Reviewerly/exHarmony huggingface dataset:
Actualization of Distance Learning in Comparison with Traditional Forms of Education in Conditions of Quarantine Security Measures

Currently, in the context of severe quarantine restrictions, one of the promising areas of the formation and development of the higher education system is the problem of its comprehensive informatization, which is based, first of all, on improving the information and educational environment for learning and the development of distance learning. Therefore, serious attention is paid to the development and implementation of modern information technologies in teaching practice. All this fully applies to the development of distance learning, which allows each person to improve their educational level with a minimum interruption from production, in their free time and according to an individual training program. Taking this into account, at the present stage of reforming the higher education system in the world, it is becoming more and more difficult to look for effective directions for training highly qualified specialists using traditional teaching aids, to respond promptly and promptly to the emerging needs of the labor market without understanding a large amount of relevant and accurate information. The article carried out a theoretical and methodological study of the very essence of distance education, and also investigated the main methods for assessing this type of education in a pandemic situation. The novelty of the article lies in the acquisition of the development of theoretical and methodological aspects of methods for assessing distance education in COVID-19 conditions.
Experimentally optimizing QKD rates via nonlocal dispersion compensation

Detection-time uncertainty due to chromatic dispersion in optical fibers decreases key rates of Quantum Key Distribution (QKD). Using nonlocal dispersion compensation, we demonstrate a 37-fold key rate increase of an entanglement-based QKD system.
Estudo de usuários do sistema de bibliotecas da universidade Federal de Santa Maria (Brasil)

This text presents the results of a study that sought to analyze the perceptions of the library system’s usersat the Federal University of Santa Maria, concerning the services and spaces offered by them. A case study was carried out anddata collection was donethrough an online questionnaire, sent to UFSM students, teachers and administrative technicians. The results showed that users are unaware of most of the spaces and services offered by libraries, which may indicate, both the under-utilization of many of them andthe convenience of the need ofa communication plan for dissemination and better guidance for users.
HUBUNGAN TINGKAT SOSIAL EKONOMI ORANGTUA DENGAN KEMANDIRIAN BELAJAR SISWA KELAS X SMA NEGERI 1 BINJAI KABUPATEN LANGKAT TAHUN PELAJARAN 2017/2018

The purpose of this study is to determine the relationship of socio-economic level of parents with learning independence students of Class X SMA Negeri 1 Binjai Kabupaten Langkat Lesson Year 2017/2018, with a population of 252 students, consists of 7 classes a sample is 49. This type of research used in this research is quantitative Correlation method. The instrument used is to spread the scale of the entire sample as inputs to determine the actual situation in the SMA Negeri 1 Binjai Kabupaten Langkat, and analysis of data using Product Moment Correlation test. From the test results hypothesis that the value r arithmetic> r table is 0.895> 0.281, so it is taken a conclusion that there is a relationship Level of socio-economic parent with independence Learning Students class X SMA Negeri 1 Binjai Kabupaten Langkat Lesson Year 2017/2018. Suggestions for future researchers to better pay attention to other psychological aspects of a student, such as self-control, emotional intelligence, while also considering more effective research time, and need to improve the quality of cooperation with parents even though the intensity of meeting and distance with the parents of students to be a bit of a constraint so that the problems faced by students is not just a responsibility of the school. Keywords: Level of Socio-Economic Parents, Learning Independence
Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes ~28µs.
LibGuides: HACC Testing: CLEP Scores

HACC's Testing Center serves students, faculty, and community members by providing a secure and confidential testing environment for academic, personal, and career advancement.
LibGuides: ScholarCheck: ScholarRank

A scholarly analysis tool to help users learn more about documents, titles, and authors and their impact in the research community. Learn about HeinOnline's author ranking system, developed from our ScholarCheck tools.
Binary Descriptors for Dense Stereo Matching

Dense local stereo matching is traditionally based on initial cost evaluation using a simple metric Dense local stereo matching is traditionally based on initial cost evaluation using a simple metric followed by sophisticated support aggregation. There is a high potential of replacing these simple metrics by robust binary descriptors. However, the available studies focus on comparing descriptors for sparse matching rather than the dense case of extracting a descriptor per each pixel. Therefore, this paper studies the design decisions of well-established binary descriptors such as BRIEF (Binary Robust Independent Elementary Features), ORB (Oriented FAST and rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints) and FREAK (Fast Retina Keypoint) to decide which one is more suitable for the dense matching case. The expremental results shows that agregation is required for use with binary descriptors to handle edges. Also, BRIEF produced the smoothnest disparity map if geometric transformations is not present. Whereas, FREAK and BRISK achieved the least overall error percentage across all regions. The lastest Middlebury Stereo benchmark is utilized in the experiments.
Supervised Machine Learning Techniques for Sentiment Analysis and its Application in Image Processing and Remote Sensing

As we see the growing social media transparency, where users want to express their opinions we also see a growing need of Sentiment analysis (SA) or Opinion mining. Sentiment Analysis is a process used to interpret user sentiments related to a specific topic. It is a field of study which is formed at the junction of Natural Language Processing (NLP), Machine learning (ML) and Information retrieval (IR). In this paper we will see various types of machine learning (ML) techniques that come under supervised learning. We will be discussing 3 techniques under the domain of Supervised ML, namely,  SVM, Naive Bayes and Maximum Entropy, which are studied and established to be the most accurate. We will see how supervised ML techniques can be applied in Image processing and Remote sensing as well.
Digital Literation and Development Emotional Social Skills at Elementary School Students in The Social Era 5.0

The change in the educational paradigm in facing the social era 5.0 inacademic units is to prepare superior human resources (teachers). The teacher actsas a learning material provider and an inspiration for the development of studentcreativity. Teachers must face two things in the social era: technology adaptationand competence, namely 21st-century life skills, namely having leadership skills,digital literacy, communication, emotional intelligence, entrepreneurship, globalcitizenship, teamwork, and problem-solving. Creativity, critical thinking,communication, and collaboration). Meanwhile, the competencies that studentsmust-have in the 21st century consist of 6 basic literacy skills (numeracy literacy,scientific literacy, information literacy, financial literacy, cultural literacy andcitizenship). In addition, students must also be able to think critically, reason, becreative, communicate, collaborate and have problem-solving skills. The sixabilities are part of digital literacy that must be understood and understood to notdeviate from existing cultural norms. Therefore, students‘ social-emotional abilitiesin facing the challenges of the 5.0 social -era need to be honed and developed so thatthey do not fall into negative things. At the social-emotional development stage,education is an essential dimension in children‘s development because elementaryschool-age brings many changes in children‘s social and emotional lives
LibGuides: Data Science Technology: Home

This guide shares information and materials for Data Science Technology, which is a new A.S. degree program offered at FSCJ.
LibGuides: Electronic Resources: What's New

This is an information guide about the Electronic Resources at NOVA Libraries. For more information, contact LTS@nvcc.edu
Induced Plithogenic Cognitive Maps with Combined Connection Matrix to investigate the glitches of online learning system

Plithogenic Cognitive Maps (PCM) introduced by Nivetha and Smarandache are extensively applied in decision making. This research work extends PCM to Induced PCM by introducing the concept of combined connection matrix (CCM). The proposed induced PCM decision making model with CCM is applied to examine the glitches of online learning system. It was observed that expert’s opinion on the associational impact between the factors considered for study in the form combined connection matrix is more advantageous on comparison with conventional connection matrix representation, as CCM is a mixture of crisp/fuzzy/intuitionistic/neutrosophic representations. The proposed model will certainly facilitate the decision makers in designing optimal solutions to the real time problems and it shall be extended based on the needs of the decision makers and employed in various other decision making environment. The shortcomings of the model are also discussed in brief.
Planning from Pixels in Environments with Combinatorially Hard Search Spaces

The ability to form complex plans based on raw visual input is a litmus test for current capabilities of artificial intelligence, as it requires a seamless combination of visual processing and abstract algorithmic execution, two traditionally separate areas of computer science. A recent surge of interest in this field brought advances that yield good performance in tasks ranging from arcade games to continuous control; these methods however do not come without significant issues, such as limited generalization capabilities and difficulties when dealing with combinatorially hard planning instances. Our contribution is two-fold: (i) we present a method that learns to represent its environment as a latent graph and leverages state reidentification to reduce the complexity of finding a good policy from exponential to linear (ii) we introduce a set of lightweight environments with an underlying discrete combinatorial structure in which planning is challenging even for humans. Moreover, we show that our methods achieves strong empirical generalization to variations in the environment, even across highly disadvantaged regimes, such as one-shot planning, or in an offline RL paradigm which only provides low-quality trajectories.
LibGuides: Library Services Platform (LSP): About Us

Website for the systemwide Library Services Platform Learn about the LSP, it's history, mission, and California Community College members
Grafs: declarative graph analytics

Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specification language for graph analytics and a synthesizer that automatically generates efficient code for five high-performance graph processing frameworks. It features novel semantics-preserving fusion transformations that optimize the specifications and reduce them to three primitives: reduction over paths, mapping over vertices and reduction over vertices. Reductions over paths are commonly calculated based on push or pull models that iteratively apply kernel functions at the vertices. This paper presents conditions, parametric in terms of the kernel functions, for the correctness and termination of the iterative models, and uses these conditions as specifications to automatically synthesize the kernel functions. Experimental results show that the generated code matches or outperforms handwritten code, and that fusion accelerates execution.
The Real Deal: Return Policies against Review Manipulation

Review manipulation is pervasive on e-platforms. Opportunistic sellers boost sales by manipulating reviews and inflating perceived product qualities. Such immoral behavior ruins market fairness and efficiency, harming social welfare. Though various technologies have been developed to detect fake reviews, review manipulation remains rampant due to the lack of economic incentives from a platform’s perspective (i.e., costs of fake-review-detection technologies and potential loss of commissions). Unlike extant literature focusing on developing advanced algorithms, we take another route to explore economic incentives via return policies. We craft a game theory-based model, endogenizing a platform’s return policy and sellers’ manipulation efforts and pricing, given heterogeneous consumers. Our results show that a full-refund policy is a powerful device to alter sellers’ misbehaviors. Yet, the policy could be a double-edged sword that either inhibits or enhances review manipulation, depending on the severity of sellers’ competition. We further identify a return-manipulation paradox – the platform is more willing to choose the policy (either full-refund or no-return) that encourages manipulation the most. In other words, the platform would hurt the welfare of sellers and consumers while maximizing its profit. To resolve the paradox, we investigate the autonomous return scheme wherein sellers are delegated to make return policies. The analyses suggest that the alternative scheme effectively lowers overall manipulation in the ecosystem and increases social welfare, compared to the platform’s dictatorial return policy (i.e., the centralized scheme). The analytical results are translated into executable actions to consumers, sellers, and platforms for healthy, sustainable e-markets.
The Use of Social Media by Managers During the Pandemic to Accomplish the Tasks

Since the outset of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), commonly referred to as the “COVID-19” pandemic, people spend more time online. The pandemic has spurred growth in the use of social media. Organizations now train employees to use digital media to coordinate work and connect from home. The study aims to produce effective discussion regarding the importance of social media and how it can assist in the recent times of Covid-19 regarding job performance. Research predicts the new ways of business conducted and how communication and coordination, knowledge exchange, training, and development are helpful in carryout effective job productivity at the workplace. We analyzed responses of more than 800 managers who reported on “go-to” social media tools and remote office practices. The research employed quantitative methodology (i.e., emailed questionnaires) and found that social media has become an important tool for conducting business operations due to social distancing and isolation. As per the results, it is clear that the utilization of social media allows Saudi Arabia's management to improve the training and development and communication and coordination among the employees while also being perceived as a highly useful tool. Therefore, we recommend that researchers conduct studies in inter-organizational and cross-national settings. Looking ahead, organizations will continue to engage in practices that ensure personal safety amid a complex web of new requirements and processes, as understanding and treatment of COVID-19 and ongoing outbreaks continues to affect and minutely transform the current work-life balance.
Archeologie voor snackers, ondiepe gravers en diepgravers: de archeoloog als verhalenverteller

Archaeology for ‘paddlers’, ‘swimmers’ and ‘divers’. This article gives a brief outline of the numerous ways in which people can get involved in archaeology. The most important message is the idea that archaeology revolves around ‘storytelling’; the archaeologist is a person who tells stories about the past. This approach can be used to appeal to three target groups: ‘paddlers’, ‘swimmers’ and ‘divers’. The article is written by a director of the Hunebedcentrum, a museum which has more than 50 years’ experience of telling stories to a wide public. It is an example of ‘archaeology for all,’ an approach that can also be applied to other activities in which archaeology has a role to play. The world has changed rapidly over the past 25 years. New media, such as smartphones, drones, audio-visual equipment and other technology, mean that the world can be experienced in a completely different way today than it could in the past. There are wonderful opportunities available to those who are receptive to new ideas. A subject such as archaeology, which stimulates people’s imagination, is particularly suitable in this context.
A Hierarchy of Nondeterminism

We study three levels in a hierarchy of nondeterminism: A nondeterministic automaton A is determinizable by pruning (DBP) if we can obtain a deterministic automaton equivalent to A by removing some of its transitions. Then, A is good-for-games (GFG) if its nondeterministic choices can be resolved in a way that only depends on the past. Finally, A is semantically deterministic (SD) if different nondeterministic choices in A lead to equivalent states. Some applications of automata in formal methods require deterministic automata, yet in fact can use automata with some level of nondeterminism. For example, DBP automata are useful in the analysis of online algorithms, and GFG automata are useful in synthesis and control. For automata on finite words, the three levels in the hierarchy coincide. We study the hierarchy for Buchi, co-Buchi, and weak automata on infinite words. We show that the hierarchy is strict, study the expressive power of the different levels in it, as well as the complexity of deciding the membership of a language in a given level. Finally, we describe a probability-based analysis of the hierarchy, which relates the level of nondeterminism with the probability that a random run on a word in the language is accepting.
LibGuides: LSC 595 Summer 2021 Merchant Professional Field Experience: GSLIS Outcomes and the Site Agreement

This guide is created to document my PFE during Summer 2021 at the Winchester Public Library, MA
Multi-block ADMM Heuristics for Mixed-Binary Optimization on Classical and Quantum Computers

Solving combinatorial optimization problems on current noisy quantum devices is currently being advocated for (and restricted to) binary polynomial optimization with equality constraints via quantum heuristic approaches. This is achieved using, e.g., the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA). We present a decomposition-based approach to extend the applicability of current approaches to quadratic plus mixed binary optimization (MBO) problems, so as to solve a broad class of real-world optimization problems. In the MBO framework, we show that the alternating direction method of multipliers (ADMM) can split the MBO into a binary unconstrained problem (that can be solved with quantum algorithms), and continuous constrained convex subproblems (that can be solved cheaply with classical optimization solvers). The validity of the approach is then showcased by numerical results obtained on several optimization problems via simulations with VQE and QAOA on the quantum circuits implemented in Qiskit, an open-source quantum computing software development framework.
Universal Sentence Representations Learning with Conditional Masked Language Model

This paper presents a novel training method, Conditional Masked Language Modeling (CMLM), to effectively learn sentence representations on large scale unlabeled corpora. CMLM integrates sentence representation learning into MLM training by conditioning on the encoded vectors of adjacent sentences. Our English CMLM model achieves state-of-the-art performance on SentEval, even outperforming models learned using (semi-)supervised signals. As a fully unsupervised learning method, CMLM can be conveniently extended to a broad range of languages and domains. We find that a multilingual CMLM model co-trained with bitext retrieval~(BR) and natural language inference~(NLI) tasks outperforms the previous state-of-the-art multilingual models by a large margin. We explore the same language bias of the learned representations, and propose a principle component based approach to remove the language identifying information from the representation while still retaining sentence semantics.
Analisis Potensi Genangan Tsunami dan Penentuan Jalur Evakuasi Berbasis Sistem Informasi Geografis di Desa Daruba Pantai – Kabupaten Pulau Morotai

Tsunami is one of the catastrophic events that threatened coastal areas in Morotai Island Regency. This is partly because the location of the regency is among several active faults. Therefore, the study aims to analyze the tsunami inundation area and determine a tsunami evacuation route in Daruba Pantai Village, South Morotai District. This study begins with the preparation of a digital elevation model and is compiled with other related maps to produce the tsunami inundation area at the study site. The study results show that a tsunami can hit 176.65 ha, with the disastrous and dangerous category being 5.02% and 0.66%. Meanwhile, specifically for the Daruba Pantai Village area with an area of 60.59 ha, the disastrous and dangerous categories are 10.72% and 1.30%, and the safe category is 36.61% and secure 23.07%. Furthermore, the preparation of the evacuation route is based on the scenario that residents from each zone of both the Neighborhood Unit (RT) and Community Unit (RW) are directed to follow the evacuation path to move towards the temporary shelter point and towards the safety zone.
The role of libraries in improving the quality of research and dissertation work

The article examines the importance of using information resources in scientific and educational activities and their impact on improving the quality of scientific research and dissertations. The role of information and library institutions in organizing online access to scientific information, completing the collection based on the analysis of user needs is shown. The author also touches upon the actual problem of plagiarism in science and provides information on the Russian system for detecting text borrowings Antiplagiat, which most closely meets the needs of scientific and educational institutions of Uzbekistan
LibGuides: How do I access the TUT Library and Information Services Electronic Resources: DISCOVERY TOOLS & SERVICES

A step-by-step guide on how to access the TUT Library and Information Services Electronic Resources This page will provide more information about Discovery Tools and Services and where to access the TUT LIS Discovery Service