Reviewerly/exHarmony huggingface dataset:
NLP based artificial intelligence method for online semantic recognition of short and long text

The current semantic recognition methods for long and short texts have low recognition accuracy and slow recognition speed for texts with obvious length differences, which can not accurately identify the deep semantics contained in the texts and restrict the development of text data. To solve this problem, based on the traditional text semantic recognition method, the natural language processing (NLP) artificial intelligence principle is introduced, and a new design of long and short text semantic recognition method is proposed. Extract the semantic features of the original text, and use the frequency method to process the lexical features in the text by weighting. Then, obtain the feature weights of the long and short texts and use the NLP artificial intelligence method to distinguish the unknown texts and identify the semantics of the long and short texts online. The experimental analysis shows that the accuracy of the semantic recognition methods proposed in this paper is more than 96. 87%. The accuracy is higher. The recall is less than 35. 27%. The recall is lower and the recognition advantage is significant compared with the traditional methods.
FQA-Net: an efficient neural network for blind image quality assessment

Guided by the neural network (NN), NN-based blind image quality assessment (BIQA) methods have improved image quality prediction accuracy. However, the training process of NN will consume a lot of time and training data. To solve the above problems, an efficient NN structure, which is called fast quality assessment network (FQA-Net), is proposed for BIQA. FQA-Net is a very simple NN, which mainly includes a convolution layer, a standard deviation measurement layer, and a regression layer. The convolution kernels in the convolution layer are a set of visual filters with characteristics of visual neurons, which are obtained by training on natural image samples. The output of the convolutional layer is a set of feature maps. Then the standard deviation of each feature map is calculated directly. Finally, a regression model is used for the mapping between the standard deviation values and the quality scores. FQA-Net not only reduces the number of parameters and the output dimensions in the training process, but also prevents network overfitting effectively. The results on seven image databases (i.e., LIVE, CSIQ, TID2013, LIVEMD, KADID-10K, LIVEC, and KonIQ-10K) show that FQA-Net has relatively low computational complexity and high computational speed and accuracy compared with other leading BIQA methods, especially when the number of training samples is very low. Moreover, the FQA-Net can provide stable prediction results across different distortion types.
Analysis of social results of tourism performing arts industry in cultural tourism convergence using fuzzy comprehensive analysis methods

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A Human Body Infrared Image Recognition Approach via DCA-Net Deep Learning Models

With the continuous exploitation of coal resources, human safety has been seriously threatened during the mining process. Therefore, it is of great significance to establish an efficient human infrared image recognition system. In this paper, three classes of infrared image data of the human body are collected by a thermal imager, namely Human, Human others and None. According to the characteristics of downhole infrared images, a distributed channel feature extraction module (DCFE) is designed, and DCA-Net is proposed based on this module. The experimental results show that the recognition rate of the network reaches 98%. Compared with other networks, this network has better recognition performance. Among them, the recognition rate of DCA-Net50 reaches 98.214%, the amount of parameters and calculations are relatively small, and the cost-effectiveness is the highest. It is suitable for the human body infrared image recognition system that requires high accuracy and high real-time performance.
Design a library information system for a green library in Bangka Regency

Abstract A library is organized for reading, counselling, research, and research purposes for information resources managed by librarians and staff, and then maintained to provide services for meeting. A place for collecting or collecting books or other materials that will be published. Needs of Reitz users. The Bangka Regency Regional Library is a library with a huge collection of books and till has many drawbacks, especially services that do not use manual information technology. So it becomes a digital library and needs to be developed to compete today. In addition, the use of paper materials can reduce the environmental problems required for papermaking. Build a computerized internet-based library information system application to run the lending process anytime, anywhere while connected to the internet and to support the Bangka Regency Regional Library and reduce excessive paper consumption. By not using paper for the data collection process, it is complete and economical in the local library. The phases that the method goes through are the planning phase, the analysis phase, the design phase, the application phase, the testing phase, and the use and maintenance phase. The library information system in the area was built open source using Slims to facilitate library management.
Mind the gap between cyber security spend, budgets and awareness

Computer Fraud & SecurityVol. 2022, No. 11 The SandboxMind the gap between cyber security spend, budgets and awarenessEric BarnesEric BarnesIntegrity360Search for more papers by this authorEric BarnesPublished Online:23 Nov 2022https://doi.org/10.12968/S1361-3723(22)70601-XAboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. ‘Cybersecurity Awareness Month’. US Cybersecurity & Infrastructure Security Agency (CISA). Accessed Nov 2022. www.cisa.gov/cybersecurity-awareness-month. Google Scholar2. ‘Is the next big cyber-threat a lack of budget or spending?’. Integrity360, 13 Mar 2019. Accessed Nov 2022. https://insights.integrity360.com/cyber-threat-as-a-lack-of-budget. Google Scholar3. ‘Attacks surge in 1H 2021 as Trend Micro blocks 41 billion cyber threats’. Trend Micro, 14 Sep 2021. Accessed Nov 2022. https://newsroom.trendmicro.com/2021-09-14-Attacks-Surge-in-1H-2021-as-Trend-Micro-Blocks-41-Billion-Cyber-Threats. Google Scholar4. ‘Cost of a data breach 2022’. IBM/Ponemon Institute. Accessed Nov 2022. www.ibm.com/reports/data-breach. Google Scholar5. ‘2022 cybersecurity spending trends: Where are organizations investing?’. Infosec Institute. Accessed Nov 2022. https://resources.infosecinstitute.com/topic/it-spending-trends/. Google Scholar6. Martin, Brian. ‘Three benchmarks to inform cyber security spending plans for 2020’. Integrity360, 29 Nov 2019. Accessed Nov 2022. https://insights.integrity360.com/security-spending. Google Scholar FiguresReferencesRelatedDetails 1 November 2022Volume 2022Issue 11ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 23 November 2022 Published in print 1 November 2022 Information© MA Business Ltd.
Communicating landslide risks during the COVID-19 pandemic: A baseline study for PHIVOLCS-Dynaslope stakeholders

Dynaslope Project, a government-funded program implemented by the Philippine Institute of Volcanology and Seismology (PHIVOLCS), develops and deploys an early warning system (EWS) for deep-seated landslides. It uses landslide sensor technology and community involvement in its EWS implementation in 50 sites all over the country. As the pandemic altered people’s modes of communication, it is important to understand how these changes relate to communicating landslide early warning information (LEWI). This research answered: How can the Dynaslope Project effectively communicate landslide risk during the COVID-19 pandemic? Specifically, it a) identified their preferred communication channels and tools during the pandemic, b) enumerated the stakeholders’ perceived participation and communication barriers, and c) measured the degree of trust of the stakeholders in the Project, the LEWI it released, and other key messages. This research was guided by the Actor-Network Theory and the concept of co-orientation which posited that, instead of a single entity defining a network or a system, it was the interactions between people, objects, and institutions that created a collective network and continuously negotiated a coherent understanding. The study employed a descriptive quantitative methodology. It used stratified random sampling to select the participants. Among the major findings include the stakeholders’ preference for offline communication like SMS and calls, the presence of communication and participation barriers including weak or no phone reception and limited access to up-to-date communication devices and the internet, and a high level of trust in the Dynaslope Project because it was implemented by a science-based national agency PHIVOLCS.
Effect of Corona Incandescent Plasma Shooting on Polyester Fibers for Geotextile Manufacturing

Abstract The development of the textile industry in Indonesia continues to show improvement and is even able to compete in the international market, including polyester fiber. Polyester fiber will be more economical if given an additional treatment to increase the added value of polyester fiber. One of the uses of polyester in the earth sector is Non-Woven Geotextile. This non-woven geotextile has relatively small pores but has high permeability. This serves as a good retainer of soil grains. Methods: Plasma technology is one way to increase the added value of polyester fiber. Jute cloth was placed on a copper plate electrode and the cloth surface was modified using incandescent plasma treatment for some time, which are 1 minute, 3 minutes and 5 minutes apart moreover with variations in distance of 4 cm, 5 cm, and 6 cm with a voltage of 3 kV. The surface modified Jute cloth using plasma was tested for absorption using a drip test, which is measuring the absorption time of the cloth before and after plasma treatment. Results: From this experiment it was found that sample D had good adsorption and adhesive properties, which was due to the plasma effect found in the O-H at 3700, which was steep and the effect of the fabric rough surface. Conclusion: It can be proven that the contact angle of sample D has a magnitude of 63.4o, the effect of fabric roughness and grafting greatly changes the wettability and adhesive properties of the fabric.
Development of a k-Nearest Neighbor (kNN) Machine Learning Model to Estimate the SPT N-Values of Valenzuela City, Philippines

Due to the intricate geological formation of geomaterials, they often exhibit a range of attributes at different sites on a given site. This has posed a problem for geotechnical engineers, as they require correct soil and rock information to plan and design geotechnical construction projects. Numerous efforts have been made at the local level to bridge this disparity by standardizing the quantification of soil parameters; nevertheless, these studies have limitations, and there are still areas with ambiguous or incorrect information. To help close this gap, this article will demonstrate an innovative technique for predicting representative Soil Penetration Test (SPT) N-values for each Valenzuela City Barangay/Zone using a k-Nearest Neighbor (k-NN) Machine Learning Model. Borehole data from the city of Valenzuela were collected and analyzed for this study. The k-NN Model’s input parameters are the borehole’s latitude, longitude, and depth, while the response parameters are the SPT N-Values. The k-NN technique was used to train the input data, identify patterns, and make classification decisions for the SPT N-Values per depth per location in Valenzuela City. The centroids of each Barangay/Zone in Valenzuela City were also extracted in Latitude and Longitude format and utilized to deploy the k-NN Model. To illustrate the findings, we presented subsurface information from Valenzuela City with SPT N-Values. For validation purposes, the accuracy rate of the machine learning model was obtained. The model’s hyperparameter in terms of k was tuned to determine if the accuracy rate could be improved.
Prior Exposure to Dynamic Visual Displays Reduces Vection Onset Latency

While compelling illusions of self-motion (vection) can be induced purely by visual motion, they are rarely experienced immediately. This vection onset latency is thought to represent the time required to resolve sensory conflicts between the stationary observer's visual and nonvisual information about self-motion. In this study, we investigated whether manipulations designed to increase the weightings assigned to vision (compared to the nonvisual senses) might reduce vection onset latency. We presented two different types of visual priming displays directly before our main vection-inducing displays: (1) 'random motion' priming displays - designed to pre-activate general, as opposed to self-motion-specific, visual motion processing systems; and (2) 'dynamic no-motion' priming displays - designed to stimulate vision, but not generate conscious motion perceptions. Prior exposure to both types of priming displays was found to significantly shorten vection onset latencies for the main self-motion display. These experiments show that vection onset latencies can be reduced by pre-activating the visual system with both types of priming display. Importantly, these visual priming displays did not need to be capable of inducing vection or conscious motion perception in order to produce such benefits.
Digital Mainstreaming of Tribals in India

India has the presence of about 635 tribal groups and sub-groups, including 73 so-called primitive tribes. Together they comprise nearly 8.2 per cent of the country's population. Tribals are often known by national terms such as native people, aboriginals, first nations, adivasi, janajati, hunter-gatherers, etc. Digital inclusion has the potential to uplift their status and bring them into the mainstream society. The Ministry of Tribal Affairs (MoTA) is responsible for planning, making policies, and coordinating program for the development and empowerment of India's Scheduled Tribe communities. Identifying the challenges and mapping out the potentialities are the key drivers for digital empowerment of the tribal communities in India. This paper is an attempt to trace out the issues, understand the potentialities, and provide suggestive measures for inclusion of tribals in the wave of digital revolution.
Lightweight 3D DenseNet with improved attention mechanism

As an important branch in the field of computer vision, the exploration of video understanding is still ongoing, and its important part of video understanding has become one of the research hot spots. Firstly, aiming at the problems of large amount of convolution network parameters and model degradation caused by the deepening of network layers, DDensenet network is proposed, and dynamic grouping convolution module (DGC) is adopted, which greatly reduces the amount of parameters, improves the calculation efficiency and alleviates the problem of gradient disappearance. Secondly, the improved convolutional attention module (S-CBMA) is added, and the adaptive module is added to its spatial attention mechanism, which can distribute the weight of features and weaken the background at the same time. DDensenet network model achieves 95.8% and 79.8% average classification accuracy on Sports8 data set and Olympic16 data set respectively. The results show that the model has faster convergence speed and higher classification performance than C3D, ResNet3d, Two-Stream and other network models.
Extracting Features from Textual Data in Class Imbalance Problems

We address class imbalance problems. These are classification problems where the target variable is binary, and one class dominates over the other. A central objective in these problems is to identify features that yield models with high precision/recall values, the standard yardsticks for assessing such models. Our features are extracted from the textual data inherent in such problems. We use n-gram frequencies as features and introduce a discrepancy score that measures the efficacy of an n-gram in highlighting the minority class. The frequency counts of n-grams with the highest discrepancy scores are used as features to construct models with the desired metrics. According to the best practices followed by the services industry, many customer support tickets will get audited and tagged as “contract-compliant” whereas some will be tagged as “over-delivered”. Based on in-field data, we use a random forest classifier and perform a randomized grid search over the model hyperparameters. The model scoring is performed using an scoring function. Our objective is to minimize the follow-up costs by optimizing the recall score while maintaining a base-level precision score. The final optimized model achieves an acceptable recall score while staying above the target precision. We validate our feature selection method by comparing our model with one constructed using frequency counts of n-grams chosen randomly. We propose extensions of our feature extraction method to general classification (binary and multi-class) and regression problems. The discrepancy score is one measure of dissimilarity of distributions and other (more general) measures that we formulate could potentially yield more effective models.
Improving the recommendation accuracy of TrustSVD via trustworthy analysis in the social network environment

Recommender systems help Internet users quickly find information they may be interested in from an extremely large amount of resources. Recent studies have shown that incorporating auxiliary social trust relationship information into the recommender system improves the accuracy of recommendations. Most existing research only considers explicit trust relationships, which result in sub-optimal recommendation performance. In this research, we present a trust model which analyses user trustworthiness based on user’s behaviours on the social networks. The proposed trust model increases the density of trust relationships by considering explicit and implicit social trust relationships and also reflects a more fine-grained and realistic trust level between users. This improved social trust information is then incorporated into TrustSVD, a matrix factorisation–based social recommendation method. By analysing the prediction result using a real-world data set, Douban-600k from the Douban Movie website, we found that our proposed method provides more accurate predictions compared with SVD++ and traditional TrustSVD, improving users’ experiences.
Negotiating the boundaries of the politically sayable: populist radical right talk scandals in the German media

The breaking of speech taboos by populist radical right (PRR) parties and the resulting talk scandals have received considerable attention, with provocative statements being seen as playing a central role in generating media attention and in shifting the boundaries of what is sayable. In Germany, breaches of taboos by the Alternative für Deutschland (AfD) have caused public outrage, with some claiming that the AfD follows a deliberate provocation strategy. This study examines the reactions to the AfD’s breaches of taboos as they play out in mainstream media. Focusing on reactions to five of the AfD’s most controversial statements, the study analyses 340 articles from the daily quality newspapers Frankfurter Allgemeine Zeitung, Süddeutsche Zeitung and Die Welt. The analysis zooms in on identifying (1) reactions to the taboo-breaking statements and how the boundaries of the respective taboos are being negotiated by the AfD and its critics, and (2) reflexive discourse on the role of breaches of taboos in the AfD’s political strategy and on the role of the media in responding to them.
Quantum State Transfer on Neighborhood Corona of Two Graphs

Given two graphs $$G_{1}$$ of order $$n_{1}$$ and $$G_{2}$$ , the neighborhood corona of $$G_{1}$$ and $$G_{2}$$ , denoted by $$G_{1}\bigstar G_{2}$$ , is the graph obtained by taking one copy of $$G_{1}$$ and taking $$n_{1}$$ copies of $$G_{2}$$ , in the meanwhile, linking all the neighbors of the i-th vertex of $$G_{1}$$ with all vertices of the i-th copy of $$G_{2}$$ . In our work, we give some conditions that $$G_{1}\bigstar G_{2}$$ is not periodic. Furthermore, we demonstrate some sufficient conditions for $$G_{1}\bigstar G_{2}$$ having no perfect state transfer. Some examples are provided to explain our results. In addition, for the reason that the graph admitting perfect state transfer is rare, we also consider pretty good state transfer on neighborhood corona of two graphs. We show some sufficient conditions for $$G_{1}\bigstar G_{2}$$ admitting pretty good state transfer.
Development of Convolutional Neural Networks for Analyzing Game Icon and Screenshot Images

Icons and screenshots are important media displayed in game distribution platforms for providing a brief understanding of the game content to the customers. In this study, we develop ensemble convolutional neural networks for icon and screenshot analysis as three applications: an automatic genre classification, a similar game searching, and a recognition quality assessment. First, the genre classifier is developed using 154358 images from 18 030 games in 17 genres. The proposed genre classifiers achieve 40.5% and 47.6% accuracies for classifying a single icon and a single screenshot, which outperform the average performance of the human testers. The accuracy can be boosted to 54.2% by aggregating results from every image of the game. The Grad-CAM is applied to analyze what models learned. Then, the feature extraction part trained by this task is transferred to the other two applications. For the similar game searching, a dissimilarity of two images is directly computed by the Euclidean distance in the feature space. We define a dissimilarity between two games which are sets of multiple images based on their image-pairwise dissimilarity. The results show that the features are successfully transferred, and the model seems to be able to cluster the games with a similar gameplay and differentiate them from the other gameplays even if they come from the same genre. For the third application, we develop a system for quality assessment of game images based on the correctness of viewers’ understanding of game content by combining multiple models from three different problem definitions. Our system can identify good-genre-representing game images which most of the human testers can recognize their genre correctly with 75.0% accuracy for icons and 76.2% accuracy for screenshots.