Reviewerly/exHarmony huggingface dataset:
Barren plateaus in quantum tensor network optimization

We analyze the barren plateau phenomenon in the variational optimization of quantum circuits inspired by matrix product states (qMPS), tree tensor networks (qTTN), and the multiscale entanglement renormalization ansatz (qMERA). We consider as the cost function the expectation value of a Hamiltonian that is a sum of local terms. For randomly chosen variational parameters we show that the variance of the cost function gradient decreases exponentially with the distance of a Hamiltonian term from the canonical centre in the quantum tensor network. Therefore, as a function of qubit count, for qMPS most gradient variances decrease exponentially and for qTTN as well as qMERA they decrease polynomially. We also show that the calculation of these gradients is exponentially more efficient on a classical computer than on a quantum computer.
Design and Development of Artificial Intelligence Knowledge Processing System for Optimizing Security of Software System

Abstract Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide the software development team with insights to act upon the security vulnerabilities proactively. Across these modules of software landscape, customer landscape, and industry landscape, data sources are leveraged using artificial intelligence approaches to identify the security insights. The focus is also on building a smart knowledge management system that integrates the information processed across modules into a central system. This central intelligence system can be further leveraged to manage software development activities proactively. In this exploration, machine learning and deep learning approaches are devised to model the data and learn from across the modules. Architecture for all the modules and their integration is also proposed. Work helps to envision a smart system for Artificial Intelligence-based knowledge management for managing software security vulnerabilities.
Twitter sentiment analysis using hybrid gated attention recurrent network

Abstract Sentiment analysis is the most trending and ongoing research in the field of data mining. Nowadays, several social media platforms are developed, among that twitter is a significant tool for sharing and acquiring peoples’ opinions, emotions, views, and attitudes towards particular entities. This made sentiment analysis a fascinating process in the natural language processing (NLP) domain. Different techniques are developed for sentiment analysis, whereas there still exists a space for further enhancement in accuracy and system efficacy. An efficient and effective optimization based feature selection and deep learning based sentiment analysis is developed in the proposed architecture to fulfil it. In this work, the sentiment 140 dataset is used for analysing the performance of proposed gated attention recurrent network (GARN) architecture. Initially, the available dataset is pre-processed to clean and filter out the dataset. Then, a term weight-based feature extraction termed Log Term Frequency-based Modified Inverse Class Frequency (LTF-MICF) model is used to extract the sentiment-based features from the pre-processed data. In the third phase, a hybrid mutation-based white shark optimizer (HMWSO) is introduced for feature selection. Using the selected features, the sentiment classes, such as positive, negative, and neutral, are classified using the GARN architecture, which combines recurrent neural networks (RNN) and attention mechanisms. Finally, the performance analysis between the proposed and existing classifiers is performed. The evaluated performance metrics and the gained value for such metrics using the proposed GARN are accuracy 97.86%, precision 96.65%, recall 96.76% and f-measure 96.70%, respectively.
YuNet: A Tiny Millisecond-level Face Detector

Abstract Great progress has been made toward accurate face detection in recent years. However, the heavy model and expensive computation costs make it difficult to deploy many detectors on mobile and embedded devices where model size and latency are highly constrained. In this paper, we present a millisecond-level anchor-free face detector, YuNet, which is specifically designed for edge devices. There are several key contributions in improving the efficiency-accuracy trade-off. First, we analyse the influential state-of-the-art face detectors in recent years and summarize the rules to reduce the size of models. Then, a lightweight face detector, YuNet, is introduced. Our detector contains a tiny and efficient feature extraction backbone and a simplified pyramid feature fusion neck. To the best of our knowledge, YuNet has the best trade-off between accuracy and speed. It has only 75856 parameters and is less than 1/5 of other small-size detectors. In addition, a training strategy is presented for the tiny face detector, and it can effectively train models with the same distribution of the training set. The proposed YuNet achieves 81.1% mAP (single-scale) on the WIDER FACE validation hard track with a high inference efficiency (Intel i7-12700K: 1.6ms per frame at 320 × 320). Because of its unique advantages, the repository for YuNet and its predecessors has been popular at GitHub and gained more than 11K stars at https://github.com/ShiqiYu/libfacedetection
A novel group recommender system for domain-independent decision support customizing a grouping genetic algorithm

Abstract Group formation is a complex task requiring computational support to succeed. In the literature, there has been considerable effort in the development of algorithms for composing groups as well as their evaluation. The most widely used approach is the Genetic Algorithm, as, it can handle numerous variables, generating optimal solutions according to the problem requirements. In this study, a novel genetic algorithm was developed for forming groups using innovative genetic operators, such as a modification of 1-point and 2-point crossover, the gene and the group crossover, to improve its performance and accuracy. Moreover, the proposed algorithm can be characterized as domain-independent, as it allows any input regardless of the domain problem; i.e., whether the groups concern objects, items or people, or whether the field of application is industry, education, healthcare, etc. The grouping genetic algorithm has been evaluated using a dataset from the literature in terms of its settings, showing that the tournament selection is better to be chosen when a quick solution is required, while the introduced gene and group crossover operators are superior to the classic ones. Furthermore, the combination of up to three crossover operators is ideal solution concerning algorithm’s accuracy and execution time. The effectiveness of the algorithm was tested in two grouping cases based on its acceptability. Both the students participated in forming collaborative groups and the professors participated in evaluating the groups of courses created were highly satisfied with the results. The contribution of this research is that it can help the stakeholders achieve an effective grouping using the presented genetic algorithm. In essence, they have the flexibility to execute the genetic algorithm in different contexts as many times as they want until to succeed the preferred output by choosing the number of operators for either greater accuracy or reduced execution time.
Perspectives on Large Language Models for Relevance Judgment

When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems. In this perspectives paper, we discuss possible ways for LLMs to support relevance judgments along with concerns and issues that arise. We devise a human--machine collaboration spectrum that allows to categorize different relevance judgment strategies, based on how much humans rely on machines. For the extreme point of "fully automated judgments", we further include a pilot experiment on whether LLM-based relevance judgments correlate with judgments from trained human assessors. We conclude the paper by providing opposing perspectives for and against the use of~LLMs for automatic relevance judgments, and a compromise perspective, informed by our analyses of the literature, our preliminary experimental evidence, and our experience as IR researchers.
Designing Cybersecurity AI Based Awareness Games for Citizens: Best Practices and Future Directions

Cybercrimes are a hazard to both people and businesses. Knowing the best techniques for cyber security is essential for defending against these threats. Despite their ability to be useful, conventional learning techniques like reading articles or attending seminars lack interest. Because of this, employing serious games for education is growing in popularity. Serious games give players an involved learning experience while educating them on a subject, like cyber security. In order to give players a useful understanding to defend themselves, this study investigates the best approaches for embedding cyber security education into serious games. Learning about cyber security is made enjoyable and more relevant by using a game-based methodology. In order to evaluate how game design aspects might be utilized to effectively instruct players on cyber security, the paper examines a variety of game design elements, including game mechanics, narrative, and feedback mechanisms. Additionally, it goes into how different players, such as novice and experienced players, might be catered to in serious games. In general, playing serious games is a fun and effective approach to educate yourself about cyber security. Designers and teachers can produce games that are both enjoyable and instructional by adhering to the quality standards described in this paper.
Many-body cavity quantum electrodynamics with driven inhomogeneous emitters

Quantum emitters coupled to optical resonators are quintessential systems for exploring fundamental phenomena in cavity quantum electrodynamics (cQED)1 and are commonly used in quantum devices acting as qubits, memories and transducers2. Many previous experimental cQED studies have focused on regimes in which a small number of identical emitters interact with a weak external drive3-6, such that the system can be described with simple, effective models. However, the dynamics of a disordered, many-body quantum system subject to a strong drive have not been fully explored, despite its importance and potential in quantum applications7-10. Here we study how a large, inhomogeneously broadened ensemble of solid-state emitters coupled with high cooperativity to a nanophotonic resonator behaves under strong excitation. We discover a sharp, collectively induced transparency (CIT) in the cavity reflection spectrum, resulting from quantum interference and collective response induced by the interplay between driven inhomogeneous emitters and cavity photons. Furthermore, coherent excitation within the CIT window leads to highly nonlinear optical emission, spanning from fast superradiance to slow subradiance11. These phenomena in the many-body cQED regime enable new mechanisms for achieving slow light12 and frequency referencing, pave a way towards solid-state superradiant lasers13 and inform the development of ensemble-based quantum interconnects9,10.
HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion

Representing human performance at high-fidelity is an essential building block in diverse applications, such as film production, computer games or videoconferencing. To close the gap to production-level quality, we introduce HumanRF, a 4D dynamic neural scene representation that captures full-body appearance in motion from multi-view video input, and enables playback from novel, unseen viewpoints. Our novel representation acts as a dynamic video encoding that captures fine details at high compression rates by factorizing space-time into a temporal matrix-vector decomposition. This allows us to obtain temporally coherent reconstructions of human actors for long sequences, while representing high-resolution details even in the context of challenging motion. While most research focuses on synthesizing at resolutions of 4MP or lower, we address the challenge of operating at 12MP. To this end, we introduce ActorsHQ, a novel multi-view dataset that provides 12MP footage from 160 cameras for 16 sequences with high-fidelity, per-frame mesh reconstructions. We demonstrate challenges that emerge from using such high-resolution data and show that our newly introduced HumanRF effectively leverages this data, making a significant step towards production-level quality novel view synthesis.
A machine learning method for distinguishing detrital zircon provenance

Abstract Zircon geochemistry provides a sensitive monitor of its parental magma composition. However, due to the complexity of the uptake of trace elements during zircon growth, identifying source magmas remains challenging, particularly for detrital grains whose petrological context is lost. We use a machine learning-based approach to explore the classifiers for zircon provenance, based on 3794 published, high-quality zircon trace element analyses compiled from I-, S-, and A-type granites. Three supervised machine learning algorithms, namely, Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) were used and trained with 11 features, including 7 trace elements (Ce, Eu, Ho, Nb, Ta, Th, and U) and 4 derived trace element ratios (Th/U, U/Yb, Ce/Ce*, and Eu/Eu*). Our results show that all three trained machine learning methods perform very well with accuracy varying from 0.86 to 0.89, and that input–output relationships captured by different ML methods are nearly consistent and can be explained by the known petrological processes. The application of our trained machine learning classifiers to detrital zircon studies will enhance the interpretability of zircon assemblages of different origins. It also helps develop interpretations, approaches, and tools that will benefit, for example, the study of continental crust evolution and mineral exploration.
Hardware Design of an Advanced-Feature Cryptographic Tile within the European Processor Initiative

This work describes the hardware implementation of a cryptographic accelerators suite, named Crypto-Tile, in the framework of the European Processor Initiative (EPI) project. The EPI project traced the roadmap to develop the first family of low-power processors with the design fully made in Europe, for Big Data, supercomputers and automotive. Each of the coprocessors of Crypto-Tile is dedicated to a specific family of cryptographic algorithms, offering functions for symmetric and public-key cryptography, computation of digests, generation of random numbers, and Post-Quantum cryptography. The performances of each coprocessor outperform other available solutions, offering innovative hardware-native services, such as key management, clock randomisation and access privilege mechanisms. The system has been synthesised on a 7 nm standard-cell technology, being the first Cryptoprocessor to be characterised in such an advanced silicon technology. The post-synthesis netlist has been employed to assess the resistance of Crypto-Tile to power analysis side-channel attacks. Finally, a demoboard has been implemented, integrating a RISC-V softcore processor and the Crypto-Tile module, and drivers for hardware abstraction layer, bare-metal applications and drivers for Linux kernel in C language have been developed. Finally, we exploited them to compare in terms of execution speed the hardware-accelerated algorithms against software-only solutions.
Artificial intelligence in the library: Gauging the potential application and implications for contemporary library services in Nigeria

Purpose: Libraries may become obsolete in the twenty-first century unless they begin to harness new technology and improve information and service delivery. This paper examines the potential application and implications of artificial intelligence for contemporary library services in Nigeria. Methods: This paper adopts the expository research approach to evaluate the application and implication of artificial intelligence in contemporary library services in Nigeria. Through systematic analysis of literature, the study addresses how academic libraries can utilize artificial intelligence to support innovative library services. Findings: The column emphasizes that, academic libraries in Nigeria have not yet adopted and applied AI, in spite of the potential that it holds for libraries. Given that there has been relatively little study linking artificial intelligence (AI) to librarianship, this may be because there is a low degree of awareness and adoption of AI's importance in libraries.Conclusions: This column is the original idea from the authors and does not reflect on any copyrighted materials. The column recommended that, academic libraries in Nigeria should fully embrace artificial intelligence like chatbots, barcodes, RFIDs, and robots for delivering quality services and libraries should also leverage on the opportunities presented by artificial intelligence to reconnect their remote users, and consequently re-establish their relevance among the user community.
Assessing the impact of cloud-based supply chain management on organizational agility: A structural equation modeling approach

This study investigates the impact of cloud-based supply chain management on organizational agility, using a Structural Equation Modeling (SEM) approach. The research is motivated by the growing importance of cloud computing in supply chain management and the need for empirical studies that assess its benefits and challenges. Drawing on a survey of 200 supply chain managers from various industries, we develop and test a theoretical model that explores the relationships between cloud adoption and organizational agility. Hypotheses for the study are tested using the structural equation model. According to the study, Cloud-based SCM has a statistically significant effect on agility and flexibility. The findings have important practical and theoretical implications and provide insights that can help managers and practitioners make informed decisions regarding the implementation of cloud-based SCM solutions and the development of strategies that enhance supply chain agility and competitiveness.
Single-emitter quantum key distribution over 175 km of fibre with optimised finite key rates

Quantum key distribution with solid-state single-photon emitters is gaining traction due to their rapidly improving performance and compatibility with future quantum networks. Here we emulate a quantum key distribution scheme with quantum-dot-generated single photons frequency-converted to 1550 nm, achieving count rates of 1.6 MHz with [Formula: see text] and asymptotic positive key rates over 175 km of telecom fibre. We show that the commonly used finite-key analysis for non-decoy state QKD drastically overestimates secure key acquisition times due to overly loose bounds on statistical fluctuations. Using the tighter multiplicative Chernoff bound to constrain the estimated finite key parameters, we reduce the required number of received signals by a factor 108. The resulting finite key rate approaches the asymptotic limit at all achievable distances in acquisition times of one hour, and at 100 km we generate finite keys at 13 kbps for one minute of acquisition. This result is an important step towards long-distance single-emitter quantum networking.
Quantum Pseudorandomness and Classical Complexity

We construct a quantum oracle relative to which $\mathsf{BQP} = \mathsf{QMA}$ but cryptographic pseudorandom quantum states and pseudorandom unitary transformations exist, a counterintuitive result in light of the fact that pseudorandom states can be "broken" by quantum Merlin-Arthur adversaries. We explain how this nuance arises as the result of a distinction between algorithms that operate on quantum and classical inputs. On the other hand, we show that some computational complexity assumption is needed to construct pseudorandom states, by proving that pseudorandom states do not exist if $\mathsf{BQP} = \mathsf{PP}$. We discuss implications of these results for cryptography, complexity theory, and quantum tomography.
The Teacher’s Strategies in Overcoming Students’ Speaking Problems at SMAN 1 Selayar

The aim of this study was to find out the teacher’s strategy and students’ speaking problems at SMAN 1 Selayar. This study used mix method namely quantitative and qualitative. There were 215 students in the population. Then, there were 20 students and 1 teacher of of SMAN 1 Selayar as sample of study. Taking of sample was carried out by cluster random sampling. The instruments used observation, questionnaires and interview quides. The results of this study showed that some strategies used by teacher in overcoming students’ speaking problems at SMA Negeri 1 Selayar namely Role Play, Number Head Together, Discussion and Grouping. While, there were 13 (65%) students who said nothing in English as one of students’ speaking problem. Then, students thought to speak English as 9 (45%) students. The worry statement in making mistake to speak English was 8 (40%) students. Students felt nervous in speaking English as 11(55%) students. Students who were not confident were 7 (35%) students. It can be concluded that there are two dominant students’ speaking problem, namely students think nothing in speaking English and be nervous in speaking English. While, the dominant strategy is Role Play. The further researcher is recommended to be more detail finding problem faced by students in order for the teacher to apply strategy based on students’ need.
Multilingual text categorization and sentiment analysis: a comparative analysis of the utilization of multilingual approaches for classifying twitter data

Text categorization and sentiment analysis are two of the most typical natural language processing tasks with various emerging applications implemented and utilized in different domains, such as health care and policy making. At the same time, the tremendous growth in the popularity and usage of social media, such as Twitter, has resulted on an immense increase in user-generated data, as mainly represented by the corresponding texts in users' posts. However, the analysis of these specific data and the extraction of actionable knowledge and added value out of them is a challenging task due to the domain diversity and the high multilingualism that characterizes these data. The latter highlights the emerging need for the implementation and utilization of domain-agnostic and multilingual solutions. To investigate a portion of these challenges this research work performs a comparative analysis of multilingual approaches for classifying both the sentiment and the text of an examined multilingual corpus. In this context, four multilingual BERT-based classifiers and a zero-shot classification approach are utilized and compared in terms of their accuracy and applicability in the classification of multilingual data. Their comparison has unveiled insightful outcomes and has a twofold interpretation. Multilingual BERT-based classifiers achieve high performances and transfer inference when trained and fine-tuned on multilingual data. While also the zero-shot approach presents a novel technique for creating multilingual solutions in a faster, more efficient, and scalable way. It can easily be fitted to new languages and new tasks while achieving relatively good results across many languages. However, when efficiency and scalability are less important than accuracy, it seems that this model, and zero-shot models in general, can not be compared to fine-tuned and trained multilingual BERT-based classifiers.