Miracle-dz/UniFAR-FTUs huggingface dataset:
Piranha: A GPU Platform for Secure Computation

Secure multi-party computation (MPC) is an essential tool for privacy-preserving machine learning (ML). However, secure training of large-scale ML models currently requires a pro-hibitively long time to complete. Given thatlarge ML inference and training tasks in the plaintext setting are signiﬁcantly accelerated by Graphical Processing Units (GPUs), this raises the natural question: can secure MPC leverage GPU acceleration? A few recent works have studied this question in the context of accelerating speciﬁc components or protocols, but do not provide a general-purpose solution. Consequently, MPC developers must be both experts in cryptographic protocol design and proﬁcient at low-level GPU kernel development to achieve good performance on any new protocol implementation. We present Piranha , a general-purpose, modular platform for accelerating secret sharing-based MPC protocols using GPUs. Piranha allows the MPC community to easily leverage the beneﬁts of a GPU without requiring GPU expertise. Piranha contributes a three-layer architecture: (1) a device layer that can independently accelerate secret-sharing protocols by providing integer-based kernels absent in current general-purpose GPU libraries, (2) a modular protocol layer that allows developers to maximize utility of limited GPU memory with in-place computation and iterator-based support for non-standard memory access patterns, and (3) an application layer that allows applications to remain completely agnostic to the underlying protocols they use. To demonstrate the beneﬁts of Piranha , we implement 3 state-of-the-art linear secret sharing MPC protocols for secure NN training: 2-party SecureML (IEEE S&P ’17), 3-party Fal-con (PETS ’21), and 4-party FantasticFour (USENIX Security ’21). Compared to their CPU-based implementations, the same protocols implemented on top of Piranha ’s protocol-agnostic acceleration exhibit a 16 − 48 × decrease
Differential effects of lipopolysaccharide on cognition, corticosterone and cytokines in socially-housed vs isolated male rats

Social isolation is an established risk factor for mental illness and impaired immune function. Evidence suggests that neuroinflammatory processes contribute to mental illness, possibly via cytokine-induced modulation of neural activity. We examined the effects of lipopolysaccharide (LPS) administration and social home cage environment on cognitive performance in the 5-Choice Serial Reaction Time Task (5CSRTT), and their effects on corticosterone and cytokines in serum and brain tissue. Male Long-Evans rats were reared in pairs or in isolation before training on the 5CSRTT. The effects of saline and LPS (150µg/kg i.p.) administration on sickness behaviour and task performance were then assessed. LPS-induced sickness behaviour was augmented in socially-isolated rats, translating to increased omissions and slower response times in the 5CSRTT. Both social isolation and LPS administration reduced impulsive responding, while discriminative accuracy remained unaffected. With the exception of reduced impulsivity in isolated rats, these effects were not observed following a second administration of LPS, revealing behavioural tolerance to repeated LPS injections. In a separate cohort of animals, social isolation potentiated the ability of LPS to increase serum corticosterone and IL-6, which corresponded to increased IL-6 in the orbitofrontal and medial prefrontal cortices and the nucleus accumbens. Basal IL-4 levels in the nucleus accumbens were reduced in socially-isolated rats. These findings are consistent with the adaptive response of reduced motivational drive following immune challenge, and identify social isolation as an exacerbating factor. Enhanced IL-6 signalling may play a role in mediating the potentiated behavioural response to LPS administration in isolated animals.
Soil CO 2 Emission Response to the Main Limiting Factors Changes During the Snow-free Period in Central Siberia

The response of soils in the boreal zone to current climate change is important for assessing the carbon sink efficiency of the forest ecosystems and the future concentration of CO 2 in the atmosphere. In this work, we examined the influence of two main limiting factors – the moisture conditions and the amount of available nitrogen in the soil on the seasonal dynamics of soil emission in the middle taiga ecosystems of Central Siberia. In the course of the study, it was found that adaptation to a sharp change in moisture conditions is long-term and stable - inhibition of the rate of soil emission is often observed. The influence of various nitrogen concentrations during the summer period is secondary in relation to seasonal climatic conditions. However, at the end of the season, when there is a decrease in precipitation and a decrease in temperature, the contribution of nitrogen leads to an increase in soil emission.
Recognizing and Extracting Cybersecurtity-relevant Entities from Text

Cyber Threat Intelligence (CTI) is information describing threat vectors, vulnerabilities, and attacks and is often used as training data for AI-based cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a strong need to develop community-accessible datasets to train existing AI-based cybersecurity pipelines to efficiently and accurately extract meaningful insights from CTI. We have created an initial unstructured CTI corpus from a variety of open sources that we are using to train and test cybersecurity entity models using the spaCy framework and exploring self-learning methods to automatically recognize cybersecurity entities. We also describe methods to apply cybersecurity domain entity linking with existing world knowledge from Wikidata. Our future work will survey and test spaCy NLP tools and create methods for continuous integration of new information extracted from text.
Strategic Development of a National Pre-tertiary Learning Analytics System

This paper elaborates the design of a National Learning Analytics (LA) and Educational Data Mining System for pre-tertiary education in Croatia. The described approach consists of the following five phases: 1) objectives setting, 2) user needs analysis, 3) data availability analysis, 4) dashboards pre-design and 5) validation of functionalities. There is an evident research gap, as well as the lack of practical examples about the development of a Learning Analytics System (LAS) for pre-tertiary education on the national and/or regional level. Therefore, this research aims to make a scientific contribution by filling the recognized gap, but also contribute to solving the practical issues of national schools’ LAS development. To increase the usability of an LAS, the involvement of all end users is essential. In the described case, six main user groups were identified: 10-18 years old students, teachers, school management and support staff, regional and national authorities responsible for education, strategic bodies and researchers and, finally, project partners that work on system development. For each of them, separate dashboard functionalities have been designed through several rounds of consultations. Consultations with users started with focus groups and panels, to brainstorm the most important issues they would like to answer in order to enhance learning and teaching. Between the two rounds of consultation, there was an evaluation phase of the relevance of the gathered questions. Finally, targeted users provided feedback on the pre-production functionalities of each dashboard and validated them. In this process, several challenges were detected, including data gathering and protection, ethical issues and interpretation of results for students who are underage, and continuous adjustments to the users’ needs.
Adaptive Interest for Emphatic Reinforcement Learning

Emphatic algorithms have shown great promise in stabilizing and improving re-inforcement learning by selectively emphasizing the update rule. Although the emphasis fundamentally depends on an interest function which defines the intrinsic importance of each state, most approaches simply adopt a uniform interest over all states (except where a hand-designed interest is possible based on domain knowledge). In this paper, we investigate adaptive methods that allow the interest function to dynamically vary over states and iterations. In particular, we leverage meta-gradients to automatically discover online an interest function that would accelerate the agent’s learning process. Empirical evaluations on a wide range of environments show that adapting the interest is key to provide significant gains. Qualitative analysis indicates that the learned interest function emphasizes states of particular importance, such as bottlenecks, which can be especially useful in a transfer learning setting.
Using (cid:96) p -norms for Fairness in Combinatorial Optimisation

The issue of fairness has received attention from researchers in many ﬁelds, including combinatorial optimisation. One way to drive the solution toward fairness is to use a modiﬁed objective function that involves so-called (cid:96) p -norms . If done in a naive way, this approach leads to large and symmetric mixed-integer nonlinear programs (MINLPs), that may be diﬃcult to solve. We show that, for some problems, one can obtain alternative MINLP formulations that are much smaller, do not suﬀer from symmetry, and have a reasonably tight continuous relaxation. We give encouraging computational results for certain vehicle routing, facility location and network design problems
Fault Analysis and Protection of Electric Vehicle Charging Station using Fast Charging Method

— In this paper the proposed model of an electrical vehicle charging station is suitable for the fast DC charging multiple electric vehicles. The station consists of a single grid-connected inverter with a DC bus where the electric vehicles are connected. The control of the individual electric vehicle charging processes is decentralized, while a separate central control deals with the power transfer from the AC grid to the DC bus. The electric power exchange does not rely on communication links between the station and vehicles, and a smooth transition to vehicle-to grid mode is also possible. Design guidelines and modelling are explained in an educational way to support implementation in Matlab/Simulink. Simulations are performed in Matlab/Simulink to illustrate the behaviour of charging station. The result shows the feasibility of the model proposed and the capability of the control system for fast charging method.
Inﬂuence of Electricity Access on Gender: Evidences from Nepal

Access to modern energy has been found to especially benefit rural women, in terms of reducing their drudgery and increasing their efficiency of time use. This paper draws from the findings of a primary household survey and qualitative study in Nepal in order to analyse how access to modern energy affects gendered aspects of health, education, income generation, and decision-making. It was found that a higher percentage of children (boys and girls) from households with electricity access were enrolled in schools and spent more hours studying. Further, electricity access in health centres assisted them to offer medical services to the people. Though these facilities helped in enhancing the overall quality of life of women, decisions regarding health and education were found to be taken mostly by men or sometimes jointly with their spouses. It was also seen that decisions regarding expenditure of income earned by women, were not taken by them independently. Further, households that have had access to electricity for a longer period of time were more likely to send a higher number of their children to school as compared to those who got access to electricity later. This means there is a time lag between the time a household gets access to electricity and the time when improvement in socioeconomic and developmental attainments (like school enrolment) begin to show up.
Analysis of Attribute Selection and Classification Algorithm Applied to Hepatitis Patients

Data mining techniques are widely used in classification, attribute selection and prediction in the field of bioinformatics because it helps to discover meaningful new correlations, patterns and trends by sifting through large volume of data, using pattern recognition technologies as well as statistical and mathematical techniques. Hepatitis is one of the most important health problem in the world. Many studies have been performed in the diagnosis of hepatitis disease but medical diagnosis is quite difficult and visual task which is mostly done by doctors. Therefore, this research is conducted to analyse the attribute selection and classification algorithm that applied to hepatitis patients. In order to achieve goals, WEKA tool is used to conduct the experiment with different attribute selector and classification algorithm . Hepatitis dataset that are used is taken from UC Irvine repository. This research deals with various attribute selector namely CfsSubsetEval, WrapperSubsetEval, GainRatioSubsetEval and CorrelationAttributeEval. The classification algorithm that used in this research are NaiveBayesUpdatable, SMO, KStar, RandomTree and SimpleLogistic. The results of the classification model are time and accuracy. Finally, it concludes that the best attribute selector is CfsSubsetEval while the best classifier is given to SMO because SMO performance is better than other classification techniques for hepatitis patients.
Voltage Multiplier Cell-Based Quasi-Switched Boost Inverter with Low Input Current Ripple

A novel single-phase single-stage voltage multiplier cell-based quasi-switched boost inverter (VMC-qSBI) is proposed in this paper. By adding the voltage multiplier cell to the qSBI, the proposed VMC-qSBI has the following merits; a decreased voltage stress on an additional switch, a high voltage gain, a continuous input current, shoot through immunity, and a high modulation index. A new pulse-width modulation (PWM) control strategy is presented for the proposed inverter to reduce the input current ripple. To improve the voltage gain of the proposed inverter, an extension is addressed by adding the VMCs. The operating principle, steady-state analysis, and impedance parameter design guideline of the proposed inverter are presented. A comparison between the proposed inverter and other impedance source-based high-voltage gain inverters is shown. Simulation and experimental results are provided to confirm the theoretical analysis.
PERSONALIZATION WITH WEB MINING

: In today’s competitive environment learners from any area are growing and they are thirsty for getting knowledge in all looks and corners. Day by day their self learning habits are increasing and they are becoming explorers of the knowledge. The subjective initiative of learners is strengthening and they want a dynamic mechanism to fulfill their needs as quick as possible. Most of them uses internet as a primary source of information but this source is very big and vast, merely it provides more accurate results as per learners interest and expectations. Personalized learning system is developed in this dissertation to provide the learning services to the learners to fulfill their needs, interests and habits through the use of web services and web mining technology. The developed system provides personalized learning environment and it is able to find out the individual differences, individual characteristics, and habits to provide results according to users interests and habits. Web mining technology is used for implementation of system aim to identify the relevant results. Basically, personalized learning system consists of five major steps to construct a personalized learning environment are: Data collection, Data preprocessing, Data analysis, Result determination, Personalized interface. All these modules use web mining techniques to achieve system goals. Finally, a typical system is constructed using . net framework to get satisfactory results.
Improving Numerical Modeling Accuracy for Fiber Orientation and Mechanical Properties of Injection Molded Glass Fiber Reinforced Thermoplastics

Local fiber alignment in fiber-reinforced thermoplastics is governed by complex flows during the molding process. As fiber-induced material anisotropy leads to non-homogeneous effective mechanical properties, accurate prediction of the final orientation state is critical for integrated structural simulations of these composites. In this work, a data-driven inverse modeling approach is proposed to improve the physics-based structural simulation of short glass fiber reinforced thermoplastics. The approach is divided into two steps: (1) optimization of the fiber orientation distribution (FOD) predicted by the Reduce Strain Closure (RSC) model, and (2) identification of the composite’s mechanical properties used in the Ramberg–Osgood (RO) multiscale structural model. In both steps, the identification of the model’s parameters was carried out using a Genetic Algorithm. Artificial Neural Networks were used as a machine learning-based surrogate model to approximate the simulation results locally and reduce the computational time. X-ray micro-computed tomography and tensile tests were used to acquire the FOD and mechanical data, respectively. The optimized parameters were then used to simulate a tensile test for a specimen injection molded in a dumbbell-shaped cavity selected as a case study for validation. The FOD prediction error was reduced by 51% using the RSC optimized coefficients if compared with the default coefficients of the RSC model. The proposed data-driven approach, which calculates both the RSC coefficients and the RO parameters by inverse modeling from experimental data, allowed improvement in the prediction accuracy by 43% for the elastic modulus and 59% for the tensile strength, compared with the non-optimized analysis.
The Specific Role of Dermatan Sulfate as an Instructive Glycosaminoglycan in Tissue Development

The crucial roles of dermatan sulfate (DS) have been demonstrated in tissue development of the cutis, blood vessels, and bone through construction of the extracellular matrix and cell signaling. Although DS classically exerts physiological functions via interaction with collagens, growth factors, and heparin cofactor-II, new functions have been revealed through analyses of human genetic disorders as well as of knockout mice with loss of DS-synthesizing enzymes. Mutations in human genes encoding the epimerase and sulfotransferase responsible for the biosynthesis of DS chains cause connective tissue disorders including spondylodysplastic type Ehlers–Danlos syndrome, characterized by skin hyperextensibility, joint hypermobility, and tissue fragility. DS-deficient mice show perinatal lethality, skin fragility, vascular abnormalities, thoracic kyphosis, myopathy-related phenotypes, acceleration of nerve regeneration, and impairments in self-renewal and proliferation of neural stem cells. These findings suggest that DS is essential for tissue development in addition to the assembly of collagen fibrils in the skin, and that DS-deficient knockout mice can be utilized as models of human genetic disorders that involve impairment of DS biosynthesis. This review highlights a novel role of DS in tissue development studies from the past decade.
Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder

Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample, (ii) alcohol use disorder (AUD) in the total sample, (iii) AUD co-occurrence with at least another SUD in the total sample (AUD+SUD), and (iv) any other SUD among BD patients with AUD. Relevant variables selected by the RFs were considered as independent variables in multiple logistic regressions to predict SUDs, adjusting for relevant covariates. AUD+SUD could be predicted in BD at an individual level with a sensitivity of 75% and a specificity of 75%. The presence of AUD+SUD was positively associated with having hypomania as the first affective episode (OR = 4.34 95% CI = 1.42–13.31), and the presence of hetero-aggressive behavior (OR = 3.15 95% CI = 1.48–6.74). Machine-learning models might be useful instruments to predict the risk of SUD in BD, but their efficacy is limited when considering socio-demographic or clinical factors alone.
Comparison of Capture Point estimation with human foot placement: Applicability and Limitations

For bipeds like humans, taking a step to recover its balance is fundamental. The question that arises is the location where the biped should step. Pratt et al. developed an algorithm to estimate the so-called Capture Point, a point on ground where a robot can step in order to bring itself to a complete stop. The objective of the current work is to test the applicability of the algorithm for the prediction of step location in humans under perturbed state.
Vulnerability of Maize Farming Systems to Climate Change: Farmers’ Opinions Differ about the Relevance of Adaptation Strategies

Climate change has negative impacts on maize cultivation in southwestern France, such as soil erosion and water stress. The vulnerability of maize farming systems to climate change must be assessed before considering potential adaptation strategies. This study focused on eliciting and understanding criteria that maize growers use to assess the vulnerability of their farming systems to climate change. To this end, we surveyed maize growers in two consecutive stages: a qualitative stage, to elicit vulnerability criteria, and a quantitative stage, to test the genericity of criteria related to the adaptation strategies. The qualitative stage identified 144 criteria that farmers used to assess vulnerability to climate change, while the quantitative stage showed that farmers’ opinions about the adaptation strategies differed. Many factors explained these differences, including structural (e.g., soil type) and psychological factors (e.g., interest in agroecology). Our typology of farmers revealed that their interest in agroecology and technology, as well as their perceptions of the risks of climate change and their attachment to their production systems, influence the type of adaptations they identify as relevant (i.e., intensification strategies, slight adjustments or agroecological innovations). Farmers’ perceptions should be considered when providing individual advice and assessing vulnerability, by including criteria related to their psychological characteristics.