Reviewerly/exHarmony huggingface dataset:
Unsupervised Text Style Transfer via an Enhanced Operation Pipeline

Unsupervised text style transfer aims to change the style attribute of the given unpaired texts while preserving the style-independent semantic content. In order to preserve the content, some methods directly remove the style-related words in texts. The remaining content, together with target stylized words, are fused to produce target samples with transferred style. In such a mechanism, two main challenges should be well addressed. First, due to the style-related words are not given explicitly in the original dataset, a detection algorithm is required to recognize the words in an unsupervised paradigm. Second, the compatibility between the remaining content and target stylized words should be guaranteed to produce valid samples. In this paper, we propose a multi-stage method following the working pipeline – Detection, Matching, and Generation. In the Detection stage, the style-related words are recognized by an effective joint method and replaced by mask tokens. Then, in the Matching stage, the contexts of the masks are employed as queries to retrieve target stylized tokens from candidates. Finally, in the Generation stage, the masked texts and retrieved style tokens are transformed to the target results by attentive decoding. On two public sentimental style datasets, experimental results demonstrate that our proposed method addresses the challenges mentioned above and achieves competitive performance compared with several state-of-the-art methods.
Texture Synthesis Optimization for Large Scale Structures

Exemplar-based texture synthesis methods aim to generate arbitrarily larger textures from limited exemplars without strong artifacts or introducing obvious unnatural visual elements. While existing non-parametric exemplar-based synthesis methods have made remarkable progress on reduce artifacts. Most of them have not been good enough to avoid introducing error-mixed larger scale in synthesis textures. In addition, in noisy large-scale regular texture, there always exists the large-scale motif texture. These large-scale motifs are difficult to correct synthesized for texture synthesis. So in this paper we focus on the problem of large scale structures synthesized problem in texture synthesis. In order to avoid introducing error large scale in texture synthesis, diamond searching with decreasing radius method is proposed in the block search step. The patch distance measure is designed by considering local principal curvature. Different from most of the existing techniques, no extra guided channel is needed. The exemplar contends large-scale regular structure will be extracted a motif at first. Then the motif will be synthesized by patch-based method. The experiment results show that the proposed method achieves significant improvements.
Two levels of meaning elaboration in psychological research

Arocha (2021) discusses the “replication crisis,” arguing for models that allow a greater complexity in the relationship between variables and processes. In this comment, we identify a more fundamental issue: the impossibility of eliminating interpretation issues with operational definitions and increased rigor in the measurements of variables and processes. Interpretation is at the core of (a) human action and (b) scientific endeavor. First, considering Vygotsky, we argue that all higher mental processes are sign mediated, influencing psychological research with humans. Second, that the understanding of research results also involves sign mediation and, therefore, it is nonneutral. We suggest two alternative approaches. There needs to be an increase in research that delivers a detailed description of psychological phenomena. Additionally, it is necessary to increase the elucidation of contextual-embeddedness research. Taking into account the two levels of meaning will underline psychology as a scientific discipline of complex phenomena.
Inferential Rules for Identifying Answers in TOEFL Texts

Knowledge modeling is required to analyze the properties and relationships in a specific domain. Several mechanisms are used to represent knowledge; one of them is the logical rule that is an intuitive way to represent schemes through a structure that facilitates matching a premise with a corresponding conclusion. In the case of the reading comprehension section in the TOEFL accreditation exams, it is necessary to analyze which pattern is linked to the question and related to the answer, so that from this scheme and the complementary information provided by the documents, a correct solution can be found. One advantage of using inferential rules is that it yields a deterministic result instead of making probabilistic management. In this paper, we propose inferential rules to model patterns to answer questions about TOEFL academic texts.
Implications on Feature Detection When Using the Benefit–Cost Ratio

Abstract In many practical machine learning applications, there are two objectives: one is to maximize predictive accuracy and the other is to minimize costs of the resulting model. These costs of individual features may be financial costs, but can also refer to other aspects, for example, evaluation time. Feature selection addresses both objectives, as it reduces the number of features and can improve the generalization ability of the model. If costs differ between features, the feature selection needs to trade-off the individual benefit and cost of each feature. A popular trade-off choice is the ratio of both, the benefit–cost ratio (BCR). In this paper, we analyze implications of using this measure with special focus to the ability to distinguish relevant features from noise. We perform simulation studies for different cost and data settings and obtain detection rates of relevant features and empirical distributions of the trade-off ratio. Our simulation studies exposed a clear impact of the cost setting on the detection rate. In situations with large cost differences and small effect sizes, the BCR missed relevant features and preferred cheap noise features. We conclude that a trade-off between predictive performance and costs without a controlling hyperparameter can easily overemphasize very cheap noise features. While the simple benefit–cost ratio offers an easy solution to incorporate costs, it is important to be aware of its risks. Avoiding costs close to 0, rescaling large cost differences, or using a hyperparameter trade-off are ways to counteract the adverse effects exposed in this paper.
A Failed attempt at creating Guidelines for Visual GUI Testing: An industrial case study

Software development is governed by guidelines that aim to improve the code's qualities, such as maintainability. However, whilst coding guidelines are commonplace for software, guidelines for testware are much less common. In particular, for GUI-based tests driven with image recognition, also referred to as Visual GUI Testing (VGT), explicit coding guidelines are missing.In this industrial case study, performed at the Swedish defence contractor Saab AB, we propose a set of coding guidelines for VGT and evaluate their impact on test scripts for an industrial, safety-critical system. To study the guidelines' effect on maintenance costs, five representative manual test cases are each translated with and without the proposed guidelines in the two VGT tools SikuliX and EyeAutomate. As such, 20 test scripts were developed, with a combined development cost of more than 100 man-hours. Three of the tests are then maintained by one researcher and two practitioners for another version of the system and costs measured to evaluate return on investment. This analysis is complemented with observations and interviews to elicit practitioners' perceptions and experiences with VGT.Results show that scripts developed with the guidelines had higher maintenance costs than scripts developed without guidelines. This is supported by qualitative results that many of the guidelines are considered inappropriate, superfluous or unnecessary due to the inherent properties of the scripts, e.g. their natural small size, linear flows, natural separation of concerns, and more. We conclude that there are differences between VGT scripts and software that prohibit direct translation of guidelines between the two. As such, we consider our study as a failure but argue that several lessons can be drawn from our results to guide future research into guidelines for VGT and GUI-based test automation.
Improved Safety On-board Using Augmented Reality Technology as a Training Tool

The current study explores the advanced technology of Augmented Reality (AR) as a digital tool of Industry 4.0. As such, we investigate the potential use of AR for the on-board training of seafarers, as a new technological tool aiming to increase their safety and operational effectiveness. Based on existing research of training through technology, we have performed a gap analysis between current training methods whereas, we are discussing the adoption maturity level and the technological obstacles. Our research is qualitative in nature, by means of in-depth interviews. The study consisted of interviews with twenty-four people from the shipping industry aiming to provide fresh insights about the augmented reality in the industry. Augmented Reality in shipping is currently at a very early stage; apparently, around half of the interviewees are not welcoming the technology in general and especially the use of AR in on-board training. According to our findings, the market seeks for the adoption of AR in on-board operations rather than training. Practical implications are also outlined.
Prerequisite-aware course ordering towards getting relevant job opportunities

Adapting learning experience according to the rapidly-changing job market is essential for students to achieve fruitful learning and successful career development. As building blocks of potential job opportunities, we focus on “technical terminologies” which are frequently required in the job market. Given a technical terminology, we aim at identifying an order of courses which contributes to the acquisition of knowledge about the terminology and also follows the prerequisite relationships among courses. To solve the course ordering problem, we develop a two-step approach, in which course-terminology relatedness is first estimated and then courses are ordered based on the prerequisite relationships and the estimated relatedness. Focusing on the second step, we propose a method based on Markov decision process (MDPOrd) and compare it with three other methods: (1) a method that orders courses based on aggregated relatedness (AggRelOrd), (2) a method that topologically sorts the courses based on personalized PageRank values (PageRankTS), and (3) a method that greedily picks courses based on the average relatedness (GVPickings). In addition to evaluating how the order prioritizes the related courses, we also evaluate from pedagogical perspectives, namely, how the order prioritizes specifically/generally fundamental courses, and how it places courses close to their prerequisites. Experimental results on two course sets show that MDPOrd outperforms the other methods in prioritizing related courses. In addition, MDPOrd is effective in ordering courses close to their prerequisites, but does not work well in highly ranking fundamental courses in the order.
Low-Complexity Training for Binary Convolutional Neural Networks Based on Clipping-Aware Weight Update

This letter presents an efficient technique to reduce the computational complexity involved in training binary convolutional neural networks (BCNN). The BCNN training shall be conducted focusing on the optimization of the sign of each weight element rather than the exact value itself in convention; in which, the sign of an element is not likely to be flipped anymore after it has been updated to have such a large magnitude to be clipped out. The proposed technique does not update such elements that have been clipped out and eliminates the computations involved in their optimization accordingly. The complexity reduction by the proposed technique is as high as 25.52% in training the BCNN model for the CIFAR-10 classification task, while the accuracy is maintained without severe degradation.
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in embeddings. Existing ANE models do not consider the specific combination between graph structure and attributes. While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node's neighborhood should be not only depicted by multi-hop nodes, but consider certain social circles. To model such information, in this paper, we propose a novel ANE model, Context Co-occurrence-aware Attributed Network Embedding (CoANE). The basic idea of CoANE is to model the context attributes for each node, and apply the convolutional mechanism to encode latent social circles. To better preserve network knowledge, we devise objective functions including positive graph likelihood, contextual negative sampling, and attribute reconstruction. We conduct experiments on five real datasets for downstream tasks, including node classification, link prediction, and community detection. The results exhibit that CoANE can significantly outperform state-of-the-art ANE models. This work was accepted to IEEE TKDE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Full paper is available at https://ieeexplore.ieee.org/document/9431700.
Two Sides to Every Story. The Truth, Post-truth, and the Blockchain Truth

One of the rallying cries of the blockchain community is that of immutability: the irreversibility of the past, the absolute truth which, once stored, remains there forever. The technology was designed with this foundational pillar in mind to ensure that changes to history are inordinately expensive and practically impossible to execute – and increasingly so, the further in the past the event which one intends to manipulate lies. This platonic view of absolute truth is in stark contrast with a world of manipulated truth, and it is not surprising that it is being revisited as a means of combating fake news. We argue that claims to the absolute nature of the blockchain are at best exaggerated, at worst misrepresented or even ‘fake news’. We discuss implicit centralised points of trust in blockchains, whether at a technological, social or governance level, and identify how these can be a threat to the ‘immutable truth’ stored within the blockchain itself. A global pandemic has unleashed an unprecedented wave of contradictory positions on anything from vaccines and face masks to ‘the new normal’. It is only natural that the pursuit of blockchain as a placebo for society's ‘truth’ problems continues.
An Inclusive Survey on Signature Recognition System

Attestation plays a crucial role to manage surveillance. So, need for authentication increases briskly. Because of the latest improvements in technology in a time where data rules everything, there is a high priority for security systems based on different biometric traits. Signature is one of the most extensively used biometric traits for verification of certain individuals. Signature plays an important role in different areas of application such as finance, banking, commercial, etc. However, in many cases, forged signatures may also be considered as original Signatures. Signatures are behavioral biometric traits and are mostly unique for each individual. Therefore, the signature verification system is one of the crucial systems that have to be produced with higher speeds and provide higher accuracy. Here in this paper, we propose different methods or techniques that help in increasing efficiency and gives the effectiveness of the overall system. We propose a method for each stage of the verification process and compare them with other alternative methods available.
Degenerate asymmetric quantum concatenated codes for correcting biased quantum errors*

In most practical quantum mechanical systems, quantum noise due to decoherence is highly biased towards dephasing. The quantum state suffers from phase flip noise much more seriously than from the bit flip noise. In this work, we construct new families of asymmetric quantum concatenated codes (AQCCs) to deal with such biased quantum noise. Our construction is based on a novel concatenation scheme for constructing AQCCs with large asymmetries, in which classical tensor product codes and concatenated codes are utilized to correct phase flip noise and bit flip noise, respectively. We generalize the original concatenation scheme to a more general case for better correcting degenerate errors. Moreover, we focus on constructing nonbinary AQCCs that are highly degenerate. Compared to previous literatures, AQCCs constructed in this paper show much better parameter performance than existed ones. Furthermore, we design the specific encoding circuit of the AQCCs. It is shown that our codes can be encoded more efficiently than standard quantum codes.
Privacy-Enhancing k-Nearest Neighbors Search over Mobile Social Networks

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.
Consistent Representation Learning Across Modalities for Zero-Shot Image Recognition

Zero-shot learning (ZSL) recently has drawn widespread attention due to the demand for scalability of object recognition in real scenes. Existing approaches typically focus on directly learning various mapping functions from the visual space to the semantic space or vice versa. However, these methods fail to explicitly capture consistent representations reflecting the nature of different modalities of the same object, which results in the deterioration of the domain shift problem in the ZSL context during the testing stage. To this end, a consistent representation learning mechanism across visual modalities and the associated semantic modalities based on common subspace learning is proposed in this paper. We further impose an orthogonal constraint on the subspace for informative representations, as well as ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> -norm regularization terms on projection matrices for automatic feature selection. Finally, an iterative process based on the ALM algorithm with an alternating direction strategy is displayed to resolve the proposed formulation. Extensive experimental results on four popular datasets demonstrate that our algorithm is promising.
Classification of Abandoned Areas for Solar Energy Projects Using Artificial Intelligence and Quantum Mechanics

The increasing demand for energy has intensified recently, requiring alternative sources to fossil fuels, which have become economically and environmentally unfeasible.On the other hand, the increasing land occupation in recent centuries is a growing problem, demanding greater efficiency, particularly in the reuse of abandoned areas, which has become an alternative.An interesting alternative would be installing energy facilities like solar, wind, biomass, and geothermal, in these areas.The objective of this paper is to develop a classification methodology, based on Artificial Intelligence (AI) and Quantum Theory (QT), to automatically carry out the classification of abandoned areas suitable for the settlement of these power plants.Artificial Neural Networks (ANNs) improved by the hybrid algorithm Quantum-behaved Particle Swarm Optimization (QPSO) together with the Levenberg-Marquardt Algorithm (LMA) were used for the classification task.In terms of Mean Squared Error (MSE), the QPSO-LMA approach achieved a decrease of 19.6% in relation to the classical LMA training with random initial weights.Moreover, the model's accuracy showed an increase of 7.3% for the QPSO-LMA over the LMA.To validate this new approach, it was also tested on six different datasets available in the UCI Machine Learning Repository and seven classical techniques established in the literature.For the problem of installing photovoltaic plants in abandoned areas, the knowledge acquired with the solar dataset can be extrapolated to other regions.
Multi-company Consumer Product Software Test Architecture Industry Experience Report

A case study is presented for a consumer hardware company which added software into the product line with little process-based test planning, including not having a software test architecture (STA). The paper presents the problem, solution, and limitations of the case study as well as future work.
English Language Training for Local Community in the Tourism Site of Karang Talun Mangrove Forest, Cilacap, Central Java

Mangrove Forest which is located at Karang Talun village, Tritih Kulon sub-district, North Cilacap, Central Java has a various potentials to develop. Its development can be conducted by utilizing local community for participating to improve with mastery of English language. Thus, it is necessary to conduct empowerment of local community through English language training to create qualified ones who can support in tourism field. The purpose of this training is to improve their English language skill and increase their economy and be able to survive by being local tour guide in the New Normal. There were 15 participants consist of traders and some employees who work in Mangrove forest. It was conducted for three days and used some methods, namely preaching method i.e, giving theory and practice learning on each unit of learning and drilling and repetition method. In addition, they were given pre test and post test to evaluate each participant’s skill. From the result, the participant’s skill in pronunciation, vocabulary and practicing dialogue has improved, but it still needs more practices. Drilling and repetition method in learning activity has been success to be applied for participants