Reviewerly/exHarmony huggingface dataset:
Cryptographic Hash Functions and Attacks – A Detailed Study

The term hash function has been used in computer science from quite some time and it refers to a function that compresses a string of arbitrary input to a string of fixed length.Cryptographic hash functions are one of the most important tools in the field of cryptography and are used to achieve a number of security goals like authenticity, digital signatures, pseudo number generation, digital steganography, digital time stamping etc.For the past few decades cryptographic hash function become the centre of attention in the cryptographic community.The security of hash function became an important topic as almost every day the world of hash function is facing a new attack.The present paper provides an extensive study on cryptographic hash functions with their applications, properties and detailed classification and also presents a detailed description of cryptographic hash algorithms.It also discusses a general classification of all kinds of possible attacks on hash function analyses some attacks on specific hash functions.
Montgomery Modular Arithmetic over Gaussian Integers

The Montgomery multiplication is an efficient method for modular arithmetic. Typically, it is used for modular arithmetic over integer rings to prevent the expensive inversion for the modulo reduction. In this work, we consider modular arithmetic over rings of Gaussian integers. Gaussian integers are subset of the complex numbers such that the real and imaginary parts are integers. In many cases Gaussian integer rings are isomorphic to ordinary integer rings. We demonstrate that the concept of the Montgomery multiplication can be extended to Gaussian integers. Due to independent calculation of the real and imaginary parts, the computation complexity of the multiplication is reduced compared with ordinary integer modular arithmetic. This concept is suitable for coding applications as well as for asymmetric key cryptographic systems, such as elliptic curve cryptography or the Rivest-Shamir-Adleman system.
Cryptanalysis and Enhancement of Harn-Lin’s Secret Sharing Scheme with Cheating Detection

Deployment of secrecy techniques is unquestionably necessitous for everyday communication over the unbarred and insecure network, as sensitive information craves bulwark from sliding into wrong whereabouts. Key negotiations using secret sharing methods, is an effectual and trenchant way to procure secrecy while transmitting information over the open internet. The secret keys so obtained, can be made use of, in encryption/decryption purposes. Methods like (t, n) threshold based, allow secret keys to be generated by combining split shares, where an individual split share have no value of its own. Existence of adversaries complicates matters by easily fooling honest participants. We identified unwanted features in Harn-Lin's secret sharing method, as extra participants/outsiders not belonging to a particular group, gets involved in secrecy reconstruction, a displeasing idiosyncrasy. Our proposed improvements over it for efficient threshold based secret sharing procedure with cheating detection is by effective inclusion of only a single extra share, however, from the dealer. Our work ensures outsiders do not mingle in group based secret key formations and also discuss ways to identify distinct cheaters.
Improved Depth Local Binary Pattern for Edge Detection of Depth Image

Depth images generally have low resolution, which leads to poor edge map from conventional edge detection methods. This necessitates the requirement of a consistent edge detection method for depth image applications. Therefore in this paper, improved depth local binary pattern (IDLBP) edge detection method based on local binary pattern is proposed for depth images. Experimentation is performed on synthetic and depth images to verify the validity of suggested technique. It is revealed that IDLBP is a fast, accurate and non-destructive edge detection technique. The depth local binary pattern, improved local binary pattern, hyper smoothing function based local binary pattern, Canny, Sobel and Laplacian of Gaussian edge detection techniques are also realized for comparative analysis. The effectiveness of suggested IDLBP algorithm is evaluated in terms of accuracy, Jaccard similarity index, specificity, sensitivity and dice similarity coefficient.
Studi Literatur Tentang Penggunaan Software Autodesk Revit Studi Kasus Perencanaan Puskesmas Sukajadi Kota Prabumulih

Computer Aided Design (CAD) has long been known to document architectural design work and information, replace the manual drawing method by hand and bring significant changes in project documentation. BIM (Building Information Modeling) is a paradigm shift replacing Conventional CAD. BIM is basically a digital platform for manufacturing virtual buildings. Autodesk Revit Software is a BIM (Building Information Modeling) based software that helps document projects more realistically with three-dimensional modeling. The purpose of this research in this Final Project is to design a 2-storey building using the Autodext Revit Software for modeling and drawing. The results of the study indicate the use of BIM-based applications in designing a building can simplify the design process, improve the efficiency of time, human resources and its subsequent stages. After going through an integrated process, the final model created has all the information from the architecture, structure, and output volumes automatically.
Multi-Agent Coordination Profiles through State Space Perturbations

The current work utilized a multi-agent reinforcement learning (MARL) algorithm embedded in a continuous predator-prey pursuit simulation environment to measure and evaluate coordination between cooperating agents. In this simulation environment, it is generally assumed that successful performance for cooperative agents necessarily results in the emergence of coordination, but a clear quantitative demonstration of coordination in this environment still does not exist. The current work focuses on 1) detecting emergent coordination between cooperating agents in a multi-agent predator-prey simulation environment, and 2) showing coordination profiles between cooperating agents extracted from systematic state perturbations. This work introduces a method for detecting and comparing the typically 'black-box' behavioral solutions that result from emergent coordination in multi-agent learning spatial tasks with a shared goal. Comparing coordination profiles can provide insights into overlapping patterns that define how agents learn to interact in cooperative multi-agent environments. Similarly, this approach provides an avenue for measuring and training agents to coordinate with humans. In this way, the present work looks towards understanding and creating artificial team-mates that will strive to coordinate optimally.
Depth image correction for intel realsense depth camera

&lt;p&gt;&lt;span&gt;Intel RealSense depth camera provides depth image using infrared projector and infrared camera. Using infrared radiation makes it possible to measure the depth with high accuracy, but the shadow of infrared radiation makes depth unmeasured regions. Intel RealSense SDK provides a postprocessing algorithm to correct it. However, this algorithm is not enough to be used and needs to be improved. Therefore, we propose a method to correct the depth image using image processing techniques. The proposed method corrects the depth using the adjacent depth information. Experimental results showed that the proposed method corrects the depth image more accurately than the Intel RealSense SDK.&lt;/span&gt;&lt;/p&gt;
Adversarial Examples in Arabic

Several studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples - perturbed inputs that cause DNN-based models to produce incorrect outputs. A variety of adversarial attacks have been proposed in the domains of computer vision and natural language processing (NLP); however, most attacks in the NLP domain have been applied to DNNs that were trained on English corpora. This paper proposes the first set of black-box adversarial attacks designed to perturb Arabic textual inputs. By intentionally violating the noun-adjective agreement in Arabic, two state-of-the-art DNN architectures are successfully fooled in the task of sentiment analysis, and classification accuracy was reduced by an average of 52.97% for the word-level BiLSTM model and 50.44% for the word-level CNN model. We believe that our findings will encourage other researchers to investigate the robustness of DNNs when applied to natural languages beyond English.
Vulnerability Scanning with Google Cloud Platform

Cloud is completely flipping the concept of business nowadays. Reducing capital and operational expenditure to a great extent already drew attention of big companies and industries. Not only startups but also large existing giant companies are shifting their data from local server to a public cloud and sometimes even taking the initiative to build a public cloud to host their sensitive data. A cloud can provide storage services, computing services and also infrastructure without any capital or operational expenses. From a single user to a multi-national million-dollar business can easily receive several types of services from a public cloud. There are numerous cloud providers in the market. Among the prominent ones, few names are mentioned here, such as Amazon AWS, Google Cloud Platform (GCP), and Microsoft Azure etc. GCP offers a wide array of services for many organizations and individuals around the world like other cloud providers. But the question is, all those data, confidential information, intellectual property of individual or industries safe enough? Information has become the most expensive commodity in modern days. A government can be shutdown, a transport system can be disabled, annoying spam calls, phishing emails, cyber bullying and more security threats are existing around us. Can we trust a public service provider? Hence our initiative aims at investigating that GCP is secure from inside attackers. Why we chose inside attackers? Well the idea is to check if GCP can detect and ignore an attack launched internally. In this case it should be secure enough to prevent from outsiders as well. The research investigation would involve Distributed Denial of Service (DDoS) attacks against a local virtual machine (VM) launched from GCP. There is a broad spectrum of VM's available in GCP compute engine service.
Gestures Based CAPTCHAs the Use of Sensor Readings to Solve CAPTCHA Challenge on Smartphones

We present novel CAPTCHA challenges based on user gestures designed for mobile. A gesture CAPTCHA challenge is a security mechanism to prevent malware from gaining access to network resources from mobile. Mobile devices contain a number of sensors that record the physical movement of the device. We utilized the accelerometer and gyroscope data as inputs to our novel CAPTCHAs to capture the physical manipulation of the device. We conducted an experimental study on a group of people. We discovered that younger people are able to solve this type of CAPTCHA challenges successfully in a short amount of time. We found that using accelerometer readings produces issues for some older people.
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review and New Framework

Recent applications of autonomous agents and robots, for example, self-driving cars, scenario-based trainers, exploration robots, service robots, have brought attention to crucial trust-related problems associated with the current generation of artificial intelligence (AI) systems. AI systems particularly dominated by the connectionist deep learning neural network approach lack capabilities of explaining their decisions and actions to others, despite their great successes. They are fundamentally non-intuitive black boxes, which renders their decision or actions opaque, making it difficult to trust them in safety-critical applications. The recent stance on the explainability of AI systems has witnessed several works on eXplainable Artificial Intelligence; however, most of the studies have focused on data-driven XAI systems applied in computational sciences. Studies addressing the increasingly pervasive goal-driven agents and robots are still missing. This paper reviews works on explainable goal-driven intelligent agents and robots, focusing on techniques for explaining and communicating agents perceptual functions (for example, senses, vision, etc.) and cognitive reasoning (for example, beliefs, desires, intention, plans, and goals) with humans in the loop. The review highlights key strategies that emphasize transparency and understandability, and continual learning for explainability. Finally, the paper presents requirements for explainability and suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots
A New Method for Fraud Detection in Credit Cards Based on Transaction Dynamics in Subspaces

This paper presents a new method for fraud detection in credit cards based on exploiting the dynamics of the card transactions. We hypothesize different behavior models in the use of the card between legitimate clients and fraudsters that are registered in the sequential pattern that follows the transactions. The method considers analyses in subspaces defined by two or three variables recorded in the transactions. From these subspaces, several dynamic features, such as transaction velocity and acceleration, are estimated as input vectors for a classification process. Linear and quadratic discriminant analysis and random forest are implemented as single classifiers. All the single classification results obtained for each of the subspaces are late fused to obtain an overall result using alpha integration algorithm. The proposed method was evaluated using a subset of real data with a very low fraud to legitimate transaction ratio. We demonstrated that the temporal dependence of card transactions exploited in different subspaces and fused to give an overall result improves the detection accuracy of fraud detection in credit cards.
Image Recognition and Annotation based Decision Making of CAPTCHAs for Human Interpretation

In current scenario of web based security, CAPTCHA security is trending and widely used in many applications. The major functional requirement of CAPTCHA is that it should be easy for the human interpretation but difficult for the bots to identify it. This feature leads to an intellectual effort for gaining accuracy. The proposed system is an image CAPTCHA which provides attractive interaction with the users that they could solve it without assistance of anyone. The main motive of this paper is to demonstrate that the CAPTCHA is secure and cannot be cracked and easier for Image Recognition and text annotation in decision-making of users to select and annotate the images where the bots cannot. The distorted images can be recognizable by users which are harder for bots that differentiates human and bots without affecting the human recognition. In this paper, selection based image CAPTCHA is dealt and an interactive and attractive image recognition and annotation based decision-making model is proposed in which 85% of participants felt easy in solving it and obtained a success rate of 97% in login access and also prevents DDoS attacks.
Deep learning based phase retrieval in quantitative phase microscopy

We propose and demonstrate a new phase retrieval method based on a deep neural network (DNN) structure. By inputting only one sample interferogram, measured from an off-axis holography based quantitative phase microscope (QPM), the DNN can output an accurate quantitative phase image of the sample without using a calibration interferogram, therefore significantly simplifying the measurements procedure. Importantly, our method can eliminate the need of performing phase unwrapping, therefore making it easy to achieve real-time phase retrieval in different program platforms. We used different types of cells as test samples to characterize the performance of our method, and we found that the accuracy of our DNNbased phase retrieval method is similar compared with the standard Fourier transform based phase method, while the background phase noise is reduced. Considering the experimental procedures and image processing steps are significantly simplified, we envision this new phase retrieval method will make QPM more easily accessible in bioimaging and material metrology applications in the future.
Need Analysis of Audio-Visual Media Development to Teach Science Materials for Young Learners

This study aims to obtain an overview of the current situation of the use of instructional media in science teaching process in elementary school, the perception and challenges faced by primary school teachers in delivering the digestive system subject matter and the formulation of learning media needs to be developed in science teaching on the digestive system materials. This study involved teachers and students in elementary schools around Surakarta in aimed to understand their accessibility potential by knowledge, types of media used, and to state their perceptions to adopt media in classroom. The results which are analyzed by using descriptive qualitative approach indicated that 1) the current use of instructional media in science learning process has not been carried out optimally. 2) There are challenges in the delivery of material, primarily abstract and conceptual materials since the teacher needs a media to visualize the process and explain it in detail and correctly so that students do not have misconceptions about the process of digestive system. 3) There is a need for learning media to be developed in learning the material, especially in form of audiovisual media. This result can be a consideration in developing science learning media for elementary school.
To Discovery The Cloud Services Authentication An Expert Based System Using Multi-Factor Authentication

In this paper, we proposed dependable operators' verification based cloud administration disclosure. In our proposed technique the specialist customer requires administration with explicit useful and specialized necessities. For protection concern, we give validation to every specialist in cloud by utilizing Multifactor confirmation process. By utilizing verification id specialists find the cloud administration dependent on administration motor. In our framework, we improve the security of the cloud web index by presenting new verification system. The validation procedure is finished by using the multiple elements like mystery key that is formed dependent on Advanced Encryption Standard calculation, biometric of client and the client's secret word accreditations. Centered Selection Contract Net Protocol is launched to identify the significant cloud administration and it assumes various jobs in cloud administration disclosure. The closeness amid the specialist's solicitation and cloud administration is figured dependent on administration details and purchaser administration utilizing Cloud cosmology. Each authentic specialist gives dependable cloud administrations to operator customers (individuals). The service capacity table identifies the cloud admin rundown and the associated ID and the admin solid cloud disclosure
Pentest on Internet of Things Devices

Internet of Things (IoT) is one of the key enabling technologies for an always-connected world and also a main enabler for generating information of interest in various application domains. A growing problem in recent years in this technology is security, as power-constrained devices that are typical of IoT applications may not always provide these implementations properly. These conditions can compromise entire environments and allow malicious agents to take control and perform malicious activities. In this article, we provide a summary of the principal vulnerabilities reported for IoT devices based on the OWASP Internet of Things Project, classified by test routine groups. Using models based on standard architectures to define and detail reproducible verification routines for each test, a selection of independent analyzes of each identified category was performed to ensure more comprehensive and accurate testing. Finally, the proposed routines are performed in a test environment to exemplify and ensure their operation, thus contributing to meeting the demand in the area for more accurate information and to assist in understanding the most common vulnerabilities.
Representing Multivariate Data by Optimal Colors to Uncover Events of Interest in Time Series Data

In this paper, we present a visualization system for users to study multivariate time series data. They first identify trends or anomalies from a global view and then examine details in a local view. Specifically, we train a neural network to project high-dimensional data to a two dimensional (2D) planar space while retaining global data distances. By aligning the 2D points with a predefined color map, high-dimensional data can be represented by colors. Because perceptual color differentiation may fail to reflect data distance, we optimize perceptual color differentiation on each map region by deformation. The region with large perceptual color differentiation will expand, whereas the region with small differentiation will shrink. Since colors do not occupy any space in visualization, we convey the overview of multivariate time series data by a calendar view. Cells in the view are color-coded to represent multivariate data at different time spans. Users can observe color changes over time to identify events of interest. Afterward, they study details of an event by examining parallel coordinate plots. Cells in the calendar view and the parallel coordinate plots are dynamically linked for users to obtain insights that are barely noticeable in large datasets. The experiment results, comparisons, conducted case studies, and the user study indicate that our visualization system is feasible and effective.
C4.5 Decision Tree Algorithm for Spatial Data, Alternatives and Performances

Using data mining techniques on spatial data is more complex than on classical data. To be able to extract useful patterns, the spatial data mining algorithms must deal with the representation of data as stack of thematic layers and consider, in addition to the object of interest itself, its neighbors linked through implicit spatial relations. The application of the classification by decision trees combined with the visualization tools represents a convenient decision support tool for spatial data analysis. The purpose of this paper is to provide and evaluate an alternative spatial classification algorithm that supports the thematic-layered data organization, by the adaptation of the C4.5 decision tree algorithm to spatial data, named S-C4.5, inspired by the SCART and spatial ID3 algorithms and the adoption of the Spatial Join Index. Our work concerns both data organization and the algorithm adaptation. Decision tree construction was experimented on traffic accident dataset and benchmarked on both computation time and memory consumption according to different experimentations: study of phenomenon by a single and then by multiple other phenomena, including one or more spatial relations. Different approaches used show compromised and balanced results between memory usage and computation time.