Reviewerly/exHarmony huggingface dataset:
Asymmetric CycleGAN for Unpaired Image-to-Image Translation Based on Dual Attention Module

Numerous studies have been conducted on image style transfer. With the advancement of artificial intelligence, the application of convolutional neural networks and Generative Adversarial Networks (GAN) has garnered considerable attention in this field. Traditional CycleGAN is limited in extracting the features of different images well and effectively for asymmetric translation due to its own completely symmetric structure. In this paper, we propose an asymmetric CycleGAN model for spring and winter image style transfer. Our method modifies one of the generators by adding dual attention module, which can capture global features through spatial and channel dimensions so as to obtain rich information of images more deeply. The model is experimentally validated to be more capable of color recognition than the original CycleGAN.
Design of a sorting and recycling system for post-consumer bottles

The recycling of discarded bottles can save resources and avoid pollution. The current machinery of recycling is faced with high cost or large manual operation. Furthermore, it cannot deal with complex classification types and environmental problems. A designment of sorting and recycling system couple with Res2net combining attention mechanism with label smoothing methods is identified and classified for different types of post-consumer bottles, the result shows that the recognition accuracy of this way has been improved. The attention mechanism and label smoothing method can not only reduce the data disturbance at the initial training stage, but also improve the accuracy. This program was tested, and its recall rate and accuracy rate reached the highest, which were respectively 91.70% and 94.16%.
A Survey on Domain-Specific Summarization Techniques

Automatic text summarization using different natural language processing techniques (NLP) has gained much momentum in recent years. Text summarization is an intensive process of extracting representative gist of the contents present in a document. Manual summarization of structured and unstructured text is a tedious task that involves immense human effort and time. There are quite a number of successful text summarization algorithms for generic documents. But when it comes specialized for a particular domain, the generic training of algorithms does not suffice the purpose. Hence, context-aware summarization of unstructured and structured text using various algorithms needs specific scoring techniques to supplement the base algorithms. This paper is an attempt to give an overview of methods and algorithms that are used for context-aware summarization of generic texts.
Comparison of Methods of Gross Error Location Technology in Hydraulic Safety Monitoring

Gross error location is an important work in data preprocessing of hydraulic safety monitoring. Several different gross error location methods are compared and introduced, and their applicable scope and limitations are discussed in this paper. Based on the normal distribution function, the traditional statistical test method can be applied to the gross error detection of repeated measurements of small samples, but it cannot adapt to the dynamic external load conditions faced by large samples and long sequences of monitoring data; the logic test method improved by "3 &#x03C3; criterion" can take the correlation of monitoring values in time series into account; ARIMA modeling method is suitable for detecting gross errors of monitoring sequences which are relatively continuous in time; for the monitoring effect quantity with clear related influencing factors, the statistical model method can effectively locate the gross error points; Grey System GM (1, 1) and GM (2, 1) models are suitable for gross error identification of data with insufficient information. The research results have certain reference value.
Research on the Detection and Recognition Algorithm of Click Chinese Character Verification Code

Network security has always been a hot topic of people’s attention. In recent years, click Chinese character verification codes have been used by major Chinese websites to prevent malicious attacks and malicious batch operations. How effective this verification code is and whether it will be cracked at a low cost is a worthy of discussion and research question. In response to this problem, this paper proposes a two-step cracking method of detection and classification. Using the more commonly used detection and classification networks, combined with some targeted changes, achieved an 83.33% cracking success rate on the test set at a relatively low cost, and made a partial answer to this question.
PEACE EDUCATION

Education for peace is a holistic approach. It nurtures knowledge ,skill,attitude and values -so that the learner can leave in harmony with self and others and handle conicts in a constructive way .peace empowers individuals to accept, create and enjoy the path of peace rather than only to be a consumer of it. It is a key tool for developing values , knowledge and life skills in a spirit of equality , empathy ,understanding and mutual respect among individuals.
On the Fractionalization of the Shift Operator on Graphs

The theory of graph signal processing has been established with the purpose of generalizing tools from classical digital signal processing to the cases where the signal domain can be modeled by an arbitrary graph. In this context, the present paper introduces the notion of fractional shift of signals on graphs, which is related to considering a non-integer power of the graph adjacency matrix. Among the results derived throughout this work, we prove that the referred fractional operator can be implemented as a linear and shift-invariant graph filter for any graph and verify its convergence to the classical fractional delay when a directed ring graph is considered. By means of a real-world example, we show that, using the proposed operator, graph filters that approximate an ideal filter better than those designed using the ordinary adjacency matrix can be obtained. An additional example dealing with noise removal from graph signals illustrates the gain provided by the mentioned filter design strategy.
Research on Affective Computing Based on Graph Sentiment Dictionary

Affective computing is an emerging field in recent years, it is proposed to allow computers understanding human emotions. At present, the most commonly used method of affective computing with textual signals is based on a sentiment lexicons. The polarity of emotion words can be divided into two types: positivity and negativity. The general method is to store common emotional words and their polarities in a list, taking them out of the list for calculation when needed. This approach can be used in basic affective computing , but it cannot solve the problems of polysemy. In view of this situation, we propose a new approach that is using a graphic dictionary to store sentiment words. The graph data structure is composed of two parts: vertices and edges, and the weights of several edges connecting the same vertex can be different from each other. The feature of the graph can solve the problem that one word may not have only one sentiment tendencies in different context. This paper constructs a graph sentiment dictionary based on the characteristics of the graph, then extracts the targets, features, sentiment words in the text as the vertices, and extracts the relationship between them as the edges to build a graph sentiment dictionary. By this way, the problem of polysemy and the lack of keywords can be solved. Compared with the traditional emotional dictionary, the effect of the graph sentiment dictionary will be better.
Assessing Digital Competencies of Students in the Fifth Cycle of Primary Education: A Diagnostic Study in the Context of Covid-19

The context of virtual education requires not only the availability of Information and Communication Technologies (ICT), but also the development of digital competencies that will mediate the teaching and learning processes. These digital competencies and the development of autonomy are established as transversal competencies in the National Curriculum of the Peruvian Ministry of Education. In addition, it is in this context that it is urgent to evaluate the digital competencies of students. For these reasons, the present study aims to analyze diagnostically the level of digital competencies in primary school students in Lima-Peru. The sample consisted of 118 students (67 females (56.8%) and 51 males (43.2%), 71 (60.2%) from fifth grade and 47 (39.8%) from sixth grade. A scale assessing 5 digital competencies was applied, which evidenced adequate levels of reliability (α = 0.83). Finally, the results showed that the students have optimal levels of development of digital competencies, but in the exploratory analysis, 2 students were identified with difficulties in the digital content creation competency and 2 with difficulties in the digital security competency.
Robust Line Based Image Fingerprinting Algorithm

Image fingerprinting is an important technology that is used by many search engines and multimedia systems for indexing, copy detection and retrieving images. However, most fingerprinting algorithms are point based algorithms. This restriction causes an impact on algorithm robustness against image manipulations. In addition, hashing algorithms that are based on dimensionality reduction have the problem of revealing the original image or low quality version of it in the fingerprint. This paper presents a robust fingerprinting algorithm that is based on line segments encoding. This algorithm calculates fingerprints based on the relations between image line segments instead of image points. The motivation is generating fast robust fingerprints that are resilient to scaling, rotation, translation, color effects, added noise and image cropping. The generated fingerprint should not reveal any part of the original image and could not be reverse engineered to recover the original image.
Inside the Machine

This chapter examines how robots work. All robots can be described in terms of sensors, actuators, overall body parts, and a control system. Control involves generation and coordination of behavior by somehow using incoming sensory information to produce appropriate actuator outputs. Autonomous vehicles are a very good exemplar of current mobile robotics as they employ many of today's cutting-edge techniques. Attempts to develop driverless vehicles go right back to the 1920s. A very significant step in this quest occurred in 1989 when Dean Pomerleau and a team at Carnegie Mellon University (CMU) demonstrated how an artificial neural network could be used to successfully steer a test vehicle around the CMU campus roads. The chapter then considers Pomerleau's system, ALVINN (autonomous land vehicle in a neural network), and studies how the artificial neural networks used in cars and robots work. Current leading-edge autonomous vehicles use a variety of sensors, including vision (cameras), LIDAR, radar, ultrasonics, and GPS information. Finally, the chapter outlines the main approaches to achieving the goal of widespread fully autonomous vehicles, looks at biomechanics, and assesses how reliable and intelligent current robots really are.
Average photon energy assessment based on modelled spectra from the National Solar Radiation Database for Lima, Peru

Abstract The photovoltaic performance under operating outdoor conditions is affected by the variability of the solar spectrum. The spectral distribution is quantifiable using the average photon energy ( APE ). In the present study, we characterize the spectral distribution in a low-latitude location such as Lima - Peru through a decade of simulated solar spectra obtained on demand from the National Solar Radiation Database and taking as a reference one year of ground-based experimental data following the decade of theoretical spectra. This characterization utilizes annual and monthly averages of irradiance-weighted APE s. The results indicate a difference of only 0.2% between the average annual APE for the decade and the annual ground-based experimental APE . Additionally, the theoretical monthly APE s for the decade show a seasonality consistent with our experimental data for the summer months but not for the winter months.
Elderly fall detection based on wireless perception in private area

With the aggravation of the aging problem, the research on the real-time monitoring technology of the elderly is of great significance. Among them, falling is the main reason threatening the health and life of the elderly. Aiming at the shortcomings of traditional fall detection technology for the elderly, this paper proposes a fall detection technology for the elderly based on wireless signal, which uses neural network for feature extraction and classification. Complete efficient and accurate detection on the premise of protecting privacy. The experimental results show that the detection rate is as high as 95%, which has a good application prospect.
Research on Acoustic Experiment of Mandarin Monophthong Based on Computer Technology

Currently, the research on minority college students with the help of computer technology support is a specific embodiment of deep integration between information technology and national education. Based on this, this paper relies on computer technology and attempts to extract the acoustic parameters of eight monophthongs of Tibetan male college students' Mandarin, makes a synchronic comparative analysis with the standard Mandarin. Through the experimental research, we find that, the acoustic vowel diagram of Tibetan male college students tends to be compressed, compared with the standard mandarin. This paper can provide some useful experience and reference for subsequent research and practice in the fields of artificial intelligence and speech processing.
Performance Analysis of Wireless Steganography based on OFDM and DFT-s-OFDM Signals over Frequency-Selective Rayleigh Fading Channels

We study the performance of a secure wireless communication technique based on the combination of OFDM and DFT-spread OFDM (DFT-s-OFDM) signaling over practical frequency-selective Rayleigh fading channels. The approach is based on a wireless steganography, where DFT-s-OFDM signals are used as cover signals and secret information is embedded in the OFDM signals, which are superposed with the cover signals. We theoretically analyze the performance of unintended receiver, which attempts to detect the existence of the embedded signal by the power or moment detector, in terms of its detection probability. We also study the bit error rate achieved by a legitimate receiver through computer simulations.
Improving cross-lingual text matching with dual-level collaborative coarse-to-fine filter alignment network

Semantic alignment is a key component in Cross-Language Text Matching (CLTM) to facilitate matching (e.g., query-document matching) between two languages. The current solutions for semantic alignment mainly perform word-level translation directly, without considering the contextual information for the whole query and documents. To this end, we propose a Dual-Level Collaborative Rough-to-Fine Filter Alignment Network (DLCCFA) to achieve better cross-language semantic alignment and document matching. DLCCFA is devised with both a coarse-grained filter in word-level and a fine-grained filter in sentence-level. Concretely, for the query in word-level, we firstly extract top-k translation candidates for each token in the query through a probabilistic bilingual lexicon. Then, a Translation Probability Attention (TPA) mechanism is proposed to obtain coarse-grained word alignment, which generates the corresponding query auxiliary sentence. Afterwards, we further propose a Bilingual Cross Attention and utilize Self-Attention to achieve fine-grained sentence-level filtering, resulting in the cross-language representation of the query. The idea is that each token in the query works as an anchor to filter the semantic noise in the query auxiliary sentence and accurately align semantics of different languages. Extensive experiments on four real-world datasets of six languages demostrate that our method can outperform the mainstream alternatives of CLTM.
Integration of Scene Image and Conversational Text for Human-Robot Dialogue

In recent years, it becomes a trend to build social dialogue systems to achieve human-robot interaction. While current conversational systems mostly focus on the dialogue utterances, visual context also plays an important role in determining the relevance of the system responses to the dialogue. In this study, we develop an integrated approach to investigate how to achieve such a visual-language task. Our approach takes both scene images and dialogue utterances into account and trains a neural model to generate machine responses of natural language. We have conducted a series of experiments to evaluate the presented approach, and the results confirm its usefulness and effectiveness.
Contrastive Learning for Multiple Models in One Supernet

Autonomous Machine Learning (AML) alludes to a learning framework having an adaptable trademark to develop the construction and boundaries on the fly. It is empowered by the way that AMLs means to adjust among stability and plasticity of a learning system. At present, deep learning-based unsupervised learning is another intriguing issue in the field of Autonomous Machine Learning, among which multi-architecture optimization is of extraordinary trouble in this research area. At the point when the current calculations face multi-intellectual model issues, it frequently sets aside a great deal of effort to ceaselessly set diverse looking through functions of different boundaries to look for the ideal model. In this work, we propose a novel technique for multi-model contrastive learning, which can get diverse unsupervised learning structures that are fit for deploying under different budgets, through a single shot super-network. Experiments reveal that our algorithm can boost the performance of the existing methods.
Is the information provided by free satellite sources suitable for predicting or evaluating the performance of photovoltaic systems in Peru? (In memoriam to Heinrich Berg)

Abstract At present, there are free access online platforms that offer, free of charge, meteorological data on Peru; however, there is no study that validates the goodness of its use in the country. These types of tools are of the utmost interest, both in the design phase of photovoltaic systems PV and in the subsequent process of analysis and evaluation of their operation if it is intended to implement monitoring systems with Class B or C precision defined in the IEC 61724: 2017 standard. In this work, the consistency of the data offered by any of these platforms and their usefulness will be analysed when they are contrasted with experimental data on the operation of real systems.
Optimized LSTM with Dimensionality Reduction Based Gene Expression Data Classification

The classification of cancer subtypes is substantial for the diagnosis and treatment of cancer.However, the gene expression data used for cancer subtype classification are high dimensional in nature and small in sample size.In this paper, an efficient dimensionality reduction with optimized long short term memory, algorithm (OLSTM) is used for gene expression data classification.The main three stages of the proposed method are explicitly pre-processing, dimensional reduction, and gene expression data classification.In the pre-processing method, the missing values and redundant values are removed for high-quality data.Following, the dimensional reduction is done by orthogonal locality preserving projections (OLPP).Finally, gene classification is done by an OLSTM classifier.Here the traditional long short term memory (LSTM) is modified using parameter optimization which uses the adaptive artificial flora optimization (AAFO) algorithm.Based on the migration and flora reproduction process, the AAFO algorithm is stimulated.Using the accuracy, sensitivity, specificity, precision, recall, and fmeasure, the proposed performance is analyzed.The test outcomes illustrate the effectiveness of the gene expression data classification with a 94.19% of accuracy value.The proposed gene expression data classification is implemented in the MATLAB platform.
Simulation, Seriation and the Dating of Roman Republican Coins

Seriation was one of the earliest applications of computers to an archaeological problem. Despite the origins of the technique in numismatics, the vast majority of coinage studies manually sequence coin hoards and issues. For many periods, the coin designs or legends can be used to provide a date. For the Republican series, however, detailed sequences rely on the use of coin hoard data. In recent years, Correspondence Analysis has become the <em>de facto</em> seriation routine of choice. For coinage studies, however, where the period of manufacture was very short, a successful seriation would leave one triangle empty in the re-arranged matrix rather than concentrating the largest values on the diagonal as would be the aim in other situations. The aim of this paper is to assess the effectiveness of various easily-available off-the-shelf open-source seriation routines that have been used in archaeology for the analysis of this type of data. Given that we know a great deal about the pattern of production of Republican coinage, it is possible to create simulated coin hoard assemblages to test the various seriation routines and assess which technique is likely to provide the most successful results. This paper presents the results of applying 14 seriation methods to 27 simulated coin hoard data sets, and discusses the results.
Epistemic Contradictions Do Not Threaten Classical Logic

Abstract Epistemic contradictions are now a well-known and often discussed phenomenon among those who study epistemic modals. These contradictions are expressed by sentences like ‘It is raining and it might not be raining’ whose oddness to the common ear demands an explanation. However, it has turned out to be a rather controversial enterprise to provide such an explanation in a sufficiently precise and general manner. According to pragmatic explanations, epistemic contradictions are semantically consistent but pragmatically defective. According to semantic explanations, one should regard epistemic contradictions as plain semantic inconsistencies. Endorsing such a semantic solution is, however, tantamount to rejecting classical logic, which predicts the consistency of epistemic contradictions. After arguing that all existing solutions to this problem face as yet unmet challenges, I will present a new solution that adequately overcomes these challenges. I will propose to view epistemic contradictions as semantically consistent but epistemically defective sentences. The main thesis emerging will be that we need neither abandon the inference rules of classical logic nor the classical truth-conditional approach to semantics to deal adequately with epistemic contradictions.