Reviewerly/exHarmony huggingface dataset:
Innovative Deep Learning-based Video Editing Tool

Deep learning has acted as the main driver of emerging technologies. Recently, it has been used more frequently to aid in the video editing process to save time. Normally it would take a human editor hour or even days and weeks to edit the video footage, while artificial intelligence (AI) could perform the task in relatively less time. To get the most advantage of the evolving technologies, we use in this work a combination of state-of-the-art deep learning models to make editing videos as easy as editing text. The video will be transcribed to editable text to allow deleting any undesired parts, in which the equivalent part in the video will be automatically removed. Similarly, adding parts to the script will generate the equivalent part in the video considering the speaker's voice and lip movement. Achieving those required functionalities, three main deep learning models are required: (i) Speech recognition model to generate the video script, (ii) Text to speech model with speaker encoder to generate the added parts with the voice of the speaker, and (iii) A lip movement generator model takes the speaker's image and syncs the lips with the added script. In this work, we focus on presenting the first two models.
Bayesian Knowledge Tracing for Assessment Results Analysis

In this paper, A Bayesian Knowledge Tracing model is utilised to perform analysis on assessment results to get human-free feedback on the quality of the questions of the assessment. The model is built and trained using PyBKT on the National Coding Competition 2018 records. The technique uses guess and slip parameters of the model to perform the analysis process. The details of the recorded data analysis, the assumptions made to build the model, the training and the validation, and the obtained results are all discussed. The method was proven to help detect problems in the given questions.
A Systematic Review of Blockchain-Based System: Transaction Throughput Latency and Challenges

Blockchain is a relatively new technology that is already gaining traction in the academic community. A peer-topeer network structure underpins blockchain technology. It is made up of blocks, which are inventories that develop as new accounts are added to the system. Blockchain's stability is ensured by cryptographic technology. Although blockchain is rapidly developing and providing us with a secure and convenient service, it is not without its drawbacks. Bitcoin suffers from low throughput and high latency, both of which are detrimental to the scalability of the blockchain. The transaction rate of Bitcoin is 4.6 transactions per second. Visa conducts 1,700 transactions every second on average. Since the number of transactions on the blockchain is increasing every day, it's critical to address scalability problems until the system hits its limit. Privacy leakage and selfish mining are explored in detail, along with potential solutions. As a future direction, we envisage focusing more on improving the efficiency of throughput and latency with the suggested solutions of authentication, monitoring selection for an epoch, and mass mining using modified proof of work algorithm.
Rate-Constrained Trellis-Coded Quantization for Large-Scale Noisy Graph Signals

We consider the issue of compressing large-scale noise-corrupted graph signals under a rate constraint, to tackle the communication resource limitations, from rate-distortion perspective. To guarantee the fidelity of the overall compression system for noisy graph signals, we adopt the technique of kernel ridge regression on graphs for preprocessing. We show that, as a compression component for the output of the kernel ridge regression, trellis-coded quantization has superior distortion performance in comparison to other feasible quantization methods for long source blocks, and hence is suitable for large-scale graph signals. Targeting at decreasing further distortion in the multiple trellis-coded quantizer system under rate restriction, we design a novel rate allocation scheme based on the intrinsic topology of graph signals and the rate-distortion characteristics of trellis-coded quantization. We perform sufficient simulation with in-depth analysis, which demonstrates both the effectiveness of the proposed trellis-coded quantization for large-scale noisy graph signals and the advantage of the proposed rate allocation scheme over the existing rate allocation schemes in the common distortion measure under communication resource constraint.
Visual perception and local features for foreground‐background segmentation

The traditional foreground-background segmentation models mainly depend on the low-level features of the image, while ignoring the visual effect. Combining visual perception and local features, a top-down segmentation model is proposed. This model regards foreground-background segmentation as a reasoning problem based on visual perception, and calculates the association between two-pixel blocks through Kullback–Leibler divergence, which solves the ill-posed problem of traditional single-pixel recognition. Meanwhile, local features are used to optimize the overall segmentation results in detail and improve the segmentation accuracy. The experimental results on the CMU-Cornell iCoseg database and the BSDS500 database show that visual perception and local features can help improve the segmentation performance to a certain extent.
Radar Intra-pulse Modulation Signal Classification Using CNN Embedding and Relation Network under Small Sample Set

For the intra-pulse modulation classification of radar signal, traditional deep learning algorithms have poor recognition performance without numerous training samples. Meanwhile, the receiver may intercept few pulse radar signals in the real scenes of electronic reconnaissance. To solve this problem, a structure which is made up of signal pretreatment by Smooth Pseudo Wigner-Ville (SPWVD) analysis algorithm, convolution neural network (CNN) and relation network (RN) is proposed in this study. The experimental results show that its classification accuracy is 94.24% under 20 samples per class training and the signal-to-noise ratio (SNR) is -4dB. Moreover, it can classify the novel types without further updating the network.
Analysis of Pixel Intensity Variation by Performing Morphological Operations for Image Segmentation On Cervical Cancer Pap Smear Image

Cervical cancer is the second largely hazardous metastatic tumor that develops in a woman&#x2019;s cervix. If it is detected at the premature stage and treated correctly then there can be less mortality ratio rate due to cervical cancer .In preliminary stage Pap smear is the simple scrutiny test generally used for the revealing of cancer. For precise screening and detection, cervical cancer is categorized as normal and abnormal cancer which includes the cell and cytoplasm in the identical structure. It is complicated task to distinguish a cancerous nucleus in the cell. Medical image processing is mainly significant but time consuming and complicated task. Medical Image preprocessing of cervical cancer pap smear images and its scrutiny is act of investigating images for recognizing objects and evaluating their impact. The primary reason of Image processing is for discovering of various kinds of unnecessary cells and exposing the amount it spreads. So for the precise segmentation of cervical cells in Pap smear image becomes an essential job to automatically identify the precancerous transforms in the cervix. Image segmentation basically refers to method of division of the image into several segments for tracing objects and borders in image. Various Image processing and segmentation algorithms are utilized to section the nucleus alone in microscopic images.The primary scope of this paper is to spotlight on how the morphological operations on cervical cancer pap smear images is achieved to fine-tune to appropriate pixel concentration and proper contrast for sorting out the tumor piece from an image. In the addressed proposed work morphological operations like erosion, dilation, opening, and closing are executed and implemented with the aid of structuring element entitled as kernel. Python libraries are used for implementation of proposed work. As the morphological transformation is applied, minimum and maximum pixel intensity is also been computed.
An Automatic Heterogenous-Based MAX-MIN Ant System with Pheromone Reconstruction Mechanism

The traveling salesman problem (TSP) is a classical combinatorial optimization problem which has been employed by graph theory. It attracts the attention of scientists because of its Non-deterministic Polynomial Complete (NP-C) characteristic to reach an optimal answer. The Ant Colony Optimization (ACO) algorithm, originated from evolutionary algorithms, is a tailored algorithm for TSP due to its convergence speed and parallelism. However, achieving admirable results without proper parameter tuning in advance is a difficult process. In this paper, a novel automatic tuning parameter method based on MAX-MIN Ant System (MMAS) is designed to make a dynamic balance between the effects and the efficacies. Embedding heterogenous characteristics and Lévy flight into the optimization process also could eliminate the consumption of tuning parameters by manual incorrectly. The elite operation, during the evolution, can provide directional guidance with a certain degree of support. Moreover, a pheromone reconstruction mechanism based on a differential information theory is engaged to guarantee the evolutionary process and get rid of the long stagnation. Abundant experiments are designed to compare the performance of the proposed AHPACO algorithm for TSP with 4 classical ACO algorithms. Experimental results indicate that the proposed AHPACO algorithm outstands the other contrast algorithms in terms of convergence speed, population diversity, and overall effects. Moreover, to validate the previous description, orthogonal tests on both involved strategies are performed.
The Development of Learning Outcomes and Prerequisite Knowledge Recommendation System

This paper describes the development process of learning outcomes and prerequisite knowledge recommendation system based on the information about an academic course. The recommendation system is designed to be implemented in the service Educational Program Maker for working with education process elements. The recommendation system aims to help university staff members during academic course creation and, as a result, make the paperwork digitizing in universities easier and faster. Learning outcomes recommendations are computed with the Universal Sentence Encoder and learning content data. Prerequisite knowledge recommendations are calculated with information regarding curricula and concept domains. Developed algorithms were modified for implementation in the service, and interface prototypes for forms with displayed recommendations were created. Models were evaluated with the hit rate metric; the results showed that final models outperform baselines.
Electroencephalogram based Movable Electric Wheelchair for Differently Abled Person

The objective of this paper is to design a movable electric wheel chair for the Quadriplegia patients. Quadriplegia patients are those people who cannot move any of their appendages in the body underneath their necks. An EEG-based cerebrum-controlled wheelchair has been developed to utilize the Brain PC interface (BCI). The designed chair uses Neurosky mind wave EEG headset. The Atmel Microcontroller is modified utilizing Arduino and MATLAB Programming language to send the BCI yield to perform forward in reverse and rotational developments in the wheelchair. This wheelchair will be a great help for the patients with mobility impairments.
Security and Privacy Evaluation Methods and Metrics in Vehicular Networks

The vehicular networks extend the internet services to road edge. They allow users to stay connected offering them a set of safety and infotainment services like weather forecasts and road conditions. The security and privacy are essential issues in computing systems and networks. They are particularly important in vehicular networks due to their direct impact on the users’ safety on road. Various researchers have concentrated their efforts on resolving these two issues in vehicular networks. A great number of researches are found in literature and with still existing open issues and security risks to be solved, the research is continuous in this area. However, the researchers may face some difficulties in choosing the correct method to prove their works or to illustrate their excellency in comparison with existing solutions. In this paper, we review a set of evaluation methodologies and metrics to measure, proof or analyze privacy and security solutions. The aim of this review is to illuminate the readers about the possible existing methods to help them choose the correct techniques to use and reduce their difficulties.
Secure Two-party Dilithium Signing Protocol

The National Institute of Standards and Technology is in the process of selecting post-quantum public-key cryptography (PQC) algorithms through a public competition-like process. Dilithium, a lattice-based signature scheme, is a PQC candidate submitted to the NIST. It offers a fairly good performance with relatively straightforward implementation. In this study, we considered a secure two-party Dilithium signing protocol. We described how to build a distributed key generation and distributed signing protocol for Dilithium. Our protocol achieves a coordinated two-party Dilithium signature generation using Brakersiki/Fan-Vercauteren homomorphic encryption scheme. Our protocol is proven secure against active adversaries corrupting one of the players. Compared with other protocols, our protocol has advantage in resisting quantum computing attacks.
Person re-identification algorithm based on multi-module convolutional neural network

For the cross-border tracking needs of target persons in real complex scenes, a person re-identification algorithm based on a multi-module convolution neural network is proposed to solve the problem of person search and matching caused by person scale change, light change, posture change and other factors in the real environment. The algorithm takes ResNet50 as the backbone network of feature extraction. The STN network module is embedded into the backbone network to overcome the impact of person scale change. The IBN network module is integrated for person image color correction to compensate for the influence of illumination change in the real scene. And A person multi-branch feature extraction module is designed to effectively reduce the impact of person posture changes. Through person image feature expression and measurement learning calculation, person similarity matching across cameras is realized. Experiments show that this method has good performance in real complex scene test data, and its Rank-1 and mAP are 98.30&#x0025; and 95.78&#x0025; respectively. It can be used for person matching and search in a real complex environment, and has certain practical value.
JomRFID Attendance Management System

Nowadays, the RFID system is very common in various industries. Currently, this system are deployed within transportation, medical, supply chain industries and more. In this research paper, it will be discussing the implementation of the RFID system in university for attendance taking purpose. The system is designed to enhance the efficiency of attendance taking in university. This system is to substitute the traditional attendance taking method. Using this system, students require to scan their students&#x0027; cards on the RFID reader, subsequently, the attendance will be recorded into the system automatically. The lecturer can save up the time on the key in the attendance on the system manually. As a result, implementing the RFID system for attendance taking is simple and efficient.
An economic analysis of information security outsourcing with competitive firms

Nowadays, firms may outsource information security operation to a managed security service provider (MSSP). Constructing a game‐theoretic model, this paper examines the strategic interaction between a MSSP and a strategic hacker in the presence of two competitive firms. We find that the MSSP's equilibrium benefit increases with the firms' loss due to user inconvenience and competition intensity when they remain high. It shows that technology similarity may benefit or harm the MSSP, dependent on its investment efficiency. Interestingly, we reveal that the hacker's benefit first decreases and then increases with technology similarity when its learning ability remains moderate.
Line Level Modi (Heritage script) OCR using Attention based Encoder-Decoder Architecture

A large number of historical documents exist in the Modi script. These documents need to be preserved and digitized so that people know more about history written in Modi. Very little work has been done for the digitization of the Modi script documents. Deep learning-based solutions help to generate text output and reduce the digitization time for Modi documents. Most of the work happened only at the character level in the Modi OCR (Optical Character Recognition). This is the first effort to recognize the line images of the Modi script.Modi OCR has three major challenges. First, there are no word boundaries in Modi script like the Marathi language. All the words are connected so word length is more as compared to other Indian languages. Second, Modi documents are mainly handwritten. Third, Text is written on plain documents which make line segmentation more difficult.This paper aims to solve the first problem with the deep neural network. We do not have a real annotated dataset for the Modi script. We generate 303571 Modi line images synthetically with noise and blur for this experiment using in-house developed MODI_SAHO font. This paper describes the usage of OpenNMT architecture for recognition due to the flexibility of hyperparameter tuning. We use a neural encoder-decoder model with the attention mechanism for the Modi OCR. We evaluated the text recognition performance on synthetically generated line images. Our method is evaluated on the basis of the text output of line images. The accuracy of the test set is achieved 99.97%. Our major contribution is to show text recognization of very long words with the encoder-decoder architecture. Our contribution helps the researcher community in building long connected words OCR.
A Multi-Feature Fusion Convolution Neural Network for Image Compression Artifacts Reduction

For effective image storage and transmission, lossy compression is normally used during the coding process, which introduces artifacts and destroys the quality of images inevitably. Though some convolutional neural network (CNN) based image compression artifacts reduction methods have been proposed, the reconstructed quality is not ideal. In this paper, an end-to-end multi-branch convolution neural network is designed for removing artifacts brought by JPEG compression. Three feature extraction branches are proposed to extract main texture features, multi-scale features and enhancement features of compressed image. With the proposed network, multi-scale features of compressed image are fully concerned, artifacts of adjacent block boundaries can be reduced effectively. Experimental results show the effectiveness of the proposed network. In particular, the PSNR-B index of the proposed network is higher than state-of-the-art methods, which indicates the better artifact reduction performance of the proposed network.
Accountants, Cybersecurity Isn't Just for “Techies”: Incorporating Cybersecurity into the Accounting Curriculum

ABSTRACT In today's business world, it is essential that accountants be concerned about cybersecurity in their organizations and in the work they perform. Despite increasing importance as well as regulatory and professional emphasis, most accounting curricula limit cybersecurity coverage to accounting information systems courses. We argue that cybersecurity concepts should be introduced at all levels of the accounting curriculum, and we provide six short, open-ended cases that highlight the relationship between cybersecurity and the accounting curriculum. Instructors can assign one or more of these cases as individual or group exercises in introductory, core, and/or advanced accounting classes. The cases address cybersecurity disclosure issues in financial reporting, the impact of a client cybersecurity breach on financial audit, the risks to tax preparers of storing personally identifiable information, calculating and evaluating the costs of cybersecurity breaches, and examining cybersecurity disclosures in 10-Ks. Case efficacy was evaluated in multiple courses and learning environments.
Deep Neural Networks and Computer Vision for Real-Time Facial Landmark Detection

Using a deep learning model and a video camera stream, this study presents a real-time face landmark identification system. Because landmark detection is the foundation of many applications, such as biometric security systems, a good model is essential for future development ideas. A real-time facial landmark detector was constructed using the ResNet50 model, a dataset of 5770 photos and a CSV file with 68 landmarks corresponding to each image.
Software Effort Estimation with Use Case Points Using Ensemble Machine Learning Models

Software development process includes estimating effort as a crucial task. The Use Case Point Analysis (UCPA) is a well know size metric that can be used to calculate effort. The software size is measured using use case diagrams in the UCPA method, using the calculated software size the effort required to complete the project is estimated. The traditional effort estimation with statistical methods is not accurate when compared to the real effort. In-accurate estimation of effort leads to problems with cost calculation and human resources calculation and it might lead to project failure. Machine learning techniques based on regression might help in estimating the effort with accuracy. In this study we proposed a method to identify best performing regression based machine learning model using two data sets Dataset1, Dataset2. Ensembles of different Machine learning methods is created and compared with individual methods and other ensemble methods to find an accurate estimation model. The results shows the individual model SVR and ensemble model GBR gives the best performance with a regression score above 98% with both data sets.
Can pose classification be used to teach Kickboxing?

Kickboxing is a combat sport, based on kicking, punching, Knee and elbow strikes and defence moves. Every kickboxing technique needs to be preformed a specific way, As there is correct postures and wrong postures to every technique. In this paper, we offer a system that can facilitate the beginners trainees to learn kickboxing. The system uses a camera to estimate poses and then, classify them into &#x201C;correct techniques&#x201D; and their common mistakes or &#x201C;wrong pose&#x201D; using ANN. Live feedback is offered by the system. Whenever the classifier recognize a wrong pose, a message is shown to indicate how to correct the posture. Our hypothesis is that, when trainees have the ability to see and recognize their wrong posters, they learn faster. We evaluate the progress of the trainees based on the time it takes to complete a simple kickboxing exercise. Two types of experiments were conducted. The first calculated the progress of trainees everyday, the other calculated the progress of trainees through three training sessions in the span on two hours. Our results show that time taken by users to preform the moves decrease with each time they use our system. This paper focuses on 3 kickboxing techniques, which are slipping, jab and front kick.