Reviewerly/exHarmony huggingface dataset:
Cloud Computing: Services, Deployment Models and Security Challenges

The period of fast improvement of data innovation has seen sensational advances in computing storage, processing capabilities, and systems administration innovation, which have empowered clients to generate, process, and share huge volume of data with high dependability and proficiency. The need to measure and disperse enormous information prompts the advancement of cloud computing, which empowers helpful, limitless and on-request network admittance to a common pool of computing assets situated in various areas. Numerous users and research organizations are presently applying cloud computing ideas for straightforward and productive answers for their computing and information sharing necessities. As we all know, Cloud computing is an emerging domain and the high-speed growth of the cloud has led to more cost-saving, elasticity, and scalability but it is also facing a large amount of privacy and security challenges. In this paper present and discuss about the cloud computing and its architecture, advantage of cloud computing and different security issue of the cloud computing.
An Energy-Efficient Low-Latency 3D-CNN Accelerator Leveraging Temporal Locality, Full Zero-Skipping, and Hierarchical Load Balance

Three-dimensional convolutional neural network (3D-CNN) has demonstrated outstanding classification performance in video recognition compared to two-dimensional CNN (2D-CNN), since 3D-CNN not only learns the spatial features of each frame, but also learns the temporal features across all frames. However, 3D-CNN suffers from intensive computation and data movement. To solve these issues, an energy-efficient low-latency 3D-CNN accelerator is proposed. Temporal locality and small differential value dropout are used to increase the sparsity of activation. Furthermore, to fully utilize the sparsity of weight and activation, a full zero-skipping convolutional microarchitecture is proposed. A hierarchical load-balancing scheme is also introduced to improve resource utilization. With the proposed techniques, a 3D-CNN accelerator is designed in a 55-nm low-power CMOS technology, bringing in up to 9.89× speedup compared to the baseline implementation. Benchmarked with C3D, the proposed accelerator achieves an energy efficiency of 4.66 TOPS/W at 100 MHz and 1.08 V supply voltage.
Unsupervised Topical Organization of Documents using Corpus-based Text Analysis

This study aims at automating the process of topical keyword organization of set of documents in an input text corpus. It is conducted in the context of a larger project to investigate efficient unsupervised learning techniques to automatically extract relevant classes and their keyword descriptions from a set of the United Nations (UN) documents, and use the latter to produce reference corpora allowing to classify future UN documents. We assume that the reference classes are unknown in advance, and thus suggest an unsupervised clustering approach which accepts as input a bunch of unstructured text documents, and produces as output groups of similar documents describing similar topics. The input document feature vectors are augmented with term co-occurrence and relatedness scores produced from a distributional thesaurus built on the same (or a related) corpus. The augmented feature vectors are then run through a hierarchical clustering process to identify groups of similar documents, which serve as candidates for topical organization and keyword extraction. Experiments on a manually labelled dataset of documents classified against the UN's Sustainable Development Goals (SDGs) confirm the quality and potential of the approach.
BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments

We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios with dense and heterogeneous traffic consisting of cars, trucks, two-and three-wheelers, and pedestrians. We describe a new semantic segmentation technique based on unsupervised domain adaptation (DA), that can identify the class or category of each region in RGB images or videos. We also present a novel self-training algorithm for multi-source DA that improves the accuracy. Our overall approach is a deep learning-based technique and consists of an unsupervised neural network that achieves 87.18% accuracy on the challenging India Driving Dataset. Our method works well on roads that may not be well-marked or may include dirt, unidentifiable debris, potholes, etc. A key aspect of our approach is that it can also identify objects that are encountered by the model for the fist time during the testing phase. We compare our method against the state-of-the-art methods and show an improvement of 5.17% − 42.9%. Furthermore, we also conduct user studies that qualitatively validate the improvements in visual scene understanding of unstructured driving environments. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Is a "Decentralized Autonomous Organization" a Panopticon?

This piece explores algorithmic governance as a strength and a vulnerability in the experience of building participatory communities known as "Decentralized Autonomous Organizations". The Cypherpunks were terrified of surveillance. They envisaged the combination of cryptography and computer technology fundamentally altering the nature of trust and reputation and built cryptographically secure blockchain-based infrastructure to counter this threat. Now, not just on chain transactions are being tracked but every move of participants in blockchain communities. Reputation in blockchain systems could become the new algorithmic authoritarianism if mis-used for social control. This piece analyzes the ways in which decentralization efforts can be a threat to themselves by exploring the question, 'Are "Decentralized Autonomous Organizations" (DAOs) the next panopticon of algorithmic governance or a different panacea, and what does this mean for human autonomy in "autonomous" systems?'. By employing ethnographic methods and case study analysis, this piece provides an important qualitative contribution to the early dynamics of the aspirations and problems of decentralized, autonomous organizations.
Human-Human Communication in Cyber Threat Situations: A Systematic Review

In cyber threat situations, decision-making processes within organizations and between the affected organization and external entities are high-stake. They require human communication entailing technical complexity, time pressure, interdisciplinary factors, and often an insufficient information basis. Communication in cyber threat situations can thus be challenging and has a variety of implications for decision-making. The cyber-physical system is a rapidly changing socio-technical system that is understudied in terms of how cyber events are communicated and acted upon to secure and maintain cyber resilience. The present study is the first to review human-to-human communication in cyber threat situations. Our aims are to outline how human-human communication performance in cybersecurity settings have been studied, to uncover areas where there is potential for developing common standards for information exchange in collaborative settings, and to provide guidance for future research efforts. The review was carried out according to the PRISMA guidelines and articles were searched for on scientific databases. Articles focusing on human-human communication in cyber threat situations published in peer reviewed journals or as conference papers were included. A total of 17 studies were included in the final review. Most of the studies were correlational and exploratory in nature. Very few studies characterize communication in useful goal-related terms. There is a need for more collaboration between cyber defense exercise-organizers and cognitive scientists. Future studies should assess how team mental model-development affects team communication and performance in cyber defense exercises.
Sunspot: A Decentralized Framework Enabling Privacy for Authorizable Data Sharing on Transparent Public Blockchains

Blockchain technologies have been boosting the development of data-driven decentralized services in a wide range of fields. However, with the spirit of full transparency, many public blockchains expose all types of data to the public such as Ethereum. Besides, the on-chain persistence of large data is significantly expensive technically and economically. These issues lead to the difficulty of sharing fairly large private data while preserving attractive properties of public blockchains. Although direct encryption for on-chain data persistence can introduce confidentiality, new challenges such as key sharing, access control, and legal rights proving are still open. Meanwhile, cross-chain collaboration still requires secure and effective protocols, though decentralized storage systems such as IPFS bring the possibility for fairly large data persistence. In this paper, we propose Sunspot, a decentralized framework for privacy-preserving data sharing with access control on transparent public blockchains, to solve these issues. We also show the practicality and applicability of Sunspot by MyPub, a decentralized privacy-preserving publishing platform based on Sunspot. Furthermore, we evaluate the security and privacy of Sunspot through theoretical analysis and experiments.
What Drives Financial Sector Development in Africa? Insights from Machine Learning

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To Supervise or Not to Supervise: How to Effectively Learn Wireless Interference Management Models?

Machine learning has become successful in solving wireless interference management problems. Different kinds of deep neural networks (DNNs) have been trained to accomplish key tasks such as power control, beamforming and admission control. There are two state-of-the-art approaches to train such DNNs based interference management models: supervised learning (i.e., fits labels generated by an optimization algorithm) and unsupervised learning (i.e., directly optimizes some system performance measure). However, it is by no means clear which approach is more effective in practice. In this paper, we conduct some theory and experiment study about these two training approaches. First, we show a somewhat surprising result, that for some special power control problem, the unsupervised learning can perform much worse than its counterpart, because it is more likely to get stuck at some low-quality local solutions. We then provide a series of theoretical results to further understand the properties of the two approaches. To our knowledge, these are the first set of theoretical results trying to understand different training approaches in learning-based wireless communication system design.
Artificial Intelligence in Forensic Science

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An Evaluation of Serverless Computing on X86 and ARM platforms: Performance and Design Implications

Serverless computing is an emerging computing paradigm in cloud computing. Serverless users benefit from the features of serverless computing (e.g., auto scaling and pay-as-you-go model), and widely adopt it. Besides, ARM servers are becoming increasingly popular nowadays. It is of great importance to explore the performance of serverless computing on ARM platforms, as well as X86 platforms. This paper provides in-depth study of serverless computing on X86 and ARM platforms. We evaluate typical characteristics (e.g., startup latency, auto scaling, and performance isolation) of popular open source serverless platforms: Knative and Open-FaaS. We gain some valuable insights in different platforms. We believe our findings can guide serverless providers to design more efficient and robust serverless systems, thereby improving user experience.
Quantum-enhanced super-sensitivity of a Mach–Zehnder interferometer with superposition of Schrödinger’s cat-like state and Fock state as inputs using a two-channel detection

We study the improvement in the phase sensitivity of a Mach–Zehnder interferometer using the superposition of Schrödinger’s cat-like state with the Fock state (SCFS) and the coherent state as inputs. With this setup, we examine the effect on phase sensitivity of the interferometer using a two-channel detection (TCD) scheme [ Opt. Express 29 , 95 ( 2021 ) OPEXFF 1094-4087 10.1364/OE.413391 ] in which we take the optimal combination of the intensities of both output ports. We find better phase sensitivity under some conditions for this setup as compared to other known combinations of inputs such as squeezed vacuum and coherent states, vacuum and coherent states, etc. Therefore, we expect that the SCFS may be an alternative nonclassical resource for improvement in the phase sensitivity of a Mach–Zehnder interferometer, having potential application in quantum sensing.
A Novel Approach for Image Denoising and Performance Analysis using SGO and APSO

Abstract Image denoising is essential to extract the information contained in an image without errors. A technique of using both wavelets and evolutionary computing tools is proposed to denoise and to improve the image quality. An adaptive thresholding-based wavelet denoising technique in the threshold function is coordinated by novel social group optimization (SGO) and accelerated particle swarm optimization (APSO) is proposed. The simulation oriented experimentation is taken out employing MATLAB and the analysis is carried out using the image property metrics similar to peak signal to noise ratio (PSNR), mean square error (MSE) and other structural similarity index metrics (SSIM).
Secure data exchange in Industrial Internet of Things

The use of the Industrial Internet of Things (IoT) is widespread, working as an enabler to implement large, scalable, reliable, and secure industrial environments. Although existing deployments do not meet security standards and have limited resources for each component which leads to several security breaches, such as trust between components, partner factories, or remote-control. These security failures can lead to critical outcomes, from theft of production information to forced production stoppages, accidents, including physical and others. The combination of blockchain-based solutions with IIoT environments is gaining momentum due to their resilience and security properties. However, chain-structured classic blockchain solutions are very resource-intensive and are not suitable for power-constrained IoT devices. To mitigate the mentioned security concerns, a secure architecture is proposed using a structured asynchronous blockchain DAG (Directed Acyclic Graph) that simultaneously provides security and transaction efficiency for the solution. The solution was modelled with special details in the use cases and sequence diagrams. Security concerns were integrated from the start, and a threat model was created using the STRIDE approach to test the security of the proposed solution. As a result, a flexible solution was been developed that significantly reduces the attack vectors in IIoT environments. The proposed architecture is versatile and flexible, is supported by an extensive security assessment, which allows it to be deployed in a variety of customizable industrial environments and scenarios, as well as to include future hardware and software extensions.
Machine-checked ZKP for NP relations: Formally Verified Security Proofs and Implementations of MPC-in-the-Head

MPC-in-the-Head (MitH) is a general framework that enables constructing efficient zero-knowledge (ZK) protocols for NP relations from secure multiparty computation (MPC) protocols. In this paper we present the first machine-checked implementations of MitH.
How Does Blockchain Security Dictate Blockchain Implementation?

Blockchain protocols come with a variety of security guarantees. For example, BFT-inspired protocols such as Algorand tend to be secure in the partially synchronous setting, while longest chain protocols like Bitcoin will normally require stronger synchronicity to be secure. Another fundamental distinction, directly relevant to scalability solutions such as sharding, is whether or not a single untrusted user is able to point to certificates, which provide incontrovertible proof of block confirmation. Algorand produces such certificates, while Bitcoin does not. Are these properties accidental? Or are they inherent consequences of the paradigm of protocol design? Our aim in this paper is to understand what, fundamentally, governs the nature of security for permissionless blockchain protocols. Using the framework developed in [12], we prove general results showing that these questions relate directly to properties of the user selection process, i.e. the method (such as proof-of-work or proof-of-stake) which is used to select users with the task of updating state. Our results suffice to establish, for example, that the production of certificates is impossible for proof-of-work protocols, but is automatic for standard forms of proof-of-stake protocols. As a byproduct of our work, we also define a number of security notions and identify the equivalences and inequivalences among them.
A multi-modal and multi-scale emotion-enhanced inference model based on fuzzy recognition

Abstract Only the label corresponding to the maximum value of the fully connected layer is used as the output category when a neural network performs classification tasks. When the maximum value of the fully connected layer is close to the sub-maximum value, the classification obtained by considering only the maximum value and ignoring the sub-maximum value is not completely accurate. To reduce the noise and improve classification accuracy, combining the principles of fuzzy reasoning, this paper integrates all the output results of the fully connected layer with the emotional tendency of the text based on the dictionary to establish a multi-modal fuzzy recognition emotion enhancement model. The provided model considers the enhancement effect of negative words, degree adverbs, exclamation marks, and question marks based on the smallest subtree on the emotion of emotional words, and defines the global emotional membership function of emojis based on the corpus. Through comparing the results of CNN, LSTM, BiLSTM and GRU on Weibo and Douyin, it is shown that the provided model can effectively improve the text emotion recognition when the neural network output result is not clear, especially for long texts.
Design of an LSTM Cell on a Quantum Hardware

The present brief proposes a methodology to implement a Long Short-Term Memory cell in the quantum framework, where inference is computed by replicating the internal structure of the cell using quantum circuits. A suitable encoding is proposed and the design of each quantum operation is detailed. A complexity analysis of the circuit is hence conducted and finally, the quantum architecture is experimentally validated both in an IBM Q simulator and with a numerical simulation on a classical device. The proposed approach leads the way for a completely quantum implementation of a Long Short-Term Memory network.
Computation-Aided Classical-Quantum Multiple Access to Boost Network Communication Speeds

A multiple access channel (MAC) consists of multiple senders simultaneously transmitting their messages to a single receiver. For the classical-quantum case (cq-MAC), achievable rates are known assuming that all the messages are decoded, a common assumption in quantum network design. However, such a conventional design approach ignores the global network structure, i.e., the network topology. When a cq-MAC is given as a part of quantum network communication, this work shows that computation properties can be used to boost communication speeds with code design dependently on the network topology. We quantify achievable quantum communication rates of codes with computation property for a two-sender cq-MAC. When the two-sender cq-MAC is a boson coherent channel with binary discrete modulation, we show that it achieves the maximum possible communication rate (the single-user capacity), which cannot be achieved with conventional design. Further, such a rate can be achieved by different detection methods: quantum (with and without quantum memory), on-off photon counting and homodyne (each at different photon power). Finally, we describe two practical applications, one of which cryptographic.
<scp>GAN‐segNet</scp>: A deep generative adversarial segmentation network for brain tumor semantic segmentation

Abstract In this study, we present a novel automatic segmentation method using a neural network model named GAN‐segNet, which can not only identify brain tumors from MRI images but also accurately delineate intratumor regions. Since brain tumors with varying shapes and sizes can appear anywhere in the brain and image quality and contrast of MRI could be inadequate, automatic segmentation remains challenging despite its importance in the clinical workflow. The proposed GAN‐segNet is an innovative modification of the Generative Adversarial Network (GAN) and can efficiently and accurately segment brain tumors. One key innovation of our GAN model is an autoencoder learning representation of input data that were added to the generative network of the above‐mentioned GAN. By doing so, information extracted through convolution operations can be meaningfully regularized. As a result, the scales of extracted features can be controlled by the added autoencoder to preserve detail. Additionally, we propose an innovative loss function based on the concept of focal loss to effectively mitigate the impact of label imbalance. The above‐mentioned combination enables the proposed GAN‐segNet model to improve the segmentation of small intratumor region(s). We demonstrate the proposed method using MRI data available from a public database, that is, Brain Tumor Segmentation Challenge 2018 database (BRATS 2018). Using the proposed GAN‐segNet model, the average Dice scores were 0.8280, 0.9022, and 0.814 for segmenting enhancing tumor core, whole tumor, and tumor core, respectively. Furthermore, positive predictive values for segmenting enhanced tumor core, whole tumor, and tumor core were 0.8496, 0.9270, and 0.8610, respectively.
Inferential Wasserstein Generative Adversarial Networks

Abstract Generative adversarial networks (GANs) have been impactful on many problems and applications but suffer from unstable training. The Wasserstein GAN (WGAN) leverages the Wasserstein distance to avoid the caveats in the minmax two-player training of GANs but has other defects such as mode collapse and lack of metric to detect the convergence. We introduce a novel inferential Wasserstein GAN (iWGAN) model, which is a principled framework to fuse autoencoders and WGANs. The iWGAN model jointly learns an encoder network and a generator network motivated by the iterative primal-dual optimization process. The encoder network maps the observed samples to the latent space and the generator network maps the samples from the latent space to the data space. We establish the generalization error bound of the iWGAN to theoretically justify its performance. We further provide a rigorous probabilistic interpretation of our model under the framework of maximum likelihood estimation. The iWGAN, with a clear stopping criteria, has many advantages over other autoencoder GANs. The empirical experiments show that the iWGAN greatly mitigates the symptom of mode collapse, speeds up the convergence, and is able to provide a measurement of quality check for each individual sample. We illustrate the ability of the iWGAN by obtaining competitive and stable performances for benchmark datasets.