Reviewerly/exHarmony huggingface dataset:
A Comparison of Machine Learning Algorithms for Automatic Cloud Resource Scaling on a Multi-Tenant Platform

Abstract In an online multi-tenant machine learning platform, the system manager would dynamically load the computing resource according to the tenant’s demand. With cloud computing services, the platform can rent or release computing resources dynamically to fulfill tenant usage which minimizes resource consumption and ensure scalability. Currently, many cloud-based services providers are using the rule-based, threshold auto-scaling mechanism. However, the rule-based method is not efficient, as the nature of availability and cost-reducing violate each other in this method, especially with the sudden increase or variation of the demand. In this paper, we compare several machine-learning-based predictive algorithms to build models based on the information of the system used to predict future usage demands. Decisions made based on this prediction save over 80% cloud resource consumption compared to the rule-based method.
Professionalism as a personal priority of a higher school teacher and a condition for high-quality training of a graduate

The professionalism of a higher school teacher and the quality of their work largely determine the quality of training of future graduates who are ready for professional advancement in the context of the development of new high-tech technologies and the increasing demands of partners in the real sector of the economy. In this regard, the research aims to find ways and mechanisms to improve the professionalism of the teacher, their training, and retraining in contemporary educational practice. The article presents the research results obtained at the Department of Pedagogy and Psychology of Penza State Technical University (Penz STU) on the concerned problems. The novelty of the research lies in the effective ideas, approaches, and principles of constructive problem-solving in educational practice proposed based on the analysis of normative, historical, pedagogical, and scientific literature of foreign (Germany, Great Britain, and the USA) and domestic practices. It is shown that the studied and characterized models of improving the professionalism and training of academic staff in higher education, identified leading principles underlying the implementation of these models, and the relationships among the goals, results, and conditions of training and retraining of academic staff in the realities of today can be in demand and successfully implemented. As promising areas for improving the professionalism of a higher school teacher to effectively address issues of high-quality training of graduates, the authors offer to provide pedagogical support as the most productive technology to support a novice teacher during their professional adjustment, conducting monitoring of the effectiveness of teachers’ activities to improve the quality of their professionalism, and encouraging effective and efficient work, as well as providing social partnership and integration of all types of educational resources.
Face recognition in four types of colour space: a performance analysis

Abstract Face detection is one of the problems in computer vision with a large number of researches works. This research work aims to analyses the effect of performing various color spaces in face detection. 1100 images provided by SFA dataset was used in this study. The proposed method was initiated by pre-processing step. Then, segmentation step by using thresholding and morphological operation was conducted. For selecting face area, some features such as eccentricity, area and texture features were used. Finally, for validating the reliability of the proposed method, UTKface dataset was applied. This study obtained more than 90% accuracy of face detection in each color space. Moreover, the highest accuracy was obtained in YCbCr color space with 96.13%. This result was also strengthened with other experiment by using UTKface dataset. This experiment achieves accuracy of 80%. According to the performance analysis, the proposed method is reliable for recognizing face characteristic.
To Score or to Score a Goal: Transitivity in Football Match Reports

This article investigates the use and non-use of objects with six transitive verbs in a corpus of English football match reports. The verbs were selected on the basis of their frequency as well as their lexico-grammatical features of "footballness" and transitivity. The study suggests that object omission may not be as pervasive as hinted at in previous studies (e.g. Bergh and Ohlander 2016; Ruppenhofer and Michaelis 2010). Regarding potential reasons for object omission, it is uncovered that the football verbs-net, save, play-are more prone to object omission than the general verbs: feed, create, take. This is attributed to the strong attraction of the former to recurrent collocates such as goal and ball. This suggests that verbs used to report on unremarkable and canonical situations (to the game of football) more readily omit the object, albeit not on a general basis, as individual differences between the verbs also emerge.
WITHDRAWN: Design and development of bird ontology using protégé

Ontology means “widely referred to”, it is the branch of philosophy that studies concepts such as existence and reality. The Main Goal of this research is to discuss how ontology is built for the classification of birds. In this work the enhanced version, Protégé 5.5.0-beta-9 is used to construct bird ontology. Ontologies are currently being used for explicit representation of the domain knowledge here the bird Ontology represents the classification of bird, the classification is based on Paleognathae- Flightless Birds and Neognathae - Flying Birds. This research implements the protégé tool to develop our own ontograf, OWL viz and it allows us to automatically publishing the Ontology which makes it available online.
Robust Visualisation of Dynamic Text Collections

Visualisations are supposed to provide intuitive ways to explore large document collections. State-of-the-art approaches usually transform high-dimensional representations of documents into 2-dimensional vectors using dimensionality reduction algorithms. These vectors are then placed into a landscape hopefully retaining semantic information regarding similarity from the high-dimensional representation. Traditionally, dimensionality reduction algorithms are developed with static collections in mind. However, many "real-world" document collections, such as news articles, scientific literature, patents, Wikipedia, or tweets, to name a few, grow and evolve over time. Visualising the temporal change of these collections poses various challenges for out-of-the-box dimensionality reduction algorithms.
The influence of a novel edge enhancement software on image quality of DR hand images of patients with rheumatoid arthritis

Introduction This study aimed to evaluate the effects of a newly developed Advanced Edge Enhancement software (AEE) (Canon Europe, Amsterdam, NL) on image quality (IQ) of Digital Radiography (DR) hand images focusing on rheumatoid arthritis (RA). Methods and materials Fifty posterior-anterior hand images with or suspected for RA were collected. For each of the 50 images, six copies were made with each their AEE algorithm settings. A total of 330 images (30 images iterated) were evaluated using relative Visual Grading Analysis (VGA) by three observers and combined into a VGA Score (VGAS). Second, 50 images of a technical Contrast Detail Radiography Phantom (CDRAD) was produced with three different AEE software settings, each at level 1,5 and without the AEE software yielding 350 CDRAD images. All images was analysed by the CDRAD Analyser and included for an objective analysis of the AEE software. Results The VGA study showed a significant difference in image quality between a standard image and images with AEE software applied. The average VGA score of the AEE software was better than the standard images (interval between 0.2 and 0.9). The AEE algorithms at level 5 scored significantly lower for noise but significantly higher for spatial resolution, sharpness and contrast in the VGA. The CDRAD images showed that all AEE algorithms had a statistically significant improvement for level 1 and deterioration for level 5 compared to the standard image. Conclusion Overall the AEE algorithm: small structure level 1 showed an improvement of all IQ criteria in the VGA and a better technical IQ. Implications for practice The AEE software ought to be considered as a useful addition to the current software, possibly enabling visualisation of structures currently visible.
Copy-Move Image Forgery Detection Using Discrete Wavelet Transform

To expressing visual information Digital images play a significant role. On social media this information in form of digital image is continually increasing. Now a day easily digital images are misrepresented by peoples because availability of software using for image processing. In this paper we represent novel approaches which identify Copy-Move forgery in image using DWT. First we read image and convert this image into grayscale image. Then apply wavelet transform. Extract overlapped high contrast blocks of the coarsest level. Then lexicographically sorting and calculate phase correlation between rows. Extract blocks from reference and match region. Then draw the duplicate region. For evaluation of our system we calculate TPR, FPR, Accuracy and AUC. Our system accuracy \({\simeq}\) 94.58%.
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Image restoration problems, i.e., recovery of an original high-quality image from the degraded observation, arise in various science and engineer areas. Over the past decades, the framelet-based methods are particularly investigated and adopted, owing to the excellent ability of sparse approximating the piecewise-smooth functions such as natural images. In this paper, we propose a novel tight frame-based ℓ2-relaxed truncated ℓ0 analysis-sparsity model that simultaneously exploiting the sparsity and support priors. The resulting nonconvex nonsmooth optimization problem is addressed by using the proposed proximal alternating adaptive hard thresholding (PAAHT) method. We also proved that the sequence generated by the proposed algorithm sublinearly converges. Numerical experiments on several typical image restoration problems demonstrate that the proposed method is more effective than the standard sparsity-inducing algorithms and outperforms several state-of-the-art methods in both objective and perceptual quality.
Dynamic SentiPhraseNet to Support Sentiment Analysis in Telugu

In the scarcity of Telugu, annotated dataset makes sentiment analysis task challenging for researchers in the recent times. A rapid growth was seen in development of annotated datasets in Telugu for sentiment analysis. SentiWordNet (SWNet) is one of them where they mapped a sentiment score to every word. However, we found that there is a limitation in unigram words of SWNet. As several unigram words are ambiguous and it is unable to contribute in sentiment analysis task without the context of word in the given sentence. To resolve such limitations of SWNet, this article proposed SentiPhraseNet (SPNet). SPNet is a collection of sentiment phrases (such as bigram and trigram to get the context of word in the given sentence). Additionally, the proposed approach is compared with existing other approaches as well. The SPNet is also extended for dynamic support which learns the unknown phrases automatically while testing the sentiment of Telugu sentences with SPNet. The proposed approach is outperformed the other existing approaches and attains an accuracy of 90.9% after testing five sets of Telugu sentences in five trials.
Constrained Learning With Non-Convex Losses

Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern statistical problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization, even obtaining a model that satisfies statistical constraints can be challenging. All the more so, a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap -- i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex) statistical problem -- and provide a practical constrained learning algorithm. These results establish a constrained counterpart to classical learning theory, enabling the explicit use of constraints in learning. We illustrate this theory and algorithm in rate-constrained learning applications arising in fairness and adversarial robustness.
Stereo matching algorithm based on binocular vision

Binocular stereo vision technology has been widely used in target detection, unmanned driving and other aspects due to its advantages of low cost and wide range of application. It is a research hotspot in the field of computer vision. Binocular stereo vision is mainly to obtain 3D depth information of the scene by analyzing two or more views of the scene. Stereo matching is the core part of the scene, and the performance of the matching algorithm directly affects the effect of 3D reconstruction. This paper mainly analyzes and summarizes the local stereo matching algorithm, global stereo matching algorithm and semi-global stereo matching algorithm by studying stereo matching algorithms at home and abroad, expounds the characteristics and performance of various matching algorithms, and makes a comparative analysis of various algorithms.
On the aggregation of compositional data

Compositional data naturally appear in many fields of application.For instance, in chemistry, the relative contributions of different chemical substances to a product are typically described in terms of a compositional data vector.Although the aggregation of compositional data frequently arises in practice, the functions formalizing this process do not fit the standard order-based aggregation framework.This is due to the fact that there is no intuitive order that carries the semantics of the set of compositional data vectors (referred to as the standard simplex).In this paper, we consider the more general betweenness-based aggregation framework that yields a natural definition of an aggregation function for compositional data.The weighted centroid is proved to fit within this definition and discussed to be linked to a very tangible interpretation.Other functions for the aggregation of compositional data are presented and their fit within the proposed definition is discussed.
Are You Not Entertained? Computational Storytelling with Non-Verbal Interaction

We describe the design and implementation of a multi-modal storytelling system. Multiple robots narrate and act out an AI-generated story whose plots can be dynamically altered via non-verbal audience feedback. The enactment and interaction focuses on gestures and facial expression, which are embedded in a computational framework that draws on cognitive-linguistic insights to enrich the storytelling experience. With the absence of in-person user studies in this late breaking research, we present the validity of the separate modules of this project and introduce it to the HRI field.
New Word Identification Algorithm in Natural Language Processing

This paper proposes a new word discovery algorithm based on mutual information and branch entropy in order to solve the problems of fast updating and complicated composition of hot words and new words on social platforms. First, the data is preprocessed, and then mutual information is used to extract and expand the candidate words, and then the branch entropy is used to filter the candidate words to obtain a valid word set. Finally, the candidate word set is compared and filtered with related dictionaries to obtain the new word set. Finally, the algorithm in this paper has a certain improvement in accuracy through experimental evaluation, which is effective and feasible.
Research on Personalized Search Based on ElasticSearch

In the context of today's big data era, with the rapid development of information technology in all walks of life, information data has exploded. People have been used to relying on the Internet to obtain effective information, but massive data is easy to cause information overload, how to quickly and efficiently obtain useful information for users has become the main problem facing search engines. In order to better study the ElasticSearch application in personalized search, this paper proposes a personalized search engine based on ElasticSearch, on the basis of improve the retrieval efficiency and retrieval accuracy, analysis of user search history records and utilization, let the search engine can be more intelligent, friendlier interact with the user, and makes search results more accord with the actual needs of users. Then, this paper tests and analyzes the search engine in many aspects. The test results show that the search rate has increased by 32.26%, which verifies the practicability, effectiveness and real-time performance of the search engine.
Different Keystrokes for Different Folks

Search engine users retrieve relevant information for an information need using keyword queries. Different users may have similar information needs, but use different query terms. The resulting user query variations can provide a wealth of useful information to IR researchers. Most recently, the keystroke-level telemetry data gathered as part of the CC-News-En collection provides important insights into how users create queries for a search task, at a level of detail not possible using a normal query log. In this demo, we present an interactive tool that enables practitioners to visualize users formulating queries. Our new tool is a temporal simulation of the typing behavior of crowdworkers, grouped by information need. It provides the ability to directly compare the cognitive behavior of multiple users simultaneously, and observe how query keyword selection and ordering happens before a final query is submitted to a search engine. To demonstrate the benefit of our tool, we include a qualitative study of four different user behavior patterns which were observed in the CC-News-En collection.
TFfinder: A Software tool to discover Temporary Field code smell

Code smells may introduce some side effects in the source code and therefore deteriorate the code quality. Martin Fowler has presented a list of 22 code smells, but all these code smells have not been paid good attention for their detection. One such code smell is Temporary field code smell. A publicly available software tool called "Designate Java" has performed a good amount of work in context of code smells. Designate Java is able to detect just 10 code smells. A recently published study has proposed a metric- based approach to detect temporary field smell but as far as we could possibly know, neither this work nor some other tool exists that can identify temporary field code smell. This paper has proposed and developed a software tool called "TFfinder", for automatic detection of temporary field code smell. TFfinder tool has been thoroughly tested on mainstream open source JAVA projects from the GitHub. Examination of results showed that all these projects taken for the investigation experienced Temporary field code smell in fluctuating degrees extending from 3% to 22%. We have likewise forked the "Designate Java" and pushed another branch containing code of the temporary field detection. At long last, we presented this branch to the original repository with the goal that the entire network can be benefitted.
A Real-Time Detection Method for Abnormal Data of Internet of Things Sensors Based on Mobile Edge Computing

Aiming at the anomaly detection problem in sensor data, traditional algorithms usually only focus on the continuity of single-source data and ignore the spatiotemporal correlation between multisource data, which reduces detection accuracy to a certain extent. Besides, due to the rapid growth of sensor data, centralized cloud computing platforms cannot meet the real-time detection needs of large-scale abnormal data. In order to solve this problem, a real-time detection method for abnormal data of IoT sensors based on edge computing is proposed. Firstly, sensor data is represented as time series; K-nearest neighbor (KNN) algorithm is further used to detect outliers and isolated groups of the data stream in time series. Secondly, an improved DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is proposed by considering spatiotemporal correlation between multisource data. It can be set according to sample characteristics in the window and overcomes the slow convergence problem using global parameters and large samples, then makes full use of data correlation to complete anomaly detection. Moreover, this paper proposes a distributed anomaly detection model for sensor data based on edge computing. It performs data processing on computing resources close to the data source as much as possible, which improves the overall efficiency of data processing. Finally, simulation results show that the proposed method has higher computational efficiency and detection accuracy than traditional methods and has certain feasibility.
Making Information Hiding Effective Again

Information hiding (IH) is an important building block for many defenses against code reuse attacks, such as code-pointer integrity (CPI), control-flow integrity (CFI) and fine-grained code (re-)randomization, because of its effectiveness and performance. It employs randomization to probabilistically “hide” sensitive memory areas, called safe areas, from attackers and ensures their addresses are not leaked by any pointers directly. These defenses used safe areas to protect their critical data, such as jump targets and randomization secrets. However, recent works have shown that IH is vulnerable to various attacks. In this article, we propose a new IH technique called SafeHidden. It continuously re-randomizes the locations of safe areas and thus prevents the attackers from probing and inferring the memory layout to find its location. A new thread-private memory mechanism is proposed to isolate the thread-local safe areas and prevent adversaries from reducing the randomization entropy. It also randomizes the safe areas after the TLB misses to prevent attackers from inferring the address of safe areas using cache side-channels. Existing IH-based defenses can utilize SafeHidden directly without any change. Our experiments show that SafeHidden not only prevents existing attacks effectively but also incurs low performance overhead.