Reviewerly/exHarmony huggingface dataset:
Clinical Databases for Breast Cancer Research

Clinical database is a collection of clinical data related to patients, which can be used for analysis and research. Clinical data can be classified into several categories: patient-related, tumor-related, diagnostics-related, treatment-related, outcome-related, administration-related, and other clinical data. Clinical databases can be classified according to the data types of clinical databases, ranges of institutes, and accessibility to data. The numbers of papers and clinical trials are rapidly increasing. Recently, more than 9000 papers related to breast cancer have been published annually, and more than 7000 papers related to human breast cancer are published annually. The speed of increase is expected to be faster and faster in future. Now, almost 8000 clinical trials are registered world widely. Main research areas of breast cancer can be classified into followings; epidemiology, screening and prevention, diagnosis, treatment, and prognosis. Clinical databases that are available for breast cancer research are also introduced in this chapter. The analysis of big data is expected to be the mainstream of breast cancer research using clinical databases. As the technology of artificial intelligence (AI) is rapidly evolving, the technology of deep learning starts to be applied for breast cancer research. In near future, AI technology is predicted to penetrate deeply the field of breast cancer research.
Drug mule for love

Purpose The purpose of this paper is to gain in-depth understandings of the stages involved in the case of a romance scam victim who was unknowingly used as a drug mule. The work compares this case with established research in this field. It also seeks to learn more about the strategies used by these cybercriminals. Design/methodology/approach The research presents a case study of a victim of a romance scam who was arrested for drug trafficking. The research involves a grounded theory analysis of interviews with the victim, legal team and family members and analysis of her instant messenger chat logs and email communications. Findings The analysis identified a variation on previous stage models of romance scams and re-names this as the “romance scammers” strategy model. It also replicates previous work on scammers’ techniques and highlights some new strategies, including positively and negatively framing messages, unconditional positive regard, activating norms of romantic relationships, cognitive immersion, manipulating role, sleep deprivation and signing is believing. Practical implications These findings could be used to help guide future similar court cases. Moreover, they can be drawn upon to advance future research on romance scams, as well as scams in general. Originality/value This is the first in-depth case study of a romance scam victim involved in drug trafficking and is the first research on romance scams to examine in depth a case, taking into account textual exchanges. While not undermining previous research, this paper provides valuable insights that are lacking in previous qualitative work on cyber scam victims.
Single sheet separation method from piled fabrics using roller hand mechanism

In order to automate the work in a garment factory, we developed a method of single sheet separation from piled fabrics by a roller hand mechanism with a rotating mechanism added to the tip of a robot hand. As a result, we succeeded in separating only single sheet of fabrics with a high probability. It is necessary to eliminate failures for practical use. However, it is difficult to eliminate failure completely due to the characteristics of fabrics. We also devised a method to judgement the success or failure of the separation to improve the reliability of the operation. As a result, the thickness of the fabric could be accurately measured and the confidence level was successfully improved.
Hard-Mining Loss Based Convolutional Neural Network for Face Recognition

Face Recognition is one of the prominent problems in the computer vision domain. Witnessing advances in deep learning, significant work has been observed in face recognition, which touched upon various parts of the recognition framework like Convolutional Neural Network (CNN), Layers, Loss functions, etc. Various loss functions such as Cross-Entropy, Angular-Softmax and ArcFace have been introduced to learn the weights of network for face recognition. However, these loss functions do not give high priority to the hard samples as compared to the easy samples. Moreover, their learning process is biased due to a number of easy examples compared to hard examples. In this paper, we address this issue by considering hard examples with more priority. In order to do so, We propose a Hard-Mining loss by increasing the loss for harder examples and decreasing the loss for easy examples. The proposed concept is generic and can be used with any existing loss function. We test the Hard-Mining loss with different losses such as Cross-Entropy, Angular-Softmax and ArcFace. The proposed Hard-Mining loss is tested over widely used Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) datasets. The training is performed over CASIA-WebFace and MS-Celeb-1M datasets. We use the residual network (i.e., ResNet18) for the experimental analysis. The experimental results suggest that the performance of existing loss functions is boosted when used in the framework of the proposed Hard-Mining loss.
Numerical Feature Transformation-based Sequence Generation Model for Multi-disease Diagnosis

The goal of computer-aided diagnosis is to predict patient’s diseases based on patient’s clinical data. The development of deep learning technology provides new help for clinical diagnosis. In this paper, we propose a new sequence generation model for multi-disease diagnosis prediction based on numerical feature transformation. Our model simultaneously uses patient’s laboratory test results and clinical text as input to diagnose and predict the disease that the patient may have. According to medical knowledge, our model can transform numerical features into descriptive text features, thereby enriching the semantic information of clinical texts. Besides, our model uses attention-based sequence generation methods to achieve the diagnosis of multiple diseases and better utilizes the correlation information between multiple diseases. We evaluate our model’s performance on a dataset of respiratory diseases from the real world, and experimental results show that our model’s accuracy reaches 42.75%, and the [Formula: see text] score reaches 65.65%, which is better than many other methods. It is suitable for the accurate diagnosis of multiple diseases.
IMPLEMENTATION EXPERIENCE OF DISTANCE LEARNING UNDER THE CIRCUMSTANCES OF THE PANDEMIC

The modern information society formulates tasks for the education of professionals who are able to adapt quickly to life situations, acquire the necessary knowledge and apply them in practice without assistance. Problems with the provision of education, which arose during the establishment of quarantine and the introduction of enhanced anti-epidemic measures in areas with a high prevalence of acute respiratory disease caused by the coronavirus, required in real life to find ways to solve them rationally, using modern technologies. The situation made teachers work with information properly, be communicative, work on the development of their own IT competence by themselves. The development of modern technologies has diversified the methods of communication between teachers and students. This article presents an analysis of the implementation of distance learning in the Zaporizhzhia region and focuses on the difficulties faced by teachers, students, their parents, and ways to reduce the problems of introducing distance learning environment in particular. Monitoring the activities of various participants in the educational process provides opportunities to systematize information and provide recommendations for overcoming such difficulties. The experience of 2020 and the results of the survey showed that there was almost no separate distance and e-learning. An online survey was conducted during March 2021 in order to determine the opinion of teachers in the Zaporozhzhia region on the organization of educational and cognitive activities of students in the form of distance learning. The effectiveness of distance learning in educational institutions of the Zaporozhzhia region depends on the technical support of the educational process, the skill of teachers involved in this process, the use of effective communication. Survey participants believe that teacher motivation has less impact on the effectiveness of distance learning than the motivation of the learner. According to many respondents, the schedule of lessons has not changed, but the usual schedule of life has changed dramatically. Many educators emphasize that they work at a computer for more than half a day. Analysis of statistical data and freely expressed opinions of teachers, participants of the survey, suggests that no effective solutions have been found for the organization of "distance" learning, but there are teachers who respond positively to the existing situation and are in innovative search.
Simba

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with finegrained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with a batch size of one, delivering an inference latency of 0.50 ms.
Revisiting Document Representations for Large-Scale Zero-Shot Learning

Zero-shot learning aims to recognize unseen objects using their semantic representations.Most existing works use visual attributes labeled by humans, not suitable for large-scale applications.In this paper, we revisit the use of documents as semantic representations.We argue that documents like Wikipedia pages contain rich visual information, which however can easily be buried by the vast amount of non-visual sentences.To address this issue, we propose a semi-automatic mechanism for visual sentence extraction that leverages the document section headers and the clustering structure of visual sentences.The extracted visual sentences, after a novel weighting scheme to distinguish similar classes, essentially form semantic representations like visual attributes but need much less human effort.On the Ima-geNet dataset with over 10,000 unseen classes, our representations lead to a 64% relative improvement against the commonly used ones.
Blockchain Technology and Fashion Industry-Opportunities and Challenges

Fashion and textile industry are one of the fastest growing sectors that involves a complex supply chain at local and global levels to procure raw materials and supply finished products to the market. The complexity of the industry demands for a system which is transparent, distributed and can protect the intellectual property rights. With key characteristics like decentralization, immutability, consensus etc., blockchain technology has the potential to enhance the exiting fashion industry by adding an extra layer of security and trust to it. One of the major challenges faced by the fashion industry is the counterfeit products flooding the market place. These fake products have a negative impact on the brand image and value. Blockchain has the ability to protect and secure the digital identities and establish authenticity in fashion industry. Despite of the exponentially growing popularity and interest in this technology, very little is known about the current state of application and use of blockchain in fashion and textile industry. This paper discusses the various aspects of the use of blockchain technology in the fashion and textile industry highlighting the benefits that blockchain could bring. The role of blockchain in providing potential solutions to the existing issues and challenges faced by the fashion industry are discussed with an insight into the current state of the blockchain technology in fashion industry. Further, the work also discusses the challenges in the integration of blockchain into the existing processes of the fashion and textile industry.
Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.
Estimating Rooftop Solar Potential in Urban Environments: A Generalized Approach and Assessment of the Galápagos Islands

Presently, many island communities are largely dependent on fossil fuel resources for energy, leaving the abundance of renewable energy resources largely untapped. Although various solar energy potential modeling tools have been developed, most require high-resolution data, which do not presently exist for many developing countries or remote areas. Here, we calculate the potential of rooftop solar systems using low-cost, readily obtainable data and methods. This approach can be replicated by local communities and decision-makers to obtain an estimate of solar potential before investing in more detailed analysis. We illustrate the use of these methods on the two major urban centers on the Galápagos Islands (Ecuador), Puerto Baquerizo Moreno and Puerto Ayora. Our results show that a minimum of 21% and 27% of the total rooftop area must be covered with today's solar energy production technology to meet the current electricity demand of Puerto Baquerizo Moreno and Puerto Ayora, respectively. Additionally, the results demonstrate that Puerto Baquerizo Moreno has a higher production potential than Puerto Ayora, making it an attractive option for solar development that does not compete with the scarce land resources, most of which must be preserved as natural protected areas.
Change of Educational Paradigm Due to E-Learning Technologies Implementation in Higher Education

The paper analyses changes in educational paradigm due to E-learning technologies introduction into higher technical education. The article considers teachers’ readiness for upcoming changes in educational paradigm. Two educational paradigms are compared: traditional and innovative, formed as a result of E-learning technologies introduction and tended to change roles and functions of higher education participants. Scientific originality of the study lies in the fact that the authors compare two educational paradigms and describe newly established roles and functions of educational process participants. The research findings are as follows: roles and functions of educational process participants under the conditions of higher education modernization are revealed; advantages of the innovative educational model over the traditional one are identified.
Federated Chinese Word Segmentation with Global Character Associations

Chinese word segmentation (CWS) is a fundamental task for Chinese information processing, which always suffers from out-ofvocabulary word issues, especially when it is tested on data from different sources.Although one possible solution is to use more training data, in real applications, these data are stored at different locations and thus are invisible and isolated among each other owing to the privacy or legal issues (e.g., clinical reports from different hospitals).To address this issue and benefit from extra data, we propose a neural model for CWS with federated learning (FL) adopted to help CWS deal with data isolation, where a mechanism of global character associations is proposed to enhance FL to learn from different data sources.Experimental results on a simulated environment with five nodes confirm the effectiveness of our approach, where our approach outperforms different baselines including some well-designed FL frameworks.
Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Abstract Purpose Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields. This also helps in having a better collaboration with governments and businesses. This study aims to investigate the development of research fields over time, translating it into a topic detection problem. Design/methodology/approach To achieve the objectives, we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents. Document embedding approaches are utilized to transform documents into vector-based representations. The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms (i.e. LDA) against a benchmark dataset. A case study is also conducted exploring the evolution of Artificial Intelligence (AI) detecting the research topics or sub-fields in related AI publications. Findings Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset. Using the proposed method, we also show how the topics have evolved in the period of the recent 30 years, taking advantage of a keyword extraction method for cluster tagging and labeling, demonstrating the context of the topics. Research limitations We noticed that it is not possible to generalize one solution for all downstream tasks. Hence, it is required to fine-tune or optimize the solutions for each task and even datasets. In addition, interpretation of cluster labels can be subjective and vary based on the readers’ opinions. It is also very difficult to evaluate the labeling techniques, rendering the explanation of the clusters further limited. Practical implications As demonstrated in the case study, we show that in a real-world example, how the proposed method would enable the researchers and reviewers of the academic research to detect, summarize, analyze, and visualize research topics from decades of academic documents. This helps the scientific community and all related organizations in fast and effective analysis of the fields, by establishing and explaining the topics. Originality/value In this study, we introduce a modified and tuned deep embedding clustering coupled with Doc2Vec representations for topic extraction. We also use a concept extraction method as a labeling approach in this study. The effectiveness of the method has been evaluated in a case study of AI publications, where we analyze the AI topics during the past three decades.
Unified Dual-view Cognitive Model for Interpretable Claim Verification

Recent studies constructing direct interactions between the claim and each single user response to capture evidence have shown remarkable success in interpretable claim verification.Owing to different single responses convey different cognition of individual users, the captured evidence belongs to the perspective of individual cognition.However, individuals' cognition of social things is not always able to truly reflect the objective.There may be one-sided or biased semantics in their opinions on a claim.The captured evidence correspondingly contains some unobjective and biased information.In this paper, we propose a Dual-view model based on the views of Collective and Individual Cognition (CICD) for interpretable claim verification.For collective cognition, we not only capture the word-level semantics based on individual users, but also focus on sentencelevel semantics (i.e., the overall responses) among all users to generate global evidence.For individual cognition, we select the top-k articles with high degree of difference and interact with the claim to explore the local key evidence fragments.To weaken the bias of individual cognition-view evidence, we devise an inconsistent loss to suppress the divergence between global and local evidence for strengthening the consistent shared evidence between the both.Experiments on three benchmark datasets confirm the effectiveness of CICD.
A Novel Reversible Data Hiding Method for 3D Model in Homomorphic Encryption Domain

Reversible data hiding in the encrypted domain (RDH-ED) is a technique that protects the privacy of multimedia in the cloud service. In order to manage three-dimensional (3D) models, a novel RDH-ED based on prediction error expansion (PEE) is proposed. First, the homomorphic Paillier cryptosystem is utilized to encrypt the 3D model for transmission to the cloud. In the data hiding, a greedy algorithm is employed to classify vertices of 3D models into reference and embedded sets in order to increase the embedding capacity. The prediction value of the embedded vertex is computed by using the reference vertex, and then the module length of the prediction error is expanded to embed data. In the receiving side, the data extraction is symmetric to the data embedding, and the range of the module length is compared to extract the secret data. Meanwhile, the original 3D model can be recovered with the help of the reference vertex. The experimental results show that the proposed method can achieve greater embedding capacity compared with the existing RDH-ED methods.
Hypernym Discovery via a Recurrent Mapping Model

Hypernym discovery aims to identify all possible hypernyms of a given term.The most recent hypernym discovery models exploit multiple mapping functions to project a term to different semantic spaces and then aggregate these embeddings to a general representation for further classification.We refer to this model as a parallel style model.In this work, we observe that there are hierarchical relations between a target terms' hypernyms.However, these hierarchical relations were not sufficiently considered in the previous parallel style model.To leverage the hierarchical relations, we propose a sequential style model that recurrently maps the query words to their hypernyms, starting from the most specific ones to the less specific ones.Empirical studies on SemEval-2018 Task 9 confirm the effectiveness of the presented model.