Reviewerly/exHarmony huggingface dataset:
Research on Water Conservancy Project Construction and Operation Management based on Cost Management

With the development and progress of the economy and society, the management level of my country’s construction projects has also been greatly improved, especially in the construction of water conservancy projects, and the cost management in the process has made significant progress; the water conservancy industry is in order to improve the market of enterprises. Competitiveness, to increase the market share of the company, it is necessary to improve the management level of the company in an all-round way, and maximize the economic benefits of the company.In order to improve the efficiency of cost management and operation management in the construction of water conservancy projects, this study conducts a theoretical analysis of the project cost management, taking the reinforcement treatment of the expansion joints of the culverts of the main canal project of the first phase of the water diversion water supply as a research case, and analyzes the expansion joints of the culverts of the project. This paper conducts a comprehensive investigation, proposes a repair plan using chemical bonding with external TPO waterstop and PTN petroleum asphalt polyurethane joint material, and makes a detailed investment estimate for project implementation. The results show that the bonding externally attached TPO waterstop and PTN petroleum asphalt polyurethane joint materials can meet the needs of this design for strengthening and strengthening the waterstop of the expansion joint of the culvert. The total static investment of construction engineering, mechanical and electrical equipment and installation engineering, construction temporary engineering, independent costs and basic reserve costs is 4,585,800 yuan, of which the total cost of underdrain expansion joint treatment is 3,594,600 yuan, which is in line with expectations, indicating that the water conservancy project of this design. The cost management plan is feasible.
A relevant document search system model using word2vec approaches

Abstract USU Repository is an institutional digital information system provided by Universitas Sumatera Utara (USU) that preserves and distributes academic papers, such as thesis and dissertation, from all departments in USU. A search box is provided to help search relevant topics from this repository. However, sometimes the search results returned were irrelevant and did not satisfy the user’s expectations. One of the causes for this situation is that the search engine could not perform optimally, particularly if the query were long and complicated. One approach that can be used to solve the problem is using semantic search. Semantic search is an information retrieval process from a sentence that involves understanding the results returned by natural language processing. In light of this approach, this paper aimed to propose a semantic search to seek the relevance between the user’s input query and academic papers returned as search results. This study implemented word2vec method in converting sentences into vectors. This study indicated average precision scores for small datasets as 46% and 73% for larger datasets.
Using Inspiration Cards for Designing Persuasive Technology to Improve Creative Situations

Persuasive technology is an established field of research that is applied in various domains. However, creative situations, collaborative, or knowledge-intensive processes are still underrepresented. Numerous established approaches exist for the design of persuasive technology. For this purpose, first applications for inspiration cards are already described in the literature. These serve as a source of inspiration in workshops and facilitate the communication between participants and designers. In this study, inspiration cards were used to facilitate the design of persuasive technology to improve creative situations. Applying a design science research approach, this study developed inspiration cards and a canvas. This artifact consists of 17 technology cards, 49 application cards, nine potentials cards and a canvas to structure the design process. The artifact was evaluated in a workshop in which a persuasive technology to support creative behavior in a creative situation was developed by the participants. The results advance the concept of inspiration cards for the design of persuasive technology, provides tools to better analyze a specific application context, that is, a creative situation, and may help promote the use of persuasive technology in an underrepresented field.
An effective action covering for multi-label learning classifier systems

In Multi-label (ML) classification, each instance is associated with more than one class label. Incorporating the label correlations into the model is one of the increasingly studied areas in the last decade, mostly due to its potential in training more accurate predictive models and dealing with noisy/missing labels. Previously, multi-label learning classifier systems have been proposed that incorporate the high-order label correlations into the model through the label powerset (LP) technique. However, such a strategy cannot take advantage of the valuable statistical information in the label space to make more accurate inferences. Such information becomes even more crucial in ML image classification problems where the number of labels can be very large. In this paper, we propose a multi-label learning classifier system that leverages a structured representation for the labels through undirected graphs to utilize the label similarities when evolving rules. More specifically, we propose a novel scheme for covering classifier actions, as well as a method to calculate ML prediction arrays. The effectiveness of this method is demonstrated by experimenting on multiple benchmark datasets and comparing the results with multiple well-known ML classification algorithms.
Vehicle Security Learning Tools and Scenarios

The rapid pace with which connected and autonomous vehicles is evolving presents security challenges that are prevalent on communication technologies. Although it is universally accepted that tremendous benefits can be derived from this emerging technology, we need to make sure that this critical infrastructure is secured and protected. Recent attacks on vehicle networks have validated the urgent need for a robust and sustained effort to stem the tide of these debilitating incursions. Our ever-increasing dependence on this type of transport system brings us to new crossroads and challenges that are confronting our economic security, privacy protection, and well-being. One major challenge is the education and training of the current and future workforce in this emerging technology. This paper explores key curriculum issues in securing modem automobiles, including the essential tools necessary to implement meaningful hands-on laboratory experiments and learning scenarios.
Learning to Enhance Low-Light Images with a Synthetic-Real Interaction Training Strategy

Recently, utilizing deep networks to improve the visual quality of low-light images has attracted widespread attention. These works attach importance to design the network architecture and loss functions. However, they heavily overlook the strong portrayal ability of the network and a critical factor that decides the performance of deep networks, i.e., training strategy. In this work, we design a kind of effective training strategy, rather than meticulously designing the architectures and loss functions. Concretely, we define a lightweight illumination estimation network which cannot obtain the ideal performance by directly using the real paired low-light images. We design a synthetic-real interaction training strategy consists of synthetic and real training phases. We synthesize the low-light image pairs by treating the depth map as the illumination. In this way, we can provide the supervised labels for simulating the smoothed property of the illumination, avoid designing the complex regularization loss which may be minor effects. Next, we fix the trained network parameters to further train it by utilizing the real paired low-light images. Actually, we can repeatedly and alternatively execute synthetic-real interaction training mechanisms to obtain more ideal performance. Extensive analyses and evaluations fully verify our effectiveness and outstanding outcomes against other state-of-the-art deep networks.
Distribution pattern and source apportionment of trace elements in refined petroleum products

The distribution pattern and sources of trace elemental contents of refined petroleum products were determined in this study. Petroleum products comprising of petrol, kerosene, and diesel samples were obtained from different commercial gas stations in Nigeria and analyzed for V, Cr, Mn, Fe, Co, Ni, Cu, and Zn using Atomic Absorption Spectrophotometry. Results showed that Zn had the highest mean concentration of 16.16 ± 2.69 mg/L in petrol while Cu had the lowest mean concentration of 0.70 ± 0.42 mg/L in kerosene. Generally, the contents of the trace elements increased from light to heavy oil. In consonance with the results of correlation matrix, principal component analysis indicated that Cr, Ni, V, and Fe were incorporated as biophile elements during sedimentation while Cu, Mn, Zn, and Co were introduced into refined petroleum products via atmospheric deposition of metal-containing particulates and/or during refining, storage and transportation.
A Compact Implementation of AES S-BOX Based on Dual Basis

In the AES, it usually made up of four parts: AddRoundKey (ARK), SubBytes (SB), ShiftRows (SR), and MixColumns (MC). Among them, AddRoundKey, ShiftRows, and MixColumns are both linear operations, SubBytes is a nonlinear operation, which is the most complicated calculation. Moreover, S-box operations are also used in DES and SM4, so it has important research significance. Area optimization is particularly important in some chips with small area requirements. In order to reduce the hardware complexity, the finite field operation of the AES S-box is generally mapped to the composite field. At the same time, the design of the circuit using the dual basis will be smaller. In this paper, we propose an optimized design for the S-box in AES, which is mainly optimized for circuit area. The S-box is designed by using dual basis, and the circuit is simplified by AND-XOR array structure, so as to achieve the characteristics of small area. Compared with the previous works, the S-Box designed in this paper has a smaller area, which is 17.76% smaller than the previous S-Box. Compared to other basis, the S-Box are 29.87% and 25.34% smaller than that of the polynomial basis and normal basis respectively.
Spatio-temporal Semantic Analysis of Safety Production Accidents in Grain Depot based on Natural Language Processing

At present, the grain industry mainly relies on the text information of production accidents entered by field personnel and expert experience to classify the causes of grain depot accidents. On the one hand, in order to accurately identify and classify the causes of grain depot accidents, security inspectors are required to have rich field work experience; On the other hand, the complicated and rare accident causes are limited by the experience of security personnel, which can easily lead to misclassification. Natural language processing (NLP) is an important direction in the field of artificial intelligence. It mainly uses an intelligent and efficient way to systematically analyze, understand and extract text data. Therefore, this paper puts forward the spatio-temporal semantic analysis of grain depot safety production accidents based on natural language processing, aiming at solving the problem of automatic classification of grain depot safety production accident text data. The text classification method based on word2vec and CNN not only considers the correlation between words, but also considers the relative position of words in the text, which has greater advantages than traditional feature selection methods. According to Word2Vec combined with CNN model, the grain accident text data is vectorized and feature extracted, which effectively improves the accuracy of grain big data classification.
Method for Automatic Determination of a 3D Trajectory of Vehicles in a Video Image

Introduction . An important part of an automotive unmanned vehicle (UV) control system is the environment analysis module. This module is based on various types of sensors, e.g. video cameras, lidars and radars. The development of computer and video technologies makes it possible to implement an environment analysis module using a single video camera as a sensor. This approach is expected to reduce the cost of the entire module. The main task in video image processing is to analyse the environment as a 3D scene. The 3D trajectory of an object, which takes into account its dimensions, angle of view and movement vector, as well as the vehicle pose in a video image, provides sufficient information for assessing the real interaction of objects. A basis for constructing a 3D trajectory is vehicle pose estimation. Aim . To develop an automatic method for estimating vehicle pose based on video data analysis from a single video camera. Materials and methods . An automatic method for vehicle pose estimation from a video image was proposed based on a cascade approach. The method includes vehicle detection, key points determination, segmentation and vehicle pose estimation. Vehicle detection and determination of its key points were resolved via a neural network. The segmentation of a vehicle video image and its mask preparation were implemented by transforming it into a polar coordinate system and searching for the outer contour using graph theory. Results . The estimation of vehicle pose was implemented by matching the Fourier image of vehicle mask signatures and the templates obtained based on 3D models. The correctness of the obtained vehicle pose and angle of view estimation was confirmed by experiments based on the proposed method. The vehicle pose estimation had an accuracy of 89 % on an open Carvana image dataset. Conclusion . A new approach for vehicle pose estimation was proposed, involving the transition from end-to-end learning of neural networks to resolve several problems at once, e.g., localization, classification, segmentation, and angle of view, towards cascade analysis of information. The accuracy level of end-to-end learning requires large sets of representative data, which complicates the scalability of solutions for road environments in Russia. The proposed method makes it possible to estimate the vehicle pose with a high accuracy level, at the same time as involving no large costs for manual data annotation and training.
Prosthetic Face Makeups and Detection

Prosthetic makeup can be used to alter the appearance of a human face drastically through molded facial parts, skin-like silicone masks, and plastic surgery. Compared to conventional disguise methods such as glasses, a fake mustache, or a wig, it is more realistic and difficult to detect. We experimented with prosthetic makeups and with an affordable near-infrared camera and a portable thermal camera for smartphones. We found the spectra signatures can reveal the features at certain facial areas such as eye sockets, nose bridge, and forehead that are almost invisible to humans and computers by visual inspection. NIR reflectance depends on the distance to the lighting source, surface material property, and face poses. Thermal signatures also depend on the breathing flow, facial temperature, and thermal properties of the prosthetics, as well as the air gap between the prosthetics and the skin. We found the location-based spectra features and multiple bands to potentially increase the detection accuracy and explainability in terms of location, material, and makeup process.
Sensors data fusion for smart decisions making: A novel bi-functional system for the evaluation of sensors contribution in classification problems

Sensor fusion has gained a lot of attention during the recent years. It is used as an application tool in different fields including semiconductor-, automotive-, medicine industries. However, finding the right sensor combination for the dedicated application is still very challenging. In this paper, we focus on applying the sensor fusion concept in reference to the prototype-based learning for object classification purposes. In fact, we present a bi-functional system architecture. The system has the feature to evaluate each sensor's contribution in a predefined classification task. The developed system will preserve the effort and the time spent by engineers to collect a huge quantity of preprocessed samples from each sensor and to try different training configurations. Our approach consists of training a model. The model learns both the predefined classes and additional parameters that represent the contribution of each sensor used in the fusion system for fulfilling the predefined classification task. We illustrate the functionality of our developed system by referring to two different application scenarios. Results validate the dual functionality of our approach as well as the simplicity of the integration of our evaluation system in any further fusion application regardless sensors inputs and classification outputs.
Intelligent Invigilation Using Video Surveillance

At the examination center, examinees daily face difficulties in finding their seating position that consumes a lot of time. Also in COVID-19 pandemic, we need to maintain social distancing while taking examination offline. So, this paper proposes system that can help examinees in knowing their seat number in their respective examination hall with the help of online Web portal. It can help in eliminating the crowd at the notice board and can also save their time. With the help of an admit card generated to which a barcode be attached, an examinee can scan that code each day and know their seating location as per a daily basis. Another system is also proposed for face recognition that can help the examiner in identifying the identity of candidates that can also alert them if they are not found in the database using deep learning. Within the examination hall, the orientation of the head and movement of the mouth of an examinee provide us the clue of suspicious behaviors. Nowadays, most of the suspicious behaviors monitoring procedures are done manually that involves a lot of invigilators in each examination hall. In the pandemic situation, we proposed an intelligent invigilation using video surveillance that can autonomously detect and track examinee’s eye gaze, head orientation, and mouth movement to robustly detect their cheating activities. Algorithms which are implemented independently include eigen-face, fisherface, and linear binary pattern histograms. With the help of webcam installed at each examinee’s desk, if any suspicious activity is detected, the system will generate an alarm indicating such behaviors.
Value-Sensitive Design for Feminist Technology: Designing for “Feminist Fatherhood” with Nonwhite Latinx and White Fathers

Value-sensitive design is an approach that seeks to explicitly center the values of design stakeholders. In doing so, the method provides a rich analytical backdrop in which to explore how participants make sense of values and embody values in their designs. In this study, I explore the broad question of how a value-sensitive design approach can be used to surface, address, and possibly reconcile the similar and different culturally informed ways we make sense of being feminist fathers. Two groups of self-proclaimed feminist fathers, white non-Latinx and nonwhite Latinx, engaged in a value-sensitive design approach to designing technology to support their conceptualizations of feminist fatherhood. Four themes around differences between the groups and the kinds of reflections the participants engaged in are summarized. Based on our findings, I contribute suggestions for adapting value-sensitive design approaches to scaffold certain kinds of reflection around authenticity and interpretation in ways that are more grounded in themes of nondominance.
Wushu Competition Field Segmentation Based on Expectation-Maximization Algorithm

This paper proposes a valid Wushu competition field segmentation algorithm based on expectation-maximization algorithm. Firstly, Wushu competition image is modeled by Gaussian mixture model. Secondly, the parameters of the Gaussian mixture model are optimally estimated and several candidate thresholds are obtained by expectation-maximum algorithm. Finally, convex sets are segmented through the convex hull algorithm corresponding to candidate thresholds. Then Wushu competition field is inferred corresponding to the convex set with the maximum density.
Developing Software Using Agile and Design Thinking Framework

There are several software companies that develop several products. Are all of them used? Even after software companies following Agile framework methodology to develop their software's, many software's still fail to meet the requirements and eventually customers end up not using them. This is usually because the software teams fail to completely understand the user requirements. Agile framework and Design Thinking framework can be used together to gain customer empathy and to overcome this problem. This study discusses how Agile framework and Design Thinking supports the same principles and can be used to reduce software development failures. A Flashcard application was developed to validate Agile framework Design Thinking framework. The results are discussed in the validation and result section.
Unpredictability properties in Algorand consensus protocol

The Algorand consensus protocol, introduced by Chen and Micali in 2016, uses a pure proof-of-stake protocol based on Byzantine agreement rather than the well-known Bitcoin proof-of-work. In this paper, we analyze specific security properties of Algorand related to "unpredictability". The unpredictability property has been initially defined by Boneh et al. in the context of single secret leader election. We show that Algorand satisfies the original unpredictability property. Then, we extend the unpredictability property to a more generic property referred to as "t-forward unpredictability" and show that for a particular setting of Algorand, the t-forward unpredictability property is not satisfied. Specifically, we provide a strategy enabling to predict the future leaders.
Team Wa’ed Al-Shrida at the Mowjaz Multi-Topic Labelling Task

This paper describes an attempt in the "Mowjaz Multi-topic Labelling Task", the competition is about classifying the Arabic articles to its topic by using artificial intelligence and neural networks, the programming language that was used to classify the datasets is "Python". The attempt was started by uploading the datasets from the "Github website", the datasets that were used in the system include three groups, train, validation, and test datasets. The "Pyarabic" and simple-transformers" libraries were used to allow the system to manipulate Arabic letters and simplify the usage of Transformer models without having to compromise on utility, respectively. The model's type that I used is "Bert" and its name is "Asafaya/Bert-base-Arabic". The accuracy of the result that was gotten is as follows: F1 macro: 0.864, F1 micro: 0.869, competition website on Codalab: 0.8430.