Reviewerly/exHarmony huggingface dataset:
Design of measuring circuit of motor speed based on Proteus

Abstract In this paper, 51 single chip microcomputer, photosensitive element and 1602 are used to design the motor speed measurement circuit based on grating method, and Proteus software is used to build the circuit and function verification. Finally the project achieves the motor speed measurement and adaptive content display under different precision, the project simulation verification results are well. At the same time, on this basis the construction of motor speed measurement circuit is discussed and promoted.
Design and implementation of a GIS calculation and investment analysis system for rooftop PV projects

Under the goal of carbon peaking and carbon neutrality, China's energy structure is gradually transitioning from fossil energy dominated to clean energy. Solar energy is the most promising clean resource, and building rooftops are important carriers for distributed PV construction, and their quantity is directly related to the construction potential of distributed PV. Therefore, monitoring and analyzing the spatial distribution of buildings and available rooftop areas are of great value for the planning and construction of distributed PV. This study takes information of relevant rooftop PV projects as data source, these projects use deep learning technology to extract building rooftops in some countries and regions, and obtain building rooftop area after vectorization, and then calculate solar PV potential and investment potential by combining relevant PV and financial indicators. Based on the multi-layer application architecture of distributed component technology and the modular design of each function, this paper constructs a carbon-neutral building rooftop solar potential GIS calculation and investment analysis system to more accurately assess the regional renewable energy potential, provide decision support for government authorities and industry enterprises to better formulate renewable energy development indicators and planning, and help renewable energy planning to be better implemented. The system will provide decision support for government authorities and industry enterprises to better formulate renewable energy development indicators and plans, and help renewable energy planning to better implement.
The Correlated Arc Orienteering Problem

This paper introduces the correlated arc orienteering problem (CAOP), where the task is to find routes for a team of robots to maximize the collection of rewards associated with features in the environment. These features can be one-dimensional or points in the environment, and can have spatial correlation, i.e., visiting a feature in the environment may provide a portion of the reward associated with a correlated feature. A robot incurs costs as it traverses the environment, and the total cost for its route is limited by a resource constraint such as battery life or operation time. As environments are often large, we permit multiple depots where the robots must start and end their routes. The CAOP generalizes the correlated orienteering problem (COP), where the rewards are only associated with point features, and the arc orienteering problem (AOP), where the rewards are not spatially correlated. We formulate a mixed integer quadratic program (MIQP) that formalizes the problem and gives optimal solutions. However, the problem is NP-hard, and therefore we develop an efficient greedy constructive algorithm. We illustrate the problem with two different applications: informative path planning for methane gas leak detection and coverage of road networks.
Experimental construction of a symmetric three-qubit entangled state and its utility in testing the violation of a Bell inequality on an NMR quantum simulator

Abstract We designed a quantum circuit to prepare a permutation-symmetric maximally entangled three-qubit state called the <?CDATA $\vert S \rangle$ ?> state and experimentally created it on an NMR quantum processor. The presence of entanglement in the state was certified by computing two different entanglement measures, namely negativity and concurrence. We used the <?CDATA $\vert S \rangle$ ?> state in conjunction with a set of maximally incompatible local measurements, to demonstrate the maximal violation of inequality number 26 in Sliwa's classification scheme, which is a tight Bell inequality for the (3, 2, 2) scenario, i.e. , the three-party, two-measurement-settings and two-measurement-outcomes scenario.
Dynamic range expansion of spatial light modulators based on a module-nπ method

It is known that the cumbersome 2π correction is needed in the traditional module-2π method (i.e., the phase wrapping method) due to the 2π deviation of the phase modulation depth of spatial light modulators (SLMs). To avoid the cumbersome 2π correction in the module-2π method, this paper proposes a module-nπ method, and it can directly utilize any full-field phase modulation depth. First, for a Gaussian phase with a phase depth of 30 rad, wrapped by the module-3.6π, it is reconstructed with the root-mean-square (RMS) values of its phase response are 0.1006λ (for the Twyman-Green interferometer) and 0.1101λ (for the Shack-Hartmann wavefront sensor method), respectively, which proves that the monitoring accuracy is relatively consistent. Subsequently, some comparative experiments based on the traditional module-2π are performed. The experimental results show that the RMS values of its phase response are 0.8886λ (for a modulation depth of 11.3 rad) and 0.2261λ (for a modulation depth of 6.28 rad), respectively. All the results have proved that the SLM with a phase modulation depth exceeding 2π (e.g., 11.3 rad) has more prominent advantages. More specifically, increasing the SLM's phase modulation depth can effectively reduce the fringe orders of the wrapped patterns generated by the module-nπ method. With the further reduction of the fringe orders, the influence of the fly-back zone error on the wavefront phase modulation is reduced, that is, the modulation accuracy is improved (the RMS values are reduced from 0.2261λ to 0.1006λ). Different from the traditional module-2π method, there is no need to consider the problems of the SLMs' over modulation or the insufficient modulation in the module-nπ method. Furthermore, it avoids the cumbersome 2π correction process, which will make the use of the SLM more convenient.
Analysis of the quality of the vocational high school library website with the WebQual 4.0 method

Introduction. The use of library websites has begun to be used massively by libraries as a source of digital library information. This study aims to analyze the quality of the vocational school library website and identify the factors that influence the quality of the website. &#x0D; Data Collection Methods. Using quantitative methods with a population of all vocational high school students in Yogyakarta. Data were obtained using a questionnaire on 357 students from three schools.&#x0D; Data Analysis. Data analysis techniques were performed using WebQual Index (WQI) and Importance Performance Analysis (IPA) analysis.&#x0D; Results and Discussion. The results of this study found a WQI value of 0.87 on the library website of SMKN 1 Bantul, the library website of SMKN 1 Godean of 0.85 and of SMKN 2 Yogyakarta of 0.81. The quality of the library website at SMKN 1 Bantul is the library website with the best quality based on user perceptions. There are five factors that are important for users, namely a positive experience when visiting the website, reliable information provided by the website, personal data security, personalization features, and website manager information.&#x0D; Conclusion. In general, the quality of the vocational high school library website in Yogyakarta is good based on three aspects, namely usability, information quality and interaction service quality.
Green streaming through utilization of AI-based content aware encoding

With the growing usage of high quality HD and ultra HD video content, adaptive bitrate streaming and constantly increasing demand for bitrates and distribution bandwidth, energy consumption and related costs grow exponentially in parallel. As such, it is vital to reduce the overall energy consumption of online video streaming. In this paper we aim to investigate, which parameters influence energy consumption for video streaming, on the client (device) side, as well as during encoding. To conduct this systematic investigation, we have set up a reproducible measurement environment that closely resembles real-world conditions, with different client devices, and video encoding workflows, each connected to energy measurement devices. In an advanced step, we additionally examine the effect of content aware encoding methods on power consumption, using an AI-based per-scene encoding solution. Finally, we discuss and evaluate the measurements and offer recommendations to reduce overall CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions for video streaming.
Research on Logistics Service Assessment for Smart City: A Users’ Review Sentiment Analysis Approach

The innovative development of logistics has become a powerful starting point and strong support for the construction of smart cities. An accurate evaluation of logistics service quality can promote intelligent transformation, and upgrading logistics enterprises can improve the urban public service infrastructure. In this study, we propose a logistics service quality evaluation model based on a combination of sentiment analysis technology and a traditional index evaluation system. With the help of sentiment analysis technology, the model focuses on extracting the sentiment characteristics of logistics service quality from user reviews and analyzing user attitudes from different aspects. We designed a new logistics service quality evaluation index system by improving the SERVQUAL model. The system uses sentiment analysis technology to explore evaluation content through feature extraction and builds relations between the evaluation content and indices. Additionally, we use sentiment orientation analysis with different indices to comprehensively evaluate service quality. The experimental analysis shows that the proposed model and algorithm have high accuracy.
Convolution Based Graph Representation Learning from the Perspective of High Order Node Similarities

Nowadays, graph representation learning methods, in particular graph neural network methods, have attracted great attention and performed well in many downstream tasks. However, most graph neural network methods have a single perspective since they start from the edges (or adjacency matrix) of graphs, ignoring the mesoscopic structure (high-order local structure). In this paper, we introduce HS-GCN (High-order Node Similarity Graph Convolutional Network), which can mine the potential structural features of graphs from different perspectives by combining multiple high-order node similarity methods. We analyze HS-GCN theoretically and show that it is a generalization of the convolution-based graph neural network methods from different normalization perspectives. A series of experiments have shown that by combining high-order node similarities, our method can capture and utilize the high-order structural information of the graph more effectively, resulting in better results.
Motivation of schoolchildren to educational activities as a support for the organization of independent work in informatics

One of the factors of successful learning is the presence of positive motivation of students. The problem of the study presented in the article is to determine the individual elements and types of independent work in the study of informatics for students with different levels of motivation. Based on the diagnosis of the motivational structure of the personality, a computer test was developed (in the format of a web app), the result of which is the student's motivational profile. Using a package of statistical data processing, clustering of profiles of 67 high school students was performed. In the course of the study, three motivational clusters were identified. The values of the variable elements of the organization of independent work in informatics are revealed. Their presence and volume will make it possible to adapt learning tasks for independent work to the characteristics of students belonging to a particular motivational cluster. Variable elements are integrated into scenarios. An example of self-study scenarios for the "Array programming" section is given. It is concluded that the construction of a student's motivational profile, the determination of his motivational characteristics, make it possible to adapt the elements of independent work, to form an individual learning trajectory.
A Comparative Analysis of Digital Forensic Investigation Tools on Facebook Messenger Applications

Technological developments make it easier for people to communicate and share information. Facebook Messenger is an instant messenger that contains multi-platform for sending text, image, sound, and video messages. Besides being used for positive purposes, this technology can also be used to carry out harmful activities. This study conducts a forensic investigation on a crime simulation in pornographic content distribution using Facebook Messenger as a communication medium on Android smartphones. Perpetrators communicate and send pornographic content in the shape of conversations, audio, and video, then delete them to eliminate traces. Every crime can leave evidence therefore after erasing the track, it can be revealed through digital forensic investigations on the smartphone devices that are used as objects to find digital evidence. The collection of evidence in this study is used four forensic tools with the research stages the National Institute of Justice (NIJ) framework. The study result can be used as evidence by investigators on handling criminal cases with the results obtained in the shape of application versions, accounts, emails, conversation, time of occurrence, pictures, audio, and video. MOBILedit Forensic Express has an accuracy of 84.85%, Wondershare Dr. Fone 36.36%, Magnet Axiom 75.76%, and Belkasoft Evidence Center 69.70%.
A New Efficient Nanodesign of Composite Gate Based on Quantum Dot Cellular Automata

The development of nanoscale technologies has come from the downsizing of electronic equipment and the inadequacy of CMOS innovation resulting from hybrid circuit establishment and its failure to respond to nanoscale. Quantum-based technologies, like quantum-dot cellular automata (QCA), are likely to succeed the present technologies due to their incredible benefits, like faster processing, smaller footprints and ultra-low energy usage. In arithmetic and comparison circuits, the fundamental gate is frequently used. Arithmetic logic circuits centered on OR, AND and NOT logic gates have a low design complexity. To obtain complicated logic gates, several architectures have been proposed in the QCA. This work offers a QCA composite gate that achieves all critical digital logic gates, including Inverter, OR, AND, NAND, NOR and exclusive gates like XOR and XNOR. All basic logic is generated in a single unit with this architecture. Only 0.09[Formula: see text][Formula: see text]m 2 of area, three three-input majority gates, one XOR, three Inverters, and 0.5 clock zones were required for the suggested circuit. As a result, a decrease of 33% in cell count is achieved compared to the previous systems. The design is evaluated and the dissipated energy is analyzed. For the research on power dissipation, the QCADesigner-E simulator is used to verify the final result. The simulation outcomes show that the suggested layout is advantageous over earlier constructions regarding the area, number of cells, clock phases and cost.
Using ARIMA model to analyse and predict bitcoin price

In this paper, Autoregressive Integrated Moving Average (ARIMA) model is used for analysing and forecasting the adjusted closing price of bitcoin. The whole dataset used is daily bitcoin closing price dating from Jan 2017 to Sep 2022. However, for testing the performance of the ARIMA model, the dataset is divided into two parts: the ARIMA model is built on training set and later using the test set to check the accuracy of the prediction. Two models, namely ARIMA (5, 2, 1) and ARIMA (0, 2, 2) are selected and comparations between them are made. ARIMA (5, 2, 1) is chosen with stepwise selection and approximated information criteria while ARIMA (0, 2, 2) is without stepwise selection and the information criteria is not approximated. Both pass the residual test and ARIMA (0, 2, 2) is slightly better according to AIC, AICc and BIC. Later, the predicting accuracy of the two models for different forecasting periods (5-day, 10-day, and long-term forecasting) are compared. It is not surprising that ARIMA performs better while making short term prediction. The 5-day and 10-day forecast works well while the long-term forecast is of limited practical value.
Application of Videoscribe Media to Cognitive Learning Outcomes in Social Science Learning at Madrasah Ibtidaiyah

The purpose study are (1) to find out whether there was an effect of videoscribe learning media on social studies learning outcomes for grade 4 at MI Nurus syafi'I, (2) to find out how much influence videoscribe learning media had on social studies learning outcomes for grade 4 at MI Nurus syafi'i This study uses a quantitative experimental research design using the PreExperimental – One Group Pretest Posttest Design research design. The results showed that there was a positive and significant influence on the videoscribe learning media on the social studies learning outcomes of fourth grade students of MI Nurusyafi'I Gedangan, as evidenced by the t-count value of 10,947&gt; 2,074, and the magnitude of the effect can be seen from Cohen's d calculations which show the result of 2.12 &gt; 0 ,8 The conclusion of this study is that there are a major influence on student cognitive learning outcomes at MI Nurus Syafii.
Automatic simulation of unseen physical events

When a feature (e.g., the letter T) becomes associated with an object (e.g., a square), we are faster to detect that feature if it later appears on that same object than if it appears elsewhere, even after the object changes location—a foundational result known as the object-specific preview benefit (OSPB). But what if that feature is a physical entity with mass and extent (e.g., a ping-pong ball with a T printed on it), and the object is a container (e.g., a wooden box) in which the ball could slide and roll? Do we (merely) create a static association between the feature and the object? Or do we represent the feature’s location “within” the object, even after the feature disappears from view? Here, we exploit the OSPB to explore how attention automatically represents the physical dynamics of unseen objects. Observers viewed a letter drop into a box, which then moved to another location before abruptly disappearing, revealing the letter in one of several locations; then, observers reported whether or not it had changed (e.g., remaining a T, or changing from a T to an L). If object-feature bindings rely on simple association, then subjects should be fastest to detect the feature in the same box-relative location where it last appeared. However, our results showed that facilitation was greatest when the feature appeared where it “should” have been, as predicted by physical simulation of how a ball slides and rolls within a moving box (which would leave it in a different box-relative location than it started). Follow-up experiments ruled out lower-level explanations, including biases toward the screen’s center and to the last screen-relative location where the feature was seen. We suggest that perception automatically simulates the forces acting on unseen objects, such that feature-object bindings incorporate complex physical interactions.
The Uncanniness of Face Swaps

Face swapping algorithms, popularly known as "deep fakes", generate synthetic faces whose movements are driven by an actor's face. To create face swaps, users construct training datasets consisting of the two faces being applied and replaced. Despite the availability of public code bases, creating a compelling, convincing face swap remains an art rather than a science because of the parameter tuning involved and the unclear consequences of parameter choices. In this paper, we investigate the effect of different dataset properties and how they influence the uncanny, eerie feeling viewers experience when watching face swaps. In one experiment, we present participants with video from the FaceForensics++ Deep Fake Detection dataset. We ask them to score the clips on bipolar adjective pairs previously designed to measure the uncanniness of computer-generated characters and faces within three categories: humanness, eeriness, and attractiveness. We find that responses to face swapped clips are significantly more negative than to unmodified clips. In another experiment, participants are presented with video stimuli of face swaps generated using deepfake models that have been trained on deficient data. These deficiencies include low resolution images, lowered numbers of images, deficient/mismatched expressions, and mismatched poses. We find that mismatches in resolution, expression, and pose and deficient expressions all induce a higher negative response compared to using an optimal training dataset. Our experiments indicate that face swapped videos are generally perceived to be more uncanny than original videos, but certain dataset properties can increase the effect, such as image resolution and quality characteristics including expression/pose match-ups between the two faces. These insights on dataset properties could be directly used by researchers and practitioners who work with face swapping to act as a guideline for higher-quality dataset construction. The presented methods additionally open up future directions for perceptual studies of face swapped videos.
Meaning maps detect the removal of local scene content but deep saliency models do not

Stored semantic knowledge gained from experience is thought to play an important role in how we guide our attention in real-world scenes. Meaning mapping (Henderson & Hayes, 2017) uses human raters to directly estimate different semantic features in scenes, and has been a useful tool in demonstrating the important role semantics play in guiding attention. However, it has recently been suggested that meaning maps do not capture semantic content in scenes, but like deep learning models of scene attention, instead represent semantically-neutral image features. To directly test this hypothesis, we compared intact scene images to scene images in which local semantic content was removed using a diffeomorphic transformation (Stojanoski & Cusack, 2014). The diffeomorphic transformation was designed to preserve image properties while removing meaning providing an ideal test for our question of interest by serving as an adversarial image. The current experiment tested whether human-generated meaning maps (N=164 original and N=164 diffeomorphed) and three state-of-the-art deep learning models (MSI-Net, DeepGaze II, and SAM-ResNet) were sensitive to the loss of semantic content in the diffeomorphed scene regions. If humans base their ratings of meaning on semantically-neutral image features, then they should rate the diffeomorphed regions similarly to the original non-diffeomorphed regions. However, if humans base their meaning ratings on semantic content, then we should observe a large decrease in the diffeomorphed regions where the semantic scene content has been removed. The results were clear: meaning maps generated by human raters showed a large decrease in the diffeomorphed scene regions, while all three deep saliency models showed a moderate increase in the diffeomorphed scene regions. These results demonstrate that meaning maps reflect local semantic content in scenes while deep saliency models reflect something else. We conclude the meaning mapping approach is an effective tool for estimating semantic content in scenes.