Reviewerly/exHarmony huggingface dataset:
Formalizing Trust in Artificial Intelligence

Trust is a central component of the interaction between people and AI, in that 'incorrect' levels of trust may cause misuse, abuse or disuse of the technology. But what, precisely, is the nature of trust in AI? What are the prerequisites and goals of the cognitive mechanism of trust, and how can we promote them, or assess whether they are being satisfied in a given interaction? This work aims to answer these questions. We discuss a model of trust inspired by, but not identical to, interpersonal trust (i.e., trust between people) as defined by sociologists. This model rests on two key properties: the vulnerability of the user; and the ability to anticipate the impact of the AI model's decisions. We incorporate a formalization of 'contractual trust', such that trust between a user and an AI model is trust that some implicit or explicit contract will hold, and a formalization of 'trustworthiness' (that detaches from the notion of trustworthiness in sociology), and with it concepts of 'warranted' and 'unwarranted' trust. We present the possible causes of warranted trust as intrinsic reasoning and extrinsic behavior, and discuss how to design trustworthy AI, how to evaluate whether trust has manifested, and whether it is warranted. Finally, we elucidate the connection between trust and XAI using our formalization.
Multi-animal pose estimation, identification and tracking with DeepLabCut

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking-features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal's identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.
Optimal ratio for data splitting

Abstract It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article, we show that the optimal training/testing splitting ratio is , where is the number of parameters in a linear regression model that explains the data well.
RNN-Stega: Linguistic Steganography Based on Recurrent Neural Networks

Linguistic steganography based on text carrier auto-generation technology is a current topic with great promise and challenges. Limited by the text automatic generation technology or the corresponding text coding methods, the quality of the steganographic text generated by previous methods is inferior, which makes its imperceptibility unsatisfactory. In this paper, we propose a linguistic steganography based on recurrent neural networks, which can automatically generate high-quality text covers on the basis of a secret bitstream that needs to be hidden. We trained our model with a large number of artificially generated samples and obtained a good estimate of the statistical language model. In the text generation process, we propose fixed-length coding and variable-length coding to encode words based on their conditional probability distribution. We designed several experiments to test the proposed model from the perspectives of information hiding efficiency, information imperceptibility, and information hidden capacity. The experimental results show that the proposed model outperforms all the previous related methods and achieves the state-of-the-art performance.
Novel mutation strategy for enhancing SHADE and LSHADE algorithms for global numerical optimization

Proposing new mutation strategies to improve the optimization performance of differential evolution (DE) is an important research study. Therefore, the main contribution of this paper goes in three directions: The first direction is introducing a less greedy mutation strategy with enhanced exploration capability, named DE/current-to-ord_best/1 (ord stands for ordered) or ord_best for short. In the second direction, we introduce a more greedy mutation strategy with enhanced exploitation capability, named DE/current-to-ord_pbest/1 (ord_pbest for short). Both of the proposed mutation strategies are based on ordering three selected vectors from the current generation to perturb the target vector, where the directed differences are used to mimic the gradient decent behavior to direct the search toward better solutions. In ord_best, the three vectors are selected randomly to enhance the exploration capability of the algorithm. On the other hand, ord_pbest is designed to enhance the exploitation capability where two vectors are selected randomly and the third is selected from the global p best vectors. Based on the proposed mutation strategies, ord_best and ord_pbest, two DE variants are introduced as EDE and EBDE, respectively. The third direction of our work is a hybridization framework. The proposed mutations can be combined with DE family algorithms to enhance their search capabilities on difficult and complicated optimization problems. Thus, the proposed mutations are incorporated into SHADE and LSHADE to enhance their performance. Finally, in order to verify and analyze the performance of the proposed mutation strategies, numerical experiments were conducted using CEC2013 and CEC2017 benchmarks. The performance was also evaluated using CEC2010 designed for Large-Scale Global Optimization. Experimental results indicate that in terms of robustness, stability, and quality of the solution obtained, both mutation strategies are highly competitive, especially as the dimension increases.
PhaseNet: A Deep Convolutional Neural Network for Two-Dimensional Phase Unwrapping

Phase unwrapping is a crucial signal processing problem in several applications that aims to restore original phase from the wrapped phase. In this letter, we propose a novel framework for unwrapping the phase using deep fully convolutional neural network termed as PhaseNet. We reformulate the problem definition of directly obtaining continuous original phase as obtaining the wrap-count (integer jump of 2 π) at each pixel by semantic segmentation and this is accomplished through a suitable deep learning framework. The proposed architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The relationship between the absolute phase and the wrap-count is leveraged in generating abundant simulated data of several random shapes. This deliberates the network on learning continuity in wrapped phase maps rather than specific patterns in the training data. We compare the proposed framework with the widely adapted quality-guided phase unwrapping algorithm and also with the well-known MATLAB's unwrap function for varying noise levels. The proposed framework is found to be robust to noise and computationally fast. The results obtained highlight that deep convolutional neural network can indeed be effectively applied for phase unwrapping, and the proposed framework will hopefully pave the way for the development of a new set of deep learning based phase unwrapping methods.
How enhancing information and communication technology has affected inequality in Africa for sustainable development: An empirical investigation

Abstract This study examines if enhancing information and communication technology reduces inequality in 48 countries in Africa for the periods 2004–2014. Three inequality indictors are used, namely, the Gini coefficient, Atkinson index, and Palma ratio. The adopted information and communication technology indicators include mobile phone penetration, internet penetration, and fixed broadband subscriptions. The empirical evidence is based on the generalised method of moments. Enhancing internet penetration and fixed broadband subscriptions has a net effect on reducing the Gini coefficient and the Atkinson index, whereas increasing mobile phone penetration and internet penetration reduces the Palma ratio. Policy implications are discussed in the light of challenges to Sustainable Development Goals.
Face Recognition Systems Under Morphing Attacks: A Survey

Recently, researchers found that the intended generalizability of (deep) face recognition systems increases their vulnerability against attacks. In particular, the attacks based on morphed face images pose a severe security risk to face recognition systems. In the last few years, the topic of (face) image morphing and automated morphing attack detection has sparked the interest of several research laboratories working in the field of biometrics and many different approaches have been published. In this paper, a conceptual categorization and metrics for an evaluation of such methods are presented, followed by a comprehensive survey of relevant publications. In addition, technical considerations and tradeoffs of the surveyed methods are discussed along with open issues and challenges in the field.
Cryptocurrency market contagion: Market uncertainty, market complexity, and dynamic portfolios

In this study, we employ a TVP-FAVAR connectedness approach in order to investigate the transmission mechanism in the cryptocurrency market. To this end, we concentrate on the top 9 cryptocurrencies by virtue of market capitalization and one market factor – based on 45 additional digital currencies – capturing the co-movements in the cryptocurrency market. The period of study spans between August 7, 2015 and May 31, 2018. We find that the dynamic total connectedness across several cryptocurrencies exhibits large dynamic variability ranging between 25% and 75%. In particular, periods of high (low) market uncertainty correspond to strong (weak) connectedness. We show that these results could be explained on the basis of increased market uncertainty that is associated with periods of highly volatile prices. In addition, despite the fact that Bitcoin still influences the cryptocurrency market substantially, we note that, recently, Ethereum has become the number one net transmitting cryptocurrency. We further note that the market gradually becomes more complex considering our connectedness approach and that this might be attributed to the unique characteristics and possibilities inherent in the technology of each cryptocurrency. A simplified application concentrating on bivariate portfolios is indicative of potential hedging opportunities using dynamic hedge ratios and dynamic portfolio weights.
Novel Medical Image Encryption Scheme Based on Chaos and DNA Encoding

In this paper, we propose a new chaos-based encryption scheme for medical images. It is based on a combination of chaos and DNA computing under the scenario of two encryption rounds, preceded by a key generation layer, and follows the permutation-substitution-diffusion structure. The SHA-256 hash function alongside the initial secret keys is employed to produce the secret keys of the chaotic systems. Each round of the proposed algorithm involves six steps, i.e., block-based permutation, pixel-based substitution, DNA encoding, bit-level substitution (i.e., DNA complementing), DNA decoding, and bit-level diffusion. A thorough search of the relevant literature yielded only this time the pixel-based substitution and the bit-level substitution are used in cascade for image encryption. The key-streams in the bit-level substitution are based on the logistic-Chebyshev map, while the sine-Chebyshev map allows producing the key-streams in the bit-level diffusion. The final encrypted image is obtained by repeating once the previous steps using new secret keys. Security analyses and computer simulations both confirm that the proposed scheme is robust enough against all kinds of attacks. Its low complexity indicates its high potential for real-time and secure image applications.
PCC Net: Perspective Crowd Counting via Spatial Convolutional Network

Crowd counting from a single image is a challenging task due to high appearance similarity, perspective changes, and severe congestion. Many methods only focus on the local appearance features and they cannot handle the aforementioned challenges. In order to tackle them, we propose a perspective crowd counting network (PCC Net), which consists of three parts: 1) density map estimation (DME) focuses on learning very local features of density map estimation; 2) random high-level density classification (R-HDC) extracts global features to predict the coarse density labels of random patches in images; and 3) fore-/background segmentation (FBS) encodes mid-level features to segments the foreground and background. Besides, the Down, Up, Left, and Right (DULR) module is embedded in PCC Net to encode the perspective changes on four directions (DULR). The proposed PCC Net is verified on five mainstream datasets, which achieves the state-of-the-art performance on the one and attains the competitive results on the other four datasets. The source code is available at https://github.com/gjy3035/PCC-Net.
Deer Hunting Optimization Algorithm: A New Nature-Inspired Meta-heuristic Paradigm

This paper proposes a novel meta-heuristic algorithm, named DHOA, which is inspired by the hunting behavior of humans toward deer. Even though the activities of the hunters differ, the way of attacking the buck/deer is based on the hunting strategy they develop. The hunting strategy depends on the movement of two hunters in their best positions, termed as leader and successor. Accordingly, each hunter updates his position until they reach the buck. The experimental results reveal that the proposed DHOA provides competitive results when compared with the state-of-the-art optimization algorithms, such as GWO, WOA, FF, PSO, etc. The experimentation is carried out with 39 benchmark functions and 3 engineering applications. Moreover, a specific application is exploited by integrating NN in DHOA (DHOA-NN), to show the efficiency of the proposed algorithm in the classification. The proposed algorithm experimented in real-time engineering applications and the performance comparison with the existing optimization algorithms proves the superiority of the DHOA algorithm.
Arabic text classification using deep learning models

Text classification or categorization is the process of automatically tagging a textual document with most relevant labels or categories. When the number of labels is restricted to one, the task becomes single-label text categorization. However, the multi-label version is challenging. For Arabic language, both tasks (especially the latter one) become more challenging in the absence of large and free Arabic rich and rational datasets. Therefore, we introduce new rich and unbiased datasets for both the single-label (SANAD) as well as the multi-label (NADiA) Arabic text categorization tasks. Both corpora are made freely available to the research community on Arabic computational linguistics. Further, we present an extensive comparison of several deep learning (DL) models for Arabic text categorization in order to evaluate the effectiveness of such models on SANAD and NADiA. A unique characteristic of our proposed work, when compared to existing ones, is that it does not require a pre-processing phase and fully based on deep learning models. Besides, we studied the impact of utilizing word2vec embedding models to improve the performance of the classification tasks. Our experimental results showed solid performance of all models on SANAD corpus with a minimum accuracy of 91.18%, achieved by convolutional-GRU, and top performance of 96.94%, achieved by attention-GRU. As for NADiA, attention-GRU achieved the highest overall accuracy of 88.68% for a maximum subsets of 10 categories on “Masrawy” dataset.
Sonic

Ever since their introduction, zero-knowledge proofs have become an important tool for addressing privacy and scalability concerns in a variety of applications. In many systems each client downloads and verifies every new proof, and so proofs must be small and cheap to verify. The most practical schemes require either a trusted setup, as in (pre-processing) zk-SNARKs, or verification complexity that scales linearly with the complexity of the relation, as in Bulletproofs. The structured reference strings required by most zk-SNARK schemes can be constructed with multi-party computation protocols, but the resulting parameters are specific to an individual relation. Groth et al. discovered a zk-SNARK protocol with a universal structured reference string that is also updatable, but the string scales quadratically in the size of the supported relations.
Selective Sparse Sampling for Fine-Grained Image Recognition

Fine-grained recognition poses the unique challenge of capturing subtle inter-class differences under considerable intra-class variances (e.g., beaks for bird species). Conventional approaches crop local regions and learn detailed representation from those regions, but suffer from the fixed number of parts and missing of surrounding context. In this paper, we propose a simple yet effective framework, called Selective Sparse Sampling, to capture diverse and fine-grained details. The framework is implemented using Convolutional Neural Networks, referred to as Selective Sparse Sampling Networks (S3Ns). With image-level supervision, S3Ns collect peaks, i.e., local maximums, from class response maps to estimate informative, receptive fields and learn a set of sparse attention for capturing fine-detailed visual evidence as well as preserving context. The evidence is selectively sampled to extract discriminative and complementary features, which significantly enrich the learned representation and guide the network to discover more subtle cues. Extensive experiments and ablation studies show that the proposed method consistently outperforms the state-of-the-art methods on challenging benchmarks including CUB-200-2011, FGVC-Aircraft, and Stanford Cars.
Design and implementation of a simple dynamical 4-D chaotic circuit with applications in image encryption

We present a simple yet highly dimensional hybrid diode bridge circuit network that can exhibit complex chaotic behaviours. Further, since our network is characterised by smooth fourth-order exponential nonlinearity, we employ a distinctive approach to assess its different properties: we examine the circuit stability near fixed points. Specifically, we evaluate dynamic complexity using the Lyaponov spectrum analysis, bifurcation analysis and phase space trajectories; additionally, we assess coexisting attractors in the parameter space using numerical and experimental analysis. Furthermore, we report assessments of our network in terms of remerging Feigenbaum trees and metastable chaos. Finally, these properties, and especially the chaotic series of the coexisting attractors, were exploited to implement a chaos-based image encryption protocol using S-box construction and PRNG generation mechanisms. To validate the performance of our protocol, we employed standard security analysis including correlation coefficient, pixel change rate, information entropy, time complexity and key space analysis whose outcome were compared alongside available state-of-the-art methods. Outcomes suggest promising applications for our chaotic circuit network in image encryption.
A better balance in metaheuristic algorithms: Does it exist?

The constant development of new metaheuristic algorithms has led to a saturation in the field of stochastic search. There are now hundreds of different algorithms that can be used to solve any problem. To produce a good performance, every metaheuristic method needs to address a satisfactory equilibrium between exploration and exploitation of the search space. Although exploration and exploitation represent two fundamental concepts in metaheuristics, the main questions about their combination and balance have not been yet completely understood. Most of the existent analyzes conducted on metaheuristic techniques consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance. This paper presents an experimental analysis that quantitatively evaluates the balance between exploration and exploitation of several of the most important and better-known metaheuristic algorithms. In the study, a dimension-wise diversity measurement is used to assess the balance of each scheme considering a representative set of 42 benchmark problems that involve multimodal, unimodal, composite and shifted functions. As a result, the analysis provides several observations that allow understanding how this balance affects the results in each type of functions, and which balance is producing better solutions.
Deep Unfolding for Communications Systems: A Survey and Some New Directions

Deep unfolding is a method of growing popularity that fuses iterative optimization algorithms with tools from neural networks to efficiently solve a range of tasks in machine learning, signal and image processing, and communication systems. This survey summarizes the principle of deep unfolding and discusses its recent use for communication systems with focus on detection and precoding in multi-antenna (MIMO) wireless systems and belief propagation decoding of error-correcting codes. To showcase the efficacy and generality of deep unfolding, we describe a range of other tasks relevant to communication systems that can be solved using this emerging paradigm. We conclude the survey by outlining a list of open research problems and future research directions.
The Influence of the Activation Function in a Convolution Neural Network Model of Facial Expression Recognition

The convolutional neural network (CNN) has been widely used in image recognition field due to its good performance. This paper proposes a facial expression recognition method based on the CNN model. Regarding the complexity of the hierarchic structure of the CNN model, the activation function is its core, because the nonlinear ability of the activation function really makes the deep neural network have authentic artificial intelligence. Among common activation functions, the ReLu function is one of the best of them, but it also has some shortcomings. Since the derivative of the ReLu function is always zero when the input value is negative, it is likely to appear as the phenomenon of neuronal necrosis. In order to solve the above problem, the influence of the activation function in the CNN model is studied in this paper. According to the design principle of the activation function in CNN model, a new piecewise activation function is proposed. Five common activation functions (i.e., sigmoid, tanh, ReLu, leaky ReLus and softplus–ReLu, plus the new activation function) have been analysed and compared in facial expression recognition tasks based on the Keras framework. The Experimental results on two public facial expression databases (i.e., JAFFE and FER2013) show that the convolutional neural network based on the improved activation function has a better performance than most-of-the-art activation functions.