Reviewerly/exHarmony huggingface dataset:
Perancangan Single Sign On Dengan Smart Card Menggunakan Active Directory Pada Politeknik Caltex Riau

The use of information technology has become a necessity to support the smooth process of work and learning, one of which is at the Caltex Riau Polytechnic (PCR). PCR has various systems and websites that can be used by users. In general, these systems and websites are developed by different developers and have their own login authentication system. Single sign on is an authentication system for users that with just one login the user can access several websites without having to log back into other websites. Smart cards are used as a means of easy login for users. In this study, designing and implementing single sign on authentication with a smart card using the samba active directory. Based on the results of functionality testing, the average user success in logging in windows, wordpress and email in single sign on using a smart card is 100%. Based on the results of performance tests that have been carried out, the average server CPU usage is 35% and server memory usage is 22% in the 5 user tests. Client login requests and user authentication processes affect CPU usage. While the percentage of memory usage is stable because there is no user activity that burdened memory.
Racial Filtering Classification Model Through Data Analysis of Racial Contents in Twitter

Stop Asian Hate or Stop Asian American Pacific Islanders (AAPI) Hate refers to the national movement against racially-motivated attacks on Asians. This protest was initiated in line with the Black Lives Matter (BLM) movement, to dismantle the ongoing hate and targeted crimes against Asians, and to educate people of such threats. Hate crimes targeting Asians have been occurring steadily across the U.S, but with the effect of COVID-19, these crimes started increasing in number. For the Stop Asian Hate movement, the matter was exacerbated with people accusing certain Asian countries as the source for COVID-19. In 2021, Asian Americans reported a single biggest increase in serious incidents of online hate and harassment with racist and xenophobic slurs blaming people of Asian descent for the spread of COVID-19. To specifically assess the impacts and measures of each movement, research was conducted to examine the racial slurs used towards Asians on social media, specifically Twitter. For analysis of the data on social media, Python programming was used to collect and analyze the ratio of racial slurs and Anti-Asian hate. In doing so, the data set was modeled through data labeling, which classified each social media tweet into one of three sub-categories. Data were classified into two types: type 1 that contains racial contents or information against Asians and type 0 that has non-racial contents. The data collection was done through Twint, a Python scraping tool for Twitter, gathering a total of over 2,000 recent tweets for keywords relevant to the movement. Then, a preprocessing step was taken through Python, involving the process of decapitalizing, lemmatizing, and tokenizing. These data were then represented by graphs and word clouds, displaying some of the most commonly used terms targeting Asians on social media. Lastly, the data went through a design of a binary classification model for filtering tweets with racial content. We compared the accuracy of classification models with three different algorithms: logistic regression, random forest, and SVM. The model created would be able to safeguard users from exposures to racist terms vastly pervaded on the internet.
Dynamics-regulated kinematic policy for egocentric pose estimation

We propose a method for object-aware 3D egocentric pose estimation that tightly integrates kinematics modeling, dynamics modeling, and scene object information. Unlike prior kinematics or dynamics-based approaches where the two components are used disjointly, we synergize the two approaches via dynamics-regulated training. At each timestep, a kinematic model is used to provide a target pose using video evidence and simulation state. Then, a prelearned dynamics model attempts to mimic the kinematic pose in a physics simulator. By comparing the pose instructed by the kinematic model against the pose generated by the dynamics model, we can use their misalignment to further improve the kinematic model. By factoring in the 6DoF pose of objects (e.g., chairs, boxes) in the scene, we demonstrate for the first time, the ability to estimate physically-plausible 3D human-object interactions using a single wearable camera. We evaluate our egocentric pose estimation method in both controlled laboratory settings and real-world scenarios.
Efficient Data Origin Authentication Scheme for Video Streaming Transmitted by Multiple Senders

This study proposes a scheme to authenticate real-time streaming data and verify the integrity of the transmitted data in a service application with multiple senders or receivers. This data origin authentication scheme provides advantages in performance compared with TESLA, a multicasting data origin authentication scheme widely applied in conventional authentication techniques. That is, it can authenticate rapidly received data, even if resource-restricted devices, such as CCTVs and drones, are equipped with fewer computing resources and memory.
Data driven semi-supervised learning

We consider a novel data driven approach for designing learning algorithms that can effectively learn with only a small number of labeled examples. This is crucial for modern machine learning applications where labels are scarce or expensive to obtain. We focus on graph-based techniques, where the unlabeled examples are connected in a graph under the implicit assumption that similar nodes likely have similar labels. Over the past decades, several elegant graph-based semi-supervised learning algorithms for how to infer the labels of the unlabeled examples given the graph and a few labeled examples have been proposed. However, the problem of how to create the graph (which impacts the practical usefulness of these methods significantly) has been relegated to domain-specific art and heuristics and no general principles have been proposed. In this work we present a novel data driven approach for learning the graph and provide strong formal guarantees in both the distributional and online learning formalizations. We show how to leverage problem instances coming from an underlying problem domain to learn the graph hyperparameters from commonly used parametric families of graphs that perform well on new instances coming from the same domain. We obtain low regret and efficient algorithms in the online setting, and generalization guarantees in the distributional setting. We also show how to combine several very different similarity metrics and learn multiple hyperparameters, providing general techniques to apply to large classes of problems. We expect some of the tools and techniques we develop along the way to be of interest beyond semi-supervised learning, for data driven algorithms for combinatorial problems more generally.
Formación docente como eje integrador de las funciones sustantivas en la Universidad Técnica de Manabí

Education is a process of constant change, in which the teacher must be at the level of the new academic and professional challenges that arise in the educational field. For this reason, teacher training is essential to enrich and update the knowledge, skills and attitudes required by educational practice, with the purpose that it is enriching for students. The present work aims to establish the relevance of teacher training as a strategic axis for the integration of substantive university functions, through the analysis of national and foreign research experiences. For the methodological approach in this work, theoretical and empirical methods have been used, such as analysis and documentary synthesis, which through it establishes the bases and the importance of an adequate and planned training of university teachers. In conclusion, teacher training provides teachers with a number of innovative tools that transform traditional teaching into a process of collective construction of learning.
EFFECTS OF AGENCY BANKING ON OPERATIONAL EFFICIENCY OF COMMERCIAL BANKS IN KENYA

The core objective of this study was to determine the relationship between agency banking and the operational efficiency of commercial banks in Kenya. The target population comprised of 15 commercial banks that utilized agency banking. The study covered the period 2014-2018 and thus the findings fundamentally reflect the events and conditions that happened during that period. Various diagnostic tests were done on the data to test normality, multicollinearity and heteroscedasticity. The data was analyzed using descriptive statistics and panel data analysis. The results revealed that agency banking has a significant effect on operational efficiency. It was found that agency banking affected the operating efficiency. The study recommends that the banks should design a digital strategy anchored on strong knowledge of their digital maturity, business operating models, flexibility and human capital. The banks should also consider fostering partnerships with technology start-ups or other third-party relationships in order to maximize the benefits of agency banking innovations. Keywords: Agency Banking, Operational Efficiency
Measuring Relative Efficiency of Physical Education Departments in Tehran using Data Envelopment Analysis

Background: Despite significant advances in the design of performance appraisal frameworks ‎and systems in recent years, many organizations still rely on traditional metrics. Purpose: The ‎present study aimed to measure the relative efficiency of physical education departments in ‎Tehran through a data envelopment analysis approach. Method: In this research, in addition to ‎identifying and determining the input and output indices of regions, an appropriate model of ‎data envelopment analysis was presented to evaluate their performance, as well as ranking ‎efficient and inefficient regions and units and providing solutions to improve inefficient ‎regions. Input indicators of this study were the ratio of student to physical education teacher, the ‎budget of physical education department, the number and surface area of sports halls at the ‎disposal of the department, and output indicators included the number of sports competitions ‎held by the department, provincial and national trophies won in competitions as well as national ‎and international athlete students of each region. Finally, DEA method was used to design a ‎performance assessment model for physical education departments in different regions. After ‎designing the research model and collecting the data, BCC, CCR and Anderson-Peterson output- ‎and input-oriented models were analyzed using DEA-SOLVER LV software. Results: The ‎results of data analysis showed that 11 out of 19 regions under study in 2017 were efficient and ‎‎8 were inefficient. Conclusion: It is necessary that the degree of changes needed to make ‎efficient the inefficient units be determined by modeling the efficient departments‎.‎
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples

Model quantization is known as a promising method to compress deep neural networks, especially for inferences on lightweight mobile or edge devices. However, model quantization usually requires access to the original training data to maintain the accuracy of the full-precision models, which is often infeasible in real-world scenarios for security and privacy issues. A popular approach to perform quantization without access to the original data is to use synthetically generated samples, based on batch-normalization statistics or adversarial learning. However, the drawback of such approaches is that they primarily rely on random noise input to the generator to attain diversity of the synthetic samples. We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries. To this end, we propose Qimera, a method that uses superposed latent embeddings to generate synthetic boundary supporting samples. For the superposed embeddings to better reflect the original distribution, we also propose using an additional disentanglement mapping layer and extracting information from the full-precision model. The experimental results show that Qimera achieves state-of-the-art performances for various settings on data-free quantization. Code is available at https://github.com/iamkanghyunchoi/qimera.
Rancang Bangun Sistem Monitoring dan Evaluasi Perkuliahan Berbasis Hybrid Programming UNIBI

To support Teaching and Learning Activities (KBM),  for a university to have innovations that can fulfill information services that must be delivered to students, lecturers and other units who interest in viewing, monitoring, and evaluating of lecturing activities, that matter accordance with the UNIBI mission to providing the best service for the academic community. To achieve this goal, the innovation is to improve the Academic Information System application (eureka.unibi.ac.id) in providing services that can be accessed by various platforms. By the design of a hybrid programming-based for system monitoring and evaluation lecturing, accordingly the SIAKAD application (eureka.unibi.ac.id) can be implemented into an application that can be accessed by various platforms such as smartphones with Android, iOS or Windows mobile operating systems. By a hybrid-based design, communication between units can occur quickly, and information from the results of monitoring and evaluation of lecturing activities can be directly disseminated and accessed by many division. Tools that use to  development process model used is the Rational Unified Process (RUP).  The RUP provides a well defined structure for the software life cycle. RUP has four phases that can be carried out iteratively which are implemented using the Ionic framework. The result of this research is a guide for developing web-based applications (eureka.unibi.ac.id) to be hybrid-based, where this hybrid-based design is the basis for implementation as an improvement in academic services. Keywords: Siakad, Monitoring, Evaluating, RUP, Lecturing activities, Framework ionic, Hybrid programming
A proposed model for achieving cloud computing strategic alignment using a mixed method approach: cloud service providers

Comparing traditional businesses, new technologies such as cloud computing have faced new challenges to achieve strategic alignment between IT and business. Therefore, identifying the factors affecting the strategic alignment in the cloud computing environment is essential. Hence, this study aims to introduce a model for cloud computing strategic alignment considering the role of IT governance. In this regard, the Luftman's strategic alignment framework was used as a reference model and was analysed using qualitative and quantitative approaches. In the qualitative part, the meta-synthesis approach was used to identify the factors, and then these factors were placed in appropriate categories using experts' ideas. Next, in the quantitative part, the chi-square test was used to analyse the hypothesis and the Friedman test was used to prioritise the concepts, categories and subcategories. The results indicate that IT governance plays an important role in creating the strategic alignment in a cloud. Moreover, two factors of security service management and the contextual and environmental factors of the cloud were identified as the important factors which help managers create value through IT investments.
Analisis Struktural Dan Relevansi Pembelajaran Bahasa Indonesia Pada Novel Emak Karya Daoed Joesoef

This study aims to describe (1) the intrinsic element of Daoak Joesoef's Emak Karya novel, and (2) its relevance in learning. This research is a descriptive qualitative study with a novel content analysis method. The results showed that: (1) Structural analysis, the theme of the novel Emak is the story of the relationship of eternal affection between a child and mother, the plot used is a forward path, characterization in the Emak novel is divided into three namely protagonists, antagonists, and extras. The point of view used is the viewpoint of the third person as the main character. The background used is the setting of the place, time and atmosphere. The dominant language style used by authors is parerelism, metaphor, symbolic, paradox, and association. The mandate used is written from the contents of the novel; and (2) Relevance as teaching material for class X high school. Â
MobILE: Model-Based Imitation Learning From Observation Alone

This paper studies Imitation Learning from Observations alone (ILFO) where the learner is presented with expert demonstrations that consist only of states visited by an expert (without access to actions taken by the expert). We present a provably efficient model-based framework MobILE to solve the ILFO problem. MobILE involves carefully trading off strategic exploration against imitation - this is achieved by integrating the idea of optimism in the face of uncertainty into the distribution matching imitation learning (IL) framework. We provide a unified analysis for MobILE, and demonstrate that MobILE enjoys strong performance guarantees for classes of MDP dynamics that satisfy certain well studied notions of structural complexity. We also show that the ILFO problem is strictly harder than the standard IL problem by presenting an exponential sample complexity separation between IL and ILFO. We complement these theoretical results with experimental simulations on benchmark OpenAI Gym tasks that indicate the efficacy of MobILE.
Renormalization method for proving frustration-free local spin chains are gapped

Key properties of a physical system depend on whether it is gapped, i.e. whether its spectral gap has a positive lower bound that is independent of system size. In quantum information theory, the question of whether a system is gapped has essential computational significance as well. Here, we introduce a rigorous renormalization method to prove that a spin chain is gapped. This approach exploits the fact that ground states of gapped systems exhibit decaying correlations. We apply the method to show that two interesting models are gapped, successfully completing proofs even where the previously established methods are inconclusive.
Semantic Answer Similarity for Evaluating Question Answering Models

The evaluation of question answering models compares ground-truth annotations with model predictions. However, as of today, this comparison is mostly lexical-based and therefore misses out on answers that have no lexical overlap but are still semantically similar, thus treating correct answers as false. This underestimation of the true performance of models hinders user acceptance in applications and complicates a fair comparison of different models. Therefore, there is a need for an evaluation metric that is based on semantics instead of pure string similarity. In this short paper, we present SAS, a cross-encoder-based metric for the estimation of semantic answer similarity, and compare it to seven existing metrics. To this end, we create an English and a German three-way annotated evaluation dataset containing pairs of answers along with human judgment of their semantic similarity, which we release along with an implementation of the SAS metric and the experiments. We find that semantic similarity metrics based on recent transformer models correlate much better with human judgment than traditional lexical similarity metrics on our two newly created datasets and one dataset from related work.
A Framework of Smart Dispensing Unit for Optimizing Sanitizer Quantity Using Iot Schematics

Nowadays, technology is helping everyone to cover their basic hygiene needs. During the COVID-19 pandemic, every person has started taking precautions, such as hand sanitization, wearing of masks, and social distancing. In this paper, the authors are focusing on hand sanitization. Latest sanitizer machines dispense sanitizer automatically because of touchless technology, but excessive use of sanitizer is harmful to the human skin. To address this issue, a smart sanitizer dispenser based on the Internet of Things (IoT) and Machine Learning (ML) has been proposed to fight against COVID-19. The proposed solution captures the image of hands and based on the size of hands and the number of germs present on the hands, the machine will dispense the sanitizer. In this way, the damage to human skin caused due to the excessive use of sanitizer can be reduced and the amount of sanitizer can also be saved.
An Anatomization Of Language Detection And Translation Using NLP Techniques

The issue with identifying language relates to process of determiningnatural language in which specific text is written. This is one of the big difficulties in the processing of natural languages. Still, they also pose a problem in improving multiclass classification in this area. Language detection and translation a significant Language Identification task are required. The language analysis method may be carried out according to tools available in a particular language if the source language is known. A successful language detection algorithm determines the achievement of the sentiment analysis task and other identification tasks. Processing natural language and machine learning techniques involve knowledge that is annotated with its language. Algorithms for natural language processing must be updated according to language's grammar. This paper proposes a secure language detection and translation technique to solve the security in natural language processing problems. Language detection algorithm based on char n-gram based statistical detector and translation Yandex API is used. While translating, there should be encryption and decryption for that we are using AESAlgorithm
Double Generative Adversarial Networks for Conditional Independence Testing

In this article, we study the problem of high-dimensional conditional independence testing, a key building block in statistics and machine learning. We propose an inferential procedure based on double generative adversarial networks (GANs). Specifically, we first introduce a double GANs framework to learn two generators of the conditional distributions. We then integrate the two generators to construct a test statistic, which takes the form of the maximum of generalized covariance measures of multiple transformation functions. We also employ data-splitting and cross-fitting to minimize the conditions on the generators to achieve the desired asymptotic properties, and employ multiplier bootstrap to obtain the corresponding $p$-value. We show that the constructed test statistic is doubly robust, and the resulting test both controls type-I error and has the power approaching one asymptotically. Also notably, we establish those theoretical guarantees under much weaker and practically more feasible conditions compared to the existing tests, and our proposal gives a concrete example of how to utilize some state-of-the-art deep learning tools, such as GANs, to help address a classical but challenging statistical problem. We demonstrate the efficacy of our test through both simulations and an application to an anti-cancer drug dataset. A Python implementation of the proposed procedure is available at https://github.com/tianlinxu312/dgcit.