Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence Recommendation System of Cancer Rehabilitation Scheme Based on IoT Technology

Based on the advantages of Internet of things, this paper focuses on the research of intelligent recommendation model for cancer patients' rehabilitation, and designs a user-friendly intelligent recommendation system of cancer rehabilitation scheme.In view of the uncertainty of the cause and time of recurrence of cancer patients, the convolutional neural network algorithm was used to predict both of them.The prediction results of the model showed that the prediction accuracy was high, reaching 92%.To solve the problem of the optimal nutrition program for the rehabilitation of cancer patients, we took the recurrence time as the objective function, and established the recommendation model of the optimal nutrition support program for the rehabilitation by using BAS algorithm.Finally, under the framework of Internet of things technology, the intelligent recommendation model of cancer rehabilitation prediction model and nutrition support program was integrated to realize the recommendation system of intelligent recommendation of rehabilitation nutrition support program for cancer rehabilitation patients according to their different characteristics.After the system simulation experiment, it was found that under the condition that the predicted recurrence location was almost unchanged (49% of simulation results and 50% of actual results), the nutritional support scheme recommended by the intelligent recommendation system could extend the postoperative recurrence time of patients by more than 95%.This recommendation system can help doctors select personalized nutrition and rehabilitation programs suitable for patients in the later stage of rehabilitation treatment according to different cancer patients, and has certain guiding significance for the field of cancer rehabilitation.
A deep learning based chatbot for cultural heritage

In this paper we propose an entertainment Chatbot based on the sequence to sequence model according to the Enconder-Decoder framework based on GRU cells for supporting user's cultural heritage path.
A Framework for the Adoption of Blockchain-Based e-Procurement Systems in the Public Sector

Public procurement constitutes a core government function for providing goods and services to citizens. The overall success of a digitized public-procurement function yields progress and economic growth for the nation. In this research, we analyze the potential of blockchain-based systems to enhance effectiveness, ease, and transparency in public procurement in the case of Nigeria and identify the current challenges facing public procurement, i.e., lack of trust and transparency among critical stakeholders in the procurement process, systems that only weakly support transaction recording and documentation, complex process structures, corruption in institutions involved in the procurement process. To address these issues, a blockchain-based framework is developed to enable interoperability of information-systems involved in the procurement process, increase citizen participation in eliciting project requirements and to enable a more transparent project monitoring and auditing. We apply the framework to a case study with respect to identifying on-chain activities that enable system interoperability, e-participation and project auditability.
Image Denoising With Generative Adversarial Networks and its Application to Cell Image Enhancement

This paper proposes an image denoising training framework based on Wasserstein Generative Adversarial Networks (WGAN) and applies it to cell image denoising. Cell image denoising is a challenging task which has high requirement on the recovery of feature details. Current popular convolutional neural network (CNN) based denoising methods encounter a blurriness issue that denoised images are blurry on texture details, which is fatal for the cell image denoising. In this paper, to solve the blurriness issue, we first theoretically analyze the cause of the blurriness issue. Subsequently, an image denoising training framework with WGAN based adversarial learning is proposed. This training framework solves the blurriness issue by guiding the denoising network to find the distribution space of real clean images rather than the distribution space of blurry images and introducing feature information. Experimental results show that this training framework can effectively solve the blurriness issue and achieve better denoising performance than the state-of-the-art denoising methods. Meanwhile, the application of this training framework on cell image denoising also achieves satisfactory performance. Recovered cell images of this training framework are clear on feature details.
Problem Domain Ontology Mining Based on Distributional Semantics

The paper presents the method of creating an extended ontology as an associative portrait of a subject area and the construction of a semantic space for intelligent knowledge extracting systems development. The ideology of semantic contextual spaces is based on the Distributional hypothesis: linguistic items with similar distributions have similar meanings. The model employed uses an extended hypothesis which includes the study of similarities and differences in contexts not only for a separate lexeme, but also for arbitrary multi-word text fragments which are considered as meaningful phrases.
ArkaeVision VR Game: User Experience Research between Real and Virtual Paestum

The design of a virtual reality (VR) cultural application is aimed at supporting the steps of the learning process-like concrete experimentation, reflection and abstraction—which are generally difficult to induce when looking at ruins and artifacts that bring back to the past. With the use of virtual technologies (e.g., holographic surfaces, head-mounted displays, motion—cation sensors) those steps are surely supported thanks to the immersiveness and natural interaction granted by such devices. VR can indeed help to symbolically recreate the context of life of cultural objects, presenting them in their original place of belonging, while they were used for example, increasing awareness and understanding of history. The ArkaeVision VR application takes advantages of storytelling and user experience design to tell the story of artifacts and sites of an important cultural heritage site of Italy, Paestum, creating a dramaturgy around them and relying upon historical and artistic content revised by experts. Visitors will virtually travel into the temple dedicated to Hera II of Paestum, in the first half of the fifth century BC, wearing an immersive viewer–HTC Vive; here, they will interact with the priestess Ariadne, a digital actor, who will guide them on a virtual tour presenting the beliefs, the values and habits of an ancient population of the Magna Graecia city. In the immersive VR application, the memory is indeed influenced by the visitors’ ability to proceed with the exploratory activity. Two evaluation sessions were planned and conducted to understand the effectiveness of the immersive experience, usability of the virtual device and the learnability of the digital storytelling. Results revealed that certainly the realism of the virtual reconstructions, the atmosphere and the “sense of the past” that pervades the whole VR cultural experience, characterize the positive feedback of visitors, their emotional engagement and their interest to proceed with the exploration.
Wearable Design Requirements Identification and Evaluation

Wearable electronics make it possible to monitor human activity and behavior. Most of these devices have not taken into account human factors and they have instead focused on technological issues. This fact could not only affect human–computer interaction and user experience but also the devices’ use cycle. Firstly, this paper presents a classification of wearable design requirements that have been carried out by combining a quantitative and a qualitative methodology. Secondly, we present some evaluation procedures based on design methodologies and human–computer interaction measurement tools. Thus, this contribution aims to provide a roadmap for wearable designers and researchers in order to help them to find more efficient processes by providing a classification of the design requirements and evaluation tools. These resources represent time and resource-saving contributions. Therefore designers and researchers do not have to review the literature. It will no be necessary to carry out exploratory studies for the purposes of identifying requirements or evaluation tools either.
Encryption and hiding text using DNA coding and hyperchaotic system

&lt;span&gt;In this paper, a secure data encryption using DNA sequence operation in a new and innovative direction different from the traditional direction, DNA coding uses the eight rules of DNA code based on a sequence of letter in text that provides the possibility of encrypting the same letter or word in more than one form in one text-based on sequence of this letter or word in the text. Then hiding technique implemented based on a hyperchaotic system. To increase the security of encryption text, we use the hyperchaotic system for obtaining the color image position that used to hide on it. The proposed steganography method hides a letter of the encrypted text in each pixel of the cover image, thus giving the possibility of hiding large text data. Some metrics have been applied to the proposed algorithm such as NPCR analyses, MSE, Correlation, and BER, The results of the simulation and security analysis showed that the new DNA coding is suitable for text encryption/decryption and that the super-chaotic map is suitable for hiding/extract the encrypted data, which indicates that the proposed encryption algorithm has a good encryption and hiding effect. Can resist brute statistical attack, force attack, differential.&lt;/span&gt;
A color image encryption algorithm based on 2D-CIMM chaotic map

To ensure the safe transmission of image information in communication, and improve the security performance of image encryption algorithm, we proposed a color image encryption algorithm with higher security based on chaotic system. Firstly, the 2-dimensional Cubic ICMIC modulation map (2D-CIMM) is designed, which has simple form, easy to construct, and high Spectral Entropy (SE) complexity. Secondly, the hash values of the original image are used to update the initial values of the 2D-CIMM map in real time, which increases the sensitivity of the image encryption algorithm to the plaintext and improves the finite precision effect. Finally, the permutation and diffusion processes of the encryption algorithm based on bit-level are performed. In addition, simulation and performance analysis show that the proposed algorithm has higher security.
Alexa, What Are You? Exploring Primary School Children’s Ontological Perceptions of Digital Voice Assistants in Open Interactions

Today’s children grow up in an environment that is increasingly characterized by digital voice assistants (DVAs), such as Alexa, Siri, or the Google Assistant. This paper argues that any attempt to investigate children’s interactions with, and perceptions of, DVAs should be based on the theoretical grounds of an ontological framework that considers children’s genuine understanding of what it means to be human and what it means to be a machine. Based on focus groups and a gamified data collection design, our empirical inquiry applied qualitative methods to explore primary school children’s (&lt;i&gt;n =&lt;/i&gt;27, age range: 6–10 years, average age: 8.6 years) open interactions with DVAs. In particular, our focus was on how DVAs were embedded in children’s general ontological belief system, and how children interpreted certain aspects of DVAs’ interactive capabilities as being genuinely humanoid or non-humanoid. On the one hand, our findings suggest that children’s interactions with DVAs might be more an end in itself than a means to an end, meaning that children primarily interact with DVAs for the sake of engaging excitement instead of using the devices’ utilitarian functionalities. On the other hand, we found that children in our sample held firm ontological beliefs about the distinct nature of humans and machines, whilst interpreting certain aspects of DVAs’ interactive capabilities as being genuinely humanoid (e.g., non-responsiveness, delayed responses, inaccuracy) and non-humanoid (e.g., permanent responsiveness, promptness, accuracy, limited conversational capacities, lack of common sense, standardized responses) at the same time.
Satellite-based solar energy potential analysis for southern states of India

Numerous efforts have been taken to exhibit scientific advancement in solar energy analysis. The current study attempts to demonstrate an approach for the solar resource variability analysis at a spatial scale in the southern states of India. This work utilized the satellite-derived modelled solar insolation datasets due to the global unavailability of in-situ meteorological data sets (especially solar radiation data). The visualization of solar insolation components including direct normal irradiance (DNI) and global horizontal irradiance (GHI) provided an opportunity to understand the energy configurations and it's potential. An average DNI variation of 5.21 kWh/m2 and GHI variation of 5.72 kWh/m2 was observed. The ancillary analysis presented a detailed map for southern states of India at a grid resolution of 100 km x 100 km. Thereafter DNI and GHI distribution map for selected random points were estimated. The precise summary statistics stated that maximum and minimum values of DNI range from 3.72 kWh/m2 to 5.59 kWh/m2 with an average value of 5.18 kWh/m2 and likewise maximum and minimum values of GHI ranges from 4.91 kWh/m2 to 5.99 kWh/m2 with an average value of 5.71 kWh/m2. It was observed that the states of Karnataka and Tamil Nadu have a better prospect for further applications. The obtained results provided datasets for further analysis to comprehend the spatial distribution at a regional or national level. These will be very much useful for better energy planning for sound decision making.
A scalable semantic framework for IoT healthcare applications

IoT-based systems for early epidemic detection have not been investigated yet in the research. The state-of-the art in sensor technology and activity recognition makes it possible to automatically detect activities of daily living (ADL). Semantic reasoning over ADLs can discover anomalies and symptoms for disorders, hence diseases and epidemics. However, semantic reasoning is computationally rather expensive and therefore unusable for real-time monitoring in large scale applications, like early epidemic detection. To overcome this limitation, this paper proposes a new scalable semantic framework based on several semantic reasoning techniques that are distributed over a semantic middleware. To reduce the number of events to process during the semantic reasoning, a complex event processing (CEP) engine is used to detect abnormal events in ADL and to generate the associated symptom indicators. To demonstrate real-time detection and scalability, the proposed framework integrates a new extension of ADLSim, a discrete event simulator that simulates long-term sequences of ADL.
Blockchain Application for the Paris Agreement Carbon Market Mechanism—A Decision Framework and Architecture

This paper evaluates the suitability of blockchain technology for the Article 6.2 carbon market mechanism of the Paris Agreement. The bottom-up approach of the Paris Agreement causes challenges to the robust accounting of mitigation outcomes and information asymmetry, both of which result from a high number of heterogeneous emission accounting systems. Blockchain is an innovative technology that can act as an aggregation platform for these fragmented systems while enhancing transparency and automating accounting processes. However, this new technology is not a panacea for all problems, and the trade-offs of applying blockchain technology need to be assessed case by case. We create and apply an eight-step decision framework for testing the applicability of the technology for the Paris Agreement Article 6.2 carbon market mechanism. The analysis shows that, under current mechanism specifications, a blockchain application can enhance transparency and increase automation, thereby eliminating information asymmetry. We outline a system architecture that allows the linking of the heterogeneous systems, the integration of an Article 6.2 exchange mechanism, and the progress tracking of climate targets. This blockchain architecture offers national Parties the opportunity to co-create a decentralised system in line with the bottom-up ethos of the Paris Agreement.
Emotion Detection in Online Social Networks: A Multilabel Learning Approach

Emotion detection in online social networks (OSNs) can benefit kinds of applications, such as personalized advertisement services, recommendation systems, etc. Conventionally, emotion analysis mainly focuses on the sentence level polarity prediction or single emotion label classification, however, ignoring the fact that emotions might coexist from users' perspective. To this end, in this work, we address the multiple emotions detection in OSNs from user-level view, and formulate this problem as a multilabel learning problem. First, we discover emotion labels correlations, social correlations, and temporal correlations from an annotated Twitter data set. Second, based on the above observations, we adopt a factor graph-based emotion recognition model to incorporate emotion labels correlations, social correlations, and temporal correlations into a general framework, and detect the multiple emotions based on the multilabel learning approach. Performance evaluation demonstrates that the factor graph-based emotion detection model can outperform the existing baselines.
Getting the ##life out of living: How Adequate Are Word-Pieces for Modelling Complex Morphology?

This work investigates the most basic units that underlie contextualized word embeddings, such as BERT -the so-called word pieces.In Morphologically-Rich Languages (MRLs) which exhibit morphological fusion and nonconcatenative morphology, the different units of meaning within a word may be fused, intertwined, and cannot be separated linearly.Therefore, when using word-pieces in MRLs, we must consider that: (1) a linear segmentation into sub-word units might not capture the full morphological complexity of words; and (2) representations that leave morphological knowledge on sub-word units inaccessible might negatively affect performance.Here we empirically examine the capacity of wordpieces to capture morphology by investigating the task of multi-tagging in Hebrew, as a proxy to evaluating the underlying segmentation.Our results show that, while models trained to predict multi-tags for complete words outperform models tuned to predict the distinct tags of WPs, we can improve the WPs tag prediction by purposefully constraining the wordpieces to reflect their internal functions.We conjecture that this is due to the naïve linear tokenization of words into word-pieces, and suggest that linguistically-informed word-pieces schemes, that make morphological knowledge explicit, might boost performance for MRLs.
ENIGMA Anonymous: Symbol-Independent Inference Guiding Machine (System Description)

We describe an implementation of gradient boosting and neural guidance of saturation-style automated theorem provers that does not depend on consistent symbol names across problems. For the gradient-boosting guidance, we manually create abstracted features by considering arity-based encodings of formulas. For the neural guidance, we use symbol-independent graph neural networks (GNNs) and their embedding of the terms and clauses. The two methods are efficiently implemented in the E prover and its ENIGMA learning-guided framework. To provide competitive real-time performance of the GNNs, we have developed a new context-based approach to evaluation of generated clauses in E. Clauses are evaluated jointly in larger batches and with respect to a large number of already selected clauses (context) by the GNN that estimates their collectively most useful subset in several rounds of message passing. This means that approximative inference rounds done by the GNN are efficiently interleaved with precise symbolic inference rounds done inside E. The methods are evaluated on the MPTP large-theory benchmark and shown to achieve comparable real-time performance to state-of-the-art symbol-based methods. The methods also show high complementarity, solving a large number of hard Mizar problems.
Impolite Pedestrian Detection by Using Enhanced YOLOv3-Tiny

In recent years, the problem of “Impolite Pedestrian” in front of the zebra crossing has aroused widespread concern from all walks of life. The traffic sector’s governance measures have become more serious. The traditional way of governance is onsite law enforcement, which requires a lot of manpower and material resources and is low efficiency. An enhanced YOLOv3-tiny model is proposed for pedestrians and vehicle detection in traffic monitoring. By modifying the backbone network structure of YOLOv3- tiny model, introducing deep detachable convolution operation, and designing the basic residual block unit of the network, the feature extraction ability of the backbone network is enhanced. The improved model is trained on the VOC2007+VOC2012 training set, and the trained model is tested for performance on the test data set. The experimental results show that: the mean Average Precision (mAP) increased from 0.672 to 0.732, increasing the measurement accuracy by 9%. The Intersection over Union (IoU) increased from 0.783 to 0.855, increasing the coverage accuracy by 7.2%. The enhanced YOLOv3-tiny model has higher measurement accuracy than the original model. Applying this model to the 1080P traffic video on the NVIDIA RTX 2080, the detection speed is 150 FPS, which can fully achieve real-time detection. Through the analysis of pedestrians and vehicle coordinates, it is judged whether or not illegal acts occur. For illegal vehicles, save three pictures as the basis for law enforcement, which forms an important supplement to off-site law enforcement.
A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections

The implementation of anomaly detection systems represents a key problem that has been focusing the efforts of scientific community. In this context, the use one-class techniques to model a training set of non-anomalous objects can play a significant role. One common approach to face the one-class problem is based on determining the geometric boundaries of the target set. More specifically, the use of convex hull combined with random projections offers good results but presents low performance when it is applied to non-convex sets. Then, this work proposes a new method that face this issue by implementing non-convex boundaries over each projection. The proposal was assessed and compared with the most common one-class techniques, over different sets, obtaining successful results.
Application of Artificial Intelligence in College Dance Teaching and Its Performance Analysis

Artificial intelligence (AI) has not been widely and deeply implemented in dance teaching of colleges. To solve the problem, this paper identifies the problems with the AI in college education and dance teaching, theoretically discusses the application strategies of the AI in dance teaching of colleges, and demonstrates the promotive effect of AI in college dance teaching. Next, a performance analysis model was established based on fuzzy interval num-ber, grey system theory, and analytic hierarchy process (AHP), and adopted to evaluate the performance of the AI application in college dance teaching. The research results shed a new light on the application of cutting-edge tech-niques in college education.