Reviewerly/exHarmony huggingface dataset:
On QA-NIZK in the BPK Model

Recently, Bellare et al. defined subversion-resistance (security in the case the CRS creator may be malicious) for NIZK. In particular, a Sub-ZK NIZK is zero-knowledge, even in the case of subverted CRS. We study Sub-ZK QA-NIZKs, where the CRS can depend on the language parameter. First, we observe that subversion zero-knowledge (Sub-ZK) in the CRS model corresponds to no-auxiliary-string non-black-box NIZK in the Bare Public Key model, and hence, the use of non-black-box techniques is needed to obtain Sub-ZK. Second, we give a precise definition of Sub-ZK QA-NIZKs that are (knowledge-)sound if the language parameter but not the CRS is subverted and zero-knowledge even if both are subverted. Third, we prove that the most efficient known QA-NIZK for linear subspaces by Kiltz and Wee is Sub-ZK under a new knowledge assumption that by itself is secure in (a weaker version of) the algebraic group model. Depending on the parameter setting, it is (knowledge-)sound under different non-falsifiable assumptions, some of which do not belong to the family of knowledge assumptions.
Modeling Product’s Visual and Functional Characteristics for Recommender Systems

An effective recommender system can significantly help customers to find desired products and assist business owners to earn more income. Nevertheless, the decision-making process of users is highly complex, not only dependent on the personality and preference of a user, but also complicated by the characteristics of a specific product. For example, for products of different domains (e.g., clothing versus office products), the product aspects that affect a user’s decision are very different. As such, traditional collaborative filtering methods that model only user-item interaction data would deliver unsatisfactory recommendation results. In this work, we focus on fine-grained modeling of product characteristics to improve recommendation quality. Specifically, we first divide a product’s characteristics into visual and functional aspects—i.e., the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visual appearance</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">functionality</i> of the product. One insight is that, the visual characteristic is very important for products of visually-aware domain (e.g., clothing), while the functional characteristic plays a more crucial role for visually non-aware domain (e.g., office products). We then contribute a novel probabilistic model, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Visual and Functional Probabilistic Matrix Factorization</i> (VFPMF), to unify the two factors to estimate user preferences on products. Nevertheless, such an expressive model poses efficiency challenge in parameter learning from implicit feedback. To address the technical challenge, we devise a computationally efficient learning algorithm based on alternating least squares. Furthermore, we provide an online updating procedure of the algorithm, shedding some light on how to adapt our method to real-world recommendation scenario where data continuously streams in. Extensive experiments on four real-word datasets demonstrate the effectiveness of our method with both offline and online protocols.
Detecting and Characterizing Extremist Reviewer Groups in Online Product Reviews

Online marketplaces often witness opinion spam in the form of reviews. People are often hired to target specific brands for promoting or impeding them by writing highly positive or negative reviews. This often is done collectively in groups. Although some previous studies attempted to identify and analyze such opinion spam groups, little has been explored to spot those groups who target a brand as a whole, instead of just products. In this article, we collected the reviews from the Amazon product review site and manually labeled a set of 923 candidate reviewer groups. The groups are extracted using frequent itemset mining over brand similarities such that users are clustered together if they have mutually reviewed (products of) a lot of brands. We hypothesize that the nature of the reviewer groups is dependent on eight features specific to a (group, brand) pair. We develop a feature-based supervised model to classify candidate groups as extremist entities. We run multiple classifiers for the task of classifying a group based on the reviews written by the users of that group to determine whether the group shows signs of extremity. A three-layer perceptron-based classifier turns out to be the best classifier. We further study behaviors of such groups in detail to understand the dynamics of brand-level opinion fraud better. These behaviors include consistency in ratings, review sentiment, verified purchase, review dates, and helpful votes received on reviews. Surprisingly, we observe that there are a lot of verified reviewers showing extreme sentiment, which, on further investigation, leads to ways to circumvent the existing mechanisms in place to prevent unofficial incentives on Amazon.
Contact Tracing to Manage COVID-19 Spread—Balancing Personal Privacy and Public Health

The coronavirus disease 2019 (COVID-19) pandemic has opened up an important conversation about how we balance interests of privacy with public health. Examples of contact tracing tools to identify exposed individuals have shown promise in facilitating early responses to minimize disease spread. These tools use geographic location data from smartphones and other devices to understand when and where infected individuals may have interacted with others. These tools, however, have come at the expense of personal data privacy, leading to concern over widespread use despite public health benefits.
Development of Secure Cloud Based Storage Using the Elgamal Hyper Elliptic Curve Cryptography with Fuzzy Logic Based Integer Selection

The technological advancements in the field of the information and communication technology led to the development of more promising cloud paradigm that allows online provision of services such as platform, software and infrastructure. The infrastructure services provided by the cloud allows the user data to be stored and accessed ubiquitously unlike the arrays that are available on the premises. However the security of the data that are being stored in the internet are still under research. This remains as major inhibitor for the adoption of cloud service in spite of its reliability, elasticity, high computational capabilities and the pay as you go possibilities. So the paper puts forth the construction of a secure storage as service in the cloud computing by utilizing the cryptography system based on Elgamal. The proposed method encrypts the data to be stored using the Elgamal that incorporates the hyper elliptic curve cryptography for twofold encryption and further utilizes the fuzzy logic to perform the integer selection that serves as the significant attribute in defining the cloud storage security. The point addition and the double based ECC is used for generating the keys for the hyper elliptic curve cryptography. The twofold security model put forth is validated using the MATLAB on the terms of efficiency observed on securing the data stored in cloud as well as the cost and the execution time endured by the proposed twofold security. The results shows that the protection offered by the twofold security is much better compared to the prevailing.
Institutions, human capital and economic growth in developing countries

Purpose This paper aims to study institutions, human capital and economic growth in developing countries. Design/methodology/approach The study applies dynamic system Generalized Method of Moments (GMM) and simultaneous quantile regression on a panel of 120 developing countries for the period of 1996-2014. Findings The findings show that human development and institutions do have a significant positive effect on economic growth. Interestingly, institutions and human development have a significant negative interactive effect on the economic growth of developing countries. This paper argues that incremental investment in human development would impact economic growth negatively in the presence of weak and dysfunctional institutions because additional stock tends to be employed in rent-seeking and socially unproductive activities. Research limitations/implications The policy makers should bear in mind the critical role played by the institutions and the initial stage of growth of a country in making their education and health policies more effective. Originality/value The most important novelty is the study of various transmission channels: political, economic and financial institutions through which human development affect economic growth in developing countries. This paper also studies the Islamic economic development concept and empirically investigates whether Muslim countries are different from their counterparts. Moreover, this study extends the existing empirical growth literature by simultaneously applying dynamic system GMM and quantile regression techniques.
Survey of Image Recognition Algorithms

Images are the basis of human vision and an important way for humans to communicate with the world. Making computers with efficient and accurate image recognition is an important technical field of artificial intelligence. In recent years, image recognition technology has developed rapidly, and many new recognition technologies have emerged. This article first outlines the development process of image recognition technology; expounds the entire technical process of image recognition; introduces the main techniques of image preprocessing and image segmentation; and then summarizes the extraction of different features of images and common image classification algorithms. Such as: KNN, SVM, BP neural network, CNN. Finally, the future research development trend of image recognition is analyzed and prospected.
Social Network Analysis in Software Development Projects: A Systematic Literature Review

Software development in project teams has become more and more complex, with increasing demands for information and decision making. Software development in projects also hugely depends on effective interaction between people, and human factors have been identified as key to successful software projects. Especially in this context, managing and analyzing social networks is highly important. The instrument of social network analysis (SNA) provides fine-grained methods for analyzing social networks in project teams, going beyond the traditional tools and techniques of project management. This paper examines the importance of the application of SNA in software development projects. We conducted a systematic literature review (SLR) of research on software development projects and social network data published between 1980 and 2019. We identified and analyzed 86 relevant studies, finding that research on software development projects spans the topics of project organization, communication management, knowledge management, version and configuration management, requirement management, and risk management. Further, we show that most studies focus on project organization and that the most common method used to gather social data relies on automated extraction from various software development repositories in the SNA context. Our paper contributes to the software development literature by providing a broad overview of published studies on the use of social networks in helping software development projects. Finally, we identify research opportunities and make suggestions for addressing existing research gaps.
Energy-Latency-Aware Task Offloading and Approximate Computing at the Mobile Edge

Task offloading with Mobile-Edge Computing (MEC) is envisioned as a promising technique for prolonging battery lifetime and enhancing the computation capacity of mobile devices. In this paper, we consider a multi-user MEC system with a Base Station (BS) equipped with a computation server assisting mobile users in executing computation-intensive real-time tasks via offloading technique. We formulate the Energy-Latency-aware Task Offloading and Approximate Computing (ETORS) problem, which aims at optimizing the trade-off between energy consumption and application completion time. Due to the centralized and mixed-integer natures of this problem, it is very challenging to derive the optimal solution in practical time. This motivates us to employ the Dual-Decomposition Method (DDM) to decompose the original problem into three subproblems-namely the Task-Offloading Decision (TOD), the CPU Frequency Scaling (CFS), and the Quality of Computation Control (QoCC). Our approach consists of two iterative layers: in the outer layer, we adopt the duality technique to find the optimal value of Lagrangian multiplier associated prime problem; and in the inner layer, we formulate the subproblems that can be solved efficiently using convex optimization techniques. We show that the computation offloading selection depends not only on the computing workload of a task, but also on the maximum completion time of its immediate predecessors and on the clock frequency as well as on the transmission power of the mobile device. Simulation results coupled with real-time experiments on a small-scale MEC testbed show the effectiveness of our proposed resource allocation scheme and its advantages over existing approaches.
Improved Classical Cryptanalysis of SIKE in Practice

The main contribution of this work is an optimized implementation of the van Oorschot-Wiener (vOW) parallel collision finding algorithm. As is typical for cryptanalysis against conjectured hard problems (e. g. factoring or discrete logarithms), challenges can arise in the implementation that are not captured in the theory, making the performance of the algorithm in practice a crucial element of estimating security. We present a number of novel improvements, both to generic instantiations of the vOW algorithm finding collisions in arbitrary functions, and to its instantiation in the context of the supersingular isogeny key encapsulation (SIKE) protocol, that culminate in an improved classical cryptanalysis of the computational supersingular isogeny (CSSI) problem. In particular, we present a scalable implementation that can be applied to the Round-2 parameter sets of SIKE that can be used to give confidence in their security levels.
Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.
SS-YOLO: An Object Detection Algorithm based on YOLOv3 and ShuffleNet

Supported by the improvement in the computing power of devices and increasing amount of data is being produced by digital society, the object detection framework based on deep learning has developed rapidly. Based on R-CNN, SSD, YOLO and other classical frameworks, many excellent object detection frameworks have appeared. YOLOv3 is at the current leading level in various aspects such as detection speed and accuracy. YOLOv3-Tiny can achieve real-time detection on the common GPU, but the accuracy is lower. SS-YOLO builds a feature extraction network with reference to the structure of ShuffleNet, which improves accuracy while ensuring the speed. The idea of SENet is added to the structure to adjust the degree of attention to the channel according to the importance of the characteristics of different channels. Compared to YOLOv3-Tiny, SS-YOLO improves the mean average precision by 6.5% and frames per second by 4 on the PASCAL VOC2007 dataset.
On One-way Functions and Kolmogorov Complexity

We prove that the equivalence of two fundamental problems in the theory of computing. For every polynomial , the following are equivalent: · One-way functions exists (which in turn is equivalent to the existence of secure private-key encryption schemes, digital signatures, pseudorandom generators, pseudorandom functions, commitment schemes, and more); · t-time bounded Kolmogorov Complexity, K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> , is mildly hard-on-average (i.e., there exists a polynomial such that no PPT algorithm can compute K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> , for more than a 1-[1/p(n)] fraction of n-bit strings). In doing so, we present the first natural, and well-studied, computational problem characterizing the feasibility of the central private-key primitives and protocols in Cryptography.
Towards efficient motion-blurred public security video super-resolution based on back-projection networks

In public security, video super-resolution has been an important technology for assisting users in effectively locating potential safety hazards. However, the video collected from video monitoring equipment is usually polluted by motion blur and noise, which results in low video quality after reconstruction. In this paper, we propose a super-resolution reconstruction model for motion-blurred video to reconstruct corresponding high-resolution video from motion-blurred low-resolution video. First, a single-frame image super-resolution model is designed based on the improved deep back-projection network, which primarily includes the design of a recursive method. Second, a multi-frame image super-resolution model is designed based on an optical motion algorithm for motion estimation and motion compensation of video frame sequences. Finally, according to the motion-blurred low-resolution video pollution condition, the de-blurring algorithm and multi-frame image super-resolution are combined to propose a super-resolution reconstruction model for motion-blurred video. Experiments show that the proposed motion-blurred video super-resolution model is superior to other contrasted algorithms in clear video and motion-blurred video reconstruction and has a better reconstruction effect and higher practical value.
Lightweight Authenticated Encryption Mode Suitable for Threshold Implementation

This paper proposes tweakable block cipher (TBC) based modes $$\mathsf {PFB\_Plus}$$ and $$\mathsf {PFB}\omega $$ that are efficient in threshold implementations (TI). Let t be an algebraic degree of a target function, e.g. $$t=1$$ (resp. $$t>1$$ ) for linear (resp. non-linear) function. The d-th order TI encodes the internal state into $$d t + 1$$ shares. Hence, the area size increases proportionally to the number of shares. This implies that TBC based modes can be smaller than block cipher (BC) based modes in TI because TBC requires s-bit block to ensure s-bit security, e.g. PFB and Romulus, while BC requires 2s-bit block. However, even with those TBC based modes, the minimum we can reach is 3 shares of s-bit state with $$t=2$$ and the first-order TI ( $$d=1$$ ). Our first design $$\mathsf {PFB\_Plus}$$ aims to break the barrier of the 3s-bit state in TI. The block size of an underlying TBC is s/2 bits and the output of TBC is linearly expanded to s bits. This expanded state requires only 2 shares in the first-order TI, which makes the total state size 2.5s bits. We also provide rigorous security proof of $$\mathsf {PFB\_Plus}$$ . Our second design $$\mathsf {PFB}\omega $$ further increases a parameter $$\omega $$ : a ratio of the security level s to the block size of an underlying TBC. We prove security of $$\mathsf {PFB}\omega $$ for any $$\omega $$ under some assumptions for an underlying TBC and for parameters used to update a state. Next, we show a concrete instantiation of $$\mathsf {PFB\_Plus}$$ for 128-bit security. It requires a TBC with 64-bit block, 128-bit key and 128-bit tweak, while no existing TBC can support it. We design a new TBC by extending SKINNY and provide basic security evaluation. Finally, we give hardware benchmarks of $$\mathsf {PFB\_Plus}$$ in the first-order TI to show that TI of $$\mathsf {PFB\_Plus}$$ is smaller than that of PFB by more than one thousand gates and is the smallest within the schemes having 128-bit security.
Image stitching with positional relationship constraints of feature points and lines

The elimination of parallax and the processing of natural issue in complex scenes are challenging tasks for image stitching. In this paper, an image stitching method with positional relationship constraints of feature points and lines, which can accomplish accurate alignment and reduce projection distortion, is proposed. At first, to reduce the computational cost and the number of outliers on subsequent feature matching, we combine the template matching to propose a quick way for detecting overlapping regions. Then, the appropriate reference image is determined to mitigate the projection distortion that the image warping is in the cases of non-planar geometry of the scenes. Furthermore, a local mesh model based on dual features is established to guide the mesh deformation. And an energy function is designed to refine alignment. In addition to alignment error terms, a novel positional relationship constraint term is proposed to improve quality of naturalness of final stitching results. Finally, experimental results demonstrate that our approach is superior to the existing image stitching algorithms in improving quality of the stitched image naturalness.
Study on pyrite thermoelectricity, ore-forming fluids and H-O-Rb-Sr isotopes of the Yongxin gold deposit, Central Asian Orogenic Belt: Implications for ore genesis and exploration

The Yongxin gold deposit is one of the prominent gold deposits in the Duobaoshan gold-copper metallogenic belt within the eastern domain of the Central Asian Orogenic Belt. The deposit has recently been discovered therefore, its genesis and economic significance remain unknown. Here, we present the results of pyrite thermoelectricity, fluid inclusion and H-O-Rb-Sr isotopic studies of different minerals with a view to decipher ore genesis and ore prospecting in the Yongxin gold deposit. The Rb-Sr isotopic composition of pyrite, quartz and calcite indicate that the deposit was formed at 114.6 ± 1.2 Ma. Fluid inclusions and H-O isotopic studies show that the Yongxin gold deposit was formed at shallow depth, with relatively low ore-forming temperatures (120–355 °C), low-medium salinity (0 wt% to 10 wt%), and low-moderate density (0.6–1.0 g/cm3). The ore-forming fluids were mainly magmatic which mixed with meteoric water triggering the precipitation of ore-forming materials. We trace the evolution of the ore fluids from deeper levels in the northwest part of the deposit to shallower levels in the southeast. The thermoelectricity parameter values (Xnp) of pyrite range from −188 to −85 and the values of erosional or exhumation level (γ) range from 97% to 71% with an average of 79.4%, suggesting that the existing ore-body is the lower part of the deposit (79.4% of the ore-body has already been eroded). Thermoelectricity of pyrite shows a vertical distribution in the axial direction without significant change in the Xnp values, indicating that the ore body extends further. Combined with the pyrite thermoelectric coefficients in different altitudes, we predict gold potential for the ores at further depth depending on the continued presence of the N-P type pyrite. Our mineralogical mappings also suggest a prospecting potential in the two zones at depth beneath the northwestern part of the deposit.