Reviewerly/exHarmony huggingface dataset:
Digital transformation in higher education: A comprehensive review of e-learning adoption

Authors:
raw_author_name: Devesh Kumar
raw_affiliation_string: School of Commerce and Management Studies, Central University of Himachal Pradesh, Dharamshala, India

Abstract: BACKGROUND: Data-driven practices are now crucial in the digital age for streamlining operations and boosting output across a range of industries. With e-Learning emerging as a cutting-edge and promising strategy to improve educational delivery and student engagement, higher education institutions are also adopting this trend. Despite the potential advantages, e-Learning adoption in higher education is still a developing topic with scant published research. OBJECTIVE: This study aims to conduct a thorough analysis of existing literature, seeking to determine the present status of e-Learning adoption in higher education while also identifying promising avenues for further research. METHODS: In this study, a systematic literature review methodology and a bibliometric approach are utilized to examine 63 papers sourced from the Scopus database, encompassing the years 2017 to 2021. These papers were specifically chosen due to their pertinence to e-Learning adoption in higher education institutions, following the exclusion and inclusion criteria outlined in the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework. The analysis techniques employed include descriptive overviews, citation analysis, and keyword co-occurrence. RESULTS: The systematic review of the literature revealed a significant increase in e-Learning adoption within higher education institutions (HEIs) in recent times. The findings highlighted several crucial factors influencing e-Learning adoption, including perceived ease of use, usefulness, learner attitude, self-efficacy, and facilitating conditions. However, further investigation is needed to better understand the impact of perceived learning and teaching styles on e-Learning acceptance in higher education settings. CONCLUSION: This study suggests conducting future investigations on e-Learning in developing countries, with a specific focus on intention, usage, adoption, and impact. Additionally, it emphasizes the importance of exploring the influence of learning and teaching styles on e-Learning acceptance within higher education institutions. These valuable insights will facilitate the development of effective strategies for the successful implementation of e-Learning in higher education settings.
Efficient bosonic nonlinear phase gates

Authors:
raw_author_name: Radim Filip
raw_affiliation_string: Department of Optics, Palacky University, 77146, Olomouc, Czech Republic

Abstract: Abstract Continuous-variable (CV) quantum information processing harnesses versatile experimental tools that leverage the power of infinite-dimensional oscillators controlled by a single qubit. Increasingly available elementary Rabi gates have been proposed as a resource for implementing universal CV gates, but the requirement of many weak, non-commuting gates is a bottleneck in scaling up such an approach. In this study, we propose a resource-efficient technique using Fourier expansion to implement arbitrary non-linear phase gates in a single oscillator. This method reduces the number of sequentially required gates exponentially. These gates represented by cubic, quartic, and other arbitrary nonlinear potentials have applications in CV quantum information processing with infinite-dimensional oscillators controlled by a single qubit. Our method outperforms previous approaches and enables the experimental realization of a wide range of applications, including the development of bosonic quantum sensors, simulations, and computation using trapped ions and superconducting circuits.
Collaborative Modality Fusion for Mitigating Language Bias in Visual Question Answering

Authors:
raw_author_name: Qiwen Lu
raw_affiliation_string: School of Computer and Information Engineering, Henan University, Kaifeng 475001, China
raw_author_name: Shengbo Chen
raw_affiliation_string: School of Computer and Information Engineering, Henan University, Kaifeng 475001, China
raw_author_name: Xiaoke Zhu
raw_affiliation_string: School of Computer and Information Engineering, Henan University, Kaifeng 475001, China

Abstract: Language bias stands as a noteworthy concern in visual question answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. In order to address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multimodal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.
The ABC of academic writing: non-native speakers’ perspective

Authors:
raw_author_name: Shinichi Nakagawa
raw_affiliation_string: Evolution and Ecology Centre and School of Biological, Earth, and Environmental Sciences, The University of New South Wales, Sydney, New South Wales, Australia; Theoretical Sciences Visiting Program, Okinawa Institute of Science and Technology Graduate University, Onna, Japan

Abstract: <h2>Abstract</h2> Academic writing is difficult, especially for non-native English speakers. We share a perspective on writing with a set of heuristics called the Writing Alphabet, consisting of Accurate, Brief, Clear, Dynamic, Engaging, Flowing, Goal, Habit, and Investment. These points can help struggling writers identify issues and, importantly, internalise good writing practices.
Using early LLMs for corpus linguistics: Examining ChatGPT's potential and limitations

Authors:
raw_author_name: Satoru UCHIDA
raw_affiliation_string: Faculty of Languages and Cultures, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka, Japan

Abstract: This study evaluates the extent to which information can be obtained from early Large Language Models (LLMs) for corpus linguistic research. Various tasks were conducted using ChatGPT 3.5, such as generating word frequency lists, collocations, words that fit certain grammatical patterns, and identifying genres. These were then compared with the search results from a large-scale general corpus (COCA). While favorable results were not achieved in identifying the genres of words or paragraphs, there was notable congruence in the frequency lists (75.0%), collocations (42.8%), and grammatical patterns (53.0%) for the top 20 items. Even when the generated items did not perfectly match those from COCA, it was evident that high-frequency items were produced. Although LLMs may not be sufficient for rigorous academic research, the results are adequate for discerning overall trends or assisting learners. In addition, the results of this study show that the ability to search at the phrase level is an advantage of using LLMs for corpus research.
Dataset of controversial news posts in Spanish from the reader's perspective

Authors:
raw_author_name: Hiram Calvo
raw_affiliation_string: Centro de Investigación en Computación, Instituto Politécnico Nacional. Av. Juan de Dios Bátiz, esq. Miguel Othón de Mendizabal, Col. Nueva Industrial Vallejo, Alcaldía Gustavo A. Madero, C.P. 07700, CDMX, México
raw_author_name: Omar Juárez Gambino
raw_affiliation_string: Escuela Superior de Cómputo, Instituto Politécnico Nacional. Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco. Alcaldía Gustavo A. Madero C.P. 07738, CDMX, México

Abstract: This paper presents a corpus of Spanish news posts obtained from X with the annotation of controversy made via crowdsourcing. A total of 60 tweets were obtained from 8 different newspapers. For the annotation task, a survey was developed and sent to 31 different participants to answer it with the controversy level they perceived from the news post summary and headline presented on the post. The most frequent selected option was assigned as the initial controversy level of the post. The final annotation of the corpus was made via an analysis of the raw data by computing the Inter Annotator Agreement (IAA). The analysis showed that the binarization of the data was the most convenient way to annotate it. A potential use for this dataset is detailed in further sections.
Criminal expertise and hacking efficiency

Authors:
raw_author_name: Asier Moneva
raw_affiliation_string: Center of Expertise Cyber Security, The Hague University of Applied Sciences, the Netherlands; Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), the Netherlands
raw_author_name: Stijn Ruiter
raw_affiliation_string: Department of Sociology, Utrecht University, the Netherlands; Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), the Netherlands

Abstract: Criminal expertise plays a crucial role in the choices offenders make when committing a crime, including their modus operandi. However, our knowledge about criminal decision making online remains limited. Drawing on insights from cyber security, we conceptualize the cybercrime commission process as the sequence of phases of the cyber kill chain that offenders go through. We assume that offenders who follow the sequence consecutively use the most efficient hacking method. Building upon the expertise paradigm, we hypothesize that participants with greater hacking experience and IT skills undertake more efficient hacks. To test this hypothesis, we analyzed data from 69 computer security and software engineering students who were invited to hack a vulnerable website in a computer lab equipped with monitoring software, which allowed to collect objective behavioral measures. Additionally, we collected individual measures regarding hacking expertise through an online questionnaire. After quantitatively measuring efficiency using sequence analysis, a regression model showed that the expertise paradigm may also apply to hackers. We discuss the implications of our novel research for the study of offender decision-making processes more broadly.
Improved monte Carlo ray-tracing algorithm based on importance sampling

Authors:
raw_author_name: Chen Gong
raw_affiliation_string: CAS Key Laboratory of Wireless-Optical Communications, University of Science and Technology of China, Hefei, Anhui 230027, China

Abstract: Abstract This paper proposes an improved ray-tracing algorithm with importance sampling (IS) to reduce the computational complexity. For multiple receiver situation, we provide improved IS including adaptive and weighted optimization.
Graph-SeTES: A Graph based Search Task Extraction using Siamese Network

Authors:
raw_author_name: Nurullah Ates
raw_affiliation_string: Department of Computer Engineering, Faculty of Computer and Informatics, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey; Faculty of Engineering, Department of Computer Engineering, Ondokuz Mayıs University, Atakum, Samsun, 55139, Turkey
raw_author_name: Yusuf Yaslan
raw_affiliation_string: Department of Computer Engineering, Faculty of Computer and Informatics, Istanbul Technical University, Maslak, Istanbul, 34469, Turkey

Abstract: Search task extraction is a sub-process for query suggestion/reformulation, personalized recommendation, and advertisement in search engines and e-commerce platforms. However, they face both internal and external challenges. Internal challenges include short and misspelled queries and incomplete keywords. External datasets include an unknown number of clusters and a limited number of labeled datasets. Deep-learning models require a large amount of data for training; however, search task datasets are rare and small. To overcome these limitations, we proposed Graph-SeTES, which integrates feature extraction with a decision network that utilizes both distance metrics and decision networks. The existing graph-clustering algorithms use the similarities between search query pairs. Because the Siamese network (SN) finds similarities between two objects, it fits search task extraction. Compared with existing models, SN requires fewer parameters for training due to the shared weights. However, it yields good results even with less labeled data, overcoming the external challenges. We benefit from both distance metrics and narrowing of the linear layers for decision networks. Graph-SeTES was compared with state-of-the-art models, and it outperformed its counterparts. The results were 6% better than those of the best baseline on the CSTE dataset, which maintained this performance difference on the WSMC12 dataset.
Mode combinability: Exploring convex combinations of permutation aligned models

Authors:
raw_author_name: Adrián Csiszárik
raw_affiliation_string: Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary; Eötvös Loránd University, Pázmány Péter sétány 1/C, Budapest, 1117, Hungary
raw_author_name: Dániel Varga
raw_affiliation_string: Alfréd Rényi Institute of Mathematics, Reáltanoda utca 13-15., Budapest, 1053, Hungary

Abstract: We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors ΘA and ΘB of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the hypercube [0,1]d and its vicinity. Our findings reveal that broad regions of the hypercube form surfaces of low loss values, indicating that the notion of linear mode connectivity extends to a more general phenomenon which we call mode combinability. We also make several novel observations regarding linear mode connectivity and model re-basin. We demonstrate a transitivity property: two models re-based to a common third model are also linear mode connected, and a robustness property: even with significant perturbations of the neuron matchings the resulting combinations continue to form a working model. Moreover, we analyze the functional and weight similarity of model combinations and show that such combinations are non-vacuous in the sense that there are significant functional differences between the resulting models.
A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

Authors:
raw_author_name: Abbas Maghsoudi
raw_affiliation_string: Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran
raw_author_name: Reza Ghezelbash
raw_affiliation_string: School of Mining Engineering, College of Engineering, University of Tehran, Tehran, Iran

Abstract: The process of data-driven clustering to uncover geochemical anomalies linked to sample catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting unique geochemical attributes within a specified study area. The Ordering Points to Identify the Clustering Structure (OPTICS) method can serve as a robust methodology for detecting geochemical anomalies in SCBs. This is attributed to its capacity to effectively manage varying cluster densities, adaptively identify cluster numbers, exhibit resilience to noise, and display minimum sensitivity to parameters. A comparison was conducted in this research between the outcomes of the OPTICS clustering algorithm and two traditional clustering techniques, namely the Gaussian Mixture Model (GMM) and K-means clustering. In the following, the Expectation-Maximization (EM) technique is employed to train the GMM for clustering. Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as two validate statistical metrics implemented to ascertain the optimal number of components (clusters) belong to the GMM. It should be noted that the effectiveness of the clustering algorithms was further assessed using the Calinski-Harabasz (CH) index and the success-rate curves. OPTICS, a density-based clustering approach, was confirmed to be more effective than K-means and GMM for identifying MVT PbZn anomalies in Varcheh district, western Iran. Furthermore, the specified anomalies show a geo-spatial correspondence with the geological facts, and it has been observed that strong anomalies are more discoverable in close proximity to MVT PbZn occurrences. This work suggests a novel anomaly detection approach based on OPTICS, which exhibits superior performance and data-modeling efficiency. The main emphasis is on effectively distinguishing geochemical anomalies from sample data originating from populations with uncertain distributions.
Self-restrained contrastive enhanced network for graph structure learning

Authors:
raw_author_name: Nan Jia
raw_affiliation_string: The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China
raw_author_name: Xiaolin Tian
raw_affiliation_string: The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China
raw_author_name: Ting Yang
raw_affiliation_string: The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China
raw_author_name: Shuo Li
raw_affiliation_string: The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China
raw_author_name: Licheng Jiao
raw_affiliation_string: The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, China

Abstract: Existing graph neural networks (GNNs) are mostly applied to representation scenes with complete graph structure. However, the graph structures of complex systems from the real world are generally missing or misconnected. In addition, due to the indistinguishability of graph node features, most of the existing GNNs are limited by the over-smoothing problem when performing classification tasks. To address these issues, we propose a model for learning and optimizing graph structure—Self-restrained Graph Contrastive Enhanced Network (GCEN). Firstly, the multiple branch generation structure is adopted to obtain various graph structures, which are updated by multi-order information fusion to obtain a new graph structure. Secondly, the performance of each generated structure branch is improved by self-restrained network, and then the structure information of the graph is continuously optimized to obtain a high-quality graph structure. Finally, the feature contrast enhancement module is utilized to adaptively adjust the features of nodes to obtain distinguishable features, thus alleviating the over-smoothing problem of GNNs in classification tasks. Extensive experiments on several benchmark datasets indicate that our GCEN is superior to several models for learning task-specific graph structure in optimizing the graph structure.
Partially reflecting jamming objects in correlation-enhanced target detection with entangled photons

Authors:
raw_author_name: V. V. Kornienko
raw_affiliation_string: Department of Electronics and Nanoengineering, Aalto University 1 , FI-00076 Espoo, Finland
raw_author_name: A. Pönni
raw_affiliation_string: Department of Electronics and Nanoengineering, Aalto University 1 , FI-00076 Espoo, Finland
raw_author_name: M. Raasakka
raw_affiliation_string: Department of Electronics and Nanoengineering, Aalto University 1 , FI-00076 Espoo, Finland
raw_author_name: I. Tittonen
raw_affiliation_string: Department of Electronics and Nanoengineering, Aalto University 1 , FI-00076 Espoo, Finland

Abstract: In the optical detection of distant objects, one can benefit from quantum light properties over those of classical light. Two-beam correlation-enhanced protocols can improve imaging or target detection even under strong background noise and in the low photon flux regime. We demonstrate that a partially reflecting jamming object introduces noise to these protocols, including quantum illumination. In contrast to background noise, the signal radiation reflected from the jammer is correlated with the corresponding idler beam. We analyze the means to mitigate this noise and introduce an indistinguishability parameter μ, showing how efficiently the jammer can be distinguished from the target. We use the quantum Chernoff bound and the density matrix orthogonalization procedure to separate the contributions from the target, the background, and the jammer. We illustrate our findings with an experiment using optical entangled photon pairs at 800 nm. Our results can be applied to the design of target detection protocols and advanced imaging techniques.
BapFL : You can Backdoor Personalized Federated Learning

Authors:
raw_author_name: Tiandi Ye
raw_affiliation_string: School of Data Science and Engineering, East China Normal University, Shanghai, China
raw_author_name: Cen Chen
raw_affiliation_string: School of Data Science and Engineering &amp; KLATASDS-MOE, East China Normal University, Shanghai, China
raw_author_name: Yinggui Wang
raw_affiliation_string: Ant Group, Beijing, China
raw_author_name: Xiang Li
raw_affiliation_string: School of Data Science and Engineering, East China Normal University, Shanghai, China
raw_author_name: Ming Gao
raw_affiliation_string: School of Data Science and Engineering &amp; KLATASDS-MOE, East China Normal University, Shanghai, China

Abstract: In federated learning (FL), malicious clients could manipulate the predictions of the trained model through backdoor attacks, posing a significant threat to the security of FL systems. Existing research primarily focuses on backdoor attacks and defenses within the generic federated learning scenario, where all clients collaborate to train a single global model. A recent study conducted by Qin et al. [24] marks the initial exploration of backdoor attacks within the personalized federated learning (pFL) scenario, where each client constructs a personalized model based on its local data. Notably, the study demonstrates that pFL methods with parameter decoupling can significantly enhance robustness against backdoor attacks. However, in this paper, we whistleblow that pFL methods with parameter decoupling are still vulnerable to backdoor attacks. The resistance of pFL methods with parameter decoupling is attributed to the heterogeneous classifiers between malicious clients and benign counterparts. We analyze two direct causes of the heterogeneous classifiers: (1) data heterogeneity inherently exists among clients and (2) poisoning by malicious clients further exacerbates the data heterogeneity. To address these issues, we propose a two-pronged attack method, BapFL , which comprises two simple yet effective strategies: (1) poisoning only the feature encoder while keeping the classifier fixed and (2) diversifying the classifier through noise introduction to simulate that of the benign clients. Extensive experiments on three benchmark datasets under varying conditions demonstrate the effectiveness of our proposed attack. Additionally, we evaluate the effectiveness of six widely used defense methods and find that BapFL still poses a significant threat even in the presence of the best defense, Multi-Krum. We hope to inspire further research on attack and defense strategies in pFL scenarios. The code is available at: https://github.com/BapFL/code.