Reviewerly/exHarmony huggingface dataset:
Detecting Outliers in Compositional Data Using Invariant Coordinate Selection

Invariant coordinate (or component) selection (ICS) is a multivariate statistical method introduced by Tyler et al. (J R Stat Soc Ser B (Stat Methodol) 71(3):549–592, 2009) and based on the simultaneous diagonalization of two scatter matrices. A model-based approach of ICS, called invariant coordinate analysis, has already been adapted for compositional data in Muehlmann et al. (Independent component analysis for compositional data. In Daouia, A, Ruiz-Gazen A (eds) Advances in Contemporary Statistics and Econometrics: Festschrift in Honor of Christine Thomas-Agnan. Springer, New York, pp. 525–545, 2021). In a model-free context, ICS is also helpful at identifying outliers Nordhausen and Ruiz-Gazen (J Multivar Anal 188:104844, 2022). We propose to develop a version of ICS for outlier detection in compositional data. This version is first introduced in coordinate space for a specific choice of isometric log-ratio coordinate system associated to a contrast matrix and follows the outlier detection procedure proposed by Archimbaud et al. (Comput Stat Data Anal 128:184–199, 2018a). We then show that the procedure is independent of the choice of contrast matrix and can be defined directly in the simplex. To do so, we establish some properties of the set of matrices satisfying the zero-sum property and introduce a simplex definition of the Mahalanobis distance and the one-step M-estimators class of scatter matrices. We also need to define the family of elliptical distributions in the simplex. We then show how to interpret the results directly in the simplex using two artificial datasets and a real dataset of market shares in the automobile industry.
Reconstruction of physical dance teaching content and movement recognition based on a machine learning model

With the technological development of movement recognition based on machine learning model algorithms, the content and movements for physical dance teaching are also seeking changes and innovations. In this paper, a set of three-dimensional convolutional neural network recognition algorithms based on a machine learning model is constructed through the collection to recognition of sports dance movement data. By collecting the skeleton information of typical movements of physical dance, a typical movement dataset of physical dance is constructed, which is recognized by the improved 3D convolutional neural network recognition algorithm under the machine learning model, and the method is validated on the public dataset. The experimental results show that the 3D CNNs in this paper can produce relatively satisfactory results for sports dance action recognition with high accuracy of action recognition, which verifies the feasibility of the 3D convolutional neural network action recognition algorithm under the machine learning model for the acquisition to recognition of sports dance actions. It illustrates that the future can be better to open a new direction of physical dance education content through machine learning models in this form.
An Educational Metaverse Experiment: The First On-Chain and In-Metaverse Academic Course

The Metaverse is not a new concept; nor its use for educational purpose. Despite its considerable attention and recent popularity, largely due to Facebook’s strategic bet, educators have been experimenting with such an environment for a while. Still though, educational activities that are delivered over a Metaverse environment require further exploration to fully understand their full potential, opportunities, and challenges, since such activities differ from traditional on-campus or e-learning education. Nowadays, the area of on-chain education in the Metaverse remains unexplored. In this paper, we are presenting the findings from our attempt to deliver the first of its kind on-chain and in the Metaverse course delivered by the University of Nicosia (UNIC). The course was offered in Fall 2022 as a Massive Open Online Course (MOOC) and was attended by 22,500 students. The paper reports interesting observations from this experiment highlighting lessons learned and open issues for further research. The findings reveal that a Metaverse-like environment that is structured for offering educational experiences to potential learners is likely to disrupt existing educational paradigms and academic practices. We are at a stage where significant amount of research and development is required to further understand and explore the opportunities for the application of a “meta” environment for education.
Pareto Optimal Layouts for Adaptive Mixed Reality

Adaptive mixed reality applications adjust their user interfaces based on the context in which they are used to provide a smooth experience for different users and environments. This involves carefully positioning UI elements, which can be challenging due to the many possible placements and need to balance competing goals. Current approaches employ global criterion optimization methods like weighted sums, which can be difficult to use, inflexible, and might not find preferred solutions. This can prevent the adaptations from meeting end-user expectations. We suggest using online multi-objective optimization methods which generate a set of Pareto optimal adaptation proposals, giving users more control and adding flexibility to the computational decision-making. We explore the feasibility of our approach by generating adaptations for a basic synthetic example and discuss relevant dimensions for a formal evaluation with end-users, including the choice of algorithm, decomposition technique, and objectives.
Neural Attention-Driven Non-Maximum Suppression for Person Detection

Non-maximum suppression (NMS) is a post-processing step in almost every visual object detector. NMS aims to prune the number of overlapping detected candidate regions-of-interest (RoIs) on an image, in order to assign a single and spatially accurate detection to each object. The default NMS algorithm (GreedyNMS) is fairly simple and suffers from severe drawbacks, due to its need for manual tuning. A typical case of failure with high application relevance is pedestrian/person detection in the presence of occlusions, where GreedyNMS doesn’t provide accurate results. This paper proposes an efficient deep neural architecture for NMS in the person detection scenario, by capturing relations of neighboring RoIs and aiming to ideally assign precisely one detection per person. The presented Seq2Seq-NMS architecture assumes a sequence-to-sequence formulation of the NMS problem, exploits the Multihead Scale-Dot Product Attention mechanism and jointly processes both geometric and visual properties of the input candidate RoIs. Thorough experimental evaluation on three public person detection datasets shows favourable results against competing methods, with acceptable inference runtime requirements.
A robust and efficient algorithm for Chinese historical document analysis and recognition

This paper presents a novel and efficient algorithm for Chinese historical document understanding, incorporating three key components: a multi-oriented text detector, a dual-path learning-based text recognizer, and a heuristic-based reading order predictor.
Developing personas for live streaming commerce platforms with user survey data

Live streaming commerce has emerged as a novel form of online marketing that offers live streaming commerce platforms a means of meeting different user groups' needs. The objective of this article is to examine the effects of age and gender on live streaming commerce platform usage and investigate user characteristics of these platforms in China. This study adopted a data-driven persona construction method combining quantitative and qualitative methods through the use of survey and interview. The survey involved 506 participants (age range = 19-70), and the interview involved 12 participants. The survey findings showed that age significantly affected users' livestream platform usage, while gender did not. Younger users had higher device proficiency and operation numbers. With more trust and device use, older users used the platforms later in the day than younger users. Interview findings revealed that gender affected users' motivations and value focus. Women tended to use the platforms as a means of entertainment. Women valued service quality and enjoyment more, while men focused on the accuracy of product information more. Four personas with significant differences were then constructed: Dedicated, Dependent, Active and Lurker. Their various needs, motivations and behavior patterns can be considered by designers to elevate the interaction of live streaming commerce platforms.
Automated Ontology Evaluation: Evaluating Coverage and Correctness using a Domain Corpus

Ontologies conceptualize domains and are a crucial part of web semantics and information systems. However, re-using an existing ontology for a new task requires a detailed evaluation of the candidate ontology as it may cover only a subset of the domain concepts, contain information that is redundant or misleading, and have inaccurate relations and hierarchies between concepts. Manual evaluation of large and complex ontologies is a tedious task. Thus, a few approaches have been proposed for automated evaluation, ranging from concept coverage to ontology generation from a corpus. Existing approaches, however, are limited by their dependence on external structured knowledge sources, such as a thesaurus, as well as by their inability to evaluate semantic relationships. In this paper, we propose a novel framework to automatically evaluate the domain coverage and semantic correctness of existing ontologies based on domain information derived from text. The approach uses a domain-tuned named-entity-recognition model to extract phrasal concepts. The extracted concepts are then used as a representation of the domain against which we evaluate the candidate ontology’s concepts. We further employ a domain-tuned language model to determine the semantic correctness of the candidate ontology’s relations. We demonstrate our automated approach on several large ontologies from the oceanographic domain and show its agreement with a manual evaluation by domain experts and its superiority over the state-of-the-art.
Design of a compact triple-channel panoramic stereo imaging system

We propose a compact form of the triple-channel panoramic annular lens (PAL) with a stereo field and no central blind area based on polarization technology, which solves the problem that the traditional stereo panoramic system always has a large and complex mirror in front to reflect light. Based on the traditional dual-channel structure, we apply polarization technology to the first reflective surface to create a third channel for the stereovision. The field of view (FoV) of the front channel is 360° × (0° - 40°), the FoV of the side channel is 360° × (40° - 105°) and the stereo FoV is 360° × (20° - 50°). The Airy radii of the front channel, the side channel, and the stereo channel are 3.374 µm, 3.372 µm, and 3.360 µm, respectively. The modulation transfer function at 147 lp/mm is greater than 0.13 in the front and stereo channels and greater than 0.42 in the side channel. The F - θ distortion of all FoVs is less than 10%. This system shows a promising way to achieve stereovision without adding complex structures on the original basis.
On Inclusion: Video Analysis of Older Adult Interactions with a Multi-Modal Voice Assistant in a Public Setting

Older adults around the world lack access to a wide range of potentially life-changing digital applications, services, and information that could be provided by voice assistants (such as Amazon’s Alexa, Google’s Assistant, or Apple’s Siri). However, older adults’ needs are underrepresented in the design of voice assistants. Because of this, we are missing opportunities for digital inclusion, and increasing risks of excluding older adults as these devices permeate public settings. In this work, we video record older adults (n=26) interacting with a multi-modal voice assistants while waiting in line at food pantries, and use Interaction Analysis to draw insights from these recordings. We find that by being agnostic to body language, audio-prosodic features, and other contextual factors, voice assistants fail to capture and react to some important aspects of interactions. We discuss design (e.g, interpreting users’ posture as a cue to wake the device when they are leaning towards the device) and research (e.g., surveillance trade-offs) implications, and argue for the use of multi-modal inputs with attention to privacy. Designing and training voice assistants to take in and appropriately respond to non-verbal cues may increase their inclusivity, helping them fulfill important needs of our aging population.
Structured State Space Decoder for Speech Recognition and Synthesis

Automatic speech recognition (ASR) systems developed in recent years have shown promising results with self-attention models (e.g., Transformer and Conformer), which are replacing conventional recurrent neural networks. Meanwhile, a structured state space model (S4) has been recently proposed, producing promising results for various long-sequence modeling tasks, including raw speech classification. The S4 model can be trained in parallel, similar to the Transformer model. In this study, we applied S4 as a decoder for ASR and text-to-speech (TTS) tasks, respectively, by comparing it with the Transformer decoder. For the ASR task, our experimental results demonstrate that the proposed model achieves a competitive word error rate (WER) of 1.88%/4.25% on the LibriSpeech test-clean/test-other set and a character error rate (CER) of 3.80%/2.63%/2.98% on the CSJ eval1/eval2/eval3 set. Furthermore, the proposed model is more robust than the standard Transformer model, particularly for long-form speech on both the datasets. In the TTS task, the proposed method outperforms the Transformer baseline.
Video Anomaly Detection for Pedestrian Surveillance

With the increase in video surveillance technology, modern human beings have more viable options to enhance safety, security, and monitoring. Automatic video surveillance is an option that provides remote monitoring with little human effort and is a computer vision task. There is no end to the applications of automatic video surveillance such as traffic monitoring, theft detection, fight detection. These are important in various places like industrial, residential and official buildings, roads, and many more. The key objective of the present study is to monitor the pedestrian streets and to provide safety and security by identifying anomalous events. However, tracking an anomalous event in itself is a tricky task because of changes in the definition of an anomaly in different scenarios. In this research, high-level features are used to enhance anomaly detection performance using an auto-encoder model. The features are derived from the pre-trained models, and the contextual properties are derived from the extracted features. The datasets used for anomaly detection on the pedestrian streets are UCSD Pedestrian Street Peds1 and Peds2. The performance is evaluated on the Receiver Operating Characteristic (ROC) curve, Area under Curve (AUC), Precision-Recall curve, Average Precision, and Equal Error Rate (EER) value.
Structured Pruning of Self-Supervised Pre-Trained Models for Speech Recognition and Understanding

Self-supervised speech representation learning (SSL) has shown to be effective in various downstream tasks, but SSL models are usually large and slow. Model compression techniques such as pruning aim to reduce the model size and computation without degradation in accuracy. Prior studies focus on the pruning of Transformers; however, speech models not only utilize a stack of Transformer blocks, but also combine a frontend network based on multiple convolutional layers for low-level feature representation learning. This frontend has a small size but a heavy computational cost. In this work, we propose three task-specific structured pruning methods to deal with such heterogeneous networks. Experiments on LibriSpeech and SLURP show that the proposed method is more accurate than the original wav2vec2-base with 10% to 30% less computation, and is able to reduce the computation by 40% to 50% without any degradation.
Approximation in morphology: A state of the art

This paper offers a state of the art of approximation within the larger domain of evaluative morphology. It provides an overview of the formal means employed by the morphology of different languages to express approximative meanings, as well as a survey of the specific approximative values that can be conveyed. We further discuss the (input and output) lexical categories involved in the expression of approximation crosslinguistically. On an intralinguistic level, we address the issue of competition between different strategies.
Scheduling Mechanisms in Serverless Computing

Currently, serverless computing is considered a critical event in the information and communication technologies domain. It provides a model with high scalability, pay-as-you-go, and a flexible approach to accessing requests using microservices. Many applications implement a microservice architecture, making them perform better than monolith architecture. Microservices are small pieces of code called functions, each of which can be used to run a series of processes. Consequently, microservices need the resources for execution. Hence, one of the critical issues is the efficient allocation of resources for microservices on the nodes, which are considered by structures called schedulers. Scheduling is the strategy of allocating tasks to resources in time. It increases the serverless domain’s performance and efficiency by maximizing resource utilization. This scheduling strategy has to consider restrictions specified by the serverless providers and the careers. Using the scheduler’s tasks and maintaining fairness between efficiency and the quality of careers service is complex. Scheduling algorithms are developed considering metrics similar to performance, priority, latency, cost, etc. Therefore, the process of resource allocation is regarded as a critical factor that can be of considerable significance to service providers. In serverless computing, service providers must ensure that their chosen scheduling strategies are satisfactory to service receivers. Although there are various scheduling techniques, it is crucial to point out that no single scheduling technique can accommodate all the requirements of various applicants. An in-depth understanding of the types of schedules and selecting the most effective scheduler for different kinds of applicant requirements is therefore crucial to provide the most efficient allocation of resources. In other words, when selected scheduling is inefficient, several problems can result both for the service provider and the service recipient. Therefore, service providers are forced to increase their costs. As a result, the cost of the applicants is increased. The consequences of this situation are that the requesters are dissatisfied with the poor quality service received and the increased cost they have and are less inclined to use a provider’s services in the future. Many scheduler strategies are available to providers; therefore, they should become familiar with a variety of them and understand their advantages, characteristics, and disadvantages. In this study, we comprehensively investigate the widely employed schedulers in serverless computing by investigating their advantages, disadvantages, and applications. The purpose of the present study is to present a comprehensive examination of various and effective scheduling techniques that can be a basis for selecting the appropriate scheduling process based on the providers’ approach.
Quantum and Semi–Quantum key Distribution in Networks

In this paper, we utilize the potential offered by multidimensional separable states (MSS) for secure and simultaneous distributions of keys in a layered network. We present protocols for both quantum and semi-quantum key distribution and discuss their robustness against various eavesdropping strategies. We provide a procedure to identify the requisite resource states to generalise these protocols for arbitrary layered networks. Finally, we study the interrelation between the local dimensionalities of states and achievable key rates in a given layer. These proposals are realisable with current technology, thanks to employment of MSS and many advances in generation, manipulation and measurement of higher-dimensional orbital angular momentum states of light.
Dealing with Evaluative Expressions and Hate Speech Metaphors with Fuzzy Property Grammar Systems

We introduce a Fuzzy Property Grammar System (FPGS), a formalism that integrates a Fuzzy Property Grammar into a linguistic grammar system to formally characterize metaphorical evaluative expressions. The main scope of this paper is to present the formalism of FPGS and to show how it might provide a formal characterization of hate speech linguistic evaluative expressions with metaphors (as fuzzy concepts), together with evaluating their degree of linguistic violence. Linguistic metaphors are full of semantic coercions. It is necessary to formally characterize the context of the communication to acknowledge the extralinguistic constraints of the pragmatic domain, which establishes whether an utterance is violent. To show the applicability of our formalism, we present a proof of concept. By compiling and tagging a 3000-tweet corpus, we have extracted a lexicon of hate speech metaphors. Furthermore, we show how FPGS architecture can deal with different types of hate speech and can identify implicit violent figurative evaluative expressions by context and type. Although we are still in the experimental phase of our project and cannot present conclusive results at the computational level, the proof-of-concept results show that our formalism can achieve the desired outcome.
Securing web applications against SQL injection attacks - A Parameterised Query perspective)

SQL Injection attack is a major threat to web applications and source of concerns to e-commerce in particular. The attack bypasses major security measures to execute malicious SQL codes and have absolute control of the database server behind the web application. Going by millions of dollars being spent yearly by various organizations to guide against the attack, it is imperative to have a better understanding of how SQL Injection works and how best it could be prevented. Using an e-commerce application as a case study, this paper demonstrates how Parameterized Queries could be used to defend SQL Injection attacks. With prepared statements in Java where LoginController was employed as the Servlet to control the application login procedure, Parameterized Queries were successfully incorporated into an e-commerce application. The implementation results shows that SQL injection is no longer possible as every code from attackers could not be executed because input was set to be data and data would be treated differently from codes.