Reviewerly/exHarmony huggingface dataset:
Efficient Protection Mechanism for CPU Cache Flush Instruction Based Attacks

CPU flush instruction-based cache side-channel attacks (cache instruction attacks) target a wide range of machines. For instance, Meltdown / Spectre combined with FLUSH+RELOAD gain read access to arbitrary data in operating system kernel and user processes, which work on cloud virtual machines, laptops, desktops, and mobile devices. Additionally, fault injection attacks use a CPU cache. For instance, Rowhammer, is a cache instruction attack that attempts to obtain write access to arbitrary data in physical memory, and affects machines that have DDR3. To protect against existing cache instruction attacks, various existing mechanisms have been proposed to modify hardware and software aspects; however, when latest cache instruction attacks are disclosed, these mechanisms cannot prevent these. Moreover, additional countermeasure requires long time for the designing and developing process. This paper proposes a novel mechanism termed FlushBlocker to protect against all types of cache instruction attacks and mitigate against cache instruction attacks employ latest side-channel vulnerability until the releasing of additional countermeasures. FlushBlocker employs an approach that restricts the issuing of cache flush instructions and the attacks that lead to failure by limiting control of the CPU cache. To demonstrate the effectiveness of this study, FlushBlocker was implemented in the latest Linux kernel, and its security and performance were evaluated. Results show that FlushBlocker successfully prevents existing cache instruction attacks (e.g., Meltdown, Spectre, and Rowhammer), the performance overhead was zero, and it was transparent in real-world applications.
Determinants Of Behavioural Endowment Intention Among Private Higher Education Alumni

Endowment or waqf fund education plays a significant role in the higher education institutions (HEI) ecosystem.In addition, the development of endowment funds or waqf in education became one of the
Workload-Driven Analysis on the Performance Characteristics of GPU-Accelerated DBMSes

This letter conducts an in-depth empirical analysis of the influence of various query characteristics on the performance of modern GPU DBMSes. Our analysis reveals that, although they can efficiently process concurrent queries, the GPU DBMSes we consider still should address various performance concerns including n-way joins, aggregates, and selective scans.
The Relationship Between Internet Usage And Cyberbullying In Social Media

Malaysia is recorded as the 9th country in the world in terms of active users of social media.Almost 80% of the total population of this country spends about 8 hours a day browsing the internet.The enforcement of the Movement Control Order (PKP) in 2020 saw a drastic increase in the traffic flow of internet usage by 32% nationwide.However, there are a few parties who abuse digital platforms by getting involved in cyberbullying activities to attract public attention and have fun on social media.Thus, this study aims to analyze the relationship between the duration of internet use and signs of cyberbullying on social media.The study was conducted using a survey design by adopting the quantitative method.A set of online questionnaires (Google Form) was distributed to 436 respondents in Melaka, and the data obtained were analyzed through SPSS 23.0 software.The study findings concluded a significant difference in the mean score of cyberbullying behavior according to the duration of internet use ((f = 8.857); (p < 0.05).In addition to exercising self-control in social contacts, especially online, all parties must collaborate to build a secure and peaceful digital ecology.
Cognitive-economic subjectivity of individual’s development in the formation of positive motivation for entrepreneurship: Evaluation of the effectiveness

Introduction. The article is devoted to the study of the problem of increasing students’ positive motivation for and interest in entrepreneurship. The purpose of the study is to evaluate and substantiate the effectiveness of cognitive-economic subjectivity of individual’s development in the formation of positive motivation for entrepreneurship. Materials and Methods. The theoretical basis of the study is the philosophy of holism. Therefore, the system-synergetic and transdisciplinary principles were used to solve the problems and to develop integrated didactic content. The ascertaining and forming stages of the educational experiment were carried out, and experimental methods of educational research and methods of mathematical statistics for processing empirical data (Wilcoxon test) were applied. Results. The article explores the effectiveness of initial preparation of students for business. The effectiveness of applying systemic-synergetic and transdisciplinary principles to the development of integrated content in physics and the basics of Economics is justified. The formation of students' cognitive-economic subjectivity, which characterizes the level of the motivational-need stage of students' readiness for entrepreneurship, is clarified. At the same time, the level of task performance indicates the formation of students’ cognitive-economic motivation for entrepreneurship. The concept of holism made it possible, on the basis of the system-synergetic principle, to single out the property of coherence of the studied independently known concepts of ‘cognitive subjectivity’ and ‘economic subjectivity’ and to prove their emergence. At the same time, cognition indicates a conscious knowledge-oriented level of practical actions of an entrepreneur, and the economic component indicates their pragmatic orientation of thinking and actions. Conclusions. The experimental study revealed the effectiveness of applying the didactic content of the specialized course of entrepreneurial education for the development of the business sector.
Classification of Indonesian Population's Level Happiness on Twitter Data Using N-Gram, Naïve Bayes, and Big Data Technology

The level of happiness is one factor that influences social interaction in the community. Therefore, the population's happiness level within the current year has become an exciting concern to be studied. Since last year, the world has been facing a COVID-19 pandemic. COVID-19 pandemic dramatically affects the happiness level of the population from a social, economic, health, education, and tourism perspective. The various affected sectors cause different levels of emotional happiness in the community in terms of social interactions in opinions and issues on social media. In addition, the number of issues on social media induce a vast data warehouse and high complexity. Big Data is a science that handles large amounts of data, which is unmanageable using traditional data processing methods or techniques. Various companies, organizations, researchers, and academics practice Big Data to extract and analyze the necessary information. Big Data is a general term used for all data collection forms of vast and complex nature. The utilization of Big Data can be valuable for a better decision-making process. This study uses Big Data Technology to evaluate the Indonesian population's happiness level on Twitter data. Method classified and technique using the N-Gram, NaÃ¯ve Bayes, and Laplacian Smoothing Technique. The emotion in this research is classified into two aspects: happy and unhappy emotions. A total of 4.306.581 tweet data is classified; the obtained results revealed 39,4% happy emotion and 60,6% unhappy emotion.
<i>Necessity</i> specifications for robustness

Robust modules guarantee to do only what they are supposed to do – even in the presence of untrusted malicious clients, and considering not just the direct behaviour of individual methods, but also the emergent behaviour from calls to more than one method. Necessity is a language for specifying robustness, based on novel necessity operators capturing temporal implication, and a proof logic that derives explicit robustness specifications from functional specifications. Soundness and an exemplar proof are mechanised in Coq.
Synthesis-powered optimization of smart contracts via data type refactoring

Since executing a smart contract on the Ethereum blockchain costs money (measured in gas ), smart contract developers spend significant effort in reducing gas usage. In this paper, we propose a new technique for reducing the gas usage of smart contracts by changing the underlying data layout. Given a smart contract P and a type-level transformation, our method automatically synthesizes a new contract P ′ that is functionally equivalent to P . Our approach provides a convenient DSL for expressing data type refactorings and employs program synthesis to generate the new version of the contract. We have implemented our approach in a tool called Solidare and demonstrate its capabilities on real-world smart contracts from Etherscan and GasStation. In particular, we show that our approach is effective at automating the desired data layout transformation and that it is useful for reducing gas usage of smart contracts that use rich data structures.
Control of Arm Robot Based on Finger Motion

Robotics is a constantly evolving field nowadays. A robot is a mechanical device that can perform physical tasks while being controlled and supervised by humans. Several robots have been created to perform risky tasks impossible for humans to act directly. The robot arm is one of the most popular types. In this project, it is proposed to control a robot arm using finger and hand movements. However, the traditional way of gesture control employs sensor-based gloves to track motion, which is time and energy-intensive due to its weight. To overcome this problem, using vision for motion tracking can be the most suited method at hand currently. The ML-based computer vision method provides real-time landmarks on different hand points. With these landmarks, we can calculate various mathematical parameters that can be used to control the robot arm. Movements that are landmarked are processed and formulated using mathematical parameters. The final output of mathematical parameters is fed to the microcontroller. Signals from the microcontroller will be the desired input signal to the motors. The rotation of the robot arm is controlled using the movement of the human arm. This project’s end application is like human operation in dull, dirty, and dangerous environments. The project’s primary goal is to protect those who come into direct contact with these environments. Bomb defusing, painting, welding, and other applications are a few examples of where this project is helpful.
Deep Learning for Instance Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content-Content Based Image Retrieval (CBIR)-a long-established research area in which improved efficiency and accuracy are needed for real-time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of instance search. In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep network architecture types, deep features, feature embedding and aggregation methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, whereby we identify milestone work, reveal connections among various methods and present the commonly used benchmarks, evaluation results, common challenges, and propose promising future directions.
Beyond calibration: estimating the grouping loss of modern neural networks

The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities. This is due to the grouping loss, created by samples with the same confidence scores but different true posterior probabilities. Proper scoring rule theory shows that given the calibration loss, the missing piece to characterize individual errors is the grouping loss. While there are many estimators of the calibration loss, none exists for the grouping loss in standard settings. Here, we propose an estimator to approximate the grouping loss. We show that modern neural network architectures in vision and NLP exhibit grouping loss, notably in distribution shifts settings, which highlights the importance of pre-production validation.
Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation

This paper presents a method for end-to-end cross-lingual text-to-speech (TTS) which aims to preserve the target language's pronunciation regardless of the original speaker's language. The model used is based on a non-attentive Tacotron architecture, where the decoder has been replaced with a normalizing flow network conditioned on the speaker identity, allowing both TTS and voice conversion (VC) to be performed by the same model due to the inherent linguistic content and speaker identity disentanglement. When used in a cross-lingual setting, acoustic features are initially produced with a native speaker of the target language and then voice conversion is applied by the same model in order to convert these features to the target speaker's voice. We verify through objective and subjective evaluations that our method can have benefits compared to baseline cross-lingual synthesis. By including speakers averaging 7.5 minutes of speech, we also present positive results on low-resource scenarios.
Deterministic constant-depth preparation of the AKLT state on a quantum processor using fusion measurements

The ground state of the spin-1 Affleck, Kennedy, Lieb and Tasaki (AKLT) model is a paradigmatic example of both a matrix product state and a symmetry-protected topological phase, and additionally holds promise as a resource state for measurement-based quantum computation. Having a nonzero correlation length, the AKLT state cannot be exactly prepared by a constant-depth unitary circuit composed of local gates. In this work, we demonstrate that this no-go limit can be evaded by augmenting a constant-depth circuit with fusion measurements, such that the total preparation time is independent of system size and entirely deterministic. We elucidate our preparation scheme using the language of tensor networks, and furthermore show that the $\mathbb{Z}_2\times\mathbb{Z}_2$ symmetry of the AKLT state directly affords this speed-up over previously known preparation methods. To demonstrate the practical advantage of measurement-assisted preparation on noisy intermediate-scale quantum (NISQ) devices, we carry out our protocol on an IBM Quantum processor. We measure both the string order and entanglement spectrum of prepared AKLT chains and, employing these as metrics, find improved results over the known (purely unitary) sequential preparation approach. We conclude with a demonstration of quantum teleportation using the AKLT state prepared by our measurement-assisted scheme. This work thus serves to provide an efficient strategy to prepare a specific resource in the form of the AKLT state and, more broadly, experimentally demonstrates the possibility for realizable improvement in state preparation afforded by measurement-based circuit depth reduction strategies on NISQ-era devices.
Lessons Learned: Defending Against Property Inference Attacks

This work investigates and evaluates multiple defense strategies against property inference attacks (PIAs), a privacy attack against machine learning models. Given a trained machine learning model, PIAs aim to extract statistical properties of its underlying training data, e.g., reveal the ratio of men and women in a medical training data set. While for other privacy attacks like membership inference, a lot of research on defense mechanisms has been published, this is the first work focusing on defending against PIAs. With the primary goal of developing a generic mitigation strategy against white-box PIAs, we propose the novel approach property unlearning. Extensive experiments with property unlearning show that while it is very effective when defending target models against specific adversaries, property unlearning is not able to generalize, i.e., protect against a whole class of PIAs. To investigate the reasons behind this limitation, we present the results of experiments with the explainable AI tool LIME. They show how state-of-the-art property inference adversaries with the same objective focus on different parts of the target model. We further elaborate on this with a follow-up experiment, in which we use the visualization technique t-SNE to exhibit how severely statistical training data properties are manifested in machine learning models. Based on this, we develop the conjecture that post-training techniques like property unlearning might not suffice to provide the desirable generic protection against PIAs. As an alternative, we investigate the effects of simpler training data preprocessing methods like adding Gaussian noise to images of a training data set on the success rate of PIAs. We conclude with a discussion of the different defense approaches, summarize the lessons learned and provide directions for future work.
Dynamics of Hopfield-Type Neural Networks with Modulo Periodic Unpredictable Synaptic Connections, Rates and Inputs

In this paper, we rigorously prove that unpredictable oscillations take place in the dynamics of Hopfield-type neural networks (HNNs) when synaptic connections, rates and external inputs are modulo periodic unpredictable. The synaptic connections, rates and inputs are synchronized to obtain the convergence of outputs on the compact subsets of the real axis. The existence, uniqueness, and exponential stability of such motions are discussed. The method of included intervals and the contraction mapping principle are applied to attain the theoretical results. In addition to the analysis, we have provided strong simulation arguments, considering that all the assumed conditions are satisfied. It is shown how a new parameter, degree of periodicity, affects the dynamics of the neural network.
Tohjm-Trained Multiscale Spatial Temporal Graph Convolutional Neural Network for Semi-Supervised Skeletal Action Recognition

In recent years, spatial-temporal graph convolutional networks have played an increasingly important role in skeleton-based human action recognition. However, there are still three major limitations to most ST-GCN-based approaches: (1) They only use a single joint scale to extract action features, or process joint and skeletal information separately. As a result, action features cannot be extracted dynamically through the mutual directivity between the scales. (2) These models treat the contributions of all joints equally in training, which neglects the problem that some joints with difficult loss-reduction are critical joints in network training. (3) These networks rely heavily on a large amount of labeled data, which remains costly. To address these problems, we propose a Tohjm-trained multiscale spatial-temporal graph convolutional neural network for semi-supervised action recognition, which contains three parts: encoder, decoder and classifier. The encoder’s core is a correlated joint–bone–body-part fusion spatial-temporal graph convolutional network that allows the network to learn more stable action features between coarse and fine scales. The decoder uses a self-supervised training method with a motion prediction head, which enables the network to extract action features using unlabeled data so that the network can achieve semi-supervised learning. In addition, the network is also capable of fully supervised learning with the encoder, decoder and classifier. Our proposed time-level online hard joint mining strategy is also used in the decoder training process, which allows the network to focus on hard training joints and improve the overall network performance. Experimental results on the NTU-RGB + D dataset and the Kinetics-skeleton dataset show that the improved model achieves good performance for action recognition based on semi-supervised training, and is also applicable to the fully supervised approach.
Academic Dishonesty and the Diamond Fraud: Attitudes of UAE Undergraduate Business Students during the COVID-19 Pandemic

This paper examines academic dishonesty by business undergraduate students in the United Arab Emirates, using the lens of the fraud diamond theory, during the Covid-19 pandemic. The study used a survey of 305 students from the college of business in a major public university in the UAE, from August 2020 to November 2020 to investigate the extent of academic dishonesty. Results revealed H1 (p&lt;0.001; p-value=0.73), H2 (p&lt;0.001; p-value=0.52), H3 (p&lt;0.001; p-value=0.76), and H4 (p&lt;0.001; p-value=0.53) resulting in acceptance of all the hypotheses. The findings indicate that pressure to maintain a scholarship status and having achieved a previous academic award are positively and significantly related to the likelihood of committing academic dishonesty. Furthermore, the rationalization factors of the fraud diamond theory are significantly and positively related to the reported incidences of academic dishonesty. Similarly, the opportunity and capability factors of the fraud diamond theory significantly predict the incidence of cheating. As a result, the study recommends that administrators should implement academic dishonesty codes, reduce the opportunity to cheat, invest in new technology for reducing cheat.
Target Identification via Multi-View Multi-Task Joint Sparse Representation

Recently, the monitoring efficiency and accuracy of visible and infrared video have been relatively low. In this paper, we propose an automatic target identification method using surveillance video, which provides an effective solution for the surveillance video data. Specifically, a target identification method via multi-view and multi-task sparse learning is proposed, where multi-view includes various types of visual features such as textures, edges, and invariant features. Each view of a candidate is regarded as a template, and the potential relationship between different tasks and different views is considered. These multiple views are integrated into the multi-task spare learning framework. The proposed MVMT method can be applied to solve the ship’s identification. Extensive experiments are conducted on public datasets, and custom sequence frames (i.e., six sequence frames from ship videos). The experimental results show that the proposed method is superior to other classical methods, qualitatively and quantitatively.