Reviewerly/exHarmony huggingface dataset:
An unsupervised root cause analysis method for satellite on-orbit anomalies based on causal discovery

For a modern satellite, there are usually thousands of telemetry parameters. How to find the root cause of the satellite anomalies using telemetry data is an important but difficult task. This paper proposes an unsupervised root cause analysis method for satellite on-orbit anomalies based on causal discovery. The method includes a component impact analysis method based on multivariate-to-one transfer entropy, which can quickly identify components that cause system anomalies; and a pruning strategy based on the change of causality are proposed to screen and identify the parameters of the components to determine the root cause of the anomalies. Simulation experiments and real case verification show that the accuracy, recall, and F1-score of the method in this paper all reach more than 80%, which is greatly improved compared with the existing methods. The case study also shows that when anomalies occur, the root cause analysis method can detect the root cause using only 4 min of anomalous information, which helps to stop the spread of anomalies more effectively.
Weather-oriented Domain Generalization of Semantic Segmentation for Autonomous Driving

Visual sensors on autonomous vehicles are vulnerable to adverse weather, which seriously reduces the performance of semantic segmentation task and threats people’s safety. Therefore, segmentation models need to carry out extensive training on a large amount of adverse weather data which is difficult and expensive to acquire to improve their robustness. To solve this problem, researchers have proposed domain generalization methods that do not need target domain (such as adverse weather) data to adapt during training. However, most of them focus on the synthetic-to-real problem which is caused by the difference in texture between real and virtual images. To address these challenges, we analyze the formation mechanism behind adverse weather, extract two kinds of weather cues and establish the relationship between them and adverse weather. On this basis, we propose CSCR, a domain randomization framework for simulating adverse weather. Specifically, CSCR includes the Common Cue Randomization (CCR) module which simulates adverse illumination style and the Specific Cue Randomization (SCR) module which randomizes weather cues that occur in specific adverse weather. We conduct extensive experiments from fair weather to fog, night, rain and snow on driving datasets. Compared with the source model, CSCR increases by more than 9% points in mIoU on average which even exceeds some domain adaptation methods while still keeping the memory of fair weather. The CSCR framework can be easily applied to existing segmentation models and significantly improve their generalization ability.
The use of augmented reality for inquiry-based activity about the phenomenon of seasons: effect on mental effort and learning outcomes

Despite the profusion of studies on the use of augmented reality (AR) for teaching, the scientific results on the students’ learning do not fully converge. On the one hand, some studies have shown AR’s benefits, including autonomy and improvement of learning by reducing mental effort. On the other hand, other studies have highlighted the strong limitations of AR technology for learning, such as cognitive over loading in some specific cases. This study investigates the impact of AR on the mental effort and learning outcomes of students who were involved in an inquiry-based science training session. The sample was composed of French Master’s degree students. A control group who used physical modeling activities and an experimental group who used an AR model for the first time studied the phenomenon of the seasons. The students were tested on their learning outcomes and mental effort during the training session. Results revealed no significant differences, except for the short-term test which showed better learning results for the control group. Moreover, a link between mental effort and learning outcomes was observed independently of the group conditions. Despite the first use of an AR model to study a complex scientific phenomenon, the experimental group (AR) performs in similar way to the control group (without AR) in long-term learning outcomes and mental effort.
Performance of the pharmaceutical professional during the COVID-19 pandemic

The COVID-19 pandemic has impacted not only the population, but also health systems around the world. Hospitals and care networks have been caught up in one of the biggest public health challenges of recent times: a novel coronavirus. In this scenario, pharmaceutical professionals perform an essential function since they are easily accessible to the population through drugstores and pharmacies and are able to offer guidance in the fight against the disease. Thus, the objective of the present study was to observe and report the services and performance of the pharmaceutical professional during the COVID-19 pandemic around the world. To this end, a bibliographic review was performed with a targeted search of scientific articles during the month of June 2020 in the PubMed and Scielo databases. The data were presented in the form of a comparative table containing the objectives of the articles, country of origin and dates of publication, as well as a description of the activities of the pharmaceutical professional cited in the different studies. Based on the results, it was observed the importance of the performance of the pharmaceutical professional in the hospital, community and clinical spheres, during the pandemic, both in the direct care of the patient and in the pharmacotherapeutic contribution in team. Therefore, the role of the pharmacist in the fight against COVID-19 is essential not only in dispensing the drug in pharmacies, but also in the primary care of the patient with reliable guidance and avoiding unnecessary trips to hospitals.
Financial investment trust mechanism based on smart contract

In this paper, a financial investment trust solution based on smart contract is proposed to solve the distrust problem in financial investment caused by information asymmetry. By utilizing the functional attributes of blockchain-based smart contracts, the financial investment trust mechanism is established. By operating this mechanism, the financial investment information is received, stored, and processed, and the information is sent to the nodes in the blockchain. By devising the algorithms of "requesting financial investment service", "successful financial investment", "product contract fraud arbitration" and "investment contract fraud arbitration", the financial investment trust mechanism is achieved. By presenting the algorithms and their invoking processes, smart contracts for the solution are written and debugged. Finally, the smart contracts are tested and validated. The smart contract source code is available in GitHub.
Detecting malicious reviews and users affecting social reviewing systems: A survey

The proliferation of attacks on On-line Social Networks (OSNs) has imposed particular attention by providers and users. This has an even higher importance for Social Reviewing Systems (SRSs), where users can be strongly conditioned by means of malevolent reviews and behavior of fake or camouflage accounts. There is a rich literature on the use of strong authentication means, encryption or privacy-preserving schemes for OSNs and SRSs, but these mechanisms only represent a first defense line. Advanced attacks may be able to bypass such a defense and to successfully threaten the system and harm its users. Therefore, it is needed to embed a second defence line to peculiar attack scenarios of SRSs that take advantage of user dynamics occurring within social networks. This survey focuses on the issue and solutions to detect malicious reviews and users so as to exclude them from social networks, protect legitimate and honest users and keep the credibility of the protected SRS as high as possible. Therefore, we provide an evaluation of the main detection solutions for the mentioned specific attacks against SRSs in according to different metrics on several standard datasets.
CNSeg: A dataset for cervical nuclear segmentation

Nuclear segmentation in cervical cell images is a crucial technique for automatic cytopathology diagnosis. Experimental evaluation of nuclear segmentation methods with datasets is helpful in promoting the advancement of nuclear segmentation techniques. However, public datasets are not enough for a reasonable and comprehensive evaluation because of insufficient quantity, single data source, and low segmentation difficulty. Therefore, we provide the largest dataset for cervical nuclear segmentation (CNSeg). It contains 124,000 annotated nuclei collected from 1,530 patients under different conditions. The image styles in this dataset cover most practical application scenarios, including microbial infection, cytopathic heterogeneity, overlapping nuclei, etc. To evaluate the performance of segmentation methods from different aspects, we divided the CNSeg dataset into three subsets, namely the patch segmentation dataset (PatchSeg) with nuclei images collected under complex conditions, the cluster segmentation dataset (ClusterSeg) with cluster nuclei, and the domain segmentation dataset (DomainSeg) with data from different domains. Furthermore, we propose a post-processing method that processes overlapping nuclei single ones. Experiments show that our dataset can comprehensively evaluate cervical nuclear segmentation methods from different aspects. We provide guidelines for other researchers to use the dataset. https://github.com/jingzhaohlj/AL-Net
Strategic and Serendipitous Knowledge Arbitrage: What We Know and What Remains Underexplored?

Knowledge management has become a major focal point of many scholarly discussions and industry decisions in the recent past. Knowledge arbitrage, concerned with enabling the flow of knowledge from surplus pools of knowledge to deficit ones, is a part of knowledge management and sharing. Strategic knowledge arbitrage and strategic knowledge serendipity (SKARSE) have been conceptualized as a strategic version of knowledge arbitrage, embodying the idea of a better, faster, and more efficient handling of knowledge assets. Despite being a part of academic research and discussions for the past two decades, which has resulted in an evident deepening of the knowledge era, the research on SKARSE has not yet reached a significant level of maturity, mandating additional and more contemporary investigations. Responding to this need, this article sought to motivate research in the area by applying the systematic literature review (SLR) approach to create an organized body of knowledge and set the future research agenda. We executed the SLR using a well-defined search protocol to identify 22 congruent studies, employing thematic analysis to aggregate and evaluate congruent evidence. As a result, we first delineated three main themes—conceptualizations of SKARSE, antecedents of SKARSE, and consequences of SKARSE—carried out a critical appraisal of the findings to identify research gaps and suggest potential research questions, and formulated a conceptual framework embodying varied aspects of SKARSE. This article offers significant implications for theory and practice.
Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning

An IoT healthcare system refers to the use of Internet of Things (IoT) devices and technologies in the healthcare industry. It involves the integration of various interconnected devices, sensors, and systems to collect, monitor, and transmit health-related data for medical purposes. Blockchain-assisted intrusion detection on IoT healthcare systems is an innovative approach to enhancing the security and privacy of sensitive medical data. By combining the decentralized and immutable nature of blockchain technology with intrusion detection systems (IDS), it is possible to create a more robust and trustworthy security framework for IoT healthcare systems. With this motivation, this study presents Blockchain Assisted IoT Healthcare System using Ant Lion Optimizer with Hybrid Deep Learning (BHS-ALOHDL) technique. The presented BHS-ALOHDL technique enables IoT devices in the healthcare sector to transmit medical data securely and detects intrusions in the system. To accomplish this, the BHS-ALOHDL technique performs ALO based feature subset selection (ALO-FSS) system to produce a series of feature vectors. The HDL model integrates convolutional neural network (CNN) features and long short-term memory (LSTM) model for intrusion detection. Lastly, the flower pollination algorithm (FPA) is exploited for the optimal hyperparameter tuning of the HDL approach, which results in an enhanced detection rate. The experimental outcome of the BHS-ALOHDL system was tested on two benchmark datasets and the outcomes indicate the promising performance of the BHS-ALOHDL technique over other models.
A Tool for the Development of Competencies in Sustainability and Carbon Footprint Reduction in Schools

This work presents a tool for developing competencies in sustainability, focusing on evaluating the environmental impact and carbon footprint in schools.The tool is based on sustainability indicators and follows a methodology derived from the ClimAct and ECF4CLIM projects.It is conceived as a support for the users and the schools to identify and evaluate the relative impact of different actions and measures, to engage them in a sustainable pathway to low-carbon schools supporting their decision-making at the different levels of users.The areas are transport, waste, green procurement, green spaces, energy, water, and indoor air quality.The tool aims to support users and schools in identifying and evaluating the impact of various actions and measures, facilitating decision-making for a sustainable and low-carbon school environment.To quantify sustainability performance, Key Performance Indicators (KPIs) have been established for each area.The results from schools that tested the tool revealed low scores in green procurement (0.59/5) and water (2.03/5), while achieving higher scores in green spaces (3.46/5) and indoor air quality (3.92/5).These findings highlight the areas where improvement is needed and provide a basis for targeted interventions.Developed on the PowerApps platform, supported by Microsoft, the tool allows access to users of different ages and knowledge levels.It has been successfully tested in six schools in Andalusia, Spain, obtaining relevant results in the seven sustainable areas defined.Based on the results, this tool will be used within co-creation and co-implementation processes for more sustainable schools and competencies acquisition by the participants.
Detection of Anomalous Energy Consumption in Commercial Activities Through Clustering Techniques

Anomalous energy consumption detection is a valuable strategy for pursuing energy efficiency.In commercial buildings, such as supermarkets, abnormal consumption can occur due to non-adequate equipment, such as lighting devices and refrigeration systems, or non-efficient HVAC plant management.Anomaly detection is usually performed on a single building by comparing its energy consumption to its usual behaviour and applying statistical or artificial intelligence-based techniques.Still, no anomaly emerges if its energy consumption is systematically high (or low).However, a more effective method for detecting anomalies would be to compare the energy consumption of a single building with that of others possessing similar characteristics.This paper then proposes an alternative approach based on clustering analysis.From this perspective, energy consumption data from a group of supermarkets are gathered in clusters to detect which presents abnormal behaviour compared to others with similar characteristics, such as the dimension and external weather conditions.An unsupervised density-based clustering algorithm for outlier detection (DBSCAN) is applied to a pool of 87 supermarkets located in Tuscany (Central Italy) to detect the abnormal ones, considering as input features the floor area, the electrical and thermal consumptions available from monthly bills, the type of the air-conditioning units, and the outdoor temperature.The analysis is performed over three years to detect recurring outliers on an annual and monthly scale to investigate possible seasonal effects.During the three years, approximately 15% of the supermarkets were consistently identified as outliers on both a monthly and annual basis.These findings were subsequently validated through an on-site inspection conducted by the energy manager of the supermarkets, revealing that 50% of the identified outliers exhibited exceptionally high thermal and electrical consumption due to improper plant operation.
Assemble the shallow or integrate a deep? Toward a lightweight solution for glyph-aware Chinese text classification

As hieroglyphic languages, such as Chinese, differ from alphabetic languages, researchers have always been interested in using internal glyph features to enhance semantic representation. However, the models used in such studies are becoming increasingly computationally expensive, even for simple tasks like text classification. In this paper, we aim to balance model performance and computation cost in glyph-aware Chinese text classification tasks. To address this issue, we propose a lightweight ensemble learning method for glyph-aware Chinese text classification (LEGACT) that consists of typical shallow networks as base learners and machine learning classifiers as meta-learners. Through model design and a series of experiments, we demonstrate that an ensemble approach integrating shallow neural networks can achieve comparable results even when compared to large-scale transformer models. The contribution of this paper includes a lightweight yet powerful solution for glyph-aware Chinese text classification and empirical evidence of the significance of glyph features for hieroglyphic language representation. Moreover, this paper emphasizes the importance of assembling shallow neural networks with proper ensemble strategies to reduce computational workload in predictive tasks.
Pragmatics Perspective of Genre-based Approach Essay Writing Textbook to Improve Students’ Writing Skills at English Education Department of Universitas PGRI Semarang

ABSTRACT This research is aimed at developing printed teaching materials with a pragmatic perspective for college students. The design of this research is descriptive qualitative by sharing questionnaire made with google form through the WhatsApp group of two classes. The unit of analysis was the responses of 40 students from 2 classes. This research was based on a qualitative analysis of the students' responses. From the responses, it can be concluded that teaching materials have a very vital role and function in the learning process. Therefore, teaching materials should be designed and developed in such a way that they are adapted to the existing curriculum and also to the needs of students. In this research, researchers will develop an English teaching material with pragmatic perspective which are able to help English learners reach the expected communicative competence. In total, there are five sections that were asked to students. Those five sections are, 1) Textbook availability, 2) Textbook Visual, 3) Textbook Contents, 4) The Textbook Currently Being Used, and 5) Text Selections.
Teaching English for Elementary School Students through Communicative Language Teaching

The aim of the implementation Communicative Language Teaching (CLT) in learning process is to change the teacher-centered learning process into student-centered learning, where students have the freedom to move and discuss all available materials. The method is given to students in grades three to six at SDN Jambangan during the Kampus Mengajar program. The CLT method is divided into four, namely the Communicative English teaching method, the practice question method, the question and Answer Session method, the guessing game method. Further, when learning is carried out, it was designed to make communicative activities such as games so that students don't feel bored. This learning process can provide opportunities for students to improve their language skills by using communicative English teaching methods. The findings showed that it is very helpful for educators to provide what is the right method to help students in dealing with existing problems and make learning fun. The students were active during the teaching and learning process. They did the teacher’s instruction cooperatively. Therefore, it can be concluded that learning can be carried out well and feels light when using the right stages. This research also helps the researcher to gain new experiences, which are very valuable experiences for life.
A Survey on Automated Leukemia Detection and Classification: Exploring Image Processing and Gene Expression Analysis Approaches

Leukemia is a haematological disorder which affects blood and bone marrow.Automated leukemia detection is significant for facilitating timely diagnoses and increasing the chances of successful treatment outcomes.Further, the classification of leukemia is crucial because each type demands a distinct treatment strategy.Deep learning and machine learning techniques are being used by computer aided diagnosis systems for their high precision in identifying leukemia from microscopic blood smear images using image processing techniques and gene microarray data using gene expression analysis techniques.However, automation of this task using image processing techniques is challenging due to the uneven structure and overlapping of cells.The large number of genes in microarray data makes the classification challenging, which is accomplished by applying a variety of feature selection techniques.In this study, recent developments in automated leukemia detection and classification employing image processing and gene expression analysis methods are explored.Furthermore, the paper compares and contrasts various techniques to provide a comprehensive overview that can assist in the continuous refinement of the detection and classification process.
Building a Corpus of Written Tasks of Swedish National Tests in English: Motivation, Method and Research Applications

This article describes a collaborative project involving the construction of a corpus of graded year 9 National Tests in written English. National Tests are standardized high stakes tests which are an important part of the Swedish education system because the results provide an indication of performance at national level, and also feed into pupils’ overall assessment. The grading of National Tests in written English has been found to be problematic for teachers, and a need for assessment training identified (Erickson and Tholin 2022). By providing a searchable database of graded written texts, together with the teacher feedback, this project aims to create a resource to support pre- and in-service teachers in interpreting knowledge requirements and assessment guidelines, and providing effective feedback. The corpus will also provide a resource for research into the features of student writing at different grade levels.&#x0D; To create the corpus, past papers from collaborating schools have been anonymized, digitized and coded. As a result, pupils’ texts can be easily sorted by a range of criteria, for example, year, gender, education type, grade achieved on the written paper and overall grade for the National Test. Teacher feedback can be accessed similarly. We outline potential research areas provided by this resource, and demonstrate how some of these might be explored. We also give examples of how the developing corpus has already been used as a resource for English teacher training programmes, and outline future plans for the project.