Reviewerly/exHarmony huggingface dataset:
Context-aware Multi-stakeholder Recommender Systems

Traditional recommender systems help users find the most relevant products or services to match their needs and preferences. However, they overlook the preferences of other sides of the market (aka stakeholders) involved in the system. In this paper, we propose to use contextual bandit algorithms in multi-stakeholder platforms where a multi-sided relevance function with adjusting weights is modeled to consider the preferences of all involved stakeholders. This algorithm sequentially recommends the items based on the contextual features of users along with the priority of the stakeholders and their relevance to the items.Our extensive experimental results on a dataset consisting of MovieLens (1m), IMDB (81k+), and a synthetic dataset show that our proposed approach outperforms the baseline methods and provides a good trade-off between the satisfaction of different stakeholders over time.
Artificial Intelligence in Library; Managing Bibliographic Data with Knowledge Base System

This study discusses Artificial Intelligence in Library; Managing Bibliographic Data With Knowledge Base System In library activities such as indexing, cataloging, literature selection, archiving and references can be assisted by an expert system. Building an expert system using the Knowledge Base System in the library must be supported by command languages, output formats, database coverage, and the existence of literature search facilities including operations to correct errors when searching for information. The research was conducted by collecting information from various sources, then examining library services that could utilize artificial intelligence and explained descriptively. It is not as easy as turning the palm of the hand to bring artificial intelligence in the library. But once it is done, the library becomes a center of knowledge that is not boring. Even libraries can be present online in cyberspace.
Named Entity Recognition for Nepali: Data Sets and Algorithms

Named Entity Recognition (NER) task involves locating Named Entities (NEs) in free text and classifying them into predefined categories such as Person Name, Location and Organization. Although the NER task has been studied widely in resource-rich languages, it has not been studied thoroughly for Nepali, a resource-poor language. In this paper, we present the systematic study of NER for Nepali language with clear Annotation Guidelines obtaining high inter-annotator agreements. The annotation produces EverestNER, the largest human annotated NER data set for Nepali which has 24,587 entities in total. It has 308,353 tokens corresponding to 15,798 sentences which are annotated into five categories: Person, Location, Organization, Date and Event. We split the EverestNER data set into EverestNER-train and EverestNER-test. These standard data sets, therefore, become the first benchmark data sets for evaluating Nepali NER systems. We release the EverestNER benchmark data sets to facilitate the research in Nepali language at https://github.com/nowalab/everest-ner. We report a comprehensive evaluation of state-of-the-art Neural and Transformer models using these data sets. We also discuss the remaining challenges for discovering NEs for Nepali.
Internal quality control of red cell concentrates in a regional blood center.

Objective: To assess the quality of the whole blood-derived red cell concentrates by measuring hematocrit. Study Design: Cross Sectional study. Setting: Regional Transfusion Center in Rawalpindi. Period: February to December 2019. Material &amp; Methods: A total of 390 whole blood-derived red cell concentrates were included using a random sampling technique. These units were evaluated for hematocrit which is a hematological parameter using Sysmex Hematology Analyzer XP 100. The measurement of hematocrit was expressed as the mean range and standard deviation (mean ± SD) using descriptive statistics. Results: A total of 390 whole blood-derived red cell concentrates were subjected to quality analysis by measuring the hematocrit. The mean range of hematocrit was found to be 68.4 ± 4.8 g/dL. The hematocrit of 98.7% units was in compliance with standard criteria according to the American Association of blood banks which suggested it to be less than 80%. Conclusion: The results of this study showed an optimum level of conformity of the quality of whole blood-derived Red Cell Concentrates with International Standards.
Towards Automated Error Analysis: Learning to Characterize Errors

Characterizing the patterns of errors that a system makes helps researchers focus future development on increasing its accuracy and robustness. We propose a novel form of "meta learning" that automatically learns interpretable rules that characterize the types of errors that a system makes, and demonstrate these rules' ability to help understand and improve two NLP systems. Our approach works by collecting error cases on validation data, extracting meta-features describing these samples, and finally learning rules that characterize errors using these features. We apply our approach to VilBERT, for Visual Question Answering, and RoBERTa, for Common Sense Question Answering. Our system learns interpretable rules that provide insights into systemic errors these systems make on the given tasks. Using these insights, we are also able to `"close the loop" and modestly improve performance of these systems.
Optimal Anonymous Independent Reward Scheme Design

We consider designing reward schemes that incentivize agents to create high-quality content (e.g., videos, images, text, ideas). The problem is at the center of a real-world application where the goal is to optimize the overall quality of generated content on user-generated content platforms. We focus on anonymous independent reward schemes (AIRS) that only take the quality of an agent's content as input. We prove the general problem is NP-hard. If the cost function is convex, we show the optimal AIRS can be formulated as a convex optimization problem and propose an efficient algorithm to solve it. Next, we explore the optimal linear reward scheme and prove it has a 1/2-approximation ratio, and the ratio is tight. Lastly, we show the proportional scheme can be arbitrarily bad compared to AIRS.
TLMOTE: A Topic-based Language Modelling Approach for Text Oversampling

Training machine learning and deep learning models on unbalanced datasets can lead to a bias portrayed by the models towards the majority classes. To tackle the problem of bias towards majority classes, researchers have presented various techniques to oversample the minority class data points. Most of the available state-of-the-art oversampling techniques generate artificial data points which cannot be comprehensibly understood by the reader, despite the synthetic data points generated being similar to the original minority class data points. In this work, we present Topic-based Language Modelling Approach for Text Oversampling (TLMOTE), a novel text oversampling technique for supervised learning from unbalanced datasets. TLMOTE improves upon previous approaches by generating data points which can be intelligibly understood by the reader, can relate to the main topics of the minority class, and introduces more variations to the synthetic data generated. We evaluate the efficacy of our approach on various tasks like Suggestion Mining SemEval 2019 Task 9 Subtasks A and B, SMS Spam Detection, and Sentiment Analysis. Experimental results verify that oversampling unbalanced datasets using TLMOTE yields a higher macro F1 score than with other oversampling techniques.
Fast Content Placement and Alignment in 3D Scenes

We present a system for fast content placement on flat surfaces in 3D scenes that leverages plane detection and computer vision. For content creators, placing and aligning objects is often a time-consuming process. Especially with AR, this problem becomes more prevalent as virtual objects should be aligned to the real world. For certain objects that should be aligned to planar regions, e.g, a virtual painting being placed on a real wall, the scene (wall) implies the rotation of the object. However, placing a virtual painting usually requires manually modifying its 3D coordinates and manually rotating it to exactly match the wall. In this work, we propose the use of plane detection to speed up the placement and alignment process for every object whose relation to surfaces has been defined, e.g., images or posters. We do this by detecting approximately planar regions in our scene and calculating planes for them. Then to place an object, one can simply click a point in the scene to select the location and with a raycast, the actual point in the 3d space is found. The object is then automatically positioned and aligned by moving it to the closest point on the plane of the region and matching the plane's rotation. Furthermore, we apply object detection and segmentation to the textured planes to gain a semantic understanding of the scene. This enables automatic placement suggestions and assistance when aligning objects to visual markers, e.g., snapping the corners of paintings to those of windows in AR.
EXPLORING BENEFITS AND CHALLENGES OF USING BLACKBOARD COLLABORATE AS A REMOTE LEARNING TOOL AMONG QASSIM UNIVERSITY EFL INTENSIVE COURSE PROGRAM STUDENTS DURING COVID-19 PANDEMIC

Due to the outbreak of COVID-19 pandemic, using Blackboard platform as a learning tool has dramatically increased in Saudi universities.Still, learners lack effective knowledge of using Blackboard platform.Accordingly, there is a pressing need to concentrate on using Blackboard platform during the COVID-19 pandemic to improve learning outcomes.Therefore, this study was conducted to explore the benefits and challenges of using Blackboard Collaborate as a learning tool by EFL ICP students during the COVID-19 pandemic.Focus group interviews were used to collect data from a purposive sample of 15 Saudi male ICP students who took part willingly in this study.Inductive thematic analysis was used to analyze the interview data.The findings showed that using Blackboard Collaborate as a learning tool can help the students enhance online cooperative learning, flexible learning in a variety of ways other than traditional classes, having access to a wide range of information for academic purposes and ability to use technology effectively.Additionally, the findings showed that the challenges faced by ICP students include insufficient technical knowledge and training, limited and poor internet connection, inability to upload and download large size files and problems with logging in password.It was intended that this research would be useful to all higher education institutions stakeholders in general.However, with only a few participants, this study was limited to EFL ICP students at Qassim University.Further research may use a quantitative or mixed-method approach to provide generalizable results.
Assessing Students’ Learning Attitude and Academic Performance Through m-Learning During the COVID-19 Pandemic

This study aimed to assess college of education students’ learning attitude and academic performance in using m-learning during the COVID-19 pandemic. The study employed a pre-test and post-test experimental research design with 50 students from the College of Education, Ikere Ekiti, Nigeria. Two research instruments were used to collect data from the participants on two occasions. The first instrument was a students’ attitude questionnaire that measured the attitude of the participants towards learning. The second instrument was the students’ academic performance test that measured the students’ scores. The differences between pre- and post-tests were measured through independent t-test. Demographic data are presented in a bar chart and show that the majority of the students own mobile devices that were suitable for learning; that the majority of the students used mobile devices for learning; and that all the respondents in the experimental group possessed mobile devices with the Zoom app. The pre-test findings revealed no significant differences in the attitude and performance of students towards m-learning and traditional learning (p&gt;0.005) while the post-test findings showed significant differences in the attitude and performance of students towards m-learning and traditional learning (p&lt;0.005). These findings suggest that m-learning should be integrated into the school curriculum.
An Overview on the Explainability of Cyber-Physical Systems

The prevalence of automating complex physical processes through learning and interactions among heterogeneous components adds to the increasing complexity of Cyber-Physical Systems (CPS) and their behavior. Popular Explainable Artificial Intelligence (XAI) methodologies usually overlook the impact of physical and virtual context when explaining the outputs of decision-making software models, which are essential factors in explaining CPS' behavior to stakeholders. Hence in this article, we survey the most relevant XAI methods used for explaining CPS' behavior to identify their shortcomings and applicability in explaining the behavior of CPS. Our main findings are (i) several papers point out the importance of context for explaining CPS, but the explanation methods lack context-awareness; (ii) the explanation delivery mechanisms using low-level visualization tools make the explanations unintelligible. Finally (iii), added information about the system's working and context may increase the actionability of the explanations. Therefore, we propose to enrich the explanations further with contextual information using Semantic Technologies, user feedback, and enhanced explanation visualization techniques to improve their understandability. To that end, context-aware explanation and better explanation presentation based on knowledge graphs and counterfactual explanations might be a promising future research direction for explainable CPS.
The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models

We study the Stochastic Gradient Descent (SGD) algorithm in nonparametric statistics: kernel regression in particular. The directional bias property of SGD, which is known in the linear regression setting, is generalized to the kernel regression. More specifically, we prove that SGD with moderate and annealing step-size converges along the direction of the eigenvector that corresponds to the largest eigenvalue of the Gram matrix. In addition, the Gradient Descent (GD) with a moderate or small step-size converges along the direction that corresponds to the smallest eigenvalue. These facts are referred to as the directional bias properties; they may interpret how an SGD-computed estimator has a potentially smaller generalization error than a GD-computed estimator. The application of our theory is demonstrated by simulation studies and a case study that is based on the FashionMNIST dataset.
SISTEM INFORMASI BERKAH BERSAMA BERBASIS WEB

Berkah Bersama Company is a core partnership company engaged in broiler farming. Within the company, there are three divisions, namely general and finance, logistics, production, and marketing. The production and marketing division plays a role in the growth of chickens to the sale of chickens. After the chicken is sold, the data on chicken growth and chicken sales will be recapitulated using Microsoft Excel. The process of data collection is still often an error in data input and at the time of data recapitulation often occurs calculation errors and losses in the final process. From these problems, the authors conduct research by building a web-based mutual blessing information system in the production and marketing division that will solve the above problems. The research method used is an incremental model that applies the stages in parallel where the first stage has not been completed, so it can go to the next stage. And the system design uses entity relationship diagrams and an integrated modeling language. The result of this research is a blessing information system with web-based production and marketing divisions that can assist and simplify data input and data recording in the production and marketing divisions.
Gaussian Mixture Model with Weighted Data for Learning by Demonstration

Cobots are robots specialized in collaborating with a human to do a task. These cobots needs to be easily re-programmed in order to adapt to a new task. Learning by Demonstration enables a non-expert user to program a cobot by demonstrating how to realize the task. Once the learning is done, the user can only improve the learning by adding new demonstrations or deleting existing ones. In this article, the proposed model gives the possibility to the user to impact the learning by choosing which parts of the demonstration has more importance. This model uses an extended version of Gaussian Mixture Model (GMM) with weighted data coupled with Gaussian Mixture Regression (GMR). This architecture was tested with two different tasks and with two robots. Results indicate better generated trajectory with the proposed approach.
PERBANDINGAN METODE ANALISIS CLUSTER HIRARKI PADA DATA MARGIN PERDAGANGAN DAN PENGANGKUTAN (MPP) KOMODITAS STRATEGIS DI INDONESIA

Trade and Transport Margins is the difference between the sales value and th e purchase value of the commodity as compensation for the trader who is the distributor of the commodity. The higher Trade and Transport Margins indicates the longer and less efficient distribution pattern, so that it can harm producers and consumers. Data is taken from the 2020 Poldis survey with strategic commodities of Rice, Eggs, Sugar, and Cooking Oil. The value of the National Trade and Transport Margins in 2020 for rice and sugar has decreased but is not followed by an increasing number of provincial Trade and Transport Margins which have decreased. It is necessary to conduct an analysis to determine the characteristics of commodities in each province, hierarchical cluster analysis by comparing the application of the average, complete, single, ward's, and centroid methods to obtain the best method. Based on the results of hierarchical cluster analysis for all methods, then compared the cophenetic correlation values ​​ and the highest correlation on the average method with a correlation of 0.8318939. Then cluster profiling was carried out using the average method, it was concluded that Cluster 1 was dominated by low Trade and Transport Margins for all commodities, so that the distribution pattern of all commodities was efficient in 28 provinces in Indonesia. Cluster 2 has the characteristics of high Trade and Transport Margins of Rice and Cooking Oil as well as eggs and moderate sugar, so the distribution pattern is still not efficient for rice and cooking oil commodities in 4 provinces in Indonesia. Cluster 3 has the characteristics of high Trade and Transport Margins of eggs and sugar as well as medium of rice and cooking oil, so the distribution pattern is still not efficient for eggs and sugar in 2 provinces in Indonesia. Based on the results of the cluster analysis, it is hoped that it can be used as a reference in formulating and establishing policies as an effort to reduce the Trade and Transport Margins value of strategic commodities in provinces in clusters 2 and 3 according to the conditions of commodity characteristics so that the distribution pattern of strategic commodities becomes more efficient.
Abnormal-Aware Multiperson Evaluation System with Improved Fuzzy Weighting

There exists a phenomenon that subjectivity highly lies in the daily evaluation process. Our research primarily concentrates on a multiperson evaluation system with anomaly detection to minimize the possible inaccuracy that subjective assessment brings. We choose the two-stage screening method, which consists of rough screening and score-weighted Kendall-τ distance to winnow out abnormal data, coupled with hypothesis testing to narrow global discrepancy. Then we use fuzzy synthetic evaluation method (FSE) to determine the significance of scores given by reviewers as well as their reliability, culminating in a more impartial weight for each reviewer in the final conclusion. The results demonstrate a clear and comprehensive ranking instead of unilateral scores, and we get to have an efficiency in filtering out abnormal data as well as a reasonably objective weight determination mechanism. We can sense that through our study, people will have a chance of modifying a multiperson evaluation system to attain both equity and a relatively superior competitive atmosphere. A preprint has previously been published (Ni, 2022).
Lyrics Generation supported by Pre-trained Models

Advancements in neural network architectures have improved the quality of several tasks in computational linguistics. Among the tasks benefited we can mention question and answer systems, dialogue systems, opinion mining and the automatic generation of texts, just to mention a few. Despite the advances, there is still room for contributions, since there are still open problems. In the case of text generation, especially in the musical genre, there are challenges for the production of texts that involve poetry and idioms. In particular, some of these challenges are linked to the treatment of metaphors and metonymy and the generation of paraphrases. This paper presents an analysis of the generation of excerpts of lyrics based on a pre-trained GPT-2 neural network model, after fine-tuning with two lyrics corpora, one in English and one in Portuguese. An analysis of the spelling, syntax and semantics of the generated texts are presented, as well as the discussion about the attempt to find a pattern in the sections generated by the implemented tool. Research demonstrates the potential for using such models in the generation of poetic texts.
Performance Comparison of Radar and Video for American Sign Language Recognition

In the past decade, there has been a great research in the developments of American Sign Language (ASL) enabled user interfaces and smart environments, especially using wearables, RGB and RGB-D video cameras, and radio frequency (RF) sensors. Each sensor modality provides distinct advantages and suffer from various problems. Although each sensor modality is studied for ASL recognition a comparison of video and RF based sensing performance in terms of ASL recognition is not available. This study aims to compare word level ASL recognition performance over the same 100 ASL glosses data from both RF and video sensors. A top-5 accuracy of 93&#x0025; was achieved while using the RF micro-Doppler spectrogram representation in a convolutional neural network (CNN) classifier, whereas with video ASL data for the same 100 words, a top-5 accuracy of 90&#x0025; was achieved. This shows that radar has comparable recognition performance to video for ASL recognition.