this paper, we present an efficient im2col algorithm, name im2cole (word “e” means efficient). The condition with different stride and pad in im2cole is well handled and the judgements in the innermost loop are removed. The procedure with pad = 1 is split into three conditions. This will reduce the pause of CPU instruction pipeline. The performances of the presented im2cole algorithm are reported with different inputs. Some discussion and performance issues are also reported. The experimental results show that the overall performance speedup of im2cole ranges from 2.12 to 4.33 compared with the original algorithm. The real application with Darknet shows that im2cole can get 20.75% whole performance improvement.
Vehicle Path Planning with Multicloud Computation Services

With the development of artificial intelligence, public cloud service platforms have begun to provide common pretrained object recognition models for public use. In this study, a dynamic vehicle path-planning system is developed, which uses several general pretrained cloud models to detect obstacles and calculate the navigation area. The Euclidean distance and the inequality based on the detected marker box data are used for vehicle path planning. Experimental results show that the proposed method can effectively identify the driving area and plan a safe route. The proposed method integrates the bounding box information provided by multiple cloud object detection services to detect navigable areas and plan routes. The time required for cloud-based obstacle identification is 2 s per frame, and the time required for feasible area detection and action planning is 0.001 s per frame. In the experiments, the robot that uses the proposed navigation method can plan routes successfully.
Organizational performance for Jordanian telecommunication companies

Understanding the factors that affect the organization's performance contributes to taking appropriate steps to improve the overall performance of organizations. Thus, this study aims to search about factors that impact on organizational performance including strategic human resource management practices (recruitment and selection, training and development, participation, performance appraisal, and compensation) and perceived organizational support. This study used a survey questionnaire as the main technique for data collection. A total of 327 questionnaires were distributed to the employees in Jordanian telecommunication companies (Zain, Umniah, and Orange), while 308 questionnaires usable were collected. The results of this study indicated that strategic human resource management practices (training & development, compensation, performance appraisal, recruitment & selection, participation) had a positive direct effect on organizational performance. Moreover, the results indicated that perceived organizational support had a positive direct effect on organizational performance. This study examined four main practices, namely training and development, compensation, performance appraisal, recruitment & selection, participation. Future studies may include other practices such as employee empowerment, teamwork, employment security, information sharing, promotion, and job description.
Heuristic Search for Activation Functions of Neural Networks Based on Gaussian Processes

Despite the powerful expressivity of neural networks with nonlinear activation functions, the underlying mechanism for deep neural networks still remains unclear. However, it can be proved that ultra-wide neural networks are equivalent to Gaussian processes, thus connecting the analysis on neural networks with Bayesian statistics and kernel methods. Moreover, recent studies on infinitely wide neural networks extend this correspondence to a specific kernel, named Neural Tangent Kernel (NTK), which governs the learning dynamics of related neural networks. Without weights and biases, the NTK recursively encodes the architecture information about the corresponding neural networks, including the activation function at each hidden layer. Inspired by this close relationship of Gaussian processes and neural networks, we propose a heuristic search method for activation functions of sufficiently wide neural networks in the NTK regime. To obtain an elegant and closed-form computation, activation functions are decomposed in the basis of Hermite polynomials, which converts the kernels in Gaussian processes into power series. Experiments show the outperformance of the obtained nonlinearities compared with other common activation functions. This work also reveals the potential utility of NTKs for guidance on neural network structure search in the future.
LabelDetection: Simplifying the Use and Construction of Deep Detection Models

Deep learning algorithms for object detection on images have been successfully applied in several fields; however, non-expert users might find difficult to adopt these techniques due to several reasons. First of all, using detection models requires some knowledge about the library employed to built them; and, in general, it is not usually possible to interact with the predictions produced by the models. In addition, training custom models is also challenging because there are several algorithms implemented in different libraries, and each of them uses its own annotation format, and is configured in a particular way. In this paper, we face all those challenges by developing LabelDetection, a graphical tool that allows non-expert users to employ models trained with different algorithms and libraries. Moreover, LabelDetection is an end-to-end application that provides the necessary features to annotate a dataset of images, and train a variety of object detection models. Finally, LabelDetection simplifies the use of advance techniques like test-time augmentation, that improves the accuracy of detection models; and data distillation, that reduces the number of images that must be annotated to train a model. LabelDetection is freely available at https://github.com/ancasag/LabelDetection.
Recommenders in Banking: An End-to-end Personalization Pipeline within ING

Retail services in corporate international banks are often constrained by the compliance and infrastructure specificities of the countries in which they operate. In addition, the business model and customers interactions through a banking app differ greatly from other major retail services in sectors like digital streaming or e-commerce platforms. We introduce ING’s Bank retail recommender system, elaborating on how we account for the global vs. local requirements, and how can we benefit from such an approach for our model selection as well as products serving.
The implementation of zero-shot algorithm on FPGA

By introducing attribute variables, the zero-shot learning method can reduce the labeled data set needed for model training under the condition of ensuring the correctness of deep learning model. In this paper, the hardware IP modules of convolution and pooling layers are designed, then by writing the hardware driver and software driver in Vivado, the deep neural network is accelerated on Zedboard. The traditional zero-shot learning method is implemented on hardware, and both performance analysis and correctness verification of the system are carried out.
On the Convexity of a Fragment of Pure Set Theory with Applications within a Nelson-Oppen Framework

The Satisfiability Modulo Theories (SMT) issue concerns the satisfiability of formulae from multiple background theories, usually expressed in the language of first-order predicate logic with equality. SMT solvers are often based on variants of the Nelson-Oppen combination method, a solver for the quantifier-free fragment of the combination of theories with disjoint signatures, via cooperation among their decision procedures. When each of the theories to be combined by the Nelson-Oppen method is convex (that is, any conjunction of its literals can imply a disjunction of equalities only when it implies at least one of the equalities) and decidable in polynomial time, the running time of the combination procedure is guaranteed to be polynomial in the size of the input formula. In this paper, we prove the convexity of a fragment of Zermelo-Fraenkel set theory, called Multi-Level Syllogistic, most of whose polynomially decidable fragments we have recently characterized.
Efficacious Governance During Pandemics Like Covid-19 Using Intelligent Decision Support Framework for User Generated Content

Jindal, Rajni Malhotra, AnshuDuring the unprecedented global health emergency caused by the pandemic Covid-19, the governments and nationalized organizations of all countries are struggling to control its spread by enforcing various measures, and also manage its social economic impact through policy intervention. In such critical situation, it becomes imperative to take data driven decisions. User generated content over online social media is an untapped data source that can be leveraged to gain insights for effective governance and decision making; and can also serve as a first hand communication medium between the various government bodies and citizens. In this paper, we have proposed a novel governance framework that leverages user generated content on social media for effective decision making by the authorities. We have used topic modelling techniques to discover social-economic trends, and to understand the issues or concerns of public interest. We used information extraction techniques like noun and verb phrase extraction, and Named Entity Recognition to measure the geographical spread, identify Covid-19 hotspots, assist in contact tracing, and discovering new health conditions. From the available literature, we could not find any intelligent framework for effective governance during pandemics, where user generated content has been utilized for real time decision making. In this paper, we address this real world problem through our proposed decision support System, and demonstrate as a proof-of-concept of how it can be used for effective governance during the pandemics through its prototype implementation.
Image Stitching Algorithm Based on Improved Harris Corners Extraction

This paper proposes an improved Harris corners extraction algorithm, which consists of coarse screening and fine screening in the corners extraction process. Firstly, image segmentation is used to find the redundant regions of feature points in the image. Secondly, the original image is divided into 3x3 blocks to separately deal with the response values of the Hession matrix of the corners contained in each block. Finally, for the corners in the three small blocks on the left of the figure, since they are the farthest from the overlapping area, only two corners with the largest response values are taken from each small block. Similarly, the corners in the middle three small blocks are required to take 20%-60% of the number of corners in each block, and the corners on the right three small blocks are required to take 90% of the number of corners in each block. After removing the redundant corners, the rest corners are the key feature points to be matched. Experimental results show that the algorithm proposed by this paper not only improves the operating speed, but also improves the registration accuracy by reducing the number of corners, so as to lay the foundation for subsequent image stitching.
Big Data Security Problem and Its Solutions

Big Data is the way to go especially for the large companies which keep a lot of information on the systems. This paper delves into the new challenges associated with big data. It points out safety challenges on Big Data as the main issues that organizations seek to address on a day-to-day basis. These challenges include securing the trusted environments, sufficient access management, performing due diligence, combating AVI vulnerabilities, and security automation. They can be solved by maintaining strict access strategies that only allow their esteemed and responsible employees to login and also set the systems in such a way that they can detect abnormalities and allow for investigations while there is still time. The paper has addressed big data challenges as well as their solutions which are always be considered in the case of the organization as they have long-term consequences if not put into consideration.
Verbose : Designing a Context-based Educational System for Improving Communicative Expressions

ESL (English as a second language) speakers tend to follow the tone structure of their first language, making their speech difficult to understand for native speakers, thereby limiting their opportunities for education and employment. To address this problem, we build an interactive smartphone-based educational mobile application using the user-centered design process. This application teaches English intonations based on globally consistent pitch patterns through conversations with a trained chat assistant, which inculcates expert linguists’ teaching principles. After co-designing the application’s parameters with primary stakeholders and expert visual designers, we assess its effectiveness by measuring the pre and post-performance of the users after the system usage, using various quantitative measures, like intonation scores, SEQ, and SUS. Feedback from users suggests that ESL speakers find significant improvement in the perception of their vocal expressions, thereby highlighting the necessity of such a system in improving the quality of conversations that people have in general.
Automatic Estimation of Age Distributions from the First Ottoman Empire Population Register Series by Using Deep Learning

Recently, an increasing number of studies have applied deep learning algorithms for extracting information from handwritten historical documents. In order to accomplish that, documents must be divided into smaller parts. Page and line segmentation are vital stages in the Handwritten Text Recognition systems; it directly affects the character segmentation stage, which in turn determines the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and the 1860s. Then, we employed horizontal projection profile-based line segmentation to the demographic information of these detected individuals in these registers. We further trained a CNN model to recognize automatically detected ages of individuals and estimated age distributions of people from these historical documents. Extracting age information from these historical registers is significant because it has enormous potential to revolutionize historical demography of around 20 successor states of the Ottoman Empire or countries of today. We achieved approximately 60% digit accuracy for recognizing the numbers in these registers and estimated the age distribution with Root Mean Square Error 23.61.
Hiding Region of Interest in Image using Fuzzy Logic

Abstract In this research, a new method for hiding image has been proposed. The proposed method relied on hiding the region of interest in the image by specifying it using fuzzy logic. Images play a large and important role in several areas. Therefore, The more important the image, the greater the need to protect and preserve it. In this research, the work was divided into two parts, the first part represents the sending side and in it the face area is defined as an important region in the image and hidden then, while the second part, represents the receiving side and in it the hidden image is received and unhide. Fuzzy logic was used in the process. The proposed algorithm provide security to the images being transmitted. Matlab language was use to perform this work.
Design of a Global Retrieval System for Characteristic Data Based on SOLR

Due to the rapid advance of informationization process, in the process of test data accumulated a large number of product features, product features data exponentially, traditional feature data used to query the database to retrieve the data content, not whole, system will target currently stored data all retrieved, and interaction is not convenient, It is not conducive to the overall control of product characteristics and subsequent analysis. In this paper, a global retrieval system of characteristic data based on Solr is designed, which aims at the retrieval design of structured data such as database table information and unstructured data of various types of documents such as Word, TXT, PDF and Excel, as well as the extension design of combined display and highlighting. After testing, the global retrieval system of characteristic data based on SOLR can efficiently retrieve the existing and accumulated characteristic data resources, and provide reliable competitiveness for enterprises in the information age.
Tweaking Business Planning With Artificial Intelligence

Griffith AI&amp;BD is a technology company that uses big data platform and artificial intelligence technology to produce products for schools. The company focuses on primary and secondary school education support and data analysis assistance system and campus ARTIFICIAL intelligence products for the compulsory education stage in the Chinese market. Through big data, machine learning and data mining, scattered on campus and distributed systems enable anyone to sign up to join the huge data processing grid, and access learning support big data analysis and matching after helping students expand their knowledge in a variety of disciplines and learning and promotion. Improve the learning process based on large data sets of students, and combine ai technology to develop AI electronic devices. To provide schools with the best learning experience to survive in a competitive world.
FA-GAL-ResNet: Lightweight Residual Network using Focused Attention Mechanism and Generative Adversarial Learning via Knowledge Distillation

Despite that deep neural networks have achieved satisfactory performance, they rely on powerful hardware for training, which is expensive and not easy to get. Therefore, the compression and acceleration of deep neural network is important. In addition, lightweight design of complex model usually leads to the decrease of model accuracy. In order to solve the problems, we propose a novel ensemble learning model called FA-GAL-ResNet, which is a lightweight residual network integrated focused attention mechanism and generative adversarial learning by knowledge distillation. In the training of knowledge distillation, the traditional single teacher network is replaced by the multi-teacher hybrid network, which enhances the learning span of student network. Meanwhile, the distilled network with attention scoring module focuses on the notable features related to the target task and the irrelevant features would be filtered by grade of scores and weights. Experimental results show that the proposed network structure improves the accuracy of image classification task, in comparison with the separate complex as well as lightweight neural networks.
Innovative Thinkings of University Labor Union in the Age of Educational Information

University labor union is a mass organization of teaching staff, and it plays a very important role in safeguarding the rights and interests of teachers, promoting university construction, participating in university management, and carrying out the education of teaching staff. This article first analyzes the changes of university labor union work mode in the age of education informatization, and then proposes innovative ideas, including improving the information literacy of teachers, mobile information services, data sharing, etc., so as to build a new pattern of university labor union.
Human Body Pose Recognition System Based on Teaching Interaction

In view of the problems of the high time cost and low accuracy of manual supervision in traditional classroom teaching, this paper proposes a human body pose recognition system based on teaching interaction. The enhanced basic network (ResNext-101 + FPN) was used in Mask R-CNN to extract the features of the input images. Then based on the behavior analysis algorithm and face detection data, the behavior data of each student in the classroom were obtained. Moreover, the behavior data were applied to support multi-dimensional visualization. The experimental results show that the system can timely and effectively reflect the learning status of students, and help teachers accurately grasp the classroom learning state of students, so as to adjust teaching strategies in a targeted way and help improve the quality of teaching.
Commentary

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. Part four of the book proposes that Barad’s feminist ‘new materialism’ provides a key to understand and act upon the relational co-production of technology and ageing. It focuses on one issue: ageing-in-place policy and how it technologically articulates ‘home’. The book then focuses on then practices of design, as forms of intervention. It also examines how social research methods might open up or close down the sociotechnical imagination. Shifting the focus from designers, engineers and policymakers, the authors proposes that social science practices themselves partake in the making of the relationship between ageing and technology. The book also focuses on the work of Mol and Law to trace how realities of ageing and technology are enacted, emphasising the plural, and multiple character of such enactment. Comfort’s insight into how technological imaginaries are linked to health measurement infrastructures and conventions is valuable.