Reviewerly/exHarmony huggingface dataset:
Filipino Online Scam Data Classification using Decision Tree Algorithms

Classification as a data mining technique is a process of finding a set of models describing and distinguishing data classes and concepts for the purpose of being able to use such a model to predict the class with an unknown label. Taking advantage of such technique as well as the other data mining techniques has been highlighted in several prior researches however, the lack of works involving cybercrime data in the Philippines suggests the lack of data mining application in facilitating cybercrime investigations in the country. Hence, this study classifies online scam unstructured data consisting of 54,059 mostly Filipino words as a significant continuation of a prior study. Further, three (3) different decision tree algorithms namely, Random Tree, J48, and Logistic Model tree (LMT) were utilized and compared in terms of their performance and prediction accuracy. The results and evaluation conducted demonstrate that among the classifiers, LMT performs best using an improved online scam data as it achieved the highest accuracy and the lowest error rate. Further researches may be conducted using other classification or data mining techniques in Weka and that weight allocation and subsequent ranking approaches be introduced or employed to the results in order to rank those classifiers that may be evaluated.
Joint Virtual and Physical Prototype Design and Testing of a Sensor Fusion Workbench for Fixed-Wing UAVs

Unmanned aerial vehicles (UAVs) require cost-efficient on board multi-sensor fusion to achieve accurate and reliable flight state estimation. The challenges behind the implementation of sensor fusion algorithms from scratch towards in-flight testing on microcontroller-based hardware are, however, demanding, since it requires not only understanding and implementing complex sensor fusion algorithms but also developing embedded software in a microcontroller. Those challenges make the process of prototyping onboard sensor fusion algorithms time-consuming, expensive, and inefficient. We present fast prototyping of a sensor fusion workbench based on extended Kalman filtering (EKF) for fixed-wing UAVs. The workbench incorporates multiple sensors, including an inertial measurement unit, a GPS receiver, and static and dynamic pressure sensors. The multi-sensor fusion algorithm has been tested under virtual flight data, as well as measured flight data including challenging environments with thermals and gusts. After performance evaluation, we transferred the algorithm from MATLAB code to C code and integrated it into an avionics device LXNAV S10. The implemented sensor fusion algorithm has been successfully tested on a manned glider under windy and turbulent environment.
Módulo de realidad virtual como facilitador de la enseñanza en robótica

The main objective of the following project was the implementation of a new teaching method for a basic level educative institution through a virtual reality (VR) teaching module. This method was carried out through virtual manuals with which the students of the academy managed to interact and perform assembly simulations, which in the future would be carried out in real conditions. The reason for using this method in an educational context is to increase the level of interest in robotics for elementary level students and to improve the Teaching-Learning process. Another of the improvements sought is the reduction of instructional time in the development of a robotics practice (assemblies of competitive robots). As a result of the application of this method, the products obtained were much more durable, achieved better performance and reduced the time to acquire knowledge.
A Hybrid Timestamping Approach for Multi-Sensor Perception Systems

Synchronized and precisely timestamped data from perception sensors is highly advantageous for the low-level fusion of multiple sensor data. Many open-available, low-cost perception sensors do neither provide hardware support for precise clock synchronization, nor provide timestamps with their measurement data. In this work, we present an approach to enable synchronization and accurate timestamping of hardware-triggerable sensors in multi-sensor perception systems.We utilize a hybrid timestamping approach, taking into account the timestamp of a hardware trigger and the software timestamp. The presented timestamping approach utilizes the trigger time to assign precise timestamps to the data streams of the perception sensors. Precise timestamps are mandatory in order to achieve a high perception performance in dynamic applications which utilize low-level data streams.Additionally, we present an implementation of the approach on a multi-sensor perception platform, archiving a timestamp precision in the range of 2 ms. An existing Robot Operating System (ROS) architecture of the platform is extended to assign hybrid timestamps to the data streams. Additionally, we present a pedestrian detection implementation which fuses the timestamped data into a representation.
A systematic decision making for functional training in competitive sports training

Decision-making is a vital aspect of any sport and competitive success, in particular open, strong, competitive team sports like soccer, volleyball, basketball, and rugby. However, in the notational analysis, it has largely ignored. To successfully win in any game and compete in every sport for individuals and teams at the national and international level, all success factors must be reconsidered to ensure a better winning decision. Hence, in this paper, Exploratory Hybridized Structural Equation Modeling Framework (HSEMF) has been proposed for decision making on functional training competitive sports training. The training environment will allow competitors to weigh up their options, determine, and mistake. However, the main feature of the environment is that athletes must be informed if they make mistakes to ensure that they do not take part in the future. The feedback should be provided to improve the performance of the athlete. The decision accuracy and performance indicators have been evaluated. Overall, the results did not show a causal relationship between changes in decision making after implementing the learning modules. However, it has provided moderate evidence of improved reaction time due to the learning modules for decision-making.
Exploiting Decryption Failures in Mersenne Number Cryptosystems

Mersenne number schemes are a new strain of potentially quantum-safe cryptosystems that use sparse integer arithmetic modulo a Mersenne prime to encrypt messages. Two Mersenne number based schemes were submitted to the NIST post-quantum standardization process: Ramstake and Mersenne-756839. Typically, these schemes admit a low but non-zero probability that ciphertexts fail to decrypt correctly. In this work we show that the information leaked from failing ciphertexts can be used to gain information about the secret key. We present an attack exploiting this information to break the IND-CCA security of Ramstake. First, we introduce an estimator for the bits of the secret key using decryption failures. Then, our estimates can be used to apply the Slice-and-Dice attack due to Beunardeau et al. at significantly reduced complexity to recover the full secret. We implemented our attack on a simplified version of the code submitted to the NIST competition. Our attack is able to extract a good estimate of the secrets using $2^12 $ decryption failures, corresponding to $2^74 $~failing ciphertexts in the original scheme. Subsequently the exact secrets can be extracted in $O(2^46 )$ quantum computational steps.
Digital whiplash: The case of digital surveillance

Abstract Digital technology is rapidly transforming human life. But our cognition is honed for an analog world. I call this the problem of digital whiplash : that the digital transformation of society, like a vehicle whose sudden acceleration injures its occupants, is too fast to be safe. I focus on the unprecedented phenomenon of digital surveillance, which I argue poses a long-term threat to human autonomy that our cognition is ill-suited to recognize or respond to. Human cognition is embodied and context-sensitive, and thus faces four problems of digital whiplash vis-à-vis digital surveillance. First is the problem of signal sparsity , that there are few if any perceptible indications of digital surveillance. Second is the problem of signal elusiveness , that the few indications there are prohibitively difficult to discover. Third is the distraction problem , that using digital technologies corrodes the cognitive abilities we need to recognize and respond to digital surveillance. Fourth is the hooking problem , that digital technologies are engineered to cultivate dependency. I address, among other objections, the idea that we choose to exchange our privacy for the use of digital technologies, so their use is in fact an expression of autonomy. The response to digital whiplash, I argue, is not to slow down the digitalization of society in its current form so that we can adapt our cognitive capacities. It is to allow ourselves the time to reflect on and debate whether digitalization, in its current form, is delivering a socially and ethically desirable future.
One Time Password (OTP) Life Cycle and Challenges: Case Study

In today's world of the internet, we give priority to secure many of our accounts over this. It could be our social media profile, our cloud storage account, or anything; everything can be accessed only via a login interface. Traditional login systems follow a simple procedure of asking for username and password to authenticate the user. But due to the exponential development in the field of technology, this login interface is vulnerable to many attacks and thus compromising the user's privacy and data. One more level has been added to the login interface known as OTP or One Time Password. The concept of OTP is that a randomly generated fixed digit code is sent to the user's physical device if the entered password is correct. This way, we can be surer that only the appropriate user is accessing the system. This whole system combined known as 2-Factor authentication system. OTP is sent directly to the user's accessible physical device only. It has many other applications like bank transactions, deletion of a social media account or cloud account, and many others. It's because of the dynamic nature of OTP. This paper discusses the lifecycle of the OTP and its issues and challenges in the current world.
DECIDABILITY OF ADMISSIBILITY: ON A PROBLEM BY FRIEDMAN AND ITS SOLUTION BY RYBAKOV

Abstract Rybakov (1984a) proved that the admissible rules of $\mathsf {IPC}$ are decidable. We give a proof of the same theorem, using the same core idea, but couched in the many notions that have been developed in the mean time. In particular, we illustrate how the argument can be interpreted as using refinements of the notions of exactness and extendibility.
Facial Emotion Recognition System Using Entire Feature Vectors and Supervised Classifier

This chapter proposes the facial expression system with the entire facial feature of geometric deformable model and classifier in order to analyze the set of prototype expressions from frontal macro facial expression. In the training phase, the face detection and tracking are carried out by constrained local model (CLM) on a standardized database. Using the CLM grid node, the entire feature vector displacement is obtained by facial feature extraction, which has 66 feature points. The feature vector displacement is computed in bi-linear support vector machines (SVMs) classifier to evaluate the facial and develops the trained model. Similarly, the testing phase is carried out and the outcome is equated with the trained model for human emotion identifications. Two normalization techniques and hold-out validations are computed in both phases. Through this model, the overall validation performance is higher than existing models.
The Study of the Classic Producer-consumer Problem in a Series of IT Courses

It is a time-consuming task for the instructors to design the examples, assignments, and projects to meet the requirement of learning outcomes. Luckily, most of the courses in information technology major are relevant and some are closely related to each other. Therefore, we propose to take advantage of the same problem which can be analyzed from different aspects under a series of IT courses. It will not only reduce the burden of the instructors to design the brand-new exercises but also help students quickly understand the foundation of the problem from what they studied and embark on exploring the high-level topics in new courses.
Enrichment of Blockchain Transaction Management with Semantic Triples

Enterprise business transactions have both public and private information; hence blockchain adaptation to an enterprise business application needs current blockchain platforms to support both public and private information. Public blockchains (permissionless) are optimized for transparency; hence the sharing of private and sensitive information is challenging. On the other hand, private blockchains (permissioned) separate information about a transaction by generating a public transaction and a set of private transactions and treat them separately. This separation weakens the cohesiveness of transaction information and develops an extra burden when it is necessary to connect both public and private information which is not duly addressed in the literature. For example, auditing, regulatory activities, certifications, and traceability need both the public and private information about transactions. This paper uses semantic triples and introduces the Triples for Transactions(T4T) model to define blockchain transactions, improve cohesiveness and resolve the extra burden of connecting both private and public transactions. This paper presents a user-driven transaction analysis, transaction modelling using the T4T model, semantic querying, and REST endpoints to enrich transaction management. Sets of semantic triples can define both public and private information about a transaction while preserving cohesiveness of the information. This approach supports point-to-point sharing of sensitive information while preserving implicit relationships between both private and public information. We have implemented an auditing scenario in the proposed approach adopting Hyperledger Fabric and compared for performance with Hyperledger Fabric. The results showed that the proposed approach reduces the number of transaction cycles by 66% compared to Hyperledger Fabric and the performance of information retrieval is in O(N). This result is a significant improvement compared to Hyperledger Fabric.
Representation of Plant Structure using XML and Its Application to Cultivation Management

The agricultural population in Japan is aging and shrinking. Therefore, it is necessary to improve the efficiency of farm work. Additionally, it takes many years to learn the skills required for farming, which discourages many young people from joining the field. As a countermeasure to this problem, smart agriculture technology is gaining increasing attention. Smart agriculture is an approach to increasing the quantity and quality of crops by leveraging advanced technologies. In smart agriculture, a large amount of data are collected from various sensors and internet of things devices. As the use of information and communication technology in agriculture spreads, the demand for data sharing between different agricultural systems increases accordingly. However, electronic data exchange interfaces for agricultural data are not standardized. Therefore, in Japan, it has been recommended to unify data formats using extensible markup language (XML). In this study, we focused on data regarding crop growth states and developed a method for representing plant structure using XML. In the proposed method, branches and fruits are extracted from plant images and their connections are expressed using the hierarchical structure of XML. Compared to conventional management of crop growth states based on images, the proposed method significantly reduces data size. Furthermore, because XML elements can be easily searched and sorted using XPath and XQuery, the XML format makes data easy to utilize for many services. For example, by counting numbers of fruits, profits and work times can be predicted. Additionally, data regarding plant structure is useful for directing farmers or robots to harvest fruits. The proposed method contributes to improving productivity and helps inexperienced farmers.
Design Decisions and Their Implications: An Ontology Quality Perspective

Objective, reproducible and quantifiable measurements based on well-defined metrics are a widespread instrument for quality assurance in engineering disciplines and also in ontology engineering. Ontology metrics allow for the assessment of their quality and the comparison of different versions of the same ontology. We argue that such a comparison and especially the view on the evolutional evolvement bears valuable insights on the effect of explicit and implicit design decisions. This paper examines the use of quality metrics in the evolution of an ontology that is used in an image recognition context in the fashion domain. Overall, 51 incremental versions were analyzed using the OntoMetrics framework by Rostock University. Using 13 selected criteria, the evolution of the ontology is quantified and the effect of design decisions on the analyzed criteria is outlined. The critical assessment of ontology metrics is further used to uncover weak spots in the ontology. These weak spots enabled the deriving of improvement recommendations.
Forecasting SNR margins has low-complexity

We use an extensive dataset from a production network to forecast signal-to-noise ratio (SNR) margin and show that a na¨ıve technique is able to forecast SNR margin better than a deep neural network (DNN).
Applying Artificial Intelligence for the Detection and Analysis of Weather Phenomena in Vehicle Sensor Data

Weather predictions arise from observatory stations on fixed locations, forming a nationwide grid. The low resolution of this grid does not allow for the prediction and discovery of local road weather conditions. This paper aims to identify weather conditions on a high-resolution scale by applying machine learning on vehicle sensor data. The model classifies anomalous samples in time series data into a road weather condition. We examine how Decision Trees can be applied to classify anomalous vehicle behavior into weather phenomena. It also specifies which preparation steps on sensor observations are advisable before a model is applied. We constructed numerous Random Forest and Gradient Boosted Tree classifiers to classify anomalies of real-world vehicle data. The grid search performed on classifier hyperparameters and input configurations shows that a well-considered feature selection and filtering has a significant impact on the accuracy.
Strategy for Well-Being in Universities: A Romanian Higher Education Approach

Student services represent one of the most important areas that has developed in every higher education institution and continues to evolve based on the students’ feedback and contextual issues. All quality assurance systems for higher education include standards to evaluate institutional capacity to provide these services. Well-being is one type of student service that universities have high interest in developing. In the current pandemic situation, significant changes towards digitalization have occurred, but at the same time, new types of student services based on counselling proved to be the most needed. Academic leadership is facing a lot of pressure to increase research visibility and deal with limited resources; therefore, a decision on well-being development is not easy. The purpose of this study is to reveal the well-being concept and practice as applied in different universities in Romania in order to build a possible institutional strategy (an introductory model). Apart from a literature review, conceptual clarifications, and best practices, the methodology includes a gap analysis and a survey based on a questionnaire to determine the students’ perceptions on the development of well-being services. The findings show the highest interest of respondents in contributing to the well-being service development. The conclusions indicate concrete steps for the establishment of a well-being strategy.
Resolving Interoperability Issues of Precision and Array with Null Value of Web Services Using WSIG-JADE Framework

Web services and agent technology play a significant role while resolving the issues related to platform interoperability. Web service interoperability organization (WS-I) provided the guidelines to remove the interoperability issues using basic profile 1.1/1.2 product. However, issues are still arising while transferring the precision values and an array with null values between different platforms like JAVA and .NET. As in a precision issue, JAVA supports data precision up to the 6th value and .NET up to the 5th value after the decimal and after increasing their limits, the whole number gets rounded off. In array with a null value issue, JAVA treats null as a value but .NET treats null as an empty string. To remove these issues, we use the WSIG-JADE framework that helps to build and demonstrate a multiagent system that does the mapping and conversions between agents and web services. It limits the number of digits to the 5th place after the decimal thereby increasing the precision in data sets, whereas it treats null as an empty string so that string length remains the same for both the platforms thereby helping in the correct count of data elements.
Arabic Offline Character Recognition Model Using Non-dominated Rank Sorting Genetic Algorithm

In recent years, there was intensive research on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR results are unsatisfactory and are still an evolving research area. Exploring the best feature extraction techniques and selecting an appropriate classification algorithm lead to superior recognition accuracy and low computational overhead. This paper presents a new Arabic OCR approach by integrating both of Extreme Learning Machine (ELM) and Non-dominated Rank Sorting Genetic Algorithm (NRSGA) in a unified framework with the aim of enhancing recognition accuracy. ELM is adopted as a neural network classifier that has a short processing time and avoids many difficulties faced by gradient-based learning methods such as learning epochs and local minima. NSRGA is utilized as a feature selection algorithm that has better convergence and spread of solutions. NSRGA emphasizes ranking among the solutions of the same front, along with elite preservation mechanism, and ensuring diversity through the nearest neighbor method reduces the run-time complexity using the simple principle of space-time trade-off. The Experimental results reveal the efficiency of the proposed model and demonstrated that the features selection approach increases the accuracy of the recognition process.
Multi-Agent Mean Field Predict Reinforcement Learning

The study of multi-agent reinforcement learning can solve many problems in real life. The current research can be divided into two aspects: one is adding the information of other agents into the critic-network to form a global critic-network, as MADDPG; the other is putting them into the actor-network, like CommNet, which takes the actions or observations from other agents into consideration. However, the two methods are faced with these problems: the action space is huge when the number of agents increases; In reality, due to the limitation of bandwidth and delay, communication often cannot perform well or even work normally. Inspired by MFRL, we design our algorithm MFPRL to solve this problem. The neighbors' average action is predicted by a separate MFP network. The experiment shows that our method achieves better results than MFRL.