Reviewerly/exHarmony huggingface dataset:
Performance Efficiency Evaluation based on ISO/IEC 25010:2011 applied to a Case Study on Load Balance and Resilient

The client-server architecture makes it necessary to implement techniques to overcome the single point of failure problem (only one server node). One of the most commonly used techniques is load balancing. Load balancing consists in distributing requests between nodes that provide a service (clusters). In this paper, we performed an evaluation based on the concept of efficiency included in ISO25010:2011. The balancers evaluated were HAProxy (High Availability Proxy) and ARR (Application Request Routing) with and without caching. The project requirements are limited using the IIS (Internet Information Services) web server. Load Balances based on caching presented the best performance in most scenarios, while HAProxy obtained better values related to CPU utilization.
The Intellectual Assumptions and Critical Thinking in Preparing a Child for School Education

The study deals with the development of pupils' intellectual assumptions and cognitive functions. The optimization strategy in the subject research are educational activities that are applied in primary education. The subject of the investigation is the identification of the impact of educational activities on the level of pupils' intellectual assumptions, operationalized at the level of pupils' general knowledge (factual, conceptual, procedural). The research design is based on a pedagogical experiment with pupils in the transition period. According the results, pupils who have been applied educational activities achieve a higher level of intellectual prerequisites at start of the primary school.
TRENDS IN VISUAL CONTENT CREATION ON THE EXAMPLE OF RUSSIAN PUBLISHING AND BOOKSELLING COMPANIES IN SOCIAL NETWORKS

Ключевые слова: визуализация,
SELECTING THE IMPORTANT FEATURES TO CLASSIFY THE ARCHAEOLOGICAL FRAGMENTS BY USING STATISTICAL TOOLS

Feature selection, the process of representing an object in the least dimensions, is one of the most important and difficult steps in pattern recognition.Therefore, meticulous selection of important features for classification is required.In this study, we propose a method based on Multidimensional Scaling (MDS) to reduce the dimensions of ancient ceramic fragment features.This method focuses on selecting the most important features based on the density of the grayscale image and texture.Finally, we use the Euclidean distance equation to classify objects into similar groups.With a database containing more than 300 images, the experiment achieved an impressive 90% success rate in accurately categorizing fragments as either similar or non-similar.These results demonstrate the effectiveness and promise of the proposed approach for image classification tasks, emphasizing the potential of statistical methods and image processing techniques for addressing complex computer vision challenges.
Efficiency in the serverless cloud paradigm: A survey on the reusing and approximation aspects

Serverless computing along with Function-as-a-Service (FaaS) is forming a new computing paradigm that is anticipated to found the next generation of cloud systems. The popularity of this paradigm is due to offering a highly transparent infrastructure that enables user applications to scale in the granularity of their functions. Since these often small and single-purpose functions are managed on shared computing resources behind the scene, a great potential for computational reuse and approximate computing emerges that if unleashed, can remarkably improve the efficiency of serverless cloud systems -- both from the user's QoS and system's (energy consumption and incurred cost) perspectives. Accordingly, the goal of this survey study is to, first, unfold the internal mechanics of serverless computing and, second, explore the scope for efficiency within this paradigm via studying function reuse and approximation approaches and discussing the pros and cons of each one. Next, we outline potential future research directions within this paradigm that can either unlock new use cases or make the paradigm more efficient.
A spatial–temporal hypergraph based method for service recommendation in the Mobile Internet of Things-enabled service platform

With the rapid progress of Mobile Internet of Things (MIoT), Location-Based Services (LBSs) have become widely popular due to the deployment of diverse devices and facilities. The utilization of spatial–temporal information has led to a considerable improvement in service recommendation in the MIoT-enabled service platform. In practical scenarios, the interaction between the user and the service is often initiated by the joint effect of previous service invocations. Many-to-many and high-order spatial–temporal relations exist among services, which are inadequately reflected by prevailing methods that rely on ordinary graphs and consider spatial–temporal relations among services as pairwise connections. Additionally, most methods indiscriminately conflate the separate influences of spatial and temporal information on user preferences, which can lead to suboptimal service recommendation performance. To this end, we propose an approach powered by spatial–temporal hypergraphs (STH) for personalized service recommendation in the MIoT-enabled service platform, which is inspired by the deep learning paradigm. Specifically, we first construct spatial and temporal hypergraphs based on spatial–temporal information. Then, we develop the spatial–temporal hypergraph neural network-based encoders to learn spatial and temporal representations of services. Finally, we introduce an auxiliary task to differentiate between distinct spatial–temporal effects for improved service recommendations. We conduct extensive experiments on four real-world datasets, and the results demonstrate the effectiveness of STH. Thus, STH not only enhances the user experience but also improves service management in the MIoT-enabled service platform.
The Influence of 'Ome TV': Exploring the Impact on Students' Listening Skills through Video Live Streaming

This quantitative research study investigates the influence of the video live streaming application 'Ome TV' on students' listening skills. Undergraduate students from a vocational university in Makassar, Indonesia participated in the study, which utilized an online questionnaire to collect data on participants' listening abilities and their experiences with 'Ome TV'. The study aims to investigate the influence of 'Ome TV' on students' listening skills, identify challenges faced by students in using the application for listening practice, understand students' perceptions of the application's effectiveness, and provide recommendations for optimizing the integration of video live streaming applications in language learning environments. The findings reveal that most students perceived an improvement in their listening skills through 'Ome TV'; however, difficulties such as infrequent usage, limited improvement in understanding words with similar sounds, security concerns, and network quality issues were reported. The study concludes with practical recommendations for educators, curriculum designers, and developers to enhance the use of video live streaming applications in language learning settings. These findings contribute to the understanding of the potential benefits and challenges associated with video live streaming applications for improving students' listening proficiency.
ProGleason-GAN: Conditional progressive growing GAN for prostatic cancer Gleason grade patch synthesis

Background and objective. Prostate cancer is one of the most common diseases affecting men. The main diagnostic and prognostic reference tool is the Gleason scoring system. An expert pathologist assigns a Gleason grade to a sample of prostate tissue. As this process is very time-consuming, some artificial intelligence applications were developed to automatize it. The training process is often confronted with insufficient and unbalanced databases which affect the generalisability of the models. Therefore, the aim of this work is to develop a generative deep learning model capable of synthesising patches of any selected Gleason grade to perform data augmentation on unbalanced data and test the improvement of classification models. Methodology. The methodology proposed in this work consists of a conditional Progressive Growing GAN (ProGleason-GAN) capable of synthesising prostate histopathological tissue patches by selecting the desired Gleason Grade cancer pattern in the synthetic sample. The conditional Gleason Grade information is introduced into the model through the embedding layers, so there is no need to add a term to the Wasserstein loss function. We used minibatch standard deviation and pixel normalisation to improve the performance and stability of the training process. Results. The reality assessment of the synthetic samples was performed with the Frechet Inception Distance (FID). We obtained an FID metric of 88.85 for non-cancerous patterns, 81.86 for GG3, 49.32 for GG4 and 108.69 for GG5 after post-processing stain normalisation. In addition, a group of expert pathologists was selected to perform an external validation of the proposed framework. Finally, the application of our proposed framework improved the classification results in SICAPv2 dataset, proving its effectiveness as a data augmentation method. Conclusions. ProGleason-GAN approach combined with a stain normalisation post-processing provides state-of-the-art results regarding Frechet’s Inception Distance. This model can synthesise samples of non-cancerous patterns, GG3, GG4 or GG5. The inclusion of conditional information about the Gleason grade during the training process allows the model to select the cancerous pattern in a synthetic sample. The proposed framework can be used as a data augmentation method.
MENINGKATKAN HASIL BELAJAR IPS MELALUI MEDIA GAMBAR PADA SISWA KELAS IV SD NEGERI 3 MOLONA

This study aims to “Improving Social Studies Learning Outcomes through Image Media in Class IV Students of SD Negeri 3 Molona ​​South Buton Regency”. This research conducred in 2 cycles, each cycle includes: planning, implementation, observation, and reflection. The subjects of this study were the fourth grade students of SD Negeri 3 Molona for the 2020/2021 academic year, totaling 20 students consisting of 8 male students and 12 female students. Student learning outcomes on the pre-cycle test the percentage of completeness reached 30% with an average value of 57, in the implementation of the first cycle the percentage of completeness reached 50% with an average value of 63,5. Cycle II reached 80% with an average value of 75,5. This shows an increase in student learning outcomes from each cycle. The increase from pre-cycle to cycle I reached 25%, and the increase from cycle I to cycle II reached 25%. The increase from pre-cycle to cycle II reached 50%. So it can be concluded that through the use of image media it can improve sicial studies learning otcomes for fourth grade students of SD Negeri 3 Molona, South Buton Regency.
Parallelizable efficient large order multiple recursive generators

The general multiple recursive generator (MRG) of maximum period has been thought of as an excellent source of pseudo random numbers. Based on a kth order linear recurrence modulo p, this generator produces the next pseudo random number based on a linear combination of the previous k numbers. General maximum period MRGs of order k have excellent empirical performance, and their strong mathematical foundations have been studied extensively. For computing efficiency, it is common to consider special MRGs with some simple structure with few non-zero terms which requires fewer costly multiplications. However, such MRGs will not have a good "spectral test" property when compared with general MRGs with many non-zero terms. On the other hand, there are two potential problems of using general MRGs with many non-zero terms: (1) its efficient implementation (2) its efficient scheme for its parallelization. Efficient implementation of general MRGs of larger order k can be difficult because the kth order linear recurrence requires many costly multiplications to produce the next number. For its parallelization scheme, for a large k, the traditional scheme like "jump-ahead parallelization method" for general MRGs becomes highly computationally inefficient. We proposed implementing maximum period MRGs with many nonzero terms efficiently and in parallel by using a MCG constructed from the MRG. In particular, we propose a special class of large order MRGs with many nonzero terms that also have an efficient and parallel implementation.
Survei Pemahaman Leksikon Ekologis Bahasa Jawa Pada Mahasiswa PGSD Universitas Sanata Dharma (Tinjauan Ekologi Linguistik)

&lt;p&gt;&lt;em&gt;This study examined the use of Lexicons related to ecological lexicons in Javanese that are commonly used by everyday students. This type of research according to Furchan (1982) was a qualitative descriptive research sub-category of survey research. Research subjects were 96 students from Java (Yogyakarta and Central Java). The instrument developed was a questionnaire. Validation of instruments using the expert judgment of Javanese linguists. The results showed students who were ecological lexicon in Javanese were 53.19%, students who did not understand the ecological lexicon of Javanese were 46.81%. More than half of the students still understand the meaning of the ecological lexicon of the Javanese language. The data was followed up with interviews. The results of the interview revealed that students' understanding of the meaning of the ecological lexicon was not entirely appropriate. Some ecological lexicons of Canada are no longer used in terms of the absence of the main factors. First, youth urbanization in Javanese society. Second, the number of speakers is getting smaller. Third, the changing natural factors, climate, and weather are commonly described by the ecological lexicon are not felt by the majority of Javanese people&lt;/em&gt;.&lt;/p&gt;
A Smart Image Encryption Technology via Applying Personal Information and Speaker-Verification System

In this paper, a framework for authorization and personal image protection that applies user accounts, passwords, and personal I-vectors as the keys for ciphering the image content was developed and connected. There were two main systems in this framework. The first involved a speaker verification system, wherein the user entered their account information and password to log into the system and provided a short voice sample for identification, and then the algorithm transferred the user’s voice (biometric) features, along with their account and password details, to a second image encryption system. For the image encryption process, the account name and password presented by the user were applied to produce the initial conditions for hyper-chaotic systems to generate private keys for image-shuffling and ciphering. In the final stage, the biometric features were also applied to protect the content of the image, so the encryption technology would be more robust. The final results of the encryption system were acceptable, as a lower correlation was obtained in the cipher images. The voice database we applied was the Pitch Tracking Database from the Graz University of Technology (PTDB-TUG), which provided the microphone and laryngoscope signals of 20 native English speakers. For image processing, four standard testing images from the University of Southern California–Signal and Image Processing Institute (USC-SIPI), including Lena, F-16, Mandrill, and Peppers, were presented to further demonstrate the effectiveness and efficiency of the smart image encryption algorithm.
A Light-Weight Artificial Neural Network for Recognition of Activities of Daily Living

Human activity recognition (HAR) is essential for the development of robots to assist humans in daily activities. HAR is required to be accurate, fast and suitable for low-cost wearable devices to ensure portable and safe assistance. Current computational methods can achieve accurate recognition results but tend to be computationally expensive, making them unsuitable for the development of wearable robots in terms of speed and processing power. This paper proposes a light-weight architecture for recognition of activities using five inertial measurement units and four goniometers attached to the lower limb. First, a systematic extraction of time-domain features from wearable sensor data is performed. Second, a small high-speed artificial neural network and line search method for cost function optimization are used for activity recognition. The proposed method is systematically validated using a large dataset composed of wearable sensor data from seven activities (sitting, standing, walking, stair ascent/descent, ramp ascent/descent) associated with eight healthy subjects. The accuracy and speed results are compared against methods commonly used for activity recognition including deep neural networks, convolutional neural networks, long short-term memory and convolutional–long short-term memory hybrid networks. The experiments demonstrate that the light-weight architecture can achieve a high recognition accuracy of 98.60%, 93.10% and 84.77% for seen data from seen subjects, unseen data from seen subjects and unseen data from unseen subjects, respectively, and an inference time of 85 μs. The results show that the proposed approach can perform accurate and fast activity recognition with a reduced computational complexity suitable for the development of portable assistive devices.
Bias in Social Media Content Management: What Do Human Rights Have to Do with It?

In a global context where political campaigning, social movements, and public discourse increasingly take place online, questions regarding the regulation of speech by social media platforms become ever more relevant. Companies like Facebook moderate content posted by users on their platforms through a mixture of automated decision making and human moderators. In this content moderation process, human rights play an ambiguous role: those who struggle with marginalization may find a space for expression and empowerment, or face exacerbation of pre-existing bias. Focusing on the role of human rights in Meta's content management, this essay explores how the protection of speech on social media platforms disadvantages the cultural, social, and economic rights of marginalized communities. This is not to say that speech on social media platforms is devoid of emancipatory potential, but that this potential is not uniformly or equally accessible. We see the incorporation of human rights considerations into decision-making processes as an avenue for alleviating this challenge. This approach faces obstacles from the platforms’ business models, which decenters human rights concerns, and from the limitations of liberal accounts of human rights. From within and against these constraints, human rights can be mobilized as emancipatory power in an effort to decrease marginalization.
Research data warehouse: using electronic health records to conduct population-based observational studies.

Electronic health records and many legacy systems contain rich longitudinal data that can be used for research; however, they typically are not readily available.At Kaiser Permanente Southern California (KPSC), a research data warehouse (RDW) has been developed and maintained since the late 1990s and widely extended in 2006, aggregating and standardizing data collected from internal and a few external sources. This article provides a high-level overview of the RDW and discusses challenges common to data warehouses or repositories for research use. To demonstrate the application of the data, we report the volume, patient characteristics, and age-adjusted prevalence of selected medical conditions and utilization rates of selected medical procedures.A total of 105 million person-years of health plan enrollment was recorded in the RDW between 1981 and 2018, with most healthcare utilization data available since early or middle 1990s. Among active enrollees on December 31, 2018, 15% were ≥65 years of age, 33.9% were non-Hispanic white, 43.3% Hispanic, 11.0% Asian, and 8.4% African American, and 34.4% of children (2-17 years old) and 72.1% of adults (≥18 years old) were overweight or obese. The age-adjusted prevalence of asthma, atrial fibrillation, diabetes mellitus, hypercholesteremia, and hypertension increased between 2001 and 2018. Hospitalization and Emergency Department (ED) visit rates appeared lower, and office visit rates seemed higher at KPSC compared to the reported US averages.Although the RDW is unique to KPSC, its methodologies and experience may provide useful insights for researchers of other healthcare systems worldwide in the era of big data analysis.
Unveiling the Black Box of PLMs with Semantic Anchors: Towards Interpretable Neural Semantic Parsing

The recent prevalence of pretrained language models (PLMs) has dramatically shifted the paradigm of semantic parsing, where the mapping from natural language utterances to structured logical forms is now formulated as a Seq2Seq task. Despite the promising performance, previous PLM-based approaches often suffer from hallucination problems due to their negligence of the structural information contained in the sentence, which essentially constitutes the key semantics of the logical forms. Furthermore, most works treat PLM as a black box in which the generation process of the target logical form is hidden beneath the decoder modules, which greatly hinders the model's intrinsic interpretability. To address these two issues, we propose to incorporate the current PLMs with a hierarchical decoder network. By taking the first-principle structures as the semantic anchors, we propose two novel intermediate supervision tasks, namely Semantic Anchor Extraction and Semantic Anchor Alignment, for training the hierarchical decoders and probing the model intermediate representations in a self-adaptive manner alongside the fine-tuning process. We conduct intensive experiments on several semantic parsing benchmarks and demonstrate that our approach can consistently outperform the baselines. More importantly, by analyzing the intermediate representations of the hierarchical decoders, our approach also makes a huge step toward the interpretability of PLMs in the domain of semantic parsing.
A Question-Answering Approach to Key Value Pair Extraction from Form-Like Document Images

In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. Specifically, KVPFormer first identifies key entities from all entities in an image with a Transformer encoder, then takes these key entities as questions and feeds them into a Transformer decoder to predict their corresponding answers (i.e., value entities) in parallel. To achieve higher answer prediction accuracy, we propose a coarse-to-fine answer prediction approach further, which first extracts multiple answer candidates for each identified question in the coarse stage and then selects the most likely one among these candidates in the fine stage. In this way, the learning difficulty of answer prediction can be effectively reduced so that the prediction accuracy can be improved. Moreover, we introduce a spatial compatibility attention bias into the self-attention/cross-attention mechanism for KVPFormer to better model the spatial interactions between entities. With these new techniques, our proposed KVPFormer achieves state-of-the-art results on FUNSD and XFUND datasets, outperforming the previous best-performing method by 7.2% and 13.2% in F1 score, respectively.