Reviewerly/exHarmony huggingface dataset:
QoS-Aware Data Center Operations Based on Chance- and Risk-Constrained Optimization

Because data centers are energy-intensive industries, data centers have made significant efforts to improve energy efficiency to save energy cost and utilize renewable energy to contribute to environmental sustainability. In addition, data centers have been considered as a potential application of demand response because they can possibly adjust energy consumption to process workload in response to time-of-use prices and intermittent renewable generation. Quality of service (QoS) should be guaranteed at the desired level to respond to users’ requests with their satisfaction in an effort to achieve energy-efficient and sustainable data center operations. Given this context, this study intends to develop a two-stage stochastic program that can be used to investigate optimal data center operations based on demand response that results in minimum energy cost with QoS guarantee against the stochastic workloads. Then, an additional chance constraint and risk constraint are considered as means of managing the level of QoS guarantee, and comprehensive numerical experiments are conducted with various parameter settings to evaluates the impact of the QoS guarantee with different levels on overall performance. The results show that the risk associated with the QoS guarantee for data centers needs to be properly managed to improve energy efficiency.
Analysis of Information Literacy on Diffable Students

The purpose of this study is to find out literary analysis information on students with disabilities at the State University of Malang.In this current study, a descriptive qualitative approach was applied as the research method.Descriptive qualitative research is research used to gain insight into an issue/problem in a natural setting.During the study, the researchers focused on analyzing the information literacy of students with disabilities that are based on their psychological condition, cognitive, and demographic.The results of this study were found analysis literacy in the context of information needs; the students need information according to the field they are studying.They have an interest in other fields such as music, sports, chess, and tend to seek information in accordance with that field.In the context of information literacy analysis based on information-seeking behavior on students with disabilities, they already have a need, and they do a search, the media used is the internet.From the aspect of information processing and use, it is found that they evaluate the information.Suggestion for further researchers or other researchers, it is hoped that the results of this study can be used as a reference or source in conducting research related to information literacy in students with disabilities.
Automatic Training Method of Deep Neural Network for Robot Vision

Thanks to the breakthrough of deep learning in machine vision, robots have been widely applied in industrial and family services in recent years. Object capture is one of the most important functions for robots, and two-dimensional object detection is the premise for robot to capture objects. However, the training cost of obtaining the high recognition rate model is very large, as the deep neural network needs huge data samples. Based on this, this paper proposes an automatic training method of deep neural network for robot vision. The Tracking-Learning-Detection (TLD) algorithm tracks and collects the object samples by online learning. Then the offline Single-Shot-Detector (SSD) model studies the features of the object so as to realize the robotic object recognition function. In order to ensure the stability and continuity of the tracking process of the target, the sample acquisition process is accomplished automatically by the robot manipulator. Two methods of manual annotation and TLD algorithm are all used in this paper for increasing the persuasiveness of the data. The results show that the time cost of automatic data annotation by TLD algorithm is 77.75% less than the manual data annotation, and the recognition rate of model trained with automatic labeling data is 97.75%, which verify the validity and feasibility of the novel method.
The Application of Projection Word Embeddings on Medical Records Scoring System

Medical records scoring is important in a health care system. Artificial intelligence (AI) with projection word embeddings has been validated in its performance disease coding tasks, which maintain the vocabulary diversity of open internet databases and the medical terminology understanding of electronic health records (EHRs). We considered that an AI-enhanced system might be also applied to automatically score medical records. This study aimed to develop a series of deep learning models (DLMs) and validated their performance in medical records scoring task. We also analyzed the practical value of the best model. We used the admission medical records from the Tri-Services General Hospital during January 2016 to May 2020, which were scored by our visiting staffs with different levels from different departments. The medical records were scored ranged 0 to 10. All samples were divided into a training set (n = 74,959) and testing set (n = 152,730) based on time, which were used to train and validate the DLMs, respectively. The mean absolute error (MAE) was used to evaluate each DLM performance. In original AI medical record scoring, the predicted score by BERT architecture is closer to the actual reviewer score than the projection word embedding and LSTM architecture. The original MAE is 0.84 ± 0.27 using the BERT model, and the MAE is 1.00 ± 0.32 using the LSTM model. Linear mixed model can be used to improve the model performance, and the adjusted predicted score was closer compared to the original score. However, the project word embedding with the LSTM model (0.66 ± 0.39) provided better performance compared to BERT (0.70 ± 0.33) after linear mixed model enhancement (p < 0.001). In addition to comparing different architectures to score the medical records, this study further uses a mixed linear model to successfully adjust the AI medical record score to make it closer to the actual physician's score.
Creative and innovative potential of future education managers

The main purpose of the study is to characterize and analyze the characteristics of the creative and innovative potential of students and future education managers. The preparation of future education managers for professional development and creative activity in educational institutions is an urgent issue of higher education. Along with scientific qualifications in the activities of education managers, the professional component is becoming increasingly important, which needs constant updating, replenishment and improvement of psychological, pedagogical, managerial knowledge, skills and abilities, personal qualities, development of abilities and creative potential. In this regard, it is important to study the activities of the education manager and, on this basis, determine the requirements for the system of his professional training. As a result of the study, the features of the creative and innovative potential of students and future education managers were identified.
MODELING AND CALIBRATION OF UNCERTAINTY IN MATERIAL PROPERTIES OF ADDITIVELY MANUFACTURED COMPOSITES

Simulations quantifying the uncertainty in structural response and damage evolution require accurate representation of the randomness of the underlying material stiffness and strength behaviors. In this paper, the mean and variance descriptions of variability of strength and stiffness of additively manufactured composite specimens are augmented with random field correlation descriptors that represent the process dependence on the property heterogeneity through microstructure variations. Two correlation lengths and a rotation parameter are introduced into randomized stiffness and strength distribution fields to capture the local heterogeneities in the microstructure of Additively Manufactured (AM) composites. We formulated a simulation and Artificial Intelligence (AI)-based technique to calibrate the correlation length and rotation parameter measures from relatively few samples of experimentally obtained strain field observations using Digital Image Correlation (DIC). The neural networks used for calibrating the correlation lengths of Karhunen-Loève Expansion (KL expansion) from the DIC images are trained using simulated stiffness and strength fields that have known correlation coefficients. A virtual DIC filter is used to add the noise and artifacts from typical DIC analysis to the simulated strain fields. A Deep Neural Network (DNN), whose architecture is optimized using Efficient Neural Architecture Search (ENAS), is trained on 150,000 simulated DIC images. The trained DNN is then used for calibration of KL expansion correlation lengths for additively manufactured composite specimens. The AM composites are loaded in tension and DIC images of the strain fields are generated and presented to the DNNs, which produce the correlation coefficients for the random fields as outputs. Compared to classical optimization methods to calibrate model parameters iteratively, neural networks, once trained, efficiently and quickly predict parameters without the need for a robust simulator and optimization methods.
Transmission and Detection of Squeezed States of Light through an Optical Fiber with a Real-time True Local Oscillator

Transmission and accurate detection of squeezed states of light will play a central role in the future quantum internet, where quantum cryptography, communication and computation will coexist in an organic system. Their coherent detection has recently proven to be crucial for photonics-based quantum computing [1] , and to provide improvements in continuous variables quantum key distribution [2] [6] and quantum sensing [3] . Often when measuring a quantum state through coherent detection, the local oscillator field is transmitted along with the signal; however, in realistic long-distance optical fiber transmissions, where the needed local oscillator optical power increases exponentially with the distance, the system’s performance deteriorates due to nonlinear Brillouin scattering, and wavelength multiplexing schemes may be needed to avoid the issue. Moreover, transmitting a local oscillator opens security loopholes in quantum key distribution systems, where an eavesdropper can defy the security assessment from the trusted parties by gaining control over the local oscillator [4] . It is therefore very important to design coherent detection schemes that make use of independent local oscillators. We demonstrate the transmission single-mode squeezed light at 1550 nm wavelength through single-mode optical fibers (10 m and 10 km lengths) and its homodyne detection, using an independent local oscillator and a real-time phase-control system. Fig. 1a depicts the implemented physical setup: 1550 nm squeezed light is generated through spontaneous parametric downconversion [5] , couples into a single-mode optical fiber and propagates towards the homodyne detector for quadrature measurement. Real-time phase control consists in detecting the interference between a 40 MHz single-sideband pilot mode -phase-locked to the downconverted pump- and the local oscillator, estimating their instantaneous phase difference and implementing a fast and broad FPGA-based phase control on the local oscillator. As shown in Fig. 1b and 1c, with the presented scheme it was possible to steadily measure up to 3.6 dB of squeezing after a 10 m optical fiber transmission, and up to 1.5 dB of squeezing after a 10 km fiber transmission, with similar performance in terms of phase error and control stability. The presented system is simple, robust and cost-effective, and it will represent a standard class of techniques to detect not only squeezed states of light, but any arbitrary quantum state of light, including Schrödinger cat states, highly useful in quantum computation and error correction.
Detector Tomography of Superconducting-Nanowire Photon-Number-Resolving Detector

Photon number resolving detection, which means to discriminate the number of incident photons, is required technology in various fields such as optical quantum information processing, quantum communication, and quantum metrology. Especially in the field of optical quantum computing, a photon-number-resolving detector is thought to play an important role in preparation of non-Gaussian states [1] which are complex quantum states required for universal quantum computation and error correction.
Neural Network based Emotion Recognition Model in Twitter Text Data

Recently, the online social networks are rising as the new field for the individuals to share their perspectives and viewpoints on various issues and subjects with their companions, family, family members, and so on. Twitter being the well-known micro-blogging tools permits the people to share their musings, mental status on explicit social, public, worldwide issues through textual content, photographs, voice and video messages and posts. In reality, in spite of the accessibility of the different types of correspondence, text is one of the most well-known methods of communication in an informal community. In the tweets, the emotional articulation has an essential function in different impact of consistent communication. The emotion extraction from twitter sentiments is more troublesome particularly from multi-dialects messages. Therefore, in this research work a novel sentiment based emotion recognition model is introduced with five steps as follows: 1) Pre-processing, 2) Keyword extraction and Sentiment Categorization, 3) Semantic similarity score measurement, 4) Feature extraction and 5) Classification. Initially, the collected raw twitter data is subjected to pre-processing, where the tokenization, stemming and stop word removal is undergone. After pre-processing, the keywords are extracted and the similarity score is measured for each of the emotion based sentiments. Then, from the computed semantic similarity score, the differential holoentropy based features and weighted probability based holoentropy features are extracted. These extracted features are subjected to emotion detection (classification) by Neural Network (NN), which outputs the detected emotion. Finally, the proposed work is compared over the existing works in terms of positive, negative and other measures.
Attention-Guided Siamese Network for Clothes-Changing Person Re-identification

Person re-identification (re-id) has achieved significant progresses in recent years. However, the existing methods generally assume that the clothes of pedestrians remain unchanged throughout the surveillance periods, which is contradict to realistic environment where pedestrians may change their clothes. Current re-id techniques may encounter a dramatic performance degradation when the pedestrians change the clothes. In this paper, we propose a novel attention-guided siamese network (AGS-Net) to solve the cross-clothes re-id challenge. The AGS-Net integrates the visual and contour information together by developing a dual-branch structure, among which one extracts powerful features from raw inputs while the other learns robust features from the sketch image. Moreover, we exploit the attention modules to emphasize reliable identity-related features considering changing clothes and avoid generating features sensitive to clothes. Specifically, we propose a clothes-change invariant constraint to learn clothes-invariant features. Experimental results verify the effectiveness of our approach.
Can Law Keep Up?

Can law keep up with runaway technology? In an era of runaway corporate surveillance, artificial intelligence, deep fakes, election hacking, social media disinformation, do-it-yourself biology, genetic modification, automation, and three-dimensional printed medical implants, law has seemed to take a back seat to rampant technological change. To listen to Silicon Valley barons, there is nothing any of us can do about it.CAN LAW KEEP UP? calls their bluff. The book provides a fresh look at law, at what it actually is, how it works, and how we can create the kind of laws that help humans survive and thrive in the face of technological change. It shows that law can keep up with technology because law is a kind of technology – social technology built by humans out of cooperative fictions like firms, nations, or money. To survive the challenges of the future, we need a new kind of law, and a new way of understanding how humans use language to cooperate, to build an ever-growing and ever-changing system of understandings about how we can secure the benefits of changing technology to all.
Development of Smart English Guiding based on Internet Data Analysis under the Background of Internet

As a tool to communicate with the world, English has been recognized by more and more people. At present, “Internet +” has penetrated into every corner of our life, and English teaching has inevitably been greatly affected, both in terms of vocabulary and grammar. However, English guiding under the background of “Internet +” has not received enough attention, let alone sufficient research. The new idea and new goal of online guiding reform in the era of “Internet +” is to realize the perfect integration of information technology and English teaching, and to realize a new mode of teaching. This paper presents the novel system to achieve the smart online model combining the data mining technology. Finally, this research work simulates the proposed model on different database to test the performance.
Self-Supervised Real-to-Sim Scene Generation

Synthetic data is emerging as a promising solution to the scalability issue of supervised deep learning, especially when real data are difficult to acquire or hard to annotate. Synthetic data generation, however, can itself be prohibitively expensive when domain experts have to manually and painstakingly oversee the process. Moreover, neural networks trained on synthetic data often do not perform well on real data because of the domain gap. To solve these challenges, we propose Sim2SG, a self-supervised automatic scene generation technique for matching the distribution of real data. Importantly, Sim2SG does not require supervision from the real-world dataset, thus making it applicable in situations for which such annotations are difficult to obtain. Sim2SG is designed to bridge both the content and appearance gaps, by matching the content of real data, and by matching the features in the source and target domains. We select scene graph (SG) generation as the downstream task, due to the limited availability of labeled datasets. Experiments demonstrate significant improvements over leading baselines in reducing the domain gap both qualitatively and quantitatively, on several synthetic datasets as well as the real-world KITTI dataset.
ANALISIS KEMAMPUAN MEMBACA PEMAHAMAN BERBASIS DARING PADA SISWA SEKOLAH DASAR

This research is motivated by the low reading comprehension ablility of sudents. This study aims to find out how the online-based learning process for reading comprension skills is and how the online-based assesment process for reading comprehension skills is. As for this type of researsh using descriptive method. Data were collected by means of in-depth interviews and daocumentation. The subjects in this study were the fifth grade elementary scools students, totaling 30 students. Based on the description of the data obtained from discussion of the results of the research that has been carried out, it can be conckuded that the online-based reading comprehension ability of SDB Tunas Harapan students is in the suffictient category, altthogh there are some who are lacking in reading comprension ablility. The results of the reading comprehension test of students who met the KKM were 23 people, while for students who did not meet the KKM were 7 people, with an average score of 71,2. From the result of this study, the online based reading comprehension ability of SDN Tunas Harapan Students is in the suffiction category, for the highest score of students on this test is 90 while the lowest score is 25. Students who have not met the KKM are due to lack of cognitive ability, and do not concentrate when reading or reading. Do not understand the material that has been delivered by the teacher so that the ability of student to answer the question becomes difficult. Support from the teacher or the people around him in reading must always be there and done so that students feel motivated and interest in readinf better in order to add knowlodge and broad insight. Keyword: Reading Comprehension Ability, Online Learning
Continuous volcano seismic monitoring in two steps applied to the Chillan volcano

In this paper, we present a continuous volcano seismic analysis performed in two steps: 1) detection and 2) classification. Detection corresponds to the segmentation of a seismic event extracted from the raw signal and classification corresponds to the event labelling, according to the physical phenomena that generated it. Here the classification is performed using two methods: first the events are represented using their spectrograms and classified by a VGG neural network and second, the events are represented using the Qerlet representation and classified using the coefficient obtained by this representation. Detection is applied before classification, and it is adjusted using an empirical criterion that evaluates the number of false positive and false negative events, in a test dataset, as a function of the amplitude threshold. Higher values of the threshold improves the system specificity, while lower values give higher sensibility. This trade-off determines the performance of the systems when the automatic continuous classification is compared with the expert&apos;s classification. The seismic signals are obtained from the Nevados del Chillan Volcano located in the south of Chile. In this paper we optimally choose the amplitude threshold for continuous event detection that best fits with expert classification.
Impacts of Using Laptop at University Level- A Survey-Based Approach

A survey-based research study has been directed to reveal the impacts of using laptops at the university level. For this research, BS Mathematics and BS Physics students, both morning and evening sessions of Universities in South Punjab, Pakistan, were taken as a population. The population consists of 240 students and 10 teachers of four Universities in South Punjab. The sample of 112 students and10 teachers was taken by the Random sampling technique. Questionnaires and interviews were used as a research tool. The consequences of the research study discovered that most of the students used laptops for study purposes like making assignments on Microsoft word and presentations on PowerPoint. The results of teachers questionnaires revealed that most of the teachers used laptops for the preparation of lecturers and for different academic activities.