Reviewerly/exHarmony huggingface dataset:
Quasi-opposition-Based Multi-verse Optimization Algorithm for Feature Selection

These days a category of algorithms known as metaheuristic algorithms is considered as more reliable for finding the solution of various optimization problems. Multi-verse optimization algorithm is one of the latest developed optimization algorithms, which mimics the behaviours of multi-verse theory in physics and impersonates the relationships amongst different universes. In machine learning, finding out the best feature subset from the original set containing the minimum number of features with maximum average accuracy (or at least remains same) is a difficult task and is considered as an optimization problem. This research proposes a quasi-oppositional learning-based multi-verse optimization algorithm which aims to improve the initial phase of setting up of random solutions along with quasi-opposite of a solution and considering the top best solutions from the set of initial solutions. The proposed algorithm has been assessed on twenty-three different benchmark functions, and furthermore, the binary variant of the proposed algorithm is developed for selecting the optimal set of features from various data sets. To gauge the performance of the proposed algorithms, various evaluation metrics are considered.
Evaluating the Effectiveness of Deterrence Theory in Information Security Compliance

There is a long-held belief that deterrence mechanisms are more useful in developing countries. Evidence on this belief is anecdotal rather than empirical. In this chapter, individual compliance to information system security policy (ISSP) is examined through the lenses of deterrence theory. The effects of certainty of detection and severity of punishment on attitude towards compliance and also ISSP compliance behaviour are investigated. A survey questionnaire was distributed to gather responses from 432 individuals who are staff of a public university in Ghana. The data was analysed using partial least square structural equation modelling (PLS-SEM). The results indicate that severity of punishment has a positive effect on attitude towards compliance and ISSP compliance behaviour. However, certainty of detection neither affected attitude towards compliance nor ISSP compliance behaviour. It is recommended that organizations enhance the severity of sanctions imposed on those who violate ISSPs. Future studies should explore how users apply neutralization techniques to evade sanctions.
Hardware implementation and optimization of advanced encryption standard (AES) algorithm based on CCSDS

The Consultative Committee for Space Data Systems (CCSDS) recommends AES algorithm as a cryptographic algorithm to maintain confidentiality and data security between spacecraft and ground systems. In implementation, there are several AES algorithm operating modes including Cipher-Block Chaining (CBC), Electronic Codebook (EBC), Cipher Feedback (CFB), Output Feedback (OFB), and counter mode (CTR). One of the operating modes of AES algorithm recommended by CCSDS is CTR because it has advantages in terms of efficiency of implementation and offers a high level of data security. LAPAN-A4 as the next generation of LAPAN satellites also uses CCSDS standard in terms of data exchange between satellites and ground stations including telemetry and telecommand (TTC) data and satellite payload data. In order to protect LAPAN-A4 satellite data, especially TTC data, in this paper AES algorithm was implemented in CTR operating mode on the FPGA which will then be implemented on LAPAN-A4 satellite onboard data handling (OBDH) as one of LAPAN-A4 satellite data security systems. To overcome the limited resource of FPGA, optimization is also done using basic iterative AES hardware reused method, which is to reuse hardware that has been designed n times for same operation for AES algorithm. From the results of implementation and optimization, efficiency of logic gates used is increasing almost a quarter less than the previous research.
Public and Private Vehicle Quantification and Classification using Vehicle Detection and Recognition

Traffic congestion in the Philippines is diverse consisting public and private vehicles. One approach of this is to design a system that can count, detect, recognize, and classify public and private vehicles from a surveillance video. This research introduces the development of the said system, to be used as a statistical data for implementing traffic rules. The dataset the researchers used consists of 13,600 images: 10,880 images for training and 2,720 images for testing. These were obtained from a gas station video source, for the vehicles frequently passing though in a gas station. The researchers used an algorithm called Convolutional Neural Network for the detecting and classifying vehicles.
Path tracking of an autonomous vehicle by means of an indirect adaptive neural controller

Due to nonlinearity of the dynamic models and the advantages of neural networks, we design an indirect adaptive neural controller for tracking the path of an autonomous vehicle. Our proposed algorithm, which includes a neural identifier, controls the lateral movement of the autonomous car. The updating laws for the identifying neural network and the controlling neural network are obtained by means of the gradient descent method. The identifying neural network is employed to estimate the Jacobian of the vehicle's lateral movement, which is then used for updating the parameters of the adaptive neural controller online. In this paper, both the radial basis function (RBF) and the multilayer perceptron (MLP) types of indirect adaptive neural controllers have been deigned. The simulation results corroborate the satisfactory performance of our proposed method in controlling the lateral motion of the examined autonomous vehicle. The results of RBF and MLP indirect adaptive neural controllers have also been compared in this work.
Fault Analysis for Lightweight Block Cipher and Security Analysis in Wireless Sensor Network for Internet of Things

The integration of wireless sensor nodes (WSNs) in the Internet of things (IoT) may generate new security challenges for establishing secure channels between low-power sensor nodes and Internet hosts. It includes a bunch of challenges right from redefining the existing ones from scratch of legacy codes to the current trend of proposing and designing new key establishment and authentication protocols. This paper is the successful attempt in resource constraint environment like wireless sensor network (WSN) to integrate cipher authentication and lightweight key management solutions in one bundle so that it can be deployed in IoT domains WSN is also a part of IoT. The cipher should be authenticated, lightweight, and should be flexible to use in the cryptanalysis. The cipher gives the better authentication techniques and performance but limited to the flexibility. Lightweight block cipher (LBC) uses less computation and secures the data with low computational cost and it is used for many purposes like radio-frequency identification (RFID) tags and sensor network nodes. Advanced Encryption Standard is applied in many devices for the encryption, but it cannot be applied to the smaller embedded devices like wearable devices, etc. LBC works for the many devices with low resources, and research on the LBC has been increased. In this research, Data Encryption Standard (DES) and analyzed algorithm are used to increase the efficiency in terms of solving time and Improved Fault Analysis (IAFA) is used to increases the security of the LBC in wireless sensor node for IoT.
Near-Duplicate Video Detection Based on Temporal and Spatial Key Points

With the development of the Internet and video editing technologies, there are a large number of near-duplicate videos on the Internet today. This can cause a lot of trouble in video content retrieval and copyright protection. It is time-consuming to manually classify a large number of near-duplicate videos A method is proposed here to automatically recognize and classify near-duplicate videos based on temporal and spatial key points. This method extracts key frame, proportion of video segment, average gray level and average segmentation ratio as the video key information, which is used to identify the approximate video. For near-duplicate video, this method has a good effect.
PP-SPEC: Securing Spectrum Allocation for Internet of Things

The explosive growth of the Internet of Things (IoT) is posing a high pressure on the spectrum resources. Dynamic spectrum allocation is an effective solution for supporting massive IoT devices with limited spectrum resources, often implemented in the form of spectrum auction. Among diverse auction formats, double auctions feature multiple buyers and multiple sellers, which is applicable to a wide range of IoT scenarios. Nevertheless, the existing double auction mechanisms mostly focus on the design of truthfulness and ignore the importance of privacy preservation. Hence, in this article, we propose PP-SPEC, a truthful and privacy-preserving double auction mechanism that takes spatial reuse, spectrum heterogeneity, and multiminded bidders into account, and provides full privacy in terms of asking prices, bidding values, and locations. To tackle the challenge of privacy-preserving comparison over ciphertext field, we carefully designed a set of generic privacy-preserving ciphertext comparison protocols (PIC), including basic PIC (B-PIC) and extended PIC (E-PIC), by integrating additive homomorphic encryption and garbled circuits. Furthermore, we have theoretically proved the truthfulness and security of PP-SPEC. We have implemented and evaluated PP-SPEC, whose experimental results show high performance and practicability of PP-SPEC for real-life applications.
An Efficient Provably Secure IBS Technique Using Integer Factorization Problem

Digital signatures are among the most fundamental primitives in cryptography, providing in an asymmetric setting, integrity, authenticity, and non-repudiation. An important technique for lightweight authentication is the identity-based signature (IBS). Recently, many attempts have been made to build IBS on the assumptions of the integer factorization problem (IFP), as they would stay secure in the future quantum era. In this article, we present an efficient provably secure IBS technique using IFP and show that it is protected against existential forgery and identity attack in the random oracle model. We also validate the outer performance of the technique in terms of security point of view and computational effectiveness. As it is intuitive, simple, and does not require paring, the resulting technique is of interest.
FPGA Implementation of Low Power Image Compression Technique

Booth multiplier has inherent advantage over normal binary multiplier. it's been adopted by Booth algorithm. This algorithm is replaced the conventional multiplier in the image compression algorithms like Discrete cosine transform (DCT) to reduce the power dissipation. The goal is to implement a Booth multiplier, Integrate with DCT and to prove booth multiplier is best so to offer comparison of binary multiplier and booth multiplier. Reducing the power dissipation area is the main goal in the design of portable computing, image processing, video processing and communication devices. Therefore different types of digital signal-processing chips are design with low power. In digital signal processing multiplier will play the major role to perform any applications. This proposed method will provide less power dissipation and less area.
A Combination of Two-Dimensional Hénon Map and Two-Dimensional Rational Map as Key Number Generator

Random numbers are utilised principally to create random strings or secure secret keys. The key is a very important part of encryption and depends on the strength and security in the encryption algorithm. Random number generators have used in a computer simulation, cryptography, statistical modelling, randomised design, and other areas where the production of an unexpected result is desirable. This paper aims to provide designing a new random key generator form on a concatenation between Two- Dimensional Hénon map and Two-Dimensional rational Map. The basic process of the proposed generator is to convert the output of the two chaotic maps into 64-bit numbers and to merge them with the XOR operation and other operations. The key resulted random sequences are studied by the NIST test group and also using the traditional statistical methods.
Spectral Bayesian Estimation for General Stochastic Hybrid Systems

General Stochastic Hybrid Systems (GSHS) have been formulated to represent various types of uncertainties in hybrid dynamical systems. In this paper, we propose computational techniques for Bayesian estimation of GSHS. In particular, the Fokker–Planck equation that describes the evolution of uncertainty distributions along GSHS is solved by spectral techniques, where an arbitrary form of probability density of the hybrid state is represented by a mixture of Fourier series. The method is based on splitting the Fokker–Planck equation represented by an integro-partial differential equation into the partial differentiation part for continuous diffusion and the integral part for discrete transition, and integrating the solution of each part. The propagated density function is used with a likelihood function in the Bayes’ formula to estimate the hybrid state for given sensor measurements. The unique feature of the proposed technique is that the probability density describing complete stochastic properties of the hybrid state is constructed without relying on the common Gaussian assumption, in contrast to other methods that compute certain expected values such as mean or variance. We apply the proposed method to the estimation of two examples: the bouncing ball model and the Dubins vehicle model. We show that the proposed technique yields propagated densities consistent with Monte Carlo simulations, more accurate estimates over the Gaussian based approach and some computational benefits over a particle filter.
A Survey of Cryptographic Techniques to Secure Genomic Data

Human genome is the unique identifier of the individual, which contains highly sensitive information about every individual. The sensitivity, longevity, and non-modifiable nature of genetic data has attracted much attention from the research community. Research on genomic data has taken a tremendous speed for finding a solution for curing various fatal diseases as like Alzheimer's, cancer, etc. Due to advances in the genome sequencing technology, it is now possible to obtain accurate and highly detailed genotypes at much-reduced costs. Such a huge research work in this domain has led us to cure some very serious problems, but at the same time, it comes up with a risk of providing security to this huge amount of highly sensitive data. In this paper, we have described some of the cryptographic applications of how we can make genomic data sharing and storage more private and secure.
Automatic Indonesian Image Caption Generation using CNN-LSTM Model and FEEH-ID Dataset

Image captioning is a challenge in computer vision research. This paper extends research on automatic image captioning generation in the Indonesian dimension. Description in Indonesian sentences is generated for unlabeled images. The dataset used is FEEH-ID, this is the first Indonesian image captioning dataset. This research is crucial due to unavailability of a corpus for image captioning in Indonesian. This paper will compare the experimental results in the FEEH-ID dataset with English, Chinese and Japanese datasets using the CNN and LSTM models. The performance of the model proposed in the test set provides promising results of 50.0 for the BLEU-1 score and 23.9 for BLEU-3, which is above average of the Bleu evaluation results in other language datasets. The merging model between CNN and LSTM displays pretty good results for the FEEH-ID dataset. The experimental results will be better with a larger dataset.
Recognition of Air-Drawn Alphanumeric Characters by Applying Slope Orientation Sequence Matching Algorithm Using Leap Motion Controller

The study focused on the recognition of letters and numbers written in free air. The information of motion trajectory is captured by a leap motion and plot as continuous stream of points. From the major points, lines will be integrated and major slopes are defined. As an application of geometry, major slopes are transformed into directions. This is also called as slope orientation. The compilation of the slope orientation referred as Slope Orientation Sequence is used to uniquely represent Alphanumeric Characters. The characters will then be recognized by the system once the generated slope orientation sequence matched with the reference slope orientation sequence in the system program. The process of sets and subsets is applied in the matching process of the algorithm. In this paper, the researchers proposed a new method that can classify Alphanumeric Characters written in air. The experiment result demonstrates the reliability of the proposed method.
Medical Social Media Systems - Implementation of the Android Application

The use of cutting-edge technologies such as the smartphones, smart bracelets, social media, and m-Health, and also mobile applications has begun to increase in medicinal services areas, due to factors such as the low cost, usability, mobility, multiple uses, and being reliable. This encouraged health institutions to increase their investment in healthcare technologies. This article presents the implementation of an integrated architecture for a mobile healthcare system, which is called Medical social media system. This system integrates wearable sensors, smartphones, and oriented social media, so as to enhance the healthcare services in the Iraqi environment. The system can be used by smartphones, running on the Android system and smart-watches. In addition, the website can be utilized by devices like desktops, notebooks, tablets, and smartphones which can be utilized maximally for data collection and analysis via user-system interactivity, utilizes wearable sensors, smartphones that automatically collect life activities information, such as exercises information like heart rate, the breathing rate, and body temperature. In order to provide healthcare services to patients, and medical workers in the Iraqi healthcare sector, the infrastructure still needs to significantly develop, and medical information needs to be shared amidst medical workers and to individuals in general. Moreover, it has been designed to store collected data in a database and to be able to produce reports to users.
Clustering Techniques and Research Challenages in Machine Learning

Clustering is a technique used to detect a group of similar data. Clustering is generally used for grouping the items with the similar information. Clustering always appears as an unsupervised learning technique. It appears as a common technique used for statistical data, machine learning and computer science analysis. Clustering is an unsupervised learning method that remains suitable for the investigation of interrelationships between samples to make a preliminary assessment of the sample structure. As it is very difficult for human to understand data in a high-dimensional space, clustering is employed. There are many real world applications for clustering. There are several ways to implement clustering algorithm such as centralized, distributed, connectivity, group, graph and density. Existing clustering algorithms are k-means, hierarchical, mean-shift, expectation-maximization, DBSCAN.
A Comparative Review of Various Techniques for Image Splicing Detection and Localization

Today, society is completely dependent on the utilization of Internet. With the increase in use of social media, millions of pictures are daily uploaded to Internet, providing opportunities for hackers to forge images. Various image editing softwares have opened the ways to image forgery, making forged images to look authentic. The manipulations of content have dissolved image trustworthiness and validation. Advancement in image forensics has introduced a number of image forgery detection techniques, to reestablish the realness in digital media. This paper endeavors to reveal various kinds of image forgery and its recognition techniques. The paper has also presented the performance of the existing splicing techniques by using quality metrics MCC and F-measure. Further, the paper evaluates different state of the art in splicing techniques, and it has been observed that the CFA artifact-based splicing localization achieves an accuracy of 99.75%. This paper features the significance of splicing localization and possible future research work in it.
A Detailed Investigation on Embedded Computing Systems for IoT Applications

The Internet of things(IoT) is a revolutionary paradigm in Information technology. IoT interconnects the various objects to internet thus being considered as virtual objects. It is considered that the IoT is the next big `thing' in internet. Internet uses the Internet protocol suite (IP/TCP) by which computer networks are interconnected in order to serve the users worldwide. Thus billions of objects which would be provided with a unique identifiers(UID's), will be outfitted using several actuators and sensors and connecting those to internet which can be accessed using technologies like real time services, RFID, embedded sensing etc. IoT aims at improving the quality of human life. It is necessary to transform the cities by improving transportation systems, infrastructure, providing waste management etc. Currently as the internet infrastructure is almost available everywhere, thus the embedded computing device can be exposed to the internet influence. Hence the real-time data can be analyzed and used to create desired outcomes. IoT has wide range of applications like Smart cities, Industrial automation, Smart home applications, healthcare, wearables, agriculture etc.
Discovering Web Services By Matching Semantic Relationships Through Ontology

The Web Service semantic annotation plays a major role in the Software Engineering. The Web Services requires the services to be semantically annotated. The Semantic annotation needs the services to be specified in the form of ontology, which shows the relationships. The Ontology is build in order to relate the web service with the keyword matching. With the help of the keyword the web services are discovered in a well formed manner. The keyword matching algorithm best suits to discover the web services based on the user request. The effective web services are identified and measured by using high throughput and low latency. The unwanted and unneeded services are filtered by using filtering method. The filtering is done and the final result is calculated and predicted by using the performance measures such as precision, accuracy, recall etc., Here, the identified web services are compared with and without applying semantic annotation. Finally, the effective web services are taken into account by calculating an accuracy measure which results in the performance factor.