Reviewerly/exHarmony huggingface dataset:
Challenges of Neural Machine Translation for Short Texts

Abstract Short texts (STs) present in a variety of scenarios, including query, dialog, and entity names. Most of the exciting studies in neural machine translation (NMT) are focused on tackling open problems concerning long sentences rather than short ones. The intuition behind is that, with respect to human learning and processing, short sequences are generally regarded as easy examples. In this article, we first dispel this speculation via conducting preliminary experiments, showing that the conventional state-of-the-art NMT approach, namely, Transformer (Vaswani et al. 2017), still suffers from over-translation and mistranslation errors over STs. After empirically investigating the rationale behind this, we summarize two challenges in NMT for STs associated with translation error types above, respectively: (1) the imbalanced length distribution in training set intensifies model inference calibration over STs, leading to more over-translation cases on STs; and (2) the lack of contextual information forces NMT to have higher data uncertainty on short sentences, and thus NMT model is troubled by considerable mistranslation errors. Some existing approaches, like balancing data distribution for training (e.g., data upsampling) and complementing contextual information (e.g., introducing translation memory) can alleviate the translation issues in NMT for STs. We encourage researchers to investigate other challenges in NMT for STs, thus reducing ST translation errors and enhancing translation quality.
Exploring privacy paradox in contact tracing apps adoption

Purpose Understanding the privacy concerns of individuals in the adoption of contact tracing apps is critical for the successful control of pandemics like COVID-19. This paper explores the privacy paradox in the adoption of contact tracing apps in Australia. Design/methodology/approach A comprehensive review of the related literature has been conducted, leading to the development of a conceptual model based on the privacy calculus theory and the antecedent-privacy concern-outcome framework. Such a model is then tested and validated using structural equation modelling on the survey data collected in Australia. Findings The study shows that perceived benefit, perceived privacy risk and trust have significant influences on the adoption of contact tracing apps. It reveals that personal innovativeness and trust have significant and negative influences on perceived privacy risk. The study further finds out that personal innovativeness is insignificant to perceived benefit. It states that perceived ease of use has an insignificant influence on perceived privacy risk in the adoption of contact tracing apps. Originality/value This study is the first attempt to use the privacy calculus theory and the antecedent–privacy concern–outcome framework for exploring the privacy paradox in adopting contact tracing apps. This leads to a better understanding of the privacy concerns of individuals in the adoption of contact tracing apps. Such an understanding can help formulate targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control through effective contact tracing for better emergency management.
Edge detection of ore and rock on the surface of explosion pile based on improved Canny operator

The detection technology and identification method of blasting fragmentation can be used to quickly quantify and feed back the blasting effect. In this paper, aiming at the phenomenon of false edge or missing edge in image edge detection operator, an improved Canny operator edge detection method is proposed. Firstly, statistical filtering is used to solve the problem of image edge blur. Then, based on the 3 × 3 gradient template of the operator, the gradient amplitude and direction of the burst image are calculated. Finally, the natural iterative method is used to obtain the image threshold, and the discontinuous edge is repaired by morphological closure operation and thinning operator to obtain the final detected edge. The experimental results show that the improved Canny operator removes the granular and flake noise of the blasting block, maintains the integrity of the edge of the explosive pile image, and has high authenticity and feasibility.
Stabilizing a Bosonic Qubit Using Colored Dissipation

Protected qubits such as the 0-π qubit, and bosonic qubits including cat qubits and Gottesman-Kitaev-Preskill (GKP) qubits offer advantages for fault tolerance. Some of these protected qubits (e.g., 0-π qubit and Kerr-cat qubit) are stabilized by Hamiltonians which have (near-)degenerate ground state manifolds with large energy gaps to the excited state manifolds. Without dissipative stabilization mechanisms the performance of such energy-gap-protected qubits can be limited by leakage to excited states. Here, we propose a scheme for dissipatively stabilizing an energy-gap-protected qubit using colored (i.e., frequency-selective) dissipation without inducing errors in the ground state manifold. Concretely we apply our colored dissipation technique to Kerr-cat qubits and propose colored Kerr-cat qubits which are protected by an engineered colored single-photon loss. When applied to the Kerr-cat qubits our scheme significantly suppresses leakage-induced bit-flip errors (which we show are a limiting error mechanism) while only using linear interactions. Beyond the benefits to the Kerr-cat qubit we also show that our frequency-selective loss technique can be applied to a broader class of protected qubits.
An Alternating-Direction-Method of Multipliers-Incorporated Approach to Symmetric Non-Negative Latent Factor Analysis

Large-scale undirected weighted networks are frequently encountered in big-data-related applications concerning interactions among a large unique set of entities. Such a network can be described by a Symmetric, High-Dimensional, and Incomplete (SHDI) matrix whose symmetry and incompleteness should be addressed with care. However, existing models fail in either correctly representing its symmetry or efficiently handling its incomplete data. For addressing this critical issue, this study proposes an Alternating-Direction-Method of Multipliers (ADMM)-based Symmetric Non-negative Latent Factor Analysis (ASNL) model. It adopts fourfold ideas: 1) implementing the data density-oriented modeling for efficiently representing an SHDI matrix’s incomplete and imbalanced data; 2) separating the non-negative constraints from the decision parameters to avoid truncations during the training process; 3) incorporating the ADMM principle into its learning scheme for fast model convergence; and 4) parallelizing the training process with load balance considerations for high efficiency. Empirical studies on four SHDI matrices demonstrate that ASNL significantly outperforms several state-of-the-art models in both prediction accuracy for missing data of an SHDI and computational efficiency. It is a promising model for handling large-scale undirected networks raised in real applications.
Emergence of Educators for Industry 5.0 An Indological Perspective

In the amid Covid-19 crisis where the world is suffering from the pandemic, the technology is ready to march forward with the new vision, concepts, and principles. The Covid-19 crisis gave humanity the new mantras of wisdom, contentment, value of products, large family concepts, and the exponential growth of the mind with a human touch. Industry 4.0 was the emergence of Industrial Internet of things where automation, Machine Learning, connectivity and real time data had been woven together. This paper deals with the Vision of Industry 5.0 leading toward Society 5.0. The smart society will be equipped with new version of education and educators 5.0, who are likely to lead the world. The humans will be equipped with Smart Collaborative and co-operating Robots to efficiently perform the undertaken tasks. This new Super-smart society will be enriched with ‘Human Touch’, personalized products. The New Education Policy (NEP) 2020 in India, is focused on the concepts of Education 5.0. This paper will also introduce the new horizons of the education system in terms of Indological Perspective. It also gives an insight to the methodologies and techniques that are required for achieving the Goals and Objectives of Society 5.0.
Breast Cancer Diagnosis (BCD) Model Using Machine Learning

In the recent years, breast cancer research has made a significant growth however there is still a scope of advancement. Breast cancer increases the statistics of mortality among women. In concern to this issue, treatment of cancer should be started at the earlier stage, to increase the chances of survival of the patient. Thus, there is a need to diagnose breast cancer at the early stage using the features from the mammograms. This paper proposes an efficient BCD model to detect breast cancer by using Support Vector Machine (SVM) with 10-fold cross validation. The complexity of the problem increases if there are many input features for the diagnosis of cancer. Thus, Principal Component Analysis (PCA) is used to reduce the feature space from a higher dimension to a lower dimension. Experiment result shows that the PCA increases the accuracy of the model. The proposed BCD model is compared with other supervised learning algorithms like Decision trees (DT), Random Forest, k- Nearest Neighbors(k-NN), Stochastic Gradient Descent (SGD), AdaBoost, Neural Network (NN), and Naïve Bayes. Evaluation parameters like F1 measure, ROC curve, Accuracy, Lift curve and Calibration Plot proves that proposed BCD model outperforms and gives the highest accuracy among other compared algorithms.
To identify the determinants of the CloudIoT technologies adoption in the Indian MSMEs: structural equation modelling approach

In everyday life, we come across multiple emerging applications of CloudIoT like the smartphone, smart television, smart city, smart factory, smart agriculture, etc. CloudIoT is the collaboration of cloud computing and internet of things technology. Even though IoT and cloud are having contradicting characteristics, the collaboration of IoT and cloud helps in solving their issues partially and gives rise to new services. The objective of this study is to develop a model using the structural equation modelling approach to identify the determinants of the CloudIoT technologies adoption in the Indian MSMEs, which have significant contribution in the national growth. The determinants considered for the study were cloud computing, perceived IT security risk, social influence, internet of things, perceived ease of use, perceived usefulness, trust, and intention to use. The data were collected from the 500 respondents of Indian MSMEs. The results of the investigation revealed that cloud computing, social influence, internet of things, perceived ease of use, trust, and perceived IT security risk positively influence the intention to use. The developed model is intended to guide the organisational managers, decision, and policymakers in understanding the relationship between the various determinants and intention to use CloudIoT technologies.
IMPROVING EFL LEARNERS� SPEAKING PROFICIENCY THROUGH INSTAGRAM VLOG

This research investigated whether Instagram Vlog contributed to the improvement of EFLs speaking ability. It also examined learners perceptions on the integration of Instagram Vlog in a speaking class. There were 28 participants involved in this research. To determine the impact of the Instagram Vlog on EFL learners speaking ability, pre- and post-tests were administered to measure the improvement. The results indicated that the implementation of Instagram Vlog into a basic-level speaking class improved the learners speaking proficiency in terms of pronunciation, fluency, vocabulary, syntax, and general use of the target language. The analysis of the questionnaire demonstrated that the utilization of Instagram vlog contributed to EFL learners speaking skill on enhancing fluency, acquiring vocabulary items, and boosting confidence and motivation.DOI:doi.org/10.24071/llt.2019.220111
Quantum Computing and AI: Impacts &amp; Possibilities

Quantum computing is one of the emerging technologies. Different communities and research organizations are working to bring quantum computing applications into reality. Artificial Intelligence is another emerging area and getting stable with time. This paper, the main objective is to find out the impact of quantum computing research growth for AI applications. Thus, the method used in this study uses computational methods. so that this research can be concluded regarding the growing impact of quantum computing research for a given AI application. This paper also presents the impact and possibilities of quantum computing in the field of artificial intelligence.
ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining

The main objective of aspect-based sentiment classification (ABSC) is to predict sentiment polarities of different aspects from sentences or documents. Recent research integrates sentiment terms into pretraining models whose accuracy impacts the ABSC performance. This paper introduces a sentiment knowledge-adaptive pretraining model (ASK-RoBERTa). A sentiment word dictionary is first built from general and field sentiment words. We develop a series of term and sentiment mining rules based on part-of-speech tagging and sentence dependency grammar. These mining rules consider word dependencies, compounding, and conjunctions. The pretraining model optimizes the mining rules to capture the dependency between aspects and sentiment words. Experimental results on multiple public benchmark datasets demonstrate the satisfactory performance of ASK-RoBERTa.
Good point set and double attractors based-QPSO and application in portfolio with transaction fee and financing cost

In order to overcome the premature convergence problem of quantum-behaved particle swarm optimization algorithm (QPSO) in the face of high-dimensional complexity, an improved QPSO based on good point set and double attractor point mechanism (GDQPSO) is presented. In the initialization phase of GDQPSO, the idea of good point set is used to allow particles to explore more uniformly and comprehensively at the beginning of the iteration. To better balance the exploration ability of individuals and the global search capability of the algorithm, the local attraction point is redefined by combining dynamic and fixed methods. Then the global perturbation mechanism is considered to maintain the diversity of the population. And the comparison results with nine intelligent algorithms on 18 benchmark functions indicate that GDQPSO has obvious advantages in convergence accuracy and stability. Finally, the simulation experiments demonstrate very competitive performance of GDQPSO while solving the portfolio model with transaction fee and financing cost when compared with other evolutionary algorithms.
A blockchain-based model with an incentive mechanism for cross-border logistics supervision and data sharing in modular construction

Sustainability in cross-border logistics requires issues such as fragmented management to be addressed. Particular challenges arise in cross-border logistics in modular construction (CLMC) because supervision is inefficient, primarily due to continued use of paper-based documentation. Researchers have developed digital platforms that integrate accurate prefabricated module location information but their centralized operation creates information security issues such as tampering. Blockchain technology can overcome this limitation but relies on user participation. This study, therefore, develops a blockchain-based supervision (BBS) model with incentives for application in CLMC. The BBS model is developed using a design science research approach to enhance supervision of CLMC and motivate users to share data promptly, and then a prototype system is developed and evaluated in a CLMC case. The results show that the system brings a positive change in product accountability (df = 8, t = 0.6601, p = 0.528) compared with current paper-based recording process (df = 8, t = 0.0035, p = 0.997), and a positive change in data traceability (df = 8, t = 1.468, p = 0.180) compared with existing process (df = 8, t = 0.042, p = 0.967). In addition, this study obtains higher scores (552) than others in evaluating the incentive mechanisms. The security analysis is also discussed through data immutability, non-repudiation, authentication, and authorization. The ﬁndings of this study pave the way for a tamper-proof, incentive-enabled supervision mechanism in modular construction.
Teacher Perceptions about ICT Integration into Classroom Instruction

This study looked at qualitative papers that focused on teacher perceptions of how technology is integrated in the classroom. It synthesized 22 qualitative research studies using the meta-ethnography method to trace, assess, and synthesize the findings in order to gain a better understanding of the diversity of teacher perceptions concerning ICT integration in the classroom. The outcomes were determined by the positive and negative attitudes teachers had toward ICT integration in the classroom. The synthesis revealed that local settings and global educational trends both influence teacher perceptions. It is stated how crucial teacher professional development programs are, and contextual elements influencing teacher perceptions about ICT integration into classroom are highlighted. Recommendations for further study are explored by integrating the review’s findings.
Fine-grained TLS services classification with reject option

The recent success and proliferation of machine learning and deep learning have provided powerful tools, which are also utilized for encrypted traffic analysis, classification, and threat detection in computer networks. These methods, neural networks in particular, are often complex and require a huge corpus of training data. Therefore, this paper focuses on collecting a large up-to-date dataset with almost 200 fine-grained service labels and 140 million network flows extended with packet-level metadata. The number of flows is three orders of magnitude higher than in other existing public labeled datasets of encrypted traffic. The number of service labels, which is important to make the problem hard and realistic, is four times higher than in the public dataset with the most class labels. The published dataset is intended as a benchmark for identifying services in encrypted traffic. Service identification can be further extended with the task of “rejecting” unknown services, i.e., the traffic not seen during the training phase. Neural networks offer superior performance for tackling this more challenging problem. To showcase the dataset’s usefulness, we implemented a neural network with a multi-modal architecture, which is the state-of-the-art approach, and achieved 97.04% classification accuracy and detected 91.94% of unknown services with 5% false positive rate.
Impact of Internal Marketing Practices on Intention to Stay in Commercial Banks in Jordan

The study aimed at identifying the impact of internal marketing practices on the intention to stay in commercial banks in Jordan. The internal marketing practices represented by (empowerment, training programs, incentives and rewards, internal communication and administrative support). The study population consists of employees of Commercial Banks in the northern region of Jordan; a sample was taken from the study population. The questionnaire was distributed to 550 of study samples, 521 were valid for statistical analysis. To achieve the objectives of the study and test hypotheses statistical analysis programs (IBM SPSS 24) and (SMARTPSLS, v. 3.2.6) were used. The study found that there is a statistically significant effect of internal marketing practices on Intention to stay in commercial banks in Jordan. Based on the study results, the researcher recommended the managers and decision makers in the commercial banks in Jordan to emphasize the importance of job commitment, which generates the desire of employees to maintain their membership in the banks and their willingness to pursue organizational goals and accept their values and objectives.
A preliminary report on the February 6, 2023 earthquakes in Türkiye

At 4:17 a.m. on Feb. 6, 2023, a magnitude-7.8 earthquake struck near Pazarcık City in south-central Türkiye, followed by a magnitude-7.5 event about 9 hours later. In this report, scientists from Türkiye explain what happened, including the complexity of the rupture itself, ground motion, building codes, and damages.
EditSum: A Retrieve-and-Edit Framework for Source Code Summarization

Existing studies show that code summaries help developers understand and maintain source code. Unfortunately, these summaries are often missing or outdated in software projects. Code summarization aims to generate natural language descriptions automatically for source code. Code summaries are highly structured and have repetitive patterns. Besides the patternized words, a code summary also contains important keywords, which are the key to reflecting the functionality of the code. However, the state-of-the-art approaches perform poorly on predicting the keywords, which leads to the generated summaries suffering a loss in informativeness. To alleviate this problem, this paper proposes a novel retrieve-and-edit approach named EditSum for code summarization. Specifically, EditSum first retrieves a similar code snippet from a pre-defined corpus and treats its summary as a prototype summary to learn the pattern. Then, EditSum edits the prototype automatically to combine the pattern in the prototype with the semantic information of input code. Our motivation is that the retrieved prototype provides a good start-point for post-generation because the summaries of similar code snippets often have the same pattern. The post-editing process further reuses the patternized words in the prototype and generates keywords based on the semantic information of input code. We conduct experiments on a large-scale Java corpus and experimental results demonstrate that EditSum outperforms the state-of-the-art approaches by a substantial margin. The human evaluation also proves the summaries generated by EditSum are more informative and useful. We also verify that EditSum performs well on predicting the patternized words and keywords.
Introduction to Homeland Security

TABLE OF CONTENTS1 Historic Overview of the Terrorist Threat 2 - Statutory Authority 3 Organizational Actions 4 Hazards 5 Safety and Security 6 Mitigation and Preparedness 7 Response and Recovery 8 - Communications 9 Technology 10 The Future of Homeland Security A1 List of Acronyms A2 U.S.A. Patriot Act Summary A3 Homeland Security Act of 2002 A4 List of State Emergency Management Offices A5 List of State Homeland Security Contacts A6 List of Coproation for National and Community Service (CNCS) Homeland Security Grantees A7 Centers for Disease Control (CDC) Preparedness and Planning Information Sites A8 Select Web Sites for Additional Information A9 Safe Conference Report A10 California State Agency Guidance: Homeland Security Advisory System A11 Communicating During Emergencies Glossary of Hazard Terms References Index