Reviewerly/exHarmony huggingface dataset:
Paley-like graphs over finite fields from vector spaces

Motivated by the well-known Paley graphs over finite fields and their generalizations, in this paper we explore a natural multiplicative-additive analogue of such graphs arising from vector spaces over finite fields. Namely, if n≥2 and U⊊Fqn is an Fq-vector space, GU is the (undirected) graph with vertex set V(GU)=Fqn and edge set E(GU)={(a,b)∈Fqn2|a≠b,ab∈U}. We describe the structure of an arbitrary maximal clique in GU and provide bounds on the clique number ω(GU) of GU. In particular, we compute the largest possible value of ω(GU) for arbitrary q and n. Moreover, we obtain the exact value of ω(GU) when U⊊Fqn is any Fq-vector space of dimension dU∈{1,2,n−1}.
Contextual anomaly detection for multivariate time series data

With the advancement of sensing technologies, sensor data collected over time have become more useful for detecting anomalies in underlying processes and systems. Sensor data are often affected by contextual variables, such as equipment settings, and can have different patterns, even in normal states depending on the contextual variables. Motivated by this problem, we propose a contextual anomaly detection method for multivariate time series data. We first build a prediction model using training data consisting of only normal observations, and then perform anomaly detection based on the prediction errors for future observations. The prediction model is based on a long short-term memory (LSTM) network that can flexibly model complex relationships between variables as well as temporal correlations between successive time points using the high expressive power of deep recurrent neural networks. In particular, to incorporate the contextual information while ensuring that it does not propagate over time but affects the response data only at specific target time points, we extend the standard LSTM by adding a layer for the contextual variables separately for each time step. The performance of the proposed method was verified with several open-source datasets and a real dataset from a global tire company.
Panoramic UAV Image Mosaic Method and Its Application in Pavement Paving Temperature Monitoring

The low-altitude technology of unmanned airborne infrared detection system is used to effectively monitor the temperature segregation in the paving stage and realize the temperature uniformity control of asphalt pavement construction. The image mosaic method can splice two images with overlapping areas together to form a panoramic image. In order to solve the problems of long time-consuming and low accuracy of aerial image mosaic algorithm, the low-temperature area of the whole pavement can be obtained quickly and accurately. In this paper, threshold segmentation technology is introduced to convert the image captured by the unmanned aerial vehicle (UAV) into a binary greyscale image so as to compensate for the mosaic error caused by temperature difference. In order to improve the efficiency and accuracy of splicing, a reference plate is used, which can provide enough feature points for splicing. Finally, the image mosaic method proposed in this paper can quickly obtain the image of the whole low-temperature area of the newly paved asphalt pavement, which has practical value and positive significance for the quality control of asphalt pavement.
Reinforcement Learning-Based Control of Single-Track Two-Wheeled Robots in Narrow Terrain

The single-track two-wheeled (STTW) robot has the advantages of small size and flexibility, and it is suitable for traveling in narrow terrains of mountains and jungles. In this article, a reinforcement learning control method for STTW robots is proposed for driving fast in narrow terrain with limited visibility and line-of-sight occlusions. The proposed control scheme integrates path planning, trajectory tracking, and balancing control in a single framework. Based on this method, the state, action, and reward function are defined for narrow terrain passing tasks. At the same time, we design the actor network and the critic network structures and use the twin delayed deep deterministic policy gradient (TD3) to train these neural networks to construct a controller. Next, a simulation platform is formulated to test the performances of the proposed control method. The simulation results show that the obtained controller allows the STTW robot to effectively pass the training terrain, as well as the four test terrains. In addition, this article conducts a simulation comparison to prove the advantages of the integrated framework over traditional methods and the effectiveness of the reward function.
Just Noticeable Difference Model for Images with Color Sensitivity

The just noticeable difference (JND) model reflects the visibility limitations of the human visual system (HVS), which plays an important role in perceptual image/video processing and is commonly applied to perceptual redundancy removal. However, existing JND models are usually constructed by treating the color components of three channels equally, and their estimation of the masking effect is inadequate. In this paper, we introduce visual saliency and color sensitivity modulation to improve the JND model. Firstly, we comprehensively combined contrast masking, pattern masking, and edge protection to estimate the masking effect. Then, the visual saliency of HVS was taken into account to adaptively modulate the masking effect. Finally, we built color sensitivity modulation according to the perceptual sensitivities of HVS, to adjust the sub-JND thresholds of Y, Cb, and Cr components. Thus, the color-sensitivity-based JND model (CSJND) was constructed. Extensive experiments and subjective tests were conducted to verify the effectiveness of the CSJND model. We found that consistency between the CSJND model and HVS was better than existing state-of-the-art JND models.
Shared Language: Linguistic Similarity in an Algebra Discussion Forum

Academic discourse communities and learning circles are characterized by collaboration, sharing commonalities in terms of social interactions and language. The discourse of these communities is composed of jargon, common terminologies, and similarities in how they construe and communicate meaning. This study examines the extent to which discourse reveals “shared language” among its participants that can promote inclusion or affinity. Shared language is characterized in terms of linguistic features and lexical, syntactical, and semantic similarities. We leverage a multi-method approach, including (1) feature engineering using state-of-the-art natural language processing techniques to select the most appropriate features, (2) the bag-of-words classification model to predict linguistic similarity, (3) explainable AI using the local interpretable model-agnostic explanations to explain the model, and (4) a two-step cluster analysis to extract innate groupings between linguistic similarity and emotion. We found that linguistic similarity within and between the threaded discussions was significantly varied, revealing the dynamic and unconstrained nature of the discourse. Further, word choice moderately predicted linguistic similarity between posts within threaded discussions (accuracy = 0.73; F1-score = 0.67), revealing that discourse participants’ lexical choices effectively discriminate between posts in terms of similarity. Lastly, cluster analysis reveals profiles that are distinctly characterized in terms of linguistic similarity, trust, and affect. Our findings demonstrate the potential role of linguistic similarity in supporting social cohesion and affinity within online discourse communities.
Data Protection and Multi-Database Data-Driven Models

Anonymization and data masking have effects on data-driven models. Different anonymization methods have been developed to provide a good trade-off between privacy guarantees and data utility. Nevertheless, the effects of data protection (e.g., data microaggregation and noise addition) on data integration and on data-driven models (e.g., machine learning models) built from these data are not known. In this paper, we study how data protection affects data integration, and the corresponding effects on the results of machine learning models built from the outcome of the data integration process. The experimental results show that the levels of protection that prevent proper database integration do not affect machine learning models that learn from the integrated database to the same degree. Concretely, our preliminary analysis and experiments show that data protection techniques have a lower level of impact on data integration than on machine learning models.
A secure and privacy-preserving word vector training scheme based on functional encryption with inner-product predicates

With the extensive applications of machine learning, it has been witnessed that machine learning has been applied in various fields such as e-commerce, mobile data processing, health analytics and behavioral analytics etc. Word vector training is usually deployed in machine learning to provide a model architecture and optimization, for example, to learn word embeddings from a large amount of datasets. Training word vector in machine learning needs a lot of datasets to train and then outputs a model, however, some of which might contain private and sensitive information, and the training phase will lead to the exposure of the trained model and user datasets. In order to offer utilizable, plausible, and personalized alternatives to users, this process usually also entails a breach of their privacy. For instance, the user data might contain of face,irirs and personal identities etc. This will release serious problem in the machine learning. In this article, we investigate the problem of training high-quality word vectors on encrypted datasets by using privacy-preserving learning algorithms. Firstly, we use a pseudo-random function to generate a statistical token for each word to help build the vocabulary of the word vector. Then we employ functional inner-product encryption to calculate the activation function to obtain the inner product, securely. Finally, we use BGN cryptosystem to encrypt and hide the sensitive datasets, and complete the homomorphic operation over the ciphertexts to perform the training procedure. In order to implement the privacy preservation of word vector training, we propose four privacy-preserving machine learning schemes to provide the privacy protection in our scheme. We analyze the security and efficiency of our protocols and give the numerical experiments. Compared with the existing solutions, it indicates that our scheme can provide a higher efficiency and less communication overhead.
DAG-Pathwidth: Graph Algorithmic Analyses of DAG-Type Blockchain Networks

This paper analyzes a blockchain network forming a directed acyclic graph (DAG), called a DAG-type blockchain, from the viewpoint of graph algorithm theory. To use a DAG-type blockchain, NP-hard graph optimization problems on the DAG are required to be solved. Although various problems for undirected and directed graphs can be efficiently solved by using the notions of graph parameters, these currently known parameters are meaningless for DAGs, which implies that it is hopeless to design efficient algorithms based on the parameters for such problems. In this work, we propose a novel graph parameter for directed graphs called a DAG-pathwidth, which represents the closeness to a directed path. This is an extension of the pathwidth, a well-known graph parameter for undirected graphs. We analyze the features of the DAG-pathwidth and prove that computing the DAG-pathwidth of a DAG (directed graph in general) is NP-complete. Finally, we propose an efficient algorithm for a variant of the maximum k-independent set problem for the DAG-type blockchain when the DAG-pathwidth of the input graph is small.
Data-Driven Hyperparameter Optimized Extreme Gradient Boosting Machine Learning Model for Solar Radiation Forecasting

The uncertainty of the non-conventional sources especially solar energy caused due to spatiotemporal factors like temperature, pressure, relative humidity etc.is continuously disrupting the productivity and reliability of an integrated power system which motivates the researcher or energy industry for strategic forecasting solutions to enhance the proper scheduling and control of solar generation power plants.Several studies have been carried out; but still the objective of achieving accurate forecasting dependent on the spatio-temporal features is not achieved.To address this critical forecasting issue in this research article a hyper parametric tuning of the Extreme Gradient Boosting (XGB) machine learning model has been carried out using two met heuristic algorithms: Moth Flame Optimization (MFO) and Grey Wolf Optimization (GWO).The dataset comprises five years of metrological attributes collected from the National Renewable Energy Laboratory (NREL) for analysis.The validation of the proposed model has been done based on the five statistical errors: Max Error (ME), Mean Absolute Error (MAE), Coefficient of Determination (R 2 ), Mean Square Error (MSE) and Root Mean Square Error (RMSE).The regressive assessment of all three models has confirmed that the XGB-MFO model outperformed the others as showing the highest R 2 score of 0.9337, 0.9011, 0.8744 and lowest RMSE values of 76.29 W•m -2 , 41.90W•m -2 and 95.94W•m -2 for Global Horizontal Irradiance (GHI), Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) respectively which ensures the proposed model implementation for the prediction and production of solar power.
MOEA/D with Constraint Objectivization and Adaptive Weight Adjustment for Constrained Optimization

In this paper, MOEA/D is extended to constrained optimization by making the constraints an objective function. An adaptive adjustment method is proposed to introduce a parameter for varying weights. The parameter for varying the weights is given in such a way that the bias of the search towards the feasible and infeasible regions can be adjusted. The parameter is tuned based on two guidelines to properly utilize infeasible solutions. The first is actively utilizing infeasible solutions with large constraint violations and encouraging global search including the infeasible regions. The second is actively utilizing infeasible solutions with small constraint violations and encouraging a search on the boundary of the feasible regions. This is expected to improve the global optimization performance to the feasible regions, which is a non-convex set, and the convergence performance to feasible solutions. We verify the usefulness of the proposed method for problems where the feasible regions are a convex set and a nonconvex set.
Authors’ Reply to ‘Comments on “Pose localization of leader-follower networks with direction measurements” [Automatica 120 (2020) 109125]’

We study modeling and identification of stationary processes with a spectral density matrix of low rank. Equivalently, we consider processes having an innovation of reduced dimension for which Prediction Error Methods (PEM) algorithms are not directly applicable. We show that these processes admit a special feedback structure with a deterministic feedback channel which can be used to split the identification in two steps, one of which can be based on standard algorithms while the other is based on a deterministic least squares fit. Identifiability of the feedback system is analyzed and a unique identifiable structure is characterized. Simulations show that the proposed procedure works well in some simple examples.
Cybercrime and its Negative Effects on Youth’s Development, the Economy and Nigeria

Cyberspace has recently been increasingly significant in the quest for efficient and effective quality service delivery across a wide range of industries, including personal services, office work, commercial, banking, financial, and entertainment. However, the ever-daily increasing development of internet technology in all human endeavours nowadays has presented new heightened computer crimes around the world including Nigeria. The frequency with which Nigerian youths get involved in this crime leaves much to be desired, as it is said to have negative effects on the future of the youth’s development, the economy and Nigeria as a nation. The primary argument presented here is to examine cybercrime and its negative effects on youth development, the economy and Nigeria as a nation. The study found that lack of employment, poverty, corruption and bad government are the major reasons youths in Nigeria engage in cyber criminalities. The paper would offer plausible suggestions on what the stakeholders and government should do to checkmate the cybercrime activities and restore the image and glory of Nigeria in the comity of nations.
Cybercrime Consciousness Among Undergraduate Students

The growing rate of cyberspace crimes in nations across the world and Nigeria in particular calls for concern. Adolescents who are typically students and more frequent on the internet constitute the most victims of cybercrimes. Lack of cybercrime consciousness has been attributed to the increasing rate of cybercrimes in Nigeria. This study assessed the level of undergraduate students’ cybercrime consciousness, as well as the difference between the level of cybercrime consciousness according to gender. Data were gathered from a sample of 171 undergraduate students of Afe Babalola University, Ado-Ekiti, Nigeria, through a survey questionnaire, and were analysed using descriptive and independent sample t-test statistics. The study found that the extent of cybercrime consciousness amongst undergraduate students is generally low. Also, the study found a significant difference on the extent of cybercrime consciousness among female and male students. It is recommended that more efforts be geared towards campaigns on the pervasiveness of cybercrimes to engender more awareness and reduce the incidence of cyberspace criminalities.
Estimating Fock-state linear optics evolution using coherent states

This paper presents two methods for simulating the interference of bosonic Fock states through linear interferometers using coherent states. The first method repeats the interferometer, injects coherent states in particular modes, and uses symmetric combinations of the outputs to reconstruct the state amplitudes of the Fock-state interference. The second method constructs a new interferometer that can be probed with coherent states on individual inputs to extract the required state amplitudes. The two approaches here show explicitly where the classical computational difficultly arises. In the first approach, the computational hardness is in the measurement post-processing, and in the second approach, it is within the construction of the required state evolution.
COVAD: Content-oriented video anomaly detection using a self attention-based deep learning model

Video anomaly detection has always been a hot topic and attracting an increasing amount of attention. Much of the existing methods on video anomaly detection depend on processing the entire video rather than considering only the significant context. This paper proposes a novel video anomaly detection method named COVAD, which mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an auto-encoded convolutional neural network and coordinated attention mechanism, which can effectively capture meaningful objects in the video and dependencies between different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can more effectively predict the future motion and appearance of objects in the video. Our proposed algorithm obtained better experimental results on multiple data sets and outperformed the baseline models considered in our analysis. At the same time we improve a visual test that can provide pixel-level anomaly explanations.
Trustworthy sealed-bid auction with low communication cost atop blockchain

Auction, widely used by firms and governments, has facilitated trillions of dollars in the world economy. However, when price is the only factor determining the winner, how to guarantee the trustworthiness of the auction outcomes, and at the same time preserve the privacy of losing bidders is a challenging issue. In this paper, to address this issue, we present a sealed-bid auction protocol with privacy preserving and public verifiability atop blockchain. No bidder is able to repudiate or change any bidding to outbid others and all public information comes with proof that even observers are able to validate the auction. Proofs post along the way to validate biddings and identify the winner are in the form of group elements with communication complexity O(log⁡(nm)), n,m denoting the upper bound bit length of the bidding price and the number of bidders respectively. The logarithmic complexity significantly reduces the communication cost and makes it practical for large-scale auction. Moreover, our protocol can be easily extended to any type of ordinal-price sealed-bid auction such as second-price with privacy preserving. We provide detailed security proofs or analysis of the required security goals. The evaluation of time cost in off-chain operations and gas cost in on-chain functions demonstrate the practicality of our protocol in real-world applications.
Generation of spin squeezing via a fully quantum degenerate parametric amplifier

Spin squeezing is one of the most attractive methods for realizing high-precision metrology. In this paper, we propose a protocol for generating spin squeezing in an atomic ensemble via a fully quantum degenerate parametric amplifier. We discuss the properties of generating spin squeezing with and without driving the pump cavity. Numerical simulation results show that the generated spin squeezing strength is sizable, and is able to be comparable to that obtained using a two-axis twisting (TAT) model. Moreover, we demonstrate that the protocol is experimentally feasible by introducing the corresponding experimental parameters. Therefore, the proposed protocol provides a promising approach to realize spin squeezing in photon-spin coupling systems.
Deep VULMAN: A deep reinforcement learning-enabled cyber vulnerability management framework

Cyber vulnerability management is a critical function of a cybersecurity operations center (CSOC) that helps protect organizations against cyber-attacks on their computer and network systems. Adversaries hold an asymmetric advantage over the CSOC, as the number of deficiencies in these systems is increasing at a significantly higher rate compared to the expansion rate of the security teams to mitigate them. The current approaches in cyber vulnerability management are deterministic and one-time decision-making methods, which do not consider future uncertainties when prioritizing and selecting vulnerabilities for mitigation. These approaches are also constrained by the sub-optimal distribution of resources, providing no flexibility to adjust their response to fluctuations in vulnerability arrivals. We propose a novel framework, Deep VULMAN, consisting of a deep reinforcement learning agent and an integer programming method to fill this gap in cyber vulnerability management process. Our sequential decision-making framework, first, determines the near-optimal amount of resources to be allocated for mitigation under uncertainty for a given system state, and then determines the optimal set of prioritized vulnerability instances for mitigation. Results show that our framework outperforms the current methods in prioritizing the selection of important organization-specific vulnerabilities, on both simulated and real-world vulnerability data, observed over a one-year period.