Reviewerly/exHarmony huggingface dataset:
Comparing human and algorithmic anomaly detection for HVAC systems applications

This paper reports the first results of a comparison of human and algorithmic anomaly detection. We are interested in how human and automated anomaly detection can be combined in the most beneficial way to improve how fault detection is practiced in building maintenance. Open source datasets with sensor data were annotated by persons with low subject matter experience, and compared with a Convolutional Autoencoder Neural Network (CAE) as well as a dedicated time series algorithm (DeepAnT), and detection metrics of human and algorithmic procedures are compared. Future comparisons will include higher levels of expertise on the human side, and more sophistication/training amount on the algorithm side. We close by discussing the advantages and caveats of our approach.
The implementation of EFL online learning: how do the non-English department students perceive it?

&#x0D; The COVID-19 outbreak influences nearly all facets of life, including education. This study conducted survey investigations to detect non-English students' views on the implementation of EFL online learning. This study used cross-sectional survey research designs employing questionnaires and interviews as data-gathering devices and procedures. The population of this study was all the first-graders of the Islamic Education Program of a State Institute for Islamic Studies situated in Central Java, Indonesia, in the academic year of 2020/2021. A number of 135 students took part as the sample of this study. The results of the study showed that there are three main phases of EFL online learning. First, at the preparation phase, they had high optimism of EFL online learning. Second, it turned into caution and forbearance at the implementation phase. At last, their optimistic level was increasing at the end (evaluation phase). This study means that it is not simple to retain the enthusiasm and optimism of students through EFL online study, particularly in non-English department classrooms. Teachers should be innovative in delivering instructions for learning, offering learning equipment, and selecting appropriate learning platforms. Teachers should also be able to comprehend pupils' psychological state to prevent boredom in online learning.
Adversarial transformation network with adaptive perturbations for generating adversarial examples

Deep neural networks are susceptible to adversarial examples which can mislead or even manipulate the predictive behaviour of deep neural networks. This raises concerns about the safety of deep learning. In this paper, to ensure rapid generation of adversarial examples, we propose an adversarial transformation network with adaptive perturbations by using the framework of a generative adversarial network. For the adversarial training phase, the direction of the adversarial perturbation is adaptively adjusted to generate more adversarial examples with transferability. Besides, the perceptual constraint based on game theory, the pixel-level constraint based on mixed norms, and the target constraint based on the C$W method are introduced to guide the optimisation of the generator. Experiments conducted on MNIST, CIFAR-10, and ImageNet show the proposed algorithm can generate adversarial examples with stronger attack abilities in a shorter time. And the proposed algorithm can generate more transferable adversarial examples when attacking models with similar structures.
Research Status, Hot Spots and Enlightenment of College Students' Information Literacy

In order to investigate the current situation and future research directions of information literacy among college students in China. In this paper, 422 core journals on college students' information literacy published by China National Knowledge Infrastructure from 2000 to 2021 are selected for bibliometric analysis, and research hotspots on college students' information literacy during this period are discussed. According to the findings of the analysis, the current domestic research on college students' information literacy primarily consists of the development of an index system, connotation discussion, investigation and analysis of the current situation, and research on college students' information literacy education strategies and methods. It is suggested, based on a review of the literature, that future research on information literacy among college students draw inspiration from three areas: assessment tools, information security, and personalized learning recommendations for students' information literacy.
Early Detection of ALL Disease Using YOLOv4 Algorithm on Peripheral Blood Smear Images

Peripheral smear is a method that allows the analysis of blood with the help of a microscope after staining the blood liquid on a glass slide. The morphological appearance and multiplicity of some blood elements are examined by this method. This examination is preferred for evaluating the response to treatment as well as providing information about the diagnosis and severity of diseases. Since manual examination of blood elements by operators contains many humanistic and environmental parameters, it is time-consuming and error-prone. Instead of this approach which does not offer a standard accuracy rate, it is planned to create a quick, cost-effective and fully automatic computer-aided system. In this study, the examination of ALL(Acute Lymphoblastic Leukaemia) malignancy which is one of the main types of leukaemia by peripheral smear method will be handled. ALL is formed by the overproduction of malignant and immature white blood cells called lymphoblasts. Since it spreads to the blood circulation and vital organs rapidly, the speed of the tests for early diagnosis is important. The peripheral blood smear test, which is a part of the diagnosis process of ALL malignancy, is a guide in terms of diagnosis and severity of the disease with the presence and multiplicity of lymphoblast cells. Therefore, a fully automatic system is designed to determine the presence and number of blasts on the given image. The model used to create this system is trained with the YOLOV4 algorithm. Training is provided via peripheral blood smear images the free and public in the ALL-IDB dataset (https://homes.di.unimi.it/scotti/all/) tagged by expert oncologists. As a result of the test process, the success rate, precision, recall, specificity and F score criteria are obtained as 98.87%, 99.08%, 99.81%, 88.88% and 99.44%, respectively. With this work, it is real-time given the total number of blast cells on the image, which is an important biomarker in determining the severity of ALL disease or evaluating the response to treatment. It is expected that this fully automatic system which produces output after 3–4 seconds for the given input will be a helpful idea for the operators.
Keynote - Requirements Conversations: A New Frontier in AI-for-RE

This extended abstract summarizes a keynote given at the 9th International Workshop on Artificial Intelligence and Requirements Engineering (AIRE). The keynote puts forward requirements conversations as a cornerstone activity in requirements engineering, and defines the role of artificial intelligence techniques that help analysts explore RE conversations.
Everyday Life Information for the People with Special Needs: A Technological Approach towards Mass Literacy

Access to everyday life information of a person with special needs seems to experience frequent hindrances in the form of Way of Life and Mastery of Life since a person of such difficulties finds the traditional mode of delivery of information from public libraries in West Bengal insufficient in getting the required information for lifelong learning. Modern day technology comes as a remedy to this. Everyday life information seeking of twenty four people with special needs of the existing six public libraries in Chanditala II and adjunct blocks of Hooghly District in West Bengal have been studied in the present investigation using micro-moment timeline interview and survey method. Recommendations have been made exploring various apps-developing platforms such as Android, Apple, and even search engines like Google etc. to endeavour to reach the zenith of mass literacy. ChromeVox, Google search through Google Voices etc. offered by Google; Text-to Speech apps offered by Android; AccessTech, Proloquo2Go, iConverse, iCommunicate etc. offered by Apple for blind and visually impaired people are some of them that can be used in public libraries in order to smoothen the everyday life information searching experience of persons with special needs like never before. DAISY Digital Talking Book, Audio Books, books procured from the Organizations such as Learning Ally (U.S.) can be made accessible in order to secure the lifelong learning of people with special needs in public libraries.
Interpretable Text Classification in Legal Contract Documents using Tsetlin Machines

Legal text contains various challenges in automated processing, compounded by the lack of detailed resources available for them. However, the ability of process such texts automatically is highly sought after. In this paper we try to parse a set of contract documents and identify key legal terminologies present in them, with the help of four text processing methods from different backgrounds: Tsetlin Machines, BERT, CNNBiLSTM and FastText. We show that the TM based approach works at par with other popular methods, with the added benefit of making available important clause literals that can act as specific linguistic cues to legal terminology.
Depth Map Decomposition for Monocular Depth Estimation

We propose a novel algorithm for monocular depth estimation that decomposes a metric depth map into a normalized depth map and scale features. The proposed network is composed of a shared encoder and three decoders, called G-Net, N-Net, and M-Net, which estimate gradient maps, a normalized depth map, and a metric depth map, respectively. M-Net learns to estimate metric depths more accurately using relative depth features extracted by G-Net and N-Net. The proposed algorithm has the advantage that it can use datasets without metric depth labels to improve the performance of metric depth estimation. Experimental results on various datasets demonstrate that the proposed algorithm not only provides competitive performance to state-of-the-art algorithms but also yields acceptable results even when only a small amount of metric depth data is available for its training.
Multi-Agent Tree Search with Dynamic Reward Shaping

Sparse rewards and their representation in multi-agent domains remains a challenge for the development of multi-agent planning systems. While techniques from formal methods can be adopted to represent the underlying planning objectives, their use in facilitating and accelerating learning has witnessed limited attention in multi-agent settings. Reward shaping methods that leverage such formal representations in single-agent settings are typically static in the sense that the artificial rewards remain the same throughout the entire learning process. In contrast, we investigate the use of such formal objective representations to define novel reward shaping functions that capture the learned experience of the agents. More specifically, we leverage the automaton representation of the underlying team objectives in mixed cooperative-competitive domains such that each automaton transition is assigned an expected value proportional to the frequency with which it was observed in successful trajectories of past behavior. This form of dynamic reward shaping is proposed within a multi-agent tree search architecture wherein agents can simultaneously reason about the future behavior of other agents as well as their own future behavior.
Prediction of Photovoltaic Energy Production Using Machine Learning Methods in the RapidMiner Application

As the penetration of using clean energy in government plans and companies is rising, many researchers are seeking the influence of multiple factors on the processes leading to producing renewable energy. Electricity via photovoltaic (PV) cells, quickly became popular in all countries due to fewer restrictions compared to other energies. In this study, we compared different machine learning methods based on the classification and prediction of solar energy output. by analyzing a specific case study in Slovakia, Finally, this model was implementedin the RapidMiner platform and the effective factors in predicting by comparing evaluation were identified.
Knowledge-Oriented Tool Complex for Developing Databases of Scientific Publications and Taking into account Semantic Web Technology

Introduction. The development of theories, methods, and algorithms for the discovery and formation of new knowledge has always occupied one of the central places for any researcher, especially if he is actively working on the creation of new scientific publications. It is known that there is no universal language for the formal description of concepts (knowledge) and systemology of transdisciplinary scientific research. And therefore, scientists face a number of priority problems, including the problem of significantly accelerating the receipt by a researcher of the cognitively structured information he needs from his sources. The tool complex for processing databases of scientific publications is oriented in this way to a researcher who has published from several tens to hundreds of scientific papers. We are not aware of search engines that could provide such information to a researcher in the shortest possible time. The toolkit implements Information Retrieval and Knowledge Discovery in Databases technologies with an emphasis on Semantic Web and cognitive graphics technologies and tools. The development of such a tool complex involves three stages: at the first stage, tools for implementing the complex, methods and algorithms for the interaction of the “User – Knowledge Engineer – Remote Endpoint” system and filling it with data are created; the second stage, the tasks of multimedia representation of figurative-conceptual structures are solved, which are described in scientific documents, and at the third stage — the solution of the problem of extracting new knowledge. Purpose. The purpose of our research was to further develop a tool complex for processing databases of scientific publications, which allows a scientist to significantly speed up the receipt of the necessary cognitively structured information from his sources. Methods. The methods and models used in the work are based on the information technologies of the Semantic Web and ontological engineering. Results. A tool complex for processing databases of scientific publications based on a remote endpoint based on the Apachi Jena Fuseki server, basic UML diagrams of functioning and examples of executing user requests have been developed. Conclusion. The article introduced and described the architectural and structural organization of the tool complex, which includes a local network from the user’s PC and the PC of the administrator-knowledge engineer and a remote endpoint based on the Apachi Jena Fuseki server, the main UML diagrams of the tool complex functioning and examples of executing user requests.
Comparative Analysis of Different Machine Learning Methods for Hate Speech Recognition in Twitter Text Data

The purpose of this work is to solve the challenges faced by us in the field of hate speech recognition on various social media platforms, that is to get a better machine learning model that can detect hate speech with greater accuracy. As the reach of the internet and mobile phones has extended abruptly in the past few years, everyone has the power to share their opinions, but some use it as an opportunity to spread hate among one another. In this paper, we used the Davidson [10] dataset which is the most popular Twitter dataset for hate speech detection, and further we implemented various machine learning-based algorithms and compared them on the basis of various parameters such as accuracy, precision score, recall and F1 scores. After the study, we found out that XGBoost when used with TF-IDF transformer embedding gave us an accuracy of 94.43%, which is the maximum among these three models for the given benchmark dataset.
Web Crawler and Classifier for News Articles

In this work, we present a crawler that collects news articles and a classifier that identifies the section to which these articles belong. Due to a large number of available sources of information, a tool for gathering and filtering news articles about specific interests is necessary. For instance, a person might be interested in news about sports or science, and it could be necessary to check several websites to obtain this kind of news finally. Therefore, in this work, we propose a web application that uses a crawler to collect news articles from different websites automatically, then a classifier determines the section of each news article, and finally, the news articles that match the section of interest are displayed in the web application.
Quantum-Safe Signing of Notification Messages in Intelligent Transport Systems

In this work, we integrated three quantum-safe digital signature algorithms, CRYSTALS-Dilithium, FALCON and Rainbow, into notification messages used in intelligent transport systems. We evaluated the performance of the algorithms by measuring the time required to sign and verify messages, as well as the size of the signed messages, and compared the quantum-safe options to the elliptic curves currently accepted by the standards. Our results show that quantum-safe digital signature algorithms could be used for signing notification messages in intelligent transport systems, with only moderate changes to performance. The results also provide an evaluation of three quantum-safe digital signature algorithms’ suitability for this purpose, thus helping to choose suitable algorithms when migrating intelligent transport systems towards quantum resistance.
Bidirectional Macro-level Discourse Parser Based on Oracle Selection

Most existing studies construct a discourse structure tree following two popular methods: top-down or bottom-up strategy. However, they often suffered from cascading errors because they can not switch the strategy of building a structure tree to avoid mistakes caused by uncertain decision-making. Moreover, due to the different basis of top-down and bottom-up methods in building discourse trees, thoroughly combining the advantages of the two methods is challenging. To alleviate these issues, we propose a Bidirectional macro-level dIscourse Parser based on OracLe selEction (BIPOLE), which combines the top-down and bottom-up strategies by selecting the suitable decision-making strategy. BIPOLE consists of a basic parsing module composed of top-down and bottom-up sub-parsers and a decision-maker for selecting a prediction strategy by considering each sub-parser state. Moreover, we propose a label-based data-enhanced oracle training strategy to generate the training data of the decision-maker. Experimental results on MCDTB and RST-DT show that our model can effectively alleviate cascading errors and outperforms the SOTA baselines significantly.
Investigating the Impact of Pre-trained Language Models on Dialog Evaluation

Recently, there is a surge of interest in applying pre-trained language models (Pr-LM) in automatic open-domain dialog evaluation. Pr-LMs offer a promising direction for addressing the multi-domain evaluation challenge. Yet, the impact of different Pr-LMs on the performance of automatic metrics is not well-understood. This paper examines eight different Pr-LMs and studies their impact on three typical automatic dialog evaluation metrics across three different dialog evaluation benchmarks. Specifically, we analyze how the choice of Pr-LMs affects the performance of automatic metrics. Extensive correlation analyses on each of the metrics are performed to assess the effects of different Pr-LMs along various axes, including pre-training objectives, dialog evaluation criteria, model size, and cross-dataset robustness. This study serves as the first comprehensive assessment of the effects of different Pr-LMs on automatic dialog evaluation.
ASSISTÊNCIA MULTIPROFISSIONAL A UM PACIENTE COM PÉ DIABÉTICO ATENDIDO EM UMA UNIDADE DE SAÚDE DA FAMÍLIA: RELATO DE EXPERIÊNCIA

Introduction: Diabetes Mellitus (DM) is a metabolic disease resulting from the lack and/or inability of insulin to be produced effectively by the pancreas, which can lead to complications that are classified as acute and chronic, with diabetic foot being one of the most common chronic complications. frequent. Due to this nature and the seriousness of the setbacks of this pathology, therapeutic resources are based, in addition to individual guidelines, on keeping glycemic levels under control, diet therapy, correcting the shoes that traumatize, antibiotic therapy and dressings, being essential the performance of a multidisciplinary team. Methodology: This is a descriptive study based on the experience report developed by a nurse and nutritionist, students of the Postgraduate Program in the Multiprofessional Residency in Family and Community Health, based on the experience acquired during the care provided to patients with chronic wound in an integrated family health unit in the city of João Pessoa/PB. Results: After a period of less than one year, the MID lesion was completely healed, although this wait lasted more than 10 years, therefore, it was a successful process that brought great satisfaction to the team, but mainly to the patient. Conclusion: The performance of the multidisciplinary team is essential in the process of monitoring care for the healing of complex injuries, including health education with diabetic patients about the daily foot care routine and prevention of the appearance of injuries. Emphasizing, in this way, the improvement of the quality of life, guarantee of comfort, reduction of pain and stress of the patient. The importance of training the multidisciplinary team regarding the treatment of injuries is also highlighted.
Synergistic Self-supervised and Quantization Learning

With the success of self-supervised learning (SSL), it has become a mainstream paradigm to fine-tune from self-supervised pretrained models to boost the performance on downstream tasks. However, we find that current SSL models suffer severe accuracy drops when performing low-bit quantization, prohibiting their deployment in resource-constrained applications. In this paper, we propose a method called synergistic self-supervised and quantization learning (SSQL) to pretrain quantization-friendly self-supervised models facilitating downstream deployment. SSQL contrasts the features of the quantized and full precision models in a self-supervised fashion, where the bit-width for the quantized model is randomly selected in each step. SSQL not only significantly improves the accuracy when quantized to lower bit-widths, but also boosts the accuracy of full precision models in most cases. By only training once, SSQL can then benefit various downstream tasks at different bit-widths simultaneously. Moreover, the bit-width flexibility is achieved without additional storage overhead, requiring only one copy of weights during training and inference. We theoretically analyze the optimization process of SSQL, and conduct exhaustive experiments on various benchmarks to further demonstrate the effectiveness of our method.