Reviewerly/exHarmony huggingface dataset:
STUDENT WORKBOOK USE: DOES IT STILL MATTER TO THE EFFECTIVENESS OF STUDENTS’ LEARNING?

One effort that teachers can do to improve their learning process is by developing learning resources for themselves and their students. Teachers can use student workbook (LKS) to be used as a source of learning for students. A student workbook that contains material summaries can help students more effectively understand the materials. Student workbook containing a variety of practice questions is believed to affect the effectiveness of student’s learning. This research aims to analyze the effects of using student workbook (LKS) towards the effectiveness of students’ learning. This research was conducted to see students' perceptions of the role of using student workbook (LKS) towards the effectiveness of student learning. To collect data, this study used a questionnaire distributed to 24 students using a survey administration application, consisting of six questions related to the students' perceptions of the role of using student workbook (LKS) towards student’s learning process. After all, participants had completed the questionnaire, the data were then categorized based on relevant findings. The findings reveal that the use of student workbook gives beneficial impact on students’ learning since it can be one of the sources of learning besides the teacher’s explanation. It also makes students easier in understanding the materials with simple content and various practices. In the end, this research hopefully can be useful as a reference or guide for future researchers who want to conduct similar research on the use of student workbook (LKS) as one of the resources in the teaching process.
An Image Classification Algorithm of Financial Instruments Based on Convolutional Neural Network

The boom of global economy has caused an explosive growth in the issuance and use of financial instruments. Traditionally, the financial instruments are recognized and classified manually, which increases the burden of financial staff and consumes lots of financial time. To solve the problems, this paper designs a convolutional neural network (CNN) for classification of financial instruments, covering components like traditional CNN, shallow convolutional layers, and cropping structure. Then, the momentum weight update was combined with weight attenuation to accelerate the model learning. In addition, the authors designed a preprocessing method for rapid pixel-level adjustment of financial instruments, enabling the proposed CNN to classify financial instruments of various sizes. Experiments show that our CNN can identify various financial instruments, and classify them at an accuracy as high as 96%.
Image stitching method by multi‐feature constrained alignment and colour adjustment

Image alignment and colour consistency are two challenging tasks for image stitching. Traditional point correspondence methods are difficult to achieve good alignments due to their insufficiency and unreliability. The results are prone to errors and distortions. On the other hand, the problem of colour inconsistency in overlapping area between image pairs is still difficult to solve, especially when the illumination difference between images is large. To solve these problems, the authors integrate point features and line features into a warping model through a designed energy function. Line features will provide geometric constraints for image stitching, and remedy the defect of point correspondences in low-textured image stitching. A global colour consistency optimization method with colour mapping via a histogram extreme point-matching algorithm is proposed. The colour characteristic of reference images will be transferred to the others to achieve a global colour consistency. The proposed method is evaluated on a series of images, and compared with other methods. The experiments demonstrate that the proposed method provides convincing stitching results and achieves satisfied colour consistency results.
SISTEM INFORMASI ADMINISTRASI PINJAMAN (KREDIT) PADA CREDO UNION MODIFIKASI (CUM) BERBASIS WEB

Modified Union credo is a cooperative-type business entity owned by a group of people in a unifying bond, which agrees to invest their money (shares) so as to create joint capital with decent business results and for productive and welfare purposes. The role of cooperatives is very important in Indonesia, because cooperatives can increase the economic growth of Indonesian society. The loan administration information system (credit) at CUM Caritas HKBP Pematangsiantar is not yet effective, because it still uses a manual system so that data is easily lost and difficult to find, and the preparation of reports takes a long time. The purpose of this study is to build a system that makes it easier for staff to process data for CUM Caritas HKBP members and improve staff performance to be more efficient and minimize errors in data processing, especially in the loan administration (credit) section of CUM Caritas HKBP. In this research, in the process of collecting data, the researcher used the method of observation, interviews and literature study and the method of system development to be used was the Prototype method. The information system to be designed will be in the form of a web and use the MySQL database. It is hoped that this research can solve the problem of Loan Administration (Credit) at CUM Caritas HKBP Pematangsiantar.
Multi-agents system of vehicle services based on Blockchain

The rapid development of the Internet of Things (IoT) is affecting several sectors, in particular the Vehicular Ad-Hoc Network (VANET). The link between the IoT and VANET is giving rise to the Internet of Vehicles (10V). With the IoV,vehicles are able to communicate with each other and with other connected objects. To ensure the exchange of information flows between the different entities, autonomy, speed and the decentralized concept must be taken into account. Blockchain technology is a distributed register technology. It is the solution to overcome these challenges and to guarantee vehicular communication. This research work deals primarily with this new technology and its applications in IoV.We then present a multi-agent model of vehicle communication based on Blockchain technology and smart contracts.
A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning

Modern large-scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computing systems. A key bottleneck of such systems is the communication overhead for exchanging information across the workers, such as stochastic gradients. Among the many techniques proposed to remedy this issue, one of the most successful is the framework of compressed communication with error feedback (EF). EF remains the only known technique that can deal with the error induced by contractive compressors which are not unbiased, such as Top-K or PowerSGD. In this paper, we propose a new and theoretically and practically better alternative to EF for dealing with contractive compressors. In particular, we propose a construction which can transform any contractive compressor into an induced unbiased compressor. Following this transformation, existing methods able to work with unbiased compressors can be applied. We show that our approach leads to vast improvements over EF, including reduced memory requirements, better communication complexity guarantees and fewer assumptions. We further extend our results to federated learning with partial participation following an arbitrary distribution over the nodes and demonstrate the benefits thereof. We perform several numerical experiments which validate our theoretical findings.
Analysis and Comparison of Deep Learning Networks for Supporting Sentiment Mining in Text Corpora

In this paper, we tackle the problem of the irony and sarcasm detection for the Italian language to contribute to the enrichment of the sentiment analysis field. We analyze and compare five deep-learning systems. Results show the high suitability of such systems to face the problem by achieving 93% of F1-Score in the best case. Furthermore, we briefly analyze the model architectures in order to choose the best compromise between performances and complexity.
Energy-Efficient Post-Processing Technique Having High Extraction Efficiency for True Random Number Generators

In this paper, we describe a post-processing technique having high extraction efficiency (ExE) for de-biasing and de-correlating a random bitstream generated by true random number generators (TRNGs). This research is based on the N-bit von Neumann (VN_N) post-processing method. It improves the ExE of the original von Neumann method close to the Shannon entropy bound by a large N value. However, as the N value increases, the mapping table complexity increases exponentially (2N), which makes VN_N unsuitable for low-power TRNGs. To overcome this problem, at the algorithm level, we propose a waiting strategy to achieve high ExE with a small N value. At the architectural level, a Hamming weight mapping-based hierarchical structure is used to reconstruct the large mapping table using smaller tables. The hierarchical structure also decreases the correlation factor in the raw bitstream. To develop a technique with high ExE and low cost, we designed and fabricated an 8-bit von Neumann with waiting strategy (VN_8W) in a 130-nm CMOS. The maximum ExE of VN_8W is 62.21%, which is 2.49 times larger than the ExE of the original von Neumann. NIST SP 800-22 randomness test results proved the de-biasing and de-correlation abilities of VN_8W. As compared with the state-of-the-art optimized 7-element iterated von Neumann, VN_8W achieved more than 20% energy reduction with higher ExE. At 0.45V and 1MHz, VN_8W achieved the minimum energy of 0.18pJ/bit, which was suitable for sub-pJ low energy TRNGs.
A Multi Camera and Multi Laser Calibration Method for 3D Reconstruction of Revolution Parts

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm.
Research on fast natural aerial image mosaic

Research on unmanned aerial vehicles (UAV) image mosaic aims to generate a high-quality panoramic image without distortions or ghosting at a high operation speed. To this end, this study first constructed a Rapid Scale-Invariant Feature Transform (RSIFT) operator to reduce computer time. Subsequently, we employ the As-Natural-As-Possible (AANAP) algorithm for image registration. An image segmentation algorithm is applied to eliminate the motion ghosting. Lastly, A special aerial images dataset for image mosaic is constructed, based on which the comparable experiments with state-of-art image mosaic methods are conducted. Experimental results demonstrate that the feature extraction speed with the proposed algorithm is improved by at least 78% compared to SIFT, and the motion ghosting is effectively eliminated, thus achieving a fast and high-quality mosaic.
Batch Mode Active Learning Based on Multi-Set Clustering

Batch mode active learning, where a batch of samples is simultaneously selected and labeled, is a challenging task.The challenge lies in how to maintain the informativeness and keep the diversity of selected samples concurrently.We propose a novel batch mode active learning that balances the informativeness and representativeness using multi-set clustering.Our method utilizes a sequential active learner to retain the informativeness by providing a ranking of unlabeled samples and constructing multiple informative sets for the subsequent clusterings.K-means clustering is used to minimize the redundancy among these samples and to improve the representativeness.Finally, the optimal batch chosen is the one minimizing the expected predictive variance on all the data.Our experimental results on a large number of benchmark datasets demonstrate excellent performance of the proposed method in comparison with current state-of-theart batch mode active learning approaches.
Methodology for the Field Evaluation of the Impact of Augmented Reality Tools for Maintenance Workers in the Aeronautic Industry

Augmented Reality (AR) enhances the comprehension of complex situations by making the handling of contextual information easier. Maintenance activities in aeronautics consist of complex tasks carried out on various high-technology products under severe constraints from the sector and work environment. AR tools appear to be a potential solution to improve interactions between workers and technical data to increase the productivity and the quality of aeronautical maintenance activities. However, assessments of the actual impact of AR on industrial processes are limited due to a lack of methods and tools to assist in the integration and evaluation of AR tools in the field. This paper presents a method for deploying AR tools adapted to maintenance workers and for selecting relevant evaluation criteria of the impact in an industrial context. This method is applied to design an AR tool for the maintenance workshop, to experiment on real use cases, and to observe the impact of AR on productivity and user satisfaction for all worker profiles. Further work aims to generalize the results to the whole maintenance process in the aeronautical industry. The use of the collected data should enable the prediction of the impact of AR for related maintenance activities.
Driven by Vision: Learning Navigation by Visual Localization and Trajectory Prediction

When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing directions. Current published self-driving models improve their performances when using additional GPS information. Here we aim to push forward self-driving research and perform route planning even in the complete absence of GPS at inference time. Our system learns to predict in real-time vehicle’s current location and future trajectory, on a known map, given only the raw video stream and the final destination. Trajectories consist of instant steering commands that depend on present traffic, as well as longer-term navigation decisions towards a specific destination. Along with our novel proposed approach to localization and navigation from visual data, we also introduce a novel large dataset in an urban environment, which consists of video and GPS streams collected with a smartphone while driving. The GPS is automatically processed to obtain supervision labels and to create an analytical representation of the traversed map. In tests, our solution outperforms published state of the art methods on visual localization and steering and provides reliable navigation assistance between any two known locations. We also show that our system can adapt to short and long-term changes in weather conditions or the structure of the urban environment. We make the entire dataset and the code publicly available.
Discrete Optimization: The Case of Generalized BCC Lattice

Recently, operations research, especially linear integer-programming, is used in various grids to find optimal paths and, based on that, digital distance. The 4 and higher-dimensional body-centered-cubic grids is the nD (n≥4) equivalent of the 3D body-centered cubic grid, a well-known grid from solid state physics. These grids consist of integer points such that the parity of all coordinates are the same: either all coordinates are odd or even. A popular type digital distance, the chamfer distance, is used which is based on chamfer paths. There are two types of neighbors (closest same parity and closest different parity point-pairs), and the two weights for the steps between the neighbors are fixed. Finding the minimal path between two points is equivalent to an integer-programming problem. First, we solve its linear programming relaxation. The optimal path is found if this solution is integer-valued. Otherwise, the Gomory-cut is applied to obtain the integer-programming optimum. Using the special properties of the optimization problem, an optimal solution is determined for all cases of positive weights. The geometry of the paths are described by the Hilbert basis of the non-negative part of the kernel space of matrix of steps.
How Bitcoin Functions As Property Law

Bitcoin replicates many of the formal aspects of real estate transactions. Bitcoin transactions have features that closely resemble grantor names, grantee names, legal descriptions, and signatures found in real property deeds. While these “Bitcoin deeds” may be interesting, they are not profound. Bitcoin goes beyond creating simple digital deeds, however, and replicates important institutional aspects of real estate transactions, in particular recordation and title assurance. Deeds to real property are recorded in a central repository (e.g., the public records office), which the parties (and the public) can search to determine title. When one grantor executes more than one deed covering the same property, recordation acts (race, notice, and race-notice) determine which grantee wins.The Bitcoin blockchain replicates the public records office, giving anyone with a computer the ability to see any Bitcoin transaction. Bitcoin mining replicates the recording of deeds, a process by which formally valid transactions between two parties become essentially a public record. When one grantor executes more than one transaction covering the same Bitcoin, a miner determines which grantee wins simply by moving one transaction to the blockchain before the others.Remarkably, Bitcoin replicates these aspects of real estate transfers without any governing authority to coordinate or supervise activities. It has no central database for the blockchain. Instead, users across the globe maintain the blockchain in identical form. Bitcoin has no recorder of deeds to time-stamp and process transactions. Instead, it relies on dispersed and competitive miners to, in effect, time-stamp transactions and add them to the blockchain. Ultimately, this Article will show that Bitcoin succeeds because it leads its community of users to a consensus about the blockchain.Thus, this Article will conclude that Bitcoin replicates elemental pieces of property law, but it does so wholly outside of traditional legal structures. Ownership is based on computer protocols, computer records, community expectations, and nothing more. Bitcoin functions as law, even though it operates outside of the law.
Sensor-based Badminton Stroke Classification by Machine Learning Methods

The use of stroke types is frequently the decisive factor in a well-matched badminton competition. It is essential to have stroke by stroke logs in practices and competitions. In this work, a smart racket system is developed to recognize and record each stroke. The racket is equipped with an acoustic sensor and an inertial measurement unit, and sensing data are transmitted to a smartphone via BT connections for further processing. The shuttlecock hitting events are detected by utilizing voiceprint, and the stroke types are classified by various machine learning algorithms including random forests, Bayesian models, and support vector machines. In our experiments, over 99.9% hitting events can be detected by the proposed voiceprint-based algorithm that outperforms most commercial solutions on the market. In addition, the average accuracy of stroke type classification is 96.5% by personalized models and 84% by generalized models.
FPGA based Identification of Frequency and Phase Modulated Signals by Time Domain Digital Techniques for ELINT Systems

In this paper, a decision tree algorithm based on time-domain digital technique is developed for the identification and classification of diverse radar intra-pulse modulated signals for the electronic intelligence system in real-time. This includes linear frequency modulation, non-linear frequency modulation, stepped frequency modulation and bi-phase modulation. The received signal is digitised and the instantaneous phase and high accuracy instantaneous frequency are estimated. The instantaneous amplitude is also estimated to get the start and stop of the pulse. Instantaneous parameters are estimated using a moving autocorrelation technique. The proposed algorithm is employed on the instantaneous frequency and the modulation is identified. The modulation type and modulation parameter are important for unique radar identification when similar radars are operating in a dense environment. Simulations are carried out at various SNR conditions and results are presented. The model for algorithm is developed using a system generator and implemented in FPGA. These results are compared when the proposed algorithm is used with the existing digital in-phase and quadrature-phase (DIQ) technique of instantaneous frequency and amplitude estimation.
Face Recognition using Haar Cascade Classifier

With development of machine learning technology many applications have been revolutionized which earlier usedto utilize high amoun to fresources. Face recognition is a crucial security application. Though this paper we present this application using optimized amount of resources and high efficiency.
Multimedia analysis platform for crime prevention and investigation

Abstract Nowadays,the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.
The Impact of Threat and Efficacy on Information Security Behavior: Applying an Extended Parallel Process Model to the Fear of Ransomware.

Information security has become an increasingly important aspect in companies and households during this time of digitalization.Cyber attacks and especially ransomware attacks are a growing threat. How people react to and perceive this threat is a central component of this study. This paper is meant to investigate how threat and efficacy influence individuals' information security behavior. For this purpose, a structural equation model was developed using the Extended Parallel Process Model (EPPM).The results show that participants who received a low threat message in their ransom demand were less afraid and more likely to deal with the issue.At the same time, they were not as confident as people who perceived a significant threat.Participants who felt that they had little adequate protection against ransomware were more fearful and therefore dealt with the topic more defensively.Conversely, they also had the intention to behave safely.