Reviewerly/exHarmony huggingface dataset:
A Critical Study on the Contributions of Training Program Application in the Success of Organization Development and Training Processes in Oman A Case Study of TRC

Most government sector in the region face the issue of lack of local training provided for the employees. Research shows the constant development in the workplace and the need to develop employee knowledge and experience through constant training. To provide an easy solution for sectors and institutions to communicate and present their needs when it comes to training programs and courses, this research aims to create a platform through mobile application to bring together government sectors and local teaching institutions to provide required training programs for employees. This is done by investigating on the issues of accessibility of the Training programs in Oman, evaluate the importance and impact of training programs on employee development, introduce a training program application and investigate on the usage benefits on the Training Program Application within government sectors. Since this research introduces a new concept, it asks: What are the issues facing organizations when it comes to training programs? To what extent does the application influence employee development? How complex is the development of a Training Program Application? And, how will government sectors benefit from the Application?&#x0D; Aside from the literature review, interviews were also made with local experts from government sectors to shed light on the effect of lack of training on the current employee situation. Online surveys were distributed to a group of employees from different departments in some local government sectors. Analysis of the responses shows the need and interest of employees on the issue of training, as the majority of employees have an interest in local courses and would use the application to benefit their work experience. The results indicate the Training Program Application affects positively on the government sectors and can provide all the needed training courses with less cost and influence employees.
L’impiego processuale del DNA fra giustizia genetica e garanzie costituzionali: quali sfide per il diritto (e per la Costituzione)

DNA analysis as a tool for investigations and definitive proof offers great opportunities for crimes’ resolution, but it also has several downsides, such as the possible infringement of individual rights, ranging from privacy to habeas corpus. Both legislators and judges are called to face new challenges, as the recent Italian experience, with the creation of the national DNA database (which fully came into operation in 2017) has taught us; in the past, such challenges saw the Constitutional Court as the legitimacy guarantor of laws pertaining forced sampling, able to oversee that personal freedom would remain intact.
LibGuides: General Information: Hours & Location

Contact and General Information about the Libraries (Staff, History, Mission, Hours, Employment, etc.)
Impression Space from Deep Template Network

It is an innate ability for humans to imagine something only according to their impression, without having to memorize all the details of what they have seen. In this work, we would like to demonstrate that a trained convolutional neural network also has the capability to "remember" its input images. To achieve this, we propose a simple but powerful framework to establish an {\emph{Impression Space}} upon an off-the-shelf pretrained network. This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression. In our framework, the impression space and image space are bridged by a layer-wise encoding and iterative decoding process. It turns out that the impression space indeed captures the salient features from images, and it can be directly applied to tasks such as unpaired image translation and image synthesis through impression matching without further network training. Furthermore, the impression naturally constructs a high-level common space for different data. Based on this, we propose a mechanism to model the data relations inside the impression space, which is able to reveal the feature similarity between images. Our code will be released.
Employing C++ Templates in the Design of a Computer Algebra Library

We discuss design aspects of the open-source Basic Polynomial Algebra Subprograms (BPAS) library. We build on standard C++11 template mechanisms to improve ease of use and accessibility. The BPAS computer algebra library looks to enable end-users to do work more easily and efficiently through optimized C code wrapped in an object-oriented and user-friendly C++ interface. Two key aspects of this interface to be discussed are the encoding of the algebraic hierarchy as a class hierarchy and a mechanism to support the combination of algebraic types as a new type. Existing libraries, if encoding the algebraic hierarchy at all, use runtime value checks to determine if two elements belong to the same ring for an incorrect false sense of type safety in an otherwise statically-typed language. On the contrary, our template metaprogramming mechanism provides true compile-time type safety and compile-time code generation. The details of this mechanism are transparent to end-users, providing a very natural interface for an end-user mathematician.
Meta-Active Learning in Probabilistically-Safe Optimization

Learning to control a safety-critical system with latent dynamics (e.g. for deep brain stimulation) requires judiciously taking calculated risks to gain information. We present a probabilistically-safe, meta-active learning approach to efficiently learn system dynamics and optimal configurations. The key to our approach is a novel integration of meta-learning and chance-constrained optimization in which we 1) meta-learn an LSTM-based embedding of the active learning sample history, 2) encode a deep learning-based acquisition function with this embedding into a mixed-integer linear program (MILP), and 3) solve the MILP to find the optimal action trajectory, trading off the predicted information gain from the acquisition function and the likelihood of safe control. We set a new state-of-the-art in active learning to control a high-dimensional system with latent dynamics, achieving a 46% increase in information gain and a 20% speedup in computation time. We then outperform baseline methods in learning the optimal parameter settings for deep brain stimulation in rats to enhance the rats’ performance on a cognitive task while safely avoiding unwanted side effects (i.e., triggering seizures).
Fully Dynamic Maximal Independent Set in Expected Poly-Log Update Time

In the fully dynamic maximal independent set (MIS) problem our goal is to maintain an MIS in a given graph $G$ while edges are inserted and deleted from the graph. The first non-trivial algorithm for this problem was presented by Assadi, Onak, Schieber, and Solomon [STOC 2018] who obtained a deterministic fully dynamic MIS with $O(m^{3/4})$ update time. Later, this was independently improved by Du and Zhang and by Gupta and Khan [arXiv 2018] to $\tilde{O}(m^{2/3})$ update time. Du and Zhang [arXiv 2018] also presented a randomized algorithm against an oblivious adversary with $\tilde{O}(\sqrt{m})$ update time. The current state of art is by Assadi, Onak, Schieber, and Solomon [SODA 2019] who obtained randomized algorithms against oblivious adversary with $\tilde{O}(\sqrt{n})$ and $\tilde{O}(m^{1/3})$ update times. In this paper, we propose a dynamic randomized algorithm against oblivious adversary with expected worst-case update time of $O(\log^4n)$. As a direct corollary, one can apply the black-box reduction from a recent work by Bernstein, Forster, and Henzinger [SODA 2019] to achieve $O(\log^6n)$ worst-case update time with high probability. This is the first dynamic MIS algorithm with very fast update time of poly-log.
"Why Fiction is Better than Reality": The Influence of Reading Fiction Narrative on Empathy ["Why Fiction is Better than Reality": Pengaruh Membaca Fiction Narrative terhadap Empati"

The fictional narrative is a form of literature often found in everyday life, such as in novels or short stories, comic strips in newspapers, and in poetry. Reading fictional literature has a positive effect, however previous research had not as yet shown any conclusive evidence that such reading increases empathy. This research was aimed at testing the influence of reading fictional narratives on empathy, mediated by narrative engagement. The method used in the research was an experimental one, with a pre-test/post-test control group design. 194 undergraduate students were actively involved as the research participants. The results of the analysis of data, using a Wilcoxon signed-rank test and a causal step approach indicated that reading a fictional narrative may significantly increase the empathy of the reader, and that the relationship of these two variables were partially mediated by narrative engagement. This research provided supportive data to the idea that fictional narratives may raise levels of empathy and that that influence is mediated by the variable of narrative engagement. The public is enjoined to read fictional narratives, such as novels, comics, short stories and poetry of a variety of genres, to experience an increase in empathy.&#x0D; &#x0D; Fiction narrative merupakan jenis bacaan yang sering ditemui dalam kehidupan sehari-hari, seperti buku novel atau cerita pendek, komik panel di surat kabar, maupun puisi. Membaca bacaan fiktif membawa dampak positif, akan tetapi penelitian terdahulu belum menunjukkan bukti yang konklusif bahwa membaca bacaan fiktif meningkatkan kemampuan empati. Penelitian ini bertujuan untuk menguji pengaruh membaca fiction narrative terhadap empati dengan dimediasi oleh narrative engagement. Metode yang digunakan dalam penelitian ini adalah eksperimen dengan pretest-posttest control group design. Terdapat 194 orang mahasiswa S1 aktif terlibat sebagai partisipan penelitian ini. Hasil analisis data dengan Wilcoxon signed-rank test dan causal step approach menunjukkan bahwa membaca fiction narrative dapat meningkatkan empati pembacanya secara signifikan, serta hubungan kedua variabel tersebut dimediasi secara parsial oleh narrative engagement. Penelitian ini menjadi dukungan data bahwa fiction narrative dapat meningkatkan empati serta pengaruh tersebut dimediasi variabel narrative engagement. Masyarakat disarankan untuk membaca bacaan fiction narrative seperti novel, komik, cerita pendek, maupun puisi dari berbagai genre agar mengalami peningkatan empati.
Risk Reduction Strategy and Risk Management on The Basis of Quality Assessments

The main issue of the study is the problem of improving the efficiency of risk management of the program. The article proposes a method for determining the quality of risk assessments of a program consisting of several projects. Risk management tasks are set and solved on the basis of risk reduction and risk avoidance strategies. The aim of the study is a systematic integrated approach to the formulation and solution of tasks to improve the efficiency of the risk management program, which allows you to objectively, fully and accurately assess the relationship of economic and technical indicators. Qualitative risk assessment can be carried out by a common method of analogy, the essence of which is to analyse the totality of data on similar projects. Here they conduct a study of impact of adverse factors on them in order to accurately determine the potential risk of new projects. The main difficulty of using the methods is the exact and correct choice of analogues, since there are no formal criteria for determining the degree of similarity of situations. All aggregate socially significant economic indicators of operational measures can become the basis of legislative regulation in the industry. Conclusions: the strategy of risk reduction is that activities are carried out that reduce either the probability or damage or both from medium to low, from high to medium or low.
LSQT: Low-Stretch Quasi-Trees for Bundling and Layout

We introduce low-stretch trees to the visualization community with LSQT, our novel technique that uses quasi-trees for both layout and edge bundling. Our method offers strong computational speed and complexity guarantees by leveraging the convenient properties of low-stretch trees, which accurately reflect the topological structure of arbitrary graphs with superior fidelity compared to arbitrary spanning trees. Low-stretch quasi-trees also have provable sparseness guarantees, providing algorithmic support for aggressive de-cluttering of hairball graphs. LSQT does not rely on previously computed vertex positions and computes bundles based on topological structure before any geometric layout occurs. Edge bundles are computed efficiently and stored in an explicit data structure that supports sophisticated visual encoding and interaction techniques, including dynamic layout adjustment and interactive bundle querying. Our unoptimized implementation handles graphs of over 100,000 edges in eight seconds, providing substantially higher performance than previous approaches.
PERBANDINGAN HASIL BELAJAR MATEMATIKA SISWA KELAS IV YANG MENGIKUTI DENGAN YANG TIDAK MENGIKUTI BIMBINGAN BELAJAR

The purpose of this study is to compare the learning outcomes of fourth grade students in elementary schools who take tutoring with students who do not take tutoring and find out the motivation of students to follow guidance. Categories of student learning outcomes with the motivation of students who take tutoring are evaluated from the very low, low, medium, high, and very high categories. The method used in this research is quantitative descriptive. This research was conducted on 98 students from a public elementary school in Pekanbaru. The instruments used to collect data are objective test questions on the material of flat shapes, statistics and angle measurements as well as questionnaires about students' motivation to follow tutoring. The results of this study indicate that the learning outcomes of students who take tutoring are better than students who do not follow tutoring, and the motivation of students who take tutoring is motivated because students' desire to obtain learning outcomes is increased and better understand the science of mathematics learned. The implication of this research is that parents need to consider their children to attend tutoring and schools need to think and design learning that can improve student learning outcomes.
NASA RTLX as a Novel Assessment Tool for Determining Cognitive Load and User Acceptance of Expert and User-based UsabilityEvaluation Methods

Background: Mobile health applications are frequently used to manage different health conditions. Diabetes is one disease where these are used in patients’ personalized disease self-management, but where the usability is often deficient. Two methods to assess usability are the cognitive walkthrough (CW), which is expert-based and the think aloud (TA) which is userbased. Both offer advantages and disadvantages and detect many types of usability problems affecting the user. There is a lack of research, however, on how the usability evaluators themselves experience performing these methods and the method impact. This can be an important aspect to include due to its possible implications for the evaluation.Objectives: In this article the focus was particularly on assessing the evaluators’ cognitive load and method acceptance while performing the described methods.Methods: In addition to the number of usability problems, and the system usability scores (SUS), the NASA RTLX instrument, novel for this purpose, was used together with in-depth interviews to assess the usability methods’ cognitive impact. Results: A total of 12 evaluators, six per method, detected 18 usability problems with the CW. Twenty were found with the TA. The SUS scores were 23.75 and 59.58 respectively. For both methods, users experienced a similarly high cognitive load with a RTLX score of 56.11 for CW and 53.47 for TA. According to the evaluators, both methods were cognitively demanding.Conclusion: The results highlight the potential significance of these dimensions for inclusion in the usability evaluation and in decision-making purposes between different usability evaluation methods.
Pedagogical basis of professional oriented technologies

The relevance of the study is also determined by the desire to prepare the future teacher, not only as a specialist in a particular subject area, but also as a person with the necessary competencies for organizing a developing educational environment: In the new socio-economic conditions, the requirements for professional competence, flexibility, and creativity have increased future specialists. The success of the activities of educational institutions depends largely on the professional and personal qualities of the teacher, his moral and intellectual potential, as well as the creative search for solving pedagogical problems. The implementation of modernization processes * of education requires updating the personal and professional potential of teachers in the development of new teaching technologies.
Image Clustering by Generative Adversarial Optimization and Advanced Clustering Criteria

Clustering is the task that has been used in numerous applications including digital image analysis and processing. Image clustering refers to the problem of segmenting image for different purposes which leads to various clustering criteria. Finding the optimal clusters represented by their centers is a hard optimization problem and it is one of the main research focuses on clustering methods. In this paper we proposed a novel generative adversarial optimization algorithm for finding the optimal cluster centers while using standard and advance clustering criteria. The proposed method was tested on seven benchmark images and results were compared with the artificial bee colony, particle swarm optimization and genetic algorithm. Based on the obtained results, the generative adversarial optimization algorithm founded better cluster centers for image clustering compared to named methods from the literature.
지역기업에 대한 정부 R&D 지원의 효과분석(2002-2016)

With acceleration of global competition and expansion of the production network where capital and labor move freely, competitiveness at a regional level, rather than as a country, is becoming an immediate national challenge. In addition, with the spread of the innovation-driven economy around the world, creation and fusion of knowledge and new technology are being applied through rapid and flexible cooperation across industries at a regional level. Recognizing that regional competitiveness is the competitiveness of the country, South Korea Government has promoted the regional industry policy since the 1990s. In 2008, the government implemented the promotion program for regional strategic industry(Post 4+9), starting with the promotion policies of innovative regional industries in 1999. In 2009, the leading industries development project in the mega-regional economy was promoted for strengthening global competitiveness of the regions; the economic cooperation industry promotion business has been promoted that can set the region through autonomous consultation between the cities and provinces since 2015. In accordance with the direction of the government, regional policies have been changed in various ways, and the spatial range has also changed from the level of cities and provinces to the one of metropolitan scale, returning to the level of cities and provinces. Research and development have been of great importance as support measures have changed from infrastructure to software-driven. In particular, R&D can be the basis for the government intervention because the investment can reach below the social optimal level as R&D has the externality, thereby playing a role of reducing the probability of market failure. However, there have been various discussions on achievements of R&D support: there was a precedent research presenting that government R&D investment plays an important role in the management of productivity and innovation level of companies. Based on these discussions, this study aims to find out the performance of regional R&D projects of national R&D projects by investigating companies that have participated in regional R&D projects to find out whether their productivity, growth, and profitability improved. For the analysis, corporate financial information panel data(2002∼2016) for 15 years of 1,493 companies were used. In addition to the growth rate analysis, statistical significance was verified by comparing the means between the groups by indicator and year.
Reconstruction in one dimension from unlabeled Euclidean lengths

Let $G$ be a $3$-connected graph with $n$ vertices and $m$ edges. Let $\mathbf{p}$ be a randomly chosen mapping of these $n$ vertices to the integer range $[1..2^b]$ for $b\ge m^2$. Let $\mathbf{l}$ be the vector of $m$ Euclidean lengths of $G$'s edges under $\mathbf{p}$. In this paper, we show that, WHP over $\mathbf{p}$, we can efficiently reconstruct both $G$ and $\mathbf{p}$ from $\mathbf{l}$. In contrast to this average case complexity, this reconstruction problem is NP-HARD in the worst case. In fact, even the labeled version of this problem (reconstructing $\mathbf{p}$ given both $G$ and $\mathbf{l}$) is NP-HARD. We also show that our results stand in the presence of small amounts of error in $\mathbf{l}$, and in the real setting with approximate length measurements. Our method is based on older ideas that apply lattice reduction to solve certain SUBSET-SUM problems, WHP. We also rely on an algorithm of Seymour that can efficiently reconstruct a graph given an independence oracle for its matroid.
Challenges to the Assessment of Damages Claims Involving Crypto-Assets in Investment Arbitration

Abstract This article addresses the particular challenges involved in valuing various types of crypto-assets as investments under treaty-based investment arbitration. The interaction of the international investment protection regime with crypto-investments has largely remained un-examined, even though increasing amounts of foreign investments have flown into the development of crypto-assets its related markets. The existing investment treaty regime is set to face significant challenges in grappling with crypto-assets as investments due to several distinct features that differentiate them from traditional asset classes. This issue is explored further in the article from the perspective of the principles governing damages under international law.
OpenDP Platform for Differential Privacy

Differential privacy is a formal, mathematical conception of privacy preservation. An algorithm is differentially private when it injects a precisely calculated quantity of noise to any statistical query, masking the possible contribution of any one individual to the result. This provides a gold standard definition of privacy protection for data scientists who want to analyze data that contains personal information that must remain private. The open source project provides several basic building blocks that can be used by people involved with sensitive data, with implementations based on vetted and mature differential privacy research. It aims to connect theoretical solutions from the academic community with practical lessons learned from real-world deployments and to make differential privacy broadly accessible to future deployments. The core library is a native runtime that is built in Rust to be memory safe and fast. It can be used to safely build differentially private releases from native code running on various devices. It has a SQL data access layer that allows users to compose analysis graphs using the SQL language. The data access layer supports a wide variety of SQL database engines. To ease deployment, it includes a sample hosted service that shows users how to compose heterogeneous queries over the same dataset, fronted by a REST-based endpoint.