Reviewerly/exHarmony huggingface dataset:
Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry

Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not.
Censored Markov Decision Processes: A Framework for Safe Reinforcement Learning in Collaboration with External Systems

The importance of safe reinforcement learning (safe RL) is widely recognized for enhancing real world systems. In this study, we construct the censored Markov decision process (CeMDP), a new Markov Decision Process (MDP) framework that describes the interaction of environment, learner and external systems, e.g., human intervention or pre-designed controller for emergency response. We also theoretically analyze the relation of CeMDP to existing frameworks such as the semi-Markov decision process, MDP with Option (OMDP) and standard MDP; the analysis clarifies that CeMDP is a special case of OMDP and can, with environment redefinition, be represented by MDP. This finding allows us to design planning and reinforcement learning algorithms for CeMDP. We confirm the validity of the theory and algorithms by numerical experiments.
Regularized Ellipsoidal Filter for the State of Discrete Dynamical Systems Using Averaged Current Measurements at a Sliding Interval

A new robust method is proposed for the ellipsoidal estimation of the current state vector of discrete dynamical systems by measuring their scalar outputs with additive limited interference of known intensity. Unlike the well-known methods of ellipsoidal estimation, it uses guaranteed restrictions not on the intensity of possible current instantaneous implementations of interference, but restrictions on their arithmetic mean value at each current time interval of a given duration. For the case of alternating interference, the method allows one to obtain more accurate estimates of the state vector of the system, since in it, unlike the existing methods, it is explicitly taken into account the prehistory of the interference and the corresponding measurements in this interval. In addition, a new regularization algorithm is proposed for constructing the intersection of the current ellipsoid with many states compatible with the measurement result. This additionally contributes to the construction of ellipsoidal estimates of the state vector of a smaller multidimensional volume.
Action Planning Logic in Intelligent IT Systems Using the Example of Bots

The logic of action planning adopted in the adaptive technology Smart Plus is considered. The key features of the approach adopted here include distinguishing between two formalisms (action planning and organization of interaction); a two-level action planning formalism; sensory data; the possibility of dynamic type generation; and the assumption of a negative result from the actions performed. The proposed formal action planning system allows the user to define the process of choosing a behaviour as an action planning process. In the framework of the formal action planning system proposed in this paper, the concept of goal attainability is formalized in terms of plans, and a foundation is created for effective inference procedures. A general approach to the automated creation of bots based on logical and linguistic models has been proposed. A general scheme of the web-based system using the chatbot was developed. One of the key features of this solution is the automation of the chain of activities, the implementation of which leads to the fulfilment of the user's query.
MILP-Aided Security Evaluation of Differential Attacks on KCipher-2

This paper investigates the security of KCipher-2 against differential attacks. We utilize an MILP-based method to evaluate the minimum number of active S-boxes in each round. We try to construct an accurate model to describe the 8-bit truncated difference propagation through the modular addition operation and the linear transformation of KCipher-2, respectively, which were omitted or simplified in the previous evaluation by Preneel et al. In our constructed model, the difference characteristics neglected in Preneel et al.'s evaluation can be taken into account and all valid differential characteristics can be covered. As a result, we reveal that the minimal number of active S-boxes is 25 over 15 rounds in the related IV setting and it is 17 over 24 rounds in the related IV-key setting. Therefore, this paper shows for the first time that KCipher-2 is secure against the related IV differential attack.
Computational Intelligence in Human Feature Analysis and Pose Selection

Using computers to detect a human's features is a difficult problem. The solution to this problem can be used in applications such as facial detection and gesture recognition. These applications require fast computation and high accuracy. In our research, we are trying to detect human poses by examining humans' features such as the arms and legs. Artificial Neural Networks have been successfully used in feature analysis and are popular for use in human pose selection. In this paper, we present the results from our research comparing various computational intelligent approaches such as Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). Among the four algorithms examined in this paper, we found that CNNs outperformed other algorithms in terms of prediction accuracy and calculation speed. Our main contribution is therefore a CNN specifically designed for learning a human's body structure and limb articulation, producing high accuracy while being robust against different body types and variation in limb articulation.
Improved Global Irradiance Decomposition by Sky Condition Classification from Measured Spectral Clearness Indices

This manuscript describes a decomposition algorithm for deriving the broadband direct normal (DNI) and diffuse horizontal (DHI) irradiances from one-minute spectral global horizontal irradiance measurements performed by the SolarSIM-G. The algorithm was calibrated and validated at four stations across Canada (Ottawa, Varennes, Egbert, and Devon) and at one station in China (Xianghe), which cumulatively represent a seven-year data set. For the DNI estimation, the root mean square error (RMSE) ranged from 26 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 48 W/m2, while the largest mean bias error (MBE) was 4 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . For the DHI estimation, the RMSE ranged from 14 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 27 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , while the largest MBE was 3 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . In addition, the integrated DNI and DHI errors for each station were less than 1% and 2%, respectively. The described method is an alternative to other measurement techniques for obtaining all three irradiance components.
Design of Smartphone Software Keyboard for Sensory Experience

In recent years, a movement in which various things are becoming mobile have been spreading. Keyboards are one example, not only battery-powered ones, but also foldable ones, and recently there appeared keyboards that can be connected to smartphones. However, it is troublesome to carry around a device additionally even though a keyboard size is minimized. Some keyboard products are projected on a desk with a laser system, but that does not give users the actual feeling of using a device. Therefore, we propose an application to turn a smartphone into a keyboard. Unlike the previous smartphone keyboard applications, it is more conscious of the “sensory experience” and proposes the implementation of physical sensation of touching. Furthermore, by using two smartphones together, it is possible to make them one full keyboard just like an actual keyboard.
Development of a sun tracking algorithm for solar panels for agricultural enterprises

Abstract In agriculture, use of solar energy is of crucial importance for water heating and functioning of greenhouses. It becomes even more important in agricultural enterprises which are located far from electric energy transmission lines. Furthermore, in Russia the importance of solar energy for agricultural enterprises can be motivated by deterioration of electric energy transmission lines. The article is devoted to the development of a sun tracking algorithm for solar panels without using GPS or sensors. The tracking algorithm has been created on the basis of well-known astronomic methods for calculation of the Sun’s position in azimuth coordinates. The control diagram using the Arduino platform is presented. The authors’ program works for the calculation of the Sun’s position complete with evaluation of error. It also works well with servo drive units. The hardware and software bundle have shown acceptable error values which did not exceed 2 % while aligning the panels with the sun.
Depth Completion with Morphological Operations: An Intermediate Approach to Enhance Monocular Depth Estimation

In the context of self-driving cars, Convolutional Neural Networks (CNNs) have improved the Single Image Depth Estimation (SIDE) field by predicting maps with accurate depth information for autonomous navigation. However, these networks are generally trained with sparse samples, generated by LIDAR laser scans, and they run at a high computational cost, demanding powerful GPUs. In this paper, we address the SIDE and depth completion tasks jointly, focusing on the design of a lightweight method to be applied in real self-driving scenarios. We introduce a fast and efficient densification algorithm, based on closing morphology, and we also propose a deep network pipeline that uses the densified reference depth maps for training. When compared to state-of-the-art methods, our network has fewer parameters, higher inference speed and yet comparable accuracy. We conduct a series of experiments in the widely exploited and public available KITTI Depth Benchmark.
ASENVA: Summarizing Anatomy Model by Aggregating Sensitive Values

Anatomy model has been developed for reducing information loss on the previous model in the field of privacy-preserving data publishing. The previous model such as k-anonymity, p-sensitive, and l-diversity employ generalization and suppression to anonymize its data, while anatomy separate quasi identifier and sensitive attributes are separated into two tables. This separation potentially reduces information loss, but they ignore the records' efficiency. The number of records tends to increase due to the original table is divided into two. In this paper, a model is proposed to handle the efficiency problem of the number of records called ASENVA. The model aggregates sensitive values on a sensitive table and summarizes quasi identifier attributes. Therefore, the number of each record is reduced since each record on both tables is unique. A simple simulation with early simple generated data shows that our proposed model can reduce the number of records, indicating that our model is more efficient.
An Efficient Sampling-Based Hybrid A* Algorithm for Intelligent Vehicles

In this paper, we propose an improved sampling-based hybrid A* (SBA*) algorithm for path planning of intelligent vehicles, which works efficiently in complex urban environments. Two main modifications are introduced into the traditional hybrid A* algorithm to improve its adaptivity in both structured and unstructured traffic scenes. Firstly, a hybrid potential field (HPF) model considering both traffic regulation and obstacle configuration is proposed to represent the vehicle's workspace, which is utilized as a heuristic function. Secondly, a set of directional motion primitives is generated by taking the prior topological structure of the workspace into account. The path planner using SBA* not only obeys traffic regulations in structured scenes but also is capable of exploring complex unstructured scenes rapidly. Finally, a post-optimization step is adopted to increase the feasibility of the path. The efficacy of the proposed algorithm is extensively validated and tested with an autonomous vehicle in real traffic scenes. The experimental results show that SBA* works well in complex urban environments.
Online Sparse Temporal Difference Learning Based on Nested Optimization and Regularized Dual Averaging

In policy evaluation of reinforcement learning tasks, the temporal difference (TD) learning with value function approximation has been widely studied. However, feature representation has a decisive influence on both accuracy of value function approximation and convergence rate. Therefore, it is important to develop the feature selection theory and methods that can efficiently prevent overfitting and improve estimation accuracy in TD learning algorithms. In this article, we propose an online sparse TD learning algorithm for policy evaluation by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -regualrization for feature selection. The per-step-time runtime computational complexity of the proposed algorithm is linear with respect to feature dimension. The loss function is defined as a nested optimization with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -regularization penalty, and the solver minimizes two suboptimization problems by running stochastic gradient descent and regularized dual averaging method, alternately. The convergence results for the fixed points are also established. The experiments on benchmarks with high-dimensional features show the abilities of learning and generalization of the proposed algorithms.
VOP detection for read and conversation speech using CWT coefficients and phone boundaries

In this paper, we propose a novel approach for accurate detection of vowel onset points (VOPs). A VOP is the instant at which a vowel begins in a speech signal. Precise identification of VOPs is important for various speech applications such as speech segmentation and speech rate modification. Existing methods detect the majority of VOPs to an accuracy of 40 ms deviation, which may not be appropriate for the above speech applications. To address this issue, we proposed a two-stage approach for accurate detection of VOPs. At the first stage, VOPs are detected using continuous wavelet transform coefficients, and the position of the detected VOPs are corrected using phone boundaries in the second stage. The phone boundaries are detected by the spectral transition measure method. Experiments are done using TIMIT and Bengali speech corpora. Performance of the proposed approach is compared with two standard signal processing based methods as well as with a recent VOP detection technique. The evaluation results show that the proposed method performs better than the existing methods.
Structural and Functional Representativity of GANs for Data Generation in Sequential Decision Making

In many sequential decision making problems progress is predominantly based on artificial data sets. This can be attributed to insufficient access to real data. Here we propose to mitigate this by using generative adversarial networks (GANs) to generate representative data sets from real data. Specifically, we investigate how GANs can generate training data for reinforcement learning (RL) problems. We distinguish structural properties (does the generated data follow the distribution of the original data), functional properties (is there a difference between the evaluation of policies for generated and real life data), and show that with a relatively small number of data points (a few thousand) we can train GANs that generate representative data for classical control RL environments.
Phase-wise Parameter Aggregation For Improving SGD Optimization

Stochastic gradient descent (SGD) is successfully applied to train deep convolutional neural networks (CNNs) on various computer vision tasks. Since fixed step-size Sgd converges to so-called error plateau, it is applied in combination with decaying learning rate to reach a favorable optimum. In this paper, we propose a simple yet effective optimization method to improve Sgd with a phase-wise decay of learning rate. Through analyzing both a loss surface around the error plateau and a structure of the Sgd optimization process, the proposed method is formulated to improve convergence as well as initialization at each training phase by efficiently aggregating the CNN parameters along the optimization sequence. The method keeps the simplicity of Sgd while touching the Sgd procedure only a few times during training. The experimental results on image classification tasks thoroughly validate the effectiveness of the proposed method in comparison to the other methods.
Reinforcement Learning based Method for Autonomous Navigation of Mobile Robots in Unknown Environments

The Reinforcement Learning is a subset of machine learning that deals with learning decisions from rewards given by the environment. The model classic reinforcement learning (RL) algorithms are usually applied to small sets of states and an action. However, in real applications, the state spaces are of a large scale and this will bring the problems in the generalization and the curse of dimensionality. In this research, authors integrate neural networks into reinforcement learning methods to generalize the value of all the states. The simulation results on the Gazebo software framework show the feasibility of the model proposed method algorithm. The robot can safely navigate an unprotected work environment and becomes a truly intelligent system with the ability to learn and adapt itself to the model.
Time and Cost Prediction Models for Language Classification Over a Large Corpus on Spark

This paper investigates performance impact regarding variation of five factors (input data size, node number, cores, memory, and disks) on the Spark big data processing framework, when applying a distributed implementation of Naïve Bayes for text classification of a large Corpus. Since performance depends on multiple factors and hardware is priced by time slice in cloud environments, knowing beforehand the effects of each factor on time and cost becomes particularly important. The idea is to explain the functional relationship between factors and performance and to develop linear predictor models for time and cost. The approach is based on the solid statistical principles of the Design of Experiments (DoE), particularly the randomized two-level fractional factorial design with replications. The research involved 48 real clusters with different hardware arrangements, and linear models with appropriate metrics were employed for screening, ranking, and measuring each factor's impact. Our findings with DoE included prediction models and showed the small influence of cores, the neutrality of memory and disks in the total execution time, and the non-significant impact of data scale on cost. Conversely, the scale was the most relevant factor for degrading the execution time, while the number of nodes was more important to improve it. Finally, costs were balanced impacted by nodes and cores. The experiments consistently evidenced the usefulness of employing DoE to analyze factor influence on cluster performance.
Multi-Label Ranking: Mining Multi-Label and Label Ranking Data

We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.
FAP

The forensic investigation of data stored on mobile devices is a common option to analyze and solve cyber-crime cases. The analysis of the installed applications extracts and collects information to clarify unknown conditions and might provide additional details. Unfortunately, some applications store messages encrypted. So, the information is only readable in the app, which sometimes require online access to start and display these messages. The demanded online access is a predicament; whereas the start of the app or the download of these messages provides new information to solve the case, the danger of remote wiping during the online connection is high. Available environments to facilitate an online access and simultaneously block other connections are available, but they fail during a forensic investigation. In this paper a novel approach for a forensic access point (FAP) is proposed. The design of FAP focuses on the implementation of an isolated environment, which allows the connection of the device and specific online services considering current requirements. The architecture is evaluated by a proof-of-concept (PoC), which proves the usability in a forensically sound manner.
Cross-layer unequal error protection using 64-HQAM and RCPT codes for robust image communication

Abstract In unequal error protection (UEP) schemes, the bits are protected according to their significance in terms of distortion reduction capability during the reconstruction process. The conventional UEP schemes are primarily designed either using forward error correction at application layer of network architecture, or hierarchical modulation at the physical layer. In this paper, an optimized cross-layer UEP scheme for set partitioning in hierarchical trees coded images is investigated. The proposed scheme uses rate compatible punctured turbo codes at application layer and two-layered 64-hierarchical QAM (64-HQAM) at physical layer. Simulation results show that proposed cross-layer UEP performs slightly better than application layer’s optimized turbo coding for additive white Gaussian noise channel but performs significantly better in flat Rayleigh fading channel, specifically under poor channel conditions. The proposed scheme is highly bandwidth efficient having the robustness performance close to that of the scheme using hierarchical 4-PAM (H 4-PAM). Complexity analysis of the proposed solution has been carried out.