Reviewerly/exHarmony huggingface dataset:
UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.
AI World Cup: Robot-Soccer-Based Competitions

Games have been used as excellent testbeds for research on artificial intelligence (AI) and computational intelligence for their diversity and complexity. In this article, we present AI World Cup, a set of AI competitions based on the game of soccer. We provide an introduction to the three challenges that concern a robot soccer match using both value-based and image-based state representations. AI Soccer runs the robot soccer match by participants managing each team of five two-wheeled robots. AI Commentator and AI Reporter observe the AI Soccer match and output real-time commentary and a summary article, respectively. Also, we introduce the AI World Cup platform along with rationale behind notable design choices. The official international AI World Cups held in 2018 and 2019 and the AI Masters competition held in 2019 as a part of the World Cyber Games are briefly discussed. Technical aspects of the strategies developed by participants are also discussed.
Phoneme-Unit-Specific Time-Delay Neural Network for Speaker Verification

Variations of speech content increase the difficulty of speaker verification. In this paper, to alleviate the negative effect of the variations, phoneme-unit-specific time-delay neural network (PUSTDNN) is proposed and applied to the state-of-the-art x-vector system. It models each phoneme unit with an individual time-delay neural network (TDNN). That is to say, each TDNN mainly deals with a phoneme unit. Compared with handling all phoneme units together, when handling a phoneme unit, a TDNN can extract more discriminative speaker information, thus improving the system performance. Two realizations of the PUSTDNN are proposed. The first one can retain speech temporal information. The second one further combines all the TDNNs in a PUSTDNN into a larger TDNN to reduce computational complexity. To avoid model overfitting, the phoneme units are obtained by clustering phonemes based on the phonetic knowledge and phonetic sparsity degree. The PUSTDNN is also compared with two other techniques, i.e., phonetic vector and multitask. Experiments on the Fisher, NIST SRE10, and VoxCeleb datasets show that the phonetic vector technique is most robust to the phoneme unit recognition accuracy. When the accuracy is high enough, the multitask performs better than the phonetic vector, and the PUSTDNN performs best and can achieve over 10% relative improvement compared with the x-vector baseline.
A Comparative Evaluation of Traditional Machine Learning and Deep Learning Classification Techniques for Sentiment Analysis

With the technological advancement in the field of digital transformation, the use of the internet and social media has increased immensely. Many people use these platforms to share their views, opinions and experiences. Analyzing such information is significant for any organization as it apprises the organization to understand the need of their customers. Sentiment analysis is an intelligible way to interpret the emotions from the textual information and it helps to determine whether that emotion is positive or negative. This paper outlines the data cleaning and data preparation process for sentiment analysis and presents experimental findings that demonstrates the comparative performance analysis of various classification algorithms. In this context, we have analyzed various machine learning techniques (Support Vector Machine, and Multinomial Naive Bayes) and deep learning techniques (Bidirectional Encoder Representations from Transformers, and Long Short-Term Memory) for sentiment analysis.
XGBXSS: An Extreme Gradient Boosting Detection Framework for Cross-Site Scripting Attacks Based on Hybrid Feature Selection Approach and Parameters Optimization

With the widespread popularity of the Internet and the transformation of the world into a global village, Web applications have been drawn increased attention over the years by companies, organizations, and social media, making it a prime target for cyber-attacks. The cross-site scripting attack (XSS) is one of the most severe concerns, which has been highlighted in the forefront of information security experts' reports. In this study, we proposed XGBXSS, a novel web-based XSS attack detection framework based on an ensemble-learning technique using the Extreme Gradient Boosting algorithm (XGboost) with extreme parameters optimization approach. An enhanced feature extraction method is presented to extract the most useful features from the developed dataset. Furthermore, a novel hybrid approach for features selection is proposed, comprising information gain (IG) fusing with sequential backward selection (SBS) to select an optimal subset reducing the computational costs and maintaining the high-performance of detector' simultaneously. The proposed framework has successfully exceeded several tests on the holdout testing dataset and achieved avant-garde results with accuracy, precision, detection probabilities, F-score, false-positive rate, false-negative rate, and AUC-ROC scores of 99.59%, 99.53 %, 99.01%, 99.27%, 0.18%, 0.98%, and 99.41%, respectively. Moreover, it can bridge the existing research gap concerning previous detectors, with a higher detection rate and lesser computational complexity. It also has the potential to be deployed as a self-reliant system, which is efficient enough to defeat such attacks, including zero-day XSS-based attacks.
Keystroke Dynamics-Based Authentication Using Unique Keypad

Authentication methods using personal identification number (PIN) and unlock patterns are widely used in smartphone user authentication. However, these authentication methods are vulnerable to shoulder-surfing attacks, and PIN authentication, in particular, is poor in terms of security because PINs are short in length with just four to six digits. A wide range of research is currently underway to examine various biometric authentication methods, for example, using the user’s face, fingerprint, or iris information. However, such authentication methods provide PIN-based authentication as a type of backup authentication to prepare for when the maximum set number of authentication failures is exceeded during the authentication process such that the security of biometric authentication equates to the security of PIN-based authentication. In order to overcome this limitation, research has been conducted on keystroke dynamics-based authentication, where users are classified by analyzing their typing patterns while they are entering their PIN. As a result, a wide range of methods for improving the ability to distinguish the normal user from abnormal ones have been proposed, using the typing patterns captured during the user’s PIN input. In this paper, we propose unique keypads that are assigned to and used by only normal users of smartphones to improve the user classification performance capabilities of existing keypads. The proposed keypads are formed by randomly generated numbers based on the Mersenne Twister algorithm. In an attempt to demonstrate the superior classification performance of the proposed unique keypad compared to existing keypads, all tests except for the keypad type were conducted under the same conditions in earlier work, including collection-related features and feature selection methods. Our experimental results show that when the filtering rates are 10%, 20%, 30%, 40%, and 50%, the corresponding equal error rates (EERs) for the proposed keypads are improved by 4.15%, 3.11%, 2.77%, 3.37% and 3.53% on average compared to the classification performance outcomes in earlier work.
An efficient multitask neural network for face alignment, head pose estimation and face tracking

While Convolutional Neural Networks (CNNs) have significantly boosted the performance of face related algorithms, maintaining accuracy and efficiency simultaneously in practical use remains challenging. The state-of-the-art methods employ deeper networks for better performance, which makes it less practical for mobile applications because of more parameters and higher computational complexity. Therefore, we propose an efficient multitask neural network, Alignment & Tracking & Pose Network (ATPN) for face alignment, face tracking and head pose estimation. Specifically, to achieve better performance with fewer layers for face alignment, we introduce a shortcut connection between shallow-layer and deep-layer features. We find the shallow-layer features are highly correspond to facial boundaries that can provide the structural information of face and it is crucial for face alignment. Moreover, we generate a cheap heatmap based on the face alignment result and fuse it with features to improve the performance of the other two tasks. Based on the heatmap, the network can utilize both geometric information of landmarks and appearance information for head pose estimation. The heatmap also provides attention clues for face tracking. The face tracking task also saves us the face detection procedure for each frame, which also significantly boost the real-time capability for video-based tasks. We experimentally validate ATPN on four benchmark datasets, WFLW, 300VW, WIDER Face and 300W-LP. The experimental results demonstrate that it achieves better performance with much less parameters and lower computational complexity compared to other light models. • Employ the structural information at shallow layer for efficient face alignment. • Generate a heatmap to provide the geometric and attention information for network. • Propose an efficient multitask framework for video-based processing. • Conduct comparison experiments and ablation studies on various datasets.
Quantum Fisher Information of a Teleported State in Heisenberg XYZ Chain With Magnetic Field and Kaplan–Shekhtman–Entin-Wohlman–Aharony Interaction

One of the teleportation versions is applied to convey an entangled two-qubit system via a class of a two-qubit XYZ Heisenberg chain model.At thermal equilibrium, the exact solution of the XYZ model with an external magnetic field and the Kaplan-Shekhtman-Entin-Wohlman-Aharony interaction (KSEWA-interaction) is proposed in X , and Z direction.The teleported quantum Fisher information is studied, where we estimate the weight parameter θ, temperature and the strength parameter of KSEWAinteraction.It is found that, the strength of the KSEWA-interaction and the magnetic field be of benefit in the case of estimating the weight function.Meanwhile, the temperature decreases the teleported information.
Gaming Beyond the Novelty Effect of Immersive Virtual Reality for Physical Rehabilitation

Immersive virtual reality (iVR) head-mounted display (HMD) systems paired with serious exercise games can positively augment physical rehabilitation process from both engagement and analytics perspectives. This article presents a serious game for iVR HMD-based long-term upper-extremity exercise. We demonstrate the capabilities of our game through a case study with five users recovering from upper-extremity injuries. We examine how our program maintains engagement and motivation over eight weeks, where users completed biweekly prescribed movements framed as protecting a virtual butterfly. We assess user experiences through a mixture of biomarkers from brainwave, heart rate, and galvanic skin response recorded at runtime, as well as motion capture and behavioral game data. Our results suggest that the iVR game was an effective medium in inducing high compliance, physical performance, and biometric changes even with increasing difficulty beyond the novelty effect period. We conclude with considerations of future work for iVR physical therapy games that adapt to biometric response.
An energy-aware resource deployment algorithm for cloud data centers based on dynamic hybrid machine learning

To meet the ever-increasing requirements of cloud users, cloud service providers have further increased the deployment of cloud data centers. Cloud users can freely choose the cloud data center that suits them according to their own business characteristics and budget expenditures. This requires cloud service providers to continuously improve service quality and reduce usage costs to expand their own user base. Mature cloud service providers will continuously optimize cloud tasks and virtual machine deployment methods to increase physical machine utilization and reduce cloud data center energy consumption. However, existing virtual machine deployment algorithms usually have low utilization of physical machines or high energy consumption of cloud data centers, thereby reducing the frequency of use by cloud users and the benefits of cloud service providers. This paper systematically analyzes virtual machine and physical machine models. At the same time, the K-means clustering algorithm for unsupervised learning and the KNN classification algorithm for supervised learning are expanded to establish a dynamic hybrid resource deployment rule. Then, an energy-aware resource deployment algorithm for cloud data centers based on dynamic hybrid machine learning (EHML) is proposed based on the theory of machine learning. This algorithm reduces energy consumption by increasing the average utilization of physical machines. Finally, the experimental test results show that the average utilization of physical machines and energy consumption of the algorithm are significantly better than those of the comparison algorithms.
Detection and Correction of Abnormal Data with Optimized Dirty Data: A New Data Cleaning Model

Each and every business enterprises require noise-free and clean data. There is a chance of an increase in dirty data as the data warehouse loads and refreshes a large quantity of data continuously from the various sources. Hence, in order to avoid the wrong conclusions, the data cleaning process becomes a vital one in various data-connected projects. This paper made an effort to introduce a novel data cleaning technique for the effective removal of dirty data. This process involves the following two steps: (i) dirty data detection and (ii) dirty data cleaning. The dirty data detection process has been assigned with the following process namely, data normalization, hashing, clustering, and finding the suspected data. In the clustering process, the optimal selection of centroid is the promising one and is carried out by employing the optimization concept. After the finishing of dirty data prediction, the subsequent process: dirty data cleaning begins to activate. The cleaning process also assigns with some processes namely, the leveling process, Huffman coding, and cleaning the suspected data. The cleaning of suspected data is performed based on the optimization concept. Hence, for solving all optimization problems, a new hybridized algorithm is proposed, the so-called Firefly Update Enabled Rider Optimization Algorithm (FU-ROA), which is the hybridization of the Rider Optimization Algorithm (ROA) and Firefly (FF) algorithm is introduced. To the end, the analysis of the performance of the implanted data cleaning method is scrutinized over the other traditional methods like Particle Swarm Optimization (PSO), FF, Grey Wolf Optimizer (GWO), and ROA in terms of their positive and negative measures. From the result, it can be observed that for iteration 12, the performance of the proposed FU-ROA model for test case 1 on was 0.013%, 0.7%, 0.64%, and 0.29% better than the extant PSO, FF, GWO, and ROA models, respectively.
Deep Sentiment Analysis: A Case Study on Stemmed Turkish Twitter Data

Sentiment analysis using stemmed Twitter data from various languages is an emerging research topic. In this paper, we address three data augmentation techniques namely Shift, Shuffle, and Hybrid to increase the size of the training data; and then we use three key types of deep learning (DL) models namely recurrent neural network (RNN), convolution neural network (CNN), and hierarchical attention network (HAN) to classify the stemmed Turkish Twitter data for sentiment analysis. The performance of these DL models has been compared with the existing traditional machine learning (TML) models. The performance of TML models has been affected negatively by the stemmed data, but the performance of DL models has been improved greatly with the utilization of the augmentation techniques. Based on the simulation, experimental, and statistical results analysis deeming identical datasets, it has been concluded that the TML models outperform the DL models with respect to both training-time (TTM) and runtime (RTM) complexities of the algorithms; but the DL models outperform the TML models with respect to the most important performance factors as well as the average performance rankings.
LiveObj: Object Semantics-based Viewport Prediction for Live Mobile Virtual Reality Streaming

Virtual reality (VR) video streaming (a.k.a., 360-degree video streaming) has been gaining popularity recently as a new form of multimedia providing the users with immersive viewing experience. However, the high volume of data for the 360-degree video frames creates significant bandwidth challenges. Research efforts have been made to reduce the bandwidth consumption by predicting and selectively streaming the user's viewports. However, the existing approaches require historical user or video data and cannot be applied to live streaming, the most attractive VR streaming scenario. We develop a live viewport prediction mechanism, namely LiveObj, by detecting the objects in the video based on their semantics. The detected objects are then tracked to infer the user's viewport in real time by employing a reinforcement learning algorithm. Our evaluations based on 48 users watching 10 VR videos demonstrate high prediction accuracy and significant bandwidth savings obtained by LiveObj. Also, LiveObj achieves real-time performance with low processing delays, meeting the requirement of live VR streaming.
Rotational Cryptanalysis from a Differential-Linear Perspective

The differential-linear attack, combining the power of the two most effective techniques for symmetric-key cryptanalysis, was proposed by Langford and Hellman at CRYPTO 1994. From the exact formula for evaluating the bias of a differential-linear distinguisher (JoC 2017), to the differential-linear connectivity table (DLCT) technique for dealing with the dependencies in the switch between the differential and linear parts (EUROCRYPT 2019), and to the improvements in the context of cryptanalysis of ARX primitives (CRYPTO 2020), we have seen significant development of the differential-linear attack during the last four years. In this work, we further extend this framework by replacing the differential part of the attack by rotational-xor differentials. Along the way, we establish the theoretical link between the rotational-xor differential and linear approximations, revealing that it is nontrivial to directly apply the closed formula for the bias of ordinary differential-linear attack to rotational differential-linear cryptanalysis. We then revisit the rotational cryptanalysis from the perspective of differential-linear cryptanalysis and generalize Morawiecki et al.’s technique for analyzing Keccak, which leads to a practical method for estimating the bias of a (rotational) differential-linear distinguisher in the special case where the output linear mask is a unit vector. Finally, we apply the rotational differential-linear technique to the permutations involved in FRIET, Xoodoo, Alzette, and SipHash. This gives significant improvements over existing cryptanalytic results, or offers explanations for previous experimental distinguishers without a theoretical foundation. To confirm the validity of our analysis, all distinguishers with practical complexities are verified experimentally.
Experimental Comparison of Optical Inline 3D Measurement and Inspection Systems

Fast optical 3D inline inspection sensors are a powerful tool to advance factory automation. Many of these visual inspection tasks require high speeds, high resolutions, and repeatability. Stereo vision, photometric stereo, light sectioning, and structured light are the most common principles for inline imaging in the several micrometers to sub-millimeter resolution range. Selecting the correct sensor principle can be challenging as manufacturers' datasheets frequently use different values to describe their systems and do not stick to proposed characterizations defined by the “Initiative Fair Data Sheet” or the VDE/VDI standards. With the help of standardized parameters, this paper aims to compare four different measurement principles, namely AIT's own single sensor light field camera method, a structured light pattern projector, a laser triangulation sensor, and a stereo camera system, with an approximate field of view of 100×100mm. We demonstrate simple yet meaningful experiments to determine lateral resolution, temporal noise, and calibration accuracy to enable an objective system comparison. Additionally, the reproduction of small surface structures and an overall performance on a challenging test object is evaluated. Results show that the measurement principles partly serve different application areas. The provided methods will help end users to select the correct sensor for specific applications.
Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model

Caricature is an artistic abstraction of the human face by distorting or exaggerating certain facial features, while still retains a likeness with the given face. Due to the large diversity of geometric and texture variations, automatic landmark detection and 3D face reconstruction for caricature is a challenging problem and has rarely been studied before. In this paper, we propose the first automatic method for this task by a novel 3D approach. To this end, we first build a dataset with various styles of 2D caricatures and their corresponding 3D shapes, and then build a parametric model on vertex based deformation space for 3D caricature face. Based on the constructed dataset and the nonlinear parametric model, we propose a neural network based method to regress the 3D face shape and orientation from the input 2D caricature image. Ablation studies and comparison with state-of-the-art methods demonstrate the effectiveness of our algorithm design. Extensive experimental results demonstrate that our method works well for various caricatures. Our constructed dataset, source code and trained model are available at https://github.com/Juyong/CaricatureFace.