Reviewerly/exHarmony huggingface dataset:
Perspective on the Double Edges of Virtual Reality in Medicine - both Addiction &amp; Treatment

Although the therapeutic potential of virtual reality has been foreseen since over half a century ago, the lack of graphical processing power made it impossible to apply in medical therapeutic sciences until last decade; nowadays, the hardware required for virtual reality is even 100 times more affordable. A head-mounted display induces immersivity engulfing the subject’s eyesight perception in a stereoscopic manner. The same tool that may aid better self understanding and bonding can also trigger psychopathological mechanisms through which the user becomes alienated from the real world. As virtual reality became even more popular during SARS-COV2 pandemic, users worldwide have spent more time into a virtual world. Depersonalization/derealization syndrome can occur if virtual reality is abused. The greater the person’s involvement in virtual reality, the greater the chance of a lack of bodily self (depersonalization). Controllers that mimic hands could prevent the subject from acknowledging the real world as true – derealization. Virtual reality’s dissociative potential is related to individual psychological traits and prolonged exposure. Children are the most prone to develop behavioral changes. Adults may develop behavioral problems related to virtual reality gaming, gambling, pornography and also social networking through created avatars. Blue light wavelength could harm sleep architecture and circadian rhythm by disrupting melatonin, therefore making virtual reality exposure problematic after sunset. State of the art reveals that using virtual reality in a therapeutic manner, actually facilitates the fight against addictions with cue therapy intended to extinguish conditioned response. The exposure to a substance (nicotine, alcohol, or any other psychoactive recreational abuse potential substances) can trigger craving in a controlled environment, that is malleable in the hands of the therapist. Virtual reality can offer an exposure perspective that is both vivid enough to be a challenge, but also safe enough to ensure patient involvement and to amplify the therapeutic alliance.
Researchers' Perceptions of Research Data Management Activities at an Academic Library in a Developing Country

This study investigated researchers perceptions of RDM at the MGGL to recommend measures to enhance managing, sharing, and reusing data. The study was underpinned by the DCC Curation lifecycle model and the CCMF which enabled the investigator to employ a descriptive research design to purposively capture data from a broad cross-section of 330 researchers. The data was analysed using SPSS to generate descriptive and inferential statistics that enabled the Investigator to answer the problem.Study findings demonstrate no explicit policies to guide each stage of data curation and capabilities. There were also inadequacies in the knowledge and skills capability, technological infrastructure and collaborative partnerships. Overall, RDM faced challenges in all the examined capabilities. These challenges limited managing, sharing, and reusing data.The study recommends establishing a CMU within the MGGL to oversee the implementation of RDM activities by assembling all the needed capabilities to support data curation activities and enable efficient managing, sharing and reusing research data
TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning

We present our contribution to the IWPT 2021 shared task on parsing into enhanced Universal Dependencies.Our main system component is a hybrid tree-graph parser that integrates (a) predictions of spanning trees for the enhanced graphs with (b) additional graph edges not present in the spanning trees.We also adopt a finetuning strategy where we first train a language-generic parser on the concatenation of data from all available languages, and then, in a second step, finetune on each individual language separately.Additionally, we develop our own complete set of pre-processing modules relevant to the shared task, including tokenization, sentence segmentation, and multiword token expansion, based on pre-trained XLM-R models and our own pre-training of character-level language models.Our submission reaches a macro-average ELAS of 89.24 on the test set.It ranks top among all teams, with a margin of more than 2 absolute ELAS over the next best-performing submission, and best score on 16 out of 17 languages.
Synthetic Experiences for Accelerating DQN Performance in Discrete Non-Deterministic Environments

State-of-the-art Deep Reinforcement Learning Algorithms such as DQN and DDPG use the concept of a replay buffer called Experience Replay. The default usage contains only the experiences that have been gathered over the runtime. We propose a method called Interpolated Experience Replay that uses stored (real) transitions to create synthetic ones to assist the learner. In this first approach to this field, we limit ourselves to discrete and non-deterministic environments and use a simple equally weighted average of the reward in combination with observed follow-up states. We could demonstrate a significantly improved overall mean average in comparison to a DQN network with vanilla Experience Replay on the discrete and non-deterministic FrozenLake8x8-v0 environment.
OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction

We propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs).The model receives as input a sentence with a target word or MWE and outputs its complexity score.Given that a key challenge with this task is the limited size of annotated data, our model relies on pretrained contextual representations from different state-of-the-art transformer-based language models (i.e., BERT and RoBERTa), and on a variety of training methods for further enhancing model generalization and robustness: multi-step fine-tuning and multi-task learning, and adversarial training.Additionally, we propose to enrich contextual representations by adding hand-crafted features during training.Our model achieved competitive results and ranked among the top-10 systems in both subtasks.
Spurious Correlations in Cross-Topic Argument Mining

Recent work in cross-topic argument mining attempts to learn models that generalise across topics rather than merely relying on withintopic spurious correlations.We examine the effectiveness of this approach by analysing the output of single-task and multi-task models for cross-topic argument mining through a combination of linear approximations of their decision boundaries, manual feature grouping, challenge examples, and ablations across the input vocabulary.Surprisingly, we show that cross-topic models still rely mostly on spurious correlations and only generalise within closely related topics, e.g., a model trained only on closed-class words and a few common open-class words outperforms a state-of-theart cross-topic model on distant target topics.
Promoting Information-Resource Sharing within the Enterprise: A Perspective of Blockchain Consensus Perception

The prevalence of information technology (IT) and information systems (IS) provides opportunities for enterprises to sustain open innovation. However, information silos that appeared with IS use have made IS inconvenient and thus impeded enterprises’ open innovation. The present study, therefore, aims to resolve this issue by helping understand how to encourage information-resource sharing within the enterprise. We first proposed a new concept—consensus perception—based on the blockchain characteristics and advantages derived from prior studies, and then developed a conceptual model based on the consensus perception and principal–agent theory. Second, we used this conceptual model to investigate whether blockchain technology (BT) can be used to promote information-resource sharing. The results showed that information security concern, perceived rewards, and openness have direct influences on information-resource sharing intention and that trust has indirect effects. The findings provide useful theoretical and practical contributions to sustain enterprises’ open innovation by adopting BT to solve the information-resource sharing issues.
Homogeneous Dirichlet wavelets on the interval diagonalizing the derivative operator, and application to free-slip divergence-free wavelets

This paper presents a new construction of a homogeneous Dirichlet wavelet basis on the unit interval, linked by a diagonal differentiation-integration relation to a standard biorthogonal wavelet basis. This allows to compute the solution of Poisson equation by renormalizing the wavelet coefficients - as in Fourier domain but using locally supported basis functions with boundary conditions-, which yields a linear complexity O(N) for this problem. Another application concerns the construction of free-slip divergence-free wavelet bases of the hypercube, in general dimension, with an associated decomposition algorithm as simple as in the periodic case.
Development of an ontology-based semantic building post-occupancy evaluation framework

Post-occupancy evaluation (POE) is a systematic method to evaluate the actual building performance against the theoretical design intents after the building has been occupied for some time, to understand how the building is performing and to capture lessons learned. The POE offers an opportunity to investigate the buildings' actual performance based upon the occupants' satisfaction levels in the aspects of building overall design, indoor environmental quality, thermal comfort, etc. However, as the key part of POE, occupant satisfaction assessment (OSA) is a missing link in the building performance evaluation (BPE) domain, and there is not a systematic evaluation method for the OSA. Moreover, it is time-consuming and error-prone to conduct the OSA manually. This paper presents from the end-user's satisfaction perspective a semantic post-occupancy evaluation ontology (POEontology) to facilitate the occupant satisfaction assessment of buildings, with the ultimate aim of optimizing building operation guidelines, and improving occupants' use experience quality and well-being. An ontology-based knowledge model has been developed to capture the fragmented knowledge of building use satisfaction assessment in the POE domain, with the benchmarking evaluation rules encoded in Semantic Web Rule Language (SWRL) to enable automatic rule-based rating and reasoning. This ontology model also enables the effective OSA-related knowledge retrieving and sharing, and promotes its implementation in the POE domain. A field study has been conducted based upon the Building Use Study (BUS) methodology to validate the proposed ontology framework.
A Software Engineering Perspective on Building Production-Ready Machine Learning Systems

This chapter discusses how to build production-ready machine learning systems. There are several challenges involved in accomplishing this, each with its specific solutions regarding practices and tool support. The chapter presents those solutions and introduces MLOps (machine learning operations, also called machine learning engineering) as an overarching and integrated approach in which data engineers, data scientists, software engineers, and operations engineers integrate their activities to implement validated machine learning applications managed from initial idea to daily operation in a production environment. This approach combines agile software engineering processes with the machine learning-specific workflow. Following the principles of MLOps is paramount in building high-quality production-ready machine learning systems. The current state of MLOps is discussed in terms of best practices and tool support. The chapter ends by describing future developments that are bound to improve and extend the tool support for implementing an MLOps approach.
LU-BZU at SemEval-2021 Task 2: Word2Vec and Lemma2Vec performance in Arabic Word-in-Context disambiguation

This paper presents a set of experiments to evaluate and compare between the performance of using CBOW Word2Vec and Lemma2Vec models for Arabic Word-in-Context (WiC) disambiguation without using sense inventories or sense embeddings.As part of the SemEval-2021 Shared Task 2 on WiC disambiguation, we used the dev.ar-ar dataset (2k sentence pairs) to decide whether two words in a given sentence pair carry the same meaning.We used two Word2Vec models: Wiki-CBOW, a pre-trained model on Arabic Wikipedia, and another model we trained on large Arabic corpora of about 3 billion tokens.Two Lemma2Vec models was also constructed based on the two Word2Vec models.Each of the four models was then used in the WiC disambiguation task, and then evaluated on the SemEval-2021 test.ar-ardataset.At the end, we reported the performance of different models and compared between using lemma-based and word-based models.
The Effects of Authentic Learning Approach on Academic Achievement and Attitudes in Social Studies Course

In this study, the effects of the authentic learning approach in social studies on academic achievement, retention of knowledge, and attitude towards the course were investigated. In the study, a quasi-experimental pre-test-post-test design with a non-equivalent control group was used. The study group consisted of 50 6th grade students. In the process, the authentic learning approach was used in the experimental and control group, the texts and instructions given in the course book were followed. The study data were collected by the “Social Studies Course Academic Achievement Test” and “Social Studies Course Attitude Scale”. As a result of the experimental process, it was obtained that the academic achievement levels of the students in the experimental and control groups in the social studies course increased and differed significantly compared to the pre-experimental process. It was observed that the measurement results in the follow-up studies conducted five weeks after the measurement after the application did not differ significantly from the post-test scores of the students in the control and experimental groups. The difference between the achievement test scores of both groups was significant, favouring the students in the experimental group. The changes in the attitudes of the students in the control group towards the social studies course were not found to be significant when compared to the pre-experimental process, while the changes in the attitudes of the students in the experimental group were significant. While there was no significant difference between the pre-test attitude scores of the students in the experimental and control groups, a significant difference was found between the post-test attitude scores favouring the experimental group. In this respect, the authentic learning approach has created an important alternative to methods based on knowledge transfer. In addition, it was observed that the authentic learning approach positively changed students’ attitudes towards the course.
Traffic Sign Recognition Based on ZYNQ

In the traffic field, the traffic sign detection and recognition system is an important part of ADAS. In this paper, a research on traffic sign recognition is carried out, and a traffic recognition sign recognition network is established with the GTSRB data set as the research object. A network computing accelerator is designed with "ARM+FPGA" framework; the functional modules of each part of the network are reasonably divided; and high-level synthesis tools are used to optimize the design of loop unwinding and loop flow to improve throughput and improve recognition speed. The experimental results show that the network has a high accuracy rate in each classification. After optimization, the processing speed of the system under the 100MHz clock is increased by 5.59 times. This method provides an effective reference for the identification of other specific signs.
ALO: AI for Least Observed People

In recent years, visual assistants of humans are taking place in the consumer market–the eye-line of humans equipped with a see-through optical display. Computer Vision Technology may play a vital role in visually challenged people to carry out their daily activities without much dependency on others. In this paper, we introduce ALO (AI for Least Observed) as an assistive glass for blind people. It can listen as a companion, read from the internet on the fly, detect surrounding objects and obstacles for freedom of movement, and recognize the faces he is communicating with. This glass can be a virtual companion of the users for social safety from unknown people, reduce the dependency of others. This system uses the camera for identifying human faces using MTCNN deep learning technique, bone conduction microphone, and google API (Application Programming Interface) for translating voice to text and text to bone conduction sound. A Market Valuable Product (MVP) has already been developed depending on our survey of over 300 visually impaired persons in Europe and Asia.
Review of Machine Learning Techniques for Network Traffic Classification

Network traffic classification is an essential tool in cyber security for the recognition and interception of cyber insider threats. Traffic classification is the first step to distinguish various applications and protocols that are available in the network. The core component of network intrusion detection is a network traffic analysis that investigates the network behavior based on traffic characterization. Network traffic classification is the centre segment of the network traffic analysis and particularly for filtering traffic in order to identify any malicious activities within the network. Numerous techniques are proposed by researchers to date varying from traditional Port-based to Machine/Deep Learning techniques. Machine Learning emerges as a prominent solution for encrypted and real-time traffic classification; exploiting the statistical properties of the network flow. In this paper various Machine/Deep Learning techniques for network traffic classification are critically evaluated. The main purpose is to investigate different traffic classification approaches (supervised, semi-supervised, unsupervised) and provide summarization for a set of trends followed by various researchers for classifying network traffic.
Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation

Content moderation is often performed by a collaboration between humans and machine learning models.However, it is not well understood how to design the collaborative process so as to maximize the combined moderator-model system performance.This work presents a rigorous study of this problem, focusing on an approach that incorporates model uncertainty into the collaborative process.First, we introduce principled metrics to describe the performance of the collaborative system under capacity constraints on the human moderator, quantifying how efficiently the combined system utilizes human decisions.Using these metrics, we conduct a large benchmark study evaluating the performance of state-of-the-art uncertainty models under different collaborative review strategies.We find that an uncertainty-based strategy consistently outperforms the widely used strategy based on toxicity scores, and moreover that the choice of review strategy drastically changes the overall system performance.Our results demonstrate the importance of rigorous metrics for understanding and developing effective moderator-model systems for content moderation, as well as the utility of uncertainty estimation in this domain. 1
A Novel Behavioral Strategy for RoboCode Platform Based on Deep Q-Learning

This paper addresses a new machine learning-based behavioral strategy using the deep Q-learning algorithm for the RoboCode simulation platform. According to this strategy, a new model is proposed for the RoboCode platform, providing an environment for simulated robots that can be programmed to battle against other robots. Compared to Atari Games, RoboCode has a fairly wide set of actions and situations. Due to the challenges of training a CNN model for such a continuous action space problem, the inputs obtained from the simulation environment were generated dynamically, and the proposed model was trained by using these inputs. The trained model battled against the predefined rival robots of the environment (standard robots) by cumulatively benefiting from the experience of these robots. The comparison between the proposed model and standard robots of RoboCode Platform was statistically verified. Finally, the performance of the proposed model was compared with machine learning based-customized robots (community robots). Experimental results reveal that the proposed model is mostly superior to community robots. Therefore, the deep Q-learning-based model has proven to be successful in such a complex simulation environment. It should also be noted that this new model facilitates simulation performance in adaptive and partially cluttered environments.
UIT-E10dot3 at SemEval-2021 Task 5: Toxic Spans Detection with Named Entity Recognition and Question-Answering Approaches

The increment of toxic comments on online space is causing tremendous effects on other vulnerable users.For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task 5: Toxic Spans Detection is one of those.This task asks competitors to extract spans that have toxicity from the given texts, and we have done several analyses to understand its structure before doing experiments.We solve this task by two approaches, Named Entity Recognition with spaCy's library and Question-Answering with RoBERTa combining with ToxicBERT, and the former gains the highest F1-score of 66.99%.
Improving Counterfactual Generation for Fair Hate Speech Detection

Bias mitigation approaches reduce models' dependence on sensitive features of data, such as social group tokens (SGTs), resulting in equal predictions across the sensitive features.In hate speech detection, however, equalizing model predictions may ignore important differences among targeted social groups, as hate speech can contain stereotypical language specific to each SGT.Here, to take the specific language about each SGT into account, we rely on counterfactual fairness and equalize predictions among counterfactuals, generated by changing the SGTs.Our method evaluates the similarity in sentence likelihoods (via pretrained language models) among counterfactuals, to treat SGTs equally only within interchangeable contexts.By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.