Reviewerly/exHarmony huggingface dataset:
Research on the application framework design and realization technology of power Marketing management information system

In the development of modern economic construction, power production and marketing as a huge and complex system engineering, to promote the innovation and development of the power industry has a positive impact. Especially after entering the development trend of economic globalization, the pressure of market competition of Chinese power grid enterprises is increasing and the market share is expanding. As a basic component of system engineering operation, power marketing link should be done well according to the brand new changes and specific questions of industry development. Therefore, on the basis of understanding the current situation of information construction and development of power marketing system, this paper deeply discusses the application architecture and implementation technology of power marketing management information system. The final experimental results show that this system design has a positive impact on the development of the electric power industry in the new era.
Comparison and Analysis of Various Autoencoders

The autoencoder is family of deep neutral network which learns to reconstruct its input. It has three main parts encoder, code, and decoder. Autoencoders are effective unsupervised learning method which encode an input into a lower dimensional representation. This representation input consist of input as features are useful for image processing applications. The size of hidden representation is lesser then the original image that’s under complete autoencoder. If the size is greater than the hidden representation that is over complete autoencoder. This paper compares and evaluates many architectures of autoencoders model.
Virtual reality boxing: Gaze-contingent manipulation of stimulus properties using blur

It has been reported that behavior of experts and novices in various sporting tasks is impervious to the introduction of blur. However, studies have used diverse methods of blurring the visual stimulus (i.e., dioptric blur and Gaussian blur), and tasks that did not always preserve the normal perception-action coupling. In the current study, we developed a novel experimental protocol to examine the effect of different levels of Gaussian blur on interception performance and eye gaze data using an immersive VR task. Importantly, this provided a realistic simulation of a real-world boxing scenario (e.g., the presence of a feint prior to the onset of different combinations of punches) in which expert combat athletes (n = 18) experienced a first-person, adaptive viewpoint of the visual environment, which could be blurred according to their gaze location (central blur, peripheral blur, no blur). We found that participants exhibited similar interception performance in the presence of central blur or peripheral blur compared to a control condition with no blur. However, interception performance was significantly better with a central blur compared to peripheral blur. Eye gaze data indicated that although participants fixated at similar areas of interest irrespective of the presence of blur, fixation duration was significantly longer with a strong level of blur in the peripheral viewing condition than all levels of central blur and the control condition. These findings can be explained by relocating attention to different areas of the environment, which thereby influenced the perception of salient information. Participants also performed better on the first punch of a sequence preceded by a foot feint compared to arm feint or no feint. Still, irrespective of feint type, performance was significantly better on the second and third punch compared to the first punch. These findings are consistent with participants using additional information from the opponent's body movements and situational probabilities to increase performance as the sequence of punches developed. Overall, these are the first evidence for the use of VR as a means to examine gaze-contingent manipulations of the environment, and hence highlight the potential for facilitating learning and transfer to a real sporting situations.
Computer Control with Face and Eye Movements Using Deep Learning and Image Processing Methods

Today's computing is one of the basic needs of every human being. Many actions are done with the mouse in the use of the computer. Individuals with physical disabilities, paralysis from the neck down, or ALS patients who have difficulty in making physical contact with the computer are having difficulty using computers. In this study, an artificial intelligence-assisted system has been developed for these individuals, where they can control the mouse with head and eye movements. In this system, facial movements and eyes are detected in real-time through the library of Haar Cascade, Dlib, and Open CV from the images acquired through the camera. When Haar Cascade is used to detect the face region, the Dlib library is used to acquire right and left eye region images from this detected face image. These eye areas are provided as an introduction to the CNN model, which is trained with 2874 eye data (https://github.com/iparaskev/simple-blink-detector), and it is determined that the eye is closed or open. The CNN model 1500 is trained on a public eye image dataset representing open and 1374 closed-eye conditions. The left eye closed and opened state allows the mouse to click left and the right eye to close and open, and the right mouse to click. In addition, the location of the face detected with Haar Cascade is used to model mouse motion. The developed system is a real-time hybrid system with a combination of different methods and has been tested on different users. According to the test results, it was observed that the system correctly identified the eyes and the closed state of these eyes, classifying the blink event with CNN in both eyes correctly. However, it has been determined that there has been a slowness in modeling mouse movement or a poor fit to facial movement. The next study will focus on this issue and improve it by fine-tuning the system with data from many people.
Will Blockchain Be a Big Deal? Reasons for Caution

The initial enthusiasm for implementing blockchain in financial markets has been dampened considerably by its collision with economic realities. Though the author warns against avoiding the Panglossian trap of viewing ours as the best of all possible worlds, he reminds us that financial institutions have evolved and won the trust of many consumers in a competitive environment as means of economizing on transactions costs. Such costs arise from the nature of transactions, including crucially the information environment in which they take place. Though new technologies such as blockchain have the potential to reduce some of these costs, they often do so without fundamentally changing the underlying economic conditions that have given rise to them. As a result, the traditional institutions such as banks and exchanges that technologists scoff at may well prove surprisingly competitive and durable in the face of technological challengers. The most successful implementation of blockchain—Bitcoin—solves a very basic transactional challenge peculiar to cryptocurrency: the double spend problem. But it does so in a very expensive way, and many other transactions pose far more complex challenges. The three cautionary tales provided by the author—the first involving securities and derivatives trading and clearing, the second commodity trading, and the third proposals to “equitize” assets—all demonstrate the need to confront “Chesterton's Fence” when evaluating the potential of blockchain in any particular application. To understand the value of a new technology for a given set of functions, one must understand the economic forces that have shaped the processes and institutions that currently perform those functions. When such forces are considered, it often becomes apparent that new technologies like blockchain will not prove superior to existing practices—and may even create adverse unintended consequences that offset and perhaps even eliminate their beneficial effects.
Prospectiva de las Tecnologías de la Información, Comunicación, Conocimiento y Aprendizaje Digitales para la Competencia Digital Docente

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.
The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things

Under the background of the Internet of things (IoT), the problems between the actual production and the environment are also prominent. The environmental cost control in the production process of manufacturing enterprises are discussed to reduce the environmental cost and promote the improvement of production efficiency. First, the environmental cost under the background of IoT is analyzed. Also, the environmental cost control methods in the production process of traditional manufacturing enterprises are investigated. Second, based on the principle of traditional genetic algorithm, the fast-nondominated sorting genetic algorithm (NSGA-II) of multiobjective genetic algorithm is introduced to complete the optimization of BP neural network (BPNN) algorithm in deep learning (DL), and the multiobjective GA optimization BPNN model is established. Finally, the multiobjective GA algorithm is used to empirically analyze the environmental cost control capability of a paper-making enterprise. It is compared with enterprises with excellent and poor environmental cost control capabilities in the same industry to find out secondary indexes. The results show that environmental costs have long-term and economic characteristics. The global search ability of BPNN optimized by multiobjective GA is improved, and the local optimal dilemma is avoided. Through empirical analysis, it is found that the comprehensive capability of the environmental cost control of the enterprise is better, scored 79 or more, and the indexes of insufficient development and advantages are obtained. As IoT rapidly develops, it is necessary to further improve the ability of enterprises in environmental cost management, which is very important to promote the development of enterprises and enhance their core competitiveness. It is hoped that this investigation can provide certain reference significance for improving the environmental cost management capability of enterprises, increasing production efficiency, and reducing environmental costs.
An optical apparatus to enable absolute angular alignment for sampling-based MTF test equipment

MTF tests, one of the most important optical metrology tasks for AR/MR glasses, analyze the DUT’s (Device Under Test) optical resolution to provide quantitative feedback for design verification and manufacturing process control. Due to the immaturity of the whole design/fabrication technical chain, current diffractive AR glasses show strong angular resolution non-uniformity across the FOV, and the measurement’s angular accuracy and consistency significantly impact the test repeatability and reproducibility. This paper presents a novel optical calibration apparatus to enable absolute angular alignment/verification, which can be implemented with a small volume and easily fit into the metrology equipment.
Comparison of the offer of selected cloud service providers from the point of view of implementing IT projects based on open code

The article presents a comparison of the offers of selected cloud computing providers in terms of their use in the process of developing and implementing IT services based on the idea of open source code. The conducted research concerns services provided according to the IaaS model in the form of a virtual machine lease and services provided according to the PaaS model in the form of database services. The presented analysis is both qualitative (subjective assessment) and quantitative, which consists in a series of measurements of the parameters of virtual instances. Based on this analysis, the best public cloud service provider for users starting to use cloud computing resources was selected.&#x0D; Keywords: cloud computing; IaaS service model; PaaS service model
Virtual Reality and Neuroarchaeology

This contribution discusses quantitative and qualitative methods in visual and neurocognitive analyses of archaeological excavations. The study and observation of on-site excavations is a very complex experience, which requires different methods of observation. How does an archaeologist look at an artefact, a stratigraphic layer or a monumental architecture? Is the level of embodiment and sensorimotor experience the same for archaeologists and non-archaeologists? The archaeological identification of layers, architectural patterns, clusters of artefacts during the excavation involves specific skills, taxonomies, and visual/sensorial comparative spatial analysis. This pilot project, entirely conducted during the COVID-19 lock down at Duke University, aimed to use eye-tracking and electroencephalography (EEG) data collection during the virtual observation of an archaeological excavation for a group of non-archaeologists and archaeologists. The virtual excavation, based on digital photogrammetry and optimized in Unity 3D, was the visual interaction test for the two groups of participants, evaluated by web-based eye-tracking methods. Preliminary results of this investigation show that archaeologists and non-archaeologists behave differently in their visual investigations of an archaeological site and that this could be explained by a differing sensorimotor embodied experience. Follow-on studies using EEG and other means for analysing neurocognitive activity are aimed at characterising the embodied sensorimotor experiences of experts and novices both in the laboratory setting and on site.
Towards a Better Understanding of the Specific Determinants of the Growth of African Manufacturing Small and Medium Enterprises (SMES)

Lack of knowledge of the new emerging determinants of African markets hinders the adaptive capacities of manufacturing SMEs operating there. The objective of this research is therefore to identify, conceptualize, and analyze these new determinants that are often overlooked in studies on the growth of manufacturing SMEs in Africa. Factors assessed include corporate growth (net income after taxes), corporate social responsibility (donations and sponsorships), investment in research and development, use of external expertise, direct and indirect taxation of companies by the government, and businesses' access to energy. A total of 1,025 small and medium enterprises in the Republic of Benin were assessed over the period 2008-2013. The information was taken from the companies' annual financial reports. Using the random effects panel regression method, this study shows the importance of the factors studied and proposes a contextualized model of analysis. The random effects model shows that variables such as sponsorship, use of external expertise, infrastructure investment (II), and indirect taxes and duties (ITD) are statistically significant across all sectors. In other words, these factors have significant impacts on business growth rates across all sectors. The study also shows the importance of factors such as the use of external expertise, investment in research and development and corporate social responsibility, particularly for medium-sized enterprises. In terms of policy implications, several behavioral and technical skills are connected to the factors identified in the study. For the development of behavioral skills, we can link donations and patronage. ITDs are linked to difficult regulatory environment. Finally, factors such as external expertise by business (UEE), II and investment in research and development (IRD), which are technical capacities on which companies invest heavily to boost their growth, could be connected.
Ancient Murals of the Shakhty Rock Shelter in the Light of Research Methods Development (Based on V. A. Ranov’s Archive Materials and Modern Data)

The article discusses the rock images of the Shakhty rock shelter. The authors’ archival research in 2021 and 2022 revealed field diaries, photographs and colour photo slides of V. A. Ranov, who was the first to find the site, in the archive of A. Donish Institute of History, Archaeology and Ethnography of National Academy of Sciences of Tajikistan. The combination of evidence obtained from these sources, and a modern examination of panels with images, made it possible to shed light on the method of recording the murals of the rock shelter in 1958. The rerecording of these images in 2019 and DStretch colour filtering of the obtained photographs made it possible to refine the contours and details of some images that are important for the attribution and subsequent interpretation of individual images. The authors assume that murals of the Shakhty rock shelter were not all created at the same time, as suggested by V. A. Ranov, dating them from the Mesolithic — early Neolithic. The presence of paint of different shades, varying degree of preservation of the shelter murals, numerous palimpsests and refined details of the images on the rock give the grounds suggest different times of their creation. Based on the results of modern research, the images of the Shakhty rock shelter can be dated from the Mesolithic to the Bronze Age.
Implementation of E-Learning Learning to Improve Student Achievement in Elementary Schools

The purpose of the study was to determine whether the implementation of e-learning could improve student achievement in SD Muhammadiyah 2 Tulangan. This study uses descriptive qualitative research so that it can describe information data in accordance with facts with methods that can describe and provide an overview. The technique used in the study was all third grade students. From the research results obtained academic and non-academic assessments, using e-learning learning can improve student achievement. The results of the study indicate that e-learning can encourage student achievement.&#x0D; Highlights:&#x0D; &#x0D; The study aimed to determine the impact of e-learning on student achievement.&#x0D; Descriptive qualitative research was employed to provide an overview of the data.&#x0D; The results indicated that e-learning positively influenced student achievement.&#x0D; &#x0D; Keywords: e-learning, student achievement, descriptive qualitative research, academic, non-academic assessments.
Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc.With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers.To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment.It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance.To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations.This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance.A comparison of the performance of these algorithms with existing ones is also given.Additionally, the future research work in the reviewed articles (if available) is also pointed out.This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study.Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms.
Open-access PIC Providers, Fabless Companies, and Their Opportunities

Like a CMOS technology, photonic integrated circuits are becoming a generic technology not only for research purpose but also for commercial use.The ecosystem needs to be further developed by the players; providers, fabless companies and users.This workshop brings a discussion and exchange opinions about the following issues.-Silicon and related materials providers -III-V and hybrid photonic integrated systems -Fabless companies -Gap between fabs technologies and fabless companies demands -Valid business and technology model -Trade-off of customization with higher investment costs vs. standardized processes with lower investment costs -Toward development of PIC R&D and product Speakers:
Fast trace ratio-based feature selection

The quality of features has a great impact on machine learning tasks. Feature selection obtains a high-quality feature subset from data, which has been widely studied because of high interpretability. In this paper, we propose a novel feature selection algorithm called trace Ratio-Based Feature Selection (RBFS), which first defines the distance of different classes and the same classes for a given sample and then projects these distances into the subspace. The margin is defined by the trace ratio of these two distances. The objective function is formulated by maximising the margin. To avoid a trivial solution, the orthogonal subspace and the L2,1 norm are incorporated into the objective function. Then, theoretically, the rewritten objective function can obtain the optimal solution through alternating iterations. In addition, power iteration is introduced to reduce the computational cost. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with six other state-of-the-art ones.