Reviewerly/exHarmony huggingface dataset:
LSMVC:Low-rank Semi-supervised Multi-view Clustering for Special Equipment Safety Warning

In order to effectively prevent accidents of special equipment, numerous management platforms utilize the multi-source data of special equipment to predict the safety state of equipment. However, there is still a lack of methods to deal with noise when fusing different data sources. This paper proposes a novel low-rank semi-supervised multi-view clustering for special equipment safety warning (LSMVC). Which achieves robust multi-view clustering by using low rank representation (LRR) to reduce the impact of noise. To solve this non-smooth optimization problem, we propose an optimization procedure based on the Alternating Direction Method of Multipliers. Finally, experiments are carried out on six real datasets including the Elevator dataset, which is collected from the actual work. The results show that the proposed clustering method can achieve better clustering performance than other clustering method.
Quantum Adaptive Agents with Efficient Long-Term Memories

Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly -- they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum information processing. We uncover the most general form a quantum agent need adopt to maximise memory compression advantages, and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favourable scaling advantages relative to memory-minimal classical agents, particularly when information must be retained about events increasingly far into the past.
Pupils’ research skills development through project-based learning in biology

The study aims to develop theoretical foundations, methods of forming research skills of pupils in biological education, and experimental substantiation of its effectiveness. The following methods were used: analysis of psychological and pedagogical, biological, scientific, and methodological literature, the definition of the theory, the innovative methods of biological education, practical work, pedagogical practice, control, questionnaires, interviews, statistical analysis, identification, and implementation of training experiments. It is proposed to develop methods and determine the content of the formation of research competence through project activities, conduct and introduce them into the educational process. It is necessary to analyze the psychological, pedagogical, biological, scientific, and methodological literature to determine the theory, innovative methods of biological education, the organization of practical work, pedagogical practice, using a statistical method, which will lead to the creation of a methodology for the formation of research skills of pupils.&#x0D; &#x0D; Keywords: project-based learning, research, methodology, reflection, group work.
Application of AI/IoT for Smart Renewable Energy Management in Smart Cities

A city is considered to be smart when the application of Artificial Intelligence (AI) and the Internet of Things (IoT) is integrated with it. This enables the collection of data from people, devices, and buildings, then analyses are performed to optimize control over infrastructure, traffic, energy, etc. A smart city is a collective framework with the integration of Information and Communication Technologies (ICT) and Cloud that makes interaction easily with one another. In this chapter, smart energy infrastructure is studied to monitor energy utilization in the city and to reduce costs and carbon emissions. Energy usage has recently shifted focus to renewable energy sources with minimal carbon emissions, emphasizing the necessity for ongoing environmental and human health preservation. Renewable energy is becoming more abundant, and the issue is to recognize and understand it in meeting the increasing demand for clean, affordable energy. Customers, distributors, and government bodies are all concerned about cost and the climate. Artificial intelligence proclaimed a new age in technology as well as in sustainable development. So, in this chapter, an implication of AI is presented and analyzed for RE research in smart environments. Along with that, an analytical study is also presented with the application of AI or IoT for smart energy management for smart cities. The main aim is to focus on and explore the efficiency level of ML/IoT techniques. This work will also provide an in-depth analysis of innovative development, deployment, analysis, and management of smart energy in smart cities.
Convolutional Neural Network: An Overview and Application in Image Classification

As the field of artificial intelligence keeps progressing each year, it is noticeable how deep learning is becoming a significant approach for information processing like image classification and speech recognition. Deep learning consists of several techniques which are advanced, have a powerful learning capacity and give high accuracy. Convolutional neural networks have become a significant approach for classification of images. It can be thought that convolutional neural network (CNN) can automatically extract features and hence feature engineering is not required. In this manuscript deep learning convolutional network is used to achieve a high accuracy in classifying the images of cats and dogs and distinctively. It is shown how CNN gives better accuracy than other techniques.
Big Data Based Smart Blockchain for Information Retrieval in Privacy-Preserving Healthcare System

In digital healthcare systems, the patients face significant problems identifying an optimal vacant and available slot for their appointment as the number of patient requests directly correlates with the slot availability. Existing smart healthcare systems facilitate the patient to reserve a particular time slot and attain real-time healthcare information. However, most of these systems need sensitive information about patients, i.e., desired destination of the healthcare providers. Moreover, existing systems utilize a centralized system which makes these systems vulnerable to numerous intruders’ attacks and security breaches (particularly related to service providers) and results in single-point failure of the entire system. In this paper, Ring Signature for permissioned Block-chain-based Private Information Retrieval scheme is proposed to improve privacy-preserving in the smart healthcare system. Our proposed Scheme initially utilizes an improved multi-transaction mode consortium block-chain constructed by different numbers of requests to the healthcare providers to achieve maximized appointment offers based on availability, transparency, and security. Our proposed Scheme is useful for information retrieval from multiple domains. The simulation results verify that the proposed Scheme provides a predominant performance in ensuring maximized patient privacy with reduced computation and communication overheads.
An Artificial Radio Frequency Fingerprint Embedding Scheme for Device Identification

Radio frequency fingerprint (RFF) is an intrinsic physical characteristic of the device caused by hardware imperfection. It can be extracted from wireless signals and has been widely applied for device identification. However, the improving precision of electronic components reduces the distinguishability of RFF, which makes it difficult to identify large amount of devices that are of the same model, especially in a low signal-to-noise ratio (SNR) scenario. In this letter, we propose an artificial radio frequency fingerprint (ARFF) embedding scheme for device identification, which greatly increases the distinguishability of fingerprints and minimally affects the performance of communication. The scheme includes an adaptive filter based RFF extraction method, a principal component analysis (PCA) based ARFF generation algorithm and a proposed ARFF embedding algorithm that eliminates the influence of the device’s original RFF to form a preset ARFF pattern. Experimental results on ZigBee devices demonstrate that our ARFF based scheme achieves 99.91% identification accuracy and simultaneously has little impact on bit error rate.
Detection of natural clusters via S-DBSCAN a Self-tuning version of DBSCAN

Density-based clustering algorithms have made a large impact on a wide range of application fields application. As more data are available with rising size and various internal organizations, non-parametric unsupervised procedures are becoming ever more important in understanding datasets. In this paper a new clustering algorithm S-DBSCAN1 is proposed in the context of knowledge discovery. S-DBSCAN belongs to the connectivity-based family such as DBSCAN but with noticeable differences and advantages as working in a differential mode. It is formalized via a very simple hierarchical process that hybridizes distance, k-nearest and Density peaks concepts. It aims at partitioning existing data into clusters until no more clustering can be done. The information delivered allows the user to intuitively deduce different sets of natural partitions in clusters at different scales. S-DBSCAN scans the database in a ordered way by applying its algorithm core (S-DBSCANCORE) with judicious input parameters. Given a set of data patterns in some space, S-DBSCANCORE groups together data patterns that are closely packed together with respect to the differential density. Data patterns whose nearest neighbors have too different densities are detected and marked as borders while the others are not visited. S-DBSCAN embeds some intelligence that makes it self-tuning (almost fully automatic) and not dependent on a global density threshold as many existing algorithms. Tests were carried out using 2-dimensional benchmark datasets of various shapes and densities. They showed that S-DBSCAN was highly effective. It also proved efficient in high dimension space when natural clusters exist and much easier to use than competitive algorithms.
Bell state analyzer for spectrally distinct photons

We demonstrate a Bell state analyzer that operates directly on frequency mismatch. Based on electro-optic modulators and Fourier-transform pulse shapers, our quantum frequency processor design implements interleaved Hadamard gates in discrete frequency modes. Experimental tests on entangled-photon inputs reveal fidelities of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>∼<!-- ∼ --></mml:mo> </mml:mrow> <mml:mn>98</mml:mn> <mml:mi mathvariant="normal">%<!-- % --></mml:mi> </mml:math> for discriminating between the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">|</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi mathvariant="normal">Ψ<!-- Ψ --></mml:mi> <mml:mo>+</mml:mo> </mml:msup> </mml:mrow> <mml:mo fence="false" stretchy="false">⟩<!-- ⟩ --></mml:mo> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo stretchy="false">|</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mi mathvariant="normal">Ψ<!-- Ψ --></mml:mi> <mml:mo>−<!-- − --></mml:mo> </mml:msup> </mml:mrow> <mml:mo fence="false" stretchy="false">⟩<!-- ⟩ --></mml:mo> </mml:math> frequency-bin Bell states. Our approach resolves the tension between wavelength-multiplexed state transport and high-fidelity Bell state measurements, which typically require spectral indistinguishability.
A three-phase search approach with dynamic population size for solving the maximally diverse grouping problem

The maximally diverse grouping problem (MDGP) aims to assign a given set of elements into a number of groups with size restrictions for the sake of maximizing the sum of diversity in these groups. MDGP is an NP-hard combinatorial optimization problem, possessing widespread application and practical importance. This paper introduces a novel hybrid algorithm, called a three-phase search approach with dynamic population size (TPSDP), for solving the problem. The proposed algorithm devises the search process into three phases with distinct functions which are iterated: (1) an undirected perturbation phase to improve the population diversity, (2) a restructure phase using a distinctive crossover operator to increase the information interaction among solutions, and (3) a directed perturbation phase to discover the adjacent local optima around current solutions. TPSDP also combines a dynamic population size strategy to reserve limited computing resources for potential solutions. The results of experiments and the Friedman test show that the overall performance of the proposed TPSDP is highly competitive with or better than previous state-of-the-art MDGP algorithms on 500 instances taken from five popular benchmark sets. Furthermore, an additional experiment of parameter analysis and a discussion of critical ingredients are presented. The source code of TPSDP is provided at https://toyamaailab.github.io/sourcedata.html.
A Novel Approach to Face Pattern Analysis

Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, PCA with an Artificial Neural Network, and even the traditional PCA-SVM to improve face recognition. PCA-SVM is better than PCA-ANN as PCA-ANN has the limitation of a small dataset. As far as classification and generalization are concerned, SVM requires fewer parameters and generates less generalization errors than an ANN. In this paper, we propose a new framework, called FRS-DCT-SVM, that uses GA-RBF for face detection and optimization and the discrete cosine transform (DCT) to extract features. FRS-DCT-SVM using GA-RBF gives better results in terms of clustering time. The average accuracy received by FRS-DCT-SVM using GA-RBF is 98.346, which is better than that of PCA-SVM and SVM-DCT (86.668 and 96.098, respectively). In addition, a comparison is made based on the training, testing, and classification times.
Flag-transitive and point-imprimitive symmetric designs with λ ≤ 10

In a paper published in 2005, Praeger and Zhou improved the upper bound on the number of points of a flag-transitive, point-imprimitive, symmetric design in terms of the number of blocks containing two points. Here we present an improvement of their result, devise a list of feasible parameter sequences for which two points are contained in at most ten blocks, and also develop new methods for the elimination of all but two such sequences. We used those methods to list all designs with given properties, with the exclusion of designs with said two sets of parameters for which we give limitations for their group of automorphisms.
Automatic optical structure optimization method of the laser triangulation ranging system under the Scheimpflug rule

Optical structure parameter design is of great importance to ensure the accuracy of asymmetry systems such as the laser triangulation ranging system. The result often depends on the experience of optical designers, and it is inevitable to introduce human errors. In this paper, an automatic optimization method of optical structure parameters applied to the laser triangulation ranging system was proposed to assist in improving the measurement accuracy. First, the optical sensitivity optimization model of the laser triangulation ranging system was constructed, and the boundary conditions were established from two aspects of laser clear imaging and sensor geometric dimension. The constraint relationship between optical structure parameters under the Scheimpflug rule was established to ensure clear laser imaging, and the transverse distance and axial distance of the sensor geometric dimension were constrained when the laser imaging length was taken into consideration. Secondly, mutation operator-based particle swarm optimization (M-PSO) algorithm was proposed to search the optimal optical sensitivity, and three typical working distances and ranges were taken as examples to design the optical parameters. In addition, the results were verified by ZEMAX simulation and also the experimental platform, the maximum optical sensitivity of the structure was 3.496 and its corresponding optimal nonlinearity was 0.039% F.S. The automatic optimization method proposed in this paper was compared with the traditional GA method and PSO method, and it was verified that the convergence efficiency of the proposed method was much higher than the traditional ones.
Photonics-enabled spiking timing-dependent convolutional neural network for real-time image classification

A photonics-enabled spiking timing-dependent convolutional neural network (CNN) is proposed by manipulating photonics multidimensional parameters in terms of wavelength, temporal and spatial, which breaks the traditional CNN architecture mapping from a spatially parallel to a time-dependent series structure. The proposed CNN with the application of real-time image recognition comprises a photonics convolution processor to accelerate the computing and an involved electronic full connection to execute the classification task. A timing-dependent series of matrix-matrix operations is conducted in the photonics convolution processor that can be achieved based on multidimensional multiplexing by the accumulation of carriers from an active mode-locked laser, dispersion latency induced by a dispersion compensation fiber, and wavelength spatial separation via a waveshaper. Incorporated with the electronic full connection, a photonics-enabled CNN is proven to perform a real-time recognition task on the MNIST database of handwritten digits with a prediction accuracy of 90.04%. Photonics enables conventional neural networks to accelerate machine learning and neuromorphic computing and has the potential to be widely used in information processing and computing, such as goods classification, vowel recognition, and speech identification.
A Contrastive Sharing Model for Multi-Task Recommendation

Multi-Task Learning (MTL) has attracted increasing attention in recommender systems. A crucial challenge in MTL is to learn suitable shared parameters among tasks and to avoid negative transfer of information. The most recent sparse sharing models use independent parameter masks, which only activate useful parameters for a task, to choose the useful subnet for each task. However, as all the subnets are optimized in parallel for each task independently, it is faced with the problem of conflict between parameter gradient updates (i.e, parameter conflict problem). To address this challenge, we propose a novel Contrastive Sharing Recommendation model in MTL learning (CSRec). Each task in CSRec learns from the subnet by the independent parameter mask as in sparse sharing models, but a contrastive mask is carefully designed to evaluate the contribution of the parameter to a specific task. The conflict parameter will be optimized relying more on the task which is more impacted by the parameter. Besides, we adopt an alternating training strategy in CSRec, making it possible to self-adaptively update the conflict parameters by fair competitions. We conduct extensive experiments on three real-world large scale datasets, i.e., Tencent Kandian, Ali-CCP and Census-income, showing better effectiveness of our model over state-of-the-art methods for both offline and online MTL recommendation scenarios.
TaxoEnrich: Self-Supervised Taxonomy Completion via Structure-Semantic Representations

Taxonomies are fundamental to many real-world applications in various domains, serving as structural representations of knowledge. To deal with the increasing volume of new concepts needed to be organized as taxonomies, researchers turn to automatically completion of an existing taxonomy with new concepts. In this paper, we propose TaxoEnrich, a new taxonomy completion framework, which effectively leverages both semantic features and structural information in the existing taxonomy and offers a better representation of candidate position to boost the performance of taxonomy completion. Specifically, TaxoEnrich consists of four components: (1) taxonomy-contextualized embedding which incorporates both semantic meanings of concept and taxonomic relations based on powerful pretrained language models; (2) a taxonomy-aware sequential encoder which learns candidate position representations by encoding the structural information of taxonomy; (3) a query-aware sibling encoder which adaptively aggregates candidate siblings to augment candidate position representations based on their importance to the query-position matching; (4) a query-position matching model which extends existing work with our new candidate position representations. Extensive experiments on four large real-world datasets from different domains show that \TaxoEnrich achieves the best performance among all evaluation metrics and outperforms previous state-of-the-art methods by a large margin.
SqueezeNeRF: Further factorized FastNeRF for memory-efficient inference

Neural Radiance Fields (NeRF) has emerged as the state-of-the-art method for novel view generation of complex scenes, but is very slow during inference. Recently, there have been multiple works on speeding up NeRF inference, but the state of the art methods for real-time NeRF inference rely on caching the neural network output, which occupies several gigabytes of disk space that limits their real-world applicability. As caching the neural network of original NeRF network is not feasible, Garbin et al. proposed "FastNeRF" which factorizes the problem into 2 sub-networks - one which depends only on the 3D coordinate of a sample point and one which depends only on the 2D camera viewing direction. Although this factorization enables them to reduce the cache size and perform inference at over 200 frames per second, the memory overhead is still substantial. In this work, we propose SqueezeNeRF, which is more than 60 times memory-efficient than the sparse cache of FastNeRF and is still able to render at more than 190 frames per second on a high spec GPU during inference.
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating Controlled Text Generation

Existing reference-free metrics have obvious limitations for evaluating controlled text generation models.Unsupervised metrics can only provide a task-agnostic evaluation result which correlates weakly with human judgments, whereas supervised ones may overfit task-specific data with poor generalization ability to other datasets.In this paper, we propose an unsupervised reference-free metric called CTRLEval, which evaluates controlled text generation from different aspects by formulating each aspect into multiple text infilling tasks.On top of these tasks, the metric assembles the generation probabilities from a pre-trained language model without any model training.Experimental results show that our metric has higher correlations with human judgments than other baselines, while obtaining better generalization of evaluating generated texts from different models and with different qualities 1 .