Reviewerly/exHarmony huggingface dataset:
Pendampingan Pengelolaan Arsip Pada Pegawai Administrasi Kantor Hukum Rolando Marpaung &amp; Partner

Archives are written records both in the form of pictures and charts which contain information about a subject (subject matter) or events that people make to help people's memory (that) too. The function of an archive is as a memory for a company or individual, as evidence, for human safety, and so on. Records management in an organization is very important, considering that archives are one of the evidences in an activity that has occurred. Therefore, archive management activities must always be maintained. Various information relating to organizational development and operational activities is always maintained by maintaining organizational records. Given the importance of archives, it is necessary to make efforts to improve and perfect the archive system optimally so that it can run well, efficiently and effectively. This activity aims to improve the skills of participants who are administrative employees of the Rolando Marpaung &amp; Partner Law Office in storing and protecting records properly. The scope of this activity was carried out at the Law Office of Rolando Marpaung &amp; Partners with all 6 (six) administrative employees participating. The results of this assistance show that based on the results of the questionnaire given to the participants, the success rate was stated to be 100%. The conclusions of this activity are (1) The implementation of this community service went smoothly according to the expectations of the community service implementation team, (2) The participants felt that this community service activity was useful, because the participants were aware of the importance of managing records for the survival of an organization, (3) The participants' responses to the community service activities carried out stated that the delivery of the material was good and the participants understood the explanations given. This is shown by the interactive atmosphere between the participants and the service team and (4) With the assistance of archive management, it facilitates the process of storing, organizing and retrieving archives.
1 Introduction

An enormous amount of data is constantly being produced around the world, both in the form of large volume as in that of large velocity. Turning the data into information requires many steps of data analysis: methods for filtering and cleaning the data, joining heterogeneous sources, extracting and selecting features, summarizing and aggregating the data, learning predictions, estimating the uncertainties of the learned model and monitoring the model fitness in its deployment. All these processes need to scale up, whether the long analysis workflows are integrated into a deep learning architecture, or not. The data ecosystems no longer allow us to take von Neumann architecture for granted where only compilers or application systems address hardware issues. Specialized architectures for accelerating machine learning have been developed, and machine learning algorithms have been tailored to novel computer architectures. Both trends are aiming at efficiency, in particular the efficient use of given resources: the real time of execution, the amount of energy, memory and communication. In the struggle for sustainability resource restrictions are of utmost importance. Energy consumption in particular receives considerable attention.We believe that resource efficiency cannot be achieved by better machine learning algorithms or by better hardware architectures alone. It demands the smart combination of hardware and algorithms.
Secure and distributed assessment of privacy-preserving GWAS releases

Genome-wide association studies (GWAS) identify correlations between the genetic variants and an observable characteristic such as a disease. Previous works presented privacy-preserving distributed algorithms for a federation of genome data holders that spans multiple institutional and legislative domains to securely compute GWAS results. However, these algorithms have limited applicability, since they still require a centralized instance to operate on the data and decide whether GWAS results can be safely disclosed, which violates privacy regulations, such as GDPR. In this work, we introduce GenDPR, a distributed middleware that leverages Trusted Execution Environments (TEEs) to securely determine a subset of the potential GWAS statistics that can be safely released. GenDPR achieves the same accuracy as centralized solutions, but requires transferring significantly less data because TEEs only exchange intermediary results but no genomes. Additionally, GenDPR can be configured to tolerate all-but-one honest-but-curious federation members colluding with the aim to expose genomes of correct members.
Information flow and levels of abstraction －an elucidation of foundational problems in philosophy of information

Information flow has been extensively discussed. There are various kinds of information flow in the environment. By using them, we produce and use information. In this paper, we first present the fundamental idea of J. Barwise and J. Seligman’s information flow. On the other hand, the area of philosophy of information is also remarkably developed. In particular, we focus on the method of Level of Abstraction (LoA) introduced by L. Floridi. The method of LoA allows us to extract suitable information from some systems. Combining the concepts of information flow and LoA, we can significantly understand these concepts.
Development of Marine Products Auction Information System

Information is needed at all times, where with the development of information technology in developing computerized systems to produce the right decisions in achieving goals, the Karanganyar Kragan Fish Auction Place (TPI) is a government institution in the field of Village Unit Cooperatives which functions as a place for buying and selling fishermen and bidder. The issue to be discussed is building a marine product auction information system that will manage the information dissemination process to make it more effective and efficient. The system built will also use the PHP and MySQL programming languages. The system development method used is the Prototype method. System planning uses data flow diagram (DFD) modeling, Entity Rational Diagram (ERD). Implementation or tie-in results of information systems Auctions for marine products can manage the dissemination of information on the results of auctions that have been carried out more efficiently
Real-time Iris Center Detection Based on Convolutional Neural Networks

It is an active field of study in studies where the iris center is referenced, such as iris center detection, gaze tracking, driver fatigue detection. In this study, an approach for real-time detection of iris centers based on convolutional neural networks is presented. The GI4E dataset was used as the dataset for the proposed approach. Experimental results estimated the test data of the proposed convolutional neural network model with an accuracy of 97.2% based on the 0.025 error corresponding to the closest position to the iris center according to the maximum normalized error criteria. The study was also tested in real time with a webcam built into the computer. While the test accuracy is satisfactory, real-time speed performance needs to be improved.
A Study of Feature Extraction techniques for Sentiment Analysis

Sentiment Analysis refers to the study of systematically extracting the meaning of subjective text . When analysing sentiments from the subjective text using Machine Learning techniques,feature extraction becomes a significant part. We perform a study on the performance of feature extraction techniques TF-IDF(Term Frequency-Inverse Document Frequency) and Doc2vec (Document to Vector) using Cornell movie review datasets, UCI sentiment labeled datasets, stanford movie review datasets,effectively classifying the text into positive and negative polarities by using various pre-processing methods like eliminating StopWords and Tokenization which increases the performance of sentiment analysis in terms of accuracy and time taken by the classifier.The features obtained after applying feature extraction techniques on the text sentences are trained and tested using the classifiers Logistic Regression,Support Vector Machines,K-Nearest Neighbours , Decision Tree and Bernoulli Nave Bayes
Encryption with Generalised Balancing Sequences and Generalised Lucas Balancing Sequences

In this paper we propose a key stream for encryptions obtained by concatenation of solutions of Diophantineequations which are generated by using some properties of Generalised Balancing Sequences.
The artist and the artwork

This chapter examines the rights of the creator of art, the artist. These include copyright, moral rights, the artist's resale right. The chapter details the protection against unauthorised reproduction afforded by copyright, explains how the artist can police the use of the artwork through moral rights. The chapter describes how copyright royalties are collected, how artist's can recover royalties upon the resale of their artwork and how artists can enforce their intellectual property rights. Finally the chapter addresses some of the rights and liabilities of graffiti artists.
The Integration of Virtual Reality (VR) and User Experience Design (UXD) in the Design of Shared Office Spaces.

Users’ satisfaction in the design of shared office spaces is one of the important aspects that everyenterprise should seek. The research provides and validates a new mechanism that could be used in thedesign process of shared office spaces, by integrating the tool of Virtual Reality (VR) and User ExperienceDesign (UXD). The proposed mechanism consists of three phases. The first phase (the experimentarticulation) illustrates the correlation between the satisfaction factors of users and the physical setting ofshared office spaces. In the second phase (the experiment tool preparation), the research articulates theprograming scripts which are necessary for the users’ interaction in the experiment using the VR tool, calledUser Interaction Tool (UIT), where these scripts are: Mouvement Script; Rotation Script; Stretch Script;and Material Script. The third phase (the experiment implementation and tool evaluation) consists of threesteps; the first one is an oriented questionnaire for determining users’ needs in shared office space whileestablishing relative weight for every aspect, and the second step is the experiment implementation for anexisting office space to explore the suggested scenarios using the proposed tool, and the third step is crucialfor comparing between the existing design model and the deduced one from the proposed mechanism toevaluate the validity of the proposed tool. After testing the tool, the research finds that users’ satisfactionbefore the experiment is about 43.352 %, while it has been raised to 86.5825 % after using the proposedmechanism. Hence, the users’ satisfaction is almost doubled after using the virtual reality tool in the designprocess of office space design.
pronoun in ortonese

In this article we will study the pronominal system in Ortonese, which belongs to the central-southern Italo-Romance languages. The analysis we propose is contrastive: we will compare the data with the Italian to highlight their differences in terms of some morphosyntactic aspects. We will examine, the stressed and unstressed personal pronouns, emphasizing the placement of the combined pronouns with the compound tenses. Then, we will focus on the rest of the pronouns, such as demonstratives, possessives, quantifiers and indefinites, also pointing out the uses of the clitics “ci” and “ne” and the presence of the pronoun “nome”, which does not exist in Italian.
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework

Knowledge distillation (KD) has been widely used for model compression and knowledge transfer. Typically, a big teacher model trained on sufficient data transfers knowledge to a small student model. However, despite the success of KD, little effort has been made to study whether KD leaks the training data of the teacher model. In this paper, we experimentally reveal that KD suffers from the risk of privacy leakage. To alleviate this issue, we propose a novel knowledge distillation method, swing distillation, which can effectively protect the private information of the teacher model from flowing to the student model. In our framework, the temperature coefficient is dynamically and adaptively adjusted according to the degree of private information contained in the data, rather than a predefined constant hyperparameter. It assigns different temperatures to tokens according to the likelihood that a token in a position contains private information. In addition, we inject noise into soft targets provided to the student model, in order to avoid unshielded knowledge transfer. Experiments on multiple datasets and tasks demonstrate that the proposed swing distillation can significantly reduce (by over 80% in terms of canary exposure) the risk of privacy leakage in comparison to KD with competitive or better performance. Furthermore, swing distillation is robust against the increasing privacy budget.
An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems

Combinatorial optimization - a field of research addressing problems that feature strongly in a wealth of scientific and industrial contexts - has been identified as one of the core potential fields of applicability of quantum computers. It is still unclear, however, to what extent quantum algorithms can actually outperform classical algorithms for this type of problems. In this work, by resorting to computational learning theory and cryptographic notions, we prove that quantum computers feature an in-principle super-polynomial advantage over classical computers in approximating solutions to combinatorial optimization problems. Specifically, building on seminal work by Kearns and Valiant and introducing a new reduction, we identify special types of problems that are hard for classical computers to approximate up to polynomial factors. At the same time, we give a quantum algorithm that can efficiently approximate the optimal solution within a polynomial factor. The core of the quantum advantage discovered in this work is ultimately borrowed from Shor's quantum algorithm for factoring. Concretely, we prove a super-polynomial advantage for approximating special instances of the so-called integer programming problem. In doing so, we provide an explicit end-to-end construction for advantage bearing instances. This result shows that quantum devices have, in principle, the power to approximate combinatorial optimization solutions beyond the reach of classical efficient algorithms. Our results also give clear guidance on how to construct such advantage-bearing problem instances.
Multi-Task Learning for Sparsity Pattern Heterogeneity: A Discrete Optimization Approach

We extend best-subset selection to linear Multi-Task Learning (MTL), where a set of linear models are jointly trained on a collection of datasets (``tasks''). Allowing the regression coefficients of tasks to have different sparsity patterns (i.e., different supports), we propose a modeling framework for MTL that encourages models to share information across tasks, for a given covariate, through separately 1) shrinking the coefficient supports together, and/or 2) shrinking the coefficient values together. This allows models to borrow strength during variable selection even when the coefficient values differ markedly between tasks. We express our modeling framework as a Mixed-Integer Program, and propose efficient and scalable algorithms based on block coordinate descent and combinatorial local search. We show our estimator achieves statistically optimal prediction rates. Importantly, our theory characterizes how our estimator leverages the shared support information across tasks to achieve better variable selection performance. We evaluate the performance of our method in simulations and two biology applications. Our proposed approaches outperform other sparse MTL methods in variable selection and prediction accuracy. Interestingly, penalties that shrink the supports together often outperform penalties that shrink the coefficient values together. We will release an R package implementing our methods.
Neural Story Planning

Automated plot generation is the challenge of generating a sequence of events that will be perceived by readers as the plot of a coherent story. Traditional symbolic planners plan a story from a goal state and guarantee logical causal plot coherence but rely on a library of hand-crafted actions with their preconditions and effects. This closed world setting limits the length and diversity of what symbolic planners can generate. On the other hand, pre-trained neural language models can generate stories with great diversity, while being generally incapable of ending a story in a specified manner and can have trouble maintaining coherence. In this paper, we present an approach to story plot generation that unifies causal planning with neural language models. We propose to use commonsense knowledge extracted from large language models to recursively expand a story plot in a backward chaining fashion. Specifically, our system infers the preconditions for events in the story and then events that will cause those conditions to become true. We performed automatic evaluation to measure narrative coherence as indicated by the ability to answer questions about whether different events in the story are causally related to other events. Results indicate that our proposed method produces more coherent plotlines than several strong baselines.
Multi-person 3D pose estimation from unlabelled data

Its numerous applications make multi-human 3D pose estimation a remarkably impactful area of research. Nevertheless, assuming a multiple-view system composed of several regular RGB cameras, 3D multi-pose estimation presents several challenges. First of all, each person must be uniquely identified in the different views to separate the 2D information provided by the cameras. Secondly, the 3D pose estimation process from the multi-view 2D information of each person must be robust against noise and potential occlusions in the scenario. In this work, we address these two challenges with the help of deep learning. Specifically, we present a model based on Graph Neural Networks capable of predicting the cross-view correspondence of the people in the scenario along with a Multilayer Perceptron that takes the 2D points to yield the 3D poses of each person. These two models are trained in a self-supervised manner, thus avoiding the need for large datasets with 3D annotations.
Computer Mediated Communication and the Promotion of World Cultural Diversity

In order to unpack the potential of CMC to promote the world cultural diversity, nomadology as the major cultural medium is considered, especially that it holds structural affinities to orality striking a tripartite media (CMC-nomad-oral). Nomadology suggests a traditional cultural structure that is already re-attracting academic interest with the advent of new media technologies. Rhizome as cultural theory used to explore social media negotiates its structural potential to promote cultural diversity, not just globalization. Considering the global reach of cyberspace, the diversity of world cultures are hosted on the same plane of interaction to "connect" and negotiate their potential of development and spread. Therefore, this study aims at introducing new perceptions to the cultural potential of CMC through arguing that the diversity of world cultures stand a chance of promotion amidst the tech-based globalization sweeping cultural production.
Improving Unsupervised Video Object Segmentation with Motion-Appearance Synergy

We present IMAS, a method that segments the primary objects in videos without manual annotation in training or inference. Previous methods in unsupervised video object segmentation (UVOS) have demonstrated the effectiveness of motion as either input or supervision for segmentation. However, motion signals may be uninformative or even misleading in cases such as deformable objects and objects with reflections, causing unsatisfactory segmentation. In contrast, IMAS achieves Improved UVOS with Motion-Appearance Synergy. Our method has two training stages: 1) a motion-supervised object discovery stage that deals with motion-appearance conflicts through a learnable residual pathway; 2) a refinement stage with both low- and high-level appearance supervision to correct model misconceptions learned from misleading motion cues. Additionally, we propose motion-semantic alignment as a model-agnostic annotation-free hyperparam tuning method. We demonstrate its effectiveness in tuning critical hyperparams previously tuned with human annotation or hand-crafted hyperparam-specific metrics. IMAS greatly improves the segmentation quality on several common UVOS benchmarks. For example, we surpass previous methods by 8.3% on DAVIS16 benchmark with only standard ResNet and convolutional heads. We intend to release our code for future research and applications.
Fast-moving object counting with an event camera

&lt;p&gt;This paper proposes the use of an event camera as a component of a vision system that enables counting of fast-moving objects - in this case, falling corn grains. These type of cameras transmit information about the change in brightness of individual pixels and are characterised by low latency, no motion blur, correct operation in different lighting conditions, as well as very low power consumption. The proposed counting algorithm processes events in real time. The operation of the solution was demonstrated on a stand consisting of a chute with a vibrating feeder, which allowed the number of grains falling to be adjusted. The objective of the control system with a PID controller was to maintain a constant average number of falling objects. The proposed solution was subjected to a series of tests to determine the correctness of the developed method operation. On their basis, the validity of using an event camera to count small, fast-moving objects and the associated wide range of potential industrial applications can be confirmed.&lt;/p&gt;
Relational Sentence Embedding for Flexible Semantic Matching

We present Relational Sentence Embedding (RSE), a new paradigm to further discover the potential of sentence embeddings. Prior work mainly models the similarity between sentences based on their embedding distance. Because of the complex semantic meanings conveyed, sentence pairs can have various relation types, including but not limited to entailment, paraphrasing, and question-answer. It poses challenges to existing embedding methods to capture such relational information. We handle the problem by learning associated relational embeddings. Specifically, a relation-wise translation operation is applied to the source sentence to infer the corresponding target sentence with a pre-trained Siamese-based encoder. The fine-grained relational similarity scores can be computed from learned embeddings. We benchmark our method on 19 datasets covering a wide range of tasks, including semantic textual similarity, transfer, and domain-specific tasks. Experimental results show that our method is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art sentence embedding methods. https://github.com/BinWang28/RSE