Reviewerly/exHarmony huggingface dataset:
Speech Acts of Apology and Request by Arab Postgraduates with Malaysian Supervisors

This study investigates the pragmatic and linguistic competencies of Arab postgraduate students and native English-speaking postgraduate students in Malaysian universities, focusing on their speech act strategies for requests and apologies. Using qualitative methods such as Discourse Completion Tests and conversations, the study addresses three research questions related to pragmatic and linguistic forms, gender differences, and cultural norms in English-speaking settings. The participants consisted of 95 individuals, including an equal number of males and females from four universities in Malaysia during the academic year 2022-2023. Of the participants, 32 were of Arab origin, and 63 were native English speakers. The study found significant differences in response rates between the two groups and emphasized the importance of understanding cultural norms for effective communication. The findings suggest that learners from Arab countries must comprehend the differences between cultural norms and conventions in English-speaking environments to communicate efficiently. The study has implications for assisting Arab-speaking graduate students in improving their communication abilities and promoting cross-cultural understanding in academic settings in Malaysia.
The effect of visual rivalry in peripheral head-mounted displays on mobility

Abstract Recent head-mounted displays and smart glasses use vision multiplexing, an optical approach where two or more views are superimposed on each other. In vision multiplexing, augmented information is presented over an observer’s natural field of view, providing field expansion and critical information during mobility situations like walking and driving. Yet despite its utility, vision multiplexing may produce visual rivalry, a phenomenon where perception alternates between the augmented information and the background scene for seconds at a time. To investigate, we compared the effect of different peripheral vision multiplexing configurations (unilateral opaque, unilateral see-through and bilateral see-through) on the detection of augmented information, incorporating at the same time real-world characteristics (target eccentricity, depth condition, and gaze movement) for a more realistic assessment. Results showed a persistently lower target detection rate in unilateral configurations than the bilateral configuration, suggesting a larger effect of binocular rivalry on target visibility. Nevertheless, this effect does become attenuated when more naturalistic elements are incorporated, and we discuss recommendations for vision multiplexing design and possible avenues for further research.
Quantum neural network cost function concentration dependency on the parametrization expressivity

Abstract Although we are currently in the era of noisy intermediate scale quantum devices, several studies are being conducted with the aim of bringing machine learning to the quantum domain. Currently, quantum variational circuits are one of the main strategies used to build such models. However, despite its widespread use, we still do not know what are the minimum resources needed to create a quantum machine learning model. In this article, we analyze how the expressiveness of the parametrization affects the cost function. We analytically show that the more expressive the parametrization is, the more the cost function will tend to concentrate around a value that depends both on the chosen observable and on the number of qubits used. For this, we initially obtain a relationship between the expressiveness of the parametrization and the mean value of the cost function. Afterwards, we relate the expressivity of the parametrization with the variance of the cost function. Finally, we show some numerical simulation results that confirm our theoretical-analytical predictions.To the best of our knowledge, this is the first time that these two important aspects of quantum neural networks are explicitly connected.
Augmented reality as a Thirdspace: Simultaneous experience of the physical and virtual

With the proliferation of devices that display augmented reality (AR), now is the time for scholars and practitioners to evaluate and engage critically with emerging applications of the medium. AR mediates the way users see their bodies, hear their environment and engage with places. Applied in various forms, including social media, e-commerce, gaming, enterprise and art, the medium facilitates a hybrid experience of physical and digital spaces. This article employs a model of real-and-imagined space from geographer Edward Soja to examine how the user of an AR app navigates the two inter-twined spaces of physical and digital, experiencing what Soja calls a 'Third-space'. The article illustrates the potential for headset-based AR to engender such a Thirdspace through the author's practice-led research project, the installation Through the Wardrobe. This installation demonstrates how AR has the potential to shift the way that users view and interact with their world with artistic applications providing an opportunity to question assumptions of social norms, identity and uses of physical space.
A comparison of Apache Solr and Elasticsearch technologies in support of large-scale data analysis

In the era of big data, data has never been more important because it contains hidden insights. Additionally, it is necessary and challenging to extract usable information from enormous volumes of data. When attempting to perform data processing and analytics in a variety of domains, developers of data-intensive systems have consequently met several challenges. In addition, full-text search is one of the most significant components of big data processing and analytics for discovering fragments of required data among large volumes of data. Due to the importance of the subject, this article begins with an examination of the characteristics, capabilities, and technical comparisons of full-text search technologies, followed by a systematic comparison of Apache Solr and Elasticsearch in terms of indexing times and queries on three separate datasets. According to our findings, based on default configuration, Apache Solr has better performance when looking at indexing times measured on three machines with different hardware specifications. Likewise, Apache Solr outperforms Elasticsearch in seven out of ten search queries. Regarding our results, on computers with restricted hardware resources, we recommend utilizing Apache Solr instead of Elasticsearch. In addition, this study provides researchers and developers of data-intensive systems with a complete comparison and suggestions for choosing the most effective full-text search engine for their task.
Sparse signals on hypergraphs

Abstract Signals with a sparse representation in a given basis as well as Laplacian eigenvectors of graphs play a big role in signal processing and machine learning. We put these topics together and look at signals on graphs that have a sparse representation in the basis of eigenvectors of the Laplacian matrix, which may appear after convolution with an unknown sparse filter. We give explicit algorithms to recover those sums by sampling the signal only on few vertices, i.e., the number of required samples is independent of the total size of the graph and takes only local properties of the graph into account. We generalize these methods to simplicial complexes.
Legal Status of Cryptocurrency Circulation in Iraq: Lessons from the United Arab Emirates and the United States

The philosophical emergence of cryptocurrencies is a consequence of the desire to get rid of the authorities’ control of the monetary system, which encouraged people to deal with it for investment or to facilitate the fulfilment of their financial obligations. The research problem is represented through the lack of legislation that regulates dealing with cryptocurrencies in Iraq. There are many technical, legal, and security risks for the use and circulation of cryptocurrency, which must be legally addressed and suitable solutions found. Therefore, this article aims to examine and analyse the legal status of cryptocurrencies in selected jurisdictions through multiple stages, from prohibition to licensing and regulation. This article adopted a qualitative approach to analyse the legal status of cryptocurrency in the selected jurisdictions. The study finding shows that the Iraqi authorities do not have the technical or legal means to prevent the illegal use of cryptocurrencies. In addition, there is a large legislative gap with the developed countries, and lawmakers need to regulate cryptocurrencies by enacting laws to protect people from falling victim to fraud.
Disruptive technologies and the rule of law

Disruptive technologies are expanding and information flow increasing. Due to the new technologies, we are facing unavoidable new challenges and ongoing changes. However, the law as the meta-technology crucial for the fourth revolution still thrives on an algorithmic and data-driven world. The law adjusts itself to the new realities, framing the ought to be’s of society, being reshaped by progressive developments. As such a mirror held up against life, it now reflects an interconnected, cosmopolitan, and global world. As a result, following the wishes and needs of markets and societies per si, corporations, private parties, universities, and even governments and natural persons are in a constant process of global legal rules creation. A process similar to the Lex mercatoria, able to effectively frame the fast-paced advances, as an autopoietic legal process - now driven by nontraditional players. A variety of technologies and processes are getting incorporated into the law - such as dispute resolution over algorithms, simplified international contracts, social “quasi-legal” sanctions widely spread by social media, protection of intellectual property, through and especially by the use of machine learning, AI and autonomous technologies - growing in importance and being materialized in previously inconceivable ways. Binding and self-enforceable, the reshaped rule of law follows society's expectations, framing autonomous technologies and artificial intelligence, yet being framed by those, ensuring our rights globally, in an effective but innovative way.
Which neural networks can be computed by an algorithm? – Generalised hardness of approximation meets Deep Learning

Abstract Classical hardness of approximation (HA) is the phenomenon that, assuming P ≠ NP, one can easily compute an ϵ‐approximation to the solution of a discrete computational problem for ϵ &gt; ϵ 0 &gt; 0, but for ϵ &lt; ϵ 0 – where ϵ 0 is the approximation threshold – it becomes intractable. Recently, a similar yet more general phenomenon has been documented in AI: Generalised hardness of approximation (GHA). This phenomenon includes the following occurrence: For any approximation threshold ϵ 1 &gt; 0, there are AI problems for which provably there exist stable neural networks (NNs) that solve the problem, but no algorithm can compute any NN that approximates the AI problem to ϵ 1 ‐accuracy. Moreover, this issue is independent of the P vs NP question and thus is a rather different mathematical phenomenon than HA. GHA implies that the universal approximation theorem for NNs only provides a partial understanding of the power of NNs in AI. Thus, a classification theory describing which NNs can be computed by algorithms to particular accuracies is needed to fill this gap. We initiate such a theory by showing the correspondence between the functions that can be computed to ϵ‐accuracy by an algorithm and those functions that can be approximated by NNs which can be computed to ϵ̂‐accuracy by an algorithm. In particular, the approximation thresholds ϵ and ϵ̂ cannot differ by more than a factor of 12. This means that computing function approximations through NNs will be optimal – in the sense of best approximation accuracy achievable by an algorithm – up to a small constant, compared to any other computational technique.
Open-source Frame Semantic Parsing

While the state-of-the-art for frame semantic parsing has progressed dramatically in recent years, it is still difficult for end-users to apply state-of-the-art models in practice. To address this, we present Frame Semantic Transformer, an open-source Python library which achieves near state-of-the-art performance on FrameNet 1.7, while focusing on ease-of-use. We use a T5 model fine-tuned on Propbank and FrameNet exemplars as a base, and improve performance by using FrameNet lexical units to provide hints to T5 at inference time. We enhance robustness to real-world data by using textual data augmentations during training.
A Random Projection k Nearest Neighbours Ensemble for Classification via Extended Neighbourhood Rule

Ensembles based on k nearest neighbours (kNN) combine a large number of base learners, each constructed on a sample taken from a given training data. Typical kNN based ensembles determine the k closest observations in the training data bounded to a test sample point by a spherical region to predict its class. In this paper, a novel random projection extended neighbourhood rule (RPExNRule) ensemble is proposed where bootstrap samples from the given training data are randomly projected into lower dimensions for additional randomness in the base models and to preserve features information. It uses the extended neighbourhood rule (ExNRule) to fit kNN as base learners on randomly projected bootstrap samples.
Managing Cyber Risk, a Science in the Making

Not a day goes by without news about a cyber attack. Fear spreads out and lots of wrong ideas circulate. This survey aims at showing how all these uncertainties about cyber can be transformed into manageable risk. After reviewing the main characteristics of cyber risk, we consider the three layers of cyber space: hardware, software and psycho-cognitive layer. We ask ourselves how is this risk different from others, how modelling has been tackled and needs to evolve, and what are the multi-facetted aspects of cyber risk management. This wide exploration pictures a science in the making and points out the questions to be solved for building a resilient society.
Analyzing the Generalizability of Deep Contextualized Language Representations For Text Classification

This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification and sentiment analysis of product reviews. A "cross-context" setting is enabled using test sets that are distinct from the training data. Specifically, in the news classification task, the models are developed on local news from India and tested on the local news from China. In the sentiment analysis task, the models are trained on movie reviews and tested on customer reviews. This comparison is aimed at exploring the limits of the representative power of today's Natural Language Processing systems on the path to the systems that are generalizable to real-life scenarios. The models are fine-tuned and fed into a Feed-Forward Neural Network and a Bidirectional Long Short Term Memory network. Multinomial Naive Bayes and Linear Support Vector Machine are used as traditional baselines. The results show that, in binary text classification, DistilBERT is significantly better than ELMo on generalizing to the cross-context setting. ELMo is observed to be significantly more robust to the cross-context test data than both baselines. On the other hand, the baselines performed comparably well to ELMo when the training and test data are subsets of the same corpus (no cross-context). DistilBERT is also found to be 30% smaller and 83% faster than ELMo. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also favorable in incorporating into real-life systems for it requires a smaller computational training budget. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional ML algorithms can still be considered as more economic alternatives to deep language representations.
Dynasparse: Accelerating GNN Inference through Dynamic Sparsity Exploitation

Graph Neural Network (GNN) inference is used in many real-world applications. Data sparsity in GNN inference, including sparsity in the input graph and the GNN model, offer opportunities to further speed up inference. Also, many pruning techniques have been proposed for model compression that increase the data sparsity of GNNs. We propose Dynasparse, a comprehensive hardware-software codesign on FPGA to accelerate GNN inference through dynamic sparsity exploitation. For this, we decouple the GNN computation kernels from the basic computation primitives, and explore hardware-software codesign as follows: 1) Hardware design: We propose a novel unified accelerator design on FPGA to efficiently execute various computation primitives. We develop a customized soft processor that is tightly coupled with the accelerator to execute a runtime system. Moreover, we develop efficient hardware mechanisms to profile the data sparsity and perform on-the-fly data format transformation to prepare the input data for various computation primitives; 2) Software design: We develop a runtime system that works synergistically with the accelerator to perform dynamic kernel-to-primitive mapping based on data sparsity. We implement Dynasparse on a state-of-the-art FPGA platform, Xilinx Alveo U250, and evaluate the design using widely used GNN models (GCN, GraphSAGE, GIN and SGC). For the above GNN models and various input graphs, the proposed accelerator and dynamic kernel-to-primitive mapping reduces the inference latency by $3.73\times$ on the average compared with the static mapping strategies employed in the state-of-the-art GNN accelerators. Compared with state-of-the-art CPU (GPU) implementations, Dynasparse achieves up to $56.9\times$ ($2.37\times$) speedup in end-to-end latency.
Deep learning-based stereo camera multi-video synchronization

Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this goal in mind, we present here a comparison of different deep learning-based systems and prove that some are efficient and generalizable enough for such a task. This study paves the way to a production ready software-based video synchronization system.
Pseudo-Euclidean Attract-Repel Embeddings for Undirected Graphs

Dot product embeddings take a graph and construct vectors for nodes such that dot products between two vectors give the strength of the edge. Dot products make a strong transitivity assumption, however, many important forces generating graphs in the real world lead to non-transitive relationships. We remove the transitivity assumption by embedding nodes into a pseudo-Euclidean space - giving each node an attract and a repel vector. The inner product between two nodes is defined by taking the dot product in attract vectors and subtracting the dot product in repel vectors. Pseudo-Euclidean embeddings can compress networks efficiently, allow for multiple notions of nearest neighbors each with their own interpretation, and can be `slotted' into existing models such as exponential family embeddings or graph neural networks for better link prediction.
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization

Electronic health records (EHRs) store an extensive array of patient information, encompassing medical histories, diagnoses, treatments, and test outcomes. These records are crucial for enabling healthcare providers to make well-informed decisions regarding patient care. Summarizing clinical notes further assists healthcare professionals in pinpointing potential health risks and making better-informed decisions. This process contributes to reducing errors and enhancing patient outcomes by ensuring providers have access to the most pertinent and current patient data. Recent research has shown that incorporating prompts with large language models (LLMs) substantially boosts the efficacy of summarization tasks. However, we show that this approach also leads to increased output variance, resulting in notably divergent outputs even when prompts share similar meanings. To tackle this challenge, we introduce a model-agnostic Soft Prompt-Based Calibration (SPeC) pipeline that employs soft prompts to diminish variance while preserving the advantages of prompt-based summarization. Experimental findings on multiple clinical note tasks and LLMs indicate that our method not only bolsters performance but also effectively curbs variance for various LLMs, providing a more uniform and dependable solution for summarizing vital medical information.
DiffPattern: Layout Pattern Generation via Discrete Diffusion

Deep generative models dominate the existing literature in layout pattern generation. However, leaving the guarantee of legality to an inexplicable neural network could be problematic in several applications. In this paper, we propose \tool{DiffPattern} to generate reliable layout patterns. \tool{DiffPattern} introduces a novel diverse topology generation method via a discrete diffusion model with compute-efficiently lossless layout pattern representation. Then a white-box pattern assessment is utilized to generate legal patterns given desired design rules. Our experiments on several benchmark settings show that \tool{DiffPattern} significantly outperforms existing baselines and is capable of synthesizing reliable layout patterns.
Delay-Aware Hierarchical Federated Learning

Federated learning has gained popularity as a means of training models distributed across the wireless edge. The paper introduces delay-aware hierarchical federated learning (DFL) to improve the efficiency of distributed machine learning (ML) model training by accounting for communication delays between edge and cloud. Different from traditional federated learning, DFL leverages multiple stochastic gradient descent iterations on local datasets within each global aggregation period and intermittently aggregates model parameters through edge servers in local subnetworks. During global synchronization, the cloud server consolidates local models with the outdated global model using a local-global combiner, thus preserving crucial elements of both, enhancing learning efficiency under the presence of delay. A set of conditions is obtained to achieve the sub-linear convergence rate of O(1/k) for strongly convex and smooth loss functions. Based on these findings, an adaptive control algorithm is developed for DFL, implementing policies to mitigate energy consumption and communication latency while aiming for sublinear convergence. Numerical evaluations show DFL's superior performance in terms of faster global model convergence, reduced resource consumption, and robustness against communication delays compared to existing FL algorithms. In summary, this proposed method offers improved efficiency and results when dealing with both convex and non-convex loss functions.