Reviewerly/exHarmony huggingface dataset:
Secure Digital Image Watermarking by Using SVD and AES

In recent times, digital data is widely used for processing and sometimes the processed data is needed to be transmitted from one organization to other. For such situations, secure transmission of digital data over the network is needed. Digital watermarking provides authority by preventing unauthorized access of multimedia data like audio, video and images. Our research work is based on an SVD-AES-based digital image watermarking scheme to provide secure transmission of digital images over networks. Initially, SVD-based watermarking is carried out. After watermarking, image is encrypted by means of an Advanced Encryption Standard (AES) algorithm. After that, encrypted image is shared over networks and finally AES decryption gives the watermarked image. The proposed method uses AES-128 encryption algorithm. The algorithm shows robust nature towards attacks like content removal, copy and paste attack and also cryptographic attacks like brute-force attack.
Data-driven approaches in FinTech: a survey

Purpose This paper aims to explore the latest study of the emerging data-driven approach in the area of FinTech. This paper attempts to provide comprehensive comparisons, including the advantages and disadvantages of different data-driven algorithms applied to FinTech. This paper also attempts to point out the future directions of data-driven approaches in the FinTech domain. Design/methodology/approach This paper explores and summarizes the latest data-driven approaches and algorithms applied in FinTech to the following categories: risk management, data privacy protection, portfolio management, and sentiment analysis. Findings This paper details out comparison between different existed works in FinTech with traditional data analytics techniques and the latest development. The framework for the analysis process is developed, and insights regarding the implementation, regulation and workforce development are provided in this area. Originality/value To the best of the authors’ knowledge, this paper is first to consider broad aspects of data-driven approaches in the application of FinTech industry to explore the potential, challenges and limitations of this area. This study provides a valuable reference for both the current and future participants.
Real-time and Online Segmentation Multi-target Tracking with Track Revival Re-identification

The first online segmentation multi-target tracking algorithm with reported real-time speeds is presented. Based on the popular and fast bounding box based tracker SORT, our method called SORTS is able to utilize segmentations for tracking while keeping the real-time speeds. To handle occlusions, which neither SORT nor SORTS do, we also present SORTS+RReID, an optional extension which uses ReID vectors to revive lost tracks from SORTS to handle occlusions. Despite only computing ReID vectors for 6.9% of the detections, ID switches are decreased by 45%. We evaluate on the MOTS dataset and run at 54.5 and 36.4 FPS for SORTS and SORT+RReID respectively, while keeping 78-79% of the sMOTSA of the current state of the art, which runs at 0.3 FPS. Furthermore, we include an experiment using a faster instance segmentation method to explore the feasibility of a complete real-time detection and tracking system. Code is available: https://github.com/ahrnbom/sorts. (Less)
COOL: A framework for conversational OLAP

The democratization of data access and the adoption of OLAP in scenarios requiring hand-free interfaces push towards the creation of smart OLAP interfaces. In this paper, we introduce COOL, a framework devised for COnversational OLap applications. COOL interprets and translates a natural language dialog into an OLAP session that starts with a GPSJ (Generalized Projection, Selection, and Join) query and continues with the application of OLAP operators. The interpretation relies on a formal grammar and on a repository storing metadata and values from a multidimensional cube. In case of ambiguous or incomplete text description, COOL can obtain the correct query either through automatic inference or user interactions to disambiguate the text. Our tests show very promising results in terms of effectiveness, efficiency, and user experience. Besides adding novel support to the interpretation and translation of complete analytical OLAP sessions, COOL achieves an average accuracy of 94% in the interpretation of GPSJ queries from real datasets.
Authority Control Today: Principles, Practices, and Trends

Authority control enhances the accessibility of library resources by controlling the choice and form of access points, improving users’ ability to efficiently find the works most relevant to their information search. While authority control and the technologies that support its implementation continue to evolve, the underlying principles and purposes remain the same. Written primarily for a new generation of librarians, this paper illuminates the importance of authority control in cataloging and library database management, discusses its history, describes current practices, and introduces readers to trends and issues in the field, including future applications beyond the library catalog.
About The Dodges Plans of the Continuous Selective Control

The mathematical methods of the statistical acceptance control (SAC) are one of the important areas of applied mathematical statistics. The main tasks of SAC are the problems of evaluating of the quality of industrial production. The growing requirement to quality of production promoted development of a number of the plans of SAC. In this paper the Dodge's plan of the continuous selective control is considered.
Classifying diverse manual material handling tasks using a single wearable sensor

The use of inertial measurement units (IMUs) for monitoring and classifying physical activities has received substantial attention in recent years, both in occupational and non-occupational contexts. However, a "user-friendly" approach is needed to promote this approach to quantify physical demands in actual workplaces. We explored the use of a single IMU for extracting information about different manual material handling (MMH) tasks (i.e., specific type of task performed, and associated duration and frequency), using a bidirectional long short-term memory network for classification. Classification performance using single IMUs placed on several body parts was compared with performance using multiple IMU configurations (2, 3, and 17 IMUs). Overall, the use of a single sensor led to satisfactory results (e.g., median accuracy >97%) in classifying MMH tasks and estimating task duration and frequency. Limited benefits were obtained using additional sensors, and several sensor locations yielded similar outcomes. Classification performance, though, was relatively inferior for push/pull vs. other tasks.
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting

The goal of continual learning (CL) is to learn a sequence of tasks without suffering from the phenomenon of catastrophic forgetting. Previous work has shown that leveraging memory in the form of a replay buffer can reduce performance degradation on prior tasks. We hypothesize that forgetting can be further reduced when the model is encouraged to remember the evidence for previously made decisions. As a first step towards exploring this hypothesis, we propose a simple novel training paradigm, called Remembering for the Right Reasons (RRR), that additionally stores visual model explanations for each example in the buffer and ensures the model has “the right reasons” for its predictions by encouraging its explanations to remain consistent with those used to make decisions at training time. Without this constraint, there is a drift in explanations and increase in forgetting as conventional continual learning algorithms learn new tasks. We demonstrate how RRR can be easily added to any memory or regularization-based approach and results in reduced forgetting, and more importantly, improved model explanations. We have evaluated our approach in the standard and few-shot settings and observed a consistent improvement across various CL approaches using different architectures and techniques to generate model explanations and demonstrated our approach showing a promising connection between explainability and continual learning. Our code is available at \url{https://github.com/SaynaEbrahimi/Remembering-for-the-Right-Reasons}
Analysis on the Current Situation and Development Trend of China’s Electrification Level and Electric Energy Substitution under the Background of Carbon Neutral

Abstract There is still room for improvement in China’s electrification level, and power substitution is an important grasp to improve the level of electrification. The implementation of electric energy substitution can promote energy transformation, realize green development and support the realization of carbon neutral goal. After analysis, the power substitution is still the main force to improve the level of electrification in various fields during the “14th five year plan”.
Empowerment Approaches in Digital Civics

The pace and magnitude of recent advancements in digital technologies have set the scene for a complete transformation of our citizenship experience. In the past, HCI research has been concerned with aligning technological progress with top-down organizational models of government institutions or exploring the needs of individual bottom-up community initiatives. However, this approach has proved too rigid for systemic issues and HCI researchers have started to meet these wicked problems halfway, rather than from two opposing sides. This involves putting emphasis on how citizens and organizations can incorporate existing technologies and involve dialogue and co-creation in their process. In this paper we highlight several directions of HCI research that follow the trail towards civic empowerment. Based on these, we identify a set of strategies that might help researchers contribute to a more inclusive orientation of civic tech.
WITHDRAWN: Automatic attendance monitoring system using facial recognition through feature-based methods (PCA, LDA)

Attendance monitoring is a vital task in any organisation. Attendance data helps keep track of the absenteeism of any employee/student. But, manual attendance is a tedious task, consumes a lot of time, and the data is not in a proper format. This project can be a saviour for faculties and institutes. The system uses facial detection through Haarcascades and facial recognition using feature-based methods, namely - PCA and LDA. PCA uses a statistical approach to reduce the number of variables used in face recognition. LDA finds the linear combination of features while preserving class separability. A comparison between the two algorithms is made on the basis of their performance on the Olivetti dataset.
A Novel Hybrid Filter for Image Despeckling Based On Improved Adaptive Wiener Filter, Bilateral Filter and Wavelet Filter

Images are widely used in engineering. But, some images such as medical ultrasound images are mainly degraded by an intrinsic noise called speckle. Therefore, de-speckling is a main pre-processing stage for degraded images. In this paper, we suggest three phases and three denoising filters. In the first phase, the coefficient of variation is computed from the noisy image. Next, fuzzy c-means (FCM) is applied to the coefficients of variation. Applying FCM leads to the fuzzy classification of image regions. Next, the second phase is a hybrid of the three denoising filters. Fast bilateral filter (BF) for homogeneous regions, improved the adaptive wiener filters (AWFs) and wavelet filter that are applied on homogeneous, detail and edge regions, respectively. The proposed improved AWF has been developed from the AWF. In the third phase, the output image is evaluated by the fuzzy logic approach. Thus, with three phases, the proposed method has a better image detail preservation compared to some other standard methods. The experimental outcomes show that the proposed denoising algorithm is able to preserve image details and edges compared with other de-speckling methods.
Improved CNN-Based Hashing for Encrypted Image Retrieval

As more and more image data are stored in the encrypted form in the cloud computing environment, it has become an urgent problem that how to efficiently retrieve images on the encryption domain. Recently, Convolutional Neural Network (CNN) features have achieved promising performance in the field of image retrieval, but the high dimension of CNN features will cause low retrieval efficiency. Also, it is not suitable to directly apply them for image retrieval on the encryption domain. To solve the above issues, this paper proposes an improved CNN-based hashing method for encrypted image retrieval. First, the image size is increased and inputted into the CNN to improve the representation ability. Then, a lightweight module is introduced to replace a part of modules in the CNN to reduce the parameters and computational cost. Finally, a hash layer is added to generate a compact binary hash code. In the retrieval process, the hash code is used for encrypted image retrieval, which greatly improves the retrieval efficiency. The experimental results show that the scheme allows an effective and efficient retrieval of encrypted images.
Developing the persona method to increase the commitment of non-industrial private forest owners in French forest policy priorities

In Europe, 60% of the forest area is privately owned, with a significant proportion of nonindustrial private forest (NIPF) being highly fragmented with, in particular, low intensity forest management. Numerous typologies have therefore been developed to provide detailed guidance to policy makers for the design of policy instruments to involve these owners in forest management approaches. However, these policy instruments fail to involve all categories of owners and there is no method made by and with people locally in charge of policy implementation to increase the commitment of NIPF owners. The main objective of this study is to adapt the persona method, which is a central user-centered design tool, to design typologies of NIPF owners as future users of policy instruments. We have designed our personas in the Vosges territory (France), in the context of the main French forest policy objective of increasing timber harvesting and NIPF sustainable management. Our results designed eight types of personas as well as a six-part composition of the persona sheet. The main interest is to provide a general methodology to forest policy and forest managers based on rigorous concepts of usercentered design processes and co-creation, to implement operational tools for NIFP management. Finally, both future developments and methodological limitations are discussed in a global research perspective.
Memes and education: opportunities, approaches and perspectives

Abstract This article presents the results of an international research of using Internet-memes in education process. The writing team set out to explore the reasons and ways of using Internet memes in education in different language spaces – English, Italian, Russian and Chinese. To achieve this goal, a number of tasks were formulated, including: 1. to study the linguistic factor of the use of memes in the educational process, 2. to classify the ways of using memes depending on the purpose, 3. to analyze the content of memes in the liberal, ideological, as well as in the field of exact sciences. The first part of the research made by Alexander Malakhov (PFUR) reveals the basic concepts associated with Internet memes. The second one written by Olga Matys (PFUR) is about using memes in a sphere of Russian liberal education in journalism. The third part of the research made by Ludovico De Serio (University of Genoa) presents an analysis using memes during studying math. The last but not the least part of this article made by Xie Dongqiang (PFUR) and reveals the aspects of an ideological studying using memes. This research was carried out using such scientific methods as analysis, comparison, description. In the course of the study, the authors used the work of such researchers as Susan Blackmore, G. Bini, M. Montagnani, Anna Zagoruyko, Maria Efremova, etc. Also there was used the content of sources such as VK publics “Memology: from antiquity to the present day” and “30 seconds before my expultion”, GeoGebra, www.padlet.com , etc.
Interest Development, Knowledge Learning, and Interactive IR

To support complex search tasks that involve prolonged search sessions and learning goals of varying difficulty, information retrieval (IR) researchers need a more comprehensive understanding of in-situ learning progresses and related factors. Among many cognitive factors associated with learning and searching, we consider interest development as an important dimension because it significantly affects users' learning performances but still remains understudied in interactive IR (IIR). To address this gap, our perspective paper proposes an interest-search-learning (ISL) model to reconceptualize learning in search and characterize the dynamic interplay of interest development, knowledge learning, and searching. Specifically, it achieves three interrelated goals: 1) characterizing the interactions between interest development, learning, and search behaviors; 2) synthesizing and proposing useful measures for capturing in-situ progresses and state variations in interest development and knowledge learning related to search behaviors; 3) identifying new research questions and directions linked to conceptualizing, building, and evaluating learning-centric IR systems. This paper uniquely integrates findings from three research communities (i.e. interest development, learning, IIR) into a cohesive framework and better structures our understanding of the multidimensional cognitive changes in search as learning (SAL). Including the exploration of interest development in SAL research will expand both conceptual and practical visions of AI-assisted learning.
A Multi-Swarm Structure for Particle Swarm Optimization: Solving the Welded Beam Design Problem

Abstract Several studies exist in the literature that utilized metaheuristics and nature-inspired algorithms to solve engineering problems. Particle Swarm Optimization (PSO) is a well-known nature-inspired algorithm that has been used for different optimization problems due to its simplicity and ability to find near-optimal solutions. However, PSO suffers from a problem of balancing between the global search and local search abilities when applied to engineering problems. Recently, a new variant of PSO based on a novel multi-swarm architecture called Multi-swarm Particle Swarm Optimization (MPSO) was proposed. The proposed MPSO was evaluated on solving normal and large-scale problems. This study evaluated the possibility of using MPSO to simulate Welded Beam Design (WDB) problem which is a mechanical engineering problem. Several simulations were performed using the proposed approach and from the results, MPSO model was observed to simulate WBD problem with better optimal solution compared to the standalone PSO. The outcome of this study further confirmed the robustness of the MPSO over the other known metaheuristics. Generally, MPSO achieved an excellent optimization performance with a fast convergence learning process.
Kannada Character Recognition Using Multi-Class SVM Method

Character recognition also truncated to OCR (Optical Character Recognition), is the translation of images: hand-written, typewritten (either mechanically or electronically), or even just a simple text printed into a machine-editable text. Among them, one of the most relevant types is handwritten character recognition. Each handwritten content is composed of symbols, alphabets, etc. that are very syllabic with a distinct font style. Character recognition is achieved through segmentation, feature ex-traction, and classification, by using any one the machine learning methods thereby making tremendous advancements. This paper keenly focuses on the recognition of handwritten Kannada alpha-bets. Different machine learning classification strategies have known to be applied to achieve this recognition. Although, in this paper, we mostly center around the procedures dependency on the solution provided by Support Vector Machine(SVM) classifiers using Python. For simplicity, we focus on only four Kannada alpha-bets in this paper.
Electro-optical logic using dual-nanobeam Mach-Zehnder interferometer switches

The maturity of integrated photonics enables many applications including high-performance computing. Digital photonic computing always considers resonator-based modulators as the key active components due to their compactness as compared to broad-spectrum Mach-Zehnder interferometers (MZIs). In this paper, we investigate the dual-nanobeam (NB) based MZI 2 × 2 switches with much smaller footprint for realizing electro-optical logic circuits. New logic gates and scalable circuits assisted by multiplexing techniques are proposed. Results show that the NB MZI is another promising candidate for electronic-photonic digital computing.
Reconstructing rock art chronology with transfer learning: A case study from Arnhem Land, Australia

In recent years, machine learning approaches have been used to classify and extract style from media and have been used to reinforce known chronologies from classical art history. In this work we employ the first ever machine learning analysis of Australian rock art using a data efficient transfer learning approach to identify features suitable for distinguishing styles of rock art. These features are evaluated in a one-shot learning setting. Results demonstrate that known Arnhem Land Rock art styles can be resolved without knowledge of prior groupings. We then analyse the activation space of learned features and report on the relationships between styles and arrange these classes into a stylistic chronology based on distance within the activation space. By generating a stylistic chronology, it is shown that the model is sensitive to both temporal and spatial patterns in the distribution of rock art in the Arnhem Land Plateau region. More broadly, this approach is ideally suited to evaluating style within any material culture assemblage and overcomes the common constraint of small training data sets in archaeological machine learning studies.
Factors Influencing Social Customer Relationship Management Implementation and its Benefits in Healthcare Industry

Social Customer Relationship Management (SCRM) becomes a buzz word which has been discussed extensively in existing scholarly works but scant consideration is given to the crucial factors affecting the adoption of the integrated social media CRM, especially in the healthcare industry. In concern with the profusely debated issue of technological organizational, and environmental factors, this study aims at identifying predominant factors affecting the implementation of Social CRM; and understanding the benefits yielding from this implementation. This study conducts a systematic review based on PRISMA method of summarizing literature mechanism which was based on the previous studies, (Agha et al., 2014; Moher et al., 2009), for the extant literature from 2008 to 2019 to sort the underlying factors affecting the implementation of Social CRM in various industries and its benefits. Findings from the literature reveal that technological factors (i.e., perceived privacy, interactivity, and compatibility), organizational factors (i.e., leadership knowledge, social media policy, and management drive) and environmental factors (i.e., bandwagon pressure, and social trust) are the key factors that influence healthcare organizations’ willingness to implement Social CRM. The conceptual framework presented in this paper advances the application of Diffusion of Technology, TOE, and IS Success used and provided for possible useful direction for future empirical research for both academics and practitioners.