Reviewerly/exHarmony huggingface dataset:
Matching-oriented Embedding Quantization For Ad-hoc Retrieval

Product quantization (PQ) is a widely used technique for ad-hoc retrieval.Recent studies propose supervised PQ, where the embedding and quantization models can be jointly trained with supervised learning.However, there is a lack of appropriate formulation of the joint training objective; thus, the improvements over previous non-supervised baselines are limited in reality.In this work, we propose the Matching-oriented Product Quantization (MoPQ), where a novel objective Multinoulli Contrastive Loss (MCL) is formulated.With the minimization of MCL, we are able to maximize the matching probability of query and ground-truth key, which contributes to the optimal retrieval accuracy.Given that the exact computation of MCL is intractable due to the demand of vast contrastive samples, we further propose the Differentiable Cross-device Sampling (DCS), which significantly augments the contrastive samples for precise approximation of MCL.We conduct extensive experimental studies on four realworld datasets, whose results verify the effectiveness of MoPQ.The code is available at https://github.com/microsoft/MoPQ. †.Work is done during the internship at Microsoft.
Countering the Influence of Essay Length in Neural Essay Scoring

Previous work has shown that automated essay scoring systems, in particular machine learning-based systems, are not capable of assessing the quality of essays, but are relying on essay length, a factor irrelevant to writing proficiency.In this work, we first show that state-of-the-art systems, recent neural essay scoring systems, might be also influenced by the correlation between essay length and scores in a standard dataset.In our evaluation, a very simple neural model shows the stateof-the-art performance on the standard dataset.To consider essay content without taking essay length into account, we introduce a simple neural model assessing the similarity of content between an input essay and essays assigned different scores.This neural model achieves performance comparable to the state of the art on a standard dataset as well as on a second dataset.Our findings suggest that neural essay scoring systems should consider the characteristics of datasets to focus on text quality.
Neural circuits and symbolic processing

The ability to use symbols is a defining feature of human intelligence. However, neuroscience has yet to explain the fundamental neural circuit mechanisms for flexibly representing and manipulating abstract concepts. This article will review the research on neural models for symbolic processing. The review first focuses on the question of how symbols could possibly be represented in neural circuits. The review then addresses how neural symbolic representations could be flexibly combined to meet a wide range of reasoning demands. Finally, the review assesses the research on program synthesis and proposes that the most flexible neural representation of symbolic processing would involve the capacity to rapidly synthesize neural operations analogous to lambda calculus to solve complex cognitive tasks.
Gamification Model Framework and its Use in E-Learning in Higher Education

The manuscript that we examined contains the introduction of gamification into e-learning lecture activities in universities. A literature study will explain conceptual differences between the techniques and methods of mechanics and game dynamics used by students. Gamification will be combined into e-learning at a university, and this has various benefits in the learning process, such as higher motivation, more fun learning, and active learning. This Paper shows the importance of gamification in learning, including in higher education. The manuscript that we created presents a different perspective on the concept of gamification in the University. The innovation in this paper describes incorporating characteristics of gamification and e-learning that can demonstrate the practical use of gamification in e-learning. The method used is a literature study. Further research expected that this framework can be applied in various universities.
Gradual Federated Learning With Simulated Annealing

Federated averaging (FedAvg) is a popular federated learning (FL) technique that updates the global model by averaging local models and then transmits the updated global model to devices for their local model update. One main limitation of FedAvg is that the average-based global model is not necessarily better than local models in the early stage of the training process so that FedAvg might diverge in realistic scenarios, especially when the data is non-identically distributed across devices and the number of data samples varies significantly from device to device. In this paper, we propose a new FL technique based on simulated annealing. The key idea of the proposed technique, henceforth referred to as \textit{simulated annealing-based FL} (SAFL), is to allow a device to choose its local model when the global model is immature. Specifically, by exploiting the simulated annealing strategy, we make each device choose its local model with high probability in early iterations when the global model is immature. From extensive numerical experiments using various benchmark datasets, we demonstrate that SAFL outperforms the conventional FedAvg technique in terms of the convergence speed and the classification accuracy.
Efficient techniques for fault detection and location of multiple controlled Toffoli-based reversible circuit

Abstract It is very important to detect and correct faults for ensuring the validity and reliability of these circuits. In this regard, a comparative study with related existing techniques is undertaken. Two techniques to achieve the testability of reversible circuits are introduced that have been improved in terms of quantum cost and fault coverage rate. Considering this aspect, the main focus of these techniques is on the efficient detection and location of faults with 100% accuracy. These techniques for fault detection in reversible circuit design, in addition to being able to produce the correct outputs, can also provide information for fault location that has already been done at a higher cost. Proposed approaches have been successfully tested for all types of SMGF, MMGF, PMGF, RGF, and SBF. In order to verify the functional correctness of the proposed scheme, it also has executed the testing over a reversible full adder circuit, and findings are checked. In the following, the proposed approach of reversible sequential circuits is presented for the first time so far. The cost metrics are evaluated for all the proposed designs and compared the estimated results against some existing design approaches of reversible circuits for better understanding.
The use of educational computer programs in the training of foreign language teachers for speech activity

The present study looks at the use of educational computer programs in professional training of students majoring in Philology. It highlights the urgency for new approaches to improve the educational process in response to the requirements of present realities. This study draws upon extensive corpus research on the use of innovative technologies in education. The findings reveal that there are certain differences in the application of this technology in teaching abroad and in the Ukrainian pedagogical practices. It was established that implementation of educational computer programs, in contrast with traditional teaching methods, has potential benefits as well as certain limitations. This research substantiates the importance of considering these factors when aiming to improve professional skills of future teachers and their linguistic competences. The conclusions drawn from the research findings allow providing recommendations concerning the improvement of professional training of foreign language teachers by means of educational computer programs.
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs

Negation is one of the most fundamental concepts in human cognition and language, and several natural language inference (NLI) probes have been designed to investigate pretrained language models' ability to detect and reason with negation.However, the existing probing datasets are limited to English only, and do not enable controlled probing of performance in the absence or presence of negation.In response, we present a multilingual (English, Bulgarian, German, French and Chinese) benchmark collection of NLI examples that are grammatical and correctly labeled, as a result of manual inspection and editing.We use the benchmark to probe the negation-awareness of multilingual language models and find that models that correctly predict examples with negation cues often fail to correctly predict their counter-examples without negation cues, even when the cues are irrelevant for semantic inference.
Revisiting ResNets: Improved Training and Scaling Strategies

Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scaling strategies. Our work revisits the canonical ResNet (He et al., 2015) and studies these three aspects in an effort to disentangle them. Perhaps surprisingly, we find that training and scaling strategies may matter more than architectural changes, and further, that the resulting ResNets match recent state-of-the-art models. We show that the best performing scaling strategy depends on the training regime and offer two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended (Tan & Le, 2019). Using improved training and scaling strategies, we design a family of ResNet architectures, ResNet-RS, which are 1.7x - 2.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. In a large-scale semi-supervised learning setup, ResNet-RS achieves 86.2% top-1 ImageNet accuracy, while being 4.7x faster than EfficientNet NoisyStudent. The training techniques improve transfer performance on a suite of downstream tasks (rivaling state-of-the-art self-supervised algorithms) and extend to video classification on Kinetics-400. We recommend practitioners use these simple revised ResNets as baselines for future research.
Robustness and static-positional accuracy of the SteamVR 1.0 virtual reality tracking system

Abstract The use of low-cost immersive virtual reality systems is rapidly expanding. Several studies started to analyse the accuracy of virtual reality tracking systems, but they did not consider in depth the effects of external interferences in the working area. In line with that, this study aimed at exploring the static-positional accuracy and the robustness to occlusions inside the capture volume of the SteamVR (1.0) tracking system. To do so, we ran 3 different tests in which we acquired the position of HTC Vive PRO Trackers (2018 version) on specific points of a grid drawn on the floor, in regular tracking conditions and with partial and total occlusions. The tracking system showed a high inter- and intra-rater reliability and detected a tilted surface with respect to the floor plane. Every acquisition was characterised by an initial random offset. We estimated an average accuracy of 0.5 ± 0.2 cm across the entire grid (XY-plane), noticing that the central points were more accurate (0.4 ± 0.1 cm) than the outer ones (0.6 ± 0.1 cm). For the Z-axis, the measurements showed greater variability and the accuracy was equal to 1.7 ± 1.2 cm. Occlusion response was tested using nonparametric Bland–Altman statistics, which highlighted the robustness of the tracking system. In conclusion, our results promote the SteamVR system for static measures in the clinical field. The computed error can be considered clinically irrelevant for exercises aimed at the rehabilitation of functional movements, whose several motor outcomes are generally measured on the scale of metres.
EMScore: Evaluating Video Captioning via Coarse-Grained and Fine-Grained Embedding Matching

Current metrics for video captioning are mostly based on the text-level comparison between reference and candidate captions. However, they have some insuperable drawbacks, e.g., they cannot handle videos without references, and they may result in biased evaluation due to the one-to-many nature of video-to-text and the neglect of visual relevance. From the human evaluator's viewpoint, a high-quality caption should be consistent with the provided video, but not necessarily be similar to the reference in literal or semantics. Inspired by human evaluation, we propose EMScore (Embedding Matching-based score), a novel reference-free metric for video captioning, which directly measures similarity between video and candidate captions. Benefit from the recent development of large-scale pre-training models, we exploit a well pre-trained vision-language model to extract visual and linguistic embeddings for computing EMScore. Specifically, EMScore combines matching scores of both coarse-grained (video and caption) and fine-grained (frames and words) levels, which takes the overall understanding and detailed characteristics of the video into account. Furthermore, considering the potential information gain, EMScore can be flexibly extended to the conditions where human-labeled references are available. Last but not least, we collect VATEX-EVAL and ActivityNet-FOIl datasets to systematically evaluate the existing metrics. VATEX-EVAL experiments demonstrate that EMScore has higher human correlation and lower reference dependency. ActivityNet-FOIL experiment verifies that EMScore can effectively identify "hallucinating" captions. The datasets will be released to facilitate the development of video captioning metrics. The code is available at: https://github.com/ShiYaya/emscore.
Dynamic stochastic resonance and image fusion based model for quality enhancement of dark and hazy images

Haze/fog is a common weather phenomenon that may exist in both day and night conditions and presents loss of contrast in captured images. For an imaging device to capture good quality photographs, it is necessary that the captured scene be well-illuminated. But sometimes due to bad illumination, which may be due to natural or manmade conditions, the captured images present degradation. To solve the ill effects of bad illumination, we propose an image enhancement algorithm that sufficiently deals with both hazy and dark images. By combining dynamic stochastic resonance and a fusion method based on illumination estimation, a local contrast enhancement of the image as seen in the fused image is achieved in the first stage. While images that are only dark are sufficiently illuminated, the effect of haze in day/night hazy images remains unchanged. In the second stage, we solve this issue by modifying the atmospheric degradation model to reduce the effect of haze. The proposed method does not rely on the estimation of atmospheric light; rather, a mean preserving algorithm to restore the mean brightness of the image has been applied. The qualitative and quantitative experiments validate the efficacy and robustness of the proposed method.
Scheduled Sampling Based on Decoding Steps for Neural Machine Translation

Scheduled sampling is widely used to mitigate the exposure bias problem for neural machine translation.Its core motivation is to simulate the inference scene during training by replacing ground-truth tokens with predicted tokens, thus bridging the gap between training and inference.However, vanilla scheduled sampling is merely based on training steps and equally treats all decoding steps.Namely, it simulates an inference scene with uniform error rates, which disobeys the real inference scene, where larger decoding steps usually have higher error rates due to error accumulations.To alleviate the above discrepancy, we propose scheduled sampling methods based on decoding steps, increasing the selection chance of predicted tokens with the growth of decoding steps.Consequently, we can more realistically simulate the inference scene during training, thus better bridging the gap between training and inference.Moreover, we investigate scheduled sampling based on both training steps and decoding steps for further improvements.Experimentally, our approaches significantly outperform the Transformer baseline and vanilla scheduled sampling on three large-scale WMT tasks.Additionally, our approaches also generalize well to the text summarization task on two popular benchmarks.
Design Guidelines for Immersive Dashboards

Immersive dashboards facilitate the presentation of large-scale complex data while offering natural interaction and real-time interactivity supporting immediate use. Few studies, however, have investigated the use of immersive dashboards to improve the visualization and analysis of abstract data. This study provides a systematic review of the literature on designing immersive dashboards for abstract data visualization and analytics. Twenty publications met the inclusion criteria. This review identifies guidelines for improving immersive dashboard design.
Linguistic Patterns for Code Word Resilient Hate Speech Identification

The permanent transition to online activity has brought with it a surge in hate speech discourse. This has prompted increased calls for automatic detection methods, most of which currently rely on a dictionary of hate speech words, and supervised classification. This approach often falls short when dealing with newer words and phrases produced by online extremist communities. These code words are used with the aim of evading automatic detection by systems. Code words are frequently used and have benign meanings in regular discourse, for instance, “skypes, googles, bing, yahoos” are all examples of words that have a hidden hate speech meaning. Such overlap presents a challenge to the traditional keyword approach of collecting data that is specific to hate speech. In this work, we first introduced a word embedding model that learns the hidden hate speech meaning of words. With this insight on code words, we developed a classifier that leverages linguistic patterns to reduce the impact of individual words. The proposed method was evaluated across three different datasets to test its generalizability. The empirical results show that the linguistic patterns approach outperforms the baselines and enables further analysis on hate speech expressions.
Predicting individual differences to cyber attacks: Knowledge, arousal, emotional and trust responses

Cyber attacks are increasingly commonplace and cause significant disruption, and therefore, have been a focus of much research. The objective of this research was to understand the factors that might lead users to fail to recognize red flags and succumb to cyber events. We investigated users’ knowledge of cyber attacks, their propensity to trust technology, arousal, emotional valence, and situational trust in response to different types and severity of cyber attacks. Our findings suggest that high-risk attacks elicited more arousal and more negative emotional valence than low-risk attacks. The attack-type manipulation revealed that phishing scenarios yielded distinctive patterns, including weaker affective responses than ransomware and other malware. The authors further examined arousal, emotional valence, and situational trust patterns among the subset of high- knowledge participants who successfully identified all the attacks and compared these responses with those of less knowledgeable peers. Our findings suggest that the more knowledgeable the user, the higher was their general propensity to trust technology, the more sensitive were their emotional responses to the manipulation of risk, and the lower their situational trust when faced with cyber attack scenarios.
A Review of Signature Recognition Using Machine Learning

Signatures have been used for years for transactions and consenting to responsibilities. Yet, online or offline, signatures can easily be falsified as there are no security measures in place to prevent this. Numerous researches have been carried out to find the most accurate and reliable signature recognition and verification system. This study examines the two problems previously mentioned. A primary goal of this study is to determine the best algorithms for recognizing signatures based on the signature type. This systematic literature review is conducted using a PRISMA flow diagram. The results indicate that offline signatures mostly use Convolutional Neural Networks (CNN) for their recognition, while online signatures use Recurrent Neural Networks (RNN) with other architectures.
Smart Payment Contract Mechanism Based on Blockchain Smart Contract Mechanism

In recent years, blockchain technology has become a hot topic in various industries. With the development and maturity of blockchain technology, it has been applied to finance, law, etc., with its advantages of decentralization, openness, information security, and concealment. The application scenarios of industry are becoming more and more abundant. Compared with the traditional TPA payment contract form, the smart contract mechanism based on blockchain technology is obviously more efficient, convenient, and safe. Against this background, we design a smart payment contract suitable for cloud storage by studying Ethereum. The relationship clause in the smart payment contract should be regulated around the contract law. The smart contract payment linkage clause can be classified into three forms, including conditional effective type, contract joint type, and contract link type, which correspond to the contract law. Therefore, the contract legal system for smart contract payment linkage clauses should follow typified thinking. Based on blockchain technology, smart contracts not only reduce the number of interactions in contract execution but also allow users to stop paying for cloud services when data is lost or damaged. The precise method is to generate each node with a private chain and place the smart contract on the private chain. With the decentralization of the blockchain private chain, the advantages of read-only data, and traceability of information, the storage of smart payment contract data is more secure. Both parties to the transaction are more trustworthy. Therefore, the proposed system has a safe and efficient smart contract payment mechanism, which brings a good user experience to users, which proves the significance and value of this research.
Deep Convolutional Symmetric Encoder—Decoder Neural Networks to Predict Students’ Visual Attention

Prediction of visual attention is a new and challenging subject, and to the best of our knowledge, there are not many pieces of research devoted to the anticipation of students’ cognition when solving tests. The aim of this paper is to propose, implement, and evaluate a machine learning method that is capable of predicting saliency maps of students who participate in a learning task in the form of quizzes based on quiz questionnaire images. Our proposal utilizes several deep encoder–decoder symmetric schemas which are trained on a large set of saliency maps generated with eye tracking technology. Eye tracking data were acquired from students, who solved various tasks in the sciences and natural sciences (computer science, mathematics, physics, and biology). The proposed deep convolutional encoder–decoder network is capable of producing accurate predictions of students’ visual attention when solving quizzes. Our evaluation showed that predictions are moderately positively correlated with actual data with a coefficient of 0.547 ± 0.109. It achieved better results in terms of correlation with real saliency maps than state-of-the-art methods. Visual analyses of the saliency maps obtained also correspond with our experience and expectations in this field. Both source codes and data from our research can be downloaded in order to reproduce our results.