Reviewerly/exHarmony huggingface dataset:
NAS-SGAN: A Semi-Supervised Generative Adversarial Network Model for Atypia Scoring of Breast Cancer Histopathological Images

Nuclear atypia scoring (NAS), forms a significant factor in determining individualized treatment plans and also for the prognosis of the disease. Automation of cancer grading using quantitative image-based analysis of histopathological images can circumvent the shortcomings of the prevailing manual grading and can assist the pathologists in cancer diagnosis. However, developing such a robust classifier model require sufficient amount of annotated data, while the labeled histopathological images are scarce and expensive to procure as annotation forms a time-consuming and laborious task. Hence, a semi-supervised learning framework combined with the deep neural network based generative adversarial training, that can improve the performance of the classification model with limited annotated data, is proposed in this paper. The proposed NAS-SGAN model consists of discriminator and generator models that are trained in an adversarial manner using both labeled and unlabeled samples. The discriminator model is designed as an unsupervised model stacked over the supervised model sharing the model parameters and learns the data distribution by extracting the discriminative features. The generator model is trained over a stable feature matching objective function following a composite GAN architecture, and its for the first time the semi-supervised GAN model is explored for the grading of breast cancer. Experimental analysis shows that the proposed model could better discriminate different cancer grades thereby improving the robustness and accuracy of the system, even with limited amount of labeled samples.
A certificateless aggregate signature scheme for security and privacy protection in VANET

In the vehicular ad hoc network, moving vehicles can keep communicating with each other by entering or leaving the network at any time to establish a new connection. However, since many users transmit a substantial number of messages, it may cause reception delays and affect the entire system. A certificateless aggregate signature scheme can provide a signature compression that keeps the verification cost low. Therefore, it is beneficial for environments constrained by time, bandwidth, and storage, such as vehicular ad hoc network. In recent years, several certificateless aggregate signature schemes have been proposed. Unfortunately, some of them still have some security and privacy issues under specific existing attacks. This article offers an authentication scheme that can improve security, privacy, and efficiency. First, we apply the certificateless aggregate signature method to prevent the onboard unit devices from leaking sensitive information when sending messages. The scheme is proven to be secure against the Type-1 [Formula: see text] and Type-2 [Formula: see text] adversaries in the random oracle model under the computational Diffie–Hellman problem assumption. Then, the performance evaluation demonstrates that our proposed scheme is more suitable for deployment in vehicular ad hoc network environments.
Job scheduling for big data analytical applications in clouds: A taxonomy study

Cloud environments have provided great help for the development of big data. Today, cloud environments have become the most active platform for big data analysis applications (BDAAs). Cloud products provide convenience for BDAA providers to provision big data analysis services to their users at a more cost-effective way. Rational utilization of cloud resources can bring higher efficiency and lower cost to BDAAs. Therefore, how to better process big data jobs in cloud environments has become a hot research topic in recent years. Efficient job scheduling schemes can make full use of cloud resources, and then achieve different goals, such as saving energy, minimizing cost and maximizing job execution efficiency. So designing suitable scheduling schemes for different problem scenarios has become the most common and effective method to optimize the execution of big data analysis jobs. To explore practical and efficient job scheduling algorithms in different scenarios, references of existing scheduling algorithms are often needed. Therefore, it is necessary to survey the research work of BDAA job scheduling in cloud environments in recent years, which can provide detailed guidance and help for future research in this field. This paper summarizes the work related to BDAA job scheduling in cloud environments, selects articles related to the topic of this survey from conferences and journals from 2016 to 2021, and divides scheduling algorithms with different research focuses into several categories. On this basis, the advantages and disadvantages of existing research work are compared, and the directions and challenges for future research are pointed out.
Visualizing a disembodied agent: young EFL learners’ perceptions of voice-controlled conversational agents as language partners

Artificial agents, such as voice-controlled conversational agents (VCAs) built into smart devices, are becoming more prevalent in daily and educational contexts, enhancing the possibility of using them as language partners. However, research has primarily focused on the cognitive or affective outcomes of using these agents, overlooking questions about language learners' perceptions of agents as human-like conversational partners and about what aspects of agents generate the social interaction schema required for language learning. To address these gaps, this exploratory study examined to what extent EFL students perceived a VCA as a human-like language partner and what knowledge regarding language partners these students used to justify their perceptions. Sixty-seven Korean EFL students, all of them being nine years old, completed three interactional tasks with a VCA designed to act as a language partner. They then participated in a drawing task and in-depth interview implemented to explore students' perceptions toward the VCA as a language partner. Thematic analysis of students' drawings and interview transcripts found that the majority of students identified human elements in the VCA, perceiving it either as a human-like partner or as something between artifact and human. This strong tendency toward anthropomorphism indicates VCAs' great potential as interactive language partners in EFL contexts. Additionally, this study discusses the reasons for and implications of students' strong anthropomorphism tendency and how students used their knowledge regarding language partners to justify their VCA perceptions. Future recommendations for the use of VCAs were suggested from both pedagogical and technological standpoints.
Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection

Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA.
ST-AL: a hybridized search based metaheuristic computational algorithm towards optimization of high dimensional industrial datasets

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration-exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94-1.00.
Sentiment classification of microblog: A framework based on BERT and CNN with attention mechanism

BERT, a boon to natural language understanding, extracts the context information of words and forms the basis of the newly-designed sentiment classification framework for Chinese microblogs. Coupled with a CNN and an attention mechanism, the BERT model takes Chinese characters as inputs for vectorization and outputs two kinds of vectors: character-level vectors and sentence-level vectors. The character-level vectors are input into the CNN, and both top-K-average pooling and attention pooling are used to mine emotions from the microblogs. The attention mechanism is used to fuse various features to the microblog vectors. The final classification results are derived via a softmax function over the microblog vectors. The results show an outstanding improvement over benchmark methods on the two classification tasks.
Large-Scale Artificial Intelligence Models

As artificial intelligence models rapidly grow in size, their potential is stimulated.
From Bag-of-Words to Pre-trained Neural Language Models: Improving Automatic Classification of App Reviews for Requirements Engineering

Popular mobile applications receive millions of user reviews. Thesereviews contain relevant information, such as problem reports and improvementsuggestions. The reviews information is a valuable knowledge source for soft-ware requirements engineering since the analysis of the reviews feedback helpsto make strategic decisions in order to improve the app quality. However, due tothe large volume of texts, the manual extraction of the relevant information is animpracticable task. In this paper, we investigate and compare textual represen-tation models for app reviews classification. We discuss different aspects andapproaches for the reviews representation, analyzing from the classic Bag-of-Words models to the most recent state-of-the-art Pre-trained Neural Languagemodels. Our findings show that the classic Bag-of-Words model, combined witha careful analysis of text pre-processing techniques, is still a competitive model.However, pre-trained neural language models showed to be more advantageoussince it obtains good classification performance, provides significant dimension-ality reduction, and deals more adequately with semantic proximity between thereviews’ texts, especially the multilingual neural language models.
Voluntary Exposure to Political Fact Checks

For political fact-checking enterprises to be effective, two conditions must be met. Voters must be interested in fact-checks, and the fact-checks must encourage voters to reevaluate their beliefs. Here, we study the former: whether voters are interested in reading fact-checks of political candidates’ statements. We use a simulated campaign environment in which participants’ exposure to fact-checks are voluntary. We find that voters are interested in fact-checking, especially for negative campaigns and personal (versus issue) campaigns. We also find that topics salient to voters are most often fact-checked. Finally, we provide evidence for the operation of a motivated reasoning process, as statements made by less preferred candidates were more deeply scrutinized.
Wearables: Fundamentals, advancements, and a roadmap for the future

Today, the term "wearable" goes beyond the traditional definition of clothing; it refers to an accessory that enables personalized mobile information processing. In this chapter, we define the concept of wearables, present their attributes, and discuss their role at the core of an ecosystem for harnessing big data. We, then present the taxonomy for wearables and trace their advancements over the years. We discuss the practical challenges associated with the use of wearables and propose the concept of a meta-wearable – in the form of a wearable motherboard – as a feasible solution. We gaze into the future of wearables and propose a transdisciplinary approach to realizing this future that will transform the field and contribute to enhancing the quality of life for everyone.
Machine Learning Paradigms

Recent major advances in Information Technologies are leading to an entirely new era in the educational process, which is characterized by the development of more engaging and human-like computer-based learning, personalization and incorporation of artificial intelligence techniques. A new research discipline, termed Learning Analytics, is emerging and examines the collection and intelligent analysis of learner and instructor data with the goal to extract information that can render electronic and/or mobile educational systems more personalized, engaging, dynamically responsive and pedagogically efficient. In this volume, internationally established authors are contributing their research ideas and results towards aspects of Learning Analytics with the purpose to (1) measure Student Engagement, to quantify the Learning Experience and to facilitate Self-Regulation, (2) to predict Student Performance (3) to be incorporated in Tools for Building Learning Materials and Educational Courses, and (4) to be used as Tools to support Learners and Educators in Synchronous and Asynchronous e-Learning.
Digital picture formats and representations

Since the origin of digital video communications, there has been a need to understand the links between the human visual system (HVS) and picture representations. Building on Chapters 1, 2, and 3, this chapter describes the picture formats, processing techniques, and assessment methods that underpin the coding process; it introduces the processing methods and sampling structures that enable compression to be so effective. Building on the mathematical preliminaries from Chapter 3, we first show how samples are represented in digital images and video signals, emphasizing the important aspects of color and spatio-temporal representations. We examine how samples are grouped to form pictures and how pictures are grouped to form sequences of pictures or videos. The mapping of light levels to brightness as perceived by the HVS is considered in Section 4.4, showing how the signal from the camera must be transformed using a nonlinear mapping (gamma correction) to ensure best use of the bits available. Next, developing further the trichromacy theory of vision introduced in Chapter 2, we introduce the area of color spaces in Section 4.5; these are important since, with relatively little effort, we can create representations that reduce the bit rate for a digital video by 50%. Finally, we introduce the important topic of image and video quality assessment – both as a means of comparing the performance of different compression systems and as a means of controlling encoder modes during compression.
Enhancing knowledge graph embedding with relational constraints

Knowledge graph embedding is studied to embed entities and relations of a knowledge graph into continuous vector spaces, which benefits a variety of real-world applications. Among existing solutions, translational models, which employ geometric translation to design score function, have drawn much attention. However, these models primarily concentrate on evidence from observing whether the triplets are plausible, and ignore the fact that the relation also implies certain semantic constraints on its subject or object entity. In this paper, we present a general framework for enhancing knowledge graph embedding with relational constraints (KRC). Specifically, we elaborately design the score function by encoding regularities between a relation and its arguments into the translational embedding space. Additionally, we propose a soft margin-based ranking loss for effectively training the KRC model, which characterizes different semantic distances between negative and positive triplets. Furthermore, we combine regularities with distributional representations to predict the missing triplets, which can possess certain robust guarantee. We evaluate our method on the tasks of knowledge graph completion and entity classification. Extensive experiments show that KRC achieves a better, or comparable performance against state-of-the-art methods. Besides, KRC makes a great improvement when dealing with long-tail entities, which have few instances in the knowledge graph.
How to Use Replication Assignments for Teaching Integrity in Empirical Archaeology

ABSTRACT The value of new archaeological knowledge is strongly determined by how credible it is, and a key measure of scientific credibility is how replicable new results are. However, few archaeologists learn the skills necessary to conduct replication as part of their training. This means there is a gap between the ideals of archaeological science and the skills we teach future researchers. Here we argue for replications as a core type of class assignment in archaeology courses to close this gap and establish a culture of replication and reproducibility. We review replication assignments in other fields and describe how to implement a replication assignment suitable for many types of archaeology programs. We describe our experience with replication in an upper-level undergraduate class on stone artifact analysis. Replication assignments can help archaeology programs give students the skills that enable transparent and reproducible research.
Homomorphic Encryption as a Service for Outsourced Images in Mobile Cloud Computing Environment

The integration of cloud computing with mobile computing and internet has given birth to mobile cloud computing. This technology offers many advantages to users, like Storage capacity, Reliability, Scalability and Real time data availability. Therefore, it is s increasing fast and it is inevitably integrated into everyday life. In MCC, data processing and data storage can be migrated into the cloud servers. However, the confidentiality of images and data is most important in today's environment. In this paper, we mainly focus on secure outsourcing of images. For this purpose, we propose a secure architecture composed by two clouds a private cloud dedicated for encryption/decryption and a second public cloud dedicated for storage. We have implemented the first cloud using openstack while respecting the encryption as a service concept. As an encryption scheme, we have used paillier's homomorphic cryptosystem designed specifically for images. The test of the homomorphic property is done by applying the Watermarking algorithm DWT.
Machine Learning Based Framework for Maintaining Privacy of Healthcare Data

The Adoption of Artificial Intelligence (AI), Machine Learning (ML), Internet of Things (IoT), cloud services, web-based software systems, and other wireless sensor devices in the healthcare infrastructure have led to phenomenal improvements and benefits in the healthcare sector.Digital healthcare has ensured early diagnosis of the diseases, greater accessibility, and mass outreach in terms of treatment.Despite this unprecedented success, the privacy and confidentiality of the healthcare data have become a major concern for all the stakeholders.Data breach reports reveal that the healthcare data industry is one of the key targets of cyber invaders.In fact the last few years have registered an unprecedented rise in healthcare data breaches.Hacking incidents and privilege abuse are the most common threats and have exposed sensitive and protected health data.Experts and researchers are working on various techniques, tools, and methods to address the security issues related to healthcare data.In this article, the main focus is on evaluating the impact of research studies done in the context of healthcare data breach reports to identify the contemporary privacy and confidentiality issues of sensitive healthcare data.Analysis of the research studies depicts that there is a need for proactive security mechanisms that will help the healthcare organizations to identify abnormal user behavior while accessing healthcare data.Moreover, studies also suggest that ML techniques would be highly effective in securing the privacy and confidentiality of the healthcare data.Working further on this premise, the present study also proposes a conceptual framework that will secure the privacy and confidentiality of healthcare data proactively.The proposed framework is based on ML techniques to detect deviated user access against Electronic Health Records.Further, fuzzy-based Analytical Network Process (ANP), a multi-criteria decision-making approach, is used to assess the accuracy of the supervised and unsupervised ML approaches for achieving a dynamic digital healthcare data security environment.
Data Mining and Knowledge Discovery in Databases

The term knowledge discovery in databases or KDD, for short, was coined in 1989 to refer to the broad process of finding knowledge in data, and to emphasize the “high-level” application of particular data mining (DM) methods. The DM phase concerns, mainly, the means by which the patterns are extracted and enumerated from data. Nowadays, the two terms are, usually, indistinctly used. Efforts are being developed in order to create standards and rules in the field of DM with great relevance being given to the subject of inductive databases. Within the context of inductive databases, a great relevance is given to the so-called DM languages. This chapter explores DM in KDD.