Reviewerly/exHarmony huggingface dataset:
Parallel and Exact Method for Solving n-Similarity Problem

Background and Objectives: The similarity is defined as the resemblance degree between two or more objects, phenomena or concepts. In various applications, designers want to build classes of objects based on the similarity criteria. So, it has an important role in automated classification, clustering methods, decision making, approximate reasoning, and diagnosis systems.Methods: n-similarity problem defined as measuring the similarity among n≥3 objects and finding a group of n objects from a dataset that have the most similarity to each other. This problem has become an important issue in information retrieval and data mining. Theory of this concept is mathematically proven, but it practically has high memory complexity and is so time-consuming. Although metaheuristics can find a near-optimal solution in a reasonable time, their results are not exact and they do not explore and compute the n-similarity value of all possible n-groups.Results: This paper is conducted to propose an exact method to solve n-similarity problem reducing the memory complexity and decreasing the execution time by parallelism using Open-MP. The experiments are performed on the application of text document resemblance.Conclusion: It has been proved that the memory complexity of the proposed method is decreased and the experimental results show that this method accelerates the speed of the computations and is more scalable compared with previous exact method.
Assessment and reliability of suspect flags in automated hematology analyzers for diagnosing white blood cell and platelet disorders

Background: The automated hematology analyzers have replaced the traditional manual methods for assessing hematological parameters. Most of the automated hematology analyzers are programmed to identify abnormalities in the form of “suspect flags.” Aims: The aim of the study was to correlate white blood cell (WBC) and platelet (PLT) flag messages provided by automated analyzers with their respective peripheral blood smear (PBS) findings. Materials and Methods: A descriptive cross-sectional study was undertaken on 100 patients who showed WBC and PLT suspect flags and/or abnormal peripheral smears. The flag messages were analyzed with their respective PBS findings. Pearson's Chi-square test was used for the statistical analysis. Results: Analyzer and PBS showed WBC defects (n = 46, 32), PLT defects (n = 39, 54), and combined defects (n = 36, 43) respectively. WBC defects included leukocytosis (n = 27, 22), leukopenia (n = 20, 18), immature granulocytes (n = 10, 3), left shift (n = 3, 2), myeloblast blast (n = 2, 1), and eosinophilia (n = 4, 4) on analyzer and PBS. Correlation between the WBC suspect flags and their peripheral smears was found to be statistically significant (P = 0.006). PLT defects included thrombocytopenia (n = 22, 30), thrombocytosis (n = 13, 17), giant PLTs (n = 10, 15), PLT clumps (n = 8, 12), and PLT debris (n = 2, 5) on analyzer and PBS. Correlation between the PLT characteristics in analyzers and in peripheral smears was statistically significant (P = 0.042). Conclusion: Suspect flags should be used as a screening tool to pick up pathological samples. These should then be followed up with a PBS examination for definitive diagnosis.
A method of generating virtual shadow dataset of buildings for the shadow detection and removal

Detecting shadows in images and restoring or removing them was a very challenging task in computer vision. Traditional researches used color information, edges, and thresholds to detect shadows, but there were errors such as not considering the penumbra area of shadow or even detecting a black area that is not a shadow. Deep learning has been successful in various fields of computer vision, and research on applying deep learning has started in the field of shadow detection and removal. However, it was very difficult and time-consuming to collect data for network learning, and there were many limited conditions for shooting. In particular, it was more difficult to obtain shadow data from buildings and satellite images, which hindered the progress of the research. In this paper, we propose a method for generating shadow data from buildings and satellites using Unity3D. In the virtual Unity space, 3D objects existing in the real world were placed, and shadows were generated using lights effects to shoot. Through this, it is possible to get all three types of images (shadow-free, shadow image, shadow mask) necessary for shadow detection and removal when training deep learning networks. The method proposed in this paper contributes to helping the progress of the research by providing big data in the field of building or satellite shadow detection and removal research, which is difficult for learning deep learning networks due to the absence of data. And this can be a suboptimal method. We believe that we have contributed in that we can apply virtual data to test deep learning networks before applying real data.
Investigating the Role of Sensor-Based Technologies in Resource-Constrained Households

In sub-Saharan Africa (SSA), domestic spaces face various challenges including insecurity, unreliable power supply and extreme weather conditions that affect poultry farming. The availability of low-cost sensors presents an opportunity to collect real-time data and utilize proactive methods to monitor these challenges. This dissertation builds on my prior studies to explore the role of sensor-based technologies to support domestic activities in this context. My research engages local technicians to design technology probes that support domestic security (M-Kulinda), power blackout monitoring (GridAlert) and poultry farming (NkhukuApp). I deployed M-Kulinda and GridAlert in Kenyan homes and I will deploy NkhukuApp in Malawian homes. This research contributes to HCI/CSCW by providing: empirical evidence about how sensors can be used in domestic spaces of this context; methodological contributions to technology probes by including local collaborators in their design process; and prototypes for supporting domestic activities in SSA.
Mutual modality learning for video action classification

The construction of models for video action classification progresses rapidly. However, the performance of those models can still be easily improved by ensembling with the same models trained on different modalities (e.g. Optical flow). Unfortunately, it is computationally expensive to use several modalities during inference. Recent works examine the ways to integrate advantages of multi-modality into a single RGB-model. Yet, there is still room for improvement. In this paper, we explore various methods to embed the ensemble power into a single model. We show that proper initialization, as well as mutual modality learning, enhances single-modality models. As a result, we achieve state-of-the-art results in the Something-Something-v2 benchmark.
VDD: A visual drift detection system for process mining

Research on concept drift detection has inspired recent advancements of process mining and expanding the growing arsenal of process analysis tools. What has so far been missing in this new research stream are techniques that support comprehensive process drift analysis in terms of localizing, drillingdown, quantifying, and visualizing process drifts. In our research, we built on ideas from concept drift, process mining, and visualization research and present a novel web-based software tool to analyze process drifts, called Visual Drift Detection (VDD). Addressing the comprehensive analysis requirements, our tool is of benefit to researchers and practitioners in the business intelligence and process analytics area. It constitutes a valuable aid to those who are involved in business process redesign projects.
National Universities in Argentina during the pandemic outbreak

Background: The health emergency that Argentina is experiencing because of the COVID-19 pandemic radically changed institutional normality at all educational levels. Universities have certainly not escaped this logic. Whilst their first response was a general shut down, along with a shift to online teaching and learning, the actions taken in response to the pandemic went far and beyond. These institutions sought to contribute, in different ways, to satisfy the new social demands linked to the outbreak of the health emergency. Aim: We aim to give an account of the actions that the national universities in Argentina took in response to the distinctive social demands linked to the pandemic outbreak. Setting: Specifically, we focussed on the academic and pedagogical decisions, the reorientation of scientific research, the availability of infrastructure and resources for the COVID-19 treatment and the responses to the social demands of local communities. Methods: We worked with institutional documents from the national universities, the National Inter-University Council, the Ministry of Education and the Ministry of Science and Technology. We also used news sources, such as newspapers articles and the universities web sites. Results: Our research brought the attention to some interesting facts on the country’s higher education system, which shows us a distinctive role that universities play in Argentina. Nevertheless, we have also found indicators regarding the difficulties that these institutions struggle with. In particular, these linked to the economic crises that the country has suffered for decades, which in turn have changed the universities specific features. Conclusion: The COVID-19 pandemic disrupted the functioning of society as a whole, and universities were no exception. Despite the strong heterogeneity that characterises national universities throughout the country, all of them agreed to make all their resources available to mitigate the effects of the health emergency.
On the Usage of Deterministic (Related-Key) Truncated Differentials and Multidimensional Linear Approximations for SPN Ciphers

Among the few works realising the search of truncated differentials (TD) and multidimensional linear approximations (MDLA) holding for sure, the optimality of the distinguisher should be confirmed via an exhaustive search over all possible input differences/masks, which cannot be afforded when the internal state of the primitive has a considerable number of words. The incomplete search is also a long-term problem in the search of optimal impossible differential (ID) and zerocorrelation linear approximation (ZCLA) since all available automatic tools operate under fixed input and output differences/masks, and testing all possible combinations of differences/masks is impracticable for now. In this paper, we start by introducing an automatic approach based on the constraint satisfaction problem for the exploration of deterministic TDs and MDLAs. Since we transform the exhaustive search into an inherent feature of the searching model, the issue of incomplete search is settled. This tool is applied to search for related-key (RK) TDs of AES-192, and a new related-key differential-linear (DL) distinguisher is identified with a TD with certainty. Due to the novel property of the distinguisher, the previous RK DL attack on AES-192 is improved. Also, the new distinguisher is explained from the viewpoint of differentiallinear connectivity table (DLCT) and thus can be regarded as the first application of DLCT in the related-key attack scenario. As the second application of the tool, we propose a method to construct (RK) IDs and ZCLAs automatically. Benefiting from the control of the nonzero fixed differential pattern and the inherent feature of exhaustive search, the new searching scheme can discover longer distinguishers and hence possesses some superiorities over the previous methods. This technique is implemented with several primitives, and the provable security bounds of SKINNY and Midori64 against impossible differential distinguishing attack are generalised.
Delivering Machine Learning Applications via Cloud Platforms: An Experience Report

Cloud technologies enable developers and organizations to focus on their product, without having to consider issues such as local server capacity, infrastructure modifications, data security, licensing or human capital. This paper attempts to explain a case in which a Machine Learning application is deployed via Amazon Web Services (AWS) tools. In doing so, it demonstrates the reasoning behind choosing a cloud-based environment instead of on-premise sources, by putting forward the advantages of the former. On the other hand, it should be noted that the application in this experience is generated with a hybrid approach: It is developed using on-premise infrastructure and then moved to the Cloud environment for the deployment phase only. In this regard, it can be read as a PaaS experience. This study is considered to be a beneficial guide for entrepreneurs and start-ups on a budget who aim at launching their products in a swift and scalable manner.
An Unsupervised Information-Theoretic Perceptual Quality Metric

Tractable models of human perception have proved to be challenging to build. Hand-designed models such as MS-SSIM remain popular predictors of human image quality judgements due to their simplicity and speed. Recent modern deep learning approaches can perform better, but they rely on supervised data which can be costly to gather: large sets of class labels such as ImageNet, image quality ratings, or both. We combine recent advances in information-theoretic objective functions with a computational architecture informed by the physiology of the human visual system and unsupervised training on pairs of video frames, yielding our Perceptual Information Metric (PIM). We show that PIM is competitive with supervised metrics on the recent and challenging BAPPS image quality assessment dataset and outperforms them in predicting the ranking of image compression methods in CLIC 2020. We also perform qualitative experiments using the ImageNet-C dataset, and establish that PIM is robust with respect to architectural details.
Investigating One Million XRefs in Thirthy Ontologies from the OBO World

The more ontologies are produced, the more need to identify mappings between them becomes important. Several practices and tools have been developed to support ontology alignment, but still, it remains a challenge. In the OBO world, ontology developers use cross reference annotations (formally using the oboInOwl:hasDbXRef property) to link a term to an external entity, including another term in another ontology (i.e., a mapping). These XRefs contains information of high value, because they were manually declared/verified by domain experts when the ontology was built. However, extracting and exploiting them remains a challenge for users due to their heterogeneous and chaotic descriptions. In this paper , we report on analysis of approximately 1 million XRefs in 30 ontologies from the OBO world. Our results show that 10.7% of these XRefs are ontology map-pings, but confirm that semantically-ambiguous usage of the cross-reference property which make it impractical or even impossible to reuse. We describe and quantify several issues identified (e.g., different kind of XRefs, miscellaneous formatting , ambiguous targets), and discuss some way to mitigate them especially by using more relevant specific properties taken from standard semantic Web metadata vocabularies.
Visual Analysis of Text Data Volume by Frequencies of Joint Use of Nouns and Adjectives

The presented research is devoted to the problems of studying the cluster structure of multidimensional data volumes.This paper presents the results of numerical experiments on the study of data volumes consisting of frequencies of joint use of adjectives and nouns.The volumes of data were obtained from samples from text collections in Russian.The aim of the research is to analyze the cluster structure of the studied volume and semantic proximity of words in clusters and subclusters.The hypothesis was used that words with similar meaning should occur in approximately the same context.In this regard, in the space of features, they will be at a relatively close distance from each other, while differing words will be at a more distant distance from each other.Research is carried out using elastic maps, which are effective tools for visual analysis of multidimensional data.The construction of elastic maps and their extensions in the space of the first three principal components makes it possible to determine the cluster structure of the studied multidimensional data volumes.The analysis of the cluster structure for the considered volume of multidimensional data is carried out.The influence of transposition of the initial data array is considered.Such analysis can be useful in the tasks of confronting negative verbal influences such as fake news, hidden propaganda, involvement in sects, verbal manipulation, etc.
Neural Sparse Representation for Image Restoration

Inspired by the robustness and efficiency of sparse representation in sparse coding based image restoration models, we investigate the sparsity of neurons in deep networks. Our method structurally enforces sparsity constraints upon hidden neurons. The sparsity constraints are favorable for gradient-based learning algorithms and attachable to convolution layers in various networks. Sparsity in neurons enables computation saving by only operating on non-zero components without hurting accuracy. Meanwhile, our method can magnify representation dimensionality and model capacity with negligible additional computation cost. Experiments show that sparse representation is crucial in deep neural networks for multiple image restoration tasks, including image super-resolution, image denoising, and image compression artifacts removal. Code is available at this https URL
Notification information system android-based for spreading school information

School is a place where students can learn a lot, but beside as a place to learning, the school also has many events and activities that must be attended by students, or even sometimes must be attended by parents. To give information or invintation to the parents, some school give information by manual letter, email, social media or online chatting, but all that method to spread information has a weakness, because sometimes information that shown or give to parents or to students, often not accepted by students or parents, and beside that, to spread message to a lot people need a lot time and need a lot cost. To solve this issue, in this study will be build Android-based information notification application with push messaging services, where if there a new information from the school, the students or parents will receive notification new information to their Android smartphone, and after that, the details of this information can be seen in the applications. To get acceptance user result, we using user acceptance test (UAT) was conducted using the TAM method, the result has positive results were 84.88% from parent or student's side and obtained 84.67% from school administration's perspective as provider information, which means this system can be accepted and as expected by the user who receiving the information and by the school as information provider.
Peningkatan Motivasi Hasil Belajar Ekonomi Melalui Model Pembelajaran Student Centered Learning

In the process of learning Economics (Accounting) required a certain strategy and strong motivation. Teachers play a very important role, especially in terms of designing learning scenarios. Teachers should be able to choose a learning model that can increase the motivation of learners, in accordance with the characteristics of subjects, and characteristics of learners so that learning can take place optimally. The aim of this classroom action research is to improve the learning of learners using the student centered learning model. This research was conducted in class XI IPS1, MAN 1 Cilegon semester 1, material Posting from Juku Kebuku Besar Journals, post to a subsidiary "using cycles, in this study the author tries to 2 cycles. The results show that in each cycle of understanding the accounting, cycle of a merchandising company on post material from a special journal to the ledger, posting to the auxiliary book, gained increased motivation and learning outcomes. In general it can be concluded that the use of student centered learning model can improve the motivation and learning outcomes of learners. &#x0D; Keywords: Student Centered Learning Model, Learn Economics Accounting
Research on Automatic Generation Method of Virtual Reality Scene and Design of User Experience Evaluation

With the continuous development of new technology, large-scale virtual reality scene is widely used in various projects, and virtual reality scene directly affects the running speed and real-time update status, and is also one of the key indicators to determine the user experience. Therefore, in view of the above problems, this paper proposes a crowdsourcing number based on the generation confrontation network model from the design of virtual reality scene according to the driven virtual reality scene automatic generation method, the real scene with human-computer interaction ability is generated to meet the design requirements. By comparing the real model, the real-time rendering model on the computer and the virtual model under the virtual reality system, this paper explores the difference of the evaluation of the design scheme by different prototype modes, and studies the cognitive differences and experience differences of the users. The experimental results show that the virtual reality evaluation is very close to the real effect in the experience, and the user experience has a certain degree of impact on the scene characters' vision, using behavior and impression provide more reliable evaluation methods, and provide new ideas for creating a scientific and comprehensive virtual reality design evaluation system.
Impact of Psychological Contract on Organizational Commitment

The paper examined the impact of psychological contract on the organizational commitment by studying the impact of independent variables of organizational policies, work environment and organizational compensation structure. The respondents chosen for this study include software developers and associated functional representatives. The sample size was 56 while purposive sampling technique was used for data collection. The findings indicate that there is a definite positive impact of psychological contract on the organizational commitment with the variables organizational work environment and organizational compensation structure dominating over organizational policies. The limitation that surfaced from the study is the small sample size and the application of descriptive statistical technique. The future scope of study is the inclusion of breakdown of the variable organizational work environment and organizational compensation structure which would provide detailed findings into these main variables.
Mixed-Lingual Pre-training for Cross-lingual Summarization

Cross-lingual Summarization (CLS) aims at producing a summary in the target language for an article in the source language. Traditional solutions employ a two-step approach, i.e. translate -> summarize or summarize -> translate. Recently, end-to-end models have achieved better results, but these approaches are mostly limited by their dependence on large-scale labeled data. We propose a solution based on mixed-lingual pre-training that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. Thus, our model can leverage the massive monolingual data to enhance its modeling of language. Moreover, the architecture has no task-specific components, which saves memory and increases optimization efficiency. We show in experiments that this pre-training scheme can effectively boost the performance of cross-lingual summarization. In NCLS dataset, our model achieves an improvement of 2.82 (English to Chinese) and 1.15 (Chinese to English) ROUGE-1 scores over state-of-the-art results.