Reviewerly/exHarmony huggingface dataset:
ANN for Assessment of Energy Consumption of 4 kW PV Modules over a Year Considering the Impacts of Temperature and Irradiance

Solar energy is considered the greatest source of renewable energy. In this paper, a case study was performed for a single-axis solar tracking model to analyze the performance of the solar panels in an office building under varying ambient temperatures and solar radiation over the course of one year (2018). This case study was performed in an office building at the College of Engineering at Shaqra University, Dawadmi, Saudi Arabia. The office building was supplied with electricity for a full year by the designed solar energy system. The study was conducted across the four seasons of the studied year to analyze the performance of a group of solar panels with the total capacity of a 4 kW DC system. The solar radiation, temperature, output DC power, and consumed AC power of the system were measured using wireless sensor networks (for temperature and irradiance measurement) and a signal acquisition system for each hour throughout the whole day. A single-axis solar tracker was designed for each panel (16 solar panels were used) using two light-dependent resistors (LDR) as detecting light sensors, one servo motor, an Arduino Uno, and a 250 W solar panel installed with an array tilt angle of 21°. Finally, an artificial neural network (ANN) was utilized to estimate energy consumption, according to the dataset of AC load power consumption for each month and the measurement values of the temperature and irradiance. The relative error between the measured and estimated energy was calculated in order to assess the accuracy of the proposed ANN model and update the weights of the training network. The maximum absolute relative error of the proposed system did not exceed 2 × 10−4. After assessment of the proposed model, the ANN results showed that the average energy in the region of the case study from a 4 kW DC solar system for one year, considering environmental impact, was around 8431 kWh/year.
Benchmarking of meteorological indices for sky cloudiness classification

Sky classification is a complex problem, due in part to such abstract conceptual definitions as clear, intermediate, and overcast, as well as other intermediate ranges. The CIE (Commission Internationale de L'Éclairage) Standard classification offers a solution to this problem, although its application requires data on the luminance distribution of the whole sky that are less commonly available. A benchmarking and classification system of ten meteorological indices is introduced in this study to classify the sky types from overcast to clear. The indices can be calculated from measurements of global, diffuse, and direct irradiance that are widely available from meteorological ground stations. The classification system uses confusion matrices, a machine-learning tool that generates a visual display of the results of supervised-learning algorithms. The CIE Standard skies classification, applied to half hourly sky-scanner measurements in Burgos (Spain), over the period June 2016 - May 2017, is used in this study as a baseline reference for a comparative review of the results from the meteorological indices and their results. They are classified by four performance ratings: Accuracy, Jaccard, Cohen, and Matthews, which feature both classification similarity and the randomness of any agreement. All meteorological indices yielded a high average degree of accuracy - close to 80% - in a detailed review of their classification. Neverthless, the results suggested that Perez's Clearness Index based on global, diffuse and direct radiation measurements offered the most precise classification of the skies, followed closely by the Klucher Clearness Index and the Perraudeau Nebulosity Index.
A Physical ZKP for Slitherlink: How to Perform Physical Topology-Preserving Computation

We propose a new technique to construct physical Zero-Knowledge Proof (ZKP) protocols for games that require a single loop draw feature. This feature appears in Slitherlink, a puzzle by Nikoli. Our approach is based on the observation that a loop has only one hole and this property remains stable by some simple transformations. Using this trick, we can transform a simple big loop, visible to anyone, into the solution loop by using transformations that do not disclose any information about the solution. As a proof of concept, we apply this technique to construct the first physical ZKP protocol for Slitherlink.
Certificateless aggregate signature scheme secure against fully chosen-key attacks

Certificateless aggregate signature (CLAS) schemes enjoy the benefits of both certificateless cryptography and aggregate signature features. Specifically, it not only simplifies the certificate management without introducing the key escrow problem but also transforms many signatures into one aggregate signature to save communication and computation cost. CLAS is a powerful cryptographic tool, yet its security should be thoroughly analyzed before being implemented. In this paper, we give a new insight into the security of CLAS schemes. We introduce a potential and realistic attack called fully chosen-key attacks that has not been considered in the traditional security models and define the security model against fully chosen-key attacks. In contrast to the traditional models, the adversary is allowed to hold all the signers’ private keys and its goal is not to forge an aggregate signature but to output invalid single signatures that can be aggregated into a valid aggregate signature. We find there is no CLAS scheme secure in traditional security models that is secure against fully chosen-key attacks and then demonstrate how to reinforce the security of an existing scheme to withstand such an attack.
(Pseudo) Random Quantum States with Binary Phase

We prove a quantum information-theoretic conjecture due to Ji, Liu and Song (CRYPTO 2018) which suggested that a uniform superposition with random binary phase is statistically indistinguishable from a Haar random state. That is, any polynomial number of copies of the aforementioned state is within exponentially small trace distance from the same number of copies of a Haar random state. As a consequence, we get a provable elementary construction of pseudorandom quantum states from post-quantum pseudorandom functions. Generating pseudorandom quantum states is desirable for physical applications as well as for computational tasks such as quantum money. We observe that replacing the pseudorandom function with a (2t)-wise independent function (either in our construction or in previous work), results in an explicit construction for quantum state t-designs for all t. In fact, we show that the circuit complexity (in terms of both circuit size and depth) of constructing t-designs is bounded by that of (2t)-wise independent functions. Explicitly, while in prior literature t-designs required linear depth (for $$t > 2$$), this observation shows that polylogarithmic depth suffices for all t. We note that our constructions yield pseudorandom states and state designs with only real-valued amplitudes, which was not previously known. Furthermore, generating these states require quantum circuit of restricted form: applying one layer of Hadamard gates, followed by a sequence of Toffoli gates. This structure may be useful for efficiency and simplicity of implementation.
An online compressor liquid floodback fault diagnosis method for variable refrigerant flow air conditioning system

Compressor liquid floodback not only can drop the performance of the variable refrigerant flow (VRF) system, but also may cause mechanical failure. Therefore, this is necessary to perform a timely online diagnosis of that fault. The present paper proposes an online compressor liquid floodback fault diagnosis method for VRF system based on back-propagation neural network (BPNN), which fills the online compressor liquid floodback fault diagnosis knowledge gap. The proposed method main article context as follow: Firstly, the sensors in VRF system record and save the data every three seconds to form the raw dataset. Secondly, after preprocessing the raw dataset, the correlation analysis is used to filter the data variables. Thirdly, the BPNN model is established by using back-propagation neural network algorithm. The testing dataset was used to verify the reliability of the model. The online dataset was used to test the model's online diagnostic capability, and its results were also compared and analyzed to the Classification and Regression Tree (CART) model. This result indicates that the BPNN method has a low degree of over-fitting and high reliability, and its online diagnostic accuracy is up to 99.48%. By comparing results of the method BPNN and CART, it shows that the former is superior to the latter, regardless of the diagnostic accuracy or the online diagnostic stability.
The effect of SOS Table learning environment on mobile learning tools acceptance, motivation and mobile learning attitude in English language learning

The aim of this study is to demonstrate effectiveness of mobile game called SOS Table in the context of the subject “Tenses in English” within the framework of mobile learning tools acceptance, learning language motivation and mobile learning attitude of the students. The target group of the research consists of preparatory class students of a school of foreign languages in a state university in Turkey. The research was carried out with mixed method research. The quantitative paradigm-based section of this study was designed with a single-group pre-test and post-test model. In this model, three different scales were applied to the participants. The participants used the SOS Table mobile game developed by the researchers for 8 weeks. After the applications, semi-structured interviews were conducted with seven participants. The quantitative data of the study were analyzed with paired sampled t-Test and the qualitative data were subjected to thematic content analysis. The results of the study indicated that the mobile application named SOS Table increased both the mobile learning tools acceptance of the participants and motivation in English, and mediated the positive attitude development for mobile learning in English. The qualitative data obtained also supported these findings.
Removal of random valued impulse noise from grayscale images using quadrant based spatially adaptive fuzzy filter

An efficient image restoration technique based on spatially linked-directional adjoining pixels and fuzzy logic for addressing moderate and highly corrupted grayscale images with Random Valued Impulse Noise is presented. The proposed technique decomposes a larger sized impulsive region of an image into numerous overlapping small patches for low as well as high density impulse noise estimation with enhanced image restoration results. This subdivision highlights the details (edges, lines and textures) present in the neighborhood and the locations of impulsive pixels are relocated in multiple regions. Direction-based fuzzy rules give appropriate reasoning for edge and texture detection in an image. A switching technique based fuzzified degree identifies a certain pixel of an image as a noise-free, noisy or edge pixel in the filtering phase. Instead of using a static threshold, a directional non-parametric approach is introduced that determines and sets the threshold adaptively which empowers the proposed filter to handle different types of images automatically. Extensive simulations are conducted on a large set of monochrome images contaminated with varying degree of impulse noise densities. Objective analysis performed using popular quantitative measures and subjective evaluation of the results shows the efficacy of the proposed filter over most of the bench-marked denoising filters.
Extracting causal relations from the literature with word vector mapping

Causal graphs play an essential role in the determination of causalities and have been applied in many domains including biology and medicine. Traditional causal graph construction methods are usually data-driven and may not deliver the desired accuracy of a graph. Considering the vast number of publications with causality knowledge, extracting causal relations from the literature to help to establish causal graphs becomes possible. Current supervised-learning-based causality extraction methods requires sufficient labeled data to train a model, and rule-based causality extraction methods are limited by the predefined patterns. This paper proposes a causality extraction framework by integrating rule-based methods and unsupervised learning models to overcome these limitations. The proposed method consists of three modules, including data preprocessing, syntactic pattern matching, and causality determination. In data preprocessing, abstracts are crawled based on attribute names before sentences are extracted and simplified. In syntactic pattern matching, these simplified sentences are parsed to obtain the part-of-speech tags, and triples are achieved based on these tags by matching the two designed syntactic patterns. In causality determination, four verb seed sets are initialized, and word vectors are constructed for the verbs in both the seed sets and the triples by applying an unsupervised machine learning model. Causal relations are identified by comparing the similarity between the verbs in each triple and that in each seed set to overcome the limitation of the seed sets. Causality extraction results on the attributes from the risk factors for Alzheimer's disease show that our method outperforms Bui's method and Alashri's method in terms of precision, recall, specificity, accuracy and F-score, with increases in the F-score of 8.29% and 5.37%, respectively.
DialPlates

In this paper we introduce a novel approach for smooth pursuits eye movement detection and demonstrate that it allows up to 160 targets to be distinguished. With this work we advance the well-established smooth pursuits technique, which allows gaze interaction without calibration. The approach is valuable for researchers and practitioners, since it enables novel user interfaces and applications to be created that employ a large number of targets, for example, a pursuits-based keyboard or a smart home where many different objects can be controlled using gaze. We present findings from two studies. In particular, we compare our novel detection algorithm based on linear regression with the correlation method. We quantify its accuracy for around 20 targets on a single circle and up to 160 targets on multiple circles. Finally, we implemented a pursuits-based keyboard app with 108 targets as proof-of-concept.
Supply Chain Management using Blockchain

As an attempt to collaborate the digital world with the physical one, we maintain a track of the journey of the dairy products from producer to consumer. IoT sensors can be linked to any product entrusted to someone else for transport, with traceable ownership, possession, and measurement parameters such as location, temperature, humidity, motion; for instance, milk, curd, etc. The final consumer can access a complete record of details and trust that the information is accurate and precise. One of the upcoming technologies, blockchain, is a great way for handling the quality of supply-chain management, since it uses the distributed public general ledger. Blockchain technology proves to be helpful in the supply-chain sector in the following manner: reduce errors, avoid product delays, eliminate fraudulent activities, improve management, increase consumer/supplier trust, and so on. In case of lack of transparency, blockchain provides recorded information about a variety of transactions in goods and/or services, which are recorded and tracked in real time.
An improved quantum projection filter

This work extends the previous quantum projection filtering scheme in Gao et al. (2019), by adding an optimality analysis result. A reformulation of the quantum projection filter is derived by minimizing the truncated Stratonovich stochastic Taylor expansion of the difference between the true quantum trajectory and its approximation on a lower-dimensional submanifold through quantum information geometric techniques. Simulation results for a qubit example demonstrate better approximation performance for the new quantum projection filter.
A new clustering method based on the bio‐inspired cuttlefish optimization algorithm

Abstract Most of the well‐known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio‐inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K‐means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFA‐Clustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.
Face Occlusion Recognition With Deep Learning in Security Framework for the IoT

Currently, the security of the Internet of Things (IoT) has aroused great concern. Face detection under arbitrary occlusion has become a key problem affecting social security. This paper designs a novel face occlusion recognition framework in the security scene of IOT, which is used to detect some crime behaviors. Our designed framework utilizes the gradient and shape cues in a deep learning model, and it has been demonstrated to be robust for its superiority to detect faces with severe occlusion. Our contributions contain three main aspects: Firstly, we present a new algorithm based on energy function for face detection; Secondly, we use the CNN models to create deep features of occluded face; Finally, to check whether the detected face is occluded, novel sparse classification model with deep learning scheme is constructed. Statistical results demonstrate that, compared with the state of the arts, our algorithm is superior in both accuracy and robustness. Our designed head detection algorithm can achieve 98.89% accuracy rate even though there are various types of severe occlusions in faces, and our designed occlusion verification scheme can achieve 97.25% accuracy rate, at a speed of 10 frames per second.
Fight Detection in Video Sequences Based on Multi-Stream Convolutional Neural Networks

Surveillance has been gradually correlating itself to forensic computer technologies. The use of machine learning techniques made possible the better interpretation of human actions, as well as faster identification of anomalous event outbursts. There are many studies regarding this field of expertise. The best results reported in the literature are from works related to deep learning approaches. Therefore, this study aimed to use a deep learning model based on a multi-stream and high level hand-crafted descriptors to be able to address the issue of fight detection in videos. In this work, we focused on the use of a multi-stream of VGG-16 networks and the investigation of conceivable feature descriptors of a video's spatial, temporal, rhythmic and depth information. We validated our method in two commonly used datasets, aimed at fight detection, throughout the literature. Experimentation has demonstrated that the association of correlated information with a multi-stream strategy increased the classification of our deep learning approach, hence, the use of complementary features can yield interesting outputs that are superior than other previous studies.
Smart toys for preschool children: A design and development research

The purpose of this study is to devise guidelines for designing, developing, and using a smart toy for preschool children. Smart toys are technologically developed toys constructed with a meaningful purpose. This study uses the design and development research method. In the analysis phase, the smart toy developed in the pilot study was analyzed. In the design phase, focus group meetings were held with early childhood teachers to determine the objectives, story, and storyboard of the smart toy. In the development phase, two prototypes were developed, and formative evaluations were conducted with preschool children, early childhood teachers, and scholars. In the evaluation phase, the smart toy was evaluated by preschool children. Many design guidelines (categorized as content, visual design, or interaction) emerged during the study.
Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems

Recently, feature selection for multilabel classification has attracted substantial attention in many fields; however, some of the available methods ignore the correlations among labels and yield low classification performance. In addition, most feature selection algorithms that are based on multilabel neighborhood rough sets (MNRS) can only deal with finite sets for multilabel data. To address the issues, this paper presents a novel hybrid filter-wrapper multilabel feature selection method that is based on binary particle swarm optimization (BPSO) and MNRS with the Lebesgue measure for multilabel neighborhood decision systems. First, to overcome the problem that the traditional correlation-based feature selection (CFS) algorithm ignores the dependencies among labels, two types of average correlation between single labels and label sets and among labels are presented. Via combination with information-entropy-based uncertainty measures, a new average correlation among labels is studied. A novel comprehensive evaluation function of CFS (NCFS) is constructed. Then, NCFS is introduced as a fitness function into the original BPSO and improved BPSO algorithms to optimize multilabel classification in the early and later stages, respectively, and the optimization process is terminated when the maximum number of iterations is reached. Next, the Lebesgue measure of the neighborhood class is developed for investigating the neighborhood approximation accuracy and the dependency degree based on MNRS. Various properties are deduced, and the relationships among these measures are used to evaluate the uncertainty and correlations among labels of multilabel data. Finally, a hybrid filter-wrapper feature selection algorithm using NCFS-BPSO is designed for preliminarily eliminating redundant features to decrease the complexity, and a heuristic forward multilabel feature selection algorithm is proposed for improving the performance of multilabel classification. Experimental results on fifteen multilabel datasets demonstrate that our proposed algorithms are effective in selecting significant features and realizing great classification performance in multilabel neighborhood decision systems.
Lagrange Coded Computing with Sparsity Constraints

In this paper, we propose a distributed coding scheme that allows for lower computation cost per computing node than the standard Lagrange Coded Computing scheme. The proposed coding scheme is useful for cases where the elements of the input data set are of large dimensions and the computing nodes have limited computation power. This coding scheme provides a trade-off between the computation cost per worker and the recovery threshold in a distributed coded computing framework. The proposed scheme is also extended to provide data privacy against at most t colluding worker nodes in the system.