Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultimate SLH: Taking Speculative Load Hardening to the Next Level

In this paper we revisit the Spectre v1 vulnerability and software-only countermeasures. Specifically, we systematically investigate the performance penalty and security properties of multiple variants of speculative load hardening (SLH). As part of this investigation we implement the “strong SLH” variant by Patrignani and Guarnieri (CCS 2021) as a compiler extension to LLVM. We show that none of the existing variants, including strong SLH, is able to protect against all Spectre v1 attacks in practice. We do this by demonstrating, for the first time, that variable-time arithmetic instructions leak secret information even if they are executed only speculatively. We extend strong SLH to include protections also against this kind of leakage, implement the resulting full protection in LLVM, and use the SPEC2017 benchmarks to compare its performance to the existing variants of SLH and to code that uses fencing instructions to completely prevent speculative execution. We show that our proposed counter-measure offers full protection against Spectre v1 attacks at much better performance than code using fences. In fact, for several benchmarks our approach is more than twice as fast.
Investigating Internet of Things Impact on e-Learning System: An Overview

—e-Learning systems have reached their peak with the revolution of smart technologies. In the past few years, the Internet of Things (IoT) has become one of the most advanced and popular technologies, affecting many different areas. Using IoT in an e-learning system is a fantastic technology that improves the e-learning system and makes it more inventive and cutting-edge. The key challenge addressed in this study is the acceptance of IoT usage in e-learning systems as well as how to improve it so that it can be utilized properly. This research concentrates on how IoT can benefit e-learning systems and how it might benefit users of e-learning systems. A comprehensive literature review was conducted to get acquainted with the important research related to IoT technology and e-learning systems through online research databases and reliable scientific journals. The first research finding is that e-learning systems need such modern techniques as IoT to enable interconnection, increase reliability, and enhance the enjoyment of the educational process. The second result is that research related to the development of new technologies like the IoT has a significant impact on enhancing the performance of new systems and bringing about positive change. This study highlights the value of IoT, particularly in e-learning systems. It aids in the development of new strategies that will improve the efficacy of e-learning systems and stimulate researchers to develop advanced technology
Holistic interpretation in locative alternation: Evidence from self-paced reading

In English and Japanese, verbs like smear and load can appear in two alternate variants while expressing nearly the same meaning. It has been argued that there is a semantic difference between locatum-as-object variant and location-as-object variant: location-as-object variant imposes holistic interpretation, but locatum-as-object variant allows both partitive interpretation and holistic interpretation. In this paper, a self-paced reading experiment was conducted to investigate whether there is a preference of interpretation (partitive / holistic) in locatum-as-object variant. The results indicate that the processing difficulty occurs when the holistic interpretation is canceled regardless of the variant type, but the interaction between VARIANT TYPE and CANCELLATION OF HOLISTIC INTERPRETATION was not found. The results suggest that the holistic interpretation is preferred not only in the location-as-object variant but also in the locatum-as-object variant. Furthermore, besides the preference of interpretation, the significant main effect observed at the verb region demonstrates that the processing of locatum-as-object variant is easier than location-as-object variant, which aligns with the argument in Aoki (2019).
Exact recovery of Granger causality graphs with unconditional pairwise tests

We study Granger Causality in the context of wide-sense stationary time series. The focus of the analysis is to understand how the underlying topological structure of the causality graph affects graph recovery by means of the pairwise testing heuristic. Our main theoretical result establishes a sufficient condition (in particular, the graph must satisfy a polytree assumption we refer to as strong causality) under which the graph can be recovered by means of unconditional and binary pairwise causality testing. Examples from the gene regulatory network literature are provided which establish that graphs which are strongly causal, or very nearly so, can be expected to arise in practice. We implement finite sample heuristics derived from our theory, and use simulation to compare our pairwise testing heuristic against LASSO-based methods. These simulations show that, for graphs which are strongly causal (or small perturbations thereof) the pairwise testing heuristic is able to more accurately recover the underlying graph. We show that the algorithm is scalable to graphs with thousands of nodes, and that, as long as structural assumptions are met, exhibits similar high-dimensional scaling properties as the LASSO. That is, performance degrades slowly while the system size increases and the number of available samples is held fixed. Finally, a proof-of-concept application example shows, by attempting to classify alcoholic individuals using only Granger causality graphs inferred from EEG measurements, that the inferred Granger causality graph topology carries identifiable features.
Effect of circuit training on selected strength variables of adolescent male basketball players

The purpose of the study was to ascertain the effect of circuit training on Pull-ups (arm and shoulder girdle strength), Bent-knee sit-ups (abdominal strength) and Standing broad-jump (legs explosive strength) of adolescent male Basketball Players. The subjects for the study were 30 male students who were in the age range of 15 to 18 years. The duration of the experimental period was twelve weeks. A 10 stations circuit was developed by the research scholar and the subjects trained thrice a week. The adaptation period of two weeks was adopted. After every two weeks the number of circuits was increased. Analysis of data was done employing t-test. The t-ratios obtained were 7.24 for pull-ups, 2.88 for bent-knee sit-ups and 3.8 for standing broad jump. All the t-ratios were found to be significant at 0.05 level of confidence with 29 degrees of freedom.
A Hybrid Feature Gene Selection Method based on Fuzzy Neighborhood Rough Set with Information Entropy

DNA microarray technique can detect tens of thousands of genes activity in cells and has been widely used in clinical diagnosis. However, microarray data has the characteristics of high dimension and small samples, moreover many irrelevant and redundant genes also decrease performance of classification algorithm. Feature gene selection is an effective method to solve this problem. This paper proposes a hybrid feature gene selection method. Firstly, a lot of irrelevant genes from original data were eliminated by using reliefF algorithm, and the candidate feature genes subset is obtained; Secondly, Fuzzy neighborhood rough set with information entropy which deals directly with continuous data is proposed to reduce redundant genes among genes subset above. Here, differential evolution algorithm is used to optimize radius before reduction by using fuzzy neighborhood rough set, because radius of neighborhood greatly affects reduction performance. The simulation results on six microarray datasets indicate that our method can obtain higher classification accuracy by using as few genes as possible, especially feature genes selected are important for understanding microarray data and identifying the pathogenic genes. The results demonstrated that this method is effective and efficient for feature genes selection.
Comparison of The Molecular Docking Properties of Three Potentially Active Agents

Antineoplastic agents are generally the drugs used to recess or prevent tumor growth which is promoted in many cases by certain factors like vascular endothelial growth factor-2 (VEGFR-2) and cyclooxygenase-2 (COX-2). These two factors seem important in angiogenesis and lymphangiogenesis in fetal, normal and neoplastic tissue. Prevention of VEGF family of proteins and cyclooxygenase-2 enzyme is a good strategy to inhibit the growth of tumor tissue eventually may give rise to recession. In this study, three potentially active and an active ligand were tested for their binding properties to two target molecules mentioned above by molecular docking study. This research is aimed to compare three different molecules according to their binding affinities, binding energies and the nature of bonds formed between the ligand and the target molecules. Showing the 3D structures will localize the fitted ligands on proteins and the possible hydrogen bonds formed were defined. Among the three proposed ligands, Ligand 1 showed the closest results to the commercial product lenalidomide®. All three ligands showed similar ∆G values and fitness scores with lenalidomide® which is an indicator of good fit, proximity and orientation with the target molecule.
University of Birmingham Séta

We present S´eta , 11 a new family of public-key encryption schemes with post-quantum security based on isogenies of supersingular elliptic curves. It is constructed from a new family of trapdoor one-way functions, where the inversion algorithm uses Petit’s so called torsion attacks on SIDH to compute an isogeny between supersingular elliptic curves given an endomorphism of the starting curve and images of torsion points. We prove the OW-CPA security of S´eta and present an IND-CCA variant using the post-quantum OAEP transformation. Several variants for key generation are explored together with their impact on the selection of parameters, such as the base prime of the scheme. We furthermore formalise an “uber” isogeny assumption framework which aims to generalize computational isogeny problems encountered in schemes including SIDH, CSDIH, OSIDH and ours. Finally, we carefully select parameters to achieve a balance between security and run-times and present experimental results from our implementation
Deadline-aware Task Scheduling for Cloud Computing using Firefly Optimization Algorithm

—Task scheduling poses a major challenge for cloud computing environments. Task scheduling ensures cost-effective task execution and improved resource utilization. It is classified as a NP-hard problem due to its nondeterministic polynomial time nature. This characteristic motivates researchers to employ meta-heuristic algorithms. The number of cloud users and computing capabilities is leading to increased concerns about energy consumption in cloud data centers. In order to leverage cloud resources in the most energy-efficient manner while delivering real-time services to users, a viable cloud task scheduling solution is necessary. This study proposes a new deadline-aware task scheduling algorithm for cloud environments based on the Firefly Optimization Algorithm (FOA). The suggested scheduling algorithm achieves a higher level of efficiency in multiple parameters, including execution time, waiting time, resource utilization, the percentage of missed tasks, power consumption, and makespan. According to simulation results, the proposed algorithm is more effective and superior to the CSO algorithm under HP2CN and NASA workload archives.
Prediction of Death Counts Based on Short-term Mortality Fluctuations Data Series using Multi-output Regression Models

—Effective public health responses to unexpected epidemiological hazards or disasters need rapid and reliable monitoring. But, monitoring fast-changing situations and ac- quiring timely, accurate, and cross-national statistics to address short-term mortality fluctuations due to these hazards is very challenging. Estimating weekly excess deaths is the most solid and accurate way to measure the mortality burden caused by short-term risk factors. The Short-term Mortality Fluctuations (STMF) data series is one of the significant collections of the Human Mortality Database (HMD) that provides the weekly death counts and rates by age and sex of a country. Sometimes, the data collected from the sources are not always represented in specific age groups rather represented by the the total number of individual death records per week. However, the researchers reclassified their dataset based on the ranges of age and sex distributions of every country so that one can easily find out how many people died in per week of each country based on an equation and earlier distribution data. The paper focuses on the implementation of multi-output regression models such as logistic regression, decision tree, random forest, k nearest neighbors, lasso, support vector regressor, artificial neural network, and recurrent neural network to correctly predict death counts for specific age groups. According to the results, random forest delivered the highest performance with an R squared coefficient value of 0.9975, root mean square error of 43.2263, and mean absolute error of 16.4069.
Design of Intrusion Detection System using Ensemble Learning Technique in Cloud Computing Environment

— The key advantage of the cloud is that it fluidly propagates to fulfil changeable requirements and provides an environment that is repeatable and can be scaled down instantly when needed. Therefore, it is necessary to protect this cloud environment from malicious attacks such as spamming, keylogging, Denial of Service (DoS), and Distributed Denial of Service (DDoS). Among these kinds of attacks, DDoS has the capability to establish a high flood of malicious attacks on the cloud environment or Software Defined Networking (SDN) based cloud environment. Hence in this work, an ensemble based deep learning technique is proposed to detect attacks in cloud and SDN based cloud environments. Here, the ensemble model is formed by combining K-means with deep learning classifiers such as Long Short term Memory (LSTM) network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Deep Neural Network (DNN). Initially, preprocessing with data cleaning and standardization is applied to the input data. Meanwhile, a random forest is implemented for extracting the minimum significant features. After that, the proposed ensemble based approach is utilized for detecting the intrusion. This approach is used to enhance the performance of the deep learning classifiers without much computational complexity. This model is trained and evaluated using two datasets as CICIDS 2018 and SDN based DDOS attack datasets. The proposed approach provides better intrusion detection performance in terms of F1 measure, precision, accuracy, and recall. By using the proposed approach, the accuracy and precision value attained is 99.685 and 0.992, respectively.
An Enhanced SVM Model for Implicit Aspect Identification in Sentiment Analysis

—Opinion Mining or Sentiment Analysis (SA) is a key component of E-commerce applications where a vast number of reviews are generated by customers. SA operates on aspect level where the views are expressed on a specific aspect of a product and have a big influence on the customers’ choices and businesses’ reputation. Aspect Based Sentiment Analysis (ABSA) is the task of categorizing text by aspect and identifying the sentiment attributed to it. Implicit Aspect Identification (IAI) is a subtask of ABSA. This paper aims to empirically investigate how external knowledge (e.g. WordNet) is integrated into SVM model to address some of its intrinsic issues when dealing with classification. To achieve this research goal, we propose an approach to improve Support Vector Machines (SVM) model to deal with IAI. Using WordNet (WN) semantic relations, we suggest an enhancement for the SVM kernel computation. Experiments are conducted on three benchmark datasets of products, laptops, and restaurant reviews. The effects of our approach are examined and analyzed according to three criteria: (i) kernel function used, (ii) different experimental settings, and (iii) SVM behavior towards Overfitting and Underfitting. The research finding of our work is that the integration of external knowledge (e.g. WordNet) is experimentally proved to be significantly helpful to SVM classification for IAI and especially for addressing Overfitting and Underfitting that are considered as two of the main structural SVM issues. The empirical results demonstrate that our approach helps SVM (i) improve its performance for the three considered kernels and under different experimental settings, and (ii) deal better with Overfitting and Underfitting.
An Analysis of Cohesive Devices in EFL Students’ Essay Writing

: Students need to be able to make effective use of cohesive devices in order to produce high-quality essays. This research aimed to investigate the use of cohesive devices in EFL students’ essay writing in the 2nd Semester of the English Language Department at a State University in North Sumatera. The data sources of this research were essays and transcripts of interviews. The method used in this research was qualitative content analysis and case study. The model used in data analysis were frequency count and content analysis by applying interactive models such as data collection, data condensation, data display, and conclusion. The research finding showed that Reference (59%) was the predominant grammatical cohesive device used by EFL students in essay writing compared to other types. Meanwhile, Ellipsis (0.3%) was the lowest. Moreover, compared to lexical cohesive devices, EFL students used Repetition at the highest rate (89%) in their essay writing whereas the percentage of Hyponyms used was the lowest (0.3%), however, Meronyms did not appear in this research. Furthermore, the students had adequate knowledge to apply grammatical cohesive devices appropriately (990 data), as opposed to the incohesive use acquired (105 data). Whereas in terms of lexical cohesive devices, all the phrases are cohesive. In this regard, the reasons for incohesive writing by students are interference from their native language and overgeneralization whereas the interference of the learning context is not found in this study. In conclusion, the understanding of cohesive devices can improve and embellish the knowledge in building a good and true sentence. This is also useful in avoid students from writing inappropriate sentences. Therefore, English writing lecturers are expected to help students facing obstacles such as the use of cohesive devices by applying explicit teaching and increasing their performance in terms of cohesive devices in essay writing.
Suitability of Wheel Ruts: Characterization of Overlooked Breeding Habitats of Alpine Newts

: The Alpine newt ( Ichthyosaura alpestris ) is sensitive to habitat destruction and alteration, and as a result there has been a decline in their populations across Europe. Due to historical landscape changes, the populations of Alpine newts drawn from lowlands and hilly areas have recently been primarily found in artiﬁcial habitats, such as wheel ruts. In some areas inhabited by the Alpine newt range, wheel ruts are the only available habitat for newt breeding, and thus the only one allowing the maintenance of their populations. However, our knowledge about this type of habitat is limited. Therefore, 60 wheel ruts in a hilly forested area in the Czech Republic were checked for the presence of Alpine newt larvae. Their characteristics were measured (conductivity, pH, dissolved oxygen, depth, water coverage, plants, presence of prey, hydroperiod). The presence of larvae was positively associated with higher values of pH, coverage of water plants, and the presence of prey. The strongest predictor was the hydrological stability of wheel ruts, and newts were found to prefer non-drying ruts. From the conservation point of view, this means that the suitability of wheel ruts for Alpine newts can be estimated quite easily. This can essentially be performed on the basis of visual inspection if it is carried out through the breeding season of newts.
mutable: A Modern DBMS for Research and Fast Prototyping

Few to zero DBMSs provide extensibility together with implementations of modern concepts, like query compilation for example. We see this as an impeding factor in academic research in our domain. Therefore, in this work, we present mu t able, a system developed at our group, that is fitted to academic research and education. mu t able features a modular design, where individual components can be composed to form a complete system. Each component can be replaced by an alternative implementation, thereby mutating the system. Our fine-granular design of components allows for precise mutation of the system. Metaprogramming and just-in-time compilation are used to remedy abstraction overheads. In this demo, we present the high-level design goals of mu t able, discuss our vision of a modular design, present some of the components, provide an outlook to research we conducted within mu t able, and demonstrate some developer-facing features.
A Study on Behavioural Intention to Use Mobile Wallet with Special Reference to Citizens in Shah Alam, Selangor, Malaysia

Internet of Things (IoT) embraces Internet technologies supporting remote and mobile applications including Mobile Wallet (M-Wallet). The M-Wallet is an electronic wallet (e-wallet), a mean that is widely used for digital payment. While literature indicated that neighboring countries advanced in the adoption of M-Wallet for digital payment, Malaysia is considerably “infant” as this technology is not extensively used. As such, a study had been conducted to investigate the users’ behavioral intention and the determinant to adopt mobile wallets as digital payment platform in a selected locality in Malaysia. Built upon three basic constructs of Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) and previous relevant research models, a model of factors influencing behavioral intention on mobile wallet for this study was proposed. Adopting the convenience sampling technique, the online questionnaire had been administered to 384 respondents. The 327 returned questionnaires had been examined and the 315 valid questionnaires were brought into analysis. Both SPSS and SmartPLS were used for descriptive analysis, internal consistency reliability analysis, indicator reliability analysis, convergent validity analysis, discriminant validity analysis, coefficient of determination (R2) analysis, path coefficients analysis and hypotheses testing where appropriate. The results revealed that Hedonic Motivation is the strongest and most crucial factor influencing the intention to use mobile wallet. The Perceived Risk, Perceived Security and Perceived Trust have significant influence on behavioral intention on mobile wallet usage.