Reviewerly/exHarmony huggingface dataset:
Normalized Gradient Descent for Variational Quantum Algorithms

Variational quantum algorithms (VQAs) are promising methods that leverage noisy quantum computers and classical computing techniques for practical applications. In VQAs, the classical optimizers such as gradient-based optimizers are utilized to adjust the parameters of the quantum circuit so that the objective function is minimized. However, they often suffer from the so-called vanishing gradient or barren plateau issue. On the other hand, the normalized gradient descent (NGD) method, which employs the normalized gradient vector to update the parameters, has been successfully utilized in several optimization problems. Here, we study the performance of the NGD methods in the optimization of VQAs for the first time. Our goal is two-fold. The first is to examine the effectiveness of NGD and its variants for overcoming the vanishing gradient problems. The second is to propose a new NGD that can attain the faster convergence than the ordinary NGD. We performed numerical simulations of these gradient-based optimizers in the context of quantum chemistry where VQAs are used to find the ground state of a given Hamiltonian. The results show the effective convergence property of the NGD methods in VQAs, compared to the relevant optimizers without normalization. Moreover, we make use of some normalized gradient vectors at the past iteration steps to propose the novel historical NGD that has a theoretical guarantee to accelerate the convergence speed, which is observed in the numerical experiments as well.
Nearest Neighbor Data Association Algorithm Based on Robust Kalman Filtering

The radar data association algorithm is one of the most difficult problems in the field of target tracking. Among them, it is easy to cause bug tracking when using the nearest neighbor data association (NNDA) algorithm. Combined with robust Kalman filtering and nearest neighbor ideas, this paper proposes the robust nearest neighbor data association (RNNDA) algorithm. This paper introduces the process of RNNDA. The simulation results show that the target tracking accuracy of RNNDA is high than NNDA. Moreover, the times of target bug tracking are reduced significantly.
Quantum scheduling optimization for UAV-enabled IoT networks

Quantum computing is a promising technology which is able to speed up computation, by several orders of magnitude, with respect to classical systems. In particular, quantum optimization can be employed to solve complex problems in many engineering fields. In this work, it is proposed to use Quantum Annealing (QA) to solve a binary optimization problem to derive an optimal scheduling plan in an Unmanned Aerial Vehicle (UAV)-enabled Internet of Things (IoT) network. Based on specific design criteria, specific problem formulation can be cast, thus providing real-time solutions.
Multipath Lightweight Deep Network Using Randomly Selected Dilated Convolution

Robot vision is an essential research field that enables machines to perform various tasks by classifying/detecting/segmenting objects as humans do. The classification accuracy of machine learning algorithms already exceeds that of a well-trained human, and the results are rather saturated. Hence, in recent years, many studies have been conducted in the direction of reducing the weight of the model and applying it to mobile devices. For this purpose, we propose a multipath lightweight deep network using randomly selected dilated convolutions. The proposed network consists of two sets of multipath networks (minimum 2, maximum 8), where the output feature maps of one path are concatenated with the input feature maps of the other path so that the features are reusable and abundant. We also replace the 3×3 standard convolution of each path with a randomly selected dilated convolution, which has the effect of increasing the receptive field. The proposed network lowers the number of floating point operations (FLOPs) and parameters by more than 50% and the classification error by 0.8% as compared to the state-of-the-art. We show that the proposed network is efficient.
TunBERT: Pretrained Contextualized Text Representation for Tunisian Dialect

Pre-trained models have accomplished high performances with the introduction of the Transformers like the Bidirectional Encoder Representations from Transformers known for BERT. Nevertheless, most of these proposed models have been trained on most represented languages (English, French, German, etc.) and few models target the under-represented languages and dialects. This work introduces a feasibility study of pre-training language models based on Transformers on Tunisian dialect as an under-represented languages. The Tunisian language model is evaluated on dialect identification task, sentiment analysis task, and reading comprehension question-answering task. Results demonstrate that, instead of using datasets from traditional sources (Wikipedia, articles, etc.), noisy web crawled data is more convenient for a under-represented language such as the Tunisian dialect. Additionally, experiments show that a reasonably small-scale dataset conducts to similar or better achievements as when using a large-scale dataset and that TunBERT model performances reach or enhance the state of the art in all three downstream tasks. The pre-trained model named TunBERT and the used datasets for the fine-tuning step are publicly released.
An improved Gaussian Mixture Method based Background Subtraction Model for Moving Object Detection in Outdoor Scene

Detection of moving objects has become an essential step in video surveillance applications. Due to lack of automatic thresholding and self-adaptive updating of background model at pixel level, foreground-background separation is not correctly classified. In this research work, we have proposed a novel approach to detect moving objects (like person and vehicle) from static scene using single stationary camera. Firstly, we used statistical background model Gaussian Mixture Model (GMM) to generate the binary mask. At this stage, we have tuned GMM parameters to update the background model pixel wise. Then, blob analysis connected components labeling and morphology operations have been applied as post processing step to detect foreground efficiently. The proposed work was experimented on two benchmark datasets PET 2006 and Highway. The performance of the proposed approach is analyzed by calculating precision, recall, f1-score and accuracy. Experimental results reveal that the proposed method performs well as compared to popular Gaussian based and Non-Gaussian based methods
A Reputation-Based Mechanism for Transaction Processing in Blockchain Systems

Blockchain protocols require nodes to verify all received transactions before forwarding them. However, massive spam transactions cause the participants in blockchain systems to consume many resources in verifying and propagating transactions. This paper proposes a reputation-based mechanism to increase the efficiency of processing transactions by considering the reputations of the sending nodes. Reputations are in turn adjusted based on the quality of transaction processing. Our proposed reputation-based mechanism offers three main contributions. First, we modify the verification strategy so that nodes set a probability of verifying a received transaction considering the likelihood of it being spam: transactions from a node with a low reputation have a high probability of being verified. Second, we optimize the transaction forwarding protocol to reduce propagation delay by prioritizing forwarding transactions to reputable receivers. Third, we design a data request protocol that provides alternative data exchange methods for nodes with different reputations. A series of simulations demonstrate the performance of our reputation-based mechanism.
Personalized Advertising Computational Techniques: A Systematic Literature Review, Findings, and a Design Framework

This work conducts a systematic literature review about the domain of personalized advertisement, and more specifically, about the techniques that are used for this purpose. State-of-the-art publications and techniques are presented in detail, and the relationship of this domain with other related domains such as artificial intelligence (AI), semantic web, etc., is investigated. Important issues such as (a) business data utilization in personalized advertisement models, (b) the cold start problem in the domain, (c) advertisement visualization issues, (d) psychological factors in the personalization models, (e) the lack of rich datasets, and (f) user privacy are highlighted and are pinpointed to help and inspire researchers for future work. Finally, a design framework for personalized advertisement systems has been designed based on these findings.
A spatiotemporal multi-feature extraction framework for opinion mining

With the rapid development of Internet technology and the explosive growth of digital text, opinion mining has become one of the important research hotspots in the field of natural language processing (NLP). In recent years, neural network based deep learning algorithms have been applied in the field of opinion mining. Considering the relation between temporal and spatial dimensions of text data and the characteristics of natural language itself, traditional deep learning algorithms cannot be comprehensive in the processing of fully feature extraction. In this paper, we propose a new deep learning framework for opinion mining, which includes a temporal feature extraction layer that consists of two layers of bidirectional simple recurrent unit (Bi-SRU) networks extracting features at the word and grammar levels; a semantic feature extraction layer that mainly contains a multi-head attention module; a spatial feature extraction layer with dilated convolution that is used to extract opinion preference features. The Internet movie database (IMDb) is used to verify the performance of the proposed framework. The experiment results show that the proposed framework can effectively improve the classification accuracy, whose performance is better than that of the compared algorithms.
Trust Me on This One: Conforming to Conversational Assistants

Conversational artificial agents and artificially intelligent (AI) voice assistants are becoming increasingly popular. Digital virtual assistants such as Siri, or conversational devices such as Amazon Echo or Google Home are permeating everyday life, and are designed to be more and more humanlike in their speech. This study investigates the effect this can have on one's conformity with an AI assistant. In the 1950s, Solomon Asch's already demonstrated the power and danger of conformity amongst people. In these classical experiments test persons were asked to answer relatively simple questions, whilst others pretending to be participants tried to convince the test person to give wrong answers. These studies were later replicated with embodied robots, but these physical robots are still rare. In light of our increasing reliance on AI assistants, this study investigates to what extent an individual will conform to a disembodied virtual assistant. We also investigate if there is a difference between a group that interacts with an assistant that communicates through text, one that has a robotic voice and one that has a humanlike voice. The assistant attempts to subtly influence participants' final responses in a general knowledge quiz, and we measure how often participants change their answer after having been given advice. Results show that participants conformed significantly more often to the assistant with a human voice than the one that communicated through text.
An Efficient Bayes Coding Algorithm for the Non-Stationary Source in Which Context Tree Model Varies from Interval to Interval

The context tree source is a source model in which the occurrence probability of symbols is determined from a finite past sequence, and is a broader class of sources that includes i.i.d. and Markov sources. This paper proposes a source model such that its subsequence is generated from a different context tree model. The Bayes code for such sources requires weighting of the posterior probability distributions for the change patterns of the context tree source and all possible context tree models. Therefore, the challenge is how to reduce this exponential order computational complexity. In this paper, we assume a special class of prior probability distribution of change patterns and context tree models, and propose an efficient Bayes coding algorithm whose computational complexity is the polynomial order. A full version of this paper is accessible at: https://arxiv.org/abs/2105.05163
Role of Shariah compliance on Cryptocurrency Acceptance among Malaysians: An Empirical Study

Cryptocurrency is the first successful application of blockchain technology that represents a breakthrough in the financial industry. Cryptocurrencies are decentralized currencies with no central issuer, allowing users to perform transactions, such as virtual payment for goods and services and fund transfer with low fees independently and effectively by utilizing blockchain platforms to ensure the legitimacy and validity of the transactions conducted. However, the acceptance rate of cryptocurrency globally and in Malaysia, is not at the desired level because of consumers' concerns such as Shariah compliance. Therefore, this paper attempts to examine user perception of the acceptance of the cryptocurrency system application from the Shariah perspectives. UTAUT is utilized in this paper, together with a Shariah compliance integration to formulate and evaluate various hypotheses that would establish the proposed conceptual model. The study used a sample of 496 persons. The hypothesis testing revealed that Performance Expectancy, Effort Expectancy, Social Influence, and Shariah compliance all affect behavioral intention to adopt cryptocurrency from a Shariah perspective in Malaysia.
Effective Rate of Minority Class Over-Sampling for Maximizing the Imbalanced Dataset Model Performance

Over-sampling is a resampling technique that has been designed to balance the imbalanced class distribution by duplicating samples of the minority class for a classification dataset. It is challenging to determine what rate of sample duplicating will be effective to maximize the model accuracy. In this research, we have proposed a method to determine an effective rate of the minority class over-sampling by which to maximize the performance of the machine learning model. We have used five over-sampling methods named Random over-sampling, SMOTE, SVMSMOTE, SMOTE Nominal, and Borderline SMOTE to evaluate the proposed method with five publicly available datasets. During the training period, we have over-sampled the minority class based on the majority class samples between the percentage ranges from 0 to 50%. Random Forest (RF) has been used as a machine learning classifier because its default hyperparameters already return great results. F1-score has been used as evaluation matrices because it is effective for imbalanced datasets. It has been seen that the proposed model has achieved a top f1-score when the minority class was over-sampled by 30–45% of the majority class samples.
Semantic Explanation of Interactive Dimensionality Reduction

Interactive dimensionality reduction helps analysts explore the high-dimensional data based on their personal needs and domain-specific problems. Recently, expressive nonlinear models are employed to support these tasks. However, the interpretation of these human-steered nonlinear models during human-in-the-loop analysis has not been explored. To address this problem, we present a new visual explanation design called semantic explanation. Semantic explanation visualizes model behaviors in a manner that is similar to users’ direct projection manipulations. This design conforms to the spatial analytic process and enables analysts better understand the updated model in response to their interactions. We propose a pipeline to empower interactive dimensionality reduction with semantic explanation using counterfactuals. Based on the pipeline, we implement a visual text analytics system with nonlinear dimensionality reduction powered by deep learning via the BERT model. We demonstrate the efficacy of semantic explanation with two case studies of academic article exploration and intelligence analysis.
The Prediction of Short-Term Bitcoin Dollar Rate (BTC/USDT) using Deep and Hybrid Deep Learning Techniques

Bitcoin as a digital cryptocurrency interests the scientists substantially in the areas of computer science, cryptography and economics. In this work, we propose to forecast the last price of Bitcoin Dollar rate in short-term or frequent trading transactions known as day-trading. In addition to statistical indicators such as maximum, minimum, and average prices, technical indicators such as Bollinger band (BB), hour-based moving average (MA), Relative Strength Index (RSI) are also evaluated as a feature set. In order to estimate the price of Bitcoin, different deep and hybrid deep learning methodologies are employed, namely convolutional neural networks (CNNs), long short-term memory networks (LSTMs), convolutional long short-term memory networks (ConvLSTM), CNN Long Short-Term Memory Network (CNN-LSTM). Extensive experiment results exhibit that the usage of ConvLSTM hybrid deep learning model is capable to estimate the price of Bitcoin with 2.4076 of MAPE result.
A multi-block clustering algorithm for high dimensional binarized sparse data

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods.
VisRuler: Visual analytics for extracting decision rules from bagged and boosted decision trees

Bagging and boosting are two popular ensemble methods in machine learning (ML) that produce many individual decision trees. Due to the inherent ensemble characteristic of these methods, they typically outperform single decision trees or other ML models in predictive performance. However, numerous decision paths are generated for each decision tree, increasing the overall complexity of the model and hindering its use in domains that require trustworthy and explainable decisions, such as finance, social care, and health care. Thus, the interpretability of bagging and boosting algorithms—such as random forest and adaptive boosting—reduces as the number of decisions rises. In this paper, we propose a visual analytics tool that aims to assist users in extracting decisions from such ML models via a thorough visual inspection workflow that includes selecting a set of robust and diverse models (originating from different ensemble learning algorithms), choosing important features according to their global contribution, and deciding which decisions are essential for global explanation (or locally, for specific cases). The outcome is a final decision based on the class agreement of several models and the explored manual decisions exported by users. We evaluated the applicability and effectiveness of VisRuler via a use case, a usage scenario, and a user study. The evaluation revealed that most users managed to successfully use our system to explore decision rules visually, performing the proposed tasks and answering the given questions in a satisfying way.
Location privacy protection of maritime mobile terminals

Mobile Edge Computing (MEC) can support various high-reliability and low-delay applications in Maritime Networks (MNs). However, security risks in computing task offloading exist. In this study, the location privacy leakage risk of Maritime Mobile Terminals (MMTs) is quantified during task offloading and relevant Location Privacy Protection (LPP) schemes of MMT are considered under two kinds of task offloading scenarios. In single-MMT and single-time offloading scenario, a dynamic cache and spatial cloaking-based LPP (DS-CLP) algorithm is proposed; and under the multi-MMTs and multi-time offloading scenario, a pseudonym and alterable silent period-based LPP (PA-SLP) strategy is proposed. Simulation results show that the DS-CLP can save the response time and communication cost compared with traditional algorithms while protecting the MMT location privacy. Meanwhile, extending the alterable silent period, increasing the number of MMTs in the maritime area or improving the pseudonym update probability can enhance the LPP effect of MMTs in PA-SLP. Furthermore, the study results can be effectively applied to MNs with poor communication environments and relatively insufficient computing resources.
SDVTracker: Real-Time Multi-Sensor Association and Tracking for Self-Driving Vehicles

Accurate motion state estimation of Vulnerable Road Users (VRUs), is a critical requirement for autonomous vehicles that navigate in urban environments. Due to their computational efficiency, many traditional autonomy systems perform multi-object tracking using Kalman Filters which frequently rely on hand-engineered association. However, such methods fail to generalize to crowded scenes and multi-sensor modalities, often resulting in poor state estimates which cascade to inaccurate predictions. We present a practical and lightweight tracking system, SDV-Tracker, that uses a deep learned model for association and state estimation in conjunction with an Interacting Multiple Model (IMM) filter. The proposed tracking method is fast, robust and generalizes across multiple sensor modalities and different VRU classes. In this paper, we detail a model that jointly optimizes both association and state estimation with a novel loss, an algorithm for determining ground-truth supervision, and a training procedure. We show this system significantly outperforms hand-engineered methods on a real-world urban driving dataset while running in less than 2.5 ms on CPU for a scene with 100 actors, making it suitable for self-driving applications where low latency and high accuracy is critical.
Dynamics Modeling of Spraying Robot using Lagrangian Method with Co-simulation Analysis

For the research on drag assist control of spraying robot, the dynamics model of the spraying robot is established using Lagrangian dynamics modeling method from the energy point of view for the spraying robot with 6-DOF. The Sim-scape dynamic model of the robot was established by simulation software, and the simulation analysis of the joint torque was carried out. The 3D robot model was imported into simulation software for motion simulation, which further verified the correctness of the dynamic torque model. Finally, the dynamic model was simplified and verified. The simulation results show that the modeling method is correct and effective, and it is an important theoretical basis for the follow-up research of drag assist control and the experiment of dynamics identification.