Reviewerly/exHarmony huggingface dataset:
An Efficient Machine Learning Classifier for Sarcasm Detection

Irony is a sarcastic term that is used to actively criticize people. Irony is frequently expressed through indirect phrases. Politics, sports, business, and social media are the different places where ironic statements can be found. Some people can perceive irony in words, while others are clueless about how others have expressed themselves. To recognize these elements of speech, various machine learning and deep learning algorithms are applied. The main purpose of this study is to propose a broad summary of the ironic and non-ironic words found in subreddit data were used to categorize the text comments. This manuscript describes about the well-known datasets that are used to create the ironic detection networks by providing a major focus on classifying the comments. The considered dataset is partitioned to make the task easier. Finally, the process used for identifying irony phrases and the most successful classifiers and evaluation criteria are also discussed.
Multiparty quantum anonymous voting with discrete modulated coherent states and an optical frequency comb

A key challenge for quantum information science is to realize large-scale, precisely controllable, practical systems for multiparty secure communications. Recently, Guidry et al . [ Nat. Photonics 16 , 52 ( 2022 ) 10.1038/s41566-021-00901-z ] have investigated the quantum optics of a Kerr-based optical frequency comb (OFC), which lays out the way for OFC acting as a quantum resource to realize a low-cost and stable multiparty continuous-variable quantum information processing. In this work, we propose a distributed quantum anonymous voting (DQAV) protocol based on discrete modulated coherent states, in which a Kerr-based OFC serves as the resource to generate multi-frequency quantum signals for multiparty voting. We consider both the single-selection and multiple-selection ballot scenarios, and design the phase compensation method for the OFC-based protocol. Voting security is ensured by the basic laws of quantum mechanics, while voting anonymity is achieved by the random assignment of different frequency sources and the homogeneity of the quantum operations taken on the same voting choice. Numerical analysis calculates the secure voting distance over the thermal-lossy channel, showing the advancement of the proposed protocol under multiparty and multivalued voting tasks.
SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance

Ad relevance modeling plays a critical role in online advertising systems including Microsoft Bing. To leverage powerful transformers like BERT in this low-latency setting, many existing approaches perform ad-side computations offline. While efficient, these approaches are unable to serve cold start ads, resulting in poor relevance predictions for such ads. This work aims to design a new, low-latency BERT via structured pruning to empower real-time online inference for cold start ads relevance on a CPU platform. Our challenge is that previous methods typically prune all layers of the transformer to a high, uniform sparsity, thereby producing models which cannot achieve satisfactory inference speed with an acceptable accuracy.
DESIGN AND DEVELOPMENT OF A SURVEILLANCE ROBOT

Conventional surveillance which was done by human, is a dull job and prone to many mistakes. Additionally, manpower required for monitoring is expensive and is not suitable for weltering in remote places. Surveillance camera-based monitoring system is a temporary solution in this scenario. However, surveillance cameras are fixed to a certain position with limited coverage area. Furthermore, surveillance cameras defunct during and after natural disasters such as earthquakes,storms, etc., while there is a necessity for search and rescue of dwindling survivors. We propose a surveillance robot that overcomes the surveillance systems’ restricted coverage area problem with a light weight and low-cost robot structure, attached with a movable surveillance camera. Instead of recording video footage in passive, the system actively telecast visual information to a RaspberryPi system connected with Wi-Fi. A face detection system based on Viola-Jones algorithm is used to detect faces on real time basis. A user-friendly and easy to manage Graphical User Interface (GUI) is introduced using PyQt5. The experimental results show that the robot can detect singleto multiple faces either eyes open or closed on real time basis.
Applications of the Hillery-Zubairy entanglement criteria to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>N</mml:mi></mml:math> -qubit systems: The Tavis-Cummings model

We consider the application of the entanglement criteria derived by Hillery and Zubairy [Phys. Rev. Lett. 96, 050503 (2006)] to the detection of entanglement in $N$-qubit systems. For $N=2$ qubits we show that, with the natural choice of operators, one of the criteria never detects entanglement; we also derive conditions for the other criterion to work and for it to have a simple relation to the negativity when it does. For general angular momenta we show the Hillery-Zubairy relations can always detect the entanglement of the (pure) states of well-defined total (J, ${J}_{z}$) if the ``test'' operators are chosen appropriately. We then show how this may be used, in particular, to develop useful criteria to detect entanglement in a system of $N$ two-level atoms interacting with a field initially in a number state (the Tavis-Cummings model).
Acquisition and Visualization of Micro-Vibration of a Sound Wave in 3D Space

The acquisition of micro-vibrations is important for analyzing machinery. In the present study, we propose a method for measuring and visualizing the three-dimensional (3D) displacements of such micro-vibrations, especially in the case of sound waves propagating through space. The proposed method uses the speckle patterns of coherent light to measure the minute displacements. Speckle patterns are useful for detecting extremely small displacements owing to their sensitivity to the pose of the object. However, it is impossible to measure the displacement at each position because the pattern changes nonlinearly with respect to large depth changes. Therefore, a method of nonlinear low-dimensional embedding of the speckle pattern is proposed to analyze the displacements and extended to measure micro-displacements in a 3D space. We divided the 3D space into multiple slices and synchronously captured each speckle pattern. The displacements in the entire 3D space were simultaneously recovered by optimizing the embedded vectors, which were consistent in a 3D lattice. The propagation of sound waves in the 3D space was visualized using the volume-rendering technique. The experiments confirmed that the proposed method correctly measured the displacements by comparing them with the ground truth captured by microphones. We also visualized the wavefront of the sound wave propagating through space.
Making the Invisible, Visible

This poster analyzes consensus methods (e.g., CDP, Delphi, RAM, and NGT) by experts in scientific and technological contexts. Consensus methods provide opportunities for experts in different geographical locations and organizations to come together to deliberate and define their level of agreement on a complex topic (e.g., public health, environment, technology use). These experts are authoritative knowledge seekers with high H-Index scores and honorific society memberships. For these very reasons, their recommendations shift paradigms and influence policies. However, there are times when group deliberations lead to multiple and contradictory recommendations.&#x0D; Three research questions were developed for this study: How do experts reach a consensus? What information-seeking behaviors do they employ (using retrieval systems and software applications? Lastly, how do they understand consensus development processes? I applied ethnographic methods, including document analysis and retrospective experience, to collect data, and used data visualization to present preliminary insights.&#x0D; Preliminary insights remove ambiguity in defining what consensus is and what it is not. Based on a historical analysis of scientific consensus, four significant shifts that have led us to modern-day consensus methods is described, and a process for which experts negotiate agreement is explained. Finally, there is a call to action for information professionals and LAM institutions to push back against invisible labor in order to convey how our profession, in particular, our subject matter expertise and systems knowledge, significantly impacts scientific consensus development.
Evaluation of Invariant Extended Kalman Filters Applied to Multi-Sensor Land Vehicle Navigation in GNSS Challenging Environments

Invariant Kalman filters have emerged in recent years, introducing group theory to the well-established Kalman Filter theory. The invariant framework holds new possibilities and benefits related to stability and guaranteed convergence of the integrated solution. However, practical investigation using real land vehicle navigation scenarios is still latent. Therefore, the primary goal of this work is to present various analysis of Invariant Kalman Filters compared to Extended Kalman Filters (EKFs) using real data collected in different environments under various conditions. Given the same inputs and initial parameters across three different trajectories, it was found that invariant filters are more robust to a wider range of scenarios than EKFs, potentially indicating more generalizable and stable models when confronted with GNSS outages. Discussions about the applicability of bias estimation within the invariant filter framework are also presented.
Dissipative Arithmetic

Large arithmetic expressions are dissipative: they lose information and are robust to perturbations. Lack of conservation gives resilience to fluctuations. The limited precision of floating point and the mixture of linear and nonlinear operations make such functions anti-fragile and give a largely stable locally flat plateau a rich fitness landscape. This slows long-term evolution of complex programs, suggesting a need for depth-aware crossover and mutation operators in tree-based genetic programming. It also suggests that deeply nested computer program source code is error tolerant because disruptions tend to fail to propagate, and therefore the optimal placement of test oracles is as close to software defects as practical.
Internet outage detection using passive analysis

Outages from natural disasters, political events, software or hardware issues, and human error [2] place a huge cost on e-commerce ($66k/minute at Amazon [1]).
Acoustic Features Extraction of Non-Electronic Disguised Voice for Speaker Identification

A lot of challenges remain despite the rapid advancement of speaker identification technology for non-electronic disguised or physically disguised voices.This is one of the most difficult components of voice disguised that has yet to be overcome by the experts.With a normal or ideal voice, the identification process is simpler and leads to a more conclusive result.A problem arises when a suspect is identified using speech samples that have been altered to hide their identity.There are both unintended and intentional disguises in this sample content.This way a person speaks can be temporarily altered by stress, anger, concern, anxiousness, or sorrow.It is common for people to disguise themselves when they get unidentified, payment or threat phone calls.The speaker changes their voice purposefully fear of being discovered.This manuscript aims to identify a speaker for multimedia applications using a non-electronically disguised voice.In speech signal processing applications requiring non-electronic disguised voice under physical speech fluctuation, it is challenging to identify the speaker.The primary goal of this study was to regulate whether or not it was conceivable to prompt precise sentiments under disguised speech circumstances.Auditory and spectrographic investigations were used to determine the impact of different disguises on personal identity and speaker recognition performances.
Protection Strategy Selection Model Based on Genetic Ant Colony Optimization Algorithm

Industrial control systems (ICS) are facing an increasing number of sophisticated and damaging multi-step attacks. The complexity of multi-step attacks makes it difficult for security protection personnel to effectively determine the target attack path. In addition, most of the current protection models responding to multi-step attacks have not deeply studied the protection strategy selection method in the case of limited budget. Aiming at the above problems, we propose a protection strategy selection model based on the Genetic Ant Colony Optimization Algorithm. The model firstly evaluates the risk of ICS through the Bayesian attack graph; next, the target attack path is predicted from multiple angles through the maximum probability attack path and the maximum risk attack path; and finally, the Genetic Ant Colony Optimization Algorithm is used to select the most beneficial protection strategy set for the target attack path under limited budget. Compared with the Genetic Algorithm and Ant Colony Optimization Algorithm, the Genetic Ant Colony Optimization Algorithm proposed in this paper can handle the local optimal problem well. Simulation experiments verify the feasibility and effectiveness of our proposed model.
Semisupervised neural biomedical sense disambiguation approach for aspect-based sentiment analysis on social networks

Patient narratives on social networks contain large amounts of objective information, such as the descriptions of examinations and interventions. Sentiment analysis (SA) models are mostly used to evaluate the conveyed sentiments by patients in these narratives to assess positive or negative clinical outcomes or to judge the impact of a drug or a medical condition. To date, many state-of-the-art SA models often result in false assessment coverage due to the natural medical entities recognition deficiency and ambiguity problem. In this work, we propose a semisupervised-based neural sense disambiguation approach that helps to substantially define ambiguities, their levels, and the relational mappings between biomedical targets and dependencies for accurate aspect-based sentiment prediction. Three main modules are proposed: (1) generate a sentiment value based on extracted concepts and their synsets, (2) encode the representations of the contextual senses and sentiment inputs, and (3) estimate an aspect-based sentiment weight based on the context-dependency sentiment units vs. the biomedical sense. Both intrinsic and extrinsic evaluation proved how the proposed method have succeeded in pruning contextual sense feature generation and showed a strong agreement for biomedical data property parameterization and ambiguity type extraction. Thus, the model offers a significant rate of discrimination of biomedical natural concept senses by critically analysing constraints from conjunctions of positive or negative contextual semantics. A total of 21% of the vocabulary is drug names, 11% is a multiword drug reaction expressions, 7% is disease symptoms, and 5% is disease-related concepts such as symptoms and related therapy terms. Furthermore, the experiments on a multisource data from Twitter and health-related forums have overshadowed sentiment assessment and achieved an accuracy of 0.91 regarding concepts-based biomedical aspects. These results provide fresh insights into how to investigate biomedical knowledge, e.g., Medical Subject Headings (MeSH) and PubMed, to clarify the correspondence of various biomedical descriptive entities, definitions, and data properties from shared medication-related content.
Emotion recognition and achievement prediction for foreign language learners under the background of network teaching

At present, there are so many learners in online classroom that teachers cannot master the learning situation of each student comprehensively and in real time. Therefore, this paper first constructs a multimodal emotion recognition (ER) model based on CNN-BiGRU. Through the feature extraction of video and voice information, combined with temporal attention mechanism, the attention distribution of each modal information at different times is calculated in real time. In addition, based on the recognition of learners' emotions, a prediction model of learners' achievement based on emotional state assessment is proposed. C4.5 algorithm is used to predict students' academic achievement in the multi-polarized emotional state, and the relationship between confusion and academic achievement is further explored. The experimental results show that the proposed multi-scale self-attention layer and multi-modal fusion layer can improve the achievement of ER task; moreover, there is a strong correlation between students' confusion and foreign language achievement. Finally, the model can accurately and continuously observe students' learning emotion and state, which provides a new idea for the reform of education modernization.
Developing Research Competence of Pre-Service EFL Teachers

Action research has become an integral part of a teaching practitioner’s professional activity improving the quality of teaching and contributing to innovations in the educational system. To be able to conduct action research efficiently, a graduate from a teacher training university should not only realize the significance of action research for their personal and professional development but also be equipped with the necessary methods and techniques and have an enquiry stance. &#x0D; The purpose of the article is to highlight the features of the process of developing students' research competence while their studying for a Bachelor’s and Master’s degree at the Department of Foreign Philology of H.S. Skovoroda Kharkiv National Pedagogical University. The described model appeared as a result of the implementation of the new national curriculum in methods of teaching English as a foreign language. &#x0D; The methodology of the study involves both a critical analysis of the models of action research currently employed in teacher education and empirical methods investigating our own daily activities as educators (observation and the participants’ structured interviews). Structured interviews of 20 student teachers were taped, transcribed and analysed by the research educators engaged in the study. &#x0D; Results. The authors described the principles and stages of developing students’ research competence at the Department of Foreign Philology of H.S. Skovoroda Kharkiv National Pedagogical University. The results of the structured interviews prove the significant role of action research in teacher education. In the course of their training, the students gradually become aware of the challenges of action research, ways to overcome them and the relevance of it for teachers’ professional and personal growth. &#x0D; Conclusions. The suggested system of developing pre-service teachers’ research competence proved its obvious benefits for educating reflective practitioners able to become agents of change.
Using Five Cards to Encode Each Integer in $$\mathbb {Z}/6\mathbb {Z}$$

Research in secure multi-party computation using a deck of playing cards, often called card-based cryptography, dates back to 1989 when Den Boer introduced the “five-card trick” to compute the logical AND function. Since then, many protocols to compute different functions have been developed. In this paper, we propose a new encoding scheme that uses five cards to encode each integer in $$\mathbb {Z}/6\mathbb {Z}$$ . Using this encoding scheme, we develop protocols that can copy a commitment with 13 cards, add two integers with 10 cards, and multiply two integers with 14 cards. All of our protocols are the currently best known protocols in terms of the required number of cards. Our encoding scheme can be generalized to encode integers in $$\mathbb {Z}/n\mathbb {Z}$$ for other values of n as well.
Programmable Hamiltonian engineering with quadratic quantum Fourier transform

Quantum Fourier transform (QFT) is a widely used building block for quantum algorithms, whose scalable implementation is challenging in experiments. Here, we propose a protocol of quadratic QFT (QQFT) considering cold atoms confined in an optical lattice. This QQFT is equivalent to QFT in the single-particle subspace and generalizes to a many-body setting through the second quantization formalism. We show this QQFT protocol can be implemented using a programmable laser potential with the digital-micromirror-device techniques recently developed in experiments. The QQFT protocol enables programmable Hamiltonian engineering and allows quantum simulations of Hamiltonian models, which are difficult to realize with conventional approaches. The flexibility of our approach is demonstrated by performing quantum simulations of one-dimensional Poincar\'e crystal physics and two-dimensional topological flat bands, where the QQFT protocol effectively generates the required long-range tunnelings despite the locality of the cold atom system. We find the discrete Poincar\'e symmetry and topological properties in the two examples, respectively, have robustness against a certain degree of noise that is potentially existent in the experimental realization. We expect this approach would open wide opportunities for optical lattice-based programmable quantum simulations.
Research data services in libraries: a systematic literature review

Purpose The purpose of this paper was to systematically collect and review all studies which have been conducted on research data services in libraries across the globe. Design/methodology/approach Six world-renowned scholarly databases were searched and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were used for the identification and selection of eligible studies. The studies were then assessed using a well-renowned framework for the quality assessment of research studies. Findings The results revealed that the libraries were engaged in providing different types of research data services such as identification, acquisition, management and preservation of research data. They also arranged training programs, supported the publishing and sharing of data and dealt with the licensing and copyright issues related to data. Research limitations/implications The results of this study would be helpful for researchers and organizations, especially those engaged in educational activities, in understanding the current scenario of research data services in the world libraries. Furthermore, the findings offer valuable insights to library professionals regarding the types of research support services available globally. This would enable them to play an integral role in planning, designing and implementing such services in their libraries. Originality/value To the best of the authors’ knowledge, this study is the first of its kind that systematically collects and reviews the literature on research data services in libraries worldwide.