Reviewerly/exHarmony huggingface dataset:
Utilization of Service Oriented Architecture to Customize Accounting Business Process in Open ERP System for Smart SME’s

Small and medium enterprises or commonly referred to as SMEs in Indonesia are business activities that are in great demand after the economic crisis which resulted in the termination of employment (PHK) experienced by most companies. The role of SMEs in the Indonesian economy has now been recognized by the wider community. However, many SMEs in Indonesia are still running their business processes separately, especially in the accounting system, so that the business processes are running poorly, financial transactions that are still recorded and reported manually and separately, sales/purchase account items that are still recorded manually, and there's no system that supports the accounting process for SMEs.The output from this research is an Open Source ERP-based Smart SME system design in the accounting module by embedding the "smart" feature, namely analytic accounting. This design expected to be the standard and best practice in the SME accounting system so that SME's actors can create and get more structured financial reports and can design an integrated system that supports a real-time data management system.The research method used in this research uses the Service Oriented Architecture (SOA) approach. This method also refers to certain services on one platform such as services for making payments, transferring payments, and checking the status of a bank account in one application. In general, all the research processes carried out refer to the step by step of SOA and there must be input and output in designing the business model. This method is used to design business process models and services based on the accounting system problems experienced by SMEs.
A Description Scheme for Video Overview Based on Scene Detection and Face Clustering

With the rapid growth of online videos, it is crucial to generate overviews of videos to help audiences make viewing decisions and save time. Video summarization and video captioning are two of the most common solutions. In this paper, we proposed a new solution in the form of a series of scene-person pairs generated from our proposed video description scheme. This new formation takes substantially less time than watching video summaries and is more acceptable than video captions. In addition, our method can be generalized to different types of videos. We also proposed a face clustering method and a scene detection method. The experimental results indicate that our methods outperform other state-of-the-art methods and are highly generalizable. As an example, a demo application is developed to demonstrate the proposed description scheme.
Reconciling Legal and Technical Approaches to Algorithmic Bias

In recent years, there has been a proliferation of papers in the algorithmic fairness literature proposing various technical definitions of algorithmic bias and methods to mitigate bias. Whether these algorithmic bias mitigation methods would be permissible from a legal perspective is a complex but increasingly pressing question at a time when there are growing concerns about the potential for algorithmic decision-making to exacerbate societal inequities. In particular, there is a tension around the use of protected class variables: most algorithmic bias mitigation techniques utilize these variables or proxies, but anti-discrimination doctrine has a strong preference for decisions that are blind to them. This Article analyzes the extent to which technical approaches to algorithmic bias are compatible with U.S. anti-discrimination law and recommends a path toward greater compatibility.This question is vital to address because a lack of legal compatibility creates the possibility that biased algorithms might be considered legally permissible while approaches designed to correct for bias might be considered illegally discriminatory. For example, a recent proposed rule from the Department of Housing and Urban Development (“HUD”), which would have established the first instance of a U.S. regulatory definition for algorithmic discrimination, would have created a safe harbor from disparate impact liability for housing-related algorithms that do not use protected class variables or close proxies. An abundance of recent scholarship has shown, however, that simply removing protected class variables and close proxies does little to ensure that the algorithm will not be biased. In fact, this approach, known as “fairness through unawareness” in the machine learning community, is widely considered naive. While the language around algorithms was removed in the final rule, this focus on the visibility of protected attributes in decision-making is central in U.S. anti-discrimination law.Causal inference provides a potential way to reconcile algorithmic fairness techniques with anti-discrimination law. In U.S. law, discrimination is generally thought of as making decisions “because of” a protected class variable. In fact, in Texas Department of Housing and Community Affairs v. Inclusive Communities Project, Inc., the case that motivated the HUD proposed rule, the Court required a “causal connection” between the decision-making process and the disproportionate outcomes. Instead of examining whether protected class variables appear in the algorithm, causal inference would allow for techniques that use protected class variables with the intent of negating causal relationships in the data tied with race. While moving from correlation to causation is challenging—particularly in machine learning, where leveraging correlations to make accurate predictions is typically the goal—doing so offers a way to reconcile technical feasibility and legal precedence while providing protections against algorithmic bias.
IT Governance Considerations for Permissioned Blockchains

ABSTRACT The operational advantages of permissioned blockchains utilize collaborative, private, immutable, append-only distributed ledgers to strategically optimize business results. Accordingly, the importance of related IT governance frameworks is growing. Strategic IT governance, especially regarding permissioned blockchains, is crucial to accurate, valid, and complete accounting data. And while permissioned blockchains such as Hyperledger Fabric, Corda, and Quorum can enhance business collaborations, attention to IT governance is critical. Reviewing blockchain IT governance is necessary if the benefits of encryption, hashing, and distributed ledgers are to be realized. Understanding the potential weaknesses of flawed capacity planning (computational and storage), cybersecurity risks, litigation uncertainty, regulatory refutation, and smart contract vulnerabilities is key. Exacerbating the situation is that many of these weaknesses are likely to grow as permissioned blockchains propagate. If internal auditors do not preemptively address these IT governance issues, both management and internal audit departments could fail their stakeholders. Data Availability: Data are available from the public sources cited in the text.
Assessment of stakeholders views on accessing quality and equity of basic education in rural communities of Abia State, Nigeria

This study aims to analyze the views of stakeholders on access to quality and equity in Basic Education in selected rural communities of Abia State, Nigeria.The study used mixed method design (quantitative and qualitative) in drawing views of a stratified sample of 432 stakeholders in the quantitative study and 72 participants of six focus group discussions.The study found out that access to quality and equity in Basic Education in the rural communities was significantly inadequate (p < 0.025) resulting from lack of qualified teachers, inadequate safety of the environment, poor facilities, very little involvement by government in maintaining the schools, learners reluctance to do their works and indiscipline, little or no stakeholders' involvement in the schools affairs.Stakeholders' involvement (most of who are parents) and their level of education were shown to be significant indicators of access to quality and equity in Basic Education although the schools were not exploiting these assets.The study concludes that if access to quality Basic Education is to become real in the rural communities, the Ministry of Education, Local Government Education Boards, stakeholders and communities need to establish strong collaboration and coordination of the activities to improve the conditions of the schools.Programmes that can assist parents and assist learners in their homework and instil morals in the learners need to be worked out by the education authorities.
Actuated Club: Modification of Golf-Club Posture with force feedback and motion prediction in VR environment

In the golf, the trajectory and posture of the golf club during the swing influence on the direction and trajectory of the hit ball. While the instructor and the system instruct how to improve the swing, it is difficult to correct the swing motion during the swing due to lack of information quantity, time for interpretation of presented information, and lack of the sense of agency in the swing motion. Therefore, in this study, we realize the modification of the swing motion during the swing using the trajectory prediction of the golf swing and the non-grounding type force display. To cope the delay between the command of the modification and the swing correction, this system learns the golf swing trajectory of the user in advance, and predicts them. If the result of the prediction suggests the club deviates from the ideal trajectory or posture, the gyroscope presents torque in the ideal trajectory direction to correct the trajectory or posture. By practicing VR golf using the proposed system, the user can always practice golf-swing with the ideal trajectory, and can make the swing practice efficient.
The Summation-Truncation Hybrid: Reusing Discarded Bits for Free

A well-established PRP-to-PRF conversion design is truncation: one evaluates an n-bit pseudorandom permutation on a certain input, and truncates the result to a bits. The construction is known to achieve tight $$2^{n-a/2}$$ security. Truncation has gained popularity due to its appearance in the GCM-SIV key derivation function (ACM CCS 2015). This key derivation function makes four evaluations of AES, truncates the outputs to n/2 bits, and concatenates these to get a 2n-bit subkey. In this work, we demonstrate that truncation is wasteful. In more detail, we present the Summation-Truncation Hybrid (STH). At a high level, the construction consists of two parallel evaluations of truncation, where the truncated $$(n-a)$$ -bit chunks are not discarded but rather summed together and appended to the output. We prove that STH achieves a similar security level as truncation, and thus that the $$n-a$$ bits of extra output is rendered for free. In the application of GCM-SIV, the current key derivation can be used to output 3n bits of random material, or it can be reduced to three primitive evaluations. Both changes come with no security loss.
Bidirectional Quantum Communication through the Composite GHZ-GHZ Channel

This paper solved the problem of transmitting quantum bits (qubits) in a multi-hop and bidirectional way. Considering that the Greenberger–Horne–Zeilinger (GHZ) states are less prone to the decoherence effects caused by the surrounding environment, we proposed a bidirectional quantum communication scheme based on quantum teleportation and the composite GHZ-GHZ states. On a multi-hop quantum path, different types of GHZ states are previously shared between the adjacent intermediate nodes. To implement qubit transmission, the sender and intermediate nodes perform quantum measurements in parallel, and then send their measurement results and the types of previously shared GHZ states to the receiver independently. Based on the received information, the receiver performs unitary operations on the local particle, thus retrieving the original qubit. Our scheme can avoid information leakage at the intermediate nodes and can reduce the end-to-end communication delay, in contrast to the hop-by-hop qubit transmission scheme.
Detecting Alzheimer’s Dementia Degree

The diagnosis of Alzheimer’s disease (AD) faces two important issues. They are how to extract the features of the rhythms of patients with AD and how to label them and reveal the degree of dementia in patients. This study defines 14 instantaneous power indicators of dementia judgment through Hilbert marginal spectrum (HMS) from rhythm waves. A warped infinite Gaussian mixture model (WiGMM) is proposed to learn the latent variables of these indicators to detect the degree of dementia. The experimental results show that HMS-based indicators are able to reflect the cognitive function of AD patients. This proposed method has the ability to detect brain cognitive status through a warped transform and Dirichlet process parameter prior to inference.
Handwritten Character Recognition

This chapter discusses isolating and recognizing individual characters. It moves closer to capturing the true problems that need to be overcome in designing high-performance character recognizers for handwritten word recognition applications. In broad terms, character recognition refers to a computer reading some part of some type of document. An easy character recognition problem is that of designing computer systems to scan and read stylized characters on the bottom of bank checks. Mail-order forms instruct us to write our characters using a certain style so that a machine can read them. Many banks and credit card firms are seeking to implement automated systems for reading amounts from checks and credit card receipts. Finally, on-line systems are becoming feasible commercial products. In an on-line system, a specialized pen and pad is used to allow the computer to measure the velocity and pressure information used in creating the characters. This added information makes handwritten character recognition more tractable.
Using the game approach in teaching general professional disciplines

Active and interactive teaching methods are now widely implemented in the educational process. The aim of the study is to consider the problems that arise when using the game approach in the process of teaching in higher professional education and, in particular, when teaching general professional disciplines, as well as to identify possible ways to solve these problems. Educational games are the object of this study. The methodological basis of the study is the analysis of scientific publications, expert assessment and generalized practical experience in identifying the main trends and problems that arise when implementing the game approach. The author examines in detail such aspects of the topic as restrictions on the use of game methods in teaching general professional disciplines, ways to apply the game approach in these conditions, and assess the effectiveness of the proposed game methods. Particular attention is paid to the use of educational games during workshops and seminars. The paper concludes that it is advisable to use individual elements of game methods during ongoing monitoring during practical exercises as an additional (auxiliary) pedagogical tool. This approach is especially important to increase students’ motivation. A number of ways to apply the game approach are proposed. The effectiveness of the proposed game methods in higher professional education is shown.
The Turing Test for Graph Drawing Algorithms

Do algorithms for drawing graphs pass the Turing Test? That is, are their outputs indistinguishable from graphs drawn by humans? We address this question through a human-centred experiment, focusing on `small' graphs, of a size for which it would be reasonable for someone to choose to draw the graph manually. Overall, we find that hand-drawn layouts can be distinguished from those generated by graph drawing algorithms, although this is not always the case for graphs drawn by force-directed or multi-dimensional scaling algorithms, making these good candidates for Turing Test success. We show that, in general, hand-drawn graphs are judged to be of higher quality than automatically generated ones, although this result varies with graph size and algorithm.
Management of Research Data in Image Format: An Exploratory Study on Current Practices

Research data management is the basis for making data more Findable, Accessible, Interoperable and Reusable. In this context, little attention is given to research data in image format. This article presents the preliminary results of a study on the habits related to the management of images in research. We collected 107 answers from researchers using a questionnaire. These researchers were PhD students, fellows and university professors from Life and Health Sciences, Exact Sciences and Engineering, Natural and Environmental Sciences and Social Sciences and Humanities. This study shows that 83.2% of researcher use images as research data, however, its use is generally not accompanied by a guidance document such as a research data management plan. These results provide valuable insights into the processes and habits regarding the production and use of images in the research context.
Adversarial Reinforcement Learning under Partial Observability in Autonomous Computer Network Defence

Recent studies have demonstrated that reinforcement learning (RL) agents are susceptible to adversarial manipulation, similar to vulnerabilities previously demonstrated in the supervised learning setting. While most existing work studies the problem in the context of computer vision or console games, this paper focuses on reinforcement learning in autonomous cyber defence under partial observability. We demonstrate that under the black-box setting, where the attacker has no direct access to the target RL model, causative attacks---attacks that target the training process---can poison RL agents even if the attacker only has partial observability of the environment. In addition, we propose an inversion defence method that aims to apply the opposite perturbation to that which an attacker might use to generate their adversarial samples. Our experimental results illustrate that the countermeasure can effectively reduce the impact of the causative attack, while not significantly affecting the training process in non-attack scenarios.
A note on precision-preserving compression of scientific data

Abstract. Lossy compression of scientific data arrays is a powerful tool to save network bandwidth and storage space. Properly applied lossy compression can reduce the size of a dataset by orders of magnitude while keeping all essential information, whereas a wrong choice of lossy compression parameters leads to the loss of valuable data. An important class of lossy compression methods is so-called precision-preserving compression, which guarantees that a certain precision of each number will be kept. The paper considers statistical properties of several precision-preserving compression methods implemented in NetCDF Operators (NCO), a popular tool for handling and transformation of numerical data in NetCDF format. We compare artifacts resulting from the use of precision-preserving compression of floating-point data arrays. In particular, we show that a popular Bit Grooming algorithm (default in NCO until recently) has suboptimal accuracy and produces substantial artifacts in multipoint statistics. We suggest a simple implementation of two algorithms that are free from these artifacts and have double the precision. One of them can be used to rectify the data already processed with Bit Grooming. We compare precision trimming for relative and absolute precision to a popular linear packing (LP) method and find out that LP has no advantage over precision trimming at a given maximum absolute error. We give examples when LP leads to an unconstrained error in the integral characteristic of a field or leads to unphysical values. We analyze compression efficiency as a function of target precision for two synthetic datasets and discuss precision needed in several atmospheric fields. Mantissa rounding has been contributed to NCO mainstream as a replacement for Bit Grooming. The Appendix contains code samples implementing precision trimming in Python3 and Fortran 95.
Discussion of: “Nonparametric regression using deep neural networks with ReLU activation function”

In the sequel, I will now discuss several aspects of the paper in more detail. Expressivity:The choice of models.As a model for the regression function, Schmidt-Hieber considers the class of compositorial functions, which are characterized by the fact that they are a composition of functions, each of which just depends on a very small
TOWARDS DISTILLATION OF DEEP NEURAL NETWORKS FOR SATELLITE ON-BOARD IMAGE SEGMENTATION

Abstract. Cubesats platforms expansion increases the need to simplify payloads and to optimize downlink data capabilities. A promising solution is to enhance on-board software, in order to take early decisions, automatically. However, the most efficient methods for data analysis are generally large deep neural networks (DNN) oversized to be loaded and processed on limited hardware capacities of cubesats. To use them, we must reduce the size of DNN while accommodating efficiency in terms of both accuracy and inference cost. In this paper, we propose a distillation method which reduces image segmentation deep neural network’s size to fit into on board processors. This method is presented through a ship detection example comparing accuracy and inference costs for several networks.
IWD towards Semantic similarity measure in ontology

Ontology plays a key role in semantic web to represent various concepts and their relations to make knowledge machine understandable. In order to provide a knowledge sharing environment, concept mapping in ontologies is required. In this regard, semantic similarity in the field of information retrieval is quite significant which refers to relatedness in ontology concepts. This semantic similarity may be computed in several ways. In this paper, a new method, based on optimization algorithm, IWD (Intelligent Water Drop), is proposed to computer similarity in ontology concepts. It is used to measure weighted path in concepts of ontology which is further used to find the semantic distance. Semantic similarity is computed on the basis of this semantic distance. Further, with the aid of a case study, a comparative analysis has been presented for the proposed method and other existing similarity methods for semantic similarity.
LCOE Analysis of Solar Tracker Application in China

In China, systemic techno-economic analysis for solar tracker has been absent.To fill the blank, by taking the economic analysis of solar tracker application as the research object and using the LCOE method widely used internationally, the techno-economic analysis model of solar tracker was established according to conditions in China.Influence factors on LCOE were analyzed by using the established model, and the relationship between each cost factor and the cost component of energy leveling of tracker was further studied.In addition, the calculation method of investment payback period based on energy leveling analysis was established, and the influence of various factors on investment payback period was revealed through an example calculation.The research results will help to measure the economy of tracker application more accurately and comprehensively, and promote the popularization and application of solar tracker.The economic analysis model of solar tracker application was established by using LCOE method.The influence factors and cost component of LCOE were analyzed with the model.The payback period of solar tracker investment was also analyzed based on LCOE method.