Reviewerly/exHarmony huggingface dataset:
Non-Interactive Zero-Knowledge Proofs with Fine-Grained Security

We construct the first non-interactive zero-knowledge (NIZK) proof systems in the fine-grained setting where adversaries' resources are bounded and honest users have no more resources than an adversary. More concretely, our setting is the $$\mathsf {NC^1}$$ -fine-grained setting, namely, all parties (including adversaries and honest participants) are in $$\mathsf {NC^1}$$ . Our NIZK systems are for circuit satisfiability (SAT) under the worst-case assumption, $$\mathsf {NC^1}\subsetneq \mathsf{\oplus L/poly}$$ . As technical contributions, we propose two approaches to construct NIZKs in the $$\mathsf {NC^1}$$ -fine-grained setting. In stark contrast to the classical Fiat-Shamir transformation, both our approaches start with a simple $$\varSigma $$ -protocol and transform it into NIZKs for circuit SAT without random oracles. Additionally, our second approach firstly proposes a fully homomorphic encryption (FHE) scheme in the fine-grained setting, which was not known before, as a building block. Compared with the first approach, the resulting NIZK only supports circuits with constant multiplicative depth, while its proof size is independent of the statement circuit size. Extending our approaches, we obtain two NIZK systems in the uniform reference string model and two non-interactive zaps (namely, non-interactive witness-indistinguishability proof systems in the plain model). While the previous constructions from Ball, Dachman-Soled, and Kulkarni (CRYPTO 2020) require provers to run in polynomial-time, our constructions are the first one with provers in $$\mathsf {NC^1}$$ .
Learning from Ideography and Labels: A Schema-aware Radical-guided Associative Model for Chinese Text Classification

Reading psychology believes text comprehension to involve a complex psychological construction process, with the reader mind being a dynamic associative system that stores an abundance of schemata. For Chinese text, in particular, the unique ideographic writing system allows its lansign to trigger semantic association and schema recalling without the need of phonetics. In contrast to previous research efforts on text classification problems, in this paper we present an interdisciplinary modeling approach that draws inspirations from the cognitive principles of ideography, schema theory and deep learning to study Chinese text classification. Specifically, we first propose a Radical-guided Associative Model (RAM) for preliminary cognitive imitation, which comprises two coupled spaces, namely the Literal Space and Associative Space. Then, taking consideration of the schemata acquired from the mind of a reader which plays a important role in influencing text-dependent information revision, we extend RAM with a systematic Schema-aware Radical-guided Associative Model (SRAM) that embeds label semantics as essential text-independent human knowledge for real-world abstraction. In SRAM, the Schema Space is introduced and a Schema Attention module is proposed with a novel loss paradigm that includes the linkage and interaction between text-dependent prior concepts and text-independent label schemata. Extensive experiments on three real-world datasets demonstrate the effectiveness and rationality of our proposed method.
Hierarchical Activity Recognition Based on Belief Functions Theory in Body Sensor Networks

Wearable human activity recognition has raised great interest among researchers in the field of Body Sensor Networks (BSNs). However, the classification of human complex activities, especially those similar activities such as walking upstairs and downstairs, is very challenging for the design of efficient activity recognition models. Considering that the hierarchical data structure is helpful to improve the performance of classifiers in effectively detecting the inter-class differences, here, we propose a novel hierarchical activity recognition method based on Belief Functions (BFs) theory. Specifically, in the training stage, we first use Long-Short Term Model (LSTM) to classify the predefined activities and then determine the similarities between each pair of activities through a confusion matrix. Based on this, the activities can be organized into a tree-based hierarchical structure through hierarchical clustering and then the Extreme Learning Machine (ELM) model for each nonleaf node in hierarchical structure can be trained, respectively. In the testing stage, we also propose a novel fast tree-based hierarchical combination rule to fuse the outputs of all trained ELMs. Finally, the proposed hierarchical activity recognition is evaluated by using two widely used UCI datasets: Smartphone and mHealth. The experimental results show that the proposed method is more accurate than the state-of-art methods.
CoLocateMe: Aggregation-Based, Energy, Performance and Cost Aware VM Placement and Consolidation in Heterogeneous IaaS Clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 9.61% more energy and <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs.
Blockchain-Based Reliable Framework for Land Registration Information System

One of the biggest problems in India is the land/property disputes for the land proprietorship issues. The main reason behind it could be the number of intermediaries in the process of land registry, which creates doubtful transactions and non-clarity of title. Maintaining transparency and tamper-proof of land titles are the challenging task, while data are moving through a number of channels. By using blockchain technology, one can solve these issues during land proprietorship transfer. This paper explains complexities in the present land registration procedure and how to resolve these issues by using blockchain technology. Furthermore, the paper provides a reliable framework configuration utilizing blockchain technology for the supports of the land proprietorship transfer in the nation. The proposed framework will remove the intermediaries and minimize the cause of fraud in the process. Using the framework, the land titles are unique and give valid and decisive authority to proprietorship.
Study of Subjective and Objective Quality Assessment of Night-Time Videos

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Specifics of Conducting Special Psychophysiological Studies with the Use of a Polygraph on the Employees of Internal Affairs Bodies Appointed to Other Positions in the System of the Ministry of Internal Affairs of Russia

The article describes the practice of using the method of special psychophysiological research (SPPR) on the polygraph in the Department of Internal Affairs in its historical context. A comparative analysis is carried out on the statistical representation of risk factors for deviant behavior that were found in employees through SPPR in 2011 and 2020. The work presents the results of the SPPR conducted as part of the professional psychological selection of candidates for service in the Department of Internal Affairs in 2018 – 2020. The identified risk factors are analyzed in the regional context. Taking into account the described results, we substantiate the necessity and relevance of developing methodological recommendations, aimed to clarify certain situations that arise in the process of conducting SPPR while transferring an individual to a higher position in the Department of Internal Af-fairs, with a different functional purpose or set of activities. As part of particularizing the content of the recommendations, we look into the specifics of conducting a pre-test interview with the subject. We show the contents of the test topics on the studied issues and their specification with regards to the functional responsibilities of employees. The methods of conducting an SPPR and the approaches to evaluating the results of the study are described.
Weakly Supervised Object Detection With Class Prototypical Network

In this paper, we aim to devise a new framework to compel the network to be equipped with the capability of detecting objects using image-level class labels as supervision. The challenge of such a weakly supervised setting mainly lies in how to make the network accurately understand both semantics and objectness of a given proposal without bounding box annotations. To this end, we contribute a concise framework, named Class Prototypical Network (CPNet). Concretely, our CPNet defines a set of learnable class prototypes to help classify object proposals. To endow the prototypes be not only discriminative for classes but also sensitive for proposals' objectness, we conduct both class-aware cross-attention and location-aware cross-attention between the feature embeddings of the learnable prototypes and the proposals. The learned attention scores are then used to form the proposal-level category information into the image-level one, making the entire framework be trained without any bounding box annotations. Besides, by applying these two kinds of attention mechanisms, the knowledge from both proposals' location and its class information can be successfully transferred into the corresponding prototypes. With the help of prototypes, our CPNet detects true positive object proposals. In addition, the CPNet further introduces a multi-head detection head to perform complementary training, preventing the model from falling into local discriminative parts and improving the model's performance on challenging non-rigid categories. We examine our CPNet on popular benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , PASCAL VOC 2007, 2012 and MS COCO 2014. Extensive experiments show our CPNet is a simple and effective framework.
The Websites Technology for Arabic Language Learning Through COVID-19 Pandemic

The pandemic of COVID 19 is transformed the people life style over the world. The teaching and learning filed is one of the most sectors were influenced by the procedures, new situations and social distancing through this pandemic. The self-learning via websites and internet used in various methods to teach the people the languages and skills. As well it used to learn the Arabic language which is one of the most widely used languages in the world. It is expected to play a critical role in educational operations and assignments. The aim of this study is to asset people to find and assess the websites for Arabic language learning based on multi criteria to support the learners. As well as to become a reference for teachers and learners to select a good quality website for non-Arabic speakers. These criteria are to evaluate Arabic learning websites to be guidelines based on functionality, usability, and learning content to serve the language skills writing, reading, listening and speaking based on correct grammars. This study focuses on Arabic language learning websites to classify them based on quantitative and qualitative methods to distinguish among them. However, this study will not assess the computer systems for learning which is out of websites scope.
Self-Organizing Fuzzy Belief Inference System for Classification

Evolving fuzzy systems (EFSs) are widely known as a powerful tool for streaming data prediction. In this article, a novel zero-order EFS with a unique belief structure is proposed for data stream classification. Thanks to this new belief structure, the proposed model can handle the interclass overlaps in a natural way and better capture the underlying multimodel structure of data streams in the form of prototypes. Utilizing data-driven soft thresholds, the proposed model self-organizes a set of prototype-based <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">if</small> – <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">then</small> fuzzy belief rules from data streams for classification, and its learning outcomes are practically meaningful. With no requirement of prior knowledge in the problem domain, the proposed model is capable of self-determining the appropriate level of granularity for rule-based construction, while enabling users to specify their preferences on the degree of fineness of its knowledge base. Numerical examples demonstrate the superior performance of the proposed model on a wide range of stationary and nonstationary classification benchmark problems.
Collaboration Between Multiple Experts for Knowledge Adaptation on Multiple Remote Sensing Sources

Due to the unique characteristics of remote sensing (RS) data, it is challenging to collect richer labeled samples for training the deep learning model compared with the natural image data. To solve this problem, recently, multi-source-single-target (MS<sup>2</sup>T) scenarios have started receiving significant attention in which the knowledge from multiple sources is integrated to transfer to a target domain with the assumption that label spaces of each source and target domain are the same. However, in real-world applications, it can be challenging to find a source domain that completely includes all classes of the target domain. Therefore, to cover all class information of the target domain, they often na&#x00EF;vely merge multiple sources into a complete single source. However, each source domain typically has a different data distribution; thus, the semantic information of each source domain can be damaged, leading to degrading the classification accuracy of the target domain. To address this problem, we propose a unified framework termed Multi-Expert Collaboration for Knowledge Adaptation (MECKA) from various sources. MECKA includes two main processes: multiple-view generation and collaborative learning. Multiview learning plays an essential role in preserving the unique characteristics of each source domain. In contrast, collaborative learning is responsible for connecting these views that leverage complementary information from each other to perform on an unseen target domain robustly. Experimental results showed that the proposed method achieved the best classification accuracy on remote sensing scene benchmark datasets on both complete and incomplete multisource unsupervised domain adaptation (UDA) tasks compared to benchmark methods.
Dynamic Neural Network for MIMO Detection

Achieving adequate precision in deep learning based communications often requires large network architectures, which results into unacceptable time delay and power consumption. This paper introduces the dynamic neural network (DyNN) into the design of wireless communications systems. DyNN allocates different samples with computation resources on demand by preforming dynamic inferences, thereby reducing the redundant computational cost and enhancing the network efficiency. We design a dynamic depth architecture that allows samples to adaptively skip layers with various dynamic strategies, from which we further develop a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</b> onfidence criterion based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</b> mproved DetNet (CD-IDetNet) and a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</b> olicy network based <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</b> mproved DetNet (PD-IDetNet) for multiple-input multiple-output (MIMO) detection. Specifically, in CD-IDetNet, a confidence criterion is adopted to control samples exiting early, while in PD-IDetNet, policy networks are trained by reinforcement learning to selectively skip layers for varying samples. Simulation results demonstrate that CD-IDetNet and PD-IDetNet detectors can respectively reduce 17.4% and 31.1% computational costs while preserving the full accuracy of IDetNet. Desirable tradeoffs between accuracy and computational complexity can be further achieved by fine-tuning the hyper-parameters of CD-IDetNet and PD-IDetNet. Moreover, over-the-air (OTA) tests are conducted to validate the effectiveness of the proposed detectors in practical systems.
DeepMinimizer: A Differentiable Framework for Optimizing Sequence-Specific Minimizer Schemes

Minimizers are k-mer sampling schemes designed to generate sketches for large sequences that preserve sufficiently long matches between sequences. Despite their widespread application, learning an effective minimizer scheme with optimal sketch size is still an open question. Most work in this direction focuses on designing schemes that work well on expectation over random sequences, which have limited applicability to many practical tools. On the other hand, several methods have been proposed to construct minimizer schemes for a specific target sequence. These methods, however, require greedy approximations to solve an intractable discrete optimization problem on the permutation space of k-mer orderings. To address this challenge, we propose: (a) a reformulation of the combinatorial solution space using a deep neural network re-parameterization; and (b) a fully differentiable approximation of the discrete objective. We demonstrate that our framework, DeepMinimizer, discovers minimizer schemes that significantly outperform state-of-the-art constructions on genomic sequences.
Data Privacy in IoT Network Using Blockchain Technology

The increasing rise in documented cases of spying and security breaches that jeopardize users’ privacy calls into question the current model, in which third parties acquire and control large amounts of personal data. Blockchain or distributed ledgers are a brand new technology system that can easily tackle such type of things with the help of IOT network system. We can use this technology in various fields like energy, health, startup, financial sector, and much more. It has the potential to provide enormous benefits and innovation. Here we will discuss various current applications that are using Blockchain technology with the help of IOT.
An Effective Communication Strategy Based on Trust: the Key Element to Adopting a Covid-19 Contact Tracking Application

To cope with the COVID-19 pandemic, contact tracing applications have been proposed to limit positive cases and reinforce other measures, especially before the appearance of vaccines. A high rate of adoption by citizens is required. This study investigates the impact of trust on the adoption of tracking applications. A survey was administered in Morocco, where the authorities proposed the «Wiqaytna» application. Structural Equation Modeling was used to test the hypotheses of the proposed model. The model explains 53% of the variance of the “intention to use” and 40.8% of the “actual use” of the application. The model was based on the UTAUT technology acceptance model and the GAM model of e-gov service acceptance. Our main objective was to study the impact of trust in the decision of Moroccans to use this type of application. Technology trust, government trust and social influence were important determinants of intention to use. The proposed model also shows that perceived awareness is an important antecedent of trust constructs. The impact of «perceived awareness» on the trust constructs (technology and government) is stronger than the social influence on the latter. Moreover, our model shows that «Perceived Awareness» has a more significant impact on «technology trust» than on «government trust». Due to their lack of interest (in seeking information) and attention (communications on the application), citizens lack information about the application's usefulness and the security of users' data. Even those who have had contact with the information they are looking for cannot often verify its credibility (e.g. the source code of the «Wiqaytna» application was available on Github). Therefore, cognitive and individual factors give way to social influence, and the intention to use becomes dependent on the norms and suggestions of influential people in the individual's environment. The latter construct is complex and has multiple determinants. Several factors act on the construction of trust in the authorities' quality of public services. Finally, the strongest relationship in the model is the effect of intention to use on using the Wiqaytna application. Based on these findings, suggestions are made for policymakers. First, a significant effort must be made to improve citizens' awareness of the importance of such an application for the control of the pandemic, even after the launch of the vaccination campaign and the application of social distancing measures. Indeed, a few posters here and there and a few commercials are not enough. An effective communication strategy must be built to explain to citizens the critical role these applications can play and reduce fears about citizens' privacy to increase the adoption rate of these applications. Secondly, the role of social influence is critical in adopting applications. This must be considered in communication campaigns and the involvement of opinion leaders and influencers to be more effective and increase the intention to use them.