Reviewerly/exHarmony huggingface dataset:
PMA-DRL: A parallel model-augmented framework for deep reinforcement learning algorithms

In recent years, aided by deep neural networks (DNNs), reinforcement learning (RL) algorithms have been achieving great success in more and more tasks. In general, model-free RL algorithms are widely applicable, but sometimes suffer from low sample efficiency. Although the environment dynamics can be incorporated into model-free RL algorithms to enhance sample efficiency, which can be transformed into the joint algorithm of model-free and model-based RL algorithms, the model bias of dynamics model may still hurt the performance. Another attractive study direction for RL algorithms, is using parallel strategy. Meanwhile, parallel RL algorithms can achieve outstanding results under less time cost, but are still with low sample efficiency, because most of such algorithms are vanilla model-free ones. Therefore, aiming to enhance the model performance for RL algorithms with DNNs, in this paper, we propose a novel parallel model-augmented framework, called PMA-DRL, to combine the dynamics model and parallel RL algorithms together. First, we introduce a local dynamics model (LDM) for each local agent model (LAM) in parallel RL algorithms. Next, we propose to build a better LDM by using an ensemble of LDMs, and such ensemble can help to reduce model bias, because the experienced observations under different LAMs are always diverse. Then, the observation diversity can be further increased by employing LDMs to explore. Finally, we implement experiments on classic control tasks (CCTs) and Atari games, which demonstrates the efficiency of our proposed framework.
Cloud for Education

Openstack is an open-source software which becomes most adoptable technology platform for many of the organizations with its highly scalable and access the resources through the internet. In this resources Which will likely have a high effect on the educational environment? Cloud platform is an excellent choice for educational institutions which are particularly under budget in order to access their information management system effectively without spending huge amount for computational and network devices. Institutions take more advantages of cloud based applications offered from their own cloud and enable their student's/staff members to perform the academic tasks. In this paper, we proposed a cloud infrastructure which provide the resources to the educational environment and the benefits to the management and also students/staff members.
Real-Time Continuous Sign Language Classification using Ensemble of Windows

Sign language recognition using wearable sensors will help in bridging the communication gap between the deaf people who use sign language and the hearing people, who do not understand signing. In this paper, classification of continuously signed sentences in real-time, using multi-modal wearable sensors is considered. Real-time classification requires segmentation of signals on which classification is carried out. Segmentation of signals with fixed window length is convenient, however, not suitable for addressing the variation in signal as static hand postures and dynamic hand motions are performed during signing. Hence, an ensemble of classifiers learned over features extracted from windows of different lengths is proposed to enhance the accuracy with which the continuously signed sentences may be classified. Results for 11 sentences signed from the Indian sign language using data from surface electromyogram and accelerometers placed on both the hands of the signers are presented. The effect of using different classifiers and different window lengths is also studied. The duration required to classify a sentence from the recorded signal segment indicates the real-time nature of the algorithm. The proposed ensemble approach classifies the sentences with higher accuracy as compared to using a single classifier learned on features extracted from fixed-duration windows.
Cyber Security Operations Centre Concepts and Implementation

Cyber security operations centres (SOCs) are attracting much attention in recent times as they play a vital role in helping businesses to detect cyberattacks, maintain cyber situational awareness, and mitigate real-time cybersecurity threats. Literature often cites the monitoring of an enterprise network and the detection of cyberattacks as core functions of an SOC. While this may be true, an SOC offers more functions than the detection of cyberattacks. For example, an SOC can provide functions that focus on helping an organisation to meet regulatory and compliance requirement. A better understanding of the functions that could be offered by an SOC is useful as this can aid businesses running an in-house SOC to extend their SOC capabilities to improve their overall cybersecurity posture. The goal of this chapter is to present the basics one needs to know about SOCs. The authors also introduce readers and IT professionals who are not familiar with SOCs to SOC concepts, types of SOC implementation, the functions and services offered by SOCs, along with some of the challenges faced by an SOC.
Survey on Face Recognition with pre filtering Techniques and their comparative study

Face is a very crucial aspect with respect to recognizing as it stands out as a unique feature. The normal human has distinct facial features that can be mapped and remembered. Security and privacy are very important topics given the trend and issues that are being faced by normal people. The face place a crucial aspect in giving out emotions and identification Facial recognition is a very flexible detection method and has the freedom that the subject wouldn't know if he's scanned. The facial features can be systematically analyzed that are common to everyone's face. The distance between the jaw line and the chin, the one between the eyes and nose, position of cheekbones all of these can be taken up as numerical values and analyze these specific features and map it to a person. There are instances where the recognition becomes a tougher due to external factors like variations in poses, emotional variants, non-controllable environmental conditions, ageing etc. In order to understand the techniques in depth and it plays a crucial role in the future of face recognition and its role in privacy and authentication. There are numerous methods that have been constructed and structured and each one has its pros and cons and other important aspects of facial recognition techniques. And we've got the comparative analysis of the available techniques and their advantages and disadvantages. We have put light in to pre-filtering techniques and edge detection methods to optimize the recognition of dicey aspects of a face, the whole idea is to understand the better aspects of the facial recognition with additional techniques that will help the future of this detection mechanism.
Bharatanatyam Dance Transcription Using Multimedia Ontology and Machine Learning

Indian classical dance is an over 5000-year-old multimodal language for expressing emotions. Preservation of dance through multimedia technology is a challenging task. In this chapter, we develop a system to generate a parseable representation of a dance performance. The system will help preserve intangible heritage, annotate performances for better tutoring, and synthesize dance performances. We first attempt to capture the concepts of the basic steps of an Indian classical dance form, named Bharatanatyam Adavus, in an ontological model. Next, we build an event-based, low-level model that relates the ontology of Adavus to the ontology of multimodal data streams (RGB-D of Kinect in this case) for a computationally realizable framework. Finally, the ontology is used for transcription into Labanotation. We also present a transcription tool for encoding the performances of Bharatanatyam Adavus to Labanotation and test it on our recorded data set. Our primary aim is to document the complex movements of dance in terms of Labanotation using the ontology.
Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC

In this paper, we revisit model predictive control (MPC) for the classical wheeled mobile robot (WMR) navigation problem. We prove that the reachable set based hierarchical MPC (HMPC), a state-of-the-art MPC, cannot handle WMR navigation in theory due to the non-existence of non-trivial linear system with an under-approximate reachable set of WMR. Nevertheless, we propose a virtual linear leader guided MPC (VLL-MPC) to enable HMPC structure. Different from current HMPCs, we use a virtual linear system with an under-approximate path set rather than the traditional trace set to guide the WMR. We provide a valid construction of the virtual linear leader. We prove the stability of VLL-MPC, and discuss its complexity. In the experiment, we demonstrate the advantage of VLL-MPC empirically by comparing it with NMPC, LMPC and anytime RRT* in several scenarios.
Data Mining Using Cat Swarm Optimization CSO Algorithm

With the dynamic structures of databases from side and the continuous changes in the stored data in another side it has become necessary using tools and algorithms of data mining. In this paper, the Cat Swarm Optimization CSO algorithm has been used for its effective search and extraction of optimal data. CSO has two modes : tracing mode and seeking mode. This paper has five test reference functions that were used out of twelve. To evaluate the algorithm and determine its relevance to the research, the test results will show that this method has many advantages in prediction, data processing speed, durability, range, and data quality. This paper emphasis that CSO is very effective in the process of data mining.
Is There a Contradiction Between Statistics and Fairness: From Intelligent Control to Explainable AI

At first glance, there seems to be a contradiction between statistics and fairness: statistics-based AI techniques lead to unfair discrimination based on gender, race, and socio-economical status. This is not just a fault of probability techniques: similar problems can happen if we use fuzzy or other techniques for processing uncertainty. To attain fairness, several authors proposed not to rely on statistics and instead, explicitly add fairness constraints into decision making. In this paper, we show that the seeming contradiction between statistics and fairness is caused mostly by the fact that the existing systems use simplified models; contradictions disappear if we replace them with more adequate (and thus more complex) statistical models.
Use of Renewable Energies in Sustainable Facilities for Rural Housing Module

Many rural homes, due to their distance to the main population centres, do not have the basic infrastructure, at the level of facilities, required by the needs demanded by its inhabitants. The design of electrical and sanitary installations of a sustainable nature is intended, and its implementation in a rural housing module. For which is based on the treatment of reliable data and existing in each place, determining the supply of renewable resources of greater predominance, such as solar, wind and rainwater. The estimation of global solar energy is determined by the method based on the Bristow-Campbell model; the Weibull distribution is used as a calculation methodology to adjust the frequency distribution of the wind speed in wind energy; likewise, a rainwater collection system is proposed, only for the toilet of the dwelling, which is treated by a biodigester; improving the quality of life of the inhabitants of the rural area through the management of sustainable facilities.
Hiding Color Image within a video file using Discrete Wavelets Transform and Artificial Neural Networks

Growing up technology services and the needing to exchange various information lead to accumulating a large amount of data across computer networks. Internet and private networks are examples of such networks. Transferring the data between computers increases the risk of un-authorizing access of unauthorized people. Protecting data is so important to be safe from unexpected and invisible threats, which are difficult to detect as most notably intrusion and infiltration.The aim of this paper is to hide a digital color image inside a digital video file using the Discrete Wavelets Transform (DWT) and Artificial Neural Networks (ANNs). The digital video file, which represents the cover, is splitted into a set of frames and partitioned into three colored layers (red, green and blue). The discrete wavelets transform technique is applied to each one of the three layers.To hide the colored image (watermark), it is firstly partitioned into the three colored layers (red, green and blue). Then, it is layered into a set of clusters. The ANNs are used as artificial hiding techniques to hide each cluster in the digital video file.The results showed that the use of the DWT and ANNs are effective and encourage in hiding data. After applying this work algorithms to a set of samples, the results also showed harmony and compatibility in the process of concealment with the treated samples.
Audio Information Hiding Based on Cochlear Delay Characteristics with Optimized Segment Selection

Audio information hiding (AIH) based on cochlear delay (CD) characteristics is a promising technique to deal with the trade-off between inaudibility and robustness requirements effectively. However, the use of phase-shift keying (PSK) for blindly detectable AIH based on CD characteristics caused abrupt phase changing (phase spread spectrum), which leads to bad inaudibility. This paper proposed the technique to reduce the spread spectrum from PSK by segment selection process with spline interpolation optimization. Objective evaluation to measure the detection accuracy (BDR) and inaudibility (PEAQ and LSD) was carried out with 102 various genre music clips dataset. Based on the evaluation result, our proposed method could successfully reduce the spread spectrum caused by PSK by having improvement on inaudibility test with adequate detection accuracy up to 1024 bps.
Performance Enhancement of Mutation and Crossover Components by using Differential Evolution Algorithm

The Differential Evolution (DE) algorithm, under the family of Evolutionary Algorithms (EAs), is one of the powerful algorithms used for solving continuous parameter optimization challenges. The simplistic nature and robustness of the classical DE algorithm have drawn researchers' attention towards its progressive enhancement. This work reports on an investigation of the behavioral changes of the classical DE algorithm, evoked when its mutation and crossover components are fine tuned for enhancement of DE's performance. The scope of this study covers the implementation of a mutation level enhancement and a crossover level enhancement, followed by their integration. The mutation and the crossover components are augmented by incorporation of Centroid DE and Superior-Superior & Superior-Inferior DE logics, respectively. The algorithms appraised in this inquiry were classical DE, Centroid based DE(cDE), Superior-Superior based DE (ssDE), Superior-Inferior DE (siDE), Centroid Superior-Superior DE (cssDE) and Centroid Superior-Inferior DE (csiDE). These algorithms were evaluated by comparison of the values of their mean objective function (MOV), and their speed, at solving the global optimization problems in a simple benchmarking function suite with 4 functions of different categories. The study concludes that the DE algorithm shows enhancement performance with modified mutation and crossover components. However, with a trend for inconsistency for varying values of its control parameters and benchmarking problems.
Maneuvering Target Tracking Algorithm Based on Multiple Models in Radar Networking

This paper studies the problem of maneuvering target tracking, and focuses on the maneuvering target tracking algorithm under the framework of autonomous multiple model, cooperative multiple model and variable multiple model structure, and deduces the processing steps of the typical tracking algorithm under the above three types of multi-model structure framework. Combined with the practical application requirements of radar networking system, a feasible method for spatial registration and time registration is proposed. The effectiveness of multi-model tracking algorithm is verified by constructing radar network simulation environment. The application of multi-model tracking algorithm in radar network system is sorted out and summarized. The conclusions given in this paper have guiding significance for the engineering implementation of radar networking system.
Text Detection Based on Text Shape Feature Analysis with Intelligent Grouping in Natural Scene Images

Developing an assistive system for visually impaired people using computer vision is an active area of research. The proposed assistive system is developed in an aim to be implemented in Braille e-book reader which facilitates visually impaired persons to recognize the text through tactile or speech output. Examples for such facilities include recognizing text in medicine pills, traffic sign detection, automatic mobile robot navigation, etc. This paper presents an automated system to recognize text in an image based on structural features like size, orientation, and distance between the successive region of interest (ROI). The system is based on two stages, the first performs text localization and the second performs the text detection. In the first stage, the localizing of text area is done by intelligent grouping algorithm. In the second stage, text detection is done based on text shape structural features. Our proposed system achieved an average of 76.26% precision rate, 75.8% of recall/sensitivity rate, and 76.03% of F-measure rate. The advantage of such a simple and lightweight model is that it can be deployed rapidly in any single-board microprocessors like Raspberry Pi and can be made to run effortlessly to produce quality results in real time.
An analysis of cryptographic algorithm strength based on S-box properties

It is well known that, the block algorithms supply security through its components (S-boxes, boolean functions, polynomials). Depending on the characteristics and properties of their components, the block algorithms provide a certain level of security. If the algorithm components are weak, then the algorithm may be weak. This paper intends to approach a part of this issue by analyzing the way in which the S-boxes' properties influence the strength of the algorithms.
Business and Risk Aspects in the era of Digital Information Technology University in Pekanbaru

Abstract Education is a part of the business, if it will also be faced by the world of education, including the development of digital information technology. The ability of universities to manage the resources in adjusting to the progress of the digital world is crucial how they are able to influence students to select universities. All information must be digitally available through accreditation related websites, the existence of regular classes and evening classes for employees, inexpensive tuition fees, the location of the campus and courses offered.
Use of Blockchain Technology in Irrigation Systems of small farmers' association

Traditionally, the energy industry has been slow in adopting new technologies and the transition to a new energy market will require a new approach by all parties involved in the energy market. Although it is a hard-to-accept scenario, especially by the big players on the power market, the pilot projects so far demonstrate that blockchain can play a major role in the future energy market, even if the technology is still immature from the conceptual point of view. This article attempts to bring solutions to support small farmers in allowing them to use irrigation systems. It proposes to create associations of small farmers to manage this situation. For this purpose, it is proposed to use green energy using photovoltaic panels. The efficiency of the proposed system will be monitored not only by the location and type of photovoltaic panels, but also by using the new blockchain technology that stimulates innovation and growth in the energy, climate and environment sectors. To accelerate the transition to the green energy economy, a SolarCoin version similar to the Bitcoin cryptomonade is also proposed.
Biometric System based on Facial Recognition

This work relates to the implementation of an effective face detection and recognition method that can be used as a module in person identification depending on features. The algorithm used in the biometric system is devised into two steps. The first step, called facial detection, is based on Haar cascade technique - composed of sets of Haar-type features. The second step, called facial recognition, is based on principal component analyses (PCA) algorithm. The recognition process is accomplished by converting the current image into feature vector using PCA technique and then the face is recognized by comparing its characteristics with the already stored persons. The complete application was implemented in C# programming language and the results were achieved using the wireless IP camera of type SM6203.
Surfel-Based Incremental Reconstruction of the Boundary Between Known and Unknown Space

This article presents the first surfel-based method for multi-view 3D reconstruction of the boundary between known and unknown space. The proposed approach integrates multiple views from a moving depth camera and it generates a set of surfels that encloses observed empty space, i.e., it models both the boundary between empty and occupied space, and the boundary between empty and unknown space. One novelty of the method is that it does not require a persistent voxel map of the environment to distinguish between unknown and empty space. The problem is solved thanks to an incremental algorithm that computes the Boolean union of two surfel bounded volumes: the known volume from previous frames and the space observed from the current depth image. A number of strategies were developed to cope with errors in surfel position and orientation. The method, implemented on CPU and GPU, was evaluated on real data acquired in indoor scenarios, and it was compared against state of the art approaches. Results show that the proposed method has a low number of false positive and false negatives, it is faster than a standard volumetric algorithm, it has a lower memory consumption, and it scales better in large environments.