Reviewerly/exHarmony huggingface dataset:
Data Annealing for Informal Language Understanding Tasks

There is a huge performance gap between formal and informal language understanding tasks.The recent pre-trained models that improved formal language understanding tasks did not achieve a comparable result on informal language.We propose data annealing transfer learning procedure to bridge the performance gap on informal natural language understanding tasks.It successfully utilizes a pretrained model such as BERT in informal language.In the data annealing procedure, the training set contains mainly formal text data at first; then, the proportion of the informal text data is gradually increased during the training process.Our data annealing procedure is model-independent and can be applied to various tasks.We validate its effectiveness in exhaustive experiments.When BERT is implemented with our learning procedure, it outperforms all the state-of-the-art models on the three common informal language tasks.
Adversarial Training for Code Retrieval with Question-Description Relevance Regularization

Code retrieval is a key task aiming to match natural and programming languages.In this work, we propose adversarial learning for code retrieval, that is regularized by questiondescription relevance.First, we adapt a simple adversarial learning technique to generate difficult code snippets given the input question, which can help the learning of code retrieval that faces bi-modal and data-scarce challenges.Second, we propose to leverage question-description relevance to regularize adversarial learning, such that a generated code snippet should contribute more to the code retrieval training loss, only if its paired natural language description is predicted to be less relevant to the user given question.Experiments on large-scale code retrieval datasets of two programming languages show that our adversarial learning method is able to improve the performance of state-of-the-art models.Moreover, using an additional duplicate question prediction model to regularize adversarial learning further improves the performance, and this is more effective than using the duplicated questions in strong multi-task learning baselines. 1
Adversarial robustness via robust low rank representations

Adversarial robustness measures the susceptibility of a classifier to imperceptible perturbations made to the inputs at test time. In this work we highlight the benefits of natural low rank representations that often exist for real data such as images, for training neural networks with certified robustness guarantees. Our first contribution is for certified robustness to perturbations measured in $\ell_2$ norm. We exploit low rank data representations to provide improved guarantees over state-of-the-art randomized smoothing-based approaches on standard benchmark datasets such as CIFAR-10 and CIFAR-100. Our second contribution is for the more challenging setting of certified robustness to perturbations measured in $\ell_\infty$ norm. We demonstrate empirically that natural low rank representations have inherent robustness properties, that can be leveraged to provide significantly better guarantees for certified robustness to $\ell_\infty$ perturbations in those representations. Our certificate of $\ell_\infty$ robustness relies on a natural quantity involving the $\infty \to 2$ matrix operator norm associated with the representation, to translate robustness guarantees from $\ell_2$ to $\ell_\infty$ perturbations. A key technical ingredient for our certification guarantees is a fast algorithm with provable guarantees based on the multiplicative weights update method to provide upper bounds on the above matrix norm. Our algorithmic guarantees improve upon the state of the art for this problem, and may be of independent interest.
Zero-Resource Knowledge-Grounded Dialogue Generation

While neural conversation models have shown great potentials towards generating informative and engaging responses via introducing external knowledge, learning such a model often requires knowledge-grounded dialogues that are difficult to obtain. To overcome the data challenge and reduce the cost of building a knowledge-grounded dialogue system, we explore the problem under a zero-resource setting by assuming no context-knowledge-response triples are needed for training. To this end, we propose representing the knowledge that bridges a context and a response and the way that the knowledge is expressed as latent variables, and devise a variational approach that can effectively estimate a generation model from a dialogue corpus and a knowledge corpus that are independent with each other. Evaluation results on three benchmarks of knowledge-grounded dialogue generation indicate that our model can achieve comparable performance with state-of-the-art methods that rely on knowledge-grounded dialogues for training, and exhibits a good generalization ability over different topics and different datasets.
The Significant Effect of Leadership and Conflict Management on Job Satisfaction

The purpose of this work is to present evidence which illustrate the significant effect of leadership and conflict management on employee satisfaction in a range of professions. Leadership and conflict management have a significant effect on job satisfaction. This is illustrated with examples of the interaction between these parameters in a range of professions. It is worth noting that a significant effect of leadership is exhibited in a range of different professional sectors and groups of employees, reflecting the universal value of leadership interacting with a wide range of parameters which may vary according to age, level of education, working conditions, cultural differences and employee expectations. Irrespective of the complexity and variety of determinant parameters, the effect of leadership and conflict management on job satisfaction has been widely exhibited in the reviewed data of the present analysis. Motivated and satisfied employees are a key in achieving organizational goals and leaders should take into account factors related to work performance, motivation and job satisfaction.
Competitive Equilibria Between Staking and On-chain Lending

Proof of Stake (PoS) is a burgeoning Sybil resistance mechanism that aims to have a digital asset ("token") serve as security collateral in crypto networks.However, PoS has so far eluded a comprehensive threat model that encompasses both Byzantine attacks from distributed systems and financial attacks that arise from the dual usage of the token as a means of payment and a Sybil resistance mechanism.In particular, the existence of derivatives markets makes malicious coordination among validators easier to execute than in Proof of Work systems.We demonstrate that it is also possible for on-chain lending smart contracts to cannibalize network security in PoS systems.When the yield provided by these contracts is more attractive than the inflation rate provided from staking, stakers will tend to remove their staked tokens and lend them out, thus reducing network security.In this paper, we provide a simple stochastic model that describes how rational validators with varying risk preferences react to changes in staking and lending returns.For a particular configuration of this model, we provide a formal proof of a phase transition between equilibria in which tokens are predominantly staked and those in which they are predominantly lent.We further validate this emergent adversarial behavior (e.g., reduced staked token supply) with agent-based simulations that sample transitions under more realistic conditions.Our results illustrate that rational, non-adversarial actors can dramatically reduce PoS network security if block rewards are not calibrated appropriately above the expected yields of on-chain lending.
TrustRec: An effective approach to exploit implicit trust and distrust relationships along with explicitones for accurate recommendations

Trust-aware recommendation approaches are widely used to mitigate the cold-start problem in recommender systems by utilizing trust networks. In this paper, we point out the problems of existing trust-aware recommendation approaches as follows: (P1) exploiting sparse explicit trust and distrust relationships; (P2) considering a misleading assumption that a user pair having a trust/distrust relationship certainly has a similar/dissimilar preference in practice; (P3) employing the transitivity of distrust relationships. Then, we propose TrustRec, a novel approach based on the matrix factorization that provides an effective solution to each of the aforementioned problems and incorporates all of them in a single matrix factorization model. Furthermore, TrustRec exploits only top-k most similar trustees and dissimilar distrustees of each user to improve both the computational cost and accuracy. The results of our extensive experiments demonstrate that TructRec outperforms existing approaches in terms of both effectiveness and efficiency.
Use of the Collaborative Wall to Improve the Teaching-Learning Conditions in the Bachelor of Visual Arts

This mixed research analyzes the use of the Collaborative Wall to improve the teaching-learning conditions in the Bachelor of Visual Arts considering data science and machine learning (linear regression). The sample is made up of 46 students who took the Geometric Representation Systems course at the National Autonomous University of Mexico (UNAM) during the 2019 school year. The Collaborative Wall is a web application that facilitates the organization and dissemination of ideas through the use of images and text. In the classroom, the students formed teams and used mobile devices to access this web application. The results of machine learning indicate that the organization of ideas in the Collaborative Wall positively influences the participation of students, motivation and learning process. Data science identifies 3 predictive models about the use of this web application in the educational field. Also, the Collaborative Wall facilitates the learning process in the classroom through the comparison and discussion of information. Finally, technological advances allow organizing creative activities that favor the active role of students.
An Investigation into the Role of Extracurricular Activities in Supporting and Enhancing Students' Academic Performance in Tertiary Foundation Programs in Oman

Sensing the pulse of globalization and the job market, the most recent trends in Oman’ s tertiary education are about teaching methods and education practices’ improvement. Recognizing the multidimensional and dynamic nature of the tertiary education learning environment, especially its foundation program cluster, and identified benefits of extra-curricular activities in having a positive impact on students, this paper reports on a study that examines the role of extracurricular activities as channels of support and foundation program students’ academic performance enhancement. In more detail, using the example of the extracurricular activities offered by the Centre for Preparatory Studies at Sultan Qaboos University, it focuses on the student-perceived factors related to the value of participation in the extracurricular activities and subsequent effects on the tertiary foundation program students’ development. The study employed a mixed study research design. The data was obtained from two instruments, namely student survey and student reflections which were then auto-analyzed using Google forms software. The findings revealed that extracurricular activities could provide a platform for new ways of learning, academic support, extra practice, variety and fun, and student skills’ improvement. Specifically, they showed some improvement on the foundation program students’ linguistic competences, critical thinking and time management skills. Also, involvement in structured extracurricular activities boosts students’ self-confidence and self-esteem, and mitigates their transitional challenges.
Image Defogging Algorithm Based on Sky Region Segmentation and Dark Channel Prior

Abstract Based on image segmentation and the dark channel prior, this paper proposes a fog removal algorithm in the HSI color space. Usually, the dark channel prior based defogging methods easily produce color distortion and halo effect when applied on images with a large sky area, because the sky region does not meet the prior assumption. For this reason, our method presents a new threshold sky region segmentation algorithm using the initial transmission map of the intensity component I. Based on the segmentation result, the initial transmission map is modified in turn, and finally refined by the guided filter. The saturation components S is reconstructed using the low frequencies of the V-transform to reduce noise, and stretched by multiplying a constant related to the initial transmission map. Experimental results show that the proposed algorithm has low time complexity and compelling fog removal result in both visual effect and quantitative measurement.
Principal Directions of Digital Transformation of Higher Education System in Sustainable Education

From the perspective of a systematic integrated approach, the article formulates the main directions of digital transformation of higher education system in sustainable education in all its components, taking into account the requirements of the modern digital economy as the leading trend in the country’s innovative development model. The authors consider essential content of the digital economy as a vector of innovative trends focused on the training of specialists of a qualitatively different level, and present the results of the formation of the digital economy in the Personnel and Education direction in 2025. One of the essential components of the modern educational process is the electronic information and educational environment, which is considered as a system that includes innovative technology platforms as an indispensable element for the generation and processing of knowledge. The article explores the prospects for improving the Russian scientific and educational system based on innovative methods of education using neural network technologies, the need for a transition to online education with integrated systems of natural and artificial intelligence. The paper identifies obstacles that significantly hamper the sustainable development of online education, one of which is the lack of teachers of the new formation who can work in the digital environment. It also presents an analysis of the results of a comprehensive study to assess the readiness of higher education to the parameters of the digital economy, showing that most universities are at the initial stage of the informatization and automation processes, which proves the relevance of the materials presented.
Representing Fitness Landscapes by Valued Constraints to Understand the Complexity of Local Search

Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fitness landscapes is poorly understood. Here we consider how fitness landscapes can be represented using valued constraints, and investigate what the structure of such representations reveals about the complexity of local search.&#x0D; First, we show that for fitness landscapes representable by binary Boolean valued constraints there is a minimal necessary constraint graph that can be easily computed. Second, we consider landscapes as equivalent if they allow the same (improving) local search moves; we show that a minimal constraint graph still exists, but is NP-hard to compute.&#x0D; We then develop several techniques to bound the length of any sequence of local search moves. We show that such a bound can be obtained from the numerical values of the constraints in the representation, and show how this bound may be tightened by considering equivalent representations. In the binary Boolean case, we prove that a degree 2 or treestructured constraint graph gives a quadratic bound on the number of improving moves made by any local search; hence, any landscape that can be represented by such a model will be tractable for any form of local search.&#x0D; Finally, we build two families of examples to show that the conditions in our tractability results are essential. With domain size three, even just a path of binary constraints can model a landscape with an exponentially long sequence of improving moves. With a treewidth-two constraint graph, even with a maximum degree of three, binary Boolean constraints can model a landscape with an exponentially long sequence of improving moves.
Publishing Node Strength Distribution With Node Differential Privacy

The challenge of graph data publishing under node-differential privacy mainly comes from the high sensitivity of the query.Compared with edge-differential privacy that can only protect the relationship between people, node-differential privacy can protect both the relationship between people and personal information.Therefore, Node-differential privacy must pay attention to the protection of personal information.This paper studies the release of node strength distribution under node-differential privacy by reducing sensitivity.We propose two algorithms to publish node strength distribution: Alternate-histogram(ALThistogram) and Density-histogram(DEN-histogram).Experimental results show that compared with the existing node strength histogram publishing algorithm, our proposed algorithm has advantages in L1-error and KS-distance, make the noise node strength distribution closer to that of the original graph.We also propose an introspective analysis to understand the influence of the projection algorithm on the node strength distribution, experiments to prove that the projection algorithm plays an important role in the node strength distribution.
An event based topic learning pipeline for neuroimaging literature mining

Abstract Neuroimaging text mining extracts knowledge from neuroimaging texts and has received widespread attention. Topic learning is an important research focus of neuroimaging text mining. However, current neuroimaging topic learning researches mainly used traditional probability topic models to extract topics from literature and cannot obtain high-quality neuroimaging topics. The existing topic learning methods also cannot meet the requirements of topic learning oriented to full-text neuroimaging literature. In this paper, three types of neuroimaging research topic events are defined to describe the process and result of neuroimaging researches. An event based topic learning pipeline, called neuroimaging Event-BTM, is proposed to realize topic learning from full-text neuroimaging literature. The experimental results on the PLoS One data set show that the accuracy and completeness of the proposed method are significantly better than the existing main topic learning methods.
RGBD-Net: Predicting Color and Depth Images for Novel Views Synthesis

We propose a new cascaded architecture for novel view synthesis, called RGBD-Net, which consists of two core components: a hierarchical depth regression network and a depth-aware generator network. The former one predicts depth maps of the target views by using adaptive depth scaling, while the latter one leverages the predicted depths and renders spatially and temporally consistent target images. In the experimental evaluation on standard datasets, RGBD-Net not only outperforms the state-of-the-art by a clear margin, but it also generalizes well to new scenes without per-scene optimization. Moreover, we show that RGBD-Net can be optionally trained without depth supervision while still retaining high-quality rendering. Thanks to the depth regression network, RGBD-Net can be also used for creating dense 3D point clouds that are more accurate than those produced by some state-of-the-art multi-view stereo methods.
Sentiment Analysis and Topic Modelling of 2018 Central Java Gubernatorial Election using Twitter Data

Twitter as a social media widely used by the public at this time. Public opinion divided into two sentiments, namely positive or negative sentiments. Public opinion on social media about a figure who runs in an election is not always in line with the actual results of the general election. This research aims to analyze sentiment and model the topic of public opinion in the 2018 Central Java Gubernatorial Election from social media twitter. So, the sentiment classification of opinions is done to predict which candidate pairs will win in this election, by looking at the most positive sentiments. The dataset used is a dataset of Indonesian tweets with the keywords ganjarpranowo and sudirmansaid for a classification model of 1600 tweets and implementation of a classification model of 1000 tweets. Naïve Bayes and SVM to develop the sentiment classification model. Latent Dirichlet Allocation (LDA) to identify patterns and find a topic from the relationship between Twitter sentiment data. The results of sentiment analysis show that SVM has the highest accuracy value than Naïve Bayes of 92.9%. The prediction results from the SVM classification model's implementation were won by the pair Ganjar Pranowo-Taj Yasin with 826 positive tweets and found two dominant topic that appeared on the positive and negative sentiments of each candidate.
Towards Transparent Human-in-the-Loop Classification of Fraudulent Web Shops

We propose an agent architecture for transparent human-in-the-loop classification. By combining dynamic argumentation with legal case-based reasoning, we create an agent that is able to explain its decisions at various levels of detail and adapts to new situations. It keeps the human analyst in the loop by presenting suggestions for corrections that may change the factors on which the current decision is based and by enabling the analyst to add new factors. We are currently implementing the agent for classification of fraudulent web shops at the Dutch Police.
Three waves of hate speech spreading faster than the pandemic in Malaysia: An analyses of outgroup populist narratives and hate speech during the COVID-19

Societal responses in disquietude circumstances during the corona virus (COVID-19) has been diverse, choosing to express themselves by engaging in online hate speech depending on the locality and pre-existing state of affairs of individual nation states. This exercise argues that there were three waves of hate speech in Malaysia during the COVID-19 pandemic. The first was a nationalistic form narrative targeting the mainland Chinese. The second one was in the form of defending the ummah because the Tabligh cluster was a large gathering which caused a significant number of infections during the initial stages of the pandemic. The last one was against the global ummah , defending the sovereign state and other Malaysians from being infected by foreign and migrant workers, especially the Rohingya refugees. We argue that they are all not in contradictory of each other, but in actual fact, glued by populist narratives of local leaders. The relativist approach, using the qualitative content analysis method was adopted to better understand the underlying factors which led to these manifestations of hate captured online and sometimes its spillover effects expressed in real life. This study concludes that there is an abstract corelation between populist exclusionary statements by political leaders and the responding hate speech sentiments being shared online during the pandemic. Keywords: Malaysian politics, political communication, political science, refugees