Reviewerly/exHarmony huggingface dataset:
Exploiting Positional Information for Session-Based Recommendation

For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect a user's current preference, a local shift of the user's intention within the session may still exist. Specifically, the interactions that take place in the early positions within a session generally indicate the user's initial intention, while later interactions are more likely to represent the latest intention. Such positional information has been rarely considered in existing methods, which restricts their ability to capture the significance of interactions at different positions. To thoroughly exploit the positional information within a session, a theoretical framework is developed in this paper to provide an in-depth analysis of the positional information. We formally define the properties of forward-awareness and backward-awareness to evaluate the ability of positional encoding schemes in capturing the initial and the latest intention. According to our analysis, existing positional encoding schemes are generally forward-aware only, which can hardly represent the dynamics of the intention in a session. To enhance the positional encoding scheme for the session-based recommendation, a dual positional encoding (DPE) is proposed to account for both forward-awareness and backward-awareness. Based on DPE, we propose a novel Positional Recommender (PosRec) model with a well-designed Position-aware Gated Graph Neural Network module to fully exploit the positional information for session-based recommendation tasks. Extensive experiments are conducted on two e-commerce benchmark datasets, Yoochoose and Diginetica and the experimental results show the superiority of the PosRec by comparing it with the state-of-the-art session-based recommender models.
Towards Hybrid Multimodal Manual and Non-Manual Arabic Sign Language Recognition: mArSL Database and Pilot Study

Sign languages are the main visual communication medium between hard-hearing people and their societies. Similar to spoken languages, they are not universal and vary from region to region, but they are relatively under-resourced. Arabic sign language (ArSL) is one of these languages that has attracted increasing attention in the research community. However, most of the existing and available works on sign language recognition systems focus on manual gestures, ignoring other non-manual information needed for other language signals such as facial expressions. One of the main challenges of not considering these modalities is the lack of suitable datasets. In this paper, we propose a new multi-modality ArSL dataset that integrates various types of modalities. It consists of 6748 video samples of fifty signs performed by four signers and collected using Kinect V2 sensors. This dataset will be freely available for researchers to develop and benchmark their techniques for further advancement of the field. In addition, we evaluated the fusion of spatial and temporal features of different modalities, manual and non-manual, for sign language recognition using the state-of-the-art deep learning techniques. This fusion boosted the accuracy of the recognition system at the signer-independent mode by 3.6% compared with manual gestures.
Factors influencing university teachers’ use of a mobile technology-enhanced teaching (MTT) platform

The main purpose of this study was to examine the critical factors influencing university teachers' use of a mobile technology-enhanced teaching (MTT) platform during the new coronavirus (COVID-19) epidemic. An integrated model with multiple factors drawing from the theoretical models and learning theories was proposed in this study to examine university teachers' intentions to use an MTT platform. The multiple factors included the individual factor (e.g., growth mindset, help seeking, and self-efficacy), the social factor (e.g., social norms), and the technological acceptance factor (e.g., perceived usefulness and perceived ease of use). The survey method was used to collect data on university teachers' perceptions of the MTT platform use, and a two-step structural equation modeling approach was used for the data analysis. Based on the path analysis of a total of 214 valid responses, the results identified that growth mindset, help seeking, and self-efficacy from the individual factor, as well as perceived usefulness from the technology acceptance factor were the significant determinants of university teachers' intentions to adopt the MTT. The contributions of this study are twofold. First, the proposed model was derived from multiple literature sources, providing a sound theoretical foundation to understand MTT platform use from an academic angle. Second, university teachers' viewpoints are a unique observation of their actual platform use, providing practical insights into the improvement and maintenance of MTT-related platforms for all educators. The findings are especially valuable during the post-COVID-19 era.
Emojis as social information in digital communication.

Facial expressions of emotion are nonverbal cues that evoke affective, inferential, and social responses during face-to-face communication. Given that communication is moving more and more from face-to-face to digital contexts, the present research tested the functional equivalence of their digital counterparts-emojis. Eleven high-powered experiments tested the general effectiveness of emojis to convey emotionality and to disambiguate discourse during digital communication, as well as predictions about their social-emotional properties derived from the Emotion as Social Information (EASI) model. Compared to messages without emojis, those including emojis were perceived as emotionally more intense and as of more extreme valence. Furthermore, the effects of emojis on perceived valence were mediated via perceived emotional intensity. This suggests that emojis are effective quasi-nonverbal cues for digital communication. Furthermore, in line with predictions of the EASI model, emojis produced patterns similar to what has been observed for facial expressions of emotion in face-to-face communication, supporting their functional equivalence. Specifically, they instigated affective (emotion contagion) and inferential (understanding) processes, which subsequently resulted in behavioral intentions (empathic concern). In terms of the predicted mediating processes, we found differences between emojis and offline facial expressions of emotion. These deviations from our predictions are attributed to inherent differences between digital and face-to-face communication and limitations in the employed methodology. In light of the present findings, we discuss a theoretical synthesis of emojis in digital communication with the EASI model and propose a research agenda to connect emotion research with predominant forms of modern communication. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Quantum coherence and distribution of N-partite bosonic fields in noninertial frame

We study the quantum coherence and its distribution of N-partite GHZ and W states of bosonic fields in the noninertial frames with arbitrary number of acceleration observers. We find that the coherence of both GHZ and W state reduces with accelerations and freezes in the limit of infinite accelerations. The freezing value of coherence depends on the number of accelerated observers. The coherence of N-partite GHZ state is genuinely global and no coherence exists in any subsystems. For the N-partite W state, however, the coherence is essentially bipartite types, and the total coherence is equal to the sum of coherence of all the bipartite subsystems.
Analysis of the potential context of Blockchain on the usability of Gamification with Game-Based Learning

Within the world of gaming, there has been a move from diversions being utilized exclusively for excitement, to recreations being utilized as a medium to teach. For this matter, there are two strategies of making amusement which are gamification or game-based learning. The previous is the utilization of diversion components, such as wellbeing focuses or pioneer sheets, and they are connected to a non-gaming stage. The last mentioned, game-based learning, incorporates creating a full-fledged amusement where the means towards the conclusion, that's to say triumph, are set in a world where the player needs to apply the lessons given to progress. Since typically an IT-related investigation, the subject chosen for this study is Blockchain, an ever-expanding division over the past decade. Diverse parts of Blockchain's makeup have been dismembered into little, comprehensible pieces of data within the applications made, which innovative understudy to the frame can take in at their claimed pace. Should this study transcend this research, it might be beneficial for the experiments to possess two games designed and created by someone who has the artistic and technical capabilities of making their own assets. This is able to leave a far better impression with the test subjects and ideally receive a far better data set for examination.
An English Network Teaching Method Supported by Artificial Intelligence Technology and WBIETS System

The traditional English teaching system has certain problems in the acquisition of teaching resources and the innovation of teaching models. In order to improve the effect of subsequent English online teaching, this paper improves the machine learning algorithm to make it a core algorithm that can be used by artificial intelligence systems. Moreover, this paper combines the WBIETS system to expand the system function, analyzes the needs of the English network teaching system, and constructs the system function modules and logical structure. The data layer, logic layer, and presentation layer in the system constructed in this paper are independent of each other and can be effectively expanded when subsequent requirements change. In addition, this paper solves the problem of acquiring English teaching resources through the WBIETS system. To evaluate the performance of the English network teaching system, this paper performs comprehensive mathematical and experimental analysis. The experimental results show that the system constructed in this paper basically meets the actual teaching requirements.
Evaluating the Robustness of Neural Language Models to Input Perturbations

High-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks.However, results for common benchmark datasets often do not reflect model reliability and robustness when applied to noisy, real-world data.In this study, we design and implement various types of character-level and wordlevel perturbation methods to simulate realistic scenarios in which input texts may be slightly noisy or different from the data distribution on which NLP systems were trained.Conducting comprehensive experiments on different NLP tasks, we investigate the ability of high-performance language models such as BERT, XLNet, RoBERTa, and ELMo in handling different types of input perturbations.The results suggest that language models are sensitive to input perturbations and their performance can decrease even when small changes are introduced.We highlight that models need to be further improved and that current benchmarks are not reflecting model robustness well.We argue that evaluations on perturbed inputs should routinely complement widely-used benchmarks in order to yield a more realistic understanding of NLP systems' robustness.
SORT-YM: An Algorithm of Multi-Object Tracking with YOLOv4-Tiny and Motion Prediction

Multi-object tracking (MOT) is a significant and widespread research field in image processing and computer vision. The goal of the MOT task consists in predicting the complete tracklets of multiple objects in a video sequence. There are usually many challenges that degrade the performance of the algorithm in the tracking process, such as occlusion and similar objects. However, the existing MOT algorithms based on the tracking-by-detection paradigm struggle to accurately predict the location of the objects that they fail to track in complex scenes, leading to tracking performance decay, such as an increase in the number of ID switches and tracking drifts. To tackle those difficulties, in this study, we design a motion prediction strategy for predicting the location of occluded objects. Since the occluded objects may be legible in earlier frames, we utilize the speed and location of the objects in the past frames to predict the possible location of the occluded objects. In addition, to improve the tracking speed and further enhance the tracking robustness, we utilize efficient YOLOv4-tiny to produce the detections in the proposed algorithm. By using YOLOv4-tiny, the tracking speed of our proposed method improved significantly. The experimental results on two widely used public datasets show that our proposed approach has obvious advantages in tracking accuracy and speed compared with other comparison algorithms. Compared to the Deep SORT baseline, our proposed method has a significant improvement in tracking performance.
Threatening Language Detection and Target Identification in Urdu Tweets

Automatic threatening language detection is an important task and most of the existing studies relied on English. However, threatening language detection in poor-resource language remains briefly addressed. In this paper, we introduce a new publicly available dataset for threatening language detection in Urdu tweets to fill the scientific gap, particularly, in the Urdu language. The proposed dataset contains 3,564 tweets manually annotated by human experts with two labels: threatening and non-threatening. The threatening tweets are further classified into two classes: threatening to an individual person or threatening to a group. This research follows a two-step approach: (i) classify a given tweet as threatening or non-threatening and (ii) classify whether a threatening tweet is used to threaten an individual or a group. We compare three forms of text representation: two count-based, where the text is represented using either character n-gram counts or word n-gram counts as feature vectors and the third text representation is based on fastText pre-trained word embeddings for Urdu. We perform several experiments using machine learning and deep learning classifiers and our study shows that MLP classifier with the combination of word n-gram features outperformed other classifiers in detecting threatening tweets. Whereas, SVM using fastText pre-trained word embedding obtained the best results for the target identification task.
Enhanced Human Activity Recognition Using Wearable Sensors via a Hybrid Feature Selection Method

The study of human activity recognition (HAR) plays an important role in many areas such as healthcare, entertainment, sports, and smart homes. With the development of wearable electronics and wireless communication technologies, activity recognition using inertial sensors from ubiquitous smart mobile devices has drawn wide attention and become a research hotspot. Before recognition, the sensor signals are typically preprocessed and segmented, and then representative features are extracted and selected based on them. Considering the issues of limited resources of wearable devices and the curse of dimensionality, it is vital to generate the best feature combination which maximizes the performance and efficiency of the following mapping from feature subsets to activities. In this paper, we propose to integrate bee swarm optimization (BSO) with a deep Q-network to perform feature selection and present a hybrid feature selection methodology, BAROQUE, on basis of these two schemes. Following the wrapper approach, BAROQUE leverages the appealing properties from BSO and the multi-agent deep Q-network (DQN) to determine feature subsets and adopts a classifier to evaluate these solutions. In BAROQUE, the BSO is employed to strike a balance between exploitation and exploration for the search of feature space, while the DQN takes advantage of the merits of reinforcement learning to make the local search process more adaptive and more efficient. Extensive experiments were conducted on some benchmark datasets collected by smartphones or smartwatches, and the metrics were compared with those of BSO, DQN, and some other previously published methods. The results show that BAROQUE achieves an accuracy of 98.41% for the UCI-HAR dataset and takes less time to converge to a good solution than other methods, such as CFS, SFFS, and Relief-F, yielding quite promising results in terms of accuracy and efficiency.
Digital Transformation of a University as a Factor of Ensuring Its Competitiveness

Digital economy brings new opportunities and threats to modern reality, covering almost all the fields of life of the population, including education. Digital economy opens up new strengths and weaknesses of a university. In this regard, a relevant task is to determine a set of goals for the development of the university and ensure movement towards these goals. The transition from the University 3.0 model to the University 4.0 model involves a comprehensive transformation of all processes in a university. Taking into account that the innovative educational and entrepreneurial activities played a leading role in the previous model, it is advisable, first of all, to ensure the digital transformation of these processes during the transition to the new model. An important factor in ensuring the competitiveness of a modern university is the digitalisation of its services. In the modern realities of the development of digital economy, universities are facing the need for digital transformation, the essence of which is based not only on the introduction of digital technologies in the activities of universities, but also on the cultural and organisational changes. The main features of the digitalisation process of the universities shall be: the university's orientation on the implementation of “smart manufacturing”; the use of digital communication technologies in the educational process; the introduction of network and distance learning; the development of basic information services; the creation and implementation of the “digital university management” service. Thus, digital transformation of the universities will make it possible for teachers, students, applicants and partners to provide more opportunities in the context of digitalisation. The transformation is impossible without the development and implementation of a digitalisation strategy that takes into account the features and specific nature of the university.
A curated, ontology-based, large-scale knowledge graph of artificial intelligence tasks and benchmarks

Abstract Research in artificial intelligence (AI) is addressing a growing number of tasks through a rapidly growing number of models and methodologies. This makes it difficult to keep track of where novel AI methods are successfully – or still unsuccessfully – applied, how progress is measured, how different advances might synergize with each other, and how future research should be prioritized. To help address these issues, we created the Intelligence Task Ontology and Knowledge Graph (ITO), a comprehensive, richly structured and manually curated resource on artificial intelligence tasks, benchmark results and performance metrics. The current version of ITO contains 685,560 edges, 1,100 classes representing AI processes and 1,995 properties representing performance metrics. The primary goal of ITO is to enable analyses of the global landscape of AI tasks and capabilities. ITO is based on technologies that allow for easy integration and enrichment with external data, automated inference and continuous, collaborative expert curation of underlying ontological models. We make the ITO dataset and a collection of Jupyter notebooks utilizing ITO openly available.
Quantum Phase Estimation Algorithm with Gaussian Spin States

Crucial drawbacks on quantum phase estimation algorithms, such as exponential time overload and the use of fragile GHZ states, can be overcome using Gaussian spin states and an adaptive measurement protocol.
Composable Sparse Fine-Tuning for Cross-Lingual Transfer

Fine-tuning the entire set of parameters of a large pretrained model has become the mainstream approach for transfer learning.To increase its efficiency and prevent catastrophic forgetting and interference, techniques like adapters and sparse fine-tuning have been developed.Adapters are modular, as they can be combined to adapt a model towards different facets of knowledge (e.g., dedicated language and/or task adapters).Sparse finetuning is expressive, as it controls the behavior of all model components.In this work, we introduce a new fine-tuning method with both these desirable properties.In particular, we learn sparse, real-valued masks based on a simple variant of the Lottery Ticket Hypothesis.Task-specific masks are obtained from annotated data in a source language, and languagespecific masks from masked language modeling in a target language.Both these masks can then be composed with the pretrained model.Unlike adapter-based fine-tuning, this method neither increases the number of parameters at inference time nor alters the original model architecture.Most importantly, it outperforms adapters in zero-shot cross-lingual transfer by a large margin in a series of multilingual benchmarks, including Universal Dependencies, MasakhaNER, and AmericasNLI.Based on an in-depth analysis, we additionally find that sparsity is crucial to prevent both 1) interference between the fine-tunings to be composed and 2) overfitting.We release the code and models at https://github.com/cambridgeltl/composable-sft.
Developing a risk-informed decision-support system for earthquake early warning at a critical seaport

Earthquake early warning (EEW) systems are used to provide timely alerts on ongoing earthquakes, which can facilitate important risk-mitigation actions before potentially damaging seismic waves reach target sites. A major shortcoming of existing EEW approaches is that the earthquake-related conditions for activating alerts are not generally defined according to a formal decision-support system (DSS) that accounts for possible risk-based consequences of triggering/not triggering the alarm. This paper exploits a next-generation risk-informed EEW DSS, which incorporates multi-criteria decision-making for evaluating the optimal decision. The proposed DSS integrates engineering-driven loss predictions associated with issuing/not issuing an EEW alert during an event, also considering possible system malfunctions. The DSS is demonstrated for the strategic Gioia Tauro seaport, located in a region with some of the highest seismic hazard in Italy. Real-time seismic risk analyses are conducted for various earthquake scenarios, accounting for event-parameter uncertainties that are integral to any EEW process and considering the multicomponent nature of the port as a system of interconnected elements. The results of these analyses are used as input to the proposed EEW DSS along with end-user risk preferences, to evaluate the optimal decision in each case and to define a series of risk-informed EEW warning thresholds for the port.
Scaling Laws for the Sensitivity Enhancement of Non-Gaussian Spin States

We identify the large-N scaling of the metrological quantum gain offered by over-squeezed spin states that are accessible by one-axis twisting, as a function of the preparation time. We further determine how the scaling is modified by relevant decoherence processes and predict a discontinuous change of the quantum gain at a critical preparation time that depends on the noise. Our analytical results provide recipes for optimal and feasible implementations of quantum enhancements with non-Gaussian spin states in existing experiments, well beyond the reach of spin squeezing.