Reviewerly/exHarmony huggingface dataset:
Proposed L-Shape Pattern on UFS ACM For Risk Analysis

At this cloud age, there is tremendous growth in business, services, resources, and cloud technology.This growth comes with risk of unsafe, unordered, and uncertainty due to unauthorized access and theft of confidential propriety data.Our objective is to model around Read, Write and Execute to resolve these unordered, unsafe, and uncertain issues.We will develop a L-Shape pattern model matching UFS ACM to minimize the accessibilities based on RIGHT & ROLE of the resources and maximize the quality of services for safety and high availability.The preventive, detective, corrective (PDC) services are the major roles for all levels of management to coordinate, control the multiple technologies and resources which are working simultaneously.It will be more ordered, accountable, and actionable on real-time access control mechanism for scalabilities, reliability, performance, and high availability of computational services.We have to make safer, certain, unified, and step-by-step normalization by applying this UFS ACM mechanism on UNIX operating system.This proposed research paper covers a wide range of areas covering optimization, normalization, Fuzzy Low, and Risk assessment.
A Static Method to Discover Deserialization Gadget Chains in Java Programs

Java deserialization vulnerability has become a server security problem at present. An attacker can execute arbitrary commands by submitting a malicious object for deserialization. However, existing methods for detecting Java deserialization vulnerability only find program paths to deserialize input data, it doesn't consider whether the program itself can build malicious deserialization objects, which may bring false positives. In order to solve this problem, people need to discover gadget chains in the program manually, however, it takes a lot of time. In this paper, we present a method for discovering gadget chains automatically and build a tool named Hawk Gadget in practice. The method is based on static analysis that analyzes every method in the program with control flow graph. We also Compare the Hawk Gadget with Gadget Inspector that is a tool to find gadget chains, the result shows that Hawk Gadget can find more gadget chains and has a lower false-positive rate. In addition, we use gadget chains found by Hawk Gadget in Apache-commons-collections-3 to generate malicious objects for CVE-2016-4437, and verify the effectiveness of gadget chains.
Visual Navigation System for Autonomous Drone using Fiducial Marker Detection

Drones have been quickly developing for civilian applications in recent years.Because of the nonlinearity of the mathematical drone model, and the importance of precise navigation to avoid possible dangers, it is necessary to establish an algorithm to localize the drone simultaneously and maneuver it to the desired destination.This paper presents a visualbased multi-stage error tolerance navigation algorithm of an autonomous drone by a tag-based fiducial marker detection in finding its target.Dynamic and kinematic models of the drone were developed by Newton-Euler.The position and orientation of the drone, related to the tag, are determined by AprilTag, which is used as feedback in a closed-loop control system with an Adjustable Proportional-Integral-Derivative (APID) controller.Parameters of the controller are tuned based on steady-State error, which is defined as the distance of the drone from the desired point.The sequence of path trajectory, that drone follows to reach the desired point, is defined as a navigation algorithm.A model of the drone was simulated in a virtual outdoor to mimic hovering in complex obstacles environment.The results present satisfactory performance of the navigation system programmed by the APID controller in comparison with the conventional Proportional-Integral-Derivative (PID) controller.It can be ascertained that the proposed navigation system based on a tag marker in the closed-loop control system is applicable to maneuvering the drone autonomously and useful for various industrial tasks in indoor/outdoor environments.
Hindrance to Requirements Engineering During Software Development with Globally Distributed Teams

With the increase in the availability of skilled software engineers, the process of global software development is being increasingly adopted by organizations, at a relatively lesser cost.This has led to software processes being more viable in a progressive manner for several reasons that include better communication technologies and the levels of maturity seen in the software industry.That being said, Organizations that undertake the decision of adopting Global software development should realize that this process isn't exactly a risk-free action and there have been many failures reported with regard to it.The existing bools of globally distributed projects portray a number of challenges.During the interactive phase, the need for particular consideration towards the requirements of the client and the globally dispersed distributed software provider teams has been indicated.This paper makes use of surveys collected from IT professionals and people working in the software industry in order to present realistic insights gathered from them.
MKULTRA (Demo)

MKULTRA is an experimental game that explores novel AI-based game mechanics. Similar in some ways to text-based interactive fiction, the player controls a character who interacts with other characters through dialog. Unlike traditional IF, MKULTRA characters have simple natural language understanding and generation capabilities, sufficient to answer questions and carry out simple tasks. The game explores a novel game mechanic, belief injection, in which players can manipulate the behavior of NPCs by injecting false beliefs into their knowledge bases. This allows for an unusual form of puzzle-based gameplay, in which the player must understand the beliefs and motivational structure of the characters well enough to understand what beliefs to inject.
Penerapan Algoritma Apriori pada Sistem Informasi Inventori Toko

In this contemporary era, meals wishes are a vital want for all people, a variety of commercial enterprise companies that sell their products are coming and growing quite significantly, besides that, dynamic web users in this country are growing quite rapidly every year. Resulting in business actors competing with each other to explore various technical options in terms of data processing to find out information on the availability of incoming goods and goods sold. In this study, a business still uses a manual system here and a computerized system will be created to assist business people in processing goods data in their business, then obtain information related to inventory and profit income. This web-primarily based totally stock statistics device additionally applies apriori information mining to decide income styles on the way to later grow to be patron advice statistics. The results obtained in this study are the creation of a web-based inventory information system by applying apriori data mining aimed at Toko Tri.
Sistem Informasi Penjualan Daging Menerapkan Model User Centered Design Berbasis Web

PT. Safana Food Makmur is a business that is engaged in selling meat, whose shop is located in the city of Depok, in sales it still uses a manual transaction system so that consumers who want to buy or view available product information must come directly to the store. And for the location of the store, only a few buyers know about it because information about the store is very limited only by word of mouth. So from the existing problems, a website-based sales information system was created, this information system in its design uses MySQL for database management, PHP programming language and with the help of the Code Igniter framework. From the research resulted in a sales information system that will be used by PT. Safana Food Makmur, this system is able to assist in product sales and promotion so as to increase sales.
Expert System Mengatasi Anxiety Disorder Pada Mahasiswa Dalam Menghadapi Tugas Akhir Metode Backward Chaining

Anxiety Disorder is a psychological disorder that can be experienced characterized by the emergence of various symptoms which cause the sufferer to experience anxiety that is so great and excessive that it is very possible to affect the sufferer psychologically or physically. The obstacles that are most often experienced by final year students in completing their final assignments are the emergence of excessive feelings of anxiety, feeling restless, easily tired, difficult to concentrate, empty thoughts. irritability, muscle tension, difficulty sleeping, cold fingers, faster heart rate, cold sweats, headache, decreased appetite, sleeplessness and chest tightness which is often referred to as Anxiety Disorder. The relevance of computerization and technology in expert systems in diagnosing anxiety disorders in final year students is needed to help students find solutions to their problems. Therefore we need a method that can assist the analysis of the expert system design that will be made in this study, namely the Backward chaining method. Backward Chaining is an inference method that works backwards towards the initial conditions. The process starts from the Goal (which is in the THEN section and the IF-THEN rule), then the search starts to run to match whether the facts match the premises in the IF section. This technological advancement can help transfer human understanding into a system form so that it can be used by many people and is not limited by time without replacing the role of humans.
Assessment of the Impact of Information Security Awareness Training Methods on Knowledge, Attitude, and Behavior

Technology is changing the way we work more than ever before.Therefore, it is critical to understand the security threats associated with these advanced tools to protect systems and data.Security is a combination of people, processes, and technology.Thus, to counter cyber-threats effectively, information security awareness (ISA) programs are an essential cornerstone of enterprise security.There are many ways in which information security knowledge can be delivered.In this paper, we have conducted an experiment to test the impact of multiple intervention strategies on knowledge, attitude, and behavior.The HAIS-Q was used to evaluate the effectiveness of training methods on the employees.Our study suggests that all the methods raised knowledge equivalently.However, having more than one delivery method to convey the same message had more impact on users' attitudes.When it comes to behavioral change, however, text-based and game-based training formats performed better than their counterparts.Additionally, employees' tendency in engaging in self-education activities and participating in future awareness programs were influenced by the intervention strategy.These findings have important implications, as ISA programs should be designed in a way that positively influences the mindset of employees and motivates them to embrace security practices in their daily activities.
Deep Learning Approach for Divergence Behavior Detection at High Density Crowd

At high-density crowd gatherings, people naturally escape from the region where any unexpected event happens. Escape in high-density crowds appears as a divergence pattern in the scene and timely detecting divergence patterns can save many human lives. In this paper, we propose to physically capture crowd normal and divergence motion patterns (or motion shapes) in form of images and train a shallow convolution neural network (CNN) on motion shape images for divergence behavior detection. Crowd motion pattern shape is obtained by extracting ridges of Lagrangian Coherent Structure (LCS) from the Finite-Time Lyapunov Exponent (FTLE) field and convert ridges into the grey-scale image. We also propose a divergence localization algorithm to pinpoint anomaly location(s). Experimentation is carried out on synthetic crowd datasets simulating normal and divergence behaviors at the high-density crowd. Comparison with state-of-the-art methods shows our method can obtain better accuracy for both divergence behavior detection and localization problems.
Presupposition Triggers: Iraqi EFL Students' Strategies in Approaching Presupposition

EFL students are observed to face various difficulties in approaching pragmatic issues; therefore, they resort to numerous strategies to arrive at convincing pragmatic interpretations, among these issues, presupposition constitutes one of the problematic areas for students. Thus, this paper aims at detecting the most common strategies taken on by Iraqi EFL university students in approaching the pragmatic interpretations of presupposition, eliciting the reasons behind presupposition failure and find out the possible solutions. Fifty Iraqi EFL students of the Department of English, College of Arts, Mustansiriyah University during the academic year 2021-2022 have been administered to a test. The paper concludes that Counterfactual Conditionals trigger ranks first since it scores the highest ratio while Implicative verbs trigger represents the lowest ratio. Most EFL students succeed to derive the suitable pragmatic interpretations of presupposition. Consequently, it can be noted that resolution strategy is preferred to the accommodation and rejection strategies.
Learning a Priority Ordering for Prioritized Planning in Multi-Agent Path Finding

Prioritized Planning (PP) is a fast and popular framework for solving Multi-Agent Path Finding, but its solution quality depends heavily on the predetermined priority ordering of the agents. Current PP algorithms use either greedy policies or random assignments to determine a total priority ordering, but none of them dominates the others in terms of the success rate and solution quality (measured by the sum-of-costs). We propose a machine-learning (ML) framework to learn a good priority ordering for PP. We develop two models, namely ML-T, which is trained on a total priority ordering, and ML-P, which is trained on a partial priority ordering. We propose to boost the effectiveness of PP by further applying stochastic ranking and random restarts. The results show that our ML-guided PP algorithms outperform the existing PP algorithms in success rate, runtime, and solution quality on small maps in most cases and are competitive with them on large maps despite the difficulty of collecting training data on these maps.
Cost effective hybrid genetic algorithm for workflow scheduling in cloud

Cloud computing plays a significant role in everyone’s lifestyle by snugly linking communities, information, and trades across the globe. Due to its NP-hard nature, recognizing the optimal solution for workflow scheduling in the cloud is a challenging area. We proposed a hybrid meta-heuristic cost-effective load-balanced approach to schedule workflow in a heterogeneous environment. Our model is based on a genetic algorithm integrated with predict earliest finish time (PEFT) to minimize makespan. Instead of assigning the task randomly to a virtual machine, we apply a greedy strategy that assigns the task to the lowest-loaded virtual machine. After completing the mutation operation, we verify the dependency constraint instead of each crossover operation, which yields a better outcome. The proposed model incorporates the virtual machine’s performance variance as well as acquisition delay, which concedes the minimum makespan and computing cost. One of the most astounding aspects of our cost-effective hybrid genetic algorithm (CHGA) is its capacity to anticipate by creating an optimistic cost table (OCT) while maintaining quadratic time complexity. Based on the results of our meticulous experiments on some real-world workflow benchmarks and comprehensive analysis of some recently successful scheduling algorithms, we concluded that the performance of our CHGA is melodious. CHGA is 14.58188%, 11.40224%, 11.75306%, and 9.78841% cheaper than standard Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Cost Effective Genetic Algorithm(CEGA), and Cost-Effective Load-balanced Genetic Algorithm (CLGA), respectively.
Acute Erythroleukemia (AML-M6) in one-year-old boy: a case report

Background: Acute Erythroleukemia (AML-M6) is a rare case with approximately 3-4% of all Acute Myeloid Leukemia (AML) cases. AML-M6 is less common in the pediatric population, with an incidence of &lt;1% of all pediatric leukemia cases. This case study aims to evaluate Acute Erythroleukemia (AML-M6) in a one-year-old boy at Prof. dr. IGNG Ngoerah Denpasar. Case Presentation: A one-year-old boy with clinical manifestations of fluctuating fever for a month, paleness, bleeding gums, bruising all over his body, swelling of both eyelids and splenomegaly. Peripheral Blood Smear (PBS) examination revealed bicytopenia with moderate normochromic normocytic anemia, severe thrombocytopenia and leukocytosis. A hypercellular picture was obtained from the examination of Bone Marrow Aspiration (BMA) with an increase in erythroblast activity of 74%, proerythroblast at 50% and normal myeloblasts activity at 10%. The patient was diagnosed with Acute Erythroleukemia (AML-M6). The patient died on day 16 of treatment. Conclusion: Based on the examination results, it was concluded that the patient was diagnosed with AML-M6 according to the PBS and BMA evaluation.
Implementation of smartcity in the field of personnel, mobile attendance case study

The smart city concept is a service-based smart city arrangement concept, which is transparent and plays a role in making it easier for the public to get information quickly and accurately. There are so many problems that can arise if the local government still applies employee absenteeism manually. One of the smart city implementation efforts that local governments can do is in the field of personnel, namely the development of mobile-based online attendance applications. This is to ensure that employee attendance data is kept safe, current, and accurate. The online attendance application must be able to ensure that all incoming data will be directly stored in the cloud. The leadership can access employee attendance data anytime, anywhere, directly from the application. Agencies can view photos, real-time entry or exit times, and location details in the form of GPS coordinates.
Comparative study on bibliometric data of cyber attacks on financial institutions

Cyber-attack is an attempt by the cyber-criminal to steal, modify, and disable computer data or information systems. There are different types of cyber-attacks performed by the cyber-criminal. The financial sector is one of the most vulnerable sectors as it deals with money. With the rise in the amount of cyber-attacks also a cyber security-related issue, there is a need to provide an overview and spread awareness regarding cyber security. This paper has compared bibliometric data of two databases- Scopus and Web of Science Core Collection on cyber-attacks in the financial sector. The database was compared on seven different parameters. This paper performed a detailed analysis by looking at the number of papers, year of publication, citation analysis, most occurred keywords, etc. The analysis concluded that there is an increase in the paper published on cyber-attacks on the financial sector from 2016. More papers are being cited since 2016.
Visually Impaired Person Assistance Based on Tensor FlowLite Technology

The most exciting thing about computer visualization is to detect a Real time object application system.This is abundantly used in many areas.With the more increase of development of deep learning such as self-driving cars, robots, safety tracking, and guiding visually impaired people, many algorithms have improved to find the relationship between video analysis and images analysis.Entire algorithms behave uniquely in the network architecture, and they have the same goal of detecting numerous objects in a composite image.It is very important to use our technology to train visually impaired people whenever they need them, as they are visually impaired and limit the movement of people in unknown places.This paper offers an application system that will identify all the possible day-to-day objects of our surroundings, and on the other side, it promotes speech feedback to the person about the sudden as well as far objects around them.This project was advanced using two different algorithms: Yolo and Yolo-v3, tested to the same criteria to measure its accuracy and performance.The SSD_MobileNet model is used in Yolo Tensor Flow and the Darknet model is used in Yolo_v3.Speech feedback: A Python library incorporated to convert statements to speech-to-speech.Both algorithms are analyzed using a web camera in a variety of circumstances to measure the correctness of the algorithm in every aspect.
TIPS: Text-Induced Pose Synthesis

In computer vision, human pose synthesis and transfer deal with probabilistic image generation of a person in a previously unseen pose from an already available observation of that person. Though researchers have recently proposed several methods to achieve this task, most of these techniques derive the target pose directly from the desired target image on a specific dataset, making the underlying process challenging to apply in real-world scenarios as the generation of the target image is the actual aim. In this paper, we first present the shortcomings of current pose transfer algorithms and then propose a novel text-based pose transfer technique to address those issues. We divide the problem into three independent stages: (a) text to pose representation, (b) pose refinement, and (c) pose rendering. To the best of our knowledge, this is one of the first attempts to develop a text-based pose transfer framework where we also introduce a new dataset DF-PASS, by adding descriptive pose annotations for the images of the DeepFashion dataset. The proposed method generates promising results with significant qualitative and quantitative scores in our experiments.
ASR - VLSP 2021: Semi-supervised Ensemble Model for Vietnamese Automatic Speech Recognition

Automatic speech recognition (ASR) is gaining huge advances with the arrival of End-to-End architectures. Semi-supervised learning methods, which can utilize unlabeled data, have largely contributed to the success of ASR systems, giving them the ability to surpass human performance. However, most of the researches focus on developing these techniques for English speech recognition, which raises concern about their performance in other languages, especially in low-resource scenarios. In this paper, we aim at proposing a Vietnamese ASR system for participating in the VLSP 2021 Automatic Speech Recognition Shared Task. The system is based on the Wav2vec 2.0 framework, along with the application of self-training and several data augmentation techniques. Experimental results show that on the ASR-T1 test set of the shared task, our proposed model achieved a remarkable result, ranked as the second place with a Syllable Error Rate (SyER) of 11.08%.
Influence of Leadership Styles on Organizational Culture, Technology Acceptance, Knowledge Management Process &amp; Capabilities on Organizational Performance

organizational performance. The objective of the study is to explore impact of leadership on organizational performance in the presence of the organizational culture where technology acceptance and knowledge management process &amp; capabilities mediate. For this empirical investigation, Microsoft questionnaire survey has been used for the data collection and 447 participants responded on this questionnaire. The findings revealed that leadership has positive association with organizational performance in the presence of organizational culture where technology acceptance and knowledge management process &amp; capabilities play mediating role between leadership and organizational performance. For the regression analysis, SPSS has been used with Hayes’ macro process to evaluate the moderated mediation effect