Reviewerly/exHarmony huggingface dataset:
Evolving Human Resource Management Practices and Employee Performance of Selected Pharmaceutical Companies in Nigeria

This paper assessed affinity of evolving human resource practices with the performance of employees of the pharmaceutical companies in Nigeria. Prior studies have addressed the concept from generalist perspective majorly short of exploring innovative thrust of human resource processes and practices and bearing on organisational performance of the pharmaceutical concerns. Study essentially relied on survey research design and questionnaire as a source of data collection. Data were analyzed through descriptive statistics including the means, standard deviation and analysis of variance. Test of hypotheses involving multiple regression analysis was conducted and results exhibited innovative human resource management&amp;rsquo;s proxies of skill-based training; selective hiring, alternative work arrangement, performance appraisal, fair compensation and autonomy work arrangement, correlating significantly with the performance of employees in the pharmaceutical companies. Recommendation was on the need for the Pharmaceutical Companies to deploy human resource policies and strategies that address issues bordering on optimum harnessing and deployment of human resources for enhanced performance. Instructive to assert also, that the human resource personnel in the healthcare industry conduct annual performance audit that provide timely and effective feedback and for corrective measures to be taken appropriately.
Connotation, characteristics and framework of coal mine safety big data

With the continuous development of automation and information technology, large amounts of safety data are produced in the processes of coal production. Most enterprises simply focus on statistics and do not conduct systematic big data analyses. Therefore, it is necessary to study the theory of coal mine safety while using big data systematically. This paper expounds on the changes in coal mine safety that have been driven by big data from three aspects: the connotation, characteristics and research framework. First, the connotation of coal mine safety big data (CMSBD) is redefined by changing the safety entities and methods. Second, the advantages and disadvantages of the big data model are compared from the perspective of feature analysis. Finally, the research paradigm and technical framework of CMSBD are designed. The results show that the management connotation of CMSBD focuses on the role of big data in coal mine safety. Compared with coal mine safety small data (CMSSD), CMSBD has both advantages and disadvantages. Therefore, CMSBD must be combined with a small data method. The research paradigm emphasizes the intersection of the research, the relevance of safety thinking, the importance of safety data analysis, and the fusion of big data with traditional small data models.
Evaluasi Program Desa Tangguh Bencana dalam Perspektif Flood Risk Management

Pacitan district is one of the five districts included in the highest disaster risk region in East Java. One of the area with a big threat of destructive flood disaster is Sirnoboyo Village, which is located between Grindulu and Jelok River. An attempt to improve community preparedness in the midst of flood hazard are carried out through Village Disaster Resilience Program. Village disaster resilience program is a form of flood risk management (FRM) as a community-based disaster risk reduction by building up self-sufficiency and adaptation. FRM is a theoritical analytic-based that is used in conjuction with Context, Input, Process, and Product (CIPP) evaluation model. The qualitative-descriptive approach was chosen to describe chronologically the evaluation of village disaster resilience program, which encounters several obstacles such as lack of active community and youth participation and under-capacity of the volunteer team. This study offers a reconstruction of village disaster resilience program execution in accordance with the FRM framework to effectively maximize community readyness over the threat of flood hazard that stakeout throughout a year.
Distributed Adaptive Filters

The chapter shows how the classical adaptive filtering algorithms can be adapted to distributed learning. In distributed learning, there is a set of adaptive filtering placed at nodes utilizing a local input and desired signals. These distributed networks of sensor nodes are located at distinct positions, which might improve the reliability and robustness of the parameter estimation in comparison to stand-alone adaptive filters. In distributed adaptive networks, parameter estimation might be obtained in a centralized form or a decentralized form. The centralized case processes the signals from all nodes of the network in a single fusion center, whereas in the decentralized case, processing is performed locally followed by a proper combination of partial estimates to result in a consensus parameter estimate. The main drawbacks of the centralized configuration are its data communication and computational costs, particularly in networks with a large number of nodes. On the other hand, the decentralized estimators require fewer data to feed the estimators and improve on robustness. The provides a discussion on equilibrium and consensus using arguments drawn from the pari-mutuel betting system. The expert opinion pool is the concept to induce improved estimation and data modeling, utilizing De-Groot’s algorithm and Markov chains as tools to probate equilibrium at consensus. It also introduces the distributed versions of the LMS and RLS adaptive filtering algorithms with emphasis on the decentralized parameter estimation case. This chapter also addresses how data broadcasting can be confined to a subset of nodes so that the overall network reduces the power consumption and bandwidth usage. Then, the chapter discusses a strategy to incorporate a data selection based on the SM adaptive filtering.
Memory Management

Memory management is an important concern for a developer, and it is a very big topic. This chapter will touch on the important points in a simplified manner to help you understand memory management in programming.
Research on College English Micro Course Teaching under the Guidance of Second Language Acquisition Theory

Under the background and premise of the continuous development of modern information technology, micro course plays an important role in teaching and has become a powerful assistant for educators. This informationbased teaching channel has attracted extensive attention in the educational field. Nowadays, micro class platform has become a powerful helper of College English teaching. The rise of micro class provides a stage for teachers to realize their own ideas. This new educational idea and method can open a new window for College English teaching, bring new reform and upgrading, and play a good role in reforming and alleviating the disadvantages of traditional teaching. Under the influence of second language acquisition theory, the application of micro class will embark on a new road, which is more in line with the development trend and needs of education in China. This paper focuses on the research of College English micro course teaching under the guidance of second language acquisition theory, hoping to provide some references and suggestions for relevant workers.
A Distinguishable Pseudo-Feature Synthesis Method for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to classify seen classes and unseen classes that are disjoint simultaneously. Hybrid approaches based on pseudo-feature synthesis are currently the most popular among GZSL methods. However, they suffer from problems of negative transfer and low-quality class discriminability, causing poor classification accuracy. To address them, we propose a novel GZSL method of distinguishable pseudo-feature synthesis (DPFS). The DPFS model can provide high-quality distinguishable characteristics for both seen and unseen classes. Firstly, the model is pretrained by a distance prediction loss to avoid overfitting. Then, the model only selects attributes of similar seen classes and makes sparse representations based on attributes for unseen classes, thereby overcoming negative transfer. After the model synthesizes pseudo-features for unseen classes, it disposes of the pseudo-feature outliers to improve the class discriminability. The pseudo-features are fed into a classifier of the model together with features of seen classes for GZSL classification. Experimental results on four benchmark datasets verify that the proposed DPFS has GZSL classification performance better than that in existing methods.
Three-dimensional monitoring of slope deformation using digital photogrammetry

To evaluate the soundness of civil engineering structures constructed on rock slopes, new measurement techniques are needed to determine the relationship between structures and rock behaviour with high accuracy and accuracy. This paper describes an example of measuring the displacement of a structure related to a landslide on a slope. The proposed technique is measured by using two image processing, one is the computation of the target centroid with intensity value and the other one is perspective projection. Thus, it is possible to measure the displacement in an arbitrary camera position. For verifying the accuracy and precision of this technique, the experiments were performed, and the result indicated that the authors can measure approximately 0.1mm of the displacement of the crack with high accuracy and precision when use the digital image taken from 0.5m of the target. In addition, the authors tried to apply this measurement system proposed herein to the actual tunnel for estimating the serviceability and it is concluded that the result of this measurement has relationship with the behaviour of the rock slope.
Intelligent Categorization of Arabic Commands Utilizing Machine Learning Techniques with Short Effective Features Vector

Different technologies are now being employed to improve the quality of life, particularly for the disabled and elderly.Speech is the quickest and most convenient method of communicating with people and technology.The majority of the works have focused on English speech; however, there is some interest in Arabic.In this study, an Arabic dataset is created, which will eventually be used to control a mobile assistant robot.Arabic is a challenging language to learn because of its many dialects, each of which has its own impact on the spoken word.The Egyptian Arabic Speech Commands (EASC) dataset was compiled from people of different backgrounds, ages, and genders who spoke in colloquial dialects.The Arabic recognition test is made more difficult by this fluctuation.Using various machine learning techniques, Arabic speech commands were classified.Mel Frequency Cepstral Coefficients were used to create an effective feature vector (MFCC).Spectral centroids and signal power are combined with MFCC to generate an enlarged features vector, which improves recognition accuracy.Because these commands will drive a robot in real time, they must be classified quickly.As a result, the training features vector's dimension is lowered by performing some statistical calculations on it.Support Vector Machines (SVM), Random Forest decision tree (RFT), Neural Network (Multi-Layer Perceptron, MLP), and k-nearest neighbors (KNN) approaches were employed as intelligent classifiers.A thorough examination of the classifiers' various parameters was carried out.With a classification accuracy of 94.84 percent, the SVM approach outperformed other techniques.We concluded in this research that the enlarged features vectors with a lower dimension are more effective for this problem and can be employed in real applications.
STUDENT’S PERCEPTION OF QUIZIZZ.COM USED AS THEIR ENGLISH LEARNING AND EVALUATION MEDIA

This study aimed to investigate the students' perceptions of Quizizz.com used as their English learning and evaluation media. The subject of the study was 50 eighth-grade students of MTs Roudhotul Muhibbin Bekasi. The research design of this study was descriptive quantitative by applying closed-ended questionnaire that is categorized as a survey instrument. The data was then analyzed through MS. Excel calculation system. The result showed positive perception of the students toward using Quizizz as their English learning and evaluation media. The students also responded that Quizizz has interesting features and displays, is easy to use, and had the motivation and interest in learning English. However, most students were still hesitant to use Quizizz.com in the future due to some factors that need to be investigated later on.
INVESTIGATION OF ENGLISH GRAMMAR LEARNING STRATEGY ON HIGH, MIDDLE, AND LOW ACHIEVERS' STUDENTS IN INDONESIA

This study investigates the English grammar learning strategy used among high, middle, and low achievers’ students in Indonesia. The aim is to reveal which types of strategies that high, middle, and low achievers’ students applied most while learning English grammar and to check whether or not there is difference grammar learning strategy used by high, middle, and low achievers’ students. A lot of researchers have concentrated their interest on the grammar learning strategy area, including in Indonesia, however, it was not seen much or still found limited references to check the grammar learning strategy among high, middle, and low achievers’ students. Besides, the grammar learning strategy instruments of other Indonesian researchers are complexly varied. This study is focused on finding the strategies that high, middle, and low achievers’ students used through Pawlak’s Grammar Learning Strategy Inventory or GLSI (2018) with 100 participants (high vs middle vs low achievers’ students) involved. The results showed that there are significant differences between the frequent use of grammar learning strategies between low, middle, and high achievers' students. Social strategies are the most frequently used grammar learning strategies by low and middle achievers’ students, while metacognitive strategies are the strategies most frequently used by high-achieving students in Indonesia.
Comparison prediction network: A novel approach to estimate apparent age

This paper presents a novel CNN model called comparison prediction network for apparent age estimation. The algorithm is structured by feature extraction and Face feature database. Compared with the existing methods, our algorithm can better deal with the problem caused by differences between apparent age and actual age, which improves the prediction precision of the model with the increasing credibility and robustness of the model prediction results, enhancing the generalization ability of the model. The algorithm has fewer parameters and is lighter than other methods, which is suitable for mobile deployment.
Low-Latency and Low-Randomness Second-Order Masked Cubic Functions

Masking schemes are the most popular countermeasure to mitigate Side-Channel Analysis (SCA) attacks. Compared to software, their hardware implementations require certain considerations with respect to physical defaults, such as glitches. To counter this extended leakage effect, the technique known as Threshold Implementation (TI) has proven to be a reliable solution. However, its efficiency, namely the number of shares, is tied to the algebraic degree of the target function. As a result, the application of TI may lead to unaffordable implementation costs. This dependency is relaxed by the successor schemes where the minimum number of d + 1 shares suffice for dth-order protection independent of the function’s algebraic degree. By this, although the number of input shares is reduced, the implementation costs are not necessarily low due to their high demand for fresh randomness. It becomes even more challenging when a joint low-latency and low-randomness cost is desired. In this work, we provide a methodology to realize the second-order glitch-extended probing-secure implementation of cubic functions with three shares while allowing to reuse fresh randomness. This enables us to construct low-latency second-order secure implementations of several popular lightweight block ciphers, including Skinny, Midori, and Prince, with a very limited number of fresh masks. Notably, compared to state-of-the-art equivalent implementations, our designs lower the latency in terms of the number of clock cycles while keeping randomness costs low.
Towards understanding and synthesis of contact-rich anthropomorphic motions through interactive cyber-physical human

This article presents perspective on the research challenge of understanding and synthesizing anthropomorphic whole-body contact motions through a platform called “interactive cyber-physical human (iCPH)” for data collection and augmentation. The iCPH platform combines humanoid robots as “physical twins” of human and “digital twins” that simulates humans and robots in cyber-space. Several critical research topics are introduced to address this challenge by leveraging the advanced model-based analysis together with data-driven learning to exploit collected data from the integrated platform of iCPH. Definition of general description is identified as the first topic as a common basis of contact motions compatible to both humans and humanoids. Then, we set continual learning of a feasible contact motion network as the second challenge by benefiting from model-based approach and machine learning bridged by the efficient analytical gradient computation developed by the author and his collaborators. The final target is to establish a high-level symbolic system allowing automatic understanding and generation of contact motions in unexperienced environments. The proposed approaches are still under investigation, and the author expects that this article triggers discussions and further collaborations from different research communities, including robotics, artificial intelligence, neuroscience, and biomechanics.
Gaining resolution when creating imagery of aging

In seeking to support healthy aging, designers have struggled to reduce their assumptions and biases toward older adults, been seen to interpret the worlds of later life through unfiltered imagery, as well as engage with stigmas, ultimately diminishing the technologies they construct. This article seeks to critically analyse this state-of-the-art from a design research perspective while engaging with the growing interdisciplinary study of aging and technologies. Toward this, we proposition "resolution" as a concept indicative of the level of detail that seeks to characterize the fidelity that representations of later life have. This concept is explored through a cultural probe study that investigated the sentiments of several older Australians regarding the inequities and social isolation brought on by the COVID-19 pandemic. Providing a diary alongside photovoice and mapping tasks, the study captured perceptions of social technology, practices, networks, and wellbeing, offering a diverse and complex picture of aging and technology. Through reflexive thematic analyses of some of these materials, this case study offers designers pathways to understanding and including older adults in their work. In determining the resolution of these images of aging, we discuss how transparency about the limitations and qualities of such participatory methods through incorporating reflexivity can influence the degree of detail such imagery gains. Ultimately this concept builds on the notion of participation configuration, supporting designers to realize better images of aging and representations of later life.
Super-resolution reconstruction of medical image via depth residual network

Recently, whole slide imaging (WSI) has become the gold standard for the clinical diagnosis of various diseases. It not only strengthens the cooperation between the computer and pathologists, but also promotes the development of remote primary diagnosis. WSI usually uses 20x and 40x objective lens scanners to scan the slides. Compared with the low-magnification lens, the high-magnification lens could provide high-resolution (HR) images, while sacrificing the large field-of-view (FOV), large depth-of-field (DOF), and high scanning efficiency instead. In this paper, we propose an image super-resolution (SR) reconstruction method based on a deep residual network (DSN), which improves flexibility and recovers the effective information neglected by the conventional network. After experimental verification, our method achieves large FOV, high scanning efficiency, and HR images simultaneously for assisting the diagnosis of pathologists.
Adaptive scanning methods for 3D scene reconstruction

This work is devoted to the research of applicability of depth determination methods for solving problems of building 3D models of rooms. The authors propose a combined method of finding the disparity and statistical signals processing using auxiliary data, obtained due to the laser illumination of the scene. Solutions used in forming 3D models of objects or the surrounding space are considered, which led to the definition of the most appropriate method for building a scanning system – the stereo-reconstruction method. The finding of disparity by naive gradient descent method is presented. The results of the scanning system are presented.
Digital Image Decoder for Efficient Hardware Implementation

Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequantizer and inverse subband transformer (parts of the digital image decoder) have been developed in such way which allows efficient hardware implementation with reduced amount of utilized logic and memory resources. It has been shown that proposed hardware realization of inverse subband transformer requires 20% lower memory capacity and uses less logic resources compared with the best state-of-the-art realizations. The proposed digital image decoder has been implemented in a low-cost FPGA device and it has been shown that it requires at least 32% less memory resources in comparison to the other state-of-the-art decoders which can process high-definition frame size. The proposed solution also requires effectively lower memory size than state-of-the-art architectures which process frame size or tile size smaller than high-definition size. The presented digital image decoder has maximum operating frequency comparable with the highest maximum operating frequencies among the state-of-the-art solutions.
Un estudio de clases virtual para promover la construcción del infinito actual en estudiantes de educación media y primer año de universidad desde la perspectiva de la teoría APOE

Chilean curriculum has recently included an optional module called "Limits, derivatives and integrals", which addresses infinitesimal calculus.On the face of it, it is expected that students understand the notion of actual infinity; however, the curriculum has a paramathematical treatment of that concept.On these grounds, this research operationalizes a virtual class study that promotes the understanding of the notion of actual infinity among upper secondary school and first-year university students.The theoretical perspective this research resorts to is APOS theory.It was observed that the proposal the research suggests promotes a successful understanding of actual infinity, since the structure of totality in the students is evident.