Reviewerly/exHarmony huggingface dataset:
Education 4.0: Gaps Research Between School Formation and Technological Development

Industry constantly changes following technological developments and innovation, resulting in changes in productivity, the labor market and education. As a result of this new industrial revolution, developed through digitization and robotics, Education 4.0 tends to combine information available in the real and virtual world. The objective of this paper was to identify research gaps, as well as to make groupings through affinity of themes. The adopted method was a literature review, which served as a basis to identify the gaps of the most relevant publications and their variations indexed in the Scopus database. From the identified gaps, it was possible to compose five groups addressing similar characteristics about Education 4.0.
Correlation Filter With Motion Detection for Robust Tracking of Shape-Deformed Targets

Target tracking is an important area of research in computer vision where stable target's tracking has been well solved. But in real world, it is difficult to ensure that the camera or lens could be fixed and the target could maintain its shape in whole video sequence. And as a result, in these unstable cases, robust tracking algorithms have to deal with the problem of target shape-deforming. Once the scenes video sequence contains shape-deformed target, tracking become a real challenging problem. Most previous tracking algorithms based on craft features only used HOG or/and CN features. This paper proposed an algorithm named as Correlation Filtering with Motion Detection (CFMD). This algorithm takes into account the camera shake and target motion information of the video sequence. After removing the effects of lens shake and camera movement, this algorithm can predict the motion information of the target, thereby effectively improving the tracking accuracy and robustness. In CFMD, the target position is determined by the weighted outputs of motion detection and correlation filter tracker. We evaluated our CMFD algorithm on the OTB-100 and VOT-2018 dataset compared with other target tracking algorithms, including Kernel Correlation Filter (KCF), Scale Adaptive with Multiple Features tracker (SAMF), Discriminative Scale Space Tracker (DSST), and Sum of Template and Pixel-wise LEarners (Staple), Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking(STRCF), Multi-Cue Correlation Filters for Robust Visual Tracking(MCCT). The experimental results showed that our algorithm owns the property of robust tracking of shape-deformed targets in video sequences containing lens shaking or camera moving and it achieves the state-of-the-art precision and tracking effects.
Applying Blockchain Technology to Enhance Clinical Trial Recruitment.

Patient recruitment for clinical trials is known to be a challenging aspect of clinical research. There are multiple competing concerns from the sponsor, patient and principal investigator's perspectives resulting in most clinical trials not meeting recruitment requirements on time. Conducting under-enrolled clinical trials affects the power of conclusive results or causes premature trial termination. The Blockchain is a distributed ledger technology originally applied in the financial sector. Its features as a peer-to-peer system with publicly audited transactions, data security, and patient privacy are a good fit for the needs of clinical trials recruitment. The "Smart Contract" is a programmable self-executing protocol that regulates the blockchain transactions. Given current recruitment challenges, we have proposed a blockchain model containing multiple trial-based contracts for trial management and patient engagement and a master smart contract for automated subject matching, patient recruitment, and trial-based contracts management.
Publicly Verifiable and Efficient Fine-Grained Data Deletion Scheme in Cloud Computing

Cloud storage, one of the most attractive services offered by cloud computing, can provide users with boundless storage capacity. Thus, users can outsource their data to the cloud server for greatly saving local storage overhead. However, in cloud storage, due to the separation between data ownership and management, users lose the direct control over their outsourced data, resulting in plenty of security and privacy problems. In this paper, we focus on the problem of verifiable outsourced data deletion, which is important but received less attention in industry and academia. We propose an efficient fine-grained outsourced data deletion scheme based on invertible Bloom filter, which can also achieve public and private verifiability of the storage and deletion results. If the cloud server does not honestly maintain/delete the data and generate corresponding evidences, users can easily detect the cloud server’s malicious behaviors with an overwhelming probability. Meanwhile, in data deletion and deletion result verification processes, the computational complexity is independent of the number of outsourced data blocks, which makes the proposed scheme be suitable for large-scale data deletion scenario. Moreover, we provide the detailed security analysis and performance evaluation, which can respectively demonstrate the security and practicability of the proposed scheme.
Deepbots: A Webots-Based Deep Reinforcement Learning Framework for Robotics

Deep Reinforcement Learning (DRL) is increasingly used to train robots to perform complex and delicate tasks, while the development of realistic simulators contributes to the acceleration of research on DRL for robotics. However, it is still not straightforward to employ such simulators in the typical DRL pipeline, since their steep learning curve and the enormous amount of development required to interface with DRL methods significantly restrict their use by researchers. To overcome these limitations, in this work we present an open-source framework that combines an established interface used by DRL researchers, the OpenAI Gym interface, with the state-of-the-art Webots robot simulator in order to provide a standardized way to employ DRL in various robotics scenarios. Deepbots aims to enable researchers to easily develop DRL methods in Webots by handling all the low-level details and reducing the required development effort. The effectiveness of the proposed framework is demonstrated through code examples, as well as using three use cases of varying difficulty.
Efektivitas Pembelajaran Google Classroom Terhadap Kemampuan Penalaran Matematis Siswa

This study aims to determine the effectiveness of google classroom learning on students' mathematical reasoning. This research is a pre-experimental research with one-shot case type involving one class of class XI students consisting of 20 students in one high school in the city of Mataram. Research instruments in the form of tests, observations and interviews. Data analysis using one sample t-test. The results showed that learning to use google classroom on students' mathematical reasoning abilities was effective. Disciplinary attitude of students can be trained in this Google classroom LMS. Another finding, in the form of improving the implementation of google classroom in learning, is the need for supporting applications that can facilitate teachers and students face to face directly via the network (web) such as the use of Google mett.
JANE: Jointly Adversarial Network Embedding

Motivated by the capability of Generative Adversarial Network on exploring the latent semantic space and capturing semantic variations in the data distribution, adversarial learning has been adopted in network embedding to improve the robustness. However, this important ability is lost in existing adversarially regularized network embedding methods, because their embedding results are directly compared to the samples drawn from perturbation (Gaussian) distribution without any rectification from real data. To overcome this vital issue, a novel Joint Adversarial Network Embedding (JANE) framework is proposed to jointly distinguish the real and fake combinations of the embeddings, topology information and node features. JANE contains three pluggable components, Embedding module, Generator module and Discriminator module. The overall objective function of JANE is defined in a min-max form, which can be optimized via alternating stochastic gradient. Extensive experiments demonstrate the remarkable superiority of the proposed JANE on link prediction (3% gains in both AUC and AP) and node clustering (5% gain in F1 score).
FR-Train: A mutual information-based approach to fair and robust training

Trustworthy AI is a critical issue in machine learning where, in addition to training a model that is accurate, one must consider both fair and robust training in the presence of data bias and poisoning. However, the existing model fairness techniques mistakenly view poisoned data as an additional bias to be fixed, resulting in severe performance degradation. To address this problem, we propose FR-Train, which holistically performs fair and robust model training. We provide a mutual information-based interpretation of an existing adversarial training-based fairness-only method, and apply this idea to architect an additional discriminator that can identify poisoned data using a clean validation set and reduce its influence. In our experiments, FR-Train shows almost no decrease in fairness and accuracy in the presence of data poisoning by both mitigating the bias and defending against poisoning. We also demonstrate how to construct clean validation sets using crowdsourcing, and release new benchmark datasets.
Few-shot relation classification by context attention-based prototypical networks with BERT

Abstract Human-computer interaction under the cloud computing platform is very important, but the semantic gap will limit the performance of interaction. It is necessary to understand the semantic information in various scenarios. Relation classification (RC) is an import method to implement the description of semantic formalization. It aims at classifying a relation between two specified entities in a sentence. Existing RC models typically rely on supervised learning and distant supervision. Supervised learning requires large-scale supervised training datasets, which are not readily available. Distant supervision introduces noise, and many long-tail relations still suffer from data sparsity. Few-shot learning, which is widely used in image classification, is an effective method for overcoming data sparsity. In this paper, we apply few-shot learning to a relation classification task. However, not all instances contribute equally to the relation prototype in a text-based few-shot learning scenario, which can cause the prototype deviation problem. To address this problem, we propose context attention-based prototypical networks. We design context attention to highlight the crucial instances in the support set to generate a satisfactory prototype. Besides, we also explore the application of a recently popular pre-trained language model to few-shot relation classification tasks. The experimental results demonstrate that our model outperforms the state-of-the-art models and converges faster.
Privileged label enhancement with multi-label learning

Label distribution learning has attracted more and more attention in view of its more generalized ability to express the label ambiguity. However, it is much more expensive to obtain the label distribution information of the data rather than the logical labels. Thus, label enhancement is proposed to recover the label distributions from the logical labels. In this paper, we propose a novel label enhancement method by using privileged information. We first apply a multi-label learning model to implicitly capture the complex structural information between instances and generate the privileged information. Second, we adopt LUPI (learning with privileged information) paradigm to utilize the privileged information and employ RSVM+ as the prediction model. Finally, comparison experiments on 12 datasets demonstrate that our proposal can better fit the ground-truth label distributions.
Semi-Supervised StyleGAN for Disentanglement Learning

Disentanglement learning is crucial for obtaining disentangled representations and controllable generation. Current disentanglement methods face several inherent limitations: difficulty with high-resolution images, primarily on learning disentangled representations, and non-identifiability due to the unsupervised setting. To alleviate these limitations, we design new architectures and loss functions based on StyleGAN (Karras et al., 2019), for semi-supervised high-resolution disentanglement learning. We create two complex high-resolution synthetic datasets for systematic testing. We investigate the impact of limited supervision and find that using only 0.25%~2.5% of labeled data is sufficient for good disentanglement on both synthetic and real datasets. We propose new metrics to quantify generator controllability, and observe there may exist a crucial trade-off between disentangled representation learning and controllable generation. We also consider semantic fine-grained image editing to achieve better generalization to unseen images.
A Relational Memory-based Embedding Model for Triple Classification and Search Personalization

Knowledge graph embedding methods often suffer from a limitation of memorizing valid triples to predict new ones for triple classification and search personalization problems.To this end, we introduce a novel embedding model, named R-MeN, that explores a relational memory network to encode potential dependencies in relationship triples.R-MeN considers each triple as a sequence of 3 input vectors that recurrently interact with a memory using a transformer self-attention mechanism.Thus R-MeN encodes new information from interactions between the memory and each input vector to return a corresponding vector.Consequently, R-MeN feeds these 3 returned vectors to a convolutional neural network-based decoder to produce a scalar score for the triple.Experimental results show that our proposed R-MeN obtains state-of-theart results on SEARCH17 for the search personalization task, and on WN11 and FB13 for the triple classification task.
CREDIT CARD FRAUD DETECTION USING DATA ANALYTICS TECHNIQUES

In this paper, we show that the power function of sub E-function f n (x) is sub E-function.Furthermore, we establish some new integral inequalities of Hadamard type involving sub E-functions and concave E-functions.
Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information

Gender bias negatively impacts many natural language processing applications, including machine translation (MT).The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and genderbalanced data.For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system.We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5% accuracy).
A different cup of {TI}? The added value of commercial threat intelligence

Commercial threat intelligence is thought to provide unmatched coverage on attacker behavior, but it is out of reach for many organizations due to its hefty price tag. This paper presents the first empirical assessment of the services of commercial threat intelligence providers. For two leading vendors, we describe what these services consist of and compare their indicators with each other. There is almost no overlap between them, nor with four large open threat intelligence feeds. Even for 22 specific threat actors – which both vendors claim to track – we find an average overlap of only 2.5% to 4.0% between the indicator feeds. The small number of overlapping indicators show up in the feed of the other vendor with a delay of, on average, a month. These findings raise questions on the coverage and timeliness of paid threat intelligence. We also conducted 14 interviews with security professionals that use paid threat intelligence. We find that value in this market is understood differently than prior work on quality metrics has assumed. Poor coverage and small volume appear less of a problem to customers. They seem to be optimizing for the workflow of their scarce resource – analyst time – rather than for the detection of threats. Respondents evaluate TI mostly through informal processes and heuristics, rather than the quantitative metrics that research has proposed.
AirCapRL: Autonomous Aerial Human Motion Capture Using Deep Reinforcement Learning

In this letter, we introduce a deep reinforcement learning (DRL) based multi-robot formation controller for the task of autonomous aerial human motion capture (MoCap). We focus on vision-based MoCap, where the objective is to estimate the trajectory of body pose, and shape of a single moving person using multiple micro aerial vehicles. State-of-the-art solutions to this problem are based on classical control methods, which depend on hand-crafted system, and observation models. Such models are difficult to derive, and generalize across different systems. Moreover, the non-linearities, and non-convexities of these models lead to sub-optimal controls. In our work, we formulate this problem as a sequential decision making task to achieve the vision-based motion capture objectives, and solve it using a deep neural network-based RL method. We leverage proximal policy optimization (PPO) to train a stochastic decentralized control policy for formation control. The neural network is trained in a parallelized setup in synthetic environments. We performed extensive simulation experiments to validate our approach. Finally, real-robot experiments demonstrate that our policies generalize to real world conditions.
Students' Perception of Lecturers' Competency and the Effect on Institution Loyalty: The Mediating Role of Students' Satisfaction.

The study investigated the impact of lecturers’ competencies on student satisfaction and student loyalty involving a total of four exogenous variables, namely, knowledge and credential, pedagogy knowledge and skill, industrial experience, and motivation of the lecturers. The mediating effect of student satisfaction was tested. The target population of the study comprised a total of 1,055,245 active students enrolled in bachelor’s degree, master’s degree, and doctoral degree programmes in Malaysia. A total of 386 valid responses were obtained through a traditional questionnaire method in eight higher education institutions. The findings revealed that, knowledge and credential, industrial experience, and motivation of lecturers all have significant positive relationships with students’ satisfaction. On the other hand, only the motivation of lecturers was found to have positive effects on student loyalty towards the institution. Students’ satisfaction was found to mediate the relationships of knowledge and credential, industrial experience and motivation of lecturers toward student loyalty. The outcome of the study also accentuated the importance of maintaining and delivering a good service quality by the institution, achieved primarily through competent lecturers as this will lead to student loyalty and institutional sustainability. In return, students will have a better understanding of the subjects taught, and the institution will be likely to sustain and to have a positive brand awareness in the market.
Differentially-Private Multi-Party Sketching for Large-Scale Statistics

Abstract We consider a scenario where multiple organizations holding large amounts of sensitive data from their users wish to compute aggregate statistics on this data while protecting the privacy of individual users. To support large-scale analytics we investigate how this privacy can be provided for the case of sketching algorithms running in time sub-linear of the input size. We begin with the well-known LogLog sketch for computing the number of unique elements in a data stream. We show that this algorithm already achieves differential privacy (even without adding any noise) when computed using a private hash function by a trusted curator. Next, we show how to eliminate this requirement of a private hash function by injecting a small amount of noise, allowing us to instantiate an efficient LogLog protocol for the multi-party setting. To demonstrate the practicality of this approach, we run extensive experimentation on multiple data sets, including the publicly available IP address data set from University of Michigan’s scans of internet IPv4 space, to determine the trade-offs among efficiency, privacy and accuracy of our implementation for varying numbers of parties and input sizes. Finally, we generalize our approach for the LogLog sketch and obtain a general framework for constructing multi-party differentially private protocols for several other sketching algorithms.
Enhancing Software Comments Readability Using Flesch Reading Ease Score

Comments are used to explain the meaning of code and ease communications between programmers themselves, quality assurance auditors, and code reviewers. A tool has been developed to help programmers write readable comments and measure their readability level. It is used to enhance software readability by providing alternatives to both keywords and comment statements from a local database and an online dictionary. It is also a word-finding query engine for developers. Readability level is measured using three different formulas: the fog index, the Flesch reading ease score, and Flesch–Kincaid grade levels. A questionnaire has been distributed to 42 programmers and 35 students to compare the readability aspect between both new comments written by the tool and the original comments written by previous programmers and developers. Programmers stated that the comments from the proposed tool had fewer complex words and took less time to read and understand. Nevertheless, this did not significantly affect the understandability of the text, as programmers normally have quite a high level of English. However, the results from students show that the tool affects the understandability of text and the time taken to read it, while text complexity results show that the tool makes new comment text that is more readable by changing the three studied variables.
Tele-Education under the COVID-19 Crisis: Asymmetries in Romanian Education

The COVID-19 pandemic has deepened social and educational asymmetries in some developing countries, such as Romania. Tele-education failed to replace face-to-face education due to the lack of symmetrical policy, connectivity, infrastructure, digitalized educational materials and digital competences. Was this issue predictable and, hence, the stakeholders’ mission failed? Our qualitative research aims at analyzing, in depth, these digitalization asymmetries, with a sample formed of Information and Communication Technology (ICT) specialists working for/with Romanian 1–4 International Standard Classification of Education (ISCED) schools. The collected primary data were processed with Atlas.ti 8. The results emphasize major key areas to be addressed with future public symmetrical policy and change management strategies: equal access to infrastructure, as well as development of compulsory and complementary digital skills (for teachers and students). The necessity to support school management in accessing funding is also required to enhance digitalization.