Reviewerly/exHarmony huggingface dataset:
DETECTION OF FAKE ACCOUNTS IN SOCIAL MEDIA

Social media is becoming increasingly used as a source of information, including events during warfare. The fake accounts of the social media are often used for a variety of cyber-attacks, information-psychological operations, and social opinion manipulating during warfare. The analysis of online social media research methods are carried out, the main metrics and attributes of fake accounts in Facebook are investigated. Each metric is assigned to the appropriate categories for the convenience of their analysis and gets a certain number of points depending on conditions from 0 to 3, which indicate how much every of the metrics influenced on conclusion about the fakeness of the account. The levels of influence have the following meanings: 0 – no influence, 1 – weak influence, 2 – significant influence, 3 – critical influence. For example, if the histogram feature reaches level 3, this means that the parameter characterizing this feature has a critical impact on account fakeness. Otherwise, if the column is at 0 or 1 level, this means that the parameter is inherent in the real account. Thus, based on the level of each of the parameters, we conclude on the fakeness or reality of a certain account. The following metrics are analyzed: likes, friends, posts and statuses, personal information about the user and the photos, considering their possible parameters and influence on the status of the account. Each metric is assigned to the appropriate categories for the convenience of their analysis. A decision-making system based on a supported vector machine is developed and has 9 inputs and single output. A series of experimental research was conducted where account analyzing as well as parameters extracting and selection are realized on Facebook. The classifier accuracy of the fake accounts detection is 97% with the special prepared dataset of the real and fake account parameters.
Triply Adaptive UC NIZK

Non-interactive zero knowledge (NIZK) enables proving the validity of NP statement without leaking anything else. We study multi-instance NIZKs in the common reference string (CRS) model, against an adversary that adaptively corrupts parties and chooses statements to be proven. We construct the first such triply adaptive NIZK that provides full adaptive soundness, as well as adaptive zero-knowledge, assuming either LWE or else LPN and DDH (previous constructions rely on non-falsifiable knowledge assumptions). In addition, our NIZKs are universally composable (UC). Along the way, we:
EvalRound Algorithm in CKKS Bootstrapping

Homomorphic encryption (HE) has opened an entirely new world up in the privacy-preserving use of sensitive data by conducting computations on encrypted data. Amongst many HE schemes targeting computation in various contexts, Cheon–Kim–Kim–Song (CKKS) scheme [8] is distinguished since it allows computations for encrypted real number data, which have greater impact in real-world applications. CKKS scheme is a levelled homomorphic encryption scheme, consuming one level for each homomorphic multiplication. When the level runs out, a special computational circuit called bootstrapping is required in order to conduct further multiplications. The algorithm proposed by Cheon et al. [7] has been regarded as a standard way to do bootstrapping in the CKKS scheme, and it consists of the following four steps: ModRaise, CoeffToSlot, EvalMod and SlotToCoeff. However, the steps consume a number of levels themselves, and thus optimizing this extra consumption has been a major focus of the series of recent research. Among the total levels consumed in the bootstrapping steps, about a half of them is spent in CoeffToSlot and SlotToCoeff steps to scale up the real number components of $$\textsf{DFT}$$ matrices and round them to the nearest integers. Each scale-up factor is very large so that it takes up one level to rescale it down. Scale-up factors can be taken smaller to save levels, but the error of rounding would be transmitted to EvalMod and eventually corrupt the accuracy of bootstrapping. EvalMod aims to get rid of the superfluous qI term from a plaintext $$\textsf{pt}+ qI$$ resulting from ModRaise, where q is the bottom modulus and I is a polynomial with small integer coefficients. EvalRound is referred to as its opposite, obtaining qI. We introduce a novel bootstrapping algorithm consisting of ModRaise, CoeffToSlot, EvalRound and SlotToCoeff, which yields taking smaller scale-up factors without the damage of rounding errors.
Efficient Zero-Knowledge Arguments in Discrete Logarithm Setting: Sublogarithmic Proof or Sublinear Verifier

We propose three interactive zero-knowledge arguments for arithmetic circuit of size N in the common random string model, which can be converted to be non-interactive by Fiat-Shamir heuristics in the random oracle model. First argument features $$O(\sqrt{\log N})$$ communication and round complexities and O(N) computational complexity for the verifier. Second argument features $$O(\log N)$$ communication and $$O(\sqrt{N})$$ computational complexity for the verifier. Third argument features $$O(\log N)$$ communication and $$O(\sqrt{N}\log N)$$ computational complexity for the verifier. Contrary to first and second arguments, the third argument is free of reliance on pairing-friendly elliptic curves. The soundness of three arguments is proven under the standard discrete logarithm and/or the double pairing assumption, which is at least as reliable as the decisional Diffie-Hellman assumption.
Deep Reinforcement Learning for the Autonomous Adaptive Behavior of Social Robots

Social assistive robots are conceived to cooperate with humans in many areas like healthcare, education, or assistance. In situations where the workforce is scarce and when these machines work with special populations like older adults or children, the behavior must be appropriate and seem natural. In this contribution, we present a Deep Reinforcement Learning model for the autonomous adaptive behavior of social robots. The model emulates some aspects of human biology by generating artificial biologically inspired functions, like sleep or entertainment, to endow robots with long-term autonomous behavior. The Deep Reinforcement Learning system overcomes classical Reinforcement Learning problems such as high dimensional state-action spaces learning which actions better suit each situation the robot is experiencing. Besides, the system aims at maintaining the robot's internal state in the best possible condition sustaining human-robot interaction. The results show that our robot Mini correctly learns how to regulate the deficits in its biological processes by selecting from six actions in a high diversity of situations that merge the state of the biological process and the external stimuli the robot perceives from the environment.
Identifying Synthetic Voices’ Qualities for Conversational Agents

The present study aims to explore user’ acceptance and perceptions toward different quality levels of synthetical voices. To achieve this, four voices have been exploited considering two main factors: the quality of the voices (low vs high) and their gender (male and female). 186 volunteers were recruited and subsequently allocated into four groups of different ages respectively, adolescents, young adults, middle-aged and seniors. After having randomly listened to each voice, participants were asked to fill the Virtual Agent Voice Acceptance Questionnaire (VAVAQ). Outcomes show that the two higher quality voices of Antonio and Giulia were more appreciated than the low-quality voices of Edoardo and Clara by the whole sample in terms of pragmatic, hedonic and attractiveness qualities attributed to the voices. Concerning preferences towards differently aged voices, it clearly appeared that they varied according to participants age’ ranges examined. Furthermore, in terms of suitability to perform different tasks, participants considered Antonio and Giulia equally adapt for healthcare and front office jobs. Antonio was also judged to be significantly more qualified to accomplish protection and security tasks, while Edoardo was classified as the absolute least skilled in conducting household chores.
Dynamic Optimal Parameter Setting with Fuzzy Argument to Metaheuristic Algorithm Variant for Fuzzy Tracking Controllers

In this work, we describe the evaluation between the original shark smell optimization method (SSO) and its variant (VSSO) in the dynamic adjustment of its main parameters applying T2FLS. The SSO metaheuristic was recently created, which demonstrates the diverse olfactory abilities of the shark in search of food, as inspiration. In this metaheuristic, multiple hunting cycles are used using two main movements forward and rotational twist, to establish a better equilibrium between the exploration and exploitation stages, with the aim of having a better process of searching for optimal solutions. The goal is the adjustment of the point values that form the FMs of the fuzzy system that we call (FSSO), the purpose is to obtain an optimal vector of values unlike the original SSO by performing dynamic adjustment in the parameters of the metaheuristic, in combination with salp swarm metaheuristics (SSA). The FSSO fuzzy system is tested with the proposed method with Benchmark CEC-2017 functions to see its functionality and thus enhance its behavior during different dimensions in addition to highlighting its characteristics in solving the problem in the last part of the work on the motor DC.
Detecting emotional ambiguity in text

An approach for determining emotional ambiguity in text data is described in this paper. The prediction confidences output from a text classifier are used to measure amount of ambiguity found in target entries. This measure can be used as a filtering mechanism to identify entries that require human feedback. This feedback loop can be implemented in a workflow which retrains a classifier model including newly disambiguated entries and resulting in a boost to classifier accuracy. This emotion ambiguity measure can be utilized to discover concrete emotional content in text data as well as reveal topics which do not have a concrete emotional consensus.
Investigation on principles for cost assignment in motion vector-based video steganography

Cost assignment in the motion vector domain remains a research focus in video steganography. Recent studies in image steganography have summarized many principles for cost assignment and achieved good results. But the basic principles for cost assignment in motion vector-based video steganography have not been fully discussed yet. Firstly, this paper proposes three principles for cost assignment in the motion vector domain, including the principle of local optimality, non-consistency in the block group, and complexity priority. Secondly, three corresponding novel practical distortion functions were designed according to the three principles. Finally, a joint distortion function is constructed based on all three principles to increase overall performance. The experimental results show that not only the three independent distortion functions can effectively resist the corresponding steganalysis attacks, but the final joint distortion can resist the three steganalysis features simultaneously. In addition, it can obtain good visual quality and coding efficiency, which can be applied to practical scenarios.
Cross-modal information balance-aware reasoning network for image-text retrieval

As a fundamental multimodal task, image-text retrieval bridges the gap between vision and language. Current mainstream methods exploit attention mechanisms to discover potential alignments between visual regions and textual words while ignoring the imbalance of image-text information. To this end, we propose a Cross-modal Information Balance-aware Reasoning Network (CIBRN), adopting information balance and similarity reasoning mechanisms to distinguish matched and unmatched image-text pairs in the paper. Specifically, a two-stage information balance scheme is employed to balance image-text information. In the first stage, a Graph Convolutional Network (GCN) with multiple convolution kernels is used to convert elements that only exist in a single modality into common elements to achieve intra-modal information balance indirectly. In the second stage, we propose an information "Add-Reduce" mechanism to realize inter-modal information balance by adding a random feature based on Gaussian distribution to each textual "word" and reducing fixed-length information from each visual "region". Subsequently, a block-based hierarchical matching method and mean-based fully connected layers are proposed to reason the relevance of images and texts. Extensive experiments on two benchmark datasets, i.e., Flickr30K and MSCOCO, demonstrate the effectiveness of the proposed model CIBRN and achieve advanced results compared to the state-of-the-art method, with a gain of 7.0% and 3.0% on rSum, respectively.
Privacy-preserving co-synthesis against sensor–actuator eavesdropping intruder

In this work, we investigate the problem of privacy-preserving supervisory control against an external passive intruder via co-synthesis of a dynamic mask, an edit function, and a supervisor. We attempt to achieve the following goals: (1) the system secret cannot be inferred by the intruder, i.e., opacity of secrets against the intruder, and the existence of the dynamic mask and the edit function should not be discovered by the intruder, i.e., covertness of dynamic mask and edit function against the intruder; (2) some safety and nonblockingness requirement should be satisfied. We assume the intruder can eavesdrop both the sensing information generated by the sensors and the control commands issued to the actuators. Our approach is to model the co-synthesis problem as a distributed supervisor synthesis problem in the Ramadge–Wonham supervisory control framework, and we propose an incremental synthesis heuristic to incrementally synthesize a dynamic mask, an edit function and a supervisor. The effectiveness of our approach is illustrated on an example about location privacy.
Pursuit of a discriminative representation for multiple subspaces via sequential games

We consider the problem of learning discriminative representations for data in a high-dimensional space with distribution supported on or around multiple low-dimensional linear subspaces. That is, we wish to compute a linear injective map of the data such that the features lie on multiple orthogonal subspaces. Instead of solving this learning problem using multiple instances of principal component analysis (PCA), we cast it as a sequential game using the closed-loop transcription (CTRL) framework recently proposed for learning discriminative and generative representations for general low-dimensional submanifolds. We prove that the equilibrium solutions to the game indeed give the correct representations. Our approach unifies classical methods of learning subspaces with modern machine learning practice, by showing that subspace learning problems may be provably solved using the modern toolkit of representation learning. In addition, our work provides the first theoretical justification for the CTRL framework in the important special case of linear subspaces. We support our theoretical findings with compelling empirical evidence. We also generalize the sequential game formulation to more general representation learning problems. Our code is publicly available on GitHub.
Electronic trading and stock market participation in Africa: Does technology induce participation?

This study examines the effect of migrating to electronic trading platforms on stock market participation among African stock exchanges. Using Bayesian estimations, we demonstrate that transitioning from a physical to an electronic stock exchange decreases trading activity on the stock exchange whilst increasing listing. Our findings further reveal that the impact of migration is more pronounced for non-Sub-Saharan African countries relative to Sub-Saharan countries. In terms of policy, the results imply that African exchanges cannot optimise the efficiency of their stock markets simply by migrating to electronic trading platforms.
Graph learning from band-limited data by graph Fourier transform analysis

A graph provides an effective means to represent the statistical dependence or similarity among signals observed at different vertices. A critical challenge is to excavate graphs underlying observed signals, because of non-convex problem structure and associated high computational requirements. This paper presents a new graph learning technique that is able to efficiently infer the graph structure underlying observed graph signals. The key idea is that we reveal the intrinsic relation between the frequency-domain representation of general band-limited graph signals, and the graph Fourier transform (GFT) basis. Accordingly, we derive a new closed-form analytic expression for the GFT basis, which depends deterministically on the observed signals (as opposed to being solved numerically and approximately in the literature). Given the GFT basis, the estimation of the graph Laplacian, more explicitly, its eigenvalues, is convex and efficiently solved using the alternating direction method of multipliers (ADMM). Simulations based on synthetic data and experiments based on public weather and brain signal datasets show that the new technique outperforms the state of the art in accuracy and efficiency.
Slipping into the world: Platforms, scale, and branding in alumni's social media writing

In this article, we draw on focus group interviews collected for the Wayfinding Project to explore how university alumni orient themselves as writers while participating in social media after graduation. By looking at alumni's self descriptions of their writing processes across public networks, we are able to trace pathways that recognize the rhetorical and communicative intentions of users, while also acknowledging the roles that serendipity, creativity, and the unexpected play in shaping these literate practices. Specifically, we point to how these alumni describe their experiences as they adapt to addressing audiences across different platforms and confront the "reach" of those platforms for engaging unexpected audiences. Several focus group participants use the term "branding" as a way to describe how they conceive of their writing across multiple social networks. These participants describe their public, networked writing as a form of managing their identities at the same time that they are "branding" themselves to manage the expectations of multiple audiences. In sum, our research shows us how the unexpected audiences generated through social media participation operate in tension with writers' deliberate shaping of their messages and their self-presentation.
Adaptive chaotic sampling particle filter to handle occlusion and fast motion in visual object tracking

We present a new particle filter method for visual object tracking to effectively handle occlusion and fast motion. The proposed approach uses a chaotic local search to model irregular motion and, compared to ordinary particle filter approaches, requires a lower number of particles. Furthermore, a new chaotic sampling procedure is used to force particles to specific areas with high values of the likelihood function with maximum diversity and a histogram of dynamical information is introduced to represent the motion over successive frames based on state space reconstruction. Finally, a new criterion is proposed to distinguish occlusion and out-of-view for appearance updating. We present numerical experiments demonstrating that the developed framework outperforms other state-of-the-art approaches dealing with irregular motions and uncertainties. According to the results on BOBOT, OTB100, OTB2013, and VOT2018, compared with traditional approaches, including methods based on deep and reinforcement learning, correlation filters, and Siamese neural networks, the proposed strategy leads to much closer convergence to the true target state, increasing the tracking accuracy. Finally, we prove analytically the convergence of the proposed method.
Facilitating hardware-aware neural architecture search with learning-based predictive models

Neural architecture search (NAS), which automatically explores the efficient model design, has achieved ground-breaking advances in recent years. To achieve the optimal model latency on deployment platforms, a performance tuning process is usually needed to select reasonable parameters and implementations for each neural network operator. As the tuning process is time-consuming, it is impractical for tuning each candidate architecture generated in the search procedure. Recent NAS systems usually utilize theoretical metrics or rule-based heuristics on-device latency to approximately estimate the model performance. Nevertheless, we discovered that there is still a gap between the estimated latency and the optimal latency, potentially causing a sub-optimal solution for neural architecture search. This paper presents an accurate and efficient approach for estimating the practical model latency on target platforms, which employs lightweight learning-based predictive models (LBPMs) to assist to obtain the realistic deployment-time model latency with acceptable run-time overhead, thereby facilitating hardware-aware neural architecture search. We propose an LBPM-based NAS framework, LBPM-NAS, and evaluate it by searching model architectures for ImageNet classification and facial landmark localization tasks on various hardware platforms. Experimental results show that the LBPM-NAS achieves up to 2.4× performance boost compared with the baselines under the same-level accuracy.
Robot 3D spatial motion measurement via vision-based method

Abstract Measuring the motion of a robot accurately is an important and integral part of evaluating the dynamic and static performance of the robot. The performance index of a robot, such as kinematic accuracy, bearing capacity, deformation, vibration, stability, and structural mode can all be calculated according to the motion displacement of the robot. Therefore, improving the robot motion measurement method, promoting the measurement accuracy, and enriching the measurement content have received considerable scholarly attention worldwide. In this paper, an approach based on binocular vision was proposed to measure the 3D spatial motion of a robot. In the process of reconstructing robot movement, a mathematical model that can facilitate the solving process and improve the accuracy of results was derived to build 3D coordinates information. A novel coordinate transformation method that is based on the singular value decomposition was drawn up to realize the transformation from camera coordinates to robot coordinates. Several experiments were carried out on the self-built three-degree-of-freedom rectangular coordinate robot platform. The marker was designed specially and glued to the end of robot, and a new train of thought was adopted to extract the marker’s feature point. The vision-based measurement results were compared with the actual coordinate value. Results of experiments demonstrated that the proposed method can successfully reconstruct the 3D spatial motion of a robot more exactly, which can meet the requirements of high-precision motion control, motion performance evaluation, and operation state evaluation.