Reviewerly/exHarmony huggingface dataset:
Information security threats encountered by Malaysian public sector data centers

&lt;span&gt;D&lt;/span&gt;&lt;span lang="EN-AU"&gt;ata centers are primarily the main targets of cybercriminals and security threats as they host various critical information and communication technology (ICT) services. Identifying the threats and &lt;/span&gt;&lt;span&gt;managing the risks associated with data centers have become a major challenge as this will enable organizations to optimize their resources to focus on the most hazardous threats to prevent the potential risks and damages. The objective of this paper is to identify major ICT security threats to data centers in the Malaysian public sector and their causes. The data for this study was collected through interview sessions. A total of 33 respondents from various government organizations were interviewed. The results revealed that the technical threats, &lt;/span&gt;&lt;span lang="EN-MY"&gt;spyware, phishing, bluesnarfing threats, social engineering and virus, trojan, malware, ransomware, viral websites threats are the major categories of threats often encountered by the malaysian public sector organizations. The&lt;/span&gt;&lt;span&gt; causes for these threats &lt;/span&gt;&lt;span lang="EN-MY"&gt;are lack of budget, competent personnel, and manpower for security tasks, user awareness; lack of &lt;/span&gt;&lt;span&gt;compliances and monitoring; insufficient security policies and procedures as well as deliberate cyber attacks. The outcome of this study will give a greater degree of awareness and understanding to the ICT security officers, who are entrusted with data center security.&lt;/span&gt;
Knowledge Discovery And Virtual Tour To Support Tourism Promotion

Planning a tourism trip is an important part for tourists so that their tour is satisfying. Service bureaus that have a function to help provide information and prepare tourist travel plans for tourists often provide random destination choices because they do not know the pattern of selecting tourist destinations. This will be detrimental to tourists when service bureaus make wrong tourism travel plans. Tourists also often find it difficult to determine which tourist destination to go to because they do not know the environmental conditions in tourist destinations. To overcome this problem, in this study, knowledge discovery and virtual tours are carried out to increase the promotion of tourism. Knowledge discovery is finding information or knowledge. Knowledge discovery uses data mining techniques to perform data analysis and find patterns. The data mining model that can be used is the frequent pattern by looking for Association Rule Mining from the data. Virtual tour is a technique that can provide 360 ??+ 180 degree images. The virtual tour will be able to show the overall environmental conditions at the tourist destination. The results that have been obtained are in the form of a quick recommendation of tourist attractions in accordance with the country of origin of tourists based on the Association rule mining values. The virtual tour has presented a 360 degree panoramic photo view to inform the environment situation in the place commented by the system.
Czech Sign Language Single Hand Alphabet Letters Classification

The paper deals with the classification of images of Czech sign language alphabet, single handed version in particular, without diacritics. The classification is performed by convolution neural network using TensorFlow computational library. Network topology, data acquisition and automatic labelling and obtained results are described in the paper. The accuracy on the test data - captured images of a person not previously seen by the network - was over 87%.
An Efficient Security Model for Password Generation and Time Complexity Analysis for Cracking the Password

Passwords tend to be one of the most popular approaches to protect operating systems and user’s data also. Most businesses rely on password protection schemes, and secure passwords are incredibly necessary to them. The proposed model typically aims to impose protection by forcing users to obey protocols to build passwords. For user protection, password has become a prevailing method in terms of exposure to scarce tools. The main problem with password is its consistency or power, i.e. how simple (or how difficult) a third person can be "assumed" to enter the tool that you use while claiming to be you. In operating systems, text-based passwords remain the primary form of authentication, following major improvements in attackers' skills in breaking passwords. The proposed Random Character Utilization with Hashing (RCUH) is used for generation of new passwords by considering user parameters. The proposed model introduces a new framework to design a password by considering nearly 10 parameters from the user and also analyze the time for cracking the generated password to provide the system strength. The proposed model aims to generate an efficient security model for password generation by considering several secret parameters from the user. To break a set of consistency passwords, analysis is also performed on time for password cracking. The tests show a close positive correlation between guessing complexity and password consistency. The proposed model is compared with the traditional password generation and cracking models. The proposed model takes much time in cracking the password that improves the systems security.
Innovative Technologies in Education

The innovative approach reflects the current state of the education system in the country. Subjects of the innovative infrastructure of the educational process ensure the socio-economic development of the economy. The task of expanding the cooperation of the business environment with scientific and educational organizations in order to provide the economy with qualified specialists is being updated. An innovative approach to education involves the processes of improving pedagogical technologies, a set of methods, techniques and means of teaching. The paper uses analytical methods, including content analysis, and offers a comprehensive approach. It allows us to present the methodological support of the issue under consideration in the unity of a practical-integrated approach to the learning process, a project approach within the needs of the business environment and tools of the business incubator for the development of youth innovative entrepreneurship by creating an environment in which students receive additional competencies by directly participating in the development and creation of new products. Project training solves the most important task of integrating science and business, which will give a high-quality result of cooperation in the form of an intellectual product in which the business is interested. An innovative approach in the education system will ensure the competitiveness of educational institutions and the human potential of the economy. It is important for the education system how quickly it changes organizationally and instrumentally, and how it is able to create new institutions. Subjects of the innovative infrastructure of the education system ensure socio-economic development and transformation of the education system. Scientific and methodological support of educational organizations is represented in the unity of socio-economic, psychological and pedagogical components.
Random Sampling-Arithmetic Mean: A Simple Method of Meteorological Data Quality Control Based on Random Observation Thought

The quality control of meteorological data has lately received great attention for its important significance to national ecological security and military security. However, the observational quality of the data has made it challenging to the quality control of meteorological data. In an effort to overcome this challenge, a random sampling-arithmetic mean (RS-AM) method based on the random observation method is proposed to solve the problem. Firstly, the reason why the arithmetic mean is not ideal for truth estimation is proved in the paper. Secondly, the method evaluates the goodness of fit between the expected distribution and the sampling distribution by repeatedly extracting the random observation vector based on the random sampling model, to find the random observation vector closest to the expected distribution. Then, the distance between the median and arithmetic mean of each set of claims is calculated by the distance formula, and the claims with the minimum distance are selected. The random extraction is continued on the selected set of claims until the stop condition is met. Finally, the truth is calculated by the method of arithmetic mean from the selected claims. Moreover, the convergence of this method is proved by theoretical derivation. Experimental results show that the proposed RS-AM method can effectively solve the problem of data observation quality. And, compared with the conflict resolution on heterogeneous data (CRH) method, the RS-AM method reduces 1.5% on MSE and 2.9% on RMSE while ensuring the error rate is basically the same.
A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

Using convolution neural network (CNN) for face recognition is being widely research with a promising significant in applications and it is interested by many authors.Moreover, the CNN model has brought successful applications in practice such as detection and identification face of people on Facebook users' photos application, they use DeepFace model.There are many articles which proposed CNN models for face recognition with using some modifications of popular models of large architectures such as VGG, ResNet, OpenFace or FaceNet.However, these models are large complexity for some applications in reality with limitations of computing resources.This paper proposes a design of CNN model with moderate complexity but still ensures the quality and efficiency of face recognition.We run experiments for evaluating the model on some popular datasets, the experiment shows effective results and indicates that the proposed model can be practically used.
Flood Disaster Identification and Decision Support System using Crowdsource Data Based on Convolutional Neural Network and 3S Technology

&lt;span&gt;Every flood causes damages to many lives and properties. Moreover, it affects the economy and lifestyle of people in the society in a short period and a long period. In consequence, this research would demonstrate techniques and flood detection analyzed through digital images and web application development for receiving reports and inspection of flood situation in every area. The process requires crowdsource data and uses 3S technology so it will receive the accurate and real-time data for making a decision in the flood management and aids to the people in the disastrous area. In this research, Convolutional neural network was applied for flood detection and classification using digital images and data from people or the victims. According to the study, it was found that convolutional neural network for flood classification has accuracy of data at the high level or 95.50%, 93.00%, 97.89%, and 0.91 which are the results of accuracy, producer accuracy, user accuracy, and kappa statistics, respectively. Besides, the use of this technique saves cost, time, and labors. Furthermore, the method could be applied to other disasters such as landslide, earthquake, and fire. It is able to monitor the incident in each type of disasters and also examines the damaged site after the incident&lt;/span&gt;&lt;em&gt;&lt;span&gt;.&lt;/span&gt;&lt;/em&gt;
GEOCHEMICAL CHARACTERISTICS OF GOLD-BEARING GRANITOIDS AT AYANFURI IN THE KUMASI BASIN, SOUTHWESTERN GHANA: IMPLICATIONS FOR THE OROGENIC RELATED GOLD SYSTEMS

This study investigates auriferous granitoids from the Esuajah and Fobinso pits within the Ayanfuri environment in the Paleoproterozoic Kumasi basin. The aim is to establish the geochemical characteristics of the granitoid gold ores and the possible deposit type which may influence mineral project development. 13 major and 51 trace elements were analyzed using XRF and ICP-MS devices, respectively. The granitoids are mainly classified as granodiorite that crystallized from a calc-alkaline magma series. The Fobinso granodiorite derived from the partial melting of the Birimian metasedimentary rocks, while the Esuajah granitoid derived from igneous rock melts. The granitoid are linked to magma source depleted in mantle material that contains crustal components through subduction processes. Major oxides of the granitoid vary lowly from the average background values derived for basin type granitoid in such terrains. Generally, the granitoid are enriched in Large Ion Lithophile Elements (LILE), while High Field Strength Elements (HFSE) and base metals are within background values when compared to Primitive Mantle (PM) values. Gold mineralisation is associated with Ag, As, Bi, Sb, Te, Pb and S in the peraluminous granitoids. Geochemical characteristics and field observations identify the deposit style as an orogenic related gold deposit type.
Nearest Neighbor Transformation of Quantum Circuits in 2D Architecture

In recent years, quantum computing has received extensive attention for its superior efficiency and application potential. For some physical architecture, one qubit can only interact with its adjacent qubits, SWAP gates are inserted to make a quantum circuit nearest neighbor compliant. The initial qubit placement algorithm in a 2D grid structure is proposed based on the constructed interaction cost metric model, and the method of dynamic grid decision is given. In order to obtain a better way of inserting SWAP gates, the gate level and circuit level interaction routing policies are put forward, and a heuristic pruning of interaction routing records is employed so as to reduce the runtime and additional quantum cost. Experimental results show that our proposed methods can achieve better performance than existing methods, and the average optimization rate of quantum cost is 21.76% and 17.23% respectively.
Dynamic Topic Tracker for KB-to-Text Generation

Recently, many KB-to-text generation tasks have been proposed to bridge the gap between knowledge bases and natural language by directly converting a group of knowledge base triples into human-readable sentences.However, most of the existing models suffer from the off-topic problem, namely, the models are prone to generate some unrelated clauses that are somehow involved with certain input terms regardless of the given input data.This problem seriously degrades the quality of the generation results.In this paper, we propose a novel dynamic topic tracker for solving this problem.Different from existing models, our proposed model learns a global hidden representation for topics and recognizes the corresponding topic during each generation step.The recognized topic is used as additional information to guide the generation process and thus alleviates the off-topic problem.The experimental results show that our proposed model can enhance the performance of sentence generation and the off-topic problem is significantly mitigated.
AND-gates in ZX-calculus: Spider Nest Identities and QBC-completeness

In this paper we exploit the utility of the triangle symbol which has a complicated expression in terms of spider diagrams in ZX-calculus, and its role within the ZX-representation of AND-gates in particular.First, we derive spider nest identities which are of key importance to recent developments in quantum circuit optimisation and T-count reduction in particular.Then, using the same rule set, we prove a completeness theorem for quantum Boolean circuits (QBCs) whose rewriting rules can be directly used for a new method of T-count reduction.We give an algorithm based on this method and show that the results of our algorithm outperform the results of all the previous best non-probabilistic algorithms.
IOT BASED STATISTICAL APPROACH FOR HUMAN CROWD DENSITY ESTIMATION-DESIGN AND ANALYSIS

In this paper we present an IoT based solution that can reduce the complexity of crowd estimation. About the human crowd estimation many technique are in existence but now a day’s more work are going on in the field of IoT, because this is era of IoT and most of the every organization is shifted towards IoT based system. So we are also proposed this system in this field and we are using the Respberry Pi-3 which are having quad core processor that can very useful and gives better result and gives accurate number even in the humans are very close to each others. This IoT based model can easily implements in the crowded areas and monitor the same in this area. The camera module in this model also helps to differentiate between human and other bodies. As this is a mobile model it can easily fix on the walls of street light and in the time of dark or in night the camera capture clear image for process in the presence of street light. So that this model gives better result almost 70% better result in compare to exiting approaches.
ASL Recognition Based on Kinematics Derived from a Multi-Frequency RF Sensor Network

As a means for leveraging technology in the design of Deaf spaces, this paper presents initial results on American Sign Language (ASL) recognition using RF sensing. RF sensors are non-contact, non-invasive, and protective of privacy, making them of special interest for use in personal areas. Using just the kinematic properties of signing as captured by the micro-Doppler signatures of a multi-frequency RF sensor network, this paper shows that native and imitation signing can be differentiated with %99 accuracy, while up to 20 ASL signs are recognized with an accuracy of %72 or higher.
THE APPLICATION OF COOPERATIVE LEARNING: JIGSAW II TECHNIQUE IN IMPROVING STUDENTS’ READING COMPREHENSION OF EXPOSITORY TEXT

The research was conducted to find out whether or not the use of Jigsaw II technique improves the reading comprehension significantly better than the use of the conventional method of the eleventh grade students of SMA Negeri 8 Takalar, it covered students’ literal comprehension, inferential comprehension and critical comprehension of expository text and whether or not the students are interested in the use of Jigsaw II technique in learning reading of expository text. This research method employed was quasi-experiment. The research data were obtained through the test and questionnaire which were analyzed by SPSS 17.0 program. Data of test were analyzed using T-test to know students’ reading comprehension in terms of literal, inferential, and critical of expository text. Data of the questionnaire were analyzed by using descriptive statistics to see the students’ interest in joining the reading class of expository text through Jigsaw II technique. The research findings indicated that (1) Jigsaw II technique gave a higher contribution to the students’ reading comprehension of expository text. The mean score of pretest of the experimental and control group was significantly different after treatment by using Jigsaw II technique. The mean score of posttest of the experimental group was higher than the control group (84.16 &gt; 62.91). Thus, the use of Jigsaw II technique was effective. Besides, all the three levels of comprehension improved after doing treatment; the literal comprehension got the highest improvement was 92.19&gt;76.69. It was followed by inferential comprehension which got 83.75&gt;66.25, and critical comprehension which got76.56 &gt; 42.81.
Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization

Recently, two evolutionary algorithms (EAs), the glowworm swarm optimization (GSO) and the firefly algorithm (FA), have been proposed. The two algorithms were inspired by the bioluminescence process that enables the light-mediated swarming behavior for mating or foraging. From our literature survey, we are convinced with much evidence that the EAs can be more effective if appropriate responsive strategies contained in the adaptive memory programming (AMP) domain are considered in the execution. This paper contemplates this line and proposes the Cyber Firefly Algorithm (CFA), which integrates key elements of the GSO and the FA and further proliferates the advantages by featuring the AMP-responsive strategies including multiple guiding solutions, pattern search, multi-start search, swarm rebuilding, and the objective landscape analysis. The robustness of the CFA has been compared against the GSO, FA, and several state-of-the-art metaheuristic methods. The experimental result based on intensive statistical analyses showed that the CFA performs better than the other algorithms for global optimization of benchmark functions.
The use of Artificial Intelligence for the Detection of Covert Channels attacks in New Generation Internet Protocol IPv6

Being instrumental to the Qatar National Vision 2030 activities and following up with “achieving security, stability and maintaining public safety” objectives, the present paper aims to safeguard the information and monitor internet communications in the region effectively. The increased dependence on internet-based technologies in all facets of life challenges the government and policymakers with the need for an effective shield mechanism against passive and active security violations. The present paper adopted an artificial intelligence-based solution for detecting suspicious communications. Further, a dataset created which was generated by simulating a number of attacks and normal communications was used for both training and testing the model. The proposed model produces an accuracy of 100% with a score of 1 for precision, recall, and F1 score. The project forward is a novel, efficient approach for detecting covert channels and suspicious communication.
Feature-Based Sentiment Analysis for Arabic Language

In light of the spread of e-commerce and emarketing, and the presence of a huge number of reviews and texts written by people to share views on products, it became necessary to give attention to extracting these opinions automatically and analyzing the feelings of the reviewers.The goal is to obtain reports evaluating products and contribute to improve services at a glance.Sentiment Analysis is a relatively recent study that deals with the processing of natural texts published in web sites and social networks.However, the processing of texts written in the Arabic language is one of the challenges that specialists face because people do not rely on standard Arabic, writing people in spoken/colloquial languages and use various dialects.This paper will present feature-based sentiment analysis for Arabic language which works on text analysis technique that breaks down text into aspects (attributes or components of a product or service), and then allocates each one a sentiment level (positive, negative or neutral).
A.I. NEURAL NETWORKS INFERENCE INTO THE IOT EMBEDDED DEVICES USING TINYML FOR PATTERN DETECTION WITHIN A SECURITY SYSTEM

The paper presents the implementation challenges of a proof of concept development for image processing with artificial intelligence neural network into an embedded device.Because of the hardware constraints of the embedded devicee.g.16 KB RAM and 128 KB of EEPROM, the deep learning/the training and the model of the neural network is processed in cloude.g.Google Cloud Platform -AI -Artificial intelligence, as shown in second section.After this step, the trained neural networks model, values and tensors are translated with TinyML into native code for the embedded devices and deployed on a specific hardware platforme.g.Arduino Nano 33 BLE Sense or SparkFun Edge Development Board Apollo3 Blue -for the neural network inferencese.g.person detection or NLP into specific area, as described in third section.The last section shows the conclusions and the security challenges for deploying neural networks into embedded systems which are used for security systems such as: monitoring and surveillance cameras, drones visual computing for securing field areas, IoT systems, etc.