Reviewerly/exHarmony huggingface dataset:
Illusionism as a Theory of Consciousness Part I

This is the first part of the article, which presents arguments in favour of an ap­proach to consciousness, which is called illusionism. This is the view that phe­nomenal consciousness is illusory. According to illusionists, our sense that our conscious experiences are like something is a result of the fact that we systemati­cally misrepresent them or their objects as having phenomenal properties. The hard problem is replaced by the problem of illusion. And the task of the theory of consciousness is to explain our illusory representations about phenomenality, not phenomenality itself. Despite having followers, illusionism is still a minority po­sition, and is often dismissed as failing to take the problem of consciousness se­riously. The presented article is aimed at refuting this accusation. In this part, the program of illusionism is outlined against the background of two approaches – radical and conservative realism, that can be considered as more traditional ones in contrast to illusionism, which reveals its strengths in comparison to them. The boundary between the “strong” and “weak” forms of illusionism is drawn. The question of the correlation of the illusionist position with the statement about the illusory nature of the external world is considered. In addition, it is discussed, whether following illusionism entail eliminativism in the question of conscious­ness in the context of experience and what is called phenomenal properties.
Data Quality Assessment of Comma Separated Values Using Linked Data Approach

With an increasing amount of structured data on the web, the need to understand and convert it into linked data is growing. One of the most frequent data formats is Comma Separated Value (CSV). However, it is not easy to describe metadata such as the datatype, data quality and data provenance along with it. Therefore, to publish CSV on the web, it is required to convert CSV into linked data format. Many approaches exist to facilitate the conversion process from structured data to linked data. However, all methods require additional domain knowledge for the conversion process. The goal of this research is to assist publishers in converting CSV files into linked data without human intervention whilst understanding its quality and root causes of data quality violations. The proposed framework consists of two modules. The first module converts the given CSV file into a knowledge graph based on a proposed ontology which is appended with data quality information. In the second module, triples that have violated the data quality constraints are identified. The results show that it is possible to convert a CSV to a knowledge graph by adding its quality information without the help of external mappings.
Development of IOT Based Digital Display Board for Students’ Lectures Update

Technology is generally improving the advertisement sector and the use of electronic medium in communication system is on geometric rise due to its flexibility, attractiveness, speed and efficiency. Various methods of information dissemination have been adopted from the use of posters, traditional billboards to electronic billboards. This work presents the development of an IOT (Internet of Things) based display board for Engineering Students in Federal Polytechnic Ile Oluji, Ondo State, Nigeria for lecture update using wireless communication channel (wi-fi); this provides ease and comfort of updating the content from remote location in the wireless network range. The command is sent to the digital display board through the Mobile Application interfaced with the wireless module to give regular update and seamless communication with students.&#x0D; The digital board basically contains a digital display module, a 5volts DC power pack, a wi-fi card, Microcontroller and Light Emitting Diodes. The administrator sends out message through the mobile led art light application to keep students updated on their lecture information. The text is received through the wireless module connected to the application and transferred to the microcontroller which decodes the message for users. The decoded message will automatically switch ON and OFF appropriate LEDs in order to display respective message sent which scrolls on the display. The display board can accept texts in different modes and styles as provided in the mobile application. The display board is useful in academic environment to regularly update students on lectures and other relevant information.
Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation

Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance. Recognizing that this data collection may be at odds with growing awareness of privacy concerns, we consider obfuscating such data before the data leaves the mobile device. The goal is to increase privacy such that it is difficult to recover sensitive features from the obfuscated data (e.g. user ids and user whereabouts), while still allowing network providers to use the data for improving network services (i.e. create accurate signal maps). To examine this privacy-utility tradeoff, we identify privacy and utility metrics and threat models suited to signal strength measurements. We then obfuscate the measurements using several preeminent techniques, spanning differential privacy, generative adversarial privacy, and information-theoretic privacy techniques, in order to benchmark a variety of promising obfuscation approaches and provide guidance to real-world engineers who are tasked to build signal maps that protect privacy without hurting utility. Our evaluation results, based on multiple, diverse, real-world signal map datasets, demonstrate the feasibility of concurrently achieving adequate privacy and utility, with obfuscation strategies which use the structure and intended use of datasets in their design, and target average-case, rather than worst-case, guarantees.
Hybrid FiST_CNN approach for Feature Extraction for Vision-Based Indian Sign Language Recognition

Indian sign language (ISL) is the commonly used language by the deaf-mute community in the Indian continent. Effective feature extraction is essential for the automatic recognition of gestures. This paper aims at developing an efficient feature extraction technique using FAST, SIFT, and CNN. Features from Fast Accelerated Segment Test(FAST) with Scale-invariant Feature Transformation(SIFT) are used to detect and compute features, respectively. CNN is used for classification with the hybridization of FAST-SIFT features. The system is implemented and tested using the python-based library Keras. The results of the proposed techniques have been tested on 34 gestures of ISL (24 alphabet sets and 10 digit sets) and then compared with the CNN and SIFT_CNN, and it is also tested on two publicly available datasets on Jochen Trisech Dataset(JTD) and NUS-II dataset. The proposed study outperformed some existing ISLR works with an accuracy of 97.89%, 95.68%, 94.90% and 95.87% for ISL-alphabets, MNIST, JTD and NUS-II, respectively.
Enthusiast versus Antagonist: Exploring the perceptions of data experts on the visualisation of uncertainty

Despite the copious number of reasons to visualise uncertainty in visualisations, there is still a reluctance to actively represent uncertainties. This paper explores the perceptions of data experts considering uncertainty visualisation and their reasoning behind lacking engagement. By documenting a series of interviews with data experts, the authors uncover the perceptions and constraints faced when contemplating uncertainty visualisation. Through several industries, the authors reveal numerous perceived benefits of uncertainty visualisation but also the strong influence end-users have on the decision to incorporate the additional information. Finally, the paper reflects on a lack of experience but also the commitment from the data experts to the use of aesthetics in developing intuitive uncertainty visualisations. Whilst also highlighting their perceived benefits around what the aesthetic could bring to both visualisation development and uncertainty visualisation design. The study documented in this paper feeds into a larger body of research on aesthetic uncertainty visualisation.
Architecture for a Modern Data-Driven Application

Now it is time to take the final step. You learned the basics of serverless services in the first part of the book. The second part of the book introduced some of the most important Azure services that are also available as serverless services. In part three, you learned concepts and patterns to help you build various aspects of solutions from these building blocks. Now it is time to finally bring it all together and design an example data-driven application in Azure using serverless services wherever possible.
Permian Arc Magmatism in the Eastern Tianshan: Implications for the Evolution of the Southern Altaids

Permian large-scale transcurrent tectonics and syn-kinematic ultramafic–felsic magmatism are prominent features in the Eastern Tianshan of the southern Altaids. The genetic relationship between coeval Ni–Cu mineralized mafic-ultramafic intrusions and felsic intrusions, and geodynamics of the region, however, remains equivocal. To assess these topics and improve our understanding of this geologically complex and economically significant region, zircon Hf isotope and bulk-rock geochemical data from Permian granitoids along the Kanggur–Huangshan Shear Zone (KHSZ) in the Eastern Tianshan are determined and interpreted in the context of previous geochronological–geochemical data. The Kungurian granitoids are high-K, calc-alkaline, metaluminous to weakly peraluminous I-type granitoids, whereas the Capitanian granitoids are calc-alkaline, peraluminous, adakitic I-type granitoids. All of the granitoid samples are enriched in light rare earth elements and large ion lithophile elements (e.g., Rb, U, and K), and depleted in high field strength elements (e.g., Nb, Ta, and Ti), similar to arc magmas, indicating that subduction-related magmatism was still active in the late Permian. Considering the exposure of ca. 280 Ma EW-trending MORB-type gabbro and mélange, adakites, I-type granites, and Alaskan-type Ni–Cu and orogenic Au deposits along the KHSZ, it is suggested that subduction of a spreading center during the Permian generated a slab window, allowing the upwelling of asthenospheric mantle. This permitted the generation and migration of mantle-derived magmas to shallower crustal levels along translithospheric faults (e.g., Kanggur and Yamansu faults), forming the mafic–ultramafic intrusions and associated Ni–Cu sulfide de­posits. The continuous migration of magma provided the necessary heat to induce partial melting of juvenile crust, generating the Permian high-K to calc-alkaline I-type granitoids. Ridge subduction-induced juvenile crustal reworking likely played a central role in the formation of the granitoids during the late Paleozoic accretionary orogenesis of the southern Altaids.
Continually Adaptive Neural Retrieval Across the Legal, Patent and Health Domain

In the past years neural retrieval approaches using contextualized language models have driven advancements in information retrieval (IR) and demonstrated great effectiveness gains for retrieval, primarily in the web domain [1, 10, 33]. This is enabled by the availability of large-scale, open-domain labelled collections [6].
A Real-Time and Convenient Taxi Assistance Mini Program for the Visually Impaired

At present, the visually impaired are inconvenient to go out because they cannot get outside information. Moreover, relying on others&#x2019; help or guiding dogs to go out will also hurt their self-esteem, so they need a tool to help them go out independently. The most popular way to go out in modern times is to take an online taxi. However, there is no relatively mature auxiliary software to guide the visually impaired to find the online taxi he or she has booked. In order to solve this problem, we used a lightweight WeChat Mini Program and computer vision technologies to create a taxi assistance software. It can recognize the vehicle ahead and license plate, track the target vehicle in real time, provide the real-time location of the target vehicle for the visually impaired, and help the visually impaired take a taxi independently.
Getting Started with NumPy

We learned the basics of Python Programming Language in the previous chapter. This chapter is fully focused on learning the basics of NumPy library. We will have a lot of hands-on programming in this chapter. While the programming is not very difficult when it comes to NumPy and Python, the concepts are worth learning. I recommend all readers to spend some time to comprehend the ideas presented in this chapter.
Investigating the Attitude of Bhutanese Teachers towards Implementing Life Skills Education

The present study examined the attitude of Bhutanese teachers towards implementing Life Skills Education in different level of Schools. The researcher used quantitative research approach with descriptive survey design. Simple random sampling technique was employed to select a total sample of 100 teachers from the Eastern part of Bhutan. The data collected were analyzed using both descriptive and inferential statistics like t test and ANOVA using SPSS software. The findings revealed that the teachers with science background hold comparatively higher attitude towards imparting Life Skills Education compare to non-science teachers and significant difference was also found between the teachers holding post graduate and B.Ed. qualification towards infusing Life Skills Education. However, the findings disclosed that there does not exist significant difference in teachers’ attitude towards Life Skills Education in term of their teaching experience.
Healthcare Data Management Using Blockchain: MyHealthMyData (MHMD) and PharmaLedger

Healthcare data is important in the development of new medicines as well as in disease prevention and health promotion. At the same time, there are significant barriers to sharing health data mainly due to privacy issues. Blockchain as a tech-nology characterized by immutability, traceability, decentralization, and crypto-graphic security might provide feasible solutions to tackling challenges such as security concerns, data format inconsistency between health data providers, and data breaches caused by unauthorized access. International projects such as MyHealthMyData and PharmaLedger provide blockchain-based solutions ad-dressing the privacy preserving issues associated with health data management. This paper provides an overview of the proposed innovative solutions of the two projects.
DeepPRFM: Pairwise Ranking Factorization Machine Based on Deep Neural Network Enhancement

In the field of personalized ranking with implicit feedback data, the pairwise learning is an important technology widely used. This method can not only learn user interest from the recorded user feedback, but also learn user preferences from the potential interaction between users and items. When modeling the interaction features between users and items, the existing methods pay more attention to modeling the high-order interaction model of interaction features but do not pay enough attention to the modeling and learning of input features of original users and items. However, the original input feature is the first-hand information that best reflects the user and the item. In order to solve the above shortcomings, we pay due attention to the original input features, integrate the high-order interaction features of users and items with the original input features, and present a new personalized ranking model DeepPRFM by using the paired learning method in this paper. In addition, the system cold start problem also hinders the pairwise learning method. We have conducted more explorations in the experimental process, and found that by increasing the proportion of negative samples, the effect of the model can be greatly improved, the training speed of the model can be accelerated and the cold start problem of the system can be alleviated. Experimental results on two real data sets demonstrate the proposed model outperforms state-of-the-art methods in personalized ranking. Code is available at: https://github.con sunchch/DeepPRFM.
A Guide to the Deployment of Global Quantum Key Distribution Networks

A guide to the deployment of global quantum key distribution (QKD) networks by using three different QKD technologies is proposed, including terrestrial QKD via optical fibers, airborne QKD via free-space optics, and spaceborne QKD based on satellites. We review the systems and methods of these QKD technologies and compare their pros and cons, in terms of channel loss, interference, distance limit, connectivity topology, deployment cost, and application scenarios. Technical criteria and deployment strategies are developed for each technology to provide global coverage of QKD networks, including intercontinental, long-haul, metro, and access networks.
Image Contrast Enhancement Algorithm Based on PSO for Batch Processing

In many cases, large amounts of images need to be enhanced automatically to get better contrast effects, in other words, need an image contrast enhancement algorithm that can be used for batch processing. Many existed image contrast enhancement algorithms can obtain good enhancement effect under manual intervention, but the need for manual intervention is not suitable for image batch processing. To solve this problem, PSO-UM model was proposed in the paper. In this paper, two parameters were set for the linear unsharp masking (UM) algorithm, and particle swarm optimization (PSO) algorithm was used to dynamically select the best parameters. The best parameters of PSO-UM model were measured through experiments. The proposed algorithm is compared with four algorithms commonly used in batch processing. The experimental results show that proposed algorithm is significantly better than other algorithms in both subjective evaluation and objective indicators, therefore the proposed algorithm is more suitable for batch processing than other algorithms.
Evaluation of normalization technique on classification with deep learning features

Cancer is one of the diseases with the highest mortality rate in the world. Dysplasia is a difficult-to-diagnose precancerous lesion, which may not have a good Hematoxylin and Eosin (H&amp;E) stain ratio, making it difficult for the histology specialist to diagnose. In this work, a method for normalizing H&amp;E stains in histological images was investigated. This method uses a generative neural network based on a U-net for image generation and a PatchGAN architecture for information discrimination. Then, the normalized histological images were employed in classification algorithms to investigate the detection of the level of dysplasia present in the histological tissue of the oral cavity. The CNN models as well as hybrid models based on learning features and machine learning algorithms were evaluated. The employment of the ResNet-50 architecture and the Random Forest algorithm provided results with an accuracy rate around 97% for the images normalized with the investigated method.
A Performance Evaluation of an Optoelectronic Cervical Screening Device in Comparison to Cytology and HPV DNA Testing

Objective: An optoelectronic screening device (OESD) is evaluated for the detection of cervical intra-epithelial neoplasia (CIN) 2+ lesions in comparison to Liquid Based Cytology (LBC) and high-risk HPV DNA (hrHPV) testing.Methods: In total 506 consecutive women referred because of abnormal cervical cytology or a positive high-risk HPV test, had an examination using OESD, LBC, and hrHPV testing.They were screened in 4 colposcopy clinics in New South Wales, Australia.In a retrospective audit, results were compared to the gold standard of colposcopy and biopsies if required.Sensitivity, specificity, area under the receiver operating characteristic (ROC)curves, and differences using McNemar tests were calculated.All results were available for comparison on 474 patients.Results: The sensitivity to detect CIN II+ lesions by OESD, LBC and hrHPV-testing was 0.72, 0.81, and 0.88, and the specificity was 0.71, 0.95, and 0.76 respectively.The age-and previous-treatment adjusted area under the ROC curve for OESD was 0.83, for LBC 0.94, and for hrHPV testing 0.89.McNemar's tests showed no significant difference in sensitivity between OESD and LBC (p = 0.26), and no significant difference in specificity between OESD and hrHPV-testing (p = 1.0) amongst patients without previous treatment.Conclusions: The optoelectronic screening device demonstrated comparable sensitivity to high quality cytology conducted in a hospital clinical setting.Specificity was comparable to hrHPV-testing in an approximate primary screening setting.OESD has the advantage of producing an immediate result and being easy to use without need of laboratory equipment.This device can potentially become an important tool in the prevention of cervical cancer, particularly in developing countries and resource-limited settings.
What’s the Fuss: The Excitement, Prospects and Software/Hardware Challenges of Distributing Entanglement over a Quantum Network

In this paper, I review the notion of a quantum network, which I define as one that can distribute entanglement between stationary qubits, and then discuss challenges relevant for the Optical Fiber Conference (OFC).
Basic Python

Basic Python programming begins with an understanding of the fundamental variables, types, and operators used in the language, as well as how to respond to errors. Basic types introduced will include Numbers, Lists, Strings, Sets, and Dictionaries. Different operations can be applied to the different data types. Learn the rules of Python programming and perform simple tasks with variables, types, and operators through the exercises provided. Learning error handling concepts will help to capture and respond to exceptions efficiently.
A deep learning framework for handwritten Ol Chiki character recognition

Ol Chiki is an Austroasiatic-Santali language used by the Santhal tribe of India. Despite being one of the official languages of India, Ol Chiki language still remains marginalized. Although there exist significant amount of work on recognizing handwritten characters of several other mainstream Indian languages (e.g. Hindi, Bengali etc.), very less number of work have been carried out for this language. In this work, a robust Handwritten Ol Chiki Character Recognition (HOCCR) system based on Deep learning has been proposed. Efficacy of the Histogram of Oriented Gradient (HOG) feature descriptor for recognizing Ol Chiki characters has been shown. A detailed comparative study has been carried out using several proposed prediction models.
Analysis of Feed-Forward Connections for Apex Frame Spotting

The performance of a convolutional neural network can be improved through deepening the network. However, it is a challenging task to optimize a deep network because of the training complexity and vanishing gradients problem. Recent works have suggested that a deeper network can be trained to obtain a decent output if shorter skip connections are provided between the layers. In this paper, we invoke the Residual Networks (summation) and Dense Networks (concatenation) properties on the VGG-M model, whereby a continuous labeling function is used for spotting an apex frame in the micro-expression recognition system. This continuous label is used to locate the apex frame with the help of Maximum Frame and Sliding Window-Maximum Frame approaches. The validity of the model is assessed by using the Mean Absolute Error, whereby the closer the spotted apex frame to the ground truth, the better the continuous label has performed in predicting the optical flow pattern. In other words, it produces a better architecture. Finally, the best result in this study improves the apex spotting by 3.26 frames of MAE value. The system can be further improved by analyzing various parallel skip connections between the convolutional layers.