Reviewerly/exHarmony huggingface dataset:
Blockchain: a strategic resource to attain and sustain competitive advantage

Purpose This paper aims to offer exploratory remarks by discussing whether blockchain can help organizations attain sustained competitive advantage in view of its increased applications and untapped potential. Organizations are yet to test its utilization as an intangible strategic resource at a time when organizational strategic landscapes – rapidly changing in a globally networked digitally empowered world – require them to enhance capability of combining resources for meeting stakeholders’ expectations. Design/methodology/approach Blockchain as an emerging technology draws frequent industry announcements and specialists’ posts on daily basis in media, and there exist inadequacies with respect to the availability of relevant studies in the extant literature on the subject of blockchain, which itself is in infancy. Keeping in view the limitations associated with the traditional understandings of scholarship underscoring, thereby that research and knowledge discovery have been restricting the progress across disciplines on account of which knowledge domains fail to make a meaningful effect; research design of the paper comprises the scholarship of integration method, which might appear to be less systematic but was more emergent in comparison to a traditional methodology of systematic literature review, and suitable for conducting this study. Findings Findings of this study suggest that blockchain with huge popularity as a technological innovation has huge potentialities and promises to be a strategic intangible resource for organizations helpful in attaining sustained advantage. However, the findings also suggest several cautionary remarks. Research limitations/implications This paper offers exploratory remarks by discussing blockchain in the context of its consideration as a significant intangible strategic resource helpful in attaining sustained competitive advantage, emphasizing the need for continuous attention and revision with its increased applications. It attempts to assess the untapped potential of blockchain incorporating ample scholarly value in this era of fourth industrial revolution. The findings offer greater significance for different stakeholders including researchers and policymakers. However, this paper limits itself by throwing light on the strategic aspects only while attempting to touch upon only those aspects of blockchain that were perceived to be helpful in understanding it as a resource of sustained competitive advantage. Originality/value This paper presents originality and value by offering exploratory remarks that can be immensely useful given the scarcity of literature on the novel blockchain with respect to its consideration as a strategic resource. This paper attempts to provide much needed underpinnings to the notion whether blockchain can help organizations attain and sustain competitive advantage.
A Survey on Recent Applications of Cloud Computing in Education: COVID-19 Perspective

Abstract Due to the outbreak of coronavirus pandemic (COVID-19) in recent times, almost all countries have been affected in a drastic way, impacting delivery mode of education in world everywhere. Several schools, colleges and universities are under lockdown or have been forced to provide online education. To meet the existing challenges due to COVID-19, it has become highly critical that educational institutes become more efficient in virtual delivery of quality teaching services. In here, cloud computing provides an excellent platform for educators to improve their teaching practices and productivity. Cloud computing presents an ideal opportunity as it not only saves costs, but also power, due to the simultaneous usage of infrastructure by several stakeholders for the purposes of teaching, learning, and research. This is a short survey paper which explores the applicability of cloud computing within the context of educational settings. It also describes a few applications such as cloud rendering, gamification and collaborative e-learning technologies. In the end, a few challenges have also been presented.
Traffic Sign Recognition Using Guided Image Filtering

In challenging lighting conditions, such as haze, rain, and weak lighting condition, the accuracy of traffic sign recognition is not very high due to missed detection or incorrect positioning. In this paper, we propose a traffic sign recognition algorithm based on Faster R-CNN and YOLOv5. Firstly, we conduct image preprocessing by using guided image filtering for the input image to remove noises. The processed images are imported into the neural networks for training and testing. The outcomes of the traffic sign recognition are promising.
The potential of feminist technoscience for advancing research in information practice

Purpose In this paper, we introduce feminist technoscience as an approach that will advance theory in information behavior and practice. Design/methodology/approach In this conceptual paper, we identify four common assumptions in information behavior and information practice research that limit theory development to date. Existing models and theories tend to rely on extractive logic, focus on a person-in-situation, depend on binary definitions and assume that information interaction changes people's lives for the better. This leads to extractive ways of discussing information interactions and limits our ability to fully theorize embodiment and affect in our discipline. Findings Feminist technoscience offers distinct ways of thinking about people, technology, bodies and power; in doing so, it responds to some perennial limitations in our research to date. Originality/value Feminist technoscience is a robust research paradigm that has not yet been fully applied in our discipline. Assumptions in information behavior and information practice research have led to models and theories that reflect a logic of extraction and are limited in their potential for characterizing both embodiment and affect. Feminist technoscience provides a way to conduct research that challenges these assumptions and addresses these limitations.
Automatic Speech Recognition

The main task of automatic speech recognition (ASR) is to convert voice signals to text transcriptions. It is one of the most important research fields in natural language processing (NLP). With more than a half century of endeavor, the word error rate (WER), which is a metric unit for transcription performance, has significantly been reduced. Particularly in recent years, due to the increase of computational power, large quantity of collected data, and efficient neural learning algorithms, the dominant power of deep learning technology further enhanced the performance of ASR systems to a practical level. However, there are still many issues that need to be further investigated for these systems to be adapted to a wide range of applications. In this chapter, we will introduce the main stream and pipeline of ASR frameworks, particularly the two dominant frameworks, i.e., Hidden Markov Model (HMM) with Gaussian Mixture model (GMM)-based ASR which dominated the field in the early decades, and deep learning model-based ASR which dominates the techniques used now. In addition, noisy robustness, which is one of the most important challenges for ASR in real applications, will also be introduced.
Structural parameter interdependencies in computational models of cognition.

Computational modeling of cognition allows latent psychological variables to be measured by means of adjustable model parameters. The estimation and interpretation of the parameters are impaired, however, if parameters are strongly intercorrelated within the model. We point out that strong parameter interdependencies are especially likely to emerge in models that combine a subjective value function with a probabilistic choice rule-a common structure in the literature. We trace structural parameter interdependencies between value function and choice rule parameters across several prominent computational models, including models on risky choice (cumulative prospect theory), categorization (the generalized context model), and memory (the SIMPLE model of free recall). Using simulation studies with a generic choice model, we show that the accuracy in parameter estimation is hampered in the presence of high parameter intercorrelations, particularly the ability to detect group differences on the parameters and associations of the parameters with external variables. We demonstrate that these problems can be alleviated by using a different specification of stochasticity in the model, for example, by assuming parameter stochasticity or a constant error term. In addition, application to two empirical data sets of risky choice shows that alleviating parameter interdependencies in this way can lead to different conclusions about the estimated parameters. Our analyses highlight a common but often neglected problem of computational models of cognition and identify ways in which the design and application of such models can be improved. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Quantum k-means algorithm based on trusted server in quantum cloud computing

Quantum k-means algorithm is widely used in solving clustering problems. However, the repeated calculation of the core subroutines overloads the quantum computer with mass computing tasks. In addition, due to the decoherence of the quantum state, the computing power of personal quantum computer is limited. Therefore, it is necessary to use high-performance quantum cloud computing to complete data processing. This paper proposes a quantum k-means algorithm in quantum cloud computing, which uploads the data to the server to execute the core subroutines SwapTest (calculating the similarity between ciphertext quantum states) and GroverOptim (quantum minimization algorithm) of quantum k-means algorithm and then sends the ciphertext to the client for further decryption using the decryption key, which reduces the load of the client. An improved quantum homomorphic encryption by t-gate updating in trusted server and data processing in semi-trusted server is introduced as well. In this scheme, the trusted server assists the semi-trusted server to execute the T-gate existing in the quantum circuit, which separates the key from the ciphertext and ensure the security of the ciphertext data processing in the quantum cloud. In our experiment, the two subroutines, SwapTest and GroverOptim, are conducted in ciphertext and plaintext conditions by using IBM Qiskit to evaluate the reliability and feasibility. The result shows that the decrypted ciphertext result is the same as the plaintext. We conclude that the proposed algorithm has a better performance in reducing the cost of load in client and protecting privacy data in cloud by contrast with the original quantum k-means algorithm.
CPC‐GSCT: Visual quality assessment for coloured point cloud based on geometric segmentation and colour transformation

Coloured point cloud (CPC) is one of the important representations of three-dimensional objects, which has been used in many fields. CPC may encounter geometric and colour distortion during its compression, simplification or other processing. Thus, the objective visual quality assessment of CPC is one of the urgent issues to be resolved in the CPC's applications. Aiming at this problem, this paper proposes a new full-reference visual quality assessment metric for CPC based on geometric segmentation and colour transformation (CPC-GSCT), which analyzes geometric distortion and colour distortion of CPC. First, considering the visual masking effect of CPC's geometric information, CPC is segmented into different regions and distributed with different weights to describe the influence of visual masking effect in CPC quality assessment. At the same time, a geometric combination feature vector is defined and extracted for measuring the CPC's geometric distortion. Then, considering the colour perception of human eyes, a colour combination feature vector is extracted to measure the CPC's colour distortion in HSV colour space. Finally, all the extracted geometric and colour features are constituted as a feature vector to predict the quality of CPC. Experimental results on three databases (IRPC, SJTU-PCQA and CPCD2.0) show that the proposed CPC-GSCT metric can achieve better performance in predicting the visual quality of CPC than relevant existing methods.
Identity preserving multi-pose facial expression recognition using fine tuned VGG on the latent space vector of generative adversarial network

<abstract> Facial expression is the crucial component for human beings to express their mental state and it has become one of the prominent areas of research in computer vision. However, the task becomes challenging when the given facial image is non-frontal. The influence of poses on facial images is alleviated using an encoder of a generative adversarial network capable of learning pose invariant representations. State-of-art results for image generation are achieved using styleGAN architecture. An efficient model is proposed to embed the given image into the latent vector space of styleGAN. The encoder extracts high-level features of the facial image and encodes them into the latent space. Rigorous analysis of semantics hidden in the latent space of styleGAN is performed. Based on the analysis, the facial image is synthesized, and facial expressions are recognized using an expression recognition neural network. The original image is recovered from the features encoded in the latent space. Semantic editing operations like face rotation, style transfer, face aging, image morphing and expression transfer can be performed on the image obtained from the image generated using the features encoded latent space of styleGAN. <italic>L</italic><sub>2</sub> feature-wise loss is applied to warrant the quality of the rebuilt image. The facial image is then fed into the attribute classifier to extract high-level features, and the features are concatenated to perform facial expression classification. Evaluations are performed on the generated results to demonstrate that state-of-art results are achieved using the proposed method. </abstract>
SecDeep

There is an increasing emphasis on securing deep learning (DL) inference pipelines for mobile and IoT applications with privacy-sensitive data. Prior works have shown that privacy-sensitive data can be secured throughout deep learning inferences on cloud-offloaded models through trusted execution environments such as Intel SGX. However, prior solutions do not address the fundamental challenges of securing the resource-intensive inference tasks on low-power, low-memory devices (e.g., mobile and IoT devices), while achieving high performance. To tackle these challenges, we propose SecDeep, a low-power DL inference framework demonstrating that both security and performance of deep learning inference on edge devices are well within our reach. Leveraging TEEs with limited resources, SecDeep guarantees full confidentiality for input and intermediate data, as well as the integrity of the deep learning model and framework. By enabling and securing neural accelerators, SecDeep is the first of its kind to provide trusted and performant DL model inferencing on IoT and mobile devices. We implement and validate SecDeep by interfacing the ARM NN DL framework with ARM TrustZone. Our evaluation shows that we can securely run inference tasks with 16× to 172× faster performance than no acceleration approaches by leveraging edge-available accelerators.
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning

Multi-step (also called n-step) methods in Reinforcement Learning (RL) have been shown to be more efficient than the 1-step method due to faster propagation of the reward signal, both theoretically and empirically, in tasks exploiting tabular representation of the value-function. Recently, research in Deep Reinforcement Learning (DRL) also shows that multi-step methods improve learning speed and final performance in applications where the value-function and policy are represented with deep neural networks. However, there is a lack of understanding about what is actually contributing to the boost of performance. In this work, we analyze the effect of multi-step methods on alleviating the overestimation problem in DRL, where multi-step experiences are sampled from a replay buffer. Specifically building on top of Deep Deterministic Policy Gradient (DDPG), we propose Multi- step DDPG (MDDPG), where different step sizes are manually set, and a variant called Mixed Multi-step DDPG (MMDDPG) where an average over different multi-step backups is used as an update target for the Q-value function. Empirically, we show that both MDDPG and MMDDPG are significantly less affected by the overestimation problem than DDPG with 1-step backup, which consequently results in better final performance and learning speed. We also discuss the advantages and disadvantages of different ways to do multi-step expansion in order to reduce approximation error, and expose the tradeoff between overestimation and underestimation that underlies offline multi-step methods. Finally, we compare the computational resource needs of MDDPG and MMDDPG with those of Twin Delayed Deep Deterministic Policy Gradient (TD3), a state-of-the-art algorithm proposed to address overestimation in actor-critic methods, since they show comparable final performance and learning speed.
DeepMal: maliciousness-Preserving adversarial instruction learning against static malware detection

Abstract Outside the explosive successful applications of deep learning (DL) in natural language processing, computer vision, and information retrieval, there have been numerous Deep Neural Networks (DNNs) based alternatives for common security-related scenarios with malware detection among more popular. Recently, adversarial learning has gained much focus. However, unlike computer vision applications, malware adversarial attack is expected to guarantee malwares’ original maliciousness semantics. This paper proposes a novel adversarial instruction learning technique, DeepMal, based on an adversarial instruction learning approach for static malware detection. So far as we know, DeepMal is the first practical and systematical adversarial learning method, which could directly produce adversarial samples and effectively bypass static malware detectors powered by DL and machine learning (ML) models while preserving attack functionality in the real world. Moreover, our method conducts small-scale attacks, which could evade typical malware variants analysis (e.g., duplication check). We evaluate DeepMal on two real-world datasets, six typical DL models, and three typical ML models. Experimental results demonstrate that, on both datasets, DeepMal can attack typical malware detectors with the mean F1-score and F1-score decreasing maximal 93.94% and 82.86% respectively. Besides, three typical types of malware samples (Trojan horses, Backdoors, Ransomware) prove to preserve original attack functionality, and the mean duplication check ratio of malware adversarial samples is below 2.0%. Besides, DeepMal can evade dynamic detectors and be easily enhanced by learning more dynamic features with specific constraints.
Research on the avalanche effect of image encryption based on the Cycle-GAN

Aiming at the problem of the weak avalanche effect in the recently proposed deep learning image encryption algorithm, this paper analyzes the causes of weak avalanche effect in the neural network of Cycle-GAN step-by-step process and proposes an image encryption algorithm combining the traditional diffusion algorithm and deep learning neural network. In this paper, first, the neural network is used for image scrambling and slight diffusion, and then the traditional diffusion algorithm is used to further diffuse the pixels. The experiment in satellite images shows that our algorithm, with the help of the further diffusion mechanism, can compensate for the weak avalanche effect of Cycle-GAN-based image encryption and can change a pixel value to the original image, and the number of pixel change rate (NPCR) and unified average changing intensity (UACI) values can achieve 99.64% and 33.49%, respectively. In addition, our method can effectively encrypt the image where the encrypted image with high information entropy and low pixel correlation is obtained. The experiment on data loss and noise attack declares our method can identify the types and intensity of attacks. What is more, the key space is big enough, and the key sensitivity is high while the key has a certain randomness.
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis

As the request for deep learning solutions increases, the need for explainability is even more fundamental.In this setting, particular attention has been given to visualization techniques, that try to attribute the right relevance to each input pixel with respect to the output of the network.In this paper, we focus on Class Activation Mapping (CAM) approaches, which provide an effective visualization by taking weighted averages of the activation maps.To enhance the evaluation and the reproducibility of such approaches, we propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches.To evaluate the appropriateness of the proposal, we compare different CAM-based visualization methods on the entire ImageNet validation set, fostering proper comparisons and reproducibility.
Stability of Self-Configuring Large Multiport Interferometers

Realistic multiport interferometers (beamsplitter meshes) are sensitive to component imperfections, and this sensitivity increases with size. Self-configuration techniques can be employed to correct these imperfections, but not all techniques are equal. This paper highlights the importance of algorithmic stability in self-configuration. Na\"{\i}ve approaches based on sequentially setting matrix elements are unstable and perform poorly for large meshes, while techniques based on power ratios perform well in all cases, even in the presence of large errors. Based on this insight, we propose a self-configuration scheme for triangular meshes that requires only external detectors and works without prior knowledge of the component imperfections. This scheme extends to the rectangular mesh by adding a single array of detectors along the diagonal.
Benchmarking the variational quantum eigensolver through simulation of the ground state energy of prebiotic molecules on high-performance computers

We use the Variational Quantum Eigensolver (VQE) as implemented in the Qiskit software package to compute the ground state energy of small molecules derived from water, H$_2$O, and hydrogen cyanide, HCN. The work aims to benchmark algorithms for calculating the electronic structure and energy surfaces of molecules of relevance to prebiotic chemistry, beginning with water and hydrogen cyanide, and to run them on the available simulated and physical quantum hardware. The numerical calculations of the algorithms for small quantum processors allow us to design more efficient protocols to be run in real hardware, as well as to analyze their performance. Future implementations on accessible quantum processing prototypes will benchmark quantum computers and provide tests of quantum advantage with heuristic quantum algorithms.