Reviewerly/exHarmony huggingface dataset:
Modelling imperfect knowledge via location semantics for realistic privacy risks estimation in trajectory data

Mobility patterns of vehicles and people provide powerful data sources for location-based services such as fleet optimization and traffic flow analysis. Location-based service providers must balance the value they extract from trajectory data with protecting the privacy of the individuals behind those trajectories. Reaching this goal requires measuring accurately the values of utility and privacy. Current measurement approaches assume adversaries with perfect knowledge, thus overestimate the privacy risk. To address this issue, we introduce a model of an adversary with imperfect knowledge about the target. The model is based on equivalence areas, spatio-temporal regions with a semantic meaning, e.g. the target's home, whose size and accuracy determine the skill of the adversary. We then derive the standard privacy metrics of k-anonymity, l-diversity and t-closeness from the definition of equivalence areas. These metrics can be computed on any dataset, irrespective of whether and what kind of anonymization has been applied to it. This work is of high relevance to all service providers acting as processors of trajectory data who want to manage privacy risks and optimize the privacy vs. utility trade-off of their services.
ShardCons - A Sharding Based Consensus Algorithm for Blockchain

Blockchain, the foundation of Bitcoin, has received extensive attentions in recent days. Blockchain-based applications are springing up, covering numerous fields including financial services, reputation system, Internet of Things (IoT), Healthcare systems, Supply Chain Management and so on. Blockchain serves as an immutable ledger which allows transactions to be securely accomplished via point-to-point connections in a distributed system without the need for a third-party. Since it is decentralized, consensus algorithms keeps hold the integrity of the transactions which are added in the chain. Consensus algorithms are the primary root of the blockchain technology and a good consensus algorithm can guarantee the fault tolerance and security of the blockchain systems. In this article, authors present a novel consensus algorithm for public blockchain which shards the miners based on their performance. Once the sharding of miners is done, the best miner from each shard is chosen to form a Super shard of miners, and then from Super shard, one miner is randomly chosen as a winner miner who will mine the next block in the blockchain network. For sharding, performance history of miners will be maintained in each miner and re-sharding will be done at regular intervals in order to bring fairness in the system. The proposed sharding based consensus algorithm solves one of the main problem of public blockchain which is scalability issue. This performance based consensus algorithm also ensures more fairness, avoids starvation, improves the trust among the miners and enhances the overall performance of the blockchain network.
AUTOSUMM: Automatic Model Creation for Text Summarization

Recent efforts to develop deep learning models for text generation tasks such as extractive and abstractive summarization have resulted in state-of-the-art performances on various datasets.However, obtaining the best model configuration for a given dataset requires an extensive knowledge of deep learning specifics like model architecture, tuning parameters etc., and is often extremely challenging for a non-expert.In this paper, we propose methods to automatically create deep learning models for the tasks of extractive and abstractive text summarization.Based on the recent advances in Automated Machine Learning and the success of large language models such as BERT and GPT-2 in encoding knowledge, we use a combination of Neural Architecture Search (NAS) and Knowledge Distillation (KD) techniques to perform model search and compression using the vast knowledge provided by these language models to develop smaller, customized models for any given dataset.We present extensive empirical results to illustrate the effectiveness of our model creation methods in terms of inference time and model size, while achieving near state-of-the-art performances in terms of accuracy across a range of datasets.
Is Everything in Order? A Simple Way to Order Sentences

The task of organizing a shuffled set of sentences into a coherent text has been used to evaluate a machine's understanding of causal and temporal relations.We formulate the sentence ordering task as a conditional textto-marker generation problem.We present Reorder-BART (RE-BART) that leverages a pre-trained Transformer-based model to identify a coherent order for a given set of shuffled sentences.The model takes a set of shuffled sentences with sentence-specific markers as input and generates a sequence of position markers of the sentences in the ordered text.RE-BART achieves the state-of-the-art performance across 7 datasets in Perfect Match Ratio (PMR) and Kendall's tau (τ ).We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets.We additionally perform several experiments to understand the functioning and limitations of our framework.2 1 3 5 4 <S1> I packed my raincoat.<S2> The forecast called for rainy.<S3> It never rained.<S4> The weather is never predictable.<S5> Instead it started to snow.
DK-Consistency: A Domain Knowledge Guided Consistency Regularization Method for Semi-supervised Breast Cancer Diagnosis

The performance of deep learning models generally relies on large and high-quality labeled datasets. However, in medical domain, as labeling process is much more laborious and time-consuming, most medical datasets are much smaller compared with natural image datasets. To mitigate this weakness, recent researches in medical image analysis adopt semi-supervised learning methods, especially consistency regularization methods to learn from a large amount of unlabeled medical data. However, as these semi-supervised learning methods are originally designed for tasks of natural images, specific properties of medical domain are not fully investigated and utilized. In this paper, we present DK-Consistency, a domain knowledge guided consistency regularization method for semi-supervised breast cancer diagnosis in ultrasound images. In DK-Consistency, domain knowledge of medical doctors is first incorporated into the generation process of perturbed samples for each unlabeled image. Then consistency regularization is adopted to force the model to make consistent predictions for unlabeled images and their perturbed samples. Extensive experiments demonstrate that, by injecting domain knowledge, DK-Consistency significantly improves the diagnostic performance of breast cancer and outperforms many state-of the-art semi-supervised methods.
We Need to Talk About train-dev-test Splits

Standard train-dev-test splits used to benchmark multiple models against each other are ubiquitously used in Natural Language Processing (NLP).In this setup, the train data is used for training the model, the development set for evaluating different versions of the proposed model(s) during development, and the test set to confirm the answers to the main research question(s).However, the introduction of neural networks in NLP has led to a different use of these standard splits; the development set is now often used for model selection during the training procedure.Because of this, comparing multiple versions of the same model during development leads to overestimation on the development data.As an effect, people have started to compare an increasing amount of models on the test data, leading to faster overfitting and "expiration" of our test sets.We propose to use a tune-set when developing neural network methods, which can be used for model picking so that comparing the different versions of a new model can safely be done on the development data.
Optimal bounds for computing α-gapped repeats

Following (Kolpakov et al., 2013; Gawrychowski and Manea, 2015), we continue the study of α-gapped repeats in strings, defined as factors of the form uvu with |uv|=|u|+|v|≤α|u|. Our main result is the O(αn) bound on the number of maximal α-gapped repeats in a string of length n, previously proved to be O(α2n) in (Kolpakov et al., 2013). For a closely related notion of maximal δ-subrepetition (maximal factors of exponent between 1+δ and 2), our result implies the O(n/δ) bound on their number, which improves the bound of (Kolpakov et al., 2010) by a log⁡n factor. We also prove an algorithmic time bound O(αn+S) (S size of the output) for computing all maximal α-gapped repeats. Our solution, inspired by (Gawrychowski and Manea, 2015), is different from the recently published proof by (Tanimura et al., 2015) of the same bound. Together with our bound on S, this implies an O(αn)-time algorithm for computing all maximal α-gapped repeats.
Cybercrime Threat Landscape amid the Movement Control Order in Malaysia

The COVID-19 pandemic has evolved rapidly and affected almost all the world's countries, creating unprecedented chaos on human lives, physical health, mental wellbeing, and the world economy. During this time, digital space has become an indispensable global means of communication, entertainment, and social interaction. However, high reliance on digital tools increases the risk of being prey to cyberattacks. In order to explore the cybercrime threat landscape, empirical research was conducted to examine the shared experiences related to cybercrime threats during the enforcement of the Movement Control Order in Malaysia. Data was collected using an online survey among 332 respondents across Malaysia. The finding stipulated that most of the respondents comprised females aged between 18 and 28 years old. The result also indicated that majority of female became victims of online phishing/malware distribution [(M = 0.58, SD = 0.13); t (113) = 2.24, p = 0.02] and online sexual harassment [(M = 0.56, SD = 0.11); t (107) = 2.38, p = 0.01]. The understanding of cybercrime experiences faced by the public during MCO is essential to help law enforcement agencies to stay vigilant on issues related to public safety and security during unprecedented conditions.
E-Commerce Picture Text Recognition Information System Based on Deep Learning

For the accuracy requirements of commodity image detection and classification, the FPN network is improved by DPFM ablation and RFM, so as to improve the detection accuracy of commodities by the network. At the same time, in view of the narrowing of channels in the application of traditional MWI-DenseNet network, a new GTNet network is proposed to improve the classification accuracy of commodities.The results show that at different levels of evaluation indexes, the dpFPN-Netv2 algorithm improved by DPFM + RFM fusion has higher target detection accuracy than RetinaNet-50 algorithm and other algorithms. And the detection time is 52 ms, which is significantly lower than 90 ms required for RetinaNet-50 detection. In terms of target recognition, compared with the traditional MWI-DenseNet neural network, the computation amount of the improved MWI DenseNet neural network is significantly reduced under different shunt ratios, and the recognition accuracy is significantly improved. The innovation of this study lies in improving the algorithm from the perspective of target detection and recognition, so as to change the previous improvement that only can be made in a single way.
Quad Swipe Pattern: A New Point-of-Entry Security Measure for Smartphone Users

In this paper, we develop a new point-of-entry security measure for smartphone users. We devise a concept, the “Quad Swipe Pattern", which includes four swipes from a user in four directions and utilizes the user’s swipe behavior for authentication. The Quad Swipe Pattern overcomes several shortcomings present in current point-of-entry security measures. We performed several experiments to demonstrate the effectiveness of the Quad Swipe Pattern in smartphone user authentication. We evaluated the Quad Swipe Pattern using five machine learning classifiers, three datasets of different sizes, and five different fingers. In addition, we studied how fusion of information from multiple fingers and multiple classifiers can improve the performance of Quad Swipe Pattern. All of our experimental results show significant promise of the Quad Swipe Pattern as a new point-of-entry security measure for smartphones. With a Neural Network model, the Quad Swipe Pattern achieves the Accuracy of 99.7%, False Acceptance Rate of 0.4%, and False Rejection Rate of 0%. With Support Vector Machine, the Quad Swipe Pattern achieves the Accuracy of 99.5%, False Acceptance Rate of 0.4%, and False Rejection Rate of 1.7%. With fusion of two best fingers, the Quad Swipe Pattern demonstrates an excellent performance of a zero Equal Error Rate.
Polar Embedding

Hierarchical relationships are invaluable information for many natural language processing (NLP) tasks.Distributional representation has become a fundamental approach for encoding word relationships, particularly embeddings in hyperbolic space showed great performance in representing hierarchies by taking advantage of their spatial properties.However, most machine learning systems do not suppose to use in such complex non-Euclidean geometries.To achieve hierarchy representations in commonly used Euclidean space, we propose Polar Embedding that learns word embeddings with the polar coordinate system.Utilizing characteristics of polar coordinates, the hierarchy of words is expressed with two independent variables: radius (generality) and angles (similarity), and their variables are optimized separately.Polar embedding shows word hierarchies explicitly and allows us to use beneficial resources such as word frequencies or word generality annotations for computing radiuses.We introduce an optimization method for learning angles in limited ranges of polar coordinates, which combining a loss function controlling gradient and distribution uniformization.Experimental results on hypernymy datasets indicate that our approach outperforms other embeddings in low-dimensional Euclidean space and competitively performs even with hyperbolic embeddings, which possess a geometric advantage.
Generic Itemset Mining Based on Reinforcement Learning

One of the biggest problems in itemset mining is the requirement of developing a data structure or algorithm, every time a user wants to extract a different type of itemsets. To overcome this, we propose a method, called <i>Generic Itemset Mining based on Reinforcement Learning</i> (GIM-RL), that offers a unified framework to train an agent for extracting any type of itemsets. In GIM-RL, the environment formulates iterative steps of extracting a target type of itemsets from a dataset. At each step, an agent performs an action to add or remove an item to or from the current itemset, and then obtains from the environment a reward that represents how relevant the itemset resulting from the action is to the target type. Through numerous trial-and-error steps where various rewards are obtained by diverse actions, the agent is trained to maximise cumulative rewards so that it acquires the optimal action policy for forming as many itemsets of the target type as possible. In this framework, an agent for extracting any type of itemsets can be trained as long as a reward suitable for the type can be defined. The extensive experiments on mining high utility itemsets, frequent itemsets and association rules show the general effectiveness and one remarkable potential (agent transfer) of GIM-RL. We hope that GIM-RL opens a new research direction towards learning-based itemset mining.
A Real-Time Cache Side-Channel Attack Detection System on RISC-V Out-of-Order Processor

Computer designers have included techniques such as speculative execution and caching to optimize speed and performance. Unfortunately, they could be exploited by the recently discovered cache-side channel attack, spectre. The purpose of this research is to resolve this problem on the open-source RISC-V architecture. Previously, software mitigation techniques and hardware modifications have been investigated on Intel or ARM system to address these issues. However, they are either difficult to implement or have resulted in a significant loss of performance. This work presents a real-time detection approach for cache side-channel attacks such as spectre on the RISC-V processor. By monitoring the CPU’s cache activity with Hardware Performance Counters and analyzing the gathered data with a neural network, we extend previous researches in the field of cache side-channel identification. Since cache side-channels frequently result in a significantly altered cache usage pattern, the proposed multi-layer perceptron network can detect an attack event with an accuracy greater than 99% in our test environment with low performance overhead. This is the first time, to our knowledge, that a spectre attack on the RISC-V architecture has been detected in run-time via hardware events and machine learning.
A Novel Integration of Social Capital, Emotional Attachment and Organizational Innovation

The social capital and attachment theory are employed to theoretically define the relationships between three social capital dimensions (trust, interaction density and cooperation), three-dimensional emotional attachment (affection, passion and connection) and organizational innovation – by considering the organizational climate as a potential moderator. This study proposes a) social capital and emotional attachment contribution to organizational innovation b) moderating role of organizational climate in the relationship between social capital, emotional attachment and organizational innovation. The suggestions conclude that keeping an account of employees’ social capital and emotional attachment may help the organization to take an innovative position in the market.
The Effect of Offline Learning Model Assisted in Practicum Discovery Learning on Learning Outcomes

The purpose of this article was to determine the effect of the offline learning model assisted by the Dilan practicum module on hot material on the learning outcomes of fifth-grade students of SD Inpres 1 Ujuna. This type of research is quantitative research with the type of experimental research using the Quasi-experimental method with the Nonequivalent Control Group Design design. The subjects in this study were fifth-grade students of SD Inpres 1 Ujuna. Data collection was carried out using multiple-choice questions, and data analysis used prerequisite tests, namely normality and homogeneity tests and hypothesis testing using independent sample t-test tests which were analyzed using SPSS Statistics version 16. Based on the results of this study, a significant value of 0.001 &lt; 0, 05 then accept the hypothesis Ha and reject Ho, meaning that the Dilan practicum module has an effect when used in improving student learning outcomes. So it can be concluded that there is an effect when using the offline learning model assisted by Dilan's practical module on hot material on the learning outcomes of fifth-grade students of SD Inpres 1 Ujuna.
Virtual Walking With Omnidirectional Movies and Foot Vibrations: Scene-Congruent Vibrations Enhance Walking-Related Sensations and Ground Material Perceptions

Walking is a human need.Even when physical disabilities or social situations, such as the COVID-19 pandemic, prevent people from walking, virtual reality can provide an opportunity for virtual walking or travel.In this study, a virtual walking system was developed in which users view omnidirectional movies while receiving scene-congruent vibrations to their left and right forefeet and heels.The timings of foot vibrations were generated by estimating the camera motion trajectory with visual simultaneous localization and mapping (visual SLAM) applied to four omnidirectional movies.Congruent vibration patterns were prepared for four ground scenes.Modulation of walking-related sensations and ground material perceptions by congruent and incongruent vibrations was verified using psychological measurements.The results showed that rhythmic foot vibration improved the sensations of self-motion, walking, leg action, and telepresence irrespective of scene-vibration congruency.Moreover, congruent vibrations were better than incongruent vibrations for walking-related sensations and telepresence in indoor corridors and snowy ground scenes.The perception of ground materials was enhanced by scene-congruent vibrations, whereas it was confused by scene-incongruent vibrations.These findings suggest that vibration patterns do not necessarily need to match the ground exactly to induce virtual walking sensations; however, scene-congruent or similar vibrations improve virtual walking sensations and ground material perception.By applying our methods, we can convert various public omnidirectional movies into realistic virtual walking experiences.
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Workers Through Explainable Artificial Intelligence

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost.It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling.In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI.To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams-DSS and automation.IDA supports knowledge workers without influencing them through automated decision-making.Specifically, we propose to use techniques of Explainable AI (XAI) while withholding concrete AI recommendations.To test this conceptualization, we develop hypotheses on the impacts of IDA and provide first evidence for their validity based on empirical studies in the literature.
Secured and Provisioned Access Authentication using Subscribed user Identity in Federated Clouds

Cloud computing has become an essential source for modern trade or market environments by abled frameworks.The exponential growth of cloud computing services in the last few years has resulted in extensive use, especially in storing and sharing the data on various cloud servers.The current trend in the cloud shows that the cloud owners use relative functions and target areas in such a way that cloud customers access or store their data either in the same servers or related servers.Simultaneously, from the security point of view, the lack of confidence about the customer's data on the cloud server is still questionable.The hour's need is to provide the cloud service in a 'single port way' by forming the joint management policy to increase customer satisfaction and profitability.In addition to this, the authentication steps also need to be improvised.This paper discusses issues on the security authentication and access provisioning of the cloud service consumers in federated clouds using subscribed user identity.This work proposes the user identity verification module (UidVM) in the cloud service consumer's authentication process to serve as a cloud broker to minimize the work overloads on the central cloud federation management system, thus enhancing the cloud security.