Reviewerly/exHarmony huggingface dataset:
Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.
Impact of migration strategies and individual stabilization on multi-scale quantum harmonic oscillator algorithm for global numerical optimization problems

Multi-scale Quantum Harmonic Oscillator Algorithm (MQHOA) is a population-based metaheuristic algorithm proposed recently. It has been proved effective and efficient to deal with unimodal and multimodal problems. Although the mechanism of replacing the worst particle with the fittest individual in MQHOA helps to fasten the iteration process, it can easily lead to premature convergence. Instead of direct replacement, several migration strategies are proposed to maintain the diversity of the population and help to obtain the global optima in difficult function evaluations. The impacts of the migration strategies and individual stabilization on the improvement of the algorithms in their effectiveness, reliability, accuracy and efficiency are well researched. A variety of multi-dimensional unimodal and multimodal benchmark functions are applied to illustrate the optimization performance of the proposed algorithms. Some of the best competitors in MQHOAs with migration strategies are selected to compare with several state-of-the-art stochastic algorithms. Experimental results presented suggest some conclusions: First, the individual stabilization mechanism does not significantly improve the performance of MQHOA. Second, random migration does not obviously help MQHOA perform much better. Third, migration strategies significantly affect the performance of MQHOA, and some of MQHOAs with migration strategies are very competitive to deal with numerical optimization problems.
Varying Speed Rate Controller for Human–Robot Teleoperation Based on Muscle Electrical Signals

With the emergence of industrial robots in recent decades, advancing the technology that drives human-robot interaction has become a research hotspot.However, the control of human-robot interaction using human gestures has presented some challenges when applied to the industrial environment.For example, when a robot is controlled with a hand motion, the hand movement space may be smaller than the robot's working space, making it difficult to meet the robot's space and accuracy requirements.The research herein was centered on building interactive scenarios through unit software, and gesture recognition sensors were used to collect spatial information about the hand.A position mapping algorithm was used to map the position of the hand to the robot end-effector and a virtual robot space constraint was added to collectively allow users to control the robot's movements.In the experiment, volunteers were asked to control the robot by using constant speed ratio (Ratio of robot speed to input value) or changing speed ratio.The speed ratio affected the stability of the experiment, adding electromyography can improve the stability.The first innovation was the use of human muscle information as input information, adding it to the interaction based on unity.The second innovation was using an algorithm to change the mapping speed ratio according to the electromyography information provided.This was the first time that electromyography was used to change the mapping speed ratio of human-robot interactions, which may enable further advancements in human-robot interaction.
Retrofitting the Partially Privileged Mode for TEE Communication Channel Protection

ARM TrustZone provides a Trusted Execution Environment (TEE) to isolate security-critical services, which are generally invoked from the Rich Execution Environment (REE) through a communication channel established by executing the Secure Monitor Call (SMC) with the general registers configured as input parameters. Unfortunately, the communication channel has been abused by adversaries to incur misbehavior of the TEE, to analyze the internal working of the TEE, and to exploit its vulnerabilities. We therefore propose the TEE defense (TFence) framework that enables the creation of a partially privileged (par-priv) process, which benefits from the coordination of the system mode and virtualization extension. More specifically, on ARM architecture, direct invocation of hypercall and SMC is not allowed in the user process; however, we limitedly escalate the privilege of the process to enable it to directly communicate with trust anchors such as hypervisor and TrustZone. This approach enabled us to remove the kernel dependency when the process communicates with the TEE, which also reduces the attack surface to the critical part of the application involved in the communication. Besides, direct communication with the hypervisor facilitates the adoption of application-shielding approaches to protect the critical part and to restrict arbitrary access to the TEE.
Cost-effective deployment of certified cloud composite services

The advent of cloud computing has radically changed the concept of distributed environments, where services can now be composed and reused at high rates. Today, service composition in the cloud is driven by the need of providing stable QoS, where non-functional properties of composite services are proven over time and composite services continuously adapt to both functional and non-functional changes of the component services. This scenario introduces substantial costs on the cloud providers that go beyond the cost of deploying component services, and require to consider the costs of continuously verifying non-functional properties of composite and component services. In this paper, we propose a cost-effective approach to certification-based cloud service composition. This approach is based, on one side, on a portable certification process for the cloud evaluating non-functional properties of composite services and, on the other side, on a cost-evaluation methodology aimed to produce the service composition that minimizes the total cost paid by the cloud providers, taking into account both deployment and certification/verification costs. Our service composition approach is driven by certificates awarded to single services and by a fuzzy-based cost evaluation methodology, and assumes certified properties as must-have requirements for service selection and composition.
EGeoIndis: An effective and efficient location privacy protection framework in traffic density detection

Traffic density detection is a useful measure to infer the environment of road condition. The common methods to achieve traffic density detection is collecting the real time vehicle locations in the areas. But processing data that consist of vehicle locations severely risks car owner's privacy. In this paper, we present a vehicle location privacy protection framework called Expanding Geo-Indistinguishability framework (EGeoIndis). By abstracting map as bitmap, we utilize Linear Programming (LP) to achieve geo-indistinguishability protection in low quality loss. We creatively combine a secret sharing protocol to resolve computational efficiency problem when we meet large-scale locations in LP and finally we achieve a better privacy guarantee than geo-indistinguishability in large areas. All our privacy data achieve the protection locally, so we don't need any third trusted party. Evaluation result shows that EGeoIndis has a better performance than classic Laplace mechanism when setting proper parameter in availability and privacy guarantee meanwhile has a good efficiency.
isAnon: Flow-Based Anonymity Network Traffic Identification Using Extreme Gradient Boosting

The abuse of anonymous communication technology brings serious challenges to network supervision. The valid identification of anonymity network traffic is a prerequisite and fundamentally important for preventing the violence of such techniques. However, due to the distinct characteristics of flow from anonymity networks including Tor, I2P, and JonDonym, existing studies don't take full advantage of these features, damaging the accuracy of identification. In this paper, we propose an effective anonymity network traffic identification model, called isAnon. Firstly, isAnon designs a novel hybrid feature selection algorithm by combining Modified Mutual Information and Random Forest (MMIRF) algorithm to filter out some irrelevant and redundant features quickly. Secondly, our proposed model applies a nested cross-validation scheme with an inner 5-fold cross-validation and an outer Monte Carlo cross-validation to prevent model overfitting. Finally, we use the Extreme Gradient Boosting (XGBoost) algorithm to identify Tor, I2P, and JonDonym networks for four scenarios. Comprehensive experimental results on several real-world anonymity network traffic datasets clearly show the effectiveness of our isAnon model compared with state-of-the-art baseline identification methods.
Reduced-Gate Convolutional LSTM Architecture for Next-Frame Video Prediction Using Predictive Coding

Spatiotemporal sequence prediction is an important problem in deep learning. We study next-frame video prediction using a deep-learning-based predictive coding framework that uses convolutional, long short-term memory (convLSTM) modules. We introduce a novel reduced-gate convolutional LSTM architecture that achieves better next-frame prediction accuracy than the original convolutional LSTM while using a smaller parameter budget, thereby reducing training time and memory requirements. We tested our reduced gate modules within a predictive coding architecture on the gray-scale and RGB video datasets. We found that our reduced-gate model has a significant reduction of approximately 40 percent of the total number of training parameters and training time in comparison with the standard LSTM model which makes it attractive for hardware implementation especially on small devices.
Accelerate Mini-batch Machine Learning Training With Dynamic Batch Size Fitting

Mini-batch Stochastic Gradient Descent (MGD) is one of the most widely used methods in Machine Learning (ML) model training. Typically, before a training process starts, researchers should manually set a fixed batch size, which is a hyper-parameter indicating the size of the random slice of the whole dataset that is trained in a single iteration. In this paper, we propose a light-weight dynamic batch size fitting algorithm based on online efficient evaluation, which has the ability of automatically tuning batch size during the train process to reach a best-so-far efficiency, but with little overhead. The experimental results have demonstrated that the algorithm is more effective compared with the commonly used fixed settings.
Image Steganography Based on Hybrid Edge Detector to Hide Encrypted Image using Vernam Algorithm

There has been a growing expansion in the use of steganography, due to the evolution in using internet technology and multimedia technology. Hence, nowadays, the information is not secured sufficiently while transmitting it over the network. Therefore, information security has taken an important role to provide security against unauthorized individuals. This paper proposes steganography and cryptography technique to secure image based on hybrid edge detector. Cryptography technique is used to encrypt a secret image by using Vernam cipher algorithm. The robust of this algorithm is depending on pseudorandom key. Therefore, pseudo-random key is generated from a nonlinear feedback shift register (Geffe Generator). While in steganography, Hybrid Sobel and Kirch edge detector have been applied on the cover image to locate edge pixels. The least significant bit (LSB) steganography technique is used to embed secret image bits in the cover image in which 3 bits are embedded in edge pixel and 2 bits in smooth pixel. The proposed method can be used in multi field such as military, medical, communication, banking, Electronic governance, and so on. This method gives an average payload ratio of 1.96 with 41.5 PSNR on average. Besides, the maximum size of secret image that can be hidden in the cover image of size 512*512 is 262*261. Also, when hiding 64800 bits in baboon cover image of size 512*512, it gives PSNR of 50.42 and MSE of 0.59.
Curse of Dimensionality in Adversarial Examples

While machine learning and deep neural networks in particular, have undergone massive progress in the past years, this ubiquitous paradigm faces a relatively newly discovered challenge, adversarial attacks. An adversary can leverage a plethora of attacking algorithms to severely reduce the performance of existing models, therefore threatening the use of AI in many safety-critical applications. Several attempts have been made to try and understand the root cause behind the generation of adversarial examples. In this paper, we try to relate the geometry of the high-dimensional space in which the model operates and optimizes, and the properties and problems therein, to such adversarial attacks. We present the mathematical background, the intuition behind the existence of adversarial examples and substantiate them with empirical results from our experiments.
Robust Generalized Fuzzy Systems Training From High-Dimensional Time-Series Data Using Local Structure Preserving PLS

Establishing fuzzy models from time-series data with predictive capabilities for numerical targets typically requires dimension reduction techniques to overcome the severe curse of dimensionality effects. Linear projection methods are promising candidates in this context as they-unlike nonlinear dimension reduction techniques-preserve interpretability of the resulting models. However, linear projections do not reveal the inherent (nonlinear, local) cluster structure of the data and are thus not ideally suited for the identification of fuzzy rule bases. To overcome this limitation, in this article, we present a new fuzzy modeling approach that combines generalized fuzzy systems modeling with a local structure preserving variant of partial least squares (PLS). In contrast to ordinary PLS, our approach maps a weighted (adjacency) graph on the directions associated with high covariance with the response in order to emphasize local data structures when constructing the latent variable (LV) space. This operates into the direction from which the (training of the) fuzzy model benefits, as therein local regions are represented by submodels in form of generalized Takagi-Sugeno (TS) fuzzy rules. The local structure preserving LV space is obtained by solving a new penalized objective function, which assures global optimality of the solutions by virtue of the specific properties of the Laplacian matrix. Local regions are characterized in two ways: through nearest neighbor points (assuming fixed local region sizes) and through density regions identified by clustering (achieving variable local region sizes). To establish a robust time-series-based forecast model, the training of a generalized TS fuzzy model is conducted in the LV space with reduced dimensionality. It is realized by an iterative robust version of Gen-Smart-EFS, allowing multiple passes over the complete datasets until convergence of the antecedent space. Consequent parameters are estimated by a fuzzily weighted elastic net approach, embedding a convex combination of ridge regression and Lasso to achieve robust solutions also in case of ill-posed problems and meeting the (more stable and interpretable) local learning spirit. The new approach is termed as LS-PLS-Fuzzy, short for local structure preserving PLS fuzzy regression. It was successfully evaluated on three real-world application scenarios including a total of 11 time-series-based prediction modeling problems with different proportions between the number of training samples and original input dimensionality. Our results show significantly improved model accuracy compared to related State-of-the-Art (SoA) modeling approaches, an alternative dimension reduction technique, and when using conventional PLS and/or nongeneralized fuzzy rules.
The Effects of Data Size on Text-Independent Automatic Speaker Identification System

Speaker recognition is a technique that automatically identifies a speaker from a recording of their speech utterance. Speaker recognition technologies are taking a new direction due to rapid progress in artificial intelligence. Research in the field of speaker recognition has shown fruitful results. There is, however, not much work done for African indigenous languages that have limited speech data resources. This paper presents how data size impacts the accuracy of an automatic speaker recognition system models, focusing on the Sepedi language as it is one of the South African under-resourced language. The speech data used is acquired from the South African Centre for Digital Language Resources. Four machine learning models, namely, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Multilayer Perceptrons (MLP) and Logistic Regression (LR) are trained under four data setting environment. LR performed better than other models with the highest accuracy of 91% while SVM obtained the highest increase of 4% in accuracy as data size increases.
Cyber Security Metrics Evaluation Using Multi-criteria Decision-Making Approach

Cyberspace is the trust-worthy environment in which message transmission over computer networks occurs. Cyber security aims to protect the information and other resources from malicious users, external or internal within the organization. Metrics is a general name that used to the set of measures that describe a suitable applications. The aim of security metrics is to make certain business and minimize business damage by preventing or lowering the impact of cyber incidents. A metric can be developed to measure cyber security trust-worthiness of a company. The measurement of security metrics can be viewed as multiple-criteria decision-making problem (MCDM). In the present paper we are comparing two MCDM approaches: AHP and Electre III to evaluate cyber security metrics.
An Object Detection by using Adaptive Structural Learning of Deep Belief Network

Deep learning forms a hierarchical network structure for representation of multiple input features. The adaptive structural learning method of Deep Belief Network (DBN) can realize a high classification capability while searching the optimal network structure during the training. The method can find the optimal number of hidden neurons for given input data in a Restricted Boltzmann Machine (RBM) by neuron generation-annihilation algorithm. Moreover, it can generate a new hidden layer in DBN by the layer generation algorithm to actualize a deep data representation. The proposed method showed higher classification accuracy for image benchmark data sets than several deep learning methods including well-known CNN methods. In this paper, a new object detection method for the DBN architecture is proposed for localization and category of objects. The method is a task for finding semantic objects in images as Bounding Box (B-Box). To investigate the effectiveness of the proposed method, the adaptive structural learning of DBN and the object detection were evaluated on the Chest X-ray image benchmark data set (CXR8), which is one of the most commonly accessible radio-logical examination for many lung diseases. The proposed method showed higher performance for both classification (more than 94.5% classification for test data) and localization (more than 90.4% detection for test data) than the other CNN methods.
On a Side Channel and Fault Attack Concurrent Countermeasure Methodology for MCU-based Byte-sliced Cipher Implementations

As IoT applications are increasingly being deployed, there comes along an ever increasing need for the security and privacy of the involved data. Since cryptographic implementations are used to achieve these goals, it is important for embedded software developers to take into consideration hardware attacks. Side Channel Analysis (SCA) and Fault Attacks (FA) are the main classes of such attacks, which can either reduce or even eliminate the security levels of an embedded design. Therefore, cryptographic implementations must address both of them at the same time. To this end, multiple solutions have been proposed to address both attacks in one solution, such as Dual Pre-charge Logic (DPL) and Encoding countermeasures. In this work, we discuss the advantages and disadvantages of the state of the art, concurrent SCA and FA countermeasures. Additionally, we propose a software countermeasure in order to provide protection against both types of attacks. The proposed countermeasure is a general approach, applicable to any byte-sliced cipher and any modern (32/64-bit) Micro-Controller Units (MCU). The proposed countermeasure is applied to an AES S-BOX implementation, for a 32-bit MCU (ARM Cortex-M3). The countermeasure has been experimentally evaluated against Correlation Power Analysis (CPA) attacks for both platforms while its fault detection capabilities are theoretically described.