Reviewerly/exHarmony huggingface dataset:
Post-merger Integration Specific Requirements Engineering Model

A goal of post-merger integration (PMI) is to combine several companies into one common entity so that Mergers and Acquisitions (M&A) strategic objectives can be achieved (some examples of objectives are growth, optimization, strength, etc.). As an integration process is related to large amounts of information to be acquired, processed, and applied during integration planning and execution phases, knowledge management can be perceived as one of the key supporting factors during the PMI. Additionally, although a PMI should cover integration of all system perspectives – social, cyber and physical; today, in many organizations, the main impact is on the cyber perspective, as it forms the infrastructure for the functioning of the whole system. This leads us to the assumption that the PMI process should be organized in such a way that information system integration becomes its central part including knowledge management activities. This research explores a requirements engineering model that can be incorporated into the PMI phase of any M&A initiative. In comparison with common requirements engineering approaches, this model extends them and is adjusted to support the following PMI features: (1) Focus on the integration and M&A strategic goals. (2) Extensive knowledge management with accumulating M&A experience, (3) Coverage of both explicit and tacit knowledge in all three system perspectives – social, cyber and physical.
Success in Digital Governance: A Literature Review on Taiwan’s COVID-19 Management

Due to the proximity to China and millions of international travelers yearly, Taiwan was thought to be at a great risk to be hit hard by the COVID-19. However, it so far managed to prevent the massive community transmission among its 23 million citizens. What leads to this success? What kinds of technology are used? Built upon the previous research and report, I argue that three major technology elements contribute to its success. First, the quarantine and cellphone tracking policy which blocks the transmission of imported cases. Second, the mask manufacturing management and distribution system help slow the spread of the disease. Third, a bunch of open-source software developed by civil society keeps people informed and even increase their voluntary compliance.
A Cop and Drunken Robber Game on n-Dimensional Infinite-Grid Graphs

A Cop and Drunken Robber (CDR) game is one variation of a famous combinatorial game, called Cops and Robbers, which has been extensively studied and applied in the area of theoretical and computer science as demonstrated by several conferences and publications. In this paper, for a natural number n, we present two strategies for a single cop to chase a drunken robber on n-dimensional infinite-grid graphs. Both strategies show that if the initial distance between the cop and the drunken robber is s, then the expected capture time is s+o(s).
HTLinker: A Head-to-Tail Linker for Nested Named Entity Recognition

Named entity recognition (NER) aims to extract entities from unstructured text, and a nested structure often exists between entities. However, most previous studies paid more attention to flair named entity recognition while ignoring nested entities. The importance of words in the text should vary for different entity categories. In this paper, we propose a head-to-tail linker for nested NER. The proposed model exploits the extracted entity head as conditional information to locate the corresponding entity tails under different entity categories. This strategy takes part of the symmetric boundary information of the entity as a condition and effectively leverages the information from the text to improve the entity boundary recognition effectiveness. The proposed model considers the variability in the semantic correlation between tokens for different entity heads under different entity categories. To verify the effectiveness of the model, numerous experiments were implemented on three datasets: ACE2004, ACE2005, and GENIA, with F1-scores of 80.5%, 79.3%, and 76.4%, respectively. The experimental results show that our model is the most effective of all the methods used for comparison.
Reading Scene Text by Fusing Visual Attention with Semantic Representations

Recognizing text in an unconstrained environment is a challenging task in computer vision. Many prevalent approaches to it employ a recurrent neural network that is difficult to train or rely heavily on sophisticated model designs for sequence modeling. In contrast to these methods, we propose a unified lexicon-free framework to enhance the accuracy of text recognition using only attention and convolution. We use a relational attention module to leverage visual patterns and word representations. To ensure that the predicted sequence captures the contextual dependencies within a word, we embed linguistic dependencies from a language model into the optimization framework. The proposed mutual attention model is an ensemble of visual cues and linguistic contexts that together improve performance. The results of experiments show that our system achieves state-of-the-art performance on datasets of texts from regular and irregular scenes. It also significantly enhances recognition performance on noisy scanned documents.
A Secure Random Number Generator with Immunity and Propagation Characteristics for Cryptography Functions

Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features.
Evolving Homomorphic Secret Sharing for Hierarchical Access Structures

Secret sharing is a cryptographic primitive that divides a secret into several shares, and allows only some combinations of shares to recover the secret. As it can also be used in secure multi-party computation protocol with outsourcing servers, several variations of secret sharing are devised for this purpose. Most of the existing protocols require the number of computing servers to be determined in advance. However, in some situations we may want the system to be “evolving”. We may want to increase the number of servers and strengthen the security guarantee later in order to improve availability and security of the system. Although evolving secret sharing schemes are available, they do not support computing on shares. On the other hand, “homomorphic” secret sharing allows computing on shares with small communication, but they are not evolving. As the contribution of our work, we give the definition of “evolving homomorphic” secret sharing supporting both properties. We propose two schemes, one with hierarchical access structure supporting multiplication, and the other with partially hierarchical access structure supporting computation of low degree polynomials. Comparing to the work with similar functionality of Choudhuri et al. (IACR ePrint 2020), our schemes have smaller communication costs.
ViewClassifier: Visual Analytics on Performance Analysis for Imbalanced Fatal Accident Data

The accurate analysis and modeling of high-dimensional imbalanced fatal accident data including its conflicting and overlapping features are significant in terms of public and transportation safety. Traditional visualization techniques and single-valued performance metrics, such as accuracy, obstruct understanding the behavior of machine learning algorithms for imbalanced fatal accident datasets. Therefore, we propose an interactive visualization tool called ViewClassifier to support the analysis of imbalanced fatal accident datasets as well as their prediction results. ViewClassifier enables users to simulate unseen/missing class features through negative sampling by displaying raw data attribute distributions via parallel coordinates. It also allows users to compare multiclass classifiers at first glance and discover model behavior and possible reasons for misclassified results by filtering and examining at instance-level via customized plots. We presented two case studies to show the usage and efficacy of the proposed visualization tool using real-world traffic accident datasets.
Learn, Generate, Rank, Explain: A Case Study of Visual Explanation by Generative Machine Learning

While the computer vision problem of searching for activities in videos is usually addressed by using discriminative models, their decisions tend to be opaque and difficult for people to understand. We propose a case study of a novel machine learning approach for generative searching and ranking of motion capture activities with visual explanation. Instead of directly ranking videos in the database given a text query, our approach uses a variant of Generative Adversarial Networks (GANs) to generate exemplars based on the query and uses them to search for the activity of interest in a large database. Our model is able to achieve comparable results to its discriminative counterpart, while being able to dynamically generate visual explanations. In addition to our searching and ranking method, we present an explanation interface that enables the user to successfully explore the model’s explanations and its confidence by revealing query-based, model-generated motion capture clips that contributed to the model’s decision. Finally, we conducted a user study with 44 participants to show that by using our model and interface, participants benefit from a deeper understanding of the model’s conceptualization of the search query. We discovered that the XAI system yielded a comparable level of efficiency, accuracy, and user-machine synchronization as its black-box counterpart, if the user exhibited a high level of trust for AI explanation.
Analysis of Cryptographic Hashing Algorithms for Image Identification in Deduplication Process

Image deduplication is the process used to prevent the storing of duplicate images in the storage space. It helps to utilize the storage space efficiently by avoiding the duplicate copies of the intended image. Deduplication can be implemented by comparing the input image that has to be stored with the image existing in the storage space. But it is a time-consuming process. So instead of comparing the images directly, it is suggested to compare their hash values. In order to identify the duplication, initially, it is required to uniquely identify the image. This can be possible with the help of hash value. Hash value can be generated for each digital data, and it remains unique for all data. The main aim of this paper is to analyze various cryptographic hashing algorithms, which are being used to uniquely identify the image. Further, this research work is going to detail and compare the performance of various hashing algorithms by considering their operations and performance and finally concluded with a better solution to improve the process of achieving an image identification.
Designated server-aided revocable identity-based keyword search on lattice

Abstract Public key encryption scheme with keyword search is a promising technique supporting search on encrypted data without leaking any information about the keyword. In real applications, it’s critical to find an effective revocation method to revoke users in multi-user cryptosystems, when user’s secret keys are exposed. In this paper, we propose the first designated server-aided revocable identity-based encryption scheme with keyword search (dSR-IBKS) from lattice. The dSR-IBKS model requires each user to keep just one private key corresponding with his identity and does not need to communicate with the key generation center or the server during key updating. We have proved that our scheme can achieve chosen keyword indistinguishability in the standard model. In particular, our scheme can designate a unique tester to test and return the search results, therefore no other entity can guess the keyword embedded in the ciphertext by generating search queries and doing the test by itself. We provide a formal security proof of our scheme assuming the hardness of the learning with errors problem on the standard model.
Prospects for academic libraries in science information environment

The author systematize history of Russian university libraries, analyze their progressing in digital environment, and discuss new possibilities for modernizing education and academic libraries, in particular. The laws and regulations for university libraries are reviewed; their functions and services are specified. As a result of global information processes intensive growth, the demand for elibraries and their services have been increasing. The students of Cherepovets State University were surveyed on the issues of library service efficiency. The survey proved the significance and relevance of library services for the students; the strengths and weaknesses of library services were identified. The authors emphasize the need for intensifying marketing component in library activities and promotion of its services, and for studying target audience demands. The prospects for academic libraries are also discussed.
Knowledge Distillation for Object Detection Based on Mutual Information

While pursuing high performance, most currently proposed object detection algorithms are composed of huge and complex neural network and large amount of parameters. As a results, many of them can not be implemented on edge devices due to the limitation of computation resources. To address this, in this paper, we designed a model compression scheme for object detection algorithm based on knowledge distillation. Firstly, a novel loss function based on mutual information have been proposed for similarity measurement between the teacher network and the student network. Then, a new knowledge distillation scheme is proposed for object detection network. The proposed algorithm has been verified on PASACL VOC dataset. The experimental results have demonstrated the effectiveness of our method.
Understanding Social Biases Behind Location Names in Contextual Word Embedding Models

Embeddings of textual data containing location names (e.g., social media posts) have essential applications in various contexts such as marketing and disaster management. In these downstream implementations, social biases behind location names are highly prone to introduce unfair results through their embeddings; for example, emergent text messages with swapped location names might result in varied rescue responses. Hence, it is critical to address social biases encoded in location names and to seek its mitigation. Prevalent works addressing biases in embeddings mainly focus on individual attributes like gender or ethnicity. Yet, a large number of social attributes behind location names (e.g., income level and population density) makes it challenging to originate the source of biases. Existing mitigation methods based on finding attribute subspaces cannot be simply applied to address social biases. Moreover, bias mitigation tends to simultaneously remove necessary semantics from embeddings, making it difficult to achieve a balance between mitigation performance and semantics retention. In this article, we first employ the concept of counterfactual fairness to investigate the social biases encoded in training data. Then, we quantify the biases in the contextual embeddings (BERT and ELMo). We report a high correlation between biases in the training data and embeddings. Next, we introduce a novel bias mitigation algorithm that customizes bias representations for any location names. The method yields debiased location name vectors for various social attributes simultaneously. The proposed algorithm achieves a better mitigation performance on overall attributes compared with a prevalent postprocessing method, while maintaining correctness by retaining semantic information.
A Tensor Sparse Representation-Based CBMIR System for Computer-Aided Diagnosis of Focal Liver Lesions and its Pilot Trial

Clinicians refer to diagnosed medical cases in order to make correct diagnosis and take appropriate treatments, due to the complexity of focal liver lesions. It's a heavy burden, however, for medical doctors to find out similar and meaningful cases from the accumulated extreme large medical datasets. Content based medical image retrieval (CBMIR) that searches for similar images in a large database has been attracting increasing research interest recently. A CBMIR system provides doctors the diagnosed cases to improve the diagnosis accuracy and confidence. This paper proposed a tensor sparse representation method to extract temporal and spatial features of multi-phase CT images, so as to provide doctors medical cases more relevant to the query one. The proposed tensor sparse representation method is applied to the retrieval of focal liver lesions (FLLs). Experiments show that the proposed method achieved better retrieval performance than conventional methods. Pilot trial was conducted and results show that diagnosis accuracy and confidence was improved significantly by the developed CBMIR system based on the proposed method.
Projectile drag coefficient identification based on extreme learning

Aerodynamic parameters play a decisive role in the ballistic characteristics of the projectile. How to accurately obtain the aerodynamic parameters of the projectile is an important task in the development process of the projectile. In order to further improve the identification accuracy of the projectile drag coefficient, this paper generates huge ballistic data through numerical simulation and uses the extreme learning method to identify the ballistic drag coefficient under three kinds of noise conditions. The method avoids the iterative updating process of weights and thresholds by randomly generating the input weights and threshold values of hidden layer neurons and overcomes the problem of long identification time of the traditional back propagation (BP) neural network algorithm. Based on the least squares principle, the Moore–Penrose generalized inverse matrix of the hidden layer output matrix was solved to determine the optimal output weight of the network, and then, the projectile drag coefficient was accurately identified. Comparing the extreme learning method with the traditional BP neural network method, the results show that the proposed method has higher identification accuracy and faster convergence speed and can effectively identify the projectile drag coefficient, which can meet the practical needs of engineering.
A High-Dimensional Modeling System Based on Analytical Hierarchy Process and Information Criteria

High-dimensional data sets frequently occur in several scientific areas, and special techniques are required to analyze these types of data sets. Especially, it becomes important to apply a suitable model in classification problems. In this study, a novel approach is proposed to estimate a statistical model for high-dimensional data sets. The proposed method uses analytical hierarchical process (AHP) and information criteria for determining the optimal PCs for the classification model. The high-dimensional “colon” and “gravier” datasets were used in evaluation part. Application results demonstrate that the proposed approach can be successfully used for modeling purposes.
Blind image super-resolution based on prior correction network

Convolutional neural networks (CNNs) based super-resolution (SR) methods have achieved remarkable progress in recent years. Most of these methods assume that the degradation is known and fixed, such as bicubic downsampling. However, the performance of CNNs-based methods suffers from a severe drop when the actual degradation mismatch the training one. This paper proposes a prior correction network (PCNet) to solve the blind SR problem, which makes CNNs-based super-resolvers trained in a fixed blur kernel but applied to other unknown blur kernels. The PCNet consists of a kernel estimation network, a correction filter module, and a correction refinement network. The kernel estimation network aims to estimate unknown blur kernel from the input low-resolution (LR) image. The correction filter module then transfers the estimated degraded domains to adapt to specific degradations (e.g., bicubic downsampling). Finally, the correction refinement network adjusts the corrected LR image to eliminate the influence of blur kernel mismatch or misestimate. Experimental results on diverse datasets show that the proposed PCNet, combined with existing CNNs-based SR methods, outperforms other state-of-the-art algorithms for blind SR. Code is available at: \url{https://github.com/caoxiang104/PCNet}.
BlockFITS: A Federated Data Augmentation Modelling for Blockchain-Based IoVT Systems

Kansra, Bhrigu Diddee, Harshita Sheikh, Tariq Hussain Khanna, Ashish Gupta, Deepak Rodrigues, Joel J. P. C.In Intelligent Transport Systems (ITS), the collection of diverse data is a major practical roadblock; not only can their data be personally identifiable, i.e. private, but also the lack of incentive for entities to participate in any kind of collaborative training is also severely limited due to the added computational expense of training collaborative models locally. In this paper, we propose BlockFITS: A Vehicle-to-BlockChain-to-Vehicle (V2B2V) federated learning enabled model training paradigm for ITS entities. In addition to which we propose a data augmentation scheme that operates with cooperative training to generate an incentive for entity participation. The immutability and decentralised features of the Blockchain system leverage the federated-like averaging of synthetically generated data samples that generate incentives for the participation of entities in such a training setup. BlockFITS can be practically deployed in future ITS systems to improve the autonomous driving system, pedestrian safety, and vehicular object detection or more due to its model-constraint-free characteristics which provide access to a synthetic and global data whilst maintaining data privacy.
Secure Medical Multimedia Data Using Symmetric Cipher Based Chaotic Logistic Mapping

Multimedia security has become a serious issue in this digital world, as more and more industries and people are turning towards digitalization. The existing encryption standards are successful in securing textual data but are not very effective for safeguarding multimedia data like images and videos. This stimulates the urge for development of new encryption systems and tools that can address the multimedia security issue. In this work we present a multimedia encryption mechanism that secures multimedia data with the help of chaotic logistic map and linear feedback shift register (LFSR). The use of chaotic map helps in providing the confusion and diffusion properties required for the encryption algorithm and also make it robust and efficient. The experimental results and analysis show that it exhibits high entropy, security against various cyber-attacks and also has lesser time and space complexities than existing techniques.