Reviewerly/exHarmony huggingface dataset:
A Multicriteria Decision Making and Fuzzy-AHP Approach for Formulating Strategy to Develop Organic Agriculture in Bengkulu Province, Indonesia

Strategy formulation was conducted to develop organic agriculture in Bengkulu Province.This research used multi-criteria decision-making tools which were analytical hierarchy process (AHP) and strength-weaknessopportunity-threat (SWOT) analysis to generate alternative strategies.Then, fuzzy analytical hierarchy process (fuzzy-AHP) was utilized to choose the best strategy for developing organic agriculture in Bengkulu Province.The results identified five strength factors, 16 weakness factors, eight opportunity factors and seven threat factors.The evaluation of internal and external factors led to the internal factor value of 2.26 and the external factor value of 2.54.This result placed the current condition of organic agriculture in the position of weakness-opportunity (WO) quadrant.The fuzzy-AHP analysis ranked the strategies, consecutively 1) strategy of developing an export-oriented competitive organic product, 2) supporting the production and marketing of organic products, and 3) developing community supported organic agriculture.
Towards a National Digital Competence Framework for Ukraine

Analysing today's approach to teaching digital competences for non-IT students in Ukraine, has shown its narrowness and inadequacy to meet needs of students with different knowledge level. Taking in consideration EU and UA Digital Competence Frameworks, international standard for digital literacy -ICDL and results of Erasmus plus CBHE project "dComFra -digital Competence framework for Ukrainian teachers and other citizens", No 598236-EPP-1-2018-1-LT-EPPKA2-CBHE-SP the authors propose a new paradigm of teaching digital competences for non-IT students. The main advantage of the new approach is the higher effectiveness of teaching digital competences for non-IT students with different knowledge levels in one training group.
Towards memory-efficient inference in edge video analytics

Video analytics pipelines incorporate on-premise edge servers to lower analysis latency, ensure privacy, and reduce bandwidth requirements. However, compared to the cloud, edge servers typically have lower processing power and GPU memory, limiting the number of video streams that they can manage and analyze. Existing solutions for memory management, such as swapping models in and out of GPU, having a common model stem, or compression and quantization to reduce the model size incur high overheads and often provide limited benefits. In this paper, we propose model merging as an approach towards memory management at the edge. This proposal is based on our observation that models at the edge share common layers, and that merging these common layers across models can result in significant memory savings. Our preliminary evaluation indicates that such an approach could result in up to 75% savings in the memory requirements. We conclude by discussing several challenges involved with realizing the model merging vision.
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency

A good open-domain chatbot should avoid presenting contradictory responses about facts or opinions in a conversational session, known as its consistency capacity.However, evaluating the consistency capacity of a chatbot is still challenging.Employing human judges to interact with chatbots on purpose to check their capacities is costly and low-efficient, and difficult to get rid of subjective bias.In this paper, we propose the Addressing Inquiries about History (AIH), an efficient and practical framework for the consistency evaluation.At the conversation stage, AIH attempts to address appropriate inquiries about the dialogue history to induce the chatbot to redeclare the historical facts or opinions.We carry out the conversation between chatbots, which is more efficient than the human-bot interaction and can also alleviate the subjective bias.In this way, we manage to rapidly obtain a dialog session that contains responses with high contradiction possibilities.At the contradiction recognition stage, we can either employ human judges or a natural language inference (NLI) model to recognize whether the answers to the inquiries are contradictory with history.Finally, we are able to rank chatbots according to the contradiction statistics.Experiments on open-domain chatbots show that our approach can efficiently and reliably assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation.We release the framework and hope to help improve the consistency capacity of chatbots. 1* Joint work with Pattern Recognition Center, WeChat AI, Tencent Inc. Yang Feng is the corresponding author.Work was done when Zekang Li and Zhengcong Fei
Image Pre-processing and Segmentation for Real-Time Subsea Corrosion Inspection

Inspection engineering is a highly important field in the Oil & Gas sector for analysing the health of offshore assets. Corrosion, a naturally occurring phenomenon, arises as a result of a chemical reaction between a metal and its environment, causing it to degrade over time. Costing the global economy an estimated US $2.5 Trillion per annum, the destructive nature of corrosion is evident. Following the downturn endured by the industry in recent times, the need to combat corrosion is escalated, as companies look to cut costs by increasing efficiency of operations without compromising critical processes. This paper presents a step towards automating solutions for real-time inspection using state-of-the-art computer vision and deep learning techniques. Experiments concluded that there is potential for the application of computer vision in the inspection domain. In particular, Mask R-CNN applied on the original images (i.e. without any form of pre-processing) was found to be most viable solution, with the results showing a mAP of 77.1%.
Augmented Reality-Based Remote Family Visits in Nursing Homes

During the COVID-19 pandemic, many nursing homes had to restrict visitations. This had a major negative impact on the wellbeing of residents and their family members. In response, residents and family members increasingly resorted to mediated communication to maintain social contact. To facilitate high-quality mediated social contact between residents in nursing homes and remote family members, we developed an augmented reality (AR)-based communication tool. In this study, we compared the user experience (UX) of AR-communication with that of video calling, for 10 pairs of residents and family members. We measured enjoyment, spatial presence and social presence, attitudes, behavior and conversation duration. In the AR-communication condition, residents perceived a 3D projection of their remote family member onto a chair placed in front of them. In the video calling condition, the family member was shown using 2D video. In both conditions, the family member perceived the resident in the video calling mode on a 2D screen. While residents reported no differences in their UX between both conditions, family members reported higher spatial presence for the AR-communication condition compared to video-calling. Conversation durations were significantly longer during AR-communication than during video calling. We tentatively suggest that there may be (unconscious) differences in UX during AR-based communication compared to video calling.
Joint Optimization of Tokenization and Downstream Model

Since traditional tokenizers are isolated from a downstream task and model, they cannot output an appropriate tokenization depending on the task and model, although recent studies imply that the appropriate tokenization improves the performance.In this paper, we propose a novel method to find an appropriate tokenization to a given downstream model by jointly optimizing a tokenizer and the model.The proposed method has no restriction except for using loss values computed by the downstream model to train the tokenizer, and thus, we can apply the proposed method to any NLP task.Moreover, the proposed method can be used to explore the appropriate tokenization for an already trained model as post-processing.Therefore, the proposed method is applicable to various situations.We evaluated whether our method contributes to improving performance on text classification in three languages and machine translation in eight language pairs.Experimental results show that our proposed method improves the performance by determining appropriate tokenizations.
AliMe Avatar: Multi-modal Content Production and Presentation for Live-streaming E-commerce

We present AliMe Avatar, a Vtuber designed for live-streaming sales in the E-commerce field. To support the emerging live shopping mode, the core of our digitial avatar is to enable customers to understand products and encourage customers to purchase in a virtual broadcasting room. Based on computer graphics & vision, natural language processing, and speech recognition & synthesis, our AI avatar is able to offer three kinds of key capabilities: custom appearance, product broadcasting, and multi-modal interaction. Currently, it has been launched online in the Taobao app, broadcasts 700+ hours and serves hundreds of thousands of customers per day. In this paper, we mainly focus on the product broadcasting part, demonstrate the system, present the underlying techniques, and share our experience in dealing with live-streaming E-commerce.
Variations on Twins in Permutations

Let $\pi$ be a permutation of the set $[n]=\{1,2,\dots, n\}$. Two disjoint order-isomorphic subsequences of $\pi$ are called twins. How long twins are contained in every permutation? The well known Erdős-Szekeres theorem implies that there is always a pair of twins of length $\Omega(\sqrt{n})$. On the other hand, by a simple probabilistic argument Gawron proved that for every $n\geqslant 1$ there exist permutations with all twins having length $O(n^{2/3})$. He conjectured that the latter bound is the correct size of the longest twins guaranteed in every permutation. We support this conjecture by showing that almost all permutations contain twins of length $\Omega(n^{2/3}/\log n^{1/3})$. Recently, Bukh and Rudenko have tweaked our proof and removed the log-factor. For completeness, we also present our version of their proof (see Remark 2 below on the interrelation between the two proofs).&#x0D; In addition, we study several variants of the problem with diverse restrictions imposed on the twins. For instance, if we restrict attention to twins avoiding a fixed permutation $\tau$, then the corresponding extremal function equals $\Theta(\sqrt{n})$, provided that $\tau$ is not monotone. In case of block twins (each twin occupies a segment) we prove that it is $(1+o(1))\frac{\log n}{\log\log n}$, while for random permutations it is twice as large. For twins that jointly occupy a segment (tight twins), we prove that for every $n$ there are permutations avoiding them on all segments of length greater than $24$.
Inductively Representing Out-of-Knowledge-Graph Entities by Optimal Estimation Under Translational Assumptions

Conventional Knowledge Graph Completion (KGC) assumes that all test entities appear during training.However, in real-world scenarios, Knowledge Graphs (KG) evolve fast with outof-knowledge-graph (OOKG) entities added frequently, and we need to efficiently represent these entities.Most existing Knowledge Graph Embedding (KGE) methods cannot represent OOKG entities without costly retraining on the whole KG.To enhance efficiency, we propose a simple and effective method that inductively represents OOKG entities by their optimal estimation under translational assumptions.Moreover, given pretrained embeddings of the in-knowledge-graph (IKG) entities, our method even needs no additional learning.Experimental results on two KGC tasks with OOKG entities show that our method outperforms the previous methods by a large margin with higher efficiency.
The Strict/Tolerant Idea and Bilattices

Strict/tolerant logic is a formally defined logic that has the same consequence relation as classical logic, though it differs from classical logic at the metaconsequence level. Specifically, it does not satisfy a cut rule. It has been proposed for use in work on theories of truth because it avoids some objectionable features arising from the use of classical logic. Here we are not interested in applications, but in the formal details themselves. We show that a wide range of logics have strict/tolerant counterparts, with the same consequence relations but differing at the metaconsequence level. Among these logics are Kleene’s $$\mathsf {K_3}$$ , Priest’s $${\mathsf {LP}}$$ , and first-degree entailment, FDE. The primary tool we use is the bilattice. But it is more than a tool, it seems to be the natural home for this kind of investigation.
Keyword-Based Knowledge Graph Exploration Based on Quadratic Group Steiner Trees

Exploring complex structured knowledge graphs (KGs) is challenging for non-experts as it requires knowledge of query languages and the underlying structure of the KGs. Keyword-based exploration is a convenient paradigm, and computing a group Steiner tree (GST) as an answer is a popular implementation. Recent studies suggested improving the cohesiveness of an answer where entities have small semantic distances from each other. However, how to efficiently compute such an answer is open. In this paper, to model cohesiveness in a generalized way, the quadratic group Steiner tree problem (QGSTP) is formulated where the cost function extends GST with quadratic terms representing semantic distances. For QGSTP we design a branch-and-bound best-first (B3F) algorithm where we exploit combinatorial methods to estimate lower bounds for costs. This exact algorithm shows practical performance on medium-sized KGs.
“Perrotta Affective Dependence Questionnaire (PAD-Q)”: Psychodiagnostic evidence and clinical profiles

Purpose: Starting from the concept of "affective dependence" and its international nosographic framework, the Perrotta Affective Dependence Questionnaire (PAD-Q) aims to study the phenomenon of "affective dependence", defi ning it as a maladaptive model of the affective-sentimental relationship of a couple, which involves the establishment or persistence of a clinically signifi cant bond, lasting at least six months and characterized by a functional impairment of the relational area, the emotional area and the somatic area.Affective addiction, not being a well-identifi ed psychopathological label in the international nosographic framework, except in the general framework of behavioral addictions, in this context is identifi ed as a maladaptive behavioral model that describes a series of personality traits afferent to several nosographically recognized psychopathological disorders [1].Methods: Administration of the PICI-1 and PAD-Q.Clinical Interview. Results:The present research work has demonstrated the reliability, effi ciency and effectiveness of the PAD-Q, in relation to the objectives and the PICI-1.In particular, it facilitated a better diagnostic framing of current affective behavioral addiction, thus allowing to focus attention on the dysfunctional traits of patients and on the correct psychodiagnostic framing and their eventual clinical treatment. Conclusions:The results of the PICI-1 on the selected population sample is perfectly compatible with the results of the PAD-Q, underlining also the trend according to which the higher the age of the population sample the higher the diagnosis of cluster B disorders, up to the highest psychotic percentage in the most mature age group.The same trend is visible in the PAD-Q data, which confi rms the greater presence of dysfunctional traits in cluster B disorders.
Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care

Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.
VR Technology to the Adjustment of Piano Playing Mentality

Piano performance requires not only skillful and excellent playing skills, but also good psychological quality and improvisation. The perfect performance is the natural language of a pianist's usual skill and mood. The various emotional activities and expressions of the overall situation in relation to the player's performance, mental issues such as the intensity and horror of the scene, seriously affect the pianist’s actual performance while playing. The piano is therefore very important for mind control. Based on this, this paper attempts to apply the current hot VR technology to piano performance and simulate the virtual piano performance scene through VR technology, so as to enhance the on-the-spot experience for piano players and continuously improve their psychological quality, so that they can better adjust their nervous bad mentality in actual combat. This article first has carried on the 3D virtual reality piano stage modeling experiment, and then selected the 20 young pianist piano training involved in day-to-day contrast test, the test group of 10 people for daily training, virtual piano stage under the environment of normal control group of 10 people ordinary daily training, training time four hours a day, for three months, all participants will take part in The Provincial Youth Piano Competition since the end of the training. The final test results showed that in the competition site, the performance level of 10 players in the test group was generally better than that of the control group. Among them, there were 2 subjects in the test group who played for a long time, while one of the control groups gave up playing in the middle. In addition, in the horizontal comparison, it is found that subjects who are older and have been learning piano for a long time have better on-the-spot mental adjustment ability and more stable performance. Boys performed better than girls, and subjects with positive, optimistic, and confident personalities performed better than those with introverted and low self-confidence.
Exploring Targeted Universal Adversarial Perturbations to End-to-End ASR Models

Although end-to-end automatic speech recognition (e2e ASR) models are widely deployed in many applications, there have been very few studies to understand models' robustness against adversarial perturbations. In this paper, we explore whether a targeted universal perturbation vector exists for e2e ASR models. Our goal is to find perturbations that can mislead the models to predict the given targeted transcript such as thank you or empty string on any input utterance. We study two different attacks, namely additive and prepending perturbations, and their performances on the state-of-the-art LAS, CTC and RNN-T models. We find that LAS is the most vulnerable to perturbations among the three models. RNN-T is more robust against additive perturbations, especially on long utterances. And CTC is robust against both additive and prepending perturbations. To attack RNN-T, we find prepending perturbation is more effective than the additive perturbation, and can mislead the models to predict the same short target on utterances of arbitrary length.
Satisfaction of students with the organisation of distance learning at the university during the period of self-isolation

The article is devoted to the description of the results of a study of students' satisfaction with the organisation of distance learning at the university, the difficulties encountered in learning under new conditions of self-isolation, resources and ways to overcome them. The study involved 1288 students of Kostroma State University of all courses, forms of study and educational levels. For statistical data processing, the SPSS 17.0 package (Spearman's correlation analysis, content analysis of students' free statements) was used. It is shown that the transition to distance learning in conditions of self-isolation is very successful, most students have adapted to the new situation. The degree of students' satisfaction with work in the remote format correlates with the assessment of their own success and with the features of the organisation of the learning process. Lack of communication with teachers and difficulties in mastering educational material form a request among students to expand the range of communication tools and teaching tools used. It was found that students experience a number of organisational and personal difficulties associated with the transition to a distance learning format. However, most students successfully overcome them using constructive coping strategies aimed at solving the problem and seeking support. The ways of increasing the degree of students’ satisfaction with work in the distance learning system, aimed at increasing the level of their psychological well-being, are proposed – focusing on a dialogical approach, organising methodological support for training courses, updating the organisational functions of curator teachers, implementing teamwork of teachers to ensure interdisciplinarity and integrability of acquired knowledge and competences.