Reviewerly/exHarmony huggingface dataset:
The Closed Loop Controller for Mini Demo Biomass Power Plant Fuel-Feeding Arm based Pneumatic System

This paper shows the establishment of a mini demo Biomass power plant fuel-feeding arm with controller, motor and pneumatics system to study how to control arm for picking and placing of fuel operation properly. The arm mechanical elements, control driver circuit and electrical components were designed, built and selected follows the real Biomass power plant fuel-feeding arm. Low level PID controller, proportional pneumatics valve and motor with encoder are utilized for defining the correct close loop position of mini demo arm. Moreover, this system is designed to plug with the Robot operating system (ROS) the easy robotics motion control system which use to calculate the desired position before sending to the robot arm close loop system. The results show the effectiveness of the established mini demo Biomass arm can archive the correct position even controlled by nonlinear air flow system.
Interactive Style Space of Deep Features and Style Innovation

Stylizing images as paintings has been a popular computer vision technique for a long time. However, most studies only consider the art styles known today, and rarely have investigated styles that have not been painted yet. We fill this gap by projecting the high-dimensional style space of Convolutional Neural Network features to the latent low-dimensional style manifold space. It is worth noting that in our visualized space, simple style linear interpolation is enabled to generate new artistic styles that would revolutionize the future of art in technology. We propose a model of an Interactive Style Space (ISS) to prove that in a manifold style space, the unknown styles are obtainable through interpolation of known styles. We verify the correctness and feasibility of our Interactive Style Space (ISS) and validate style interpolation within the space.
Effectiveness of Peer Review in Teaching and Learning User Centered Conceptual Design Among Large Cohorts of Information Technology Students

Conceptual design often sets the direction of the development of a product. Educating Software Engineering and Information Technology (IT) students to consider the needs of users during the conceptual design stage is important. The assessment of conceptual design is a time consuming process. When budgets are limited, financial resources are inadequately allocated to allow staff to spend quality time assessing the conceptual design artifacts of large classes. We present a methodology to teach, capture and evaluate conceptual design artifacts for large classes. In two separate studies, two groups of over 185 undergraduate IT students reviewed their peers' design artifacts using a comprehensive rubric. Their reviews were later checked by two researchers using the same rubric. It was found that not only were the student-peers' reviews close to the researchers' reviews, it was possible to give students valuable and timely feedback and scaffold them to reflect, an essential characteristic for professional competence. In addition, we found that assessment by staff was not feasible due to inadequate resources. We conclude that for large classes, conceptual design artifacts can be evaluated and valuable feedback provided in a timely manner by peers with the guidance of a comprehensive rubric.
Comparison of Personal Security Protocols

One of the most critical issues in modern society is the possibility of efficient users’ authentication in all the systems and items creating the IoT. There is a need to determine persons’ rights and privileges to use devices they operate in their daily routines. This work aims to explore the most promising research avenues that could prove the user identity based on their behavior, personal characteristics, or history, preferably on-the-fly. There is already a wide range of techniques used, from simple biometrics, through the patterns describing the keystroke and cadence of a given individual during any performed operation. All those methods are considered for their usefulness in more contactless or decentralized systems in a systematic review. That will form a factual research basis for a further avenue of personalized cryptographic protocols. These would not determine what a person has or knows, but more likely – who such a person is and what makes them unique. The issue of false positives and negatives should also balance the tight line between strict metrics of peoples’ behavior and privacy issues.
Energy-Aware Disk Storage System for Cloud Data Centers

In the era of overflowing data, storage technology has emerged as an indispensable paradigm for processing and storing data in cloud data centers. These data centers provide infrastructure as a service (IaaS) using high computing that uses power hungry disks. However, the deployment of the data raises disk energy efficiency challenge. Therefore, an innovative model for energy-efficient storage within cloud data centers is needed. So, this article proposes an efficient storage system for storing large amount of data in cloud data centers. The proposed system uses workload distribution with data replication and intelligently selects the disk for executing the request. The disk is selected using idle time productivity such that unnecessary spins can be reduced which ultimately saves 79.9% of the total energy consumption.
Frame Synchronization of Navigation Message Based on Cumulative Sum Method

In this paper, a frame synchronization algorithm based on the cumulative sum is proposed in order to solve the frame synchronization problem of navigation messages. First, it is concluded that the synchronization code appears periodically on the basis of the analysis of the inherent structural characteristics of navigation messages. Second, on the basis of the above conclusion, the sequence to be synchronized is divided according to a fixed length, and the cumulative sum processing is carried out to obtain the cumulative sequence. Last, by observing the periodic distribution of the cumulative sequence, the correct synchronization of navigation messages is achieved. The simulation experiments have verified the effectiveness of the proposed algorithm that shows certain practical engineering values.
Leveraging video data to better understand driver-pedestrian interactions in a smart city environment

New data sources such as video promise to provide insights into how humans navigate urban infrastructure and enable analysis of human-in-the-loop interactions. This work considers the deployment of portable video data collection units to understand human-driver interactions at unsignalized intersections. Specifically, we present preliminary data collection and results that highlight the value of video data in capturing the nuanced interactions of pedestrians with vehicles when navigating urban streets.
Temporal action proposal generation Based on Boundary Probability

Temporal action proposal generation is widely used in video processing, action recognition and other fields. Aiming at the problems of the existing methods of temporal action proposal generation that fail to effectively use the Temporal information of video frames and the blurring of action boundaries, this paper proposes a method of time-series action proposal based on boundary probability, and the existing method based on slider and action score temporal action proposal generation method is different. In this paper, a time series boundary probability generation network is generated to generate the boundary sequence in order to optimize the time series boundary fuzzy problem; at the same time, the proposal evaluation network is constructed to optimize the time series proposal segment; in addition, the improved Maximum value suppression algorithm--DTS-NMS to further improve the temporal action proposal generation results. The experimental results show that the constructed model has achieved competitive effects on the two public datasets of ActivityNet-1.3 and THUMO14 (represented as large datasets and small and medium datasets, respectively).
격이론의 문제점과 격자질의 개념적 (비)타당성

The primary purpose of this paper is to show the theoretical and empirical problems of Case theory suggested in GB and the Minimalist Program. The secondary purpose of the paper is to criticize the notion of Case feature, arguing that the feature is not necessary for computation but for interpretation at the interfaces. From a pure ontological view, the notion of Case feature is not necessary for computation. The morphological Case manifests the relationship between nominals and functional heads.
The Role of Blockchain Security in the Internet of Things

Blockchain may be a new technology within the Internet of Things (IoT). It functions with the public, distributed, decentralized, and instantaneous ledger to stockpile various transactions within the nodes of IoT. A Blockchain comprises of sequences of blocks with each component linked to the previous block sequences. Individual blocks contain previous block hash, cryptography hash code, and stored sets of knowledge. The transactions, which happen within the Blockchain are the elemental elements, which transfer data from one IoT node to a different one. The nodes vary in physical composition, though all of them have actuators, sensors, and programs; thus, they will communicate with one another. The function of Blockchain in IoT is to ensure a secure process of transferring data through the nodes. The technology is efficient and usable publicly without worrying about knowledge pilferage. The blockchain is important for IoT because it enables the secure transfer of data between its nodes in an environment, which is heterogeneous. The communications in blockchain are traceable to a person who has the permission to exchange information within the IoT. The system may help in IoT to reinforce the safety of communication data. This paper, reconnoitered the weakness of IoT security, the importance of Blockchain technology in IoT, the integration of blockchain in IoT, and therefore the design architecture of the integrating of IoT and Blockchain.
A Comfort Feedback Method for Remote Fitting

Garment e-commerce has become an indispensable part of the garment retail industry. However, the lack of a fitting process in garment e-commerce has led to high return rates and operating costs. The remote fitting method based on the fitting robot helps solve the above problem, but the accuracy of the fitting results needs to be improved to ensure the effectiveness of the remote fitting. The comfort of clothing is the main factor affecting the fitting results. This paper presents a comfort feedback method based on the fitting robot to obtain and feed back the comfort information of remote fitting. This method includes a pressure measuring device, a comfort conversion model, and a real-time feedback interface. And the test results demonstrate that this method is effective. This method’s application provides consumers with a new fitting experience and more accurate fitting results, which will promote the development of garment e-commerce.
Permutation Tests for Two-Sample Means of High-dimensional Data

The generation of high-dimensional data has led people to research permutation tests. Permutation tests are widely used in practice, but unduly strong conditions are often required for its validity. In this paper, we use a modified statistic which calls for only marginal standardization, and calculate the test statistics using pseudo samples that are generated through the Bootstrap method. We show that the corresponding permutation test is consistent under mild conditions. By comparing with some existing methods are made by simulation, the use of permutation tests for two sample means in high dimensional settings has better performance.
Federation learning optimization using distillation

Federated learning is a special type of distributed machine learning that enables a large number of edge computing devices to train models collaboratively without sharing any private data. This method of decentralized training model data is always not local, which is private and safe. Provided a guarantee. However, because federated learning faces the challenges of heterogeneous problems: 1) heterogeneous models among devices; 2) differences in real data, which do not obey independent and identical distribution, resulting in poor performance of traditional federated learning algorithms. To solve the above problems, a distributed training method based on knowledge distillation is proposed. By introducing a personalized model on each device side, the personalized model is used to improve the performance of the global model on the device side, thereby improving the ability of the global model. The improvement of the performance of the local model benefits from the effect of knowledge distillation, which can guide the improvement of the global model by transferring "dark knowledge" between heterogeneous networks. Experimental results show that this method can significantly improve the accuracy of classification tasks, and at the same time meet the needs of heterogeneous users.
SOLUTION OF A NONLINEAR TWO-STAGE PROBLEM OF LOCATION OF PRODUCTION

A mathematical model of problem with limiting the production of products and the volume of transportation through the transfer points is developed. The production function is nonlinear, and the transport function is linear. The solving method of this problem is suggested. Transfer points are divided into several transfer points. The problem is reduced to the usual transport problem. The numerical example is compiled.
Dictionary Based Brute Force Attack – Study Case of Montenegro and China

One of the most common cyber-attacks is brute force attack which puts users at a high security risk. This paper deals with statistical analysis and comparison of passwords’ strength between two countries - Montenegro and PR of China through the help of extensive digital survey. In-depth analysis has helped us conclude what is the role of culture and computer literacy while creating stronger passwords and improving cyber security. Also, the results show the passwords’ differences that help the creation of a perfect dictionary that can be used as a starting point for programming a brute force attack. One more purpose of this research is to help employees in law enforcement dealing with digital forensics, to find digital evidence in cyber incidents in the shortest amount of time.
Detecting the Point of Release of Virtual Projectiles in AR/VR

Our aim is to detect the point of release of a thrown virtual projectile in VR/AR. We capture the full-body motion of 18 participants throwing virtual projectiles and extract motion features, such as position, velocity, rotation and rotational velocity for arm joints. Frame-level binary classifiers that estimate the point of release are trained and evaluated using a metric that prioritizes detection timing to obtain a ranking of joints and motion features. We find that wrist joint and rotation motion feature are most accurate, which can can help when placing simple motion tracking sensors for real-time throw detection.
Decentralized Voting System Using Block Chain Technology

Voting is an integral part of any organization which helps organizations to take important decisions after taking a consensus from others and help implement any idea/decision in a procedural manner. But current voting methods like ballot box and digital voting systems face a lot of security threats like DDoS attacks, fake votes, alteration and manipulation of votes, malware attacks, etc. This creates a sense of mistrust amongst the traditional voting systems. In proposed system, use of blockchain technology to minimize the flaws of existing system and make voting process more secure, reliable, and transparent. To resolve the issues in current existing voting system this paper suggests use of Blockchain Technology for enhancing security and decentralization of data.
Towards Information Diversity Through Separable Cascade Modules for Image Super Resolution

Convolution neural networks (CNNs) have been widely applied to learn the mapping function between low-resolution (LR) images and high-resolution (HR) images. With the aid of deep networks operating through cascading collaborative modules, several works have effectively improved image quality. In these works, the local cascading module is usually treated as an atomic term to extend, which is a convenient way to deepen network structures for feature extraction and information fusion. However, this limits the representation of later reconstruction layers because of its lack of abundant features if local atomic modules can output only one type of information. For instance, residual blocks only contain residual information. In this paper, we propose a separable mechanism in module design to increase the representation capability of deep neural networks. For the sake of diverse features, we obtain two-stream features with four separable modules based on residual learning and attention mechanisms. Through a contiguous memory (CM) mechanism, it ultimately combines low-level features with high-level features. Similar to the residual-in-residual (RIR) structure, we propose an attention-in-attention (AIA) framework to deepen our networks. Experimental results demonstrate the effectiveness of our method when applied to several image datasets.
UKIRF: An Item Rejection Framework for Improving Negative Items Sampling in One-Class Collaborative Filtering

Collaborative Filtering (CF) is one of the most successful techniques in recommender systems. Most CF scenarios depict positive-only implicit feedback, which means that negative feedback is unavailable. Therefore, One-Class Collaborative Filtering (OCCF)techniques have been tailored to tackling these scenarios. Nonetheless, several OCCF models still require negative observations during training, and thus, a popular approach is to consider randomly selected unknown relationships as negative. This work brings forward a novel and non-random approach for selecting negative items called Unknown Item Rejection Framework (UKIRF). More specifically, we instantiate UKIRF using similarity approaches, i.e., TF-IDF and Cosine, to reject items similar to those a user interacted with. We apply UKIRF to different OCCF models in different datasets and show that it improves the recall rates up to 24% when compared to random sampling.
Creation of a Callbot Module for Automatic Processing of a Customer Service Calls

Bots are now more and more present in our daily life, we can find them in the form of chatbots, voicebots or FAQs. Since the number one source of customer demand is phone calls, callbot becomes a requirement in order to ensure a 24/7 presence and to minimize customer wait times in addition to demining charges. Callbots present a new kind of machine-human interface.
Analysis Classification Opinion of Policy Government Announces Cabinet Reshuffle on YouTube Comments Using 1D Convolutional Neural Networks

YouTube social media has been equipped with a comment column facility so that viewers can comment on YouTube video information in the form of comments or opinions that lead to likes, dislikes, and neutrality. With the increase in the number of viewers, there were also more comments on various writing kinds, both symbolic and numeric. The author wants to take these comments into useful information using sentiment analysis using the 1D Convolutional Neural Networks method. From the results of this study, classification can be done very well with the CNN model and accuracy by using variations of epoch 10, 30, 150, and 300 with the best results of 100%, loss: 1.6%. This study also compared the classification reports for precision, f1-score recall, and accuracy with the Naïve Bayes 93% and CNN methods, with an accuracy of 96%.
Adversarial Attacks on Multi-Network Mining: Problem Definition and Fast Solutions

Multi-sourced networks naturally appear in many application domains, ranging from bioinformatics, social networks, neuroscience to management. Although state-of-the-art offers rich models and algorithms to find various patterns when input networks are given, it has largely remained nascent on how vulnerable the mining results are due to the adversarial attacks. In this paper, we address the problem of attacking multi-network mining through the way of deliberately perturbing the networks to alter the mining results. The key idea of the proposed method admiring is effective and efficient influence functions on the Sylvester equation defined over the input networks, which plays a central and unifying role in various multi-network mining tasks. The proposed algorithms bear three main advantages, including (1) effectiveness, being able to accurately quantify the rate of change of the mining results in response to attacks; (2) efficiency, scaling linearly with more than 100 times speed-up over the straight-forward implementation without any quality loss; and (3) generality, being applicable to a variety of multi-network mining tasks (e.g., graph kernel, network alignment, cross-network node similarity) with different attacking strategies (e.g., edge/node removal, attribute alteration).
A MODEL OF TECHNOLOGICALLY CHOOSING EMPLOYEES IN AN AEROSPACE INDUSTRY PROJECT

The challenges of interviewing candidates for positions in cybersecure communications of aerospace industry. Data and experiments conducted as evidence that the improvements to the current model of selecting employees are needed in the real-world applications. A proposal of a newly developed method of selecting job candidates using information technology.
On The Radon–Nikodym Machine Learning Parallelization

A novel Radon–Nikodym approach to machine learning (ML) is developed, implemented numerically, tested on various datasets, and it’s parallelization is discussed. It consists in building a “wavefunction” ψ(x), a linear function on input attributes x then constructing a classificator of the form ⟨ψ2f⟩/⟨ψ2⟩. The solution is obtained from a generalized eigenproblem |f|ψ[i]⟩ = λ[i]|ψ[i]⟩ with left– and right– hand side matrices calculated from the input dataset. A solution to classification problem (predict f on an unseen x) is found without using a norm testing how predicted f differs from the one observed. Possible outcomes and their probabilities are obtained separately using the Lebesgue quadrature technique, this makes the method robust to input data with outliers, spikes, etc. A remarkable feature is the knowledge of the invariant group (what input attributes transforms do not change the solution). Radon–Nikodym approach is typically slower than the other methods used, this is the cost of being a “norm–free”. A parallel implementation is expected to improve this deficiency.
Hierarchical Context Guided Aggregation Network for Stereo Matching

Nowadays, CNN-based stereo matching methods achieved remarkable performance, and how to efficiently exploit contextual information in cost aggregation stage is the key to improve performance. In this paper, we propose a simple yet efficient network named Hierarchical Context Guided Aggregation Network (HCGANet). Specifically, a novel cost aggregation module is developed to replace widely used 3D convolutions. Firstly, we construct pyramid cost volumes which carry multi-level distinctive and discriminative representation. Additionally, an intra-level aggregation module is presented for single-level regularization and contextual information learning. Moreover, we develop an inter-level aggregation module to hierarchically regularize cost volumes via the guidance from coarser scales. The proposed aggregation module is lightweight and complementary, further improving the robustness and performance of disparity estimation. Extensive experiments demonstrate that the proposed method achieves superior results for both efficiency and accuracy on Scene-Flow and KITTI benchmarks.