Reviewerly/exHarmony huggingface dataset:
Rating prediction based on the graph Fourier basis and PSD estimation from the perspective of graph signal reconstruction

Accurately and quickly predicting unknown ratings from fewer known ratings has always been a challenging problem for recommender systems. Inspired by emerging graph signal processing techniques, this paper proposes a rating prediction method via a graph signal reconstruction technique. The most novel point is that the proposed method does not need to construct (or learn) the graph structures in advance and then estimate the power spectral density (PSD), but directly estimates the graph Fourier basis and the PSD from data. Furthermore, we present an approximate solution strategy to significantly reduce the computational complexity of the reconstructed model. Finally, we conduct experiments with two public databases to test the proposed method and compare it with existing methods. The experiments give an encouraging result in both prediction accuracy and efficiency.
The role of social norms and traditions in the Chinese enterprise social management mechanism functioning

The article examines the role of social norms and cultural traditions in the implementation of the social management mechanism in Chinese enterprises. The article reveals the features of a sociological approach to the study of management mechanisms, analyzes cultural values and traditions, as well as social norms that can influence the formation of a social management mechanism in an enterprise, an empirical study (n=152) of the influence of social norms and traditions on the enterprise management process. As a result, it was revealed that social norms and traditions play an important role in the mechanism of social management of an enterprise. It is concluded that considering the cultural values of Confucianism, Taoism, Legalism and Mohism makes it possible to make enterprise management more reasonable, which leads to an increase in the efficiency of employees and the enterprise as a whole. Further research may be devoted to studying the features of social management in Chinese companies.
Brain-derived neuromorphic computing with 3D electronic-photonic integrated circuits

Reverse engineering the brain has been our dream and a grand challenge for many decades. The human brain has immense learning capabilities at extreme energy efficiencies and scale, which no artificial system has been able to match. We propose that utilization of a brain-derived—rather than a brain-inspired—architecture will lead to a paradigm shift, enabling the development of intelligent agents that can work in tandem with humans on complex tasks in noisy, unpredictable environments. Key to this paradigm shift will be the integration of hardware that emulates the processing characteristics of the brain with design principles based on fundamental aspects of neural plasticity and circuit design in the human brain. To achieve this grand challenge, we pursue a new neuromorphic computing paradigm enabled by modular integration of energy-efficient 3D-electronic-photonic-ICs (3D EPICs) with attojoule nanophotonic neurons in photonic neural networks.
Using feature selection to evaluate pathological speech after training with a serious game

To evaluate the effectiveness of speech therapy, speech features before and after treatment can be compared, focussing on those features that changed most during treatment.In the current study acoustic features were automatically extracted from speech of patients affected by Parkinson's Disease who had received speech treatment.Praat and openSMILE were used for feature extraction.Through feature selection, the top ten most characterizing features for pre vs. post-treatment were found.Further analysis of these features confirmed that after treatment the speakers spoke louder with lower pitch, which were the goals of the treatment.
The Effect of Artificial Intelligence and Robotics on Learning in the Higher Education Sector

Artificial Intelligence is a combination of algorithms that can be taught to machines to effectively perform tasks and solve problems (Agrawal, Gans & Goldfarb, 2017). Computing especially artificial intelligence has played an important role in approaching modern management as businesses are rapidly developing and require fast yet rational responses (Alvesson, 2015). Artificial intelligence has been defined in many ways following four approaches which are thinking humanly, thinking rationally, acting humanly and acting rationally (Britgo, 2019). These approaches explain how artificial intelligence can process information and react like humans with rationality. Artificial intelligence has been defined as the theory and development of computer systems that are able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. This paper intends to determine the effect of Artificial intelligence and robotics on learning in the higher education sector.
Data Breach Announcement Effect on Bank Operations and Performance

A downside of the digital economy is that banks are prone to experiencing data breaches resulting from, for example, cyberattacks, system glitches, and employee negligence. We investigate how a data breach announcement affects bank operations and stock performance. Our findings indicate that banks have an outflow of insured and brokered deposits after a data breach announcement. Large banks experience almost no impact from a data breach announcement. Furthermore, we find that deposits transfer from banks with data breaches to banks with no history of experiencing data breaches. We find evidence that banks try to develop strategies after a data breach supported by the changes in loan activities and interest margins. Overall, the loan activities increase, and deposit rates decrease after a data breach announcement. In terms of an impact on stock return, data breaches negatively affect stock returns in both the short term and long term.
User Privacy in Recommender Systems

Recommender systems process abundances of user data to generate recommendations that fit well to each individual user. This utilization of user data can pose severe threats to user privacy, e.g., the inadvertent leakage of user data to untrusted parties or other users. Moreover, this data can be used to reveal a user’s identity, or to infer very private information as, e.g., gender. Instead of the plain application of privacy-enhancing techniques, which could lead to decreased accuracy, we tackle the problem itself, i.e., the utilization of user data. With this, we aim to equip recommender systems with means to provide high-quality recommendations that respect users’ privacy.
Identifying and Representing Knowledge Delta in Scientific Literature

The process of continuously keeping up to date with the state-of-the-art on a specific research topic is a challenging task for researchers not least due to the rapid increase of published research. In this research proposal, we define the term Knowledge Delta (KD) between scientific articles which refers to the differences between pairs of research articles that are similar in some aspects. We propose a three-phase research methodology to identify and represent the KD between articles. We intend to explore the effect of applying different text representations on extracted facts from scientific articles on the downstream task of KD identification.
The Impact of Cross-Lingual Adjustment of Contextual Word Representations on Zero-Shot Transfer

Large multilingual language models such as mBERT or XLM-R enable zero-shot cross-lingual transfer in various IR and NLP tasks. Cao et al. [8] proposed a data- and compute-efficient method for cross-lingual adjustment of mBERT that uses a small parallel corpus to make embeddings of related words across languages similar to each other. They showed it to be effective in NLI for five European languages. In contrast we experiment with a topologically diverse set of languages (Spanish, Russian, Vietnamese, and Hindi) and extend their original implementations to new tasks (XSR, NER, and QA) and an additional training regime (continual learning). Our study reproduced gains in NLI for four languages, showed improved NER, XSR, and cross-lingual QA results in three languages (though some cross-lingual QA gains were not statistically significant), while mono-lingual QA performance never improved and sometimes degraded. Analysis of distances between contextualized embeddings of related and unrelated words (across languages) showed that fine-tuning leads to “forgetting” some of the cross-lingual alignment information. Based on this observation, we further improved NLI performance using continual learning. Our software is publicly available https://github.com/pefimov/cross-lingual-adjustment .
Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data

Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce. Here, we demonstrate how recent advances in machine learning, combined with a recently published multilingual data set with standardized fine-grained product category information, enable robust product attribute extraction in challenging transfer learning settings. Our models can reliably predict product attributes across online shops, languages, or both. Furthermore, we show that our models can be used to match product taxonomies between online retailers.
The System for Efficient Indexing and Search in the Large Archives of Scanned Historical Documents

The paper introduces software capable of indexing and searching large archives of scanned historical documents. The system capabilities are demonstrated on the collection containing documents from the archives of the post-Soviet security services. The backend of the system was designed with a focus on flexibility (it is actually already being used for other related tasks) and scalability to larger volumes of data. The graphical user interface design has been consulted with historians interested in using the archived documents and was developed in several iterations, gradually including the changes induced both by the user’s requests and by our improving knowledge about the nature of the processed data.
A Comprehensive Overview of Consumer Conflicts on Social Media

The use of social media platforms is increasingly prevalent in society, providing brands with a multitude of opportunities to interact with consumers. However, literature has shown this increased usage has negative impacts for users who have experienced depression, anxiety, and stress and brands who see increasing volumes of hate within their communities such as bullying, conflicts, complaints, and harmful content. Existing research focuses on extreme forms of conflict, largely ignoring the lesser forms which still pose a significant threat to consumer and brand welfare. This research aims to capture the full spectrum of online conflict, providing a comprehensive overview of the problem from an interdisciplinary marketing and computer science perspective. I propose a further investigation into online hate, utilising big data analysis to establish an understanding of triggers, consequences and brand responses to online hate. Initially, I will conduct a systematic literature review exploring the definitions and methodology used within the hate research domain. Secondly, I will conduct an investigation into state-of-the-art models and classification systems, producing an analysis on the prevalence of hate and its various forms on social media. Finally, I plan to establish the features of social media data which constitute triggers for online conflicts. Then, through a combination of user studies, sentiment analysis, and emotion detection I will examine the consequences of these conflicts. This project represents a unique opportunity to combine cutting edge marketing theories with big data analysis, this collaborative approach will offer a considerable contribution to academic literature.
Text Information Retrieval in Tetun

Tetun is one of Timor-Leste’s official languages alongside Portuguese. It is a low-resource language with over 932,000 speakers that started developing when Timor-Leste restored its independence in 2002. Newspapers mainly use Tetun and more than ten national online news websites actively broadcast news in Tetun every day. However, since information retrieval-based solutions for Tetun do not exist, finding Tetun information on the internet and digital platforms is challenging. This work aims to investigate and develop solutions that can enable the application of information retrieval techniques to develop search solutions for Tetun using Tetun INL and focus on the ad-hoc text retrieval task. As a result, we expect to have effective search solutions for Tetun and contribute to the innovation in information retrieval for low-resource languages, including making Tetun datasets available for future researchers.
TweetStream2Story: Narrative Extraction from Tweets in Real Time

The rise of social media has brought a great transformation to the way news are discovered and shared. Unlike traditional news sources, social media allows anyone to cover a story. Therefore, sometimes an event is already discussed by people before a journalist turns it into a news article. Twitter is a particularly appealing social network for discussing events, since its posts are very compact and, therefore, contain colloquial language and abbreviations. However, its large volume of tweets also makes it impossible for a user to keep up with an event. In this work, we present TweetStream2Story, a web app for extracting narratives from tweets posted in real time, about a topic of choice. This framework can be used to provide new information to journalists or be of interest to any user who wishes to stay up-to-date on a certain topic or ongoing event. As a contribution to the research community, we provide a live version of the demo, as well as its source code.
PyGaggle: A Gaggle of Resources for Open-Domain Question Answering

Text retrieval using dense–sparse hybrids has been gaining popularity because of their effectiveness. Improvements to both sparse and dense models have also been noted, in the context of open-domain question answering. However, the increasing sophistication of proposed techniques places a growing strain on the reproducibility of results. Our work aims to tackle this challenge. In Generation Augmented Retrieval (GAR), a sequence-to-sequence model was used to generate candidate answer strings as well as titles of documents and actual sentences where the answer string might appear; this query expansion was applied before traditional sparse retrieval. Distilling Knowledge from Reader to Retriever (DKRR) used signals from downstream tasks to train a more effective Dense Passage Retrieval (DPR) model. In this work, we first replicate the results of GAR using a different codebase and leveraging a more powerful sequence-to-sequence model, T5. We provide tight integration with Pyserini, a popular IR toolkit, where we also add support for the DKRR-based DPR model: the combination demonstrates state-of-the-art effectiveness for retrieval in open-domain QA. To account for progress in generative readers that leverage evidence fusion for QA, so-called fusion-in-decoder (FiD), we incorporate these models into our PyGaggle toolkit. The result is a reproducible, easy-to-use, and powerful end-to-end question-answering system that forms a starting point for future work. Finally, we provide evaluation tools that better gauge whether models are generalizing or simply memorizing.
Pre-processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering

One of the contributions of the landmark Dense Passage Retriever (DPR) work is the curation of a corpus of passages generated from Wikipedia articles that have been segmented into non-overlapping passages of 100 words. This corpus has served as the standard source for question answering systems based on a retriever–reader pipeline and provides the basis for nearly all state-of-the-art results on popular open-domain question answering datasets. There are, however, multiple potential drawbacks to this corpus. First, the passages do not include tables, infoboxes, and lists. Second, the choice to split articles into non-overlapping passages results in fragmented sentences and disjoint passages that models might find hard to reason over. In this work, we experimented with multiple corpus variants from the same Wikipedia source, differing in passage size, overlapping passages, and the inclusion of linearized semi-structured data. The main contribution of our work is the replication of Dense Passage Retriever and Fusion-in-Decoder training on our corpus variants, allowing us to validate many of the findings in previous work and giving us new insights into the importance of corpus pre-processing for open-domain question answering. With better data preparation, we see improvements of over one point on both the Natural Questions dataset and the TriviaQA dataset in end-to-end effectiveness over previous work measured using the exact match score. Our results demonstrate the importance of careful corpus curation and provide the basis for future work.
Selecting ICT in Alternative Training

The research goal is to investigate pedagogical students’ preferences of ICT in alternative (distance) education. The primary research issue concerns selecting the most helpful ICT for temporary distance education of pedagogical students. It splits into more specific issues concerning (1) the ranking of ICT in temporary distance education in different groups of students, (2) gadgets mostly applied in distance education, (3) technologies preferred by students and teachers for learning new information, (4) and technologies for testing knowledge and skills. We apply the case study method and method of ranking in the research. Internet survey serves as a data collection technique. The results show that full-time and part-time students select the same three essential technologies for alternative education, but their selection of ICT differs from teachers’ preferences. Besides, we have registered some gender differences in ICT selection. The results deviate from other scholars’ research as students’ preferences of ICT in this paper depend on the distance of education. The research results may be of interest to educators engaged in teacher training.
Designing Useful Conversational Interfaces for Information Retrieval in Career Decision-Making Support

The proposal is an interdisciplinary problem-focused study to explore the usefulness of conversational information retrieval (CIR) in a complex domain. A research-through-design methodology will be used to identify the informational, practical, affective, and ethical requirements for a CIR system in the specific context of Career Education, Information, Advice & Guidance (CEIAG) services for young people in Scotland. Later phases of the research will use these criteria to identify appropriate techniques in the literature, and design and evaluate artefacts intended to meet these. This research will use an interdisciplinary approach to further understanding on the use and limitations of dialogue systems as intermediaries for information retrieval where there are a wide range of possible information tasks and specific users’ needs may be ambiguous.
A Reproducibility Study of Question Retrieval for Clarifying Questions

The use of clarifying questions within a search system can have a key role in improving retrieval effectiveness. The generation and exploitation of clarifying questions is an emerging area of research in information retrieval, especially in the context of conversational search. In this paper, we attempt to reproduce and analyse a milestone work in this area. Through close communication with the original authors and data sharing, we were able to identify a key issue that impacted the original experiments and our independent attempts at reproduction; this issue relates to data preparation. In particular, the clarifying questions retrieval task consists of retrieving clarifying questions from a question bank for a given query. In the original data preparation, such question bank was split into separate folds for retrieval – each split contained (approximately) a fifth of the data in the full question bank. This setting does not resemble that of a production system; in addition, it also was only applied to learnt methods, while keyword matching methods used the full question bank. This created inconsistency in the reporting of the results and overestimated findings. We demonstrate this through a set of empirical experiments and analyses.
Entity Embeddings for Entity Ranking: A Replicability Study

Knowledge Graph embeddings model semantic and structural knowledge of entities in the context of the Knowledge Graph. A nascent research direction has been to study the utilization of such graph embeddings for the IR-centric task of entity ranking. In this work, we replicate the GEEER study of Gerritse et al. [9] which demonstrated improvements of Wiki2Vec embeddings on entity ranking tasks on the DBpediaV2 dataset. We further extend the study by exploring additional state-of-the-art entity embeddings ERNIE [27] and E-BERT [19], and by including another test collection, TREC CAR, with queries not about person, location, and organization entities. We confirm the finding that entity embeddings are beneficial for the entity ranking task. Interestingly, we find that Wiki2Vec is competitive with ERNIE and E-BERT. Our code and data to aid reproducibility and further research is available at https://github.com/poojahoza/E3R-Replicability .
Digital Competence in University Students: Insights from Various Training Programs

The paper describes the current approaches to the formation of digital competence of a contemporary person. We compare the approaches to the definition of digital competence. Besides, we identify and substantiate the indicators of achieving students’ digital competence. More than that, we provide examples of indicators of achieving students’ digital competence depending on their majors. The paper analyzes the definition of the concept of blended education. We reveal that various scholars consider blended education at the level of the concept of modern education, learning technology, and teaching method. The basics of modern didactics allowed us to conclude that blended education is a teaching method, along with full-time, part-time, and distance education. The consideration of blended education from the standpoint of a teaching method has become the basis for building a structural and functional model of the formation of a person’s digital competence. The research illustrates component features of the structural–functional model of the formation of the students’ digital competence, including the target-, content-, organization-, and efficiency-based components. The goals, objectives, and principles of building the model are disclosed within the target-based component. We consider the principle of proactive training as the primary one. It consists in building a teaching process aimed at creating conditions for the training of future professionals who will be in demand in a dynamically changing labor market and have competencies of the future professions.