Reviewerly/exHarmony huggingface dataset:
CCRO: Citation’s Context &amp; Reasons Ontology

Research papers can be visualized as a networked information space that contains a collection of information entities, inter-connected by directed links, commonly known as citation graph.There is a possibility to enrich the citation graph with meaningful relations using semantic tags.We have discovered and evaluated more than 150 citations' reasons from the existing published literature to be represented as citation tags.Many of these reasons have overlapped and diffused meanings.Annotating such a large volume of citation graphs with citation's reasons manually is nearly impossible, giving rise to a need to discover the citation's reasons automatically with high accuracy.The first step towards this is developing a minimal set of citation's context and reasons that are disjoint in nature.It would be a great help to the reasoning system if these reasons are represented in a formal way in the form of an ontology.By adopting a welldefined scientific methodology to formulate an ontology of citation reasons, we have reduced 150 reasons into only eight disjoint reasons, using an iterative process of sentiment analysis, collaborative meanings, and experts' opinions.Based on our findings and experiments, we have proposed a citation's context and reasons ontology (CCRO) that provides abstract conceptualization required to organize citations' relations.CCRO has been verified, validated, and assessed by using the well-defined procedures and tools proposed in the literature for ontology evaluation.The results show that the proposed ontology is concise, complete, and consistent.For the instantiation and mapping of ontology classes on real data, we have developed a mapping graph among the verbs with predicative complements in the English Language, the verbs extracted from the selected corpus using the NLP and CCRO classes.Using this mapping graph, the mapping of ontology classes in each citation's sentiment is explained with a complete mapping on the selected dataset.
Particle Grey Wolf Optimizer (PGWO) Algorithm and Semantic Word Processing for Automatic Text Clustering

Text mining refers to the process of extracting the high-quality information from the text. It is broadly used in applications, like text clustering, text categorization, text classification, etc. Recently, the text clustering becomes the facilitating and challenging task used to group the text document. Due to some irrelevant terms and large dimension, the accuracy of text clustering is reduced. In this paper, the semantic word processing and novel Particle Grey Wolf Optimizer (PGWO) is proposed for automatic text clustering. Initially, the text documents are given as input to the pre-processing step which caters the useful keyword for feature extraction and clustering. Then, the resultant keyword is applied to wordnet ontology to find out the synonyms and hyponyms of every keyword. Subsequently, the frequency is determined for every keyword which is used to build the text feature library. Since the text feature library contains the larger dimension, the entropy is utilized to select the most significant feature. Finally, the new algorithm Particle Grey Wolf Optimizer (PGWO) is developed by integrating the particle swarm optimization (PSO) into the grey wolf optimizer (GWO). Thus, the proposed algorithm is used to assign the class labels to generate the different clusters of text documents. The simulation is performed to analyze the performance of the proposed algorithm, and the proposed algorithm is compared with existing algorithms. The proposed method attains the clustering accuracy of 80.36% for 20 Newsgroup dataset and the clustering accuracy of 79.63% for Reuter which ensures the better automatic text clustering.
Combining Monitoring and Autonomous Feedback Requests to Elicit Actionable Knowledge of System Use

[Context and motivation] To validate developers' ideas of what users might want and to understand user needs, it has been proposed to collect and combine system monitoring with user feedback. [Question/problem] So far, the monitoring data and feedback have been collected passively, hoping for the users to get active when problems emerge. This approach leaves unexplored opportunities for system improvement when users are also passive or do not know that they are invited to offer feedback. [Principal ideas/results] In this paper, we show how we have used goal monitors to identify interesting situations of system use and let a system autonomously elicit user feedback in these situations. We have used a monitor to detect interesting situations in the use of a system and issued automated requests for user feedback to interpret the monitoring observations from the users' perspectives. [Contribution] The paper describes the implementation of our approach in a Smart City system and reports our results and experiences. It shows that combining system monitoring with proactive, autonomous feedback collection was useful and surfaced knowledge of system use that was relevant for system maintenance and evolution. The results were helpful for the city to adapt and improve the Smart City application and to maintain their internet-of-things deployment of sensors.
Ultrasound tissue characterization based on the Lempel–Ziv complexity with application to breast lesion classification

Building upon the recent successes in the application of information-theoretic concepts (e.g. Shannon entropy) in quantitative ultrasound, the authors propose a novel tissue characterization method based on the Lempel–Ziv complexity. In this procedure, standard ultrasound B-Mode images are mapped onto words over finite alphabets before the corresponding Lempel–Ziv complexity of ultrasound images is calculated. Such complexity metric may be used to differentiate between types of tissues. Here, the method is utilized as a binary classifier for the malignancy of breast lesions. The method is tested on OASBUD – an open-access breast lesions image database. Images of 48 malignant and 48 benign lesions were used – two images for each lesion. The new procedure slightly outperforms the state-of-art classifier based on pixel entropy as measured in the size of area under the receiver operating curve (ROC AUC), which suggests that it may serve as a basis for computer-assisted breast cancer ultrasound diagnosis and possibly in other standard applications of the quantitative ultrasound.
A state based energy optimization framework for dynamic virtual machine placement

The dynamic optimization of virtual machine (VM) placement is to dynamically adjust the placement of VMs on physical machines (PMs) to accomplish some objectives with certain constraints. On one hand, the number of possible combinations of PMs and VMs can be extremely large, which make the optimal solution very hard to get. On the other hand, the optimized solution needs be reachable from the old solution. To solve the problem from both sides, a partitioned optimization framework is proposed. First, four different states of PM, i.e. off, sleeping, ready and running, are introduced with different energy consumption. Running pool, sleeping pool and off pool are set up which partition PMs based on their different states. The classification helps us build the energy consumption model which is needed to evaluate mapping solutions. To decide if a new solution is better than the old one, only three parts of energy need be considered, i.e. energy changes for PMs in different states, energy consumed for changing the states of PMs, and extra energy consumption for migrating VMs. An energy model composed of these three parts is built as the optimization objective. A method is presented to decide the most suitable range to conduct the energy optimization through excluding some PMs in the sleep or off pool if the best solution achieved with those PMs included cannot be better than old solution. A memetic algorithm combining the partheno-genetic algorithm with the multiplayer random evolutionary game theory is proposed to achieve the global optimal solution and generate the executable live migration sequence from old mapping to the target one at the same time. According to our experimental results, our method can decrease the optimization range remarkably. Within the optimized scales, the proposed algorithm performed very well to approach the global optimal solution and guarantee the solution’s feasibility from old solution at the same time.
Informative trees by visual pruning

The aim of this study is to provide visual pruning and decision tree selection for classification and regression trees. Specifically, we introduce an unedited tree graph to be made informative for recursive tree data partitioning. A decision tree is visually selected through a dendrogram-like procedure or through automatic tree-size selection. Our proposal is a one-step procedure whereby the most predictive paths are visualized. This method appears to be useful in all real world cases where tree-path interpretation is crucial. Experimental evaluations using real world data sets are presented. The performance was very similar to Classification and Regression Trees (CART) benchmarking methodology, showing that our method is a valid alternative to the well-known method of cost-complexity pruning.
FIMA: Fault Intensity Map Analysis

We present a new statistical fault analysis technique called fault intensity map analysis (FIMA) that evaluates the responses of cryptographic implementations to biased-fault injections with varying intensities. FIMA exploits information from fault bias, as well as the correlation between fault distribution and intensity, to retrieve the secret key with fewer fault injections than existing techniques. FIMA generalizes several existing statistical fault analysis techniques, such as fault sensitivity analysis (FSA), differential fault intensity analysis (DFIA), ciphertext-only fault analysis (CFA), and statistical ineffective fault analysis (SIFA). FIMA has the flexibility of using different observables, e.g., faulty ciphertexts, correct ciphertexts under ineffective fault inductions, and data-dependent intensity profiles, and is successful against a wide range of countermeasures. In this paper, we use FIMA to retrieve the entire 128-bit secret key of the Ascon authenticated cipher, a CAESAR finalist for lightweight applications. On a software implementation of Ascon, simulations show that FIMA recovers the secret key with fewer than 50% of the fault injections required by previous techniques that rely on fault bias alone; furthermore, in the presence of error-detection and infective countermeasures, FIMA is $$6\times $$ more efficient than previous bias-based techniques.
Hierarchical Convolutional Neural Network Architecture in Distributed Facial Recognition System

Authors propose an efficient distributed facial recognition system based on the cascade of Convolutional Neural Networks. The traditional approach utilizing the centralized schema of recognition system has its main disadvantage concluding a high traffic and computing load of the central processing server: the more video surveillance cameras are served by the central unit, the more its load rate. But most of the time the high-quality video stream does not consist of facial information and the computational resources are being wasted. The main principle of our recognition system is the distributed hierarchical processing network utilizing the "coarse-fine" paradigm. Each source video stream is processed in-place by the tiny SoC computer which acts as an Edge Computing Unit and detects the presence of a face fragment in a video frame and crops the bounds of the ROI. The resulting stream including ROI is relayed to the main server if the face is detected.
Towards AI-Powered Multiple Cloud Management

Cloud users worldwide are looking at the next generation of artificial intelligence (AI) powered cloud management tools to automate cloud performance tuning and anomaly detection. To be effective across clouds, AI tools need a common representation of cloud services and support for machine learning optimization targeting multiple objectives. We put forward the notion that ontology-based models can support both.
The Principles of Creating a Balanced Investment Portfolio for Cryptocurrencies

Despite their novelty, cryptocurrencies have by now acquired certain popularity due to convenience in making payments, high speed of transactions and the application of modern technology to ensure transaction security among other things. The aim of this research is to evaluate cryptocurrencies as an investment instrument. The tasks of the research are as follows: (i) to evaluate a hypothesis about potential profitability of the cryptocurrency investment portfolio, (ii) to analyse cryptocurrency investment profitability, (iii) to assess the attractiveness of creating an investment portfolio of cryptocurrencies, and (iv) to provide recommendations to a potential investor for creating an investment portfolio comprised of cryptocurrencies. The research methods used include statistical data analysis and econometric modelling. Applied results were obtained using correlation-covariance analysis yielding a balanced profitable investment portfolio according to the Markowitz model. The results of the research have allowed the authors to conclude that cryptocurrencies would be an attractive investment instrument; the following indicators can be used to assess the attractiveness of cryptocurrency: market capitalization, the objectives of currency creators (issuing tokens on different resources to currency holders, bounty programs, etc.), trend assessment, and the analysis of reproducibility and replicability of the platform on which the cryptocurrency is built.
On the Impact of Refactoring Operations on Code Naturalness

Recent studies have demonstrated that software is natural, that is, its source code is highly repetitive and predictable like human languages. Also, previous studies suggested the existence of a relationship between code quality and its naturalness, presenting empirical evidence showing that buggy code is “less natural” than non-buggy code. We conjecture that this qualitynaturalness relationship could be exploited to support refactoring activities (e.g., to locate source code areas in need of refactoring). We perform a first step in this direction by analyzing whether refactoring can improve the naturalness of code. We use state-of-the-art tools to mine a large dataset of refactoring operations performed in open source systems. Then, we investigate the impact of different types of refactoring operations on the naturalness of the impacted code. We found that (i) code refactoring does not necessarily increase the naturalness of the refactored code; and (ii) the impact on the code naturalness strongly depends on the type of refactoring operations.
Handwritten Character Recognition using Deep Learning and Neural Network

Handwritten character recognition is among the most challenging research areas in pattern recognition and image processing. With everything going digital, applications of handwritten character recognition are emerging in different offices, educational institutes, healthcare units, commercial units and banks etc., where the documents that are handwritten are dealt more frequently. Many researchers have worked with recognition of characters of different languages but there is comparatively less work carried for Devanagari Script. In past few years, however the work carried out in this direction is increasing to a great extent. Handwritten Devanagari Character Recognition is more challenging in comparison to the recognition of the Roman characters. The complexity is mostly due to the presence of a header line known as shirorekha that connects the Devanagari characters to form a word. The presence of this header line makes the segmentation process of characters more difficult. There is uniqueness to the handwriting styles of every individual which adds to the complexity. In this paper, a recognition system based on neural network has been proposed for Devanagari (Marathi) alphabets. Each of the characters that are extracted through query image is resized and is then passed to the neural networks for the process of recognition.
A Multi-Target Track Association Algorithm in Underwater Multi-Sensors Environments

In order to solve the problem of multi-target trajectory correlation in underwater environments, such as the error of underwater measurement and the inconsistency of the targets reported by sensors, we leverage the gradation pre-processing to eliminate the noise and propose the Gaussian mixture model by the topology information between the trajectories. A novel mixed integer nonlinear programming is constructed to determine the relationship between underwater target trajectories, and the correlation bias is reduced by recursing the sensor bias estimation continuously. At the same time, the idea of weighting is introduced to maximize the clustering expectation. The optimal closure solution of the GMM is achieved with the expectation maximization clustering. The corresponding relationship of the trajectory is gotten at the expectation maximization stage, and finally obtains the trajectory correlation result of the underwater targets. The simulation results show that the GMP algorithm has better positive correlation rate and robustness than other algorithms with different target numbers in different dimensions, different sensor angular ranging errors, and different sensor detection probabilities. The GMP algorithm has advantages in the complex underwater environments with multiple noise and high false alarms, and it has accuracy and robustness in underwater multi-target trajectory correlation.
A Target Location Method Based on Swarm Probability Fusion

The existing target location methods either depend on the sensors' high performance or the communication bandwidth, which are not suitable to directly apply to the effective fusion and target recognition of observation information of the large-scale heterogeneous swarm. In this paper, a target location method based on swarm probability fusion is proposed, which can represent the sensor's data in the heterogeneous swarms as the probability distribution map, and then carry out image recognition processing after normalized distributed fusion to obtain target information. The effectiveness of the method is verified by the effectiveness tests, the singlenode's direction finding accuracy tests, and the swarm nodes' number tests. Preliminary analysis shows that single-node's direction finding accuracy has a significant effect on the swarm positioning accuracy and the convergence speed, while the number of nodes has a small effect on positioning accuracy and mainly affects the convergence speed.
Towards Visualized User Profile Analysis from Massive Web Log

Nowadays, mining user profiles from web log, and then recommending interested items becomes a research hot. In the meantime, user's behavior habits can be learned to improve the usage experience. However, current web frameworks are separated with the front-end and the back-end parts, and then the web logs are jumbled together. Always a single user's action can generate multiple logs, which makes it particularly difficult to recover user's behavior and analyse his habits. To overcome above difficulties, a sequence pattern mining method based on PrefixSpan is designed to extract the common sequence pattern of user's log as their operation behaviors. Further more, W-Kmeans is used to cluster their operation behaviors and summarize their habits. Finally, we design a multi-dimensional user profile label system to depict the user's features from multiple perspectives, and use the WordCloud to draw the profiles.
Von einem Phänomen der Buchgeschichte zur Herausforderung für die Digital Humanities: Leporellos in der Kinder- und Jugendliteratur

With their characteristic folded shape, leporellos represent a traditional format and should be viewed in the context of an occidental book history rich in extravagant book forms – whereby the focus here is not on the decoration but on the material form and shape of the book. In the multi-faceted book culture of the 19th century, they experienced a veritable heyday – not only, but also as books for children and young people. The contemporary leporellos presented here are non-fiction books insofar as knowledge transfer plays a central role – often in the shape of a narrative framework in the sense of an expanded narrative. The imparting of knowledge goes beyond the actual themes of the books and also implies a certain level of knowledge about books: it is also about learning about the possibilities of the leporello as an unconventional kind of book. It will be noticed that different traditions of leporellos and of contents presented in this specific shape in a historical perspective are cleverly linked. For the Digital Humanities, unconventional book forms represent a challenge: This concerns, among other things, the differentiated keywording of, for example, picture books and movable books in library catalogues as well as strategies for the digitization of object-like books.
A Development of a Wrist-Worn Fall Detection using Binary Neural Network

This paper presents a development of wrist-worn device for fall detection which can be a fall detector and localizer. The system consists of a wristband and a mobile phone. The wristband acts as a fall detector and the mobile phone acts as an IoT gateway for locating and transmitting the fall information to the people involved. The fall detection method is based on a threshold-based and a binary neural network that uses to reduce resources and computational requirements. Experimental results showed that the accuracy of the proposed method was 97.23%. However, for some movement patterns such as hand clapping or hand waving, the system might be given a low accuracy.
Estimating water requirements of tomato plant based on Forecast-Evapotranspiration Equation in Semangat Village, Merdeka Sub-district, Karo Regency, North Sumatera Province, Indonesia

Abstract Tomato plant is one of the horticulture products that produce in Semangat Village. One of the problems focused by tomato’s farmer was how much water will give. Estimating water requirements for tomato plants will make it easier for farmers to provide water according to the phase of plant growth. The aims of the research was to estimating water requirements of tomato plants in Semangat Village Merdeka District, Karo Residence. The methods divided two steps, they were analysis the forecast-potential evapotranspiration, and estimating water requirements of tomato plant where calculated by the relation of crop coefficient and forecast-potential evapotranspiration. The results showed forecast-potential evapotranspiration (ETo) in Semangat Village were 3.36 – 6.89 mm/day during January until July, 2020. The water requirements of tomato plant showed 3.141 mm/day in the early phase, 6.02 mm/day in the middle phase, and 4.188 mm/day in the final phase. The conclusion showed that the water requirement of tomato plants for all of growth phase was increase as high as crop coefficient of tomato plants.