Reviewerly/exHarmony huggingface dataset:
Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models

The growth of domain-specific applications of semantic models, boosted by the recent achievements of unsupervised embedding learning algorithms, demands domain-specific evaluation datasets.In many cases, contentbased recommenders being a prime example, these models are required to rank words or texts according to their semantic relatedness to a given concept, with particular focus on top ranks.In this work, we give a threefold contribution to address these requirements: (i) we define a protocol for the construction, based on adaptive pairwise comparisons, of a relatedness-based evaluation dataset tailored on the available resources and optimized to be particularly accurate in top-rank evaluation; (ii) we define appropriate metrics, extensions of well-known ranking correlation coefficients, to evaluate a semantic model via the aforementioned dataset by taking into account the greater significance of top ranks.Finally, (iii) we define a stochastic transitivity model to simulate semantic-driven pairwise comparisons, which confirms the effectiveness of the proposed dataset construction protocol.
Cyber-Physical-Social Semantic Link Network

Is there any cause-effect link between thinking, experiencing and knowledge? This problem has challenged philosophers and scientists for centuries. Understanding and representing reality is a key step toward finding the link. Semantics modelling is an approach to understanding and representing reality.
Video Question Answering on Screencast Tutorials

This paper presents a new video question answering task on screencast tutorials. We introduce a dataset including question, answer and context triples from the tutorial videos for a software. Unlike other video question answering works, all the answers in our dataset are grounded to the domain knowledge base. An one-shot recognition algorithm is designed to extract the visual cues, which helps enhance the performance of video question answering. We also propose several baseline neural network architectures based on various aspects of video contexts from the dataset. The experimental results demonstrate that our proposed models significantly improve the question answering performances by incorporating multi-modal contexts and domain knowledge.
Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection

Off-topic spoken response detection, the task aiming at predicting whether a response is off-topic for the corresponding prompt, is important for an automated speaking assessment system. In many real-world educational applications, off-topic spoken response detectors are required to achieve high recall for off-topic responses not only on seen prompts but also on prompts that are unseen during training. In this paper, we propose a novel approach for off-topic spoken response detection with high off-topic recall on both seen and unseen prompts. We introduce a new model, Gated Convolutional Bidirectional Attention-based Model (GCBiA), which applies bi-attention mechanism and convolutions to extract topic words of prompts and key-phrases of responses, and introduces gated unit and residual connections between major layers to better represent the relevance of responses and prompts. Moreover, a new negative sampling method is proposed to augment training data. Experiment results demonstrate that our novel approach can achieve significant improvements in detecting off-topic responses with extremely high on-topic recall, for both seen and unseen prompts.
Analysing the fall 2020 Emotet campaign

In this report, we analyse the latest campaign of Emotet that had a significant impact in several countries worldwide. We leverage the data of a specifically crafted dataset, which contains emails, documents, executables and domains from the latest campaign. The goal is to analyse the attack vector, map the infrastructure used in various stages of the campaign and perform a surface analysis of Emotet's malicious payloads to assess their potential impact.
Gaussian Mean Field Regularizes by Limiting Learned Information

Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for learning parameters and hidden variables. Empirically, a regularizing effect can be observed that is poorly understood. In this work, we show how mean field inference improves generalization by limiting mutual information between learned parameters and the data through noise. We quantify a maximum capacity when the posterior variance is either fixed or learned and connect it to generalization error, even when the KL-divergence in the objective is scaled by a constant. Our experiments suggest that bounding information between parameters and data effectively regularizes neural networks on both supervised and unsupervised tasks.
Evaluation of Disadvantaged Regions in East Java Based-on the 33 Indicators of the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration Using the Ensemble ROCK (Robust Clustering Using Link) Method

East Java province is a large province in Indonesia, in which Surabaya is the second largest metropolitan city after Jakarta.Various problems of development inequality in East Java have caused East Java to be defined as a disadvantaged area in 2015.The determination of disadvantaged regions is carried out every 5 years using 6 criteria and 33 indicators that have been set by the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration.However, from several studies that have been conducted on the determination of disadvantaged regions, there is no research applies 33 indicators as a whole.So in this study, an evaluation of the determination of disadvantaged regions will be carried out using 33 indicators that have been determined by The Ministry of Villages, Development of Disadvantaged Regions, and Transmigration.Criteria data used are the results of the 2014 and 2018 surveys.These data are in the form of numerical data and categorical data.The method used is ensemble Robust Clustering Using Link (ROCK), which is a clustering method that can accommodate mixed data both categorical and numerical, using the concept of distance to measure the similarity or closeness between a pair of data points.The best cluster results for evaluating the determination of disadvantaged regions in 2020 consist of 4 clusters with the smallest Sw and Sb ratio of 0.3873984 and the optimum threshold value of 0.04.The results of the clustering, place Trenggalek, Bondowoso, Situbondo, Probolinggo, Tuban, Pamekasan, Sumenep, Bangkalan, and Sampang regions as disadvantaged regions in East Java.
Reflections of Social Media, Television Series and Programs on Family Privacy According to Pre-Service Teachers’ Views

This research aims to examine the views of pre-service teachers about the reflections of social media, television series and programs on family privacy.The case study design, which is one of the qualitative research designs, was used in the study.The data obtained in the research were analysed through content analysis.A semi-structured interview form was used as a data collection tool in the research.According to the findings of the study, pre-service teachers think that social media in general damages family privacy.Pre-service teachers claim the following reasons why social media damages family privacy, the need to share, social media addiction, sharing happiness, over-sharing; wannabe and informing according to the purpose of using social media tools.It is seen that the preservice teachers who participated in the study do not consider that the television series is careful about family privacy.The pre-service teachers who participated in the research stated that television programs are not suitable for family privacy.They state that there are cookery and marriage programs on television in general and that these programs harm the family structure and family privacy, which is sacred and precious.Pre-service teachers state, in general, that there is no family privacy.Pre-service teachers express that family privacy should be known well and it should not be shared.If the family shares, the child will also share, and what other people think is also a factor.
Training Data Generation for AI Based on Parametric Eigenspace Method

In this paper, we propose a method for automatic labeling of captured video images by using parametric eigenspace method for AI learning data. The experimental results showed that about 70% of the categorized data was labeled correctly. We also tried to improve the accuracy of labeling by extracting the moving object regions. The experimental results showed slight improvement in the accuracy of labeling.
The Problem of Fraudulent Content on the Web: Deep Learning Approaches

With the fast pace of life and advancement in technology, people rely more on social networking sites for the happenings around the globe. The misinformation or rumors spread across especially during emergency situations such as Pulwama Attack 2019 or The Attack of 26/11 or a natural calamity like the Kerala Flood 2018 can have a devastating effect on individuals and society. Spurious news in such a scenario would not only give rise to panic among the individuals but in some cases, it may also target a particular community. In this paper, we try to understand the term Spurious news in more detail and analyze the situation of Spurious news in India in a broader manner. We will also review the various state-of-the-art techniques that exist for solving the fraudulent news problem from different aspects such as deep learning, machine learning, etc.
Two-Excitation Routing via Linear Quantum Channels

Routing quantum information among different nodes in a network is a fundamental prerequisite for a quantum internet. While single-qubit routing has been largely addressed, many-qubit routing protocols have not been intensively investigated so far. Building on the many-excitation transfer protocol in arXiv:1911.12211, we apply the perturbative transfer scheme to a two-excitation routing protocol on a network where multiple two-receivers block are coupled to a linear chain. We address both the case of switchable and permanent couplings between the receivers and the chain. We find that the protocol allows for efficient two-excitation routing on a fermionic network, although for a spin-$\frac{1}{2}$ network only a limited region of the network is suitable for high-quality routing.
Vax-a-Net: Training-Time Defence Against Adversarial Patch Attacks

We present Vax-a-Net; a technique for immunizing convolutional neural networks (CNNs) against adversarial patch attacks (APAs). APAs insert visually overt, local regions (patches) into an image to induce misclassification. We introduce a conditional Generative Adversarial Network (GAN) architecture that simultaneously learns to synthesise patches for use in APAs, whilst exploiting those attacks to adapt a pre-trained target CNN to reduce its susceptibility to them. This approach enables resilience against APAs to be conferred to pre-trained models, which would be impractical with conventional adversarial training due to the slow convergence of APA methods. We demonstrate transferability of this protection to defend against existing APAs, and show its efficacy across several contemporary CNN architectures.
Post-quantum digital signature schemes: setting a hidden group with two-dimensional cyclicity

For the construction of post-quantum digital signature schemes that satisfy the strengthened criterion of resistance to quantum attacks, an algebraic carrier is proposed that allows one to define a hidden commutative group with two-dimensional cyclicity. Formulas are obtained that describe the set of elements that are permutable with a given fixed element. A post-quantum signature scheme based on the considered finite non-commutative associative algebra is described.
LPCF: Robust Correlation Tracking via Locality Preserving Tracking Validation

In visual tracking, the tracking model must be updated online, which often leads to undesired inclusion of corrupted training samples, and hence inducing tracking failure. We present a locality preserving correlation filter (LPCF) integrating a novel and generic decontamination approach, which mitigates the model drift problem. Our decontamination approach maintains the local neighborhood feature points structures of the bounding box center. This proposed tracking-result validation approach models not only the spatial neighborhood relationship but also the topological structures of the bounding box center. Additionally, a closed-form solution to our approach is derived, which makes the tracking-result validation process could be accomplished in only milliseconds. Moreover, a dimensionality reduction strategy is introduced to improve the real-time performance of our translation estimation component. Comprehensive experiments are performed on OTB-2015, LASOT, TrackingNet. The experimental results show that our decontamination approach remarkably improves the overall performance by 6.2%, 12.6%, and 3%, meanwhile, our complete algorithm improves the baseline by 27.8%, 34.8%, and 15%. Finally, our tracker achieves the best performance among most existing decontamination trackers under the real-time requirement.
The impact of online learning on the education system in the context of the spread of coronavirus infection

The globalization of the economy and the development of international cooperation, which have been a key priority for many countries over the past few decades, have shown negative consequences for the whole world when an epidemiological threat arises. The transition to online learning has become virtually the only practical response of the education system to the emerging challenge. As part of the preparation of the scientific work, a sociological survey was conducted among students of economic directions about their attitude to online learning during the pandemic. The survey showed that students are generally satisfied with the organization of training in the online learning format and rather highly assess the readiness of the university and teachers to quickly switch to online learning format. The article highlights the main directions of development of the education system, due to the inevitability of the development of online learning and distance education.
Higher Education E-Learning Usability Analysis Using System Usability Scale

The COVID-19 pandemic encourages almost every institution to optimize the use of information and communication technology to maintain consistency and competitive advantage. One of the efforts made by Higher education is developing and optimizing online-based learning (e-learning) models. E-learning plays an important role in the success of achieving learning objectives at Telkom Institute of Technology Purwokerto (ITTP), which is conducted between lecturers and students. The problem faced in optimizing e-learning system at ITTP is related to usability. The purpose of this study is to evaluate the usability level of the ITTP e-learning system using the System Usability Scale (SUS) method. Based on the testing of questionnaire instruments using SPSS software, it was obtained the result that the questionnaire used for the study was valid, and the reliability rate was 0.758. Based on the results of system tests to 100 respondents or users, it is known that the SUS score is 55.3. The value indicates that the acceptability ranges are in the Marginal Low range. It can be seen from the adjective rating, SUS score from the ITTP e-learning system is in the CATEGORY OK and according to Curved Grade Scale (CGS) is in grade D. Based on Net Promoter Scale (NPS), the score 55.3 indicates that the ITTP e-learning system users tend to be in the Detractor category.
TAN-NTM: Topic Attention Networks for Neural Topic Modeling.

Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, most existing neural models either take document bag-of-words (BoW) or sequence of tokens as input followed by variational inference and BoW reconstruction to learn topic-word distribution. However, leveraging topic-word distribution for learning better features during document encoding has not been explored much. To this end, we develop a framework TAN-NTM, which processes document as a sequence of tokens through a LSTM whose contextual outputs are attended in a topic-aware manner. We propose a novel attention mechanism which factors in topic-word distribution to enable the model to attend on relevant words that convey topic related cues. The output of topic attention module is then used to carry out variational inference. We perform extensive ablations and experiments resulting in ~9-15 percentage improvement over score of existing SOTA topic models in NPMI coherence on several benchmark datasets - 20Newsgroups, Yelp Review Polarity and AGNews. Further, we show that our method learns better latent document-topic features compared to existing topic models through improvement on two downstream tasks: document classification and topic guided keyphrase generation.
A Targeted Universal Attack on Graph Convolutional Network

Graph-structured data exist in numerous applications in real life. As a state-of-the-art graph neural network, the graph convolutional network (GCN) plays an important role in processing graph-structured data. However, a recent study reported that GCNs are also vulnerable to adversarial attacks, which means that GCN models may suffer malicious attacks with unnoticeable modifications of the data. Among all the adversarial attacks on GCNs, there is a special kind of attack method called the universal adversarial attack, which generates a perturbation that can be applied to any sample and causes GCN models to output incorrect results. Although universal adversarial attacks in computer vision have been extensively researched, there are few research works on universal adversarial attacks on graph structured data. In this paper, we propose a targeted universal adversarial attack against GCNs. Our method employs a few nodes as the attack nodes. The attack capability of the attack nodes is enhanced through a small number of fake nodes connected to them. During an attack, any victim node will be misclassified by the GCN as the attack node class as long as it is linked to them. The experiments on three popular datasets show that the average attack success rate of the proposed attack on any victim node in the graph reaches 83% when using only 3 attack nodes and 6 fake nodes. We hope that our work will make the community aware of the threat of this type of attack and raise the attention given to its future defense.
Unpredictable random number generator

The fundamental link between physics and computation leads to an exciting field of quantum computation. Since the birth of the programmable digital computer, random number generators in computers have been developed. In principle, the pseudo random number generators currently implemented in digital computers are predictable. However, a quantum random number generator is employed to ideally generate unpredictable random numbers. In this essay, we explain why a one-qubit quantum computer can be a quantum random number generator and how this leads to justifying the foundations of quantum mechanics.
Security of Two NIST Candidates in the Presence of Randomness Reuse

The National Institute of Standards and Technology (NIST) is working on the standardization of post-quantum algorithms. In February 2019, NIST announced 26 candidate post-quantum cryptosystems, including NewHope and LAC, had entered the second round. In order to investigate the resilience of various candidate algorithms in key reuse situations, a series of work has been carried out. In fact, randomness also has the risk of reuse, and in the real word random number generators (RNGs) frequently fail and produce bad randomness. In this work, we assess the resilience of candidate NewHope-CPA-KEM and LAC.KE in randomness reuse situations. In particular, we propose a method, which can recover the reused randomness after several communications. NewHope-CPA-KEM and LAC.KE are based on NewHope-CPA-PKE and LAC.CPA, respectively. The key to our method is that they share a common feature: if public key satisfies certain conditions, the ciphertext will reveal information about the randomness of encryption. The recovered randomness can be used to attack another session where the same randomness is used.
New Results in Reduced Round AES - 256 Impossible Differential Cryptanalysis

Security of Crypto systems is usually analyzed through different cryptanalytic methods.Since Advance Encryption Standard (AES) is one of the most widely used and popular block cipher, a number of attacks have already been proposed on it.Lots of reduced round attacks on AES are available in the literature.In this paper, two efficient reduced round impossible differential attacks are introduced against AES -256.The attacks proposed here show how an attack can modified for betterment.The first one is a new 8 th round attack, which shows the data complexity and time complexity can get interchanged without affecting the memory requirement, by introducing proper change in the attack procedure.The second cryptanalysis is carried out in which four round impossible differential begins from third round only, wherein conventional attacks it starts from second round itself.This difference in attack procedure leads to reduction in data as well as time complexities.Moreover, the interchange of Add Round Key and Mix Column operations done in the 7 th round of conventional impossible attacks can be avoided here.A conventional attack appeared in the literature is taken as the main reference.Comparison of the complexities is also given.
Identifying semantic relations between sentences by solving an anaphora

This paper presents the method of semantic relations identification by solving the anaphora, specifically to determine the nature of relations between sentences in scientific texts. Authors explored the use of the pronoun "anaphora", developed a mathematical predicate model to determine the anaphora and antecedent and described the relationships between them for fragments in the Ukrainian language. Authors conducted an experiment based on the developed model and the logical network that was build based on presented predicate model.
Selecting Best CPU frequency for energy saving in cluster using genetic algorithm

Abstract Dynamic voltage and frequency scaling (DVFS) is a technique mainly used for reducing the consumed energy of computer’s processor. Its only drawback detracting the performance of parallel application when executing them over parallel platform. However, genetic algorithm is introduced and applied in a heterogeneous cluster architecture for modeling the best trade-off between the energy saving and performance degradation of the parallel application in the same time. The suggested algorithm selects the best frequencies vector to achieve that targets by offering equal trade-off between them. The genetic algorithm simultaneously gives minimum energy consumption and minimal performance degradation via its objective function. All experiment will apply using SimGrid simulator. The experiments show that the algorithm reduces the energy consumption of the message passing application by (24% with 8000 problem size and 22% for 4000 problem size) and in almost cases improve the application performance up to 3%.