Reviewerly/exHarmony huggingface dataset:
Fairness in ERC token markets: A Case Study of CryptoKitties

Fairness is an important trait of open, free markets. Ethereum is a platform meant to enable digital, decentralized markets. Though many researchers debate the market's fairness, there are few discussions around the fairness of automated markets, such as those hosted on Ethereum. In this paper, using pilot studies, we consider unfair factors caused by adding the program. Because CryptoKitties is one of the major blockchain-based games and has been in operation for an extended period of time, we focus on its market to examine fairness. As a result, we concluded that a gene determination algorithm in this game has little randomness, and a significant advantage to gain profit is given to players who know its bias over those who do not. We state incompleteness and impact of the algorithm and other factors. Besides, we suppose countermeasures to reduce CryptoKitties' unfairness as a market.
Connecting Sphere Manifolds Hierarchically for Regularization

This paper considers classification problems with hierarchically organized classes. We force the classifier (hyperplane) of each class to belong to a sphere manifold, whose center is the classifier of its super-class. Then, individual sphere manifolds are connected based on their hierarchical relations. Our technique replaces the last layer of a neural network by combining a spherical fully-connected layer with a hierarchical layer. This regularization is shown to improve the performance of widely used deep neural network architectures (ResNet and DenseNet) on publicly available datasets (CIFAR100, CUB200, Stanford dogs, Stanford cars, and Tiny-ImageNet).
Structural changes in the professional training of higher education students in Russia

The article provides a statistical analysis of the dynamics and structure of professional training of higher education students. The main goal of the work is to identify the conformity of degrees of university graduates with the needs of the modern labor market. The empirical base of the study was compiled according to official statistics in Russia for 2013-2018. During the period under review, there was a 3.19% decrease in students studying under the Bachelor's degree program, while the number of long first degree students decreased by a factor of 3.39, and the number of students studying under Master's degree programs, in contrast, increased by a factor of 2.85. There is a tendency to reduce the share of accepted applications for university admission from the total number of applications submitted for all study programs, with the exception of Master's degree programs. The results obtained indicate that professions, which are popular with university students, are not in high demand in the modern labor market. Holders of a Bachelor's degree preferred mainly the humanitarian professions (related to Law, Economics, and Pedagogy). However, the related study programs are characterized by an overdemand and a high level of competition. Structural changes in the Bachelor's and Master's degree programs have occurred in terms of increasing the share of academic and engineering programs. The calculations made it possible to trace the progressive change in the structure of student training under the long first degree programs towards the growth of the medical direction. A reduction in the primary link of the long first degree is due to its transformation to the basic form of Bachelor training.
Sparse KELM Online Prediction Model Based on Forgetting Factor

In the process of online prediction of nonstationary time series by kernel extreme learning machine (KELM), two problems appear that the order of kernel matrix is increasing and the system dynamic characteristics are difficult to be determined. A sparse KELM state prediction model based on forgetting factor (FF) is proposed. Firstly, by introducing the forgetting factor, a new objective function is constructed to make the elements in the sparse dictionary have different weights according to the time distance, so as to ensure the effective tracking of the dynamic changes of the model. By studying the relationship between KELM and kernel recursive least-squares (KRLS), KRLS is extended to the online sparse KELM framework. To control the growth of network structure, and realize the recursion and update of dictionary parameters, the samples are sparse by using approximate linear dependence (ALD) criterion. The experimental results show that compared with KB-KELM, FOKELM, NOS-KELM and KRLSELM, FF-KRLSELM can reduce the average root mean square error by 48% and 36%, and the average relative error by 37% and 36%, and has good dynamic tracking ability and adaptability.
STUDENTS’ PERCEPTION TOWARD ENGLISH FOR SPECIFIC PURPOSES IMPLEMENTATION IN STIE BINA KARYA DURING THE PANDEMIC

The spread of Covid-19 in Indonesia  has paralyzed almost in all system in our live. It significantly affect education system in all over the world. The physical distancing emerge in order to stop the covid 19 transmission.Teachers have to set the way of learning for students on several learning platforms so students can learn effectively and attractively to prevent the boredom at home. No exception for learning English for Specific Purposes. This study is going to describe students’ perception in the implementation of English for Specific Purposes in STIE Bina Karya during the pandemic. This study applied the descriptive qualitative approach which took the data from 108 informants from students who had taken two semesters in pandemic situation. The result of this study were the ESP implementation during the pandemic did not run 100%. Only two from four skills were assessed by the lecturer through the Google Classroom and Youtube Channel. But so far, the informants/the students were still enjoyed learning ESP even in online class. Keywords: pandemic, ESP, perception
DEVELOPMENT OF A MODEL OF THE CONTENT OF TEACHING COMPUTER ARCHITECTURE TO STUDENTS OF ENGINEERING AND PEDAGOGICAL PROFILE

The article deals with the existing methodical systems of learning computer architecture by students of engineering and pedagogical specialty with the use of information and computer technologies and means of simulation modeling. The analysis of these methods revealed the advantages and disadvantages of each methodological system. The identified problem of perception and understanding of physical processes by applicants for higher education which are taking place in the electronic elements of microprocessor systems and their parallelism and transience determined the development of a model of the content of learning the architecture of microprocessors with use of simulation computer modeling. The authors of the article develop and study the system of skills in the architecture of microprocessor mechanisms and the system of knowledge of the course "Architecture of Microprocessors" according to the current State Standard of Higher Education of Ukraine. A detailed analysis of methodological systems of learning computer architecture by students of engineering and pedagogical specialty allowed developing a general model of the content of learning architecture of microprocessor mechanisms that corresponds to the goals set by State Standard of Higher Education. The presented general model of the content of learning architecture of microprocessor mechanisms consists of 6 modules: module 1 – History of computer mechanisms development; module 2 – Calculation systems; module 3 – Logical foundations of computers; module 4 – Computer structure; module 5 – Microprocessors; module 6 – Assembler programming language. These modules provide solutions to an independent group of problems and are integral units of information and logically complete parts of the real process of learning the architecture of microprocessors. Thus, to increase the activity of learning, it is advisable to select and use a variety of learning methods adequate to each educational topic. Key words: computer architecture, information and computer technologies, electronic elements of microprocessor systems, methodological systems, system of skills, system of knowledge, general model of the content of learning.
Increasing Problem-Solving Ability for Students with Visual Impairments and Intellectual Disabilities

Introduction: Despite the national effort to focus on prioritization of mathematical concepts, teachers of students with sensory and cognitive disabilities often do not know how to provide quality instruction to address mathematical skills. Research is limited for this population of students in the area of mathematics. This study is a replication of previous research to determine whether schema-based instruction (SBI) increases problem-solving ability for students with concurrent visual impairments and intellectual disabilities at the secondary level. Method: A multiple-baseline-across-students design was used to study the effects of SBI on problem-solving skills of three high school students with concurrent visual impairments and intellectual disabilities. During the four-to-five-week intervention sessions, students utilized the find, organize, plan, and solve (FOPS) strategy to solve addition and subtraction word problems. Results: The three students improved their problem-solving ability using the FOPS. Mastery increased from 13.2 during baseline to an overall level of 72.5%. Discussion: High school students with visual impairments and intellectual disabilities demonstrated improvement in problem-solving ability after SBI was taught. Limitations: Limitations of this study include: only three students participated in the study, the duration of the intervention was four to five weeks, and utilizing the FOPS strategy with combined visual impairments and intellectual disabilities within peer-reviewed research has yet to be established. Future studies that address high school students with multiple disabilities are needed to support or contradict this study’s findings. These studies could incorporate other mathematical skills. Implications for practitioners: When using SBI to teach problem-solving skills, teachers of students with visual impairments and intellectual disabilities can simplify the language they use, use repetitive operation words, and include pictures for those students with low vision.
Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement

Low light conditions in aerial images adversely affect the performance of several vision based applications. There is a need for methods that can efficiently remove the low light attributes and assist in the performance of key vision tasks. In this work, we propose a new method that is capable of enhancing the low light image in a self-supervised fashion, and sequentially apply detection and segmentation tasks in an end-to-end manner. The proposed method occupies a very small overhead in terms of memory and computational power over the original algorithm and delivers superior results. Additionally, we propose the generation of a new low light aerial dataset using GANs, which can be used to evaluate vision based networks for similar adverse conditions.
Automated Focal Loss for Image based Object Detection.

Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter. Manually tuning this hyperparameter for each training task is highly time-consuming. With automated focal loss we introduce a new loss function which substitutes this hyperparameter by a parameter that is automatically adapted during the training progress and controls the amount of focusing on hard training examples. We show on the COCO benchmark that this leads to an up to 30% faster training convergence. We further introduced a focal regression loss which on the more challenging task of 3D vehicle detection outperforms other loss functions by up to 1.8 AOS and can be used as a value range independent metric for regression.
Transparency in the Patent System – Artificial Intelligence and the Disclosure Requirement

The patent system is fundamentally challenged by the recent breakthroughs in Artificial Intelligence (AI). While issues such as patentability and non-obviousness of AI-related inventions have already attracted attention, this paper deals with the specific research question whether patent law’s disclosure requirement is (also) under attack by AI. Building on the presumption that the patent system’s legitimacy – at least to some extent – is founded on a working disclosure requirement, the issue at hand is highly relevant: If the disclosure requirement were fundamentally challenged by AI, the patent system would run the risk of losing its legitimacy.The research question can also be framed through the lens of transparency. Conditioning the grant of patents on the disclosure of inventions by patentees, the patent system arguably aims to increase transparency. But as AI is scolded for being particularly non-transparent and often compared to a 'black box' one might ask whether the use of AI in the inventive process jeopardises or decreases the desired transparency.This paper argues that the disclosure requirement indeed is challenged by AI, but that the challenges are less grievous than in other areas of patent law. To be more precise: use of AI may lead to new disclosure deficits and some AI-related patents may be considered insufficiently disclosed because they lack reproducibility. But timely anticipation of these risks allows patent law to adapt, particularly because AI is not only a threat but could also remedy imbalances in the disclosure requirement. Provided that the issues are addressed properly, AI may lead even to more instead of less transparency.The research question will be answered in four steps: The paper first examines the disclosure requirement in patent law (II.) and the state of the art regarding AI (III.). It then combines these parts in order to address the looming challenges of an AI-confronted disclosure requirement (IV.) before highlighting new opportunities brought about by AI (V.) and drawing conclusions (VI.).
Toric Codes from Order Polytopes

In this article we investigate a class of linear error correcting codes in relation with the order polytopes. In particular we consider the order polytopes of tree posets and bipartite posets. We calculate the parameters of the associated toric variety codes.
Towards Ontology and Blockchain Based Measurement, Reporting, and Verification For Climate Action

Efforts that incentivize GHG emissions reduction are hampered by inability to transparently and uniformly account for impact. The UN has espoused blockchain’s potential to address transparency, and novel Measurement, Reporting, and Verification (MRV) systems that constitute the state-of-the-art in using blockchain to address this need. However, positive effect is diluted without sufficient uniformity: Different blockchains idiosyncratically reporting carbon credits based on differing standards does not constitute a solution. In this paper, we specify a top-level architecture that uses Semantic Web ontologies to complement a blockchain-based, transparent, uniform (sharable) MRV system. Our design conceptualizes transforming “smart standards” designed and harmonized using IT into ontologies of quantification methodologies and verification standards, which in turn can be used develop smart contracts executable on, and interoperable between, different blockchains. We pose this within the context of the real-life Reciclo Orgánicos project, an international collaboration to reduce GHG emissions from Chile’s municipal waste sector.
DRIV100: In-The-Wild Multi-Domain Dataset and Evaluation for Real-World Domain Adaptation of Semantic Segmentation

Together with the recent advances in semantic segmentation, many domain adaptation methods have been proposed to overcome the domain gap between training and deployment environments. However, most previous studies use limited combinations of source/target datasets, and domain adaptation techniques have never been thoroughly evaluated in a more challenging and diverse set of target domains. This work presents a new multi-domain dataset DRIV100 for benchmarking domain adaptation techniques on in-the-wild road-scene videos collected from the Internet. The dataset consists of pixel-level annotations for 100 videos selected to cover diverse scenes/domains based on two criteria; human subjective judgment and an anomaly score judged using an existing road-scene dataset. We provide multiple manually labeled ground-truth frames for each video, enabling a thorough evaluation of video-level domain adaptation where each video independently serves as the target domain. Using the dataset, we quantify domain adaptation performances of state-of-the-art methods and clarify the potential and novel challenges of domain adaptation techniques. The dataset is available at https://doi.org/10.5281/zenodo.4389243.
Generalized Micro-Service Architecture for Web Based Database Management Systems

In web-based database management systems, execution of multiple select queries or the queries requesting a large amount of data records from a single database management service via http requests is heavily affected by the performance of the database management service, rather than database engine. Although almost every popular Relational Database Management System (RDBMS) is designed with an architecture to provide higher efficiency, ultimately a single database management service will read the database and serve the results through http in the common scenario. So, a drastic performance reduction is visible while serving the http requests with the fetched data from the databases. This research examines the efficiency difference between micro-service architecture and mono service architecture for web-based database management systems by analyzing the time taken to get the response with data for READ operations with different record sizes. This research proposes a simple and generic web service architecture in managing HTTP requests which require fetching data from the underlying SQL adhering the micro service concepts.
A Compendium of Classification Techniques, Tools and Evaluation Datasets for Twitter Sentiment Analysis

Social networking sites and micro blogs provide tremendous amount of real time data every day. Sentiment analysis or opinion mining aims to automate the process of sentiment extraction from the user content available online. Twitter in recent years due to its high subscriber rate and diverse audience, has become increasingly powerful in representing and changing user opinions over an object or event. This paper focuses on research conducted within the field of twitter sentiment analysis. The objective is to comprehensively investigate the task of sentiment analysis and its sub processes and identify the different tools, techniques or other resources used or applied on twitter data during the process. A Systematic Literature Review (SLR) has been conducted to identify 40 researches, relevant to sentiment identification and analysis. The work presented covers major tools and techniques used during sentiment mining process and maybe utilized by researchers or practitioners for identifying potential research directions as well as suggest possible software development areas that need to be explored.
Language interference in elt writing class

This research about The Effect of Using Language Interference in ELT Writing Class at The Tenth Grade of Athirah Bone. It is intended to find out two problems of writing. The first problem is the types of errors made by the students in students’ writing skills of the tenth grade of Athirah Bone. This research used a qualitative method by implementing interview. The sample consisted of 9 students from Athirah Bone. The result shows that the students’ perception of the types of students’ errors in writing consist of five perceptions. The first is students’ perceptions relate to the feeling. The second is students’ perceptions relate with the first language’ influence students in mastering the second language. The third is the students’ perception relates to the students’ experiences and kinds of interferences that students have made on writing. The fourth is the students’ perception relates to the factors that cause students to do interfere in writing. The last is about the students’ perception of the difficulties in writing. The second problem is the factors that cause students to do interfere in writing through the observation, it is found that there are four factors that make the students of the tenth grade of Athirah Bone do interfere in writing. The first is do not master spelling. The second is still confused about using the article. The third is Missing using singular and plural nouns. The last is missing space.
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present \textbf{DiSCoL} (\textbf{Di}alogue \textbf{S}ystems through \textbf{Co}versational \textbf{L}ine guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly \textbf{convlines}) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL's pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to \textit{control} the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
Mapping Wage Theft with Data Science

Wage theft is almost 'normal' in some industries, but hard to detect. Predictive algorithms can help regulators and give workers an edge.
Fine-tuning BERT-based models for Plant Health Bulletin Classification

In the era of digitization, different actors in agriculture produce numerous data. Such data contains already latent historical knowledge in the domain. This knowledge enables us to precisely study natural hazards within global or local aspects, and then improve the risk prevention tasks and augment the yield, which helps to tackle the challenge of growing population and changing alimentary habits. In particular, French Plants Health Bulletins (BSV, for its name in French Bulletin de Sant{\'e} du V{\'e}g{\'e}tal) give information about the development stages of phytosanitary risks in agricultural production. However, they are written in natural language, thus, machines and human cannot exploit them as efficiently as it could be. Natural language processing (NLP) technologies aim to automatically process and analyze large amounts of natural language data. Since the 2010s, with the increases in computational power and parallelization, representation learning and deep learning methods became widespread in NLP. Recent advancements Bidirectional Encoder Representations from Transformers (BERT) inspire us to rethink of knowledge representation and natural language understanding in plant health management domain. The goal in this work is to propose a BERT-based approach to automatically classify the BSV to make their data easily indexable. We sampled 200 BSV to finetune the pretrained BERT language models and classify them as pest or/and disease and we show preliminary results.