Reviewerly/exHarmony huggingface dataset:
What Does Industry Suggest to Academia on Software Engineering Education?

Software professionals might face difficulties after beginning their careers due to misalignment of the skills learnt during their university education. Although Software Engineering (SE) is shaped by both the industry and academia, whose goals and contributions are different, there are some mismatches in perceptions of software practitioners about academic activities; hence, the level of Industry Academia Collaboration (IAC) in software industry is low compared to other industrial sectors. To investigate the skills gaps and understand different opinions of practitioners to increase such IACs, we conducted an online survey. 628 software practitioners, whose undergraduate degree was completed in Turkey, responded to the survey. In this study, we explored the suggestion of software practitioners on SE education. We believe that this study sheds light on improving mutual understanding to close the gaps between academia and industry.
Explainable AI for Software Defect Prediction with Gradient Boosting Classifier

Explainability is one of the most investigated quality attributes and nowadays, it has an increasing interest of the stakeholders using Artificial Intelligence (AI), especially Machine Learning software. Since AI-based software is different from traditional software in having a black-box nature, it has become very important to understand the logic behind the predictions made. In this study, we focus on the explainability of Gradient Boosting (GB) classifier used for software defect prediction (SDP). We apply post-hoc model-agnostic methods, namely “Explain like I am a 5-year old” (ELl5), “Local Interpretable Model Agnostic Explanations” (LIME), and “SHapley Additive exPlanations” (SHAP), over an SDP dataset offered by NASA, in order to shed light on the explainability of GB classifier. More specifically, we use ELI5 and LIME to explain instances locally, and SHAP to get both local and global explanations. The results suggest a post-hoc and model-agnostic way to quantify explainability, and indicate that all three methods used in this study performed consistent results with each other while explaining the GB model.
Impact of Conventional and AI-based Image Coding on AI-based Face Recognition Performance

AI-based face recognition has become increasingly appealing for daily life applications due to its high performance. At the same time, image coding is very commonly used and, thus, many applications perform face recognition with decoded images. However, using decoded images, which may suffer from compression artifacts, may impact the final decision-making process of AI-based face recognition systems and, thus, its overall recognition performance. This paper studies the impact of image coding on the overall face verification performance of a popular and high performing face recognition solution, ArcFace. Face recognition using both original and decoded images, with several compression rates and qualities, is considered. Tests were performed using the Labeled Faces in the Wild (LFW) face dataset, with its images coded using conventional image coding standards, notably JPEG, JPEG 2000, and JPEG XL, as well as three emerging AI-based image codecs. As expected, the experimental results show that coding can have a significant impact on face recognition performance, with its impact becoming increasingly relevant as the coding rate is reduced. It is also observed that the recent AI-based image codecs appear to offer slightly better recognition performance for the same coding rates as a consequence of their better RD performance.
Introduction to Cognitive Computing

Cognitive computing (CC) is an evolving area in the current artificial intelligence (AI) domain. It mainly involves mimicking human intelligence, such as understanding a situation and giving the correct reaction. It also mimics human sense organs by way of collecting information from its surroundings. Humans collect data through their senses, process them, and give reactions based on previous experience, so a primary part of cognitive computing is collecting and processing data. This is cognitive computing, a branch used to make decisions with the assistance of humans. Humans cannot store more data than that. High-speed processing of data is also not possible. So, based on the application, CC will collect structured and unstructured data from connected devices, process them with its high computing power, and provide high-quality decisions such as diagnosing, predicting, and specifying. The main objective of IoT is to improve the performance of the IoT in terms of its intelligence using cognitive computing technology. Cognitive analytics combines both data analytics and visual analytics to make the system fully automatic, and it removes the human from the loop. It has more analytical techniques to analyse large amounts of data. It converts unstructured and semi-structured data into structured data. It deals with both high-level and low-level feature extraction to make the system accurate. This chapter is organized in a way that is ideal for gaining a thorough understanding of cognitive computing. It starts with a deep introduction with an overview of data science and cognitive science. This is followed by technologies developed in this field and examples of their applications.
Cryptanalysis of stream cipher LIZARD using division property and MILP based cube attack

LIZARD is a lightweight stream cipher designed by Hamann et al. in 2017 for power constrained devices. In this work, we have done cryptanalysis of LIZARD by improving modified Three Subset Division Property proposed by Hao et al. in EUROCRYPT 2020. We have improved Hao et al.'s attack model using the following methods — first we found a set of secret key bits (J) that are involved in the superpoly and then estimated the degree (d) of the superpoly using degree estimation strategy introduced by Q.Wang et al. in CRYPTO 2018. With the knowledge of J and d, we evaluate only those monomials with degree less than or equal to d. This improvement to Hao's attack model reduces the time complexity from 2|I|+|J| to {2|I|∗∑i=0d|J|Ci}. Further, since removing even number of trails do not have impact on the final result, we present a propagation reduction property which eliminates the even number of trails and this improves the algorithm efficiency too. Using the improved attack model we have recovered superpoly and key for the reduced initialization rounds 223 and 224. The time complexity to recover superpoly for round 223 is 2112 and for round 224 it is 2101. The time complexity for key recovery process is 2119.
Online health assessment method based on belief rule base with sliding time window considering input correlation and redundancy

This paper develops a new health assessment method based on belief rule base with sliding time window considering correlation and redundancy of input information (BRB-WCR). The new proposed method aims to solve four problems in health assessment of dynamic complex system: lack of system fault data, uncertainty of expert knowledge, correlation of characteristic and high real-time requirement of system. The traditional BRB model can address the first two problems while it assumes that the input characteristics are completely independent. In order to solve the two remaining problems, a new BRB-WCR model is developed, where the correlation and redundancy of characteristics are considered and the sliding time window for online training data is introduced to improve the real time performance of the model. Moreover, to address the uncertainty of expert knowledge, a new optimization model is developed to train the parameters of the BRB-WCR model. To illustrate the effectiveness of the developed method, a case study of inertial navigation platform is conducted.
University Blockchain Research Initiative (UBRI): Boosting blockchain education and research

Since its conceptualization, blockchain technology has witnessed continuous and rapid development, bringing profound changes to computer science, law, and economics. In 2008, the initial blockchain system was merely a growing list of records linked together using cryptography. Today, however, blockchains have become the foundation of most digital currencies, robust cloud computing platforms, and dependable databases for tracking supply chain information. Despite challenges and controversies, blockchain technology has the potential to help build a trustworthy and efficient digital world.
Innovative Analysis Ready Data (ARD) product and process requirements, software system design, algorithms and implementation at the midstream as <i>necessary-but-not-sufficient</i> precondition of the downstream in a new notion of Space Economy 4.0 - Part 1: Problem background in Artificial General Intelligence (AGI)

Aiming at the convergence between Earth observation (EO) Big Data and Artificial General Intelligence (AGI), this two-part paper identifies an innovative, but realistic EO optical sensory image-derived semantics-enriched Analysis Ready Data (ARD) product-pair and process gold standard as linchpin for success of a new notion of Space Economy 4.0. To be implemented in operational mode at the space segment and/or midstream segment by both public and private EO big data providers, it is regarded as necessary-but-not-sufficient “horizontal” (enabling) precondition for: (I) Transforming existing EO big raster-based data cubes at the midstream segment, typically affected by the so-called data-rich information-poor syndrome, into a new generation of semantics-enabled EO big raster-based numerical data and vector-based categorical (symbolic, semi-symbolic or subsymbolic) information cube management systems, eligible for semantic content-based image retrieval and semantics-enabled information/knowledge discovery. (II) Boosting the downstream segment in the development of an ever-increasing ensemble of “vertical” (deep and narrow, user-specific and domain-dependent) value–adding information products and services, suitable for a potentially huge worldwide market of institutional and private end-users of space technology. For the sake of readability, this paper consists of two parts. In the present Part 1, first, background notions in the remote sensing metascience domain are critically revised for harmonization across the multi-disciplinary domain of cognitive science. In short, keyword “information” is disambiguated into the two complementary notions of quantitative/unequivocal information-as-thing and qualitative/equivocal/inherently ill-posed information-as-data-interpretation. Moreover, buzzword “artificial intelligence” is disambiguated into the two better-constrained notions of Artificial Narrow Intelligence as part-without-inheritance-of AGI. Second, based on a better-defined and better-understood vocabulary of multidisciplinary terms, existing EO optical sensory image-derived Level 2/ARD products and processes are investigated at the Marr five levels of understanding of an information processing system. To overcome their drawbacks, an innovative, but realistic EO optical sensory image-derived semantics-enriched ARD product-pair and process gold standard is proposed in the subsequent Part 2.
A Study on the Improvement Plan Based on the Current Status Recognition of Fisheries and Shipping High School Teachers

이 연구는 수산･해운계고등학교 교사들이 인식하는 수산･해운계고등학교의 현황과 개선점을 분석하여 수산･해운업의 성장과 발전에 도움을 주고자 연구되었다. 전국에 분포되어 있는 11개 수산해운계고등학교에 근무하고 있는 교사 23명을 대상으로 현상학적으로 접근하였으며 연구결과를 요약하면 다음과 같다. 첫째, 수산･해운계 고등학교에 근무하고 있는 교사들은 학교와 학생들에게 열정과 열의가 있고 수산해운의 미래에 위기감을 느끼고 있으며 모두 고민하고 준비해야 한다는 것을 인식하고 있었다. 둘째, 자기인식에서는 대부분 ‘상’수준의 만족감을 나타냈지만 사회적 인식은 거의 대부분의 교사가 부정적이라고 했다. 셋째, 학생 지도에 관한 내용을 분석한 결과 정서적인 측면에서 자존감이 낮으며 학습 동기가 없고 인지적 측면은 글을 읽고 이해하는 문해력이 낮아 전공교과학습이나 실습과 프로젝트를 하는데 어려움이 있고 환경요인은 주위사람들의 관심과 지지가 부족하고 진로와 취업요인에서는 취업처가 다양하지 못하여 어려움을 겪고 있다고 했다. 넷째, 필요한 역량은 서로 협업하고 학생을 상담하고 정서를 지지할 수 있는 상담기법이나 의사소통기술을 익히는 것이 필요하고 변화되는 산업구조에 필요한 인재를 양성하고 인성함양에 필요한 지식과 기술을 습득할 수 있는 역량이 필요하다고 했다. 다섯째, 개선점은 스마트한 인재양성을 위해 학교간 서로 노력해야 하며 수산･해운업과 관련된 다양한 공부를 해야 하며 미래 수산･해운업과 연결할 수 있는 실천적 경험연구가 필요하다고 하였다. 여섯째, 외부요인을 보면, 특히 수산･해운계고등학교는 비대면 수업이 장기화되면서 장기결석이 늘어나고 수업에 활성화가 어려웠다고 하였다.This study was conducted to help the growth and development of the fisheries and maritime industries by analyzing the current status and improvements of fisheries and smaritime high schools recognized by teachers of fisheries and maritime high schools. The phenomenological approach was made to 23 teachers working at 11 fisheries and maritime high schools distributed across the country, and the research results are summarized as follows. First, teachers working at fisheries and maritime high schools recognized that they had passion and enthusiasm for schools and students, felt a sense of crisis in the future of fisheries and maritime, and that they all had to think and prepare. Second, in self-awareness, most of the teachers showed satisfaction at the 'upper' level, but in social perception, most of the teachers were negative. Third, as a result of analyzing the contents of student guidance, there is low self-esteem in the emotional aspect, no motivation for learning, and low literacy in the cognitive aspect, so it is difficult to study, practice, and project. Fourth, it is necessary to learn counseling techniques or communication skills that can cooperate with each other, consult students, and support emotions, and to cultivate human resources necessary for changing industrial structures and acquire knowledge and skills necessary to cultivate personality. Fifth, it was said that the improvement points should be made between schools to foster smart talent, various studies related to fisheries and maritime, and practical experience research that can be connected to future fisheries and shipping industries is needed. Sixth, looking at external factors, especially in fisheries and maritime High School, long-term absences increased due to prolonged non-face-to-face classes, and it was difficult to activate classes.
ImitAL: Learned Active Learning Strategy on Synthetic Data

Active Learning (AL) is a well-known standard method for efficiently obtaining annotated data by first labeling the samples that contain the most information based on a query strategy. In the past, a large variety of such query strategies has been proposed, with each generation of new strategies increasing the runtime and adding more complexity. However, to the best of our knowledge, none of these strategies excels consistently over a large number of datasets from different application domains. Basically, most of the existing AL strategies are a combination of the two simple heuristics informativeness and representativeness, and the big differences lie in the combination of the often conflicting heuristics. Within this paper, we propose ImitAL, a domain-independent novel query strategy, which encodes AL as a learning-to-rank problem and learns an optimal combination between both heuristics. We train ImitAL on large-scale simulated AL runs on purely synthetic datasets. To show that ImitAL was successfully trained, we perform an extensive evaluation comparing our strategy on 13 different datasets, from a wide range of domains, with 7 other query strategies.
Private and Reliable Neural Network Inference

Reliable neural networks (NNs) provide important inference-time reliability guarantees such as fairness and robustness. Complementarily, privacy-preserving NN inference protects the privacy of client data. So far these two emerging areas have been largely disconnected, yet their combination will be increasingly important.
Contrastive Bayesian Analysis for Deep Metric Learning

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin.
Multi-Attribute Subspace Clustering via Auto-Weighted Tensor Nuclear Norm Minimization

Self-expressiveness based subspace clustering methods have received wide attention for unsupervised learning tasks. However, most existing subspace clustering methods consider data features as a whole and then focus only on one single self-representation. These approaches ignore the intrinsic multi-attribute information embedded in the original data feature and result in one–attribute self-representation. This paper proposes a novel multi-attribute subspace clustering (MASC) model that understands data from multiple attributes. MASC simultaneously learns multiple subspace representations corresponding to each specific attribute by exploiting the intrinsic multi-attribute features drawn from original data. In order to better capture the high-order correlation among multi-attribute representations, we represent them as a tensor in low-rank structure and propose the auto-weighted tensor nuclear norm (AWTNN) as a superior low-rank tensor approximation. Especially, the non-convex AWTNN fully considers the difference between singular values through the implicit and adaptive weights splitting during the AWTNN optimization procedure. We further develop an efficient algorithm to optimize the non-convex and multi-block MASC model and establish the convergence guarantees. A more comprehensive subspace representation can be obtained via aggregating these multi-attribute representations, which can be used to construct a clustering-friendly affinity matrix. Extensive experiments on eight real-world databases reveal that the proposed MASC exhibits superior performance over other subspace clustering methods.
Data-Driven Monitoring for Distributed Sensor Networks: An End-to-End Strategy Based on Collaborative Learning

This article proposes an end-to-end event monitoring method for a distributed sensor network with non-concentrated label noises based on a collaborative soft-label network (CSLN). The proposed CSLN framework is composed of a fundamental learner (FL) and a collaborative label modification. FL is designed based on a bidirectional gated recurrent unit (BiGRU) with attention to tackle the indistinct boundary problem by modeling multiscale dependencies. BiGRU can learn latent representations through capturing local dependencies in a bidirectional time flow. The attention mechanism is capable of modeling long-range dependencies through assigning learnable weights to the latent representations. Then, the collaborative label modification is established to reduce label noises by combining a truncated loss function and a dual-space smoothing technique. The truncated loss function can prevent FL from overfitting to noisy labels by isolating them in optimization. The dual-space smoothing technique can generate soft labels based on the local similarities. Furthermore, the proposed CSLN method is optimized by a bi-loop recursive strategy to reduce label noises gradually through alternatively training FL and generating soft labels. The feasibility and effectiveness of the proposed method are validated through real-field experiments of perimeter security applications based on distributed optical fiber sensors (DOFSs).
Research on denoising method of chinese ancient character image based on chinese character writing standard model

Abstract Ancient documents are historical evidence of cultural inheritance, and the damage brought by natural and human factors to ancient documents is inevitable, resulting in the collected images of ancient Chinese characters containing a large amount of noise, which seriously affects the accuracy of subsequent image recognition and thus creates a great obstacle to the digitization of ancient documents. To address the complexity of ancient text structure, this paper proposes a Chinese ancient text image denoising method based on the Chinese character writing standard model. The method firstly adds four additional local branches based on the global branching, and uses the supplementary character detail information to weaken the phenomenon of strokes adhering to noise due to the lack of local details; secondly, it introduces the simulation noise of ancient documents to simulate the real ancient character image morphology, which can be used for the adversarial training of this method. In the training process, the minimum absolute value deviation, smoothing loss, structural consistency loss and the refined loss function formed by the adversarial loss are used to iteratively optimize the parameters. Finally, experiments prove that the model in this paper can increase the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) of the image by at least 23.8% and 11.4%, and the user evaluation index (UV) has also reached more than 80%.
On the Proper Role of Linguistically Oriented Deep Net Analysis in Linguistic Theorising

A lively research field has recently emerged that uses experimental methods to probe the linguistic behaviour of modern deep networks. While work in this tradition often reports intriguing results about the grammatical skills of deep nets, it is not clear what their implications for linguistic theorising should be. As a consequence, linguistically oriented deep net analysis has had very little impact on linguistics at large. In this chapter, I suggest that deep networks should be treated as theories making explicit predictions about the acceptability of linguistic utterances. I argue that if we overcome some obstacles standing in the way of seriously pursuing this idea, we will gain a powerful new theoretical tool, complementary to mainstream algebraic approaches.