Reviewerly/exHarmony huggingface dataset:
Image Processing Approach for Segmentation of WBC Nuclei based on K-Means Clustering

Pathologists do manual microscopic assessment to check for abnormality which is very time consuming process. Automating blood cells classification is a requirement to aid them for robust and accurate results. White blood cells are used to diagnose some particular diseases like Leukemia, Immune Deficiency Syndrome and many others. They help in monitoring the fitness of a person. Segmentation is the foremost step in automatic blood cell classification which is a perplexing task because of alterations in appearance of cells under microscope. In this paper, we have presented a successful technique for automated white blood cells' nuclei segmentation by keeping in view the fact of variation in light intensity. We have combined the contrast adjustment, K-means clustering and threshold (to remove false objects) to segment nuclei from blood smear image. This technique is tested on all five types of white blood cells to compare relative performance. The results of the proposed method showed that the Dice score, Sensitivity, False Positive Rate (FPR) and Accuracy are 97.81%, 98.29%, 0.14% and 99.78%, respectively for nuclei segmentation.
Team Sports for Game AI Benchmarking Revisited

Sport games are among the oldest and best established genres of computer games. Sport-inspired environments, such as RoboCup, have been used for AI benchmarking for years. We argue that, in spite of the rise of increasingly more sophisticated game genres, team sport games will remain an important testbed for AI benchmarking due to two primary factors. First, there are several genre-specific challenges for AI systems that are neither present nor emphasized in other types of games, such as team AI and frequent replanning. Second, there are unmistakable nonskill-related goals of AI systems, contributing to player enjoyment, that are most easily observed and addressed within a context of a team sport, such as showing creative and emotional traits. We analyze these factors in detail and outline promising directions for future research for game AI benchmarking, within a team sport context.
Sensor-Based Human Activity and Behavior Computing

A major goal of human activity and behavior recognition (HAR) is to recognize activities and behaviors from a series of action data for different subjects under different environmental conditions. The use of wearables, smart devices, and vision-based systems enables the collection of human action and behavior data for greater health benefits, rehabilitation, elderly care, and monitoring. This chapter overviews the state-of-the-art in activity and behavior recognition based on sensor data. First, we provided a general architecture of HAR describing primary data sources and pre-processing techniques. Then, we describe robust feature extraction and selection strategies with a comparative analysis of statistical and deep learning-based models. Additionally, we provide information about more than 100 benchmark datasets and repositories in this domain. The paper concludes with a discussion of major issues and challenges, highlighting open issues and scopes that need to be approached in prospective research.
Accelerating DNNs inference with predictive layer fusion

Many modern convolutional neural neworks (CNNs) rely on bottleneck block structures where the activation tensor is mapped between higher dimensions using an intermediate low dimension, and convolved with depthwise feature filters rather than multi-channel filters. Because most of the computation lies in computing the large dimensional tensors, however, such networks cannot be scaled without significant computation costs.
SynCGAN: Using Learnable Class Specific Priors to Generate Synthetic Data for Improving Classifier Performance on Cytological Images

One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data. This makes it difficult for deep learning algorithms to perform well due to a lack of variations in the input space. While generative adversarial networks have shown promise in the field of synthetic data generation, but without a carefully designed prior the generation procedure can not be performed well. In the proposed approach we have demonstrated the use of automatically generated segmentation masks as learnable class-specific priors to guide a conditional GAN for the generation of patho-realistic samples for cytology image. We have observed that augmentation of data using the proposed pipeline called "SynCGAN" improves the performance of state of the art classifiers such as ResNet-152, DenseNet-161, Inception-V3 significantly.
Disruptive Technologies for Disruptive Innovations: Challenges and Opportunities

Disruptive technologies continuously and significantly alter the way people communicate and collaborate as well as the way industries operate today and in the future. To create new business models and opportunities, several combinations of disruptive technologies are being introduced nowadays. Among these technologies, cloud computing, IoT, Blockchain, artificial intelligence, social networks and media, big data, and 5G are mostly used. For instance, Blockchain technology made distributed solutions feasible and popular. On the other hand, big data and the social media are two contemporary technologies which lament significant impact on business and society. This paper presents a holistic approach to integration perspectives of these technologies considering many challenges like security and privacy. This paper also surveys the most relevant work in order to analyze how some of these technologies could potentially improve each other.
Anime Style Transfer With Spatially-Adaptive Normalization

Image style transfer has always been a popular topic in the field of computer vision. Many researchers have achieved transfer image texture characteristics, such as converting real photos to painting styles. Different from these style transfer tasks, our target is to colorize the anime line art according to the color scheme of the given reference image. We transfer the color of the anime characters' hair, clothes, skin, etc. to another grayscale anime line art. In this paper, we propose a model based on Spatially-Adaptive (DE) Normalization (SPADE) to achieve anime style transfer. In addition, we use a data augmentation method to solve the "lazy" problem of neural networks.
Strategic Guidelines of the State Library Policy

The Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia was held on November 11—12, 2020 in the online format. The event was organized by the Ministry of Culture of the Russian Federation, the Russian National Library and the Russian State Library. The main goal of the meeting is to ensure participation of the heads of federal and central regional libraries in the formation and implementation of the state library policy. The topic of the 2020 Meeting is “The Library Profession and Scientific and Educational Work of Libraries”. The scientific content of the meeting was basically determined by the “Strategy for the development of librarianship in the Russian Federation for the period up to 2030”, the draft development of which has been already completed as a whole, but requires the deployment of large-scale research work to form the unified system for monitoring the activities of libraries in the country. The meeting focused on the following issues: organization of scientific research work in libraries; training of professional staff; modernization of librarianship and the role of information technologies in the modern development of libraries and digitalization. The relevance of these topics was proved in the discourse on the development of higher and further professional education in the library sector, improvement of availability of information in the modern conditions. During the sessions, there were summed up the results of the 7th All-Russian competition “Library Analytics” among the central libraries of the subjects of the Russian Federation, the 8th All-Russian competition “The Best Professional Book of the Year” and the 7th All-Russian library review competition for the best electronic publication on culture and art.
Application of Machine Intelligence-Based Knowledge Graphs for Software Engineering

This chapter focuses on knowledge graphs application in software engineering. It starts with a general exploration of artificial intelligence for software engineering and then funnels down to the area where knowledge graphs can be a good fit. The focus is to put together work done in this area and call out key learning and future aspirations. The knowledge management system's architecture, specific application of the knowledge graph in software engineering like automation of test case creation and aspiring to build a continuous learning system are explored. Understanding the semantics of the knowledge, developing an intelligent development environment, defect prediction with network analysis, and clustering of the graph data are exciting explorations.
ArcIoT: Enabling Intuitive Device Control in the Internet of Things through Augmented Reality

Increasing numbers of smart devices have enriched the possibilities of the IoT. These advances also impose significant cognitive overhead on the end-users; to interact with IoT devices, a user must be knowledgeable about the devices, the applications, the network, etc. We present ArcIoT, a system that enables intuitive interaction with IoT devices by leveraging Augmented Reality (AR) and a novel relative indoor localization service. In ArcIoT, the user points a smartphone camera towards a device to control, chooses the device by a tap on the screen, and interacts with the displayed interface. Continuously and transparently, ArcIoT maintains a map of devices in the environment, which allows it to quickly and reliably control them. Compared to existing solutions, ArcIoT does not need predefined markers to recognize a device or require any change to physical infrastructure. ArcIoT is also robust to changes in the locations of the IoT devices in the environment. We successfully deployed ArcIoT in different home environments and evaluated its performance to demonstrate that ArcIoT helps users control devices in everyday environments accurately, responsively and intuitively.
CRIL: Continual Robot Imitation Learning via Generative and Prediction Model

Imitation learning (IL) algorithms have shown promising results for robots to learn skills from expert demonstrations. However, they need multi-task demonstrations to be provided at once for acquiring diverse skills, which is difficult in real world. In this work we study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one, thus reducing the burden of multitask IL and accelerating the process of new task learning at the same time. We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics-aware prediction model to generate pseudo trajectories from all learned tasks in the new task learning process. Our experiments on both simulation and real-world manipulation tasks demonstrate the effectiveness of our method.
Defining the relationship between IT Service management and knowledge management: towards improved performance

Regarding the continuous changes in Information Technologies and the necessity for managing them, IT service management (ITSM) should be enriched with knowledge management (KM) to align with the requirements of the digital era. The aim of this research is to identify the relationship between ITSM and KM in order to set up a foundation for designing an integrated ITSM–KM model. Unlike the previous endeavours that only considered the general relationship between ITSM and KM, we focus on the relationship between processes and activities of them in detail. We applied fuzzy cognitive map (FCM) to identify the relationships in both process and activity levels. Also, we specified the key performance indicators of ITSM processes which will be improved in response to this integration using FAHP technique. The results of this research indicate that the efficiency and effectiveness of ITSM processes will increase when they are integrated with KM activities.
Toward a historical ontology of the infopolitics of data-driven decision-making (DDDM) in education

This paper interrogates the fundamental logic of data-driven decision-making (DDDM) as it has taken hold in education and argues for a critical analysis of data-driven education via an attitude of historical ontology. Though influenced by Foucault's understanding of this concept, I center Colin Koopman's recent analysis of the 'informational person' to point attention to the ways in which the very formatting of data may be understood as historically contingent and, thus, more contestable. After examining the background of DDDM and relevant critiques of it, I suggest that investigating the socio-historical formations of data itself are important for indicating its potential dangers within education. Such a critical analysis identifies the doubled politics of data formatting, with its ability to tether subjectivity to data, while potentially baking modes of power into a variety of technological data apparatuses. To conclude, I consider Koopman's work in terms of how it may extend critical studies of educational datafication.
Sentiment Analysis of Chinese E-commerce Reviews Based on BERT

The popularity of the Internet has brought profound influence to electronic commerce. A kind of review-oriented consumption mode is gradually expanding in the market and consumers will refer to the reviews provided by consumers who bought the product in the past. How to accurately analyze users' sentiments from massive data of e-commerce reviews has become one of the key issues for e-commerce platforms. Current standard sentiment analysis classifies overall sentiment of e-commerce reviews without an extended description of the entity. We set up an optimized Aspect-based sentiment analysis (ABSA) that includes four elements: aspect, category, polarity, and opinion. Aiming at the above problems, this paper proposes a Chinese e-commerce reviews sentiment analysis algorithm based on BERT. By using pre-training model, we use the BIO(B-begin,I-inside,O-outside) data labeling pattern to label entities and study sentiment analysis by the annotation data. Experimental results on the Taobao cosmetics review datasets show that compared with the ordinary deep learning methods, our approach in the accuracy rate and the F1 score has significant improvement.
$$\text{ W-OTS}^{+}$$ Up My Sleeve! A Hidden Secure Fallback for Cryptocurrency Wallets

We introduce a new key generation mechanism where users can generate a "back up key", securely nested inside the secret key of a signature scheme.
Learning word representation by jointly using neighbor and syntactic contexts

Interpretability is a significant aspect of the distributed word representation learning model. Although the most advanced pretrained models have achieved the best results till date, the interpretability of a pretrained model is difficult to explain clearly. For this reason, based on the interpretability of distributed word embeddings, this paper presents a method of learning word representation using joint context. At present, the existing distributed word representation models for learning word representations usually focus on either neighbor or syntactic context. We argue that it is necessary to simultaneously model both contexts. In particular, the point mutual information obtained by combining the two types of contexts can efficiently express the correlation between the words. We propose two alternative distribution models for learning word representations by employing the neighbor and syntactic contexts via a simple and effective joint learning framework. Furthermore, the proposed models are trained on a public corpus, and the learned representations are evaluated in word analogy, word similarity, and sentence classification tasks. The experimental results demonstrate the potential of the proposed method.
Duo-LDL method for Label Distribution Learning based on pairwise class dependencies

Label Distribution Learning (LDL) is a new learning paradigm with numerous applications in various domains. It is a generalization of both standard multiclass classification and multilabel classification. Instead of a binary value, in LDL, each label is assigned a real number which corresponds to a degree of membership of the object being classified to a given class. In this paper a new neural network approach to Label Distribution Learning (Duo-LDL), which considers pairwise class dependencies, is introduced. The method is extensively tested on 15 well-established benchmark sets, against 6 evaluation measures, proving its competitiveness to state-of-the-art non-neural LDL approaches. Additional experimental results on artificially generated data demonstrate that Duo-LDL is especially effective in the case of most challenging benchmarks, with extensive input feature representations and numerous output classes.
Market Behavior Analysis using Descriptive Approach

In the field of marketing, substantial voluminous of data has been produced every day, but most of them are being unused. In the current market scenario, most of the supermarkets and retail showrooms are unaware of their stock status, sales percentage on a particular period, frequently and infrequently selling products. This paper proposes a big data platform (BDP) to overcome the problem as mentioned above. These inferences can be revealed from the data that are being incorporated in the marketing industry, and the analyzed data will give us a clear view about the frequent items in an average selling frequency of product, and least selling product. A visualization technique has been deployed to understand the statistics. Therefore, this system overcomes the drawbacks of a marketing system and also can be used widely in a variety of industries.
Click This, Not That: Extending Web Authentication with Deception

With phishing attacks, password breaches, and brute-force login attacks presenting constant threats, it is clear that passwords alone are inadequate for protecting the web applications entrusted with our personal data. Instead, web applications should practice defense in depth and give users multiple ways to secure their accounts.
Modified ML-kNN and Rank SVM for Multi-label Pattern Classification

Abstract To develop an efficient multi-label classifier is the main objective of this paper. In multi-label learning tasks such as classification, each example is associated with a set of labels, and the task is to predict the label set whose size is unknown apriory for each unseen example. In a realistic scenario each object or entity belongs to a multi-label category. Multi-Label k-Nearest Neighbor (ML-kNN), Rank-SVM (Ranking Support Vector Machine) are two popular techniques used for multi-label pattern classification. ML-kNN is a multi-label version of standard kNN and Rank SVM is a multi-label extension of standard SVM. The main aim of this work is to enhance the performance of these methods. Multi-label classifiers generally consider ranking loss, Hamming loss, one error, average precision and coverage as a performance metrics.
Theory of quasi-exact fault-tolerant quantum computing and valence-bond-solid codes

In this work, we develop the theory of quasi-exact fault-tolerant quantum (QEQ) computation, which uses qubits encoded into quasi-exact quantum error-correction codes ("quasi codes"). By definition, a quasi code is a parametric approximate code that can become exact by tuning its parameters. The model of QEQ computation lies in between the two well-known ones: the usual noisy quantum computation without error correction and the usual fault-tolerant quantum computation, but closer to the later. Many notions of exact quantum codes need to be adjusted for the quasi setting. Here we develop quasi error-correction theory using quantum instrument, the notions of quasi universality, quasi code distances, and quasi thresholds, etc. We find a wide class of quasi codes which are called valence-bond-solid codes, and we use them as concrete examples to demonstrate QEQ computation.
Multi-Relational Graph Convolutional Networks for Skeleton-Based Action Recognition

In motion, the interaction relationship between the human body parts is diversified. However, the existing action recognition methods based on the graph convolution neural networks (GCNs) can only deal with a single relation of skeletons. Even some works describe different relations of the skeleton, the adjacency matrices of different relation graphs are added together. This paper proposes a multi-relational GCNs for action recognition following the idea of describing different relations between entities by knowledge graphs. The natural connection relation, symmetric connection relation, and global connection relation of the human body parts are modeled respectively. The features of the relations are transmitted and integrated through the network, which can improve the representation ability of features. Meanwhile, this paper proposes a two-stream multi-relational graph convolution networks (2S-MRGCNs), which processes the joint flow and the body part flow of skeleton data respectively to represent the action more comprehensively. The experimental results show that the 2S-MRGCNs model proposed in this paper has achieved state-of-the-art results on action recognition in kinetics and NTU-RGB+D datasets.
Forward and backward secure keyword search with flexible keyword shielding

Dynamic Searchable Symmetric Encryption (DSSE) has gained increasing popularity as it enables users to perform both file updates and ciphertext retrieval over encrypted data. However, existing DSSE schemes still lead to privacy leakage (e.g., forward and backward privacy) in the dynamic setting. Some forward and backward secure DSSE schemes have been proposed, but still cannot support the keyword shielding flexibly. To solve this challenging issue, we propose a Forward and Backward Authorized Keyword Search (FB-AKS) scheme with recoverable keyword shielding by using trapdoor permutations and puncturable encryption in this paper. Compared with existing forward and backward private schemes, FB-AKS achieves keyword authorization flexibly (e.g., keyword shielding, keyword un-shielding). The formal security analysis proves that FB-AKS achieves forward and backward security. And extensive experiments demonstrate that FB-AKS has less computation and storage overheads.
Stable first-arrival picking through adaptive threshold determination and spatial constraint clustering

First-arrival picking is a critical step of determining the onsets of signals in seismic exploration. Existing approaches are usually sensitive to background noise and user-specified parameters. In this paper, we present the first-arrival picking through adaptive threshold determination and spatial constraint clustering (FASC) algorithm with four steps. The mean filtering step steadily forms an extended signal region. The adaptive threshold determination step stably obtains the first-arrival range in a bottom-up manner. The spatial constraint clustering step clusters the data within the range. The adaptive cluster-selecting step selects the appropriate cluster as first arrivals. Experiments are undertaken on two field datasets. The results show that FASC is more accurate than the three classic and latest algorithms.