Reviewerly/exHarmony huggingface dataset:
Research on Computer-Aided Diagnosis Method Based on Symptom Filtering and Weighted Network

In the process of disease identification, as the number of diseases increases, the collection of both diseases and symptoms becomes larger. However, existing computer-aided diagnosis systems do not completely solve the dimensional disaster caused by the increasing data set. To address the above problems, we propose methods of using symptom filtering and a weighted network with the goal of deeper processing of the collected symptom information. Symptom filtering is similar to a filter in signal transmission, which can filter the collected symptom information, further reduce the dimensional space of the system, and make the important symptoms more prominent. The weighted network, on the other hand, mines deeper disease information by modeling the channels of symptom information, amplifying important information, and suppressing unimportant information. Compared with existing hierarchical reinforcement learning models, the feature extraction methods proposed in this paper can help existing models improve their accuracy by more than 10%.
Towards Reproducible Machine Learning Research in Information Retrieval

While recent progress in the field of machine learning (ML) and information retrieval (IR) has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions failing to provide the necessary information in order to ensure subsequent reproducibility. Despite the introduction of self-check mechanisms before submission (such as the Reproducibility Checklist, criteria for evaluating reproducibility during reviewing at several major conferences, artifact review and badging framework, and dedicated reproducibility tracks and challenges at major IR conferences, the motivation for executing reproducible research is lacking in the broader information community. We propose this tutorial as a gentle introduction to help ensure reproducible research in IR, with a specific emphasis on ML aspects of IR research.
MVP: Robust Multi-View Practice for Driving Action Localization

Distracted driving causes thousands of deaths per year, and how to apply deep-learning methods to prevent these tragedies has become a crucial problem. In Track3 of the 6th AI City Challenge, researchers provide a high-quality video dataset with densely action annotations. Due to the small data scale and unclear action boundary, the dataset presents a unique challenge to precisely localize all the different actions and classify their categories. In this paper, we make good use of the multi-view synchronization among videos, and conduct robust Multi-View Practice (MVP) for driving action localization. To avoid overfitting, we fine-tune SlowFast with Kinetics-700 pre-training as the feature extractor. Then the features of different views are passed to ActionFormer to generate candidate action proposals. For precisely localizing all the actions, we design elaborate post-processing, including model voting, threshold filtering and duplication removal. The results show that our MVP is robust for driving action localization, which achieves 28.49% F1-score in the Track3 test set.
From TEM pulse switching to computing with light

Exploiting light-matter interactions for computing has become a new paradigm in this field for efficient and high-speed computing systems. In this communication we will present an overview of our recent and ongoing efforts in exploiting electromagnetic waves for computing processes.
Does Exposure to Health-related Information and Peer Effects Affect the Nutritional Status of Adolescents in Urban and Rural Areas?

BACKGROUND: The influence of health information exposure particularly from peer influences on the health and nutrition of adolescents and its effect on the nutritional status of female adolescents. AIM: The purpose of this research is to acknowledge the health information exposure and peer influences on female adolescents in urban and rural areas. METHODS: This study used a cross-sectional study design. The sample was 96 female adolescents with 44 from State Junior High School 1 Purwokerto representative of the urban area and 52 female adolescents from State Junior High School 1 Patikraja as representative from rural area. Exposure data and health information sources were collected using a questionnaire, peer influences measurement using Peer Influence Scale (PIS) questionnaire, and nutrition status measurement using Body Mass Index per age. The Statistics Test used in this study was Chi-square and Fisher Exact with 5% accuracy. RESULTS: No significant association was found between health information exposure and peer influences with nutrition status in female adolescents (p = 0.29; 0.77 &gt; 0.05) and there was also no significant difference in acceptance source and health nutrition information topic of female adolescents in urban and rural areas. However, there was a significant difference between peer influences in female adolescents in urban and rural areas (p = 0.00 &lt; 0.05). CONCLUSION: In both groups of female adolescents from urban and rural, there was comparable acceptance in their sources of information regarding health and nutrition. There was a significant difference in the effect of peer influences between female adolescents in urban and rural areas. However, peer influences does not have any effect on their nutrition status.
Users: Can't Work With Them, Can't Work Without Them?

If we could design the ideal IR "effectiveness" experiment (as distinct from an IR "efficiency" experiment), what would it look like? It would probably be a lab-based observational study [3] involving multiple search systems masked behind a uniform interface, and with hundreds (or thousands) of users each progressing some "real" search activity they were interested in. And we'd plan to (non-intrusively, somehow) capture per-snippet, per-document, per-SERP, and per-session annotations and satisfaction responses. The collected data could then be compared against a range of measured "task completion quality" indicators, and also against search effectiveness metric scores computed from the elements contained in the SERPs that were served by the systems. That's a tremendously big ask! So we often use offline evaluation techniques instead, employing test collections, static qrels sets, and effectiveness metrics [6]. We abstract the user into a deterministic evaluation script, supposing for pragmatic reasons that we know what query they would issue, and at the same time assuming that we can apply an effectiveness metric to calculate how much usefulness (or satisfaction) they will derive from any given SERP. The great advantage of this approach is that aside from the process of collecting the qrels, it is free of the need for users, meaning that it is repeatable. Indeed, we often do repeat, iterating to set parameters (and to rectify programming errors). Then, once metric scores have been computed, we carry out one or more paired statistical tests and draw conclusions as to relative system effectiveness.
Digital Educational Consulting in the Professional Training of Future Teachers

The article deals with the problem of professional training of future teachers in the conditions of digitalization of education. Particular attention is paid to changes in the structure and process of education due to the rapid development of new technologies, as well as the active informatization and digitalization of society. The authors emphasize the possibilities of digital educational consulting as one of the young areas, through which it is possible to minimize risks and create conditions for pedagogical support of both future teachers and young professionals. The aim of this study is to identify the significance of information and educational consulting in the professional training of future teachers. Materials and methods of research: theoretical methods (analysis and synthesis of scientific and methodological literature on the topic of research) were used in the research. Results of the study and discussion: the activities of educational institutions of higher education for adaptability to new conditions and enterprises of various actions regarding the training of competitive and adapted to the new realities of graduates of teacher training areas were analyzed; various sources were analyzed to identify the essence and semantic characteristics of the concept of «digital competence»; the essence and role of digital educational consulting in professional training were outlined Conclusions: the most advantageous way to solve the problems associated with the preparation of future teachers in the digital transformation of education is the use of a new direction in the field of consulting – «digital educational consulting», which provides skilled support for the subjects of the educational process based on the use of digital resources and digital technologies.
Automatic Reading Detection during Online Search Sessions

Information Retrieval (IR) systems provide users with a magnitude of information. Complex information needs of users result normally in entire online search sessions that can not be reduced to a singular query. During such sessions complex search activities are executed that comprise several aspects of the users search behavior. One crucial aspect is the users reading activity. To advance towards more adaptive IR systems which recognize the desired interests, we focus on a user model designed for automatic reading detection. This might serve as a measurement for the user investment towards a particular web-page, which should correlate with its relevance. In this work, we propose an entire pipeline from data representation to model prediction. We comparatively evaluate 9 models for automatic reading detection to achieve an accuracy of 79.23% (or 20.77% error-rate). By using such models, we argue that we are able to analyze aspects of the users search behavior and to draw conclusions about their underlying search activity.
PUS

A crucial performance bottleneck in most interprocedural static analyses is solving pointer analysis constraints. We present Pus, a highly efficient solver for inclusion-based pointer analysis. At the heart of Pus is a new constraint solving algorithm that significantly advances the state-of-the-art. Unlike the existing algorithms (i.e., wave and deep propagation) which construct a holistic constraint graph, at each stage Pus only considers partial constraints that causally affect the final fixed-point computation. In each iteration Pus extracts a small causality subgraph and it guarantees that only processing the causality subgraph is sufficient to reach the same global fixed point. Our extensive evaluation of Pus on a wide range of real-world large complex programs yields highly promising results. Pus is able to analyze millions of lines of code such as PostgreSQL in 10 minutes on a commodity laptop. On average, Pus is more than 7× faster in solving context-sensitive constraints, and more than 2× faster in solving context-insensitive constraints compared to the state of the art wave and deep propagation algorithms. Moreover, Pus has been used to find tens of previous unknown bugs in high-profile codebases including Linux, Redis, and Memcached.
Arm

In recent years, there has been increasing interest in adopting published neural retrieval models learned from corpora for text retrieval. Although these models achieve excellent retrieval performance, in terms of popular accuracy metrics, on datasets they have been trained, their performance on new text data might degrade. To obtain the desired retrieval performance on both the data used in training and the latest data collected after training, the simple approach of learning a new model from both datasets is not always feasible since the annotated dataset used in training is often not published along with the learned model. Knowledge amalgamation (KA) is an emerging technique to deal with this problem of inaccessibility of data used in previous training. KA learns a new model (called a student model) from new data by reusing (called amalgamating) a number of trained models (called teacher models) instead of accessing the teachers' original training data. However, in order to efficiently learn an accurate student model, the classical KA approach requires manual selection of an appropriate subset of teacher models for amalgamation. This manual procedure for selecting teacher models prevents the classical KA from being scaled to retrieval tasks for which a large number of candidate teacher models are ready to be reused.
Intelligent System for Recommendation of Mobile Services to Consumers

This paper presents an intelligent system for recommendation of services to mobile users (consumers) by considering the current context. The system builds up and dynamically manages personal profiles of consumers, aiming to facilitate and optimize the service discovery and recommendation process, in support of consumers’ choices for getting the best ‘cost / quality of experience (QoE)’ ratio when utilizing different mobile services.
A Common Framework for Exploring Document-at-a-Time and Score-at-a-Time Retrieval Methods

Document-at-a-time (DaaT) and score-at-a-time (SaaT) query evaluation techniques are different approaches to top-k retrieval with inverted indexes. While modern systems are dominated by DaaT, the academic literature has seen decades of debate about the merits of each. Recently, there has been renewed interest in SaaT methods for learned sparse lexical models, where studies have shown that transformers generate "wacky weights" that appear to reduce opportunities for optimizations in DaaT methods. However, researchers currently lack an easy-to-use SaaT system to support further exploration. This is the gap that our work fills. Starting with a modern SaaT system (JASS), we built Python bindings in order to integrate into the DaaT Pyserini IR toolkit (Lucene). The result is a common frontend to both a DaaT and a SaaT system. We demonstrate how recent experiments with a wide range of learned sparse lexical models can be easily reproduced. Our contribution is a framework that enables future research comparing DaaT and SaaT methods in the context of modern neural retrieval models.
A Diary Study of Social Explanations for Recommendations in Daily Life

We report a diary study of the explanations for the recommendations to characterize the social features in these explanations recorded by five participants over two months. The study reveals several social explanation categories (e.g., personal opinions and personal experiences) and their relationship with user contexts (e.g., location, relevant experience) and recommender attributes (e.g., integrity, expertise) illustrated in a network diagram. Specifically, personal opinions and experiences are two prominent social explanations, mainly associated with user contexts (e.g., users’ preferences and users’ experiences) and several recommender attributes (e.g., politeness, benevolence, and experience). Finally, we discuss several design implications for social explanations and anticipate the value of our findings regarding designing personalized social explanations in recommender systems that aim to build rapport with users, such as conversational recommender systems.
The library liaison’s Training guide to collection management (ALCTS monograph)

"The library liaison’s Training guide to collection management (ALCTS monograph)." Journal of Electronic Resources Librarianship, 34(2), pp. 160–161
Major Challenges Currently Facing the Software Industry

66% of software projects fail, according to the Standish Group's 2020 Chaos Report [1]. When we see that number, it comes to our mind the 1968 NATO conference on Software Engineering where leaders came together to discuss the state of software development which they described as "Software Crisis". If more than 50 years from that conference we estimate failed software projects to 66%, we cannot say that the "Software Crisis" is over. To evaluate that statement, we can look at the list of problems that conference listed and see whether they continue to be manifested. The conference formulated that software projects are: Unreliable, delivered late, impossible to maintain, costly to modify, performing at an inadequate level, and exceeding budget costs. Does any of this family of problem sound familiar? This paper surveys major challenges currently facing the software industry.
13th International Workshop on Personalized Access to Cultural Heritage (PATCH 2022) - Towards Hybrid CH Experience

ACM PATCH 2022, the 13th International Workshop on Personalized Access to Cultural Heritage, is organized in conjunction with the 30th International Conference on User Modeling, Adaptation and Personalization. It is the meeting point between researchers and practitioners working on personalization in cultural heritage, aiming to enhance the user experience in digital and physical Cultural Heritage sites. The PATCH workshops started in 2007 and they are typically held in conjunction with UMAP, IUI and recently AVI Conference series. This paper summarizes the main ideas addressed in the papers accepted for presentation at PATCH 2022 and for publication in the workshop proceedings.
An Ensemble Model (Simple Average) for Malaria Cases in North India

Malaria is an infectious disease borne due to mosquitoes that attacks humans and other animals’ bodies. Malaria is a part of the plasmodium group caused by single-celled microorganisms. This study proposes the use of ensemble model using the three regression algorithms that are linear regression, support vector machine (SVM), and auto-Arima techniques and comparing their results. Predictions of plasmodium virus cases are made with the use of linear regression, support vector machine, and auto-Arima algorithms. The accuracy of prediction is measured by calculating the explained variance score, mean squared error rate, and root mean squared error rate. Our aim is to get better prediction results compared to the individual algorithms by combining the results of these individual models. The proposed work determines the accuracy of linear regression, support vector machine, and auto-Arima and ensembles together to find the trend of prediction using simple Average. A comparison of performance among the three regression techniques indicated the SVM model performs the best and has small RMSE and MAE values. But, by introducing the technique of ensemble modeling using simple average, combining the prediction of these three algorithms results in the lowest RMSE and MAE values.
Privacy information protection algorithm of ultra dense network nodes based on edge computing

In order to solve the problems of high node loss rate, high time overhead and high risk of privacy disclosure in network node privacy information protection, an ultra dense network node privacy information protection algorithm based on edge computing is proposed. The weight update algorithm is used to detect the security vulnerabilities of ultra dense network nodes. According to the detection results, the characteristics of node vulnerabilities are obtained, the sensitive label information of node vulnerabilities is protected through the weighted graph, and the k-anonymity technology is used to anonymize the privacy information of ultra dense network nodes; Finally, edge computing is used to protect the privacy information of nodes. The experimental results show that the node loss rate of the proposed method is always less than 2%, the time overhead is small, and the risk coefficient of privacy disclosure is small.
Wavelet based Video Compression techniques for Industrial monitoring applications

Abstract The principle of industrial machine monitoring and vehicle camera transmission system are focusing on this paper. Video compression is widely used in many industrial applications like continuous monitoring of machines which consumes more storage by capturing every motion detection in machine, hence video coding is highly recommended for video compression without any loss in the actual video. By using wavelet transform delivers the superior localization both in frequency and time domain and its results showcases the better performance while comparing with discrete cosine transform. A comparative analysis has been carried on Video compression using Haar and orthogonal (Daubechies) wavelet. Duplicate coefficients of discrete wavelet transform is reduced by using quantization technique. It aims to attain minimum error while preserving the high peak signal to noise ratio and image quality in the acceptable range. Using PSNR as measure of quality, this paper shows that Daubechies wavelet provides the better quality of video compared to Haar wavelet. Evaluation of performance is depends upon on compression ratio, PSNR, MSE, and SSIM.
A Virtual Assistant for the Movie Domain Exploiting Natural Language Preference Elicitation Strategies

In this paper, we present a strategy to introduce natural language preference elicitation in a virtual assistant for the movie domain. Our approach allows users to express preferences on objective movie features (e.g., actors, directors, etc.) that are extracted from a structured knowledge base, as well as on subjective features that are collected by mining movie reviews. The effectiveness of the approach was evaluated in a user study (N=103), where our strategy was integrated in a virtual assistant that acquires users’ preferences expressed in form of natural language statements and generates a suitable movie recommendation. Results showed that users experience some difficulties in expressing their preferences in terms of subjective features. However, when people succeed in expressing their preferences by also using subjective properties, this generally leads to better recommendations.
Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation

This paper introduces the schemes of Team LingJing’s experiments in NLPCC-2022-Shared-Task-4 Multi-modal Dialogue Understanding and Generation (MDUG). The MDUG task can be divided into two phases: multi-modal context understanding and response generation. To fully leverage the visual information for both scene understanding and dialogue generation, we propose the scene-aware prompt for the MDUG task. Specifically, we utilize the multi-tasking strategy for jointly modelling the scene- and session- multi-modal understanding. The visual captions are adopted to aware the scene information, while the fixed-type templated prompt based on the scene- and session-aware labels are used to further improve the dialogue generation performance. Extensive experimental results show that the proposed method has achieved state-of-the-art (SOTA) performance compared with other competitive methods, where we rank the 1-st in all three subtasks in this MDUG competition.
Theory and application of labelling techniques for interpretability logics

Abstract The notion of a critical successor [5] in relational semantics has been central to most classic modal completeness proofs in interpretability logics. In this paper we shall work with a more general notion, that of an assuring successor . This will enable more concisely formulated completeness proofs, both with respect to ordinary and generalised Veltman semantics. Due to their interesting theoretical properties, we will devote some space to the study of a particular kind of assuring labels, the so‐called full labels and maximal labels . After a general treatment of assuringness, we shall apply it to obtain a completeness result for the modal logic w.r.t. generalised semantics for a restricted class of frames.