Reviewerly/exHarmony huggingface dataset:
Spectral Clustering of Web Services by Fusing Document-based and Tag-based Topics Similarity

This paper proposes a Web services clustering method based on network and integration tags. Firstly, a Document-Tag LDA model(DTag-LDA), is proposed, which considers the tag information of Web services and the tag can describe the effective information of documents accurately. Through the unified modeling of integrated description document and tag information, the web service network is constructed, and then the network is clustered to improve the clustering effect. Based on the first model, we further propose an efficient Document weight and Tag weight-LDA model(DTw-LDA), which fused multi-modal data network. To further improve the clustering accuracy, the model constructs the network for describing text and tag respectively, and then merges the two networks to generate web service network clustered. In addition, we also design experiments to verify that the used auxiliary information can help to extract more accurate semantics by conducting service classification. The proposed model is evaluated on the real-world data-sets and the experimental results show that the accuracy and recall rate are more than 0.7.And the method has obvious advantages in precision, recall, purity and other performance.
GRTr: Generative-Retrieval Transformers for Data-Efficient Dialogue Domain Adaptation

Domain adaptation has recently become a key problem in dialogue systems research. Deep learning, while being the preferred technique for modeling such systems, works best given massive training data. However, in real-world scenarios, such resources are rarely available for new domains, and the ability to train with a few dialogue examples can be considered essential. Pre-training on large data sources and adapting to the target data has become the standard method for few-shot problems within the deep learning framework. In this paper, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grt</small> r, a hybrid generative-retrieval model based on the large-scale general-purpose language model GPT[2] fine-tuned to the multi-domain <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</small> eta <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lwo</small> z dataset. In addition to robust and diverse response generation provided by the GPT[2], our model is able to estimate generation confidence, and is equipped with retrieval logic as a fallback for the cases when the estimate is low. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grt</small> r is the winning entry at the fast domain adaptation task of DSTC-8 in human evaluation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> 4% improvement over the 2nd place system). It also attains superior performance to a series of baselines on automated metrics on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</small> eta <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lwo</small> z and <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</small> ulti <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">woz</small> , a multi-domain dataset of goal-oriented dialogues. In this paper, we also conduct a study of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">grt</small> r's performance in the setup of limited adaptation data, evaluating the model's overall response prediction performance on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</small> eta <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lwo</small> z and goal-oriented performance on <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</small> ulti <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">woz</small> .
FP-Nets for Blind Image Quality Assessment

Feature-Product networks (FP-nets) are a novel deep-network architecture inspired by principles of biological vision. These networks contain the so-called FP-blocks that learn two different filters for each input feature map, the outputs of which are then multiplied. Such an architecture is inspired by models of end-stopped neurons, which are common in cortical areas V1 and especially in V2. The authors here use FP-nets on three image quality assessment (IQA) benchmarks for blind IQA. They show that by using FP-nets, they can obtain networks that deliver state-of-the-art performance while being significantly more compact than competing models. A further improvement that they obtain is due to a simple attention mechanism. The good results that they report may be related to the fact that they employ bio-inspired design principles.
Research on Realization of Python Professional English Translator

Abstract The main purpose of professional English translator is to translate more ambiguity and more professional words and sentences to meet the needs of the work. This article combines web crawler based on Python language, and analyzes Python programming and online crawler implementation. This article uses this as a basis to study how to design a professional English translator, and study the structure, business workflow, and functions of the translator. The test results prove that the designed program can effectively realize the crawler function, so that it can obtain a large amount of consistent data in a very short time, and return these related data to the user through the interface connection design. This can not only meet the needs of users for quick translation and search of English words and sentences, but also meet the needs of actual work.
Interpolation in Auto Encoders with Bridge Processes

Auto encoding models have been extensively studied in recent years. They provide an efficient framework for sample generation, as well as for analysing feature learning. Furthermore, they are efficient in performing interpolations between data-points in semantically meaningful ways. In this paper, we introduce a method for generating sequence samples from auto encoders trained on flattened sequences (e.g video sample from auto encoders trained on single frames); as well as a canonical, dimension independent method for generating stochastic interpolations. The distribution of interpolation paths is represented as the distribution of a bridge process constructed from an artificial random data generating process in the latent space, having the prior distribution as its invariant distribution.
Improved MPC Algorithms for Edit Distance and Ulam Distance

Edit distance is one of the most fundamental problems in combinatorial optimization to measure the similarity between strings. Ulam distance is a special case of edit distance where no character is allowed to appear more than once in a string. Recent developments have been very fruitful for obtaining fast and parallel algorithms for both edit distance and Ulam distance. In this work, we present an almost optimal MPC (massively parallel computation) algorithm for Ulam distance and improve MPC algorithms for edit distance. Our algorithm for Ulam distance is almost optimal in the sense that (1) the approximation factor of our algorithm is 1+ε1+ε, (2) the round complexity of our algorithm is constant, (3) the total memory of our algorithm is almost linear (~O <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ε</sub> (n)Õε(n)), and (4) the overall running time of our algorithm is almost linear which is the best known for Ulam distance. We also improve the work of Hajiaghayi et al. for edit distance in terms of total memory. The best previously known MPC algorithm for edit distance requires ~O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2x</sup> )Õ(n2x) machines when the memory of each machine is bounded by ~O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1-x</sup> )Õ(n1-x). In this work, we improve the number of machines to ~O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(9/5)x</sup> )Õ(n(9/5)x) while keeping the memory limit intact. Moreover, the round complexity of our algorithm is constant and the total running time of our algorithm is truly subquadratic. However, our improvement comes at the expense of a constant factor in the approximation guarantee of the algorithm. This improvement is inspired by the recent techniques of Boroujeni et al. and Chakraborty et al. for obtaining truly subquadratic time algorithms for edit distance.
Approaches for Semantic Relatedness Computation for Big Data

Computation of semantic relatedness between words, concepts, sentences or documents is a fundamental task in many areas like Text Mining, Information Retrieval and so on but as we know that data is increasing day by day Leading to Big Data. Traditional softwares which were much efficient and heavily used are not able to handle Big Data i.e. they cannot give result in reasonable amount of time for Big Data. So Hadoop and Spark like tools have been developed to handle Big Data. Similarly our different algorithms and formulas, being efficient on small data, needs to be updated and tuned according to big data. Thus in text mining semantic mining leads to the extraction of semantic vector or context vector and using different formula like Mutual Information (MI), Balanced Mutual Information (BMI), Normalized Google Distance(NGD), etc. techniques to calculate fuzzy membership values of elements of vector to its corresponding concepts. Thus Semantic Similarity is calculated between two concepts by considering repeatedly the semantic similarity or information gain between two elements of each vector. Then Semantic similarity between two concepts is calculated. Thus we have a matrix similar to Vector Space Model (VSM) but these contains fuzzy membership values rather than frequency of terms. We have proposed Map Reduce based Semantic Relatedness computation technique for Big Data.
Application of Artificial Intelligence in Cultural Heritage Protection

Abstract With the continuous development of science and technology, artificial intelligence has become the subject of extensive discussion in recent years. Artificial intelligence technology uses deep learning to apply this, it shows the trend of high development, such as the previous Go game that shocked the world, the fact that artificial intelligence “Alphago” defeated the world Go champion, people realized that the development of artificial intelligence exceeded people Imagination. Various social industries are gradually introducing artificial intelligence technology to give them new vitality for development. This is exactly how the cultural heritage protection process should be combined with artificial intelligence technology. Cultural protection should enter the digital era, using virtual reality technology and applying segmentation algorithms to segment the prospects of cultural heritage. Complete all aspects of cultural heritage protection without applications. This article introduces the process of artificial intelligence in the protection of cultural heritage using virtual reality technology to realize the digitization of cultural heritage, realizing that the protection of cultural heritage keeps pace with the times. The experimental results of this paper show that the virtual reality technology combined with artificial intelligence is applied to the importance of cultural heritage protection, which improves the vitality of cultural heritage and more perfectly shows the charm of cultural heritage application.
Enhanced $k$-Means Clustering Algorithm for Detection of Human Intestinal Parasites

Helminthiases disease has been diagnosed as one of the most common health problems in children and adults that is caused by human intestinal parasites named helminth. An immediate diagnosis system is required for the identification and classification of helminth to avoid the development of parasites in human intestine. In response to the needs for a rapid and precise identification of helminth, this research proposes analyzing the segmentation performance analysis of unsupervised colour image segmentation of helminth parasites through enhanced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means clustering (EKM) algorithm on the modified global contrast stretching (MGCS) and the modified linear contrast stretching (MLCS) enhancement techniques. By segmenting the helminth parasites using EKM clustering algorithm, the concept of variance and a new version of transferring process for clustered members are used to assist the assignation of data to the proper centre during the process of clustering, so that good segmented image can be obtained. A total of 100 helminth parasites images have been analyzed with the approach comprising of 50 images from each species of the Ascaris Lumbricoides Ova (ALO) and the Trichuris Trichiura Ova (TTO). Overall, the average results generated by the enhanced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means clustering algorithm on MGCS is higher than MLCS. The proposed technique achieved 99.88% accuracy, 95.68% sensitivity alongside 99.95% specificity. Ultimately, the result demonstrates that the proposed technique is suitable for image segmentation of the helminth parasites images.
Feature Extraction Functions for Neural Logic Rule Learning

Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output. In this paper, we propose feature extracting functions for integrating human knowledge abstracted as logic rules into the predictive behaviour of a neural network. These functions are embodied as programming functions, which represent the applicable domain knowledge as a set of logical instructions and provide a modified distribution of independent features on input data. Unlike other existing neural logic approaches, the programmatic nature of these functions implies that they do not require any kind of special mathematical encoding, which makes our method very general and flexible in nature. We illustrate the performance of our approach for sentiment classification and compare our results to those obtained using two baselines.
Cost Minimization Algorithm for Provisioning Cloud Resources

Cloud Computing offers resources as a utility that can be accessed and rented via web browsers. It has also made it easy for paying for resources with different ways of pricing. However, that could result in overpaying for resources or underutilizing the reserved resources. In this paper, we focus on two main options in the pricing model, namely, pay-per-use and reserved instances. In the first one, users can pay for what they use only while the second option offers up to 72% discount but they have to pay in advance for the whole reserved period. In this paper, we present two algorithms for provisioning cloud resources to help cloud customers pick the most cost-effective plans for their jobs.
Infographie, data visualization, and the danger of reality distortion: The case study of Australia fire 2020

Abstract Nowadays, the stream of information to promote environmental consciousness are profoundly dominated by the existence of infographics and data visualization. Beside visually attractive, it is crucial for them to deliver a clear and concise information. This research will uncover infographics and data visualization in conjunction with news about the fire in Australia continent, early in 2020. Some of these visualizations were potentially giving a misleading interpretation to its reader. This research aims to explore the effectiveness of infographics and data visualization channel from the perspective of visual communication design discipline; both from its aesthetics dimension and its content data accuracy. As the outcome, this research is expected to be a reminder that the amount of information in the world is limitless. It is our shared responsibility to be mindful towards each and every material that we consume and share with others.
Securing the UNICODE Data transmission on IoT Environment using Hybrid Encryption Scheme (HES)

Data Security offers praxis for the protection of the various forms of information. Information is transmitted in various kinds as ASCII textual content, image, video, audio, UNICODE text, and so on. Various algorithms are recommended by different scientists for dealing with copy protection. The current cryptographic algorithms are concentrating on ASCII textual content. Within the electronic data transmission era, a lot of symmetric cryptographic algorithms are unveiled by using S boxes. S box is a crucial part of certain clusters of cryptographic algorithms. A few cryptographic algorithms rely on a fixed S box, which produces insecurity to electronic information. The current S box idea manages ASCII copy just. This particular study newspaper provides the 2 sets of information that are carried out by AMES, the Hardy wall algorithm, the very first class applied to a concealed procedure. The next class was viewed as a method of each embedding as well as encryption. The information within the 2nd class is cut back towards the scope of enough for the modulation procedure, by removing the high-value properties of its then getting rid of them out of the mother's info trend. The procedure for encrypting the 2 sets of information will come in concert with the Hybrid Encryption Scheme (HES) feature. Algorithms have been designed to conceal as well as encrypt 1 as well as 2-dimensional information. Decryption algorithms have been designed to send back encrypted details to the original forms of theirs, as well as buying the imitation information. Results were satisfied with the security level on data transmission and also this scheme has reduced the number of attacks on comparing with various existing algorithms.
A 3.6 TOPS/W Hybrid FP-FXP Deep Learning Processor with Outlier Compensation for Image-to-Image Application

A Hybrid floating-point (FP) and fixed-point (FXP) deep learning processor with an outlier-aware channel splitting algorithm is proposed for image-to-image applications on mobile devices. Since the high quality of the reconstructed image through deep learning based image-to-image application requires high bit-precision (> FP16), the mobile processor suffers from the high computation power and large external memory access (EMA). In this work, the proposed algorithm reduces 16-bit FP data to 8-bit FXP data, and only few outliers (<; 10%) are computed in 16-bit FP while maintaining the image reconstruction quality. Therefore, it reduces EMA by 45.5% Moreover, the hierarchical processor accelerates these dense 8bit FXP data and sparse 16-bit FP data, and the functional L2 memory aggregates the convolution output of them by forming the pipeline, which reduces 98% of latency. The proposed system is simulated in 28nm COMS technology, and it occupies 4.16mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The hierarchical processor successfully demonstrates the x 4 scale Full-HD super-resolution generation achieving 76 frames-per-second (fps) with 133.3 mW power-consumption at 0.9 V supply and 3.6 TOPS/W of energy-efficiency which is × 3.27 higher than the previous 16-bit FXP processor.
Part Coaxiality Detection Based on Polynomial Interpolation Subpixel Edge Detection Algorithm

In order to meet the requirements of rapid detection of coaxiality and high precision in the assembly process of aerospace small parts, a subpixel edge detection algorithm based on polynomial interpolation is proposed. First, Gaussian filtering is used to preprocess the image, and then the gray image and the direction template are convolved to obtain the gradient image. Then the gradient image is binarized to obtain the edges of the parts, and finally the edge points are subjected to quadratic polynomial interpolation to obtain the subpixel edges. Improved least squares method is used to fit the circular edges of the parts and calculate the coaxiality. Through the edge extraction experiment of small parts, the accuracy of the coaxiality obtained by this algorithm (PIS) is better than that of algorithm based on canny operator. It is a method that can accurately calculate the coaxiality of small parts.
Skeletal Growth Assessment Using Segmented Middle Phalanx with Active Shape Modelling

Bone growth assessment is a task performed on radiographs to predict the final adult height, to diagnose growth disorders, to predict the patient's stage of growth by monitoring skeletal development. The modified center phalanx of the middle finger stages was used to assess the growth and development of the subject. Hand X-rays are pre-processed and MP3 bones are segmented to extract the features which are the epiphyseal gap. It helps was to compute the MP3 stages by measuring the epiphyseal gap between a phalanx of middle finger bone from X-ray images with the point distribution segmentation model. And to assess the correlation that exists between MP3 stages and the height growth rate. Participants were aged between eight to fourteen years. Intra-observer and inter-observer reliability of MP3 staging was assessed using correlation coefficient, percentage of agreement and the Cohen weighted kappa coefficient. ANOVA was used to test the differences between CVM stages. Among 998 participants, the peak in participant's height growth velocity occurred at MP3 stage G (P5 0.001). The research shows the significant relation between body height and the middle phalanx of the hand.
Towards On-the-fly Self-Adaptation of Stream Parallel Patterns

Stream processing applications compute streams of data and provide insightful results in a timely manner, where parallel computing is necessary for accelerating the application executions. Considering that these applications are becoming increasingly dynamic and long-running, a potential solution is to apply dynamic runtime changes. However, it is challenging for humans to continuously monitor and manually self-optimize the executions. In this paper, we propose self-adaptiveness of the parallel patterns used, enabling flexible on-the-fly adaptations. The proposed solution is evaluated with an existing programming framework and running experiments with a synthetic and a real-world application. The results show that the proposed solution is able to dynamically self-adapt to the most suitable parallel pattern configuration and achieve performance competitive with the best static cases. The feasibility of the proposed solution encourages future optimizations and other applicabilities.