Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Support Range based Rare Pattern Mining over Data Streams

—Rare itemset mining is a relatively recent topic of study in data mining. In certain application domains, such as online banking transaction analysis, sensor data analysis, and stock market analysis, rare patterns are patterns with low support and high confidence that are extremely interesting when compared to frequent patterns. Numerous applications generate large amounts of continuous data streams. We require efficient algorithms capable of processing data streams in order to analyze them and find unique patterns. The strategies developed for static databases cannot be used to data streams. As a result, we require algorithms created expressly for data stream processing in order to extract critical unique patterns. Rare pattern mining is still in its infancy, with only a few ways available. To address this is developed the Dynamic Support Range-based Hybrid-Eclat Algorithm (DSRHEA), an Eclat-based technique for mining unique patterns from a data stream using bit-set vertical mining with two item-based optimizations. The detected patterns are kept in a prefix-based rare pattern tree that uses double hashing to maintain the unusual pattern in the data stream. Testing showed that the proposed method did well in terms of how long it took to run, how many rare patterns it made and accuracy.
Federated Matrix Factorization with Privacy Guarantee

Matrix factorization (MF) approximates unobserved ratings in a rating matrix, whose rows correspond to users and columns correspond to items to be rated, and has been serving as a fundamental building block in recommendation systems. This paper comprehensively studies the problem of matrix factorization in different federated learning (FL) settings, where a set of parties want to cooperate in training but refuse to share data directly. We first propose a generic algorithmic framework for various settings of federated matrix factorization (FMF) and provide a theoretical convergence guarantee. We then systematically characterize privacy-leakage risks in data collection, training, and publishing stages for three different settings and introduce privacy notions to provide end-to-end privacy protections. The first one is vertical federated learning (VFL), where multiple parties have the ratings from the same set of users but on disjoint sets of items. The second one is horizontal federated learning (HFL), where parties have ratings from different sets of users but on the same set of items. The third setting is local federated learning (LFL), where the ratings of the users are only stored on their local devices. We introduce adapted versions of FMF with the privacy notions guaranteed in the three settings. In particular, a new private learning technique called embedding clipping is introduced and used in all the three settings to ensure differential privacy. For the LFL setting, we combine differential privacy with secure aggregation to protect the communication between user devices and the server with a strength similar to the local differential privacy model, but much better accuracy. We perform experiments to demonstrate the effectiveness of our approaches.
Visualizing Web Application Execution Logs to Improve Software Security Defect Localization

Interactive web-based applications play an important role for both service providers and consumers. However, web applications tend to be complex, produce high-volume data, and are often ripe for attack. Attack analysis and remediation are complicated by adversary obfuscation and the difficulty in assembling and analyzing logs. In this work, we explore the web application analysis task through log file fusion, distillation, and visualization. Our approach consists of visualizing the logs of web and database traffic with detailed function execution traces. We establish causal links between events and their associated behaviors. We evaluate the effectiveness of this process using data volume reduction statistics, user interaction models, and usage scenarios. Across a set of scenarios, we find that our techniques can filter at least 97.5% of log data and reduce analysis time by 93–96%.
1_1611_TQM Framework for Healthcare Sectors

Purpose: A qualitative research was carried out with an aim of understanding and addressing the challenges of integrating TQM in the healthcare industry. It examines the existing inadequacies in the service quality, and barriers in implementation of TQM practices, Methodology/Approach: A conceptual model is developed to explore the issues arising TQM implementation. There are three major components of TQM: such as barriers to implementation, Critical Success Factors and Benefits of TQM. Interviews with health workers and patients were conducted based on prestructured questionnaires. Seven hypotheses were developed to investigate how TQM can be achieved irrespective of surmounting barriers. Findings: Findings suggest TQM can be implemented in the right environment with committed leadership and supportive infrastructure, which would drive SQ, improved customers, and employees’ satisfaction and loyalty, increase profitability and shareholder values. TQM can deliver high quality medical care for overall performance of the healthcare industry. Research Limitation/Implication: Interviews were conducted with semistructured research questionnaires. There may be some inevitable biases present in questionnaires and evaluation of review. Originality/Value of paper: The study benefits from insights from medical personnel and patients’ perspective, in exploring the SQ attributes, i.e., quality circle, continuous improvement, employee empowerment and customer focused approach. Category: Research paper
THE IMPACT OF INNOVATION, ORGANIZATIONAL, TECHNOLOGICAL CAPITAL ON INNOVATION PERFORMANCE OF SMEs: THE MEDIATING EFFECT OF INNOVATIVE INTELLIGENCE

Innovation is crucial for SMEs to survive and impacts the national economy. The present study is conducted in the context of knowledge-based SMEs in Malaysia concerning structural capital (technology capital, innovation capital, and organizational capital), innovative intelligence, and innovation performance. A survey was carried out through a face-face interview and online. A total of 136 usable questionnaires were then analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). This study has shed light on the role of the structural capital of knowledge-based SMEs on innovation performance. While structural capital is significant for organizations to achieve their competitive advantage and should be given necessary attention compared to human capital, innovation and technological capital were not fully capitalized. The presence of innovative intelligence in SMEs helps to be innovative. This result will be encouraging to SMEs in other developing countries. The results obtained are useful for SMEs, especially knowledge-based firms where technology and innovation are critical to achieving a competitive advantage. The results are also significant for agencies that handle SMEs in high-tech and policymakers to increase performance and promote economic stability.
Effect of tunisian pomegranate peel extract on the oxidative sta- bility of corn oil under heating conditions

The effect of pomegranate peel extract (PPE) on the oxidative stability of corn oil during heating was studied. Oxidation was followed by determining peroxide value (PV), p-anisidine value (p-AV), free fatty acid value (FFA), conjugated dienes (CD), conjugated trienes hydroperoxides (CT) and the calculated total oxidation value (TOTOX). Polyphenol (TPC) and ortho-diphenol (TOPC) contents as well as the antioxidant activity of each oil sample were evaluated before and after heating. PPE showed a significant inhibitory effect on lipid oxidation. Heating samples for 8 hours supplemented by PPE to a level of 1000 ppm resulted in the highest significant decreases in investigated indices compared to the control and BHT values. It was concluded that the antioxidant activity of PPE delayed oxidation and can be used in the food industry to prevent and reduce lipid deterioration in oil.
The Implementation Corporate Social Responsibility Factors That Affect the Corporate Political Activities in Riau Province

This study shows the implementation Corporate Social Responsibility factors that affect the Corporate Political Activities in Riau Province. The research problem is still coming up from the companies side due to the implementation of corporate social responsibility. The research problem is that there are still companies that make the implementation of corporate social responsibility a form of company political activity. This research is descriptive qualitative, the method of collecting data in the form of information interviews. Analysis of the data obtained is done through data collection, verification, presentation, and conclusions. The results of the study are three factors that influence CSR part of the CPA, namely individual factors, institutional factors and
CSR PRACTICES DISCLOSURE’S IMPACT ON CORPORATE FINANCIAL PERFORMANCE AND MARKET PERFORMANCE: EVIDENCE OF MALAYSIAN PUBLIC LISTED COMPANIES

Disclosure of CSR practices in public listed companies in Malaysia was made compulsory recently. Hence, its contribution to the firm performance based on financial and market indicators has often been questioned. This study explores using partial least square – structural equation modelling (PLS-SEM), the predictive accuracy and relevancy of CSR practices disclosure on corporate financial performance given by ROA and market performance given by Tobin’s Q. Using a sample size of 200 randomly selected public listed firms in Malaysia, the effect of CSR practices on corporate financial performance based on an accounting and market measures was determined. Findings of this study showed that CSR practices have a significant and positive impact on ROA and Tobin’s Q. Reputation mediates the relationship between CSR practices and ROA but not with Tobin’s Q. CSR practices and reputation could explain 52.9% of variance in ROA but only 6.3% of variance in Tobin’s Q. CSR practices have a large effect size on reputation and ROA but negligible effect size on Tobin’s Q. Thus, these findings imply that CSR practices can relate significantly to accounting-based but not market-based financial performance. Further studies to explore internal and external factors such as third-party assurance, industry type and other environmental factors as well as extending the timeline might provide more insights to understand how CSR practices can influence financial performance.
Study of Endocrine Factors Influencing the Reproductive Stages of Indian Salmon, Eleutheronema tetradactylum

Eleutheronema tetradactylum is a polynemid fish species commonly known as Indian salmon and locally called as “Kaala” in Tamil. The population of this commercially valuable species and their availability has been drastically reduced and also the knowledge on reproductive biology of fish is minimum. The present study focuses on the influence of endocrine factors such as steroid hormones and peptides towards its reproduction. Based upon the histomorphological scrutiny, it has been understood that Indian salmon breeds during early January and early June as a biannual breeder. Previtellogenic, Vitellogenic and Post-Vitellogenic ovaries were tested for steroids such as Estradiol-17 β , Testosterone, Pregnenolone, Progesterone, 17 α -hydroxyprogesterone 20 β -hydroxyprogesterone, 17, 20 α -dihydroxyprogesterone and 17,20 β -dihydroxy progesterone using HPLC. Alongside, genetic expression of insulin receptor-b (IRb), leptin receptor (LR) and 3 β -hydroxysteroid dehydrogenase (3 β -HSD) were analyzed in the gonads of E. tetradactylum using RT-PCR (Real-Time). A Progesterone, precursor of majority of the steroids and a 17 α -hydroxyprogesterone, metabolite of progesterone significantly differed in the stages of ovary. The Expression IRb and LR were observed in both ovary and testis, and 3 β -HSD in the previtellogenic ovary. The results suggest that IRb might be playing a significant role in the gonad of male and female salmon. Leptin might play an indirect role in the gonadal maturation. While 3 β -HSD expression, signifies the steroidogenic indication in the gonadal development and maturation in E. tetradactylum . Identification of IRb in the developing ovary of polynemid species in the present study is the first report. The study highlights the steroids and peptides might have their own role to contribute in the reproduction of E. tetradactylum while the synergetic effects of them in the gonads is a question that should be considered in the future.
Symmetries and Complexity

The Constraint Satisfaction Problem (CSP) and a number of problems related to it have seen major advances during the past three decades. In many cases the leading driving force that made these advances possible has been the so-called algebraic approach that uses symmetries of constraint problems and tools from algebra to determine the complexity of problems and design solution algorithms. In this presentation we give a high level overview of the main ideas behind the algebraic approach illustrated by examples ranging from the regular CSP, to counting problems, to optimization and promise problems, to graph isomorphism. 2012 ACM Subject Classification Theory of computation → Complexity classes; Theory of computation → Design and analysis of algorithms
Transcallosal Ipsilateral Motor Pathway from the Unaffected Hemisphere in a Patient with Traumatic Brain Injury

Methods: One patient and six age-matched normal subjects were enrolled. A 42-year-old left hemiparetic male who suffered from brain injury by motor vehicle accident 9 years ago showed a leukomalactic lesion in the right corona radiata and parietotemporal lobe. His left extremities were completely paralyzed initially, but recovered slowly over 2 years. At the time of the evaluation, he was able to grasp and release an object, and to walk with spastic gait pattern. DTT was performed using 1.5 T with a Synergy-L Sensitivity Encoding head coil. DTT was obtained with termination criteria of FA <0.2 and an angle change >45°.
Large Scale Learning with the Gaussian Process Latent Variable Model

In this paper we apply the latest techniques in sparse Gaussian process regression (GPR) to the Gaussian process latent variable model (GP-LVM). We review three techniques and discuss how they may be implemented in the context of the GP-LVM. We brieﬂy consider a GPR toy problem to highlight the strengths and weaknesses of the diﬀerent approaches before studying the perfomance of these techniques on a benchmark visualisation data set.
Evaluation of Different Methods for Total DNA Extraction from Sago Pith Residue OF BIOCHEMISTRY, MICROBIOLOGY AND BIOTECHNOLOGY

Direct extraction of DNA from the environment has become major importance for molecular analyses for the study of microbial communities in soil and other decomposing agrowaste. The presence of humic substances in Sago pith residue not only results in low DNA quality but also can lead to PCR amplification inhibition which may hinder most molecular studies. Many of the published protocols have been found to be unsuitable to obtained high amount of yield and low of humic acids contamination. This study presents the evaluation of three different methods for extracting total DNA from Sago pith residue. The methods evaluated were enzymatic lysis, glass bead homogenization and freeze-thaw treatment. Each method were evaluated by 260/280 nm absorbance ratio for protein contamination, 260/230 nm absorbance ratio for other contaminants and PCR amplification for molecular work suitability. Among the three methods, freeze-thaw treatment provided the highest yield of DNA, 5.06±0.01 µg/g of Sago pith residue. Nevertheless, all three methods resulted in poor DNA quality which could be used for PCR amplification. Additional steps of agarose electrophoresis and silica column purification were found to be effective for increasing the quality of the extracted DNA and were validated by positive PCR amplifications.
New Query Optimization Techniques in the Spark Engine of Azure Synapse

The cost of big-data query execution is dominated by stateful operators. These include sort and hash-aggregate that typically materialize intermediate data in memory, and exchange that materializes data to disk and transfers data over the network. In this paper we focus on several query optimization techniques that reduce the cost of these operators. First, we introduce a novel exchange placement algorithm that improves the state-of-the-art and significantly reduces the amount of data exchanged. The algorithm simultaneously minimizes the number of exchanges required and maximizes computation reuse via multi-consumer exchanges. Second, we introduce three partial push-down optimizations that push down partial computation derived from existing operators ( group-bys , intersections and joins ) below these stateful operators. While these optimizations are generically applicable we find that two of these optimizations ( partial aggregate and partial semi-join push-down ) are only beneficial in the scale-out setting where exchanges are a bottleneck. We propose novel extensions to existing literature to perform more aggressive partial push-downs than the state-of-the-art and also specialize them to the big-data setting. Finally we propose peephole optimizations that specialize the implementation of stateful operators to their input parameters. All our optimizations are implemented in the spark engine that powers azure synapse. We evaluate their impact on TPCDS and demonstrate that they make our engine 1.8X faster than Apache Spark 3.0. 1.
Nutritional and anticancer therapy in cancer patients with low muscle mass

Úbytek svalové hmoty je jev, se kterým se setkáváme v onkologii relativně často. Nejen celková hmotnost, ale rovněž složení tělesné hmoty (zastoupení svalové a tukové tkáně) má zásadní vliv na efektivitu protinádorové léčby, případně její toxicitu. Na svalový metabolismus působí různá protinádorová léčiva odlišně. Shrnujeme nejen působení cytostatik a cílené terapie, ale zabýváme se rovněž moderní imunoonkologickou léčbou. Především pak souvislostí mezi tělesným složením organismu a protinádorovou účinností. Jsou shrnuta základní doporučení pro nutriční postupy u pacientů s nízkou svalovou hmotou. Klíčová slova: nutriční terapie, sarkopenie, chemoterapie, cílená terapie, imunoterapie.
SepFusion: Finding Optimal Fusion Structures for Visual Sound Separation

Multiple modalities can provide rich semantic information; and exploiting such information will normally lead to better performance compared with the single-modality counterpart. However, it is not easy to devise an effective cross-modal fusion structure due to the variations of feature dimensions and semantics, especially when the inputs even come from different sensors, as in the field of audio-visual learning. In this work, we propose SepFusion, a novel framework that can smoothly produce optimal fusion structures for visual-sound separation. The framework is composed of two components, namely the model generator and the evaluator. To construct the generator, we devise a lightweight architecture space that can adapt to different input modalities. In this way, we can easily obtain audio-visual fusion structures according to our demands. For the evaluator, we adopt the idea of neural architecture search to select superior networks effectively. This automatic process can significantly save human efforts while achieving competitive performances. Moreover, since our SepFusion provides a series of strong models, we can utilize the model family for broader applications, such as further promoting performance via model assembly, or providing suitable architectures for the separation of certain instrument classes. These potential applications further enhance the competitiveness of our approach.
Augmented Human Assistance (AHA)

Aging and sedentarism are two main challenges for social and health systems in modern societies. To face these challenges a new generation of ICT based solutions is being developed to promote active aging, prevent sedentarism and find new tools to support the large populations of patients that suffer chronic conditions as result of aging. Such solutions have the potential to transform healthcare by optimizing resource allocation, reducing costs, improving diagnoses and enabling novel therapies, thus increasing quality of life. The primary goal of the “AHA: Augmented Human Assistance” project is to develop novel assistive technologies to promote exercise among the elderly and patients of motor disabilities. For exercise programs to be effective, it is essential that users and patients comply with the prescribed schedule and perform the exercises following established protocols. Until now this has been achieved by human monitoring in rehabilitation and therapy session, where the clinicians or therapists permanently accompany users or patient. In many cases, exercises are prescribed for home performance, in which case it is not possible to validate their execution. In this context, the AHA project is an integrative and cross-disciplinary approach of 4 Portuguese universities, the CMU, and 2 Portuguese industry partners, that combines innovation and fundamental research in the areas of human-computer interaction, robotics, serious games and physiological computing (see partner list in Appendix A). In the project, we capitalize on recent innovations and aim at enriching the capabilities and range of application of assistive devices via the combination of (1) assistive robotics; (2) technologies that use well-understood motivational techniques to induce people to do their exercises in the first place, and to do them correctly and completely; (3) tailored and relevant guidance in regard to health care and social support and activities; and (4) technologies to self-monitoring and sharing of progress with health-care providers,
SQAPP: No-Reference Speech Quality Assessment Via Pairwise Preference

Automatic speech quality assessment remains challenging, as we lack complete models of human auditory perception. Many existing full-reference models correlate well with human perception, but cannot be used in real-world scenarios where ground truth clean reference recordings are not available. On the other hand no-reference metrics typically suffer from several shortcomings, such as lack of robustness to unseen perturbations and reliance on (limited) labeled data for training. Moreover, noise or large variance among the labels makes it difficult to learn generalizable representations, especially for recordings with subtle differences. This paper proposes a learning framework for estimating the quality of a recording without any reference, and without any human judgments. The main component of this framework is a pairwise quality-preference strategy that reduces label noise, thereby making learning more robust. From pairwise preferences, we first learn a content invariant quality ordering; and then we re-target the model to predict quality on an absolute scale. We show that the resulting learned metric is well-calibrated with human judgments. Since it is a deep network, the metric is differentiable, making it suitable as a loss function for downstream tasks. For example, we show that adding this metric to an existing speech enhancement method yields significant improvement.