Reviewerly/exHarmony huggingface dataset:
SemSegLoss: A python package of loss functions for semantic segmentation

Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. In recent years, various research papers proposed different loss functions used in case of biased data, sparse segmentation, and unbalanced dataset. In this paper, we introduce SemSegLoss, a python package consisting of some of the well-known loss functions widely used for image segmentation. It is developed with the intent to help researchers in the development of novel loss functions and perform an extensive set of experiments on model architectures for various applications. The ease-of-use and flexibility of the presented package have allowed reducing the development time and increased evaluation strategies of machine learning models for semantic segmentation. Furthermore, different applications that use image segmentation can use SemSegLoss because of the generality of its functions. This wide range of applications will lead to the development and growth of AI across all industries.
Cryptanalysis of a Honeyword System in the IoT Platform

Password is one of the most well-known authentication methods in accessing many Internet of Things (IoT) devices. The usage of passwords, however, inherits several drawbacks and emerging vulnerabilities in the IoT platform. However, many solutions have been proposed to tackle these limitations. Most of these defense strategies suffer from a lack of computational power and memory capacity and do not have immediate cover in the IoT platform. Motivated by this consideration, the goal of this article is fivefold. First, we analyze the feasibility of implementing a honeyword-based defense strategy to prevent the latest developed server-side threat on the IoT domain’s password. Second, we perform thorough cryptanalysis of a recently developed honeyword-based method to evaluate its advancement in preventing the threat and explore the best possible way to incorporate it in the IoT platform. Third, we verify that we can add a honeyword-based solution to the IoT infrastructure by ensuring specific guidelines. Fourth, we propose a generic attack model, namely, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">matching attack</i> utilizing the compromised password file to perform the security check of any legacy-UI approach for meeting the all essential flatness security criterion. Last, we compare the matching attack’s performance with the corresponding one of a benchmark technological methods over the legacy-UI model and confirm that our attack has 5%–22% more vulnerable than others.
Research on Innovation and Entrepreneurship Education for College Students from the Perspective of Big Data

Abstract Information technology promotes the wide application of big data and causes the constant reform of the teaching mode in colleges and universities. How to make college students adapt to the changeable market environment and improve the effect of innovation and entrepreneurship education has become a topic. Analyze the current situation of college students’ innovation and entrepreneurship education through big data. This paper analyzes college students’ professional ethics, job-hunting concept, innovation and practice ability cultivation, creative thinking, entrepreneurial psychological bearing ability, entrepreneurial core competitiveness, innovation and entrepreneurship comprehensive quality, etc. Through the analysis of the current situation of college students’ innovation and entrepreneurship education, we find out the positive influencing factors and consolidate and improve them. In view of the negative influence factors, this paper puts forward reform ideas and countermeasures. From the government, schools, teachers and students, the society summed up the innovation and entrepreneurship education model, to establish a set of constantly improved innovation and entrepreneurship curriculum system. In this way, the innovative and entrepreneurial talents training scheme adapted to the changing times can be formed and the healthy development of talents can be promoted.
Hero: On the Chaos When PATH Meets Modules

Ever since its first release in 2009, the Go programming language (Golang) has been well received by software communities. A major reason for its success is the powerful support of library-based development, where a Golang project can be conveniently built on top of other projects by referencing them as libraries. As Golang evolves, it recommends the use of a new library-referencing mode to overcome the limitations of the original one. While these two library modes are incompatible, both are supported by the Golang ecosystem. The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management (DM) issues, incurring reference inconsistencies and even build failures. Motivated by the problem, we conducted an empirical study to characterize the DM issues, understand their root causes, and examine their fixing solutions. Based on our findings, we developed Hero, an automated technique to detect DM issues and suggest proper fixing solutions. We applied Hero to 19,000 popular Golang projects. The results showed that Hero achieved a high detection rate of 98.5% on a DM issue benchmark and found 2,422 new DM issues in 2,356 popular Golang projects. We reported 280 issues, among which 181 (64.6%) issues have been confirmed, and 160 of them (88.4%) have been fixed or are under fixing. Almost all the fixes have adopted our fixing suggestions.
Low Rank Based End-to-End Deep Neural Network Compression

Deep neural networks (DNNs), despite their performance on a wide variety of tasks, are still out of reach for many applications as they require significant computational resources. In this paper, we present a low-rank based end-to-end deep neural network compression framework with the goal of enabling DNNs performance to computationally constrained devices. The proposed framework includes techniques for low-rank based structural approximation, quantization and lossless arithmetic coding. Many of these techniques have been accepted in the MPEG working draft on compressed Neural Network Representations. We demonstrate the efficacy of the proposed framework via extensive experiments on a variety of DNNs for various tasks considered in this standardization activity. These techniques provide impressive performance on DNNs used in ImageNet Large-Scale Visual Recognition Challenge by compressing VGG16 by 61x, ResNet50 by almost 15x, and MobileNetV2 by almost 7x.
Architecting Internet of Things Systems with Blockchain

Blockchain offers a distributed ledger to record data collected from Internet of Thing (IoT) devices as immutable and tamper-proof transactions and securely shared among authorized participants in a Peer-to-Peer (P2P) network. Despite the growing interest in using blockchain for securing IoT systems, there is a general lack of systematic research and comprehensive review of the design issues on the integration of blockchain and IoT from the software architecture perspective. This article presents a catalog of architectural tactics for the design of IoT systems supported by blockchain as a result of a Systematic Literature Review (SLR) on IoT and blockchain to extract the commonly reported quality attributes, design decisions, and relevant architectural tactics for the architectural design of this category of systems. Our findings are threefold:&lt;?brk?&gt; (i) identification of security, scalability, performance, and interoperability as the commonly reported quality attributes; (ii) a catalog of twelve architectural tactics for the design of IoT systems supported by blockchain; and (iii) gaps in research that include tradeoffs among quality attributes and identified tactics. These tactics might provide architects and designers with different options when searching for an optimal architectural design that meets the quality attributes of interest and constraints of a system.
Evaluation of Virtual Reality opportunities during Pandemic

As Virtual Reality (VR) continues to expand from the fictional world and permeates every aspect of our life, it is becoming important for both professionals and students to understand the various aspects of VR. The Covid-19 pandemic has brought about unprecedented change in today's world, yet its timing is fortuitous. Researchers are using ingenious technologies for speeding up the development to end this hazard. Virtual Reality also plays an important role in combating this pandemic, through sensory-based virtual communication. In the present world, we are surrounded by multiple technologies. This paper provides an overview of the basics of VR, the hardware and the history of VR, from its origins in the 1950s to the present-day. Different applications of VR such as medicine, entertainment, education, tourism, real estate, and others are discussed in this paper.
Onboarding in Software Product Lines: Concept Maps as Welcome Guides

With a volatile labour and technological market, onboarding is becoming increasingly important. The process of incorporating a new developer, a.k.a. the newcomer, into a software development team is reckoned to be lengthy, frustrating and expensive. Newcomers face personal, interpersonal, process and technical barriers during their incorporation, which, in turn, affects the overall productivity of the whole team. This problem exacerbates for Software Product Lines (SPLs), where their size and variability combine to make onboarding even more challenging, even more so for developers that are transferred from the Application Engineering team into the Domain Engineering team, who will be our target newcomers. This work presents concept maps on the role of sensemaking scaffolds to help to introduce these newcomers into the SPL domain. Concept maps, used as knowledge visualisation tools, have been proven to be helpful for meaningful learning. Our main insight is to capture concepts of the SPL domain and their interrelationships in a concept map, and then, present them incrementally, helping newcomers grasp the SPL and aiding them in exploring it in a guided manner while avoiding information overload. This work's contributions are four-fold. First, concept maps are proposed as a representation to introduce newcomers into the SPL domain. Second, conceptmaps are presented as the means for a guided exploration of the SPL core assets. Third, a feature-driven concept map construction process is introduced. Last, the usefulness of concept maps as guides for SPL onboarding is tested through a formative evaluation.
Modeling Inter-aspect Relationship with Conjunction for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis is currently a main focus within the domain of sentiment analysis, whose target is to identify the sentiment polarities of specific aspect terms. The ongoing research is absent of exploiting the inter-aspect relationship while mainly focus on modeling the aspect terms and its context independently. To address this problem, we propose a model integrating the conjunction information and the sentiment of the preceding aspect term. As such, the inter-aspect relation between adjacent aspect terms can be precisely modeled and applied to sentiment classification. Experimental results on SemEval 2014 and MAMS show that our model outperform the baseline methods, especially dealing with the multi-aspect terms, which establishes a strong evidence of the effectiveness of the proposed method.
An Effective Automatic Breast Cancer Identification using Vedic Mathematics

Breast cancer in recent years has been pervasive and become one of the leading causes of death. Breast cancer typically is a malignant tumour that has been start to growth from cells of the breast and eventually affects the surrounding cells. The women affected by the breast cancer cause high mortality rate. So the detection of breast cancer at an early stage helps to increase the survival rate and possibility of suitable treatment by healthcare professional. The breast cancer is detected by mammogram and followed by Biopsy. We proposed a method here to identify the breast cancer using image processing techniques which is blended with Vedic Mathematics. It consists of stages: Image pre-processing, Segmentation, Feature Extraction. In the proposed method, desired features of the cancer regions are extracted through regional descriptors. The main aim of this paper is to assist the pathologists to improve the accuracy, efficiency in detecting grade of cancer and to minimize the inter-observer variation.
Deaf Users’ Preferences Among Wake-Up Approaches during Sign-Language Interaction with Personal Assistant Devices

Personal-assistant devices like Amazon Alexa and Google Assistant are increasingly popular among consumers. Users activate these systems through some type of wake-up approach, e.g. using a wake-word "Alexa" or "Ok, Google." Voice-based interaction poses accessibility barriers for Deaf and Hard of Hearing (DHH) users, and technologies for sign-language recognition are improving. We therefore explore wake-up interactions for DHH users for potential personal assistant devices that understand sign language commands. Interviews with DHH users (N=21) motivated the design of six wake-up approaches, and we produced video prototypes demonstrating each using a Wizard-of-Oz approach. These prototypes were evaluated in a follow-up study in which DHH users (N=12) identified factors that influenced their preference among approaches. This study contributes empirical knowledge about DHH ASL signers' preferences and concerns with wake-up interaction, thereby providing guidance for future designers of these systems.
Symmetric Reconstruction of Functional Liver Segments and Cross-Individual Correspondence of Hepatectomy

Accurate localization and analyses of functional liver segments are crucial in devising various surgical procedures, including hepatectomy. To this end, they require the extraction of a liver from computed tomography, and then the identification of resection correspondence between individuals. The first part is usually impeded by inherent deficiencies, as present in medical images, and vast anatomical variations across subjects. While the model-based approach is found viable to tackle both issues, it is often undermined by an inadequate number of labeled samples, to capture all plausible variations. To address segmentation problems by balancing between accuracy, resource consumption, and data availability, this paper presents an efficient method for liver segmentation based on a graph-cut algorithm. One of its main novelties is the incorporation of a feature preserving a metric for boundary separation. Intuitive anatomical constraints are imposed to ensure valid extraction. The second part involves the symmetric conformal parameterization of the extracted liver surface onto a genus-0 domain. Provided with a few landmarks specified on two livers, we demonstrated that, by using a modified Beltrami differential, not only could they be non-rigidly registered, but also the hepatectomy on one liver could be envisioned on another. The merits of the proposed scheme were elucidated by both visual and numerical assessments on a standard MICCAI SLIVER07 dataset.
Shrinkage Priors on Complex-Valued Circular- Symmetric Autoregressive Processes

We investigate shrinkage priors on power spectral densities for complex-valued circular-symmetric autoregressive processes. We construct shrinkage predictive power spectral densities, which asymptotically dominate (i) the Bayesian predictive power spectral density based on the Jeffreys prior and (ii) the estimative power spectral density with the maximum likelihood estimator, where the Kullback-Leibler divergence from the true power spectral density to a predictive power spectral density is adopted as a risk. Furthermore, we propose general constructions of objective priors for Kähler parameter spaces by utilizing a positive continuous eigenfunction of the Laplace-Beltrami operator with a negative eigenvalue. We present numerical experiments on a complex-valued stationary autoregressive model of order 1.
Large-Scale Historical Watermark Recognition: dataset and a new consistency-based approach

Historical watermark recognition is a highly practical, yet unsolved challenge for archivists and historians. With a large number of well-defined classes, cluttered and noisy samples, different types of representations, both subtle differences between classes and high intra-class variation, historical watermarks are also challenging for pattern recognition. In this paper, overcoming the difficulty of data collection, we present a large public dataset with more than 6k new photographs, allowing for the first time to tackle at scale the scenarios of practical interest for scholars: one-shot instance recognition and cross-domain one-shot instance recognition amongst more than 16k fine-grained classes. We demonstrate that this new dataset is large enough to train modern deep learning approaches, and show that standard methods can be improved considerably by using mid-level deep features. More precisely, we design both a matching score and a feature fine-tuning strategy based on filtering local matches using spatial consistency. This consistency-based approach provides important performance boost compared to strong baselines. Our model achieves 55% top-1 accuracy on our very challenging 16,753-class one-shot cross-domain recognition task, each class described by a single drawing from the classic Briquet catalog. In addition to watermark classification, we show our approach provides promising results on fine-grained sketch-based image retrieval.
Joint Reinforcement Learning and Game Theory Bitrate Control Method for 360-Degree Dynamic Adaptive Streaming

A joint reinforcement learning (RL) and game theory method is presented for segment-level continuous bitrate selection and tile-level bitrate allocation in tile-based 360-degree streaming to increase users’ quality of experience (QoE). First, a viewpoint prediction method based on single-user (SU) viewpoint traces and the saliency map (SM) model is presented to model viewing behaviours. Second, an RL method is proposed to predict segment bitrate and a cooperative bargaining game theory is proposed for bitrate allocation optimization to choose a suitable bitrate for every tile with the help of the viewpoint prediction map. Performance evaluation results indicate that the proposed method can outperform the state-of-the-art methods in terms of different QoE objectives.
Applications of Blockchain Technologies in Health Services: A General Framework for Policymakers

Developments in information and communication technologies lead to radical changes in traditional business models. This transformation process is rapidly changing the principles underpinning existing systems and governance models and makes the traditional role of centralized institutions questionable. Perhaps the newest and most important example of these changes is the "Blockchain" technology. Blockchain claims to provide a deep-rooted solution to the problem of "trust" that exists in traditional commercial relations. Blockchain technology is a technology that does not require a central structure and allows the storage and transmission of commercial or value-containing data (money, identity, valuable papers, etc.) safely and quickly. This contributes to reduced costs, increased efficiency, reduced errors as a result of continuous storage of records in the chain, and the reliability of records kept. Blockchain technology enables it to be implemented in many sectors such as finance, manufacturing, logistics, energy, health care, retail, telecommunications, media, insurance, as well as in public transactions thanks to its technological infrastructure and smart contracts. Due to the cost-cutting effect of blockchain technologies, the use of this technology is of great importance for the health sector and interest in this field is increasing. Blockchain's applications in the medical field cover a wide range of processes, including electronic health records, health insurance, biomedical research, drug supply, purchasing processes, and medical education. Blockchain networks have many promising uses in the healthcare sector, from increasing transparency in the drug supply chain to creating and sharing unchangeable medical records. In the health sector, blockchain technologies can be used at different stages, from drug and medical product development processes to diagnosis, from the e-prescription process to better preservation and use of patient records.
Distributed Ledger technology systems in securities post-trading services. Evidence from European global systemic banks

Building on the fast developing – yet not consolidated – literature on blockchain and the financial system, this paper aims to investigate how Distributed Ledger Technologies (DLTs) may affect the banking sector performance by influencing the efficiency of the securities post-trading process. Three different scenarios have been developed and used as a framework to assess the impact of the adoption of blockchain technologies on the cost reduction in a sample of 12 Global Systemic Banks in Europe. By using DLT systems, the expected savings in the post-trading processes are sizable and mainly concentrated in IT expenditures and wages associated with core post-trade functions. Moreover, DLT systems emerge as a catalyst for innovation in post-trade process, as they improve timing window and data quality.
Distributed Dual Coordinate Ascent in General Tree Networks and Communication Network Effect on Synchronous Machine Learning

Due to the big size of data and limited data storage volume of a single computer or a single server, data are often stored in a distributed manner. Thus, performing large-scale machine learning operations with the distributed datasets through communication networks is often required. In this paper, we study the convergence rate of the distributed dual coordinate ascent for distributed machine learning problems in a general tree-structured network. Since a tree network model can be understood as the generalization of a star network, our algorithm can be thought of as the generalization of the distributed dual coordinate ascent in a star network. We provide the convergence rate of the distributed dual coordinate ascent over a general tree network in a recursive manner and analyze the network effect on the convergence rate. Secondly, by considering network communication delays, we optimize the distributed dual coordinate ascent algorithm to maximize its convergence speed. From our analytical result, we can choose the optimal number of local iterations depending on the communication delay severity to achieve the fastest convergence speed. In numerical experiments, we consider machine learning scenarios over communication networks, where local workers cannot directly reach to a central node due to constraints in communication, and demonstrate that the usability of our distributed dual coordinate ascent algorithm in tree networks.