Reviewerly/exHarmony huggingface dataset:
Expert Opinions on Website Development

Many traditional information system development methodologies are based on outdated concepts from the 1970s. These methodologies are commonly used to develop websites, and it is therefore not surprising that they are not that effective since have never been created for this purpose. Although these methods have many drawbacks in terms of use in web design, some of their basic principles are still applicable. These methods include, for example, the waterfall model, the SSADM method, RAD, or prototyping. There are many articles on how to reduce the size of an image, how to use color or animation effectively. No doubt, all these attributes are important components of web design. Unfortunately, the proposed methodologies do not address the overall web development process as a comprehensive output of partial graphic, programming, and copywriting activities. Due to the lack of specialized literature devoted to the process and management part of web design, a questionnaire survey was organized, supplemented by structured interviews with web design experts. This survey was conducted through the LinkedIn professional network and was attended by a total of 238 respondents, while 18 of them participated also in a structured online interview.
Phase congruency and ODBTC based image retrieval

In this study, the authors investigate content based image retrieval (CBIR) using ordered-dither block truncation coding (ODBTC) and phase congruency feature (PCF). Relevant feature extraction plays a vital role for retrieving the image in CBIR. The unique reason to choose PCF with ODBTC is that it detects the edges and corners during variation of image while preserving image brightness and contrast. Combining the PCF and ODBTC features improves CBIR system usage in various visual data processing domains. Thus, yields a better CBIR system which assists in the reduction of storage space, decreases retrieval time and increases accuracy of the system. The precision and recall are used as performance metrics to evaluate the proposed method based on retrieval of relevant images. Extensive experimental results with Corel 1 K (1000 images), Corel 10 K (10000 images) and CALTECH 256 (30144 images) proves that the proposed method is more desirable than antecedent proposed CBIR system in terms of accuracy, precision and recall.
Global Deep Feature Representation for Person Re-Identification

Person re-identification (re-ID) has attracted tremendous attention in the field of computer vision, especially in intelligent visual surveillance (IVS). The propose of re-ID is to retrieval the interest person across different cameras. There are still lots of challenges and difficulties that are the same appearance such as clothes, the lens distance, various poses and different shooting angles, all of which influence the performance of re-ID. In this paper, we propose a novel architecture, called global deep convolutional network (GDCN), which applies classical convolutional network as the backbone network and calculates the similarity between query and gallery. We evaluate the proposed GDCN on three large-scale public datasets: Market-1501 by 92.72% in Rank-1 and 88.86% in mAP, CHUK03 by 60.78% in Rank-1 and 62.47% in mAP, DukeMTMC-re-ID by 82.22% in Rank-1 and 77.99% in mAP, respectively. Besides, we compare the experimental results with previous work to verify the state-of-art performance of the proposed method that is implemented by NVIDIA Ge-Force GTX 1080Ti.
Local Stiffness Estimation of the Human Eardrum via the Virtual Fields Method

This work proposes a method from which a heterogeneous and isotropic stiffness can be estimated from human eardrums. Under normal hearing conditions, neither the bending nor the membrane strains can be neglected on such a sample. Due to this, the Virtual Fields Method was adapted by invoking specific assumptions related to the Kirchhoff Love plate model, so both strain types could be taken into account during the inverse method. Apart from this contribution, specific virtual fields were designed, so the virtual work could be computed on enclosed domains. By neglecting spatial stiffness variations within the prescribed regions, such an estimation could be performed locally. Those attempts were validated on the basis of a Finite-element model and the identified stiffness distribution was in agreement with the reference one. Due to the quality of the preliminary results, the proposed method has shown itself to be a reliable tool to identify heterogeneous material properties on shell-like structures.
An Improved Monkey Algorithm with Descending Factor

Nature inspired meta-heuristics has turn out to be an intimidating and ordinary field of research in the midst of researchers who are trying to solve complex optimization problems. Monkey algorithm is new addition in the class of swarm intelligence, which is a good option for complex optimization problems. This paper puts forward an improved monkey algorithm with descending factor, whose basic idea is that a descending factor is used in the climbing process of the basic monkey algorithm to ensure the algorithm escape from local optimal solution and find the global optimal solution. The proposed algorithm tests over benchmark functions and results show that it receives better results for the considered problems.
A Detection and Alarm Algorithm against the Third-Party Excavation

It often happens that the third-party excavation damage to underground pipelines in cities. A detection and alarm algorithm to prevent the third-party construction excavation is proposed in order to give early warning for the damage of the third-party excavation. Through the construction of excavation signal database, the time-frequency joint feature vectors of different types of excavation signals are extracted, and the machine learning classification model of excavation signals is constructed with the idea of deep learning to predict and classify common types of the third-party excavation signals, so as to capture and monitor the third-party excavation signals in real time. Once the excavation event is detected to be happening, remote alarm will be carried out to realize the early warning of underground pipeline excavation damage, and then the safe operation of underground pipelines is effectively guaranteed.
Compact Representation of Graphs with Small Bandwidth and Treedepth

We consider the problem of compact representation of graphs with small bandwidth as well as graphs with small treedepth. These parameters capture structural properties of graphs that come in useful in certain applications. We present simple navigation oracles that support degree and adjacency queries in constant time and neighborhood query in constant time per neighbor. For graphs of bandwidth k, our oracle takes (k+ âŒˆ log 2kâŒ‰)n +o(kn) bits. By way of an enumeration technique, we show that (k-5√k-4)n-O(k^2) bits are required to encode a graph of bandwidth k and size n. For graphs of treedepth k, our oracle takes (k+âŒˆlog k âŒ‰ +2)n + o(kn) bits. We present a lower bound that certifies our oracle is succinct for certain values of k ∊ o(n).
Intelligent HTTP Request Distribution Strategies in One and Two-Layer Architectures of Cloud-Based Web Systems

Web Cloud systems are very popular today. One of the main problems in cloud computing today is the better use of distributed resources and applying them to achieve higher throughput. To solve those problems load distribution mechanisms are implemented. A two-level decision HTTP request distribution strategy working in a one-layer architecture is presented in the article. The proposed solution uses fuzzy-neural models yielding minimal service times in the web cloud. The article contains the results of experiments in which a new solution is compared with intelligent request strategies used in two-layer architectures. Discussion of results and final conclusions are presented at the end.
Movie Recommendations Based on a Recurrent Neural Network Model

Movie streaming services like Netflix, Hulu, Amazon Prime, and others are increasingly used by consumers to discover video content. For example, in 2017 Netflix subscribers collectively watched more than 140 million hours per day and Netflix surpassed $11 billion in revenue in 2017. Undoubtedly, movie streaming services have become an integral part of how we consume video content today, and the importance of movie recommendation systems cannot be understated—they are an integral part of how we consume video content. This chapter discusses the most popular approaches for collaborative filtering. These methods work by computing neighborhoods of similar users or items. The chapter proposes a deep learning approach for collaborative filtering based on an autoencoder. It demonstrates that the approach outperforms the neighborhood-based baseline. Deep learning, which is essentially just deep artificial neural networks, is able to learn complex decision boundaries for classification or complex non-linear regressions.
Extended Target Tracking Using Non-linear Observations

With the development of modern wireless technologies, large bandwidth signals and large antenna arrays are widely adopted. It facilitates the high resolution detection and tracking of objects, which is of great importance in numerous applications, such as autonomous vehicles, internet of things, and so on. In this paper, we mainly focus on the target positioning and tracking problem based on the “non-linear" measurements, namely the range and angle, where both the location and the contour information are considered. The random matrix based theory is adopted for this extended target localization issue. Both single and multiple targets scenarios are considered. Numerical results validate our analysis, and show that the proposed tracking frameworks can work for real applications such as the autonomous vehicle and so on.
End-to-End Feature Learning for Multi-label Image Retrieval

Hashing code similarity search is an effective method for large-scale image retrieval, since it provides fast search calculating and memory operating. In image retrieval, hash code based on deep convolutional networks can learn accurate image semantic information and compact hash code simultaneously. The existing methods generally extract the overall features of the image and generate corresponding hash codes. When the image contains multiple targets, this method may be suboptimal for describing the image, thus, limiting the accuracy of multi-label images. This paper focuses on the use of deep convolutional hash coding for multi-label image problems. In order to solve this problem, the authors propose a deep convolutional neural network structure suitable for multi-label image feature extraction. This network can also be used for end-to-end feature learning and further improve image retrieval accuracy. In this method, each target in the image is represented by a hash code and extensively evaluated across multiple data sets, making substantial improvement over the most advanced supervised and unsupervised hashing methods.
Automated Java exceptions explanation using natural language generation techniques

Abstract A common problem encountered by novice programmers and students taking introductory programming courses is how to understand exception messages and how to locate the corresponding errors. Such messages are typically too difficult for them to interpret without seeking help from colleagues or through online forums. Tackling this problem can help speed up their learning curves and hence increase their productivity. This paper proposes using natural language generation (NLG) techniques and the Rhetorical Structure Theory, which specifies how text spans are related and aggregated, to convert Java exception messages into readable and understandable natural language (NL) statements. An additional advantage is the possibility of adding more information and explanations that help students understand different exception types, their causes, and how they can be avoided. As a proof of concept, we developed Java Exceptions in the NL (JENL) tool to process Java exceptions using NLG techniques. Empirical evaluation has shown extreme satisfaction among Java programming subjects (including student novice programmers) with the capabilities of JENL as well as its usability.
Effect of Giving N Value on ADA N Method for Classification of Imbalanced Nominal Data

Class imbalances in data mining research are quite detrimental because there are difficulties in classifying minority classes (small number of instances) correctly. Oversampling is a method of balancing class distribution by randomly replicating instances in minority classes. multiclass classification might achieve a lower performance than binary classification as the boundaries among the classes may overlap. This issue may become more complex when facing imbalanced data. This study presents test results giving different k values in nearest neighbor searches which are used to balance the class using the ADASYN-N method. The results show that giving different k values can affect the performance of the ADASYN-N method depending on the number of dataset instances.
Word Length Interval Dependency for Mobile Rapid Sentence Reading

We have developed a multi-lingual enhanced Rapid Serial Visual Presentation (RSVP) system "MulLinR", which supports languages which are written continuously (non-spaced) and spelled with non-alphabetic characters. Our system currently supports Japanese as well as English, and experiment with variation of presentation time intervals for Japanese version, depending on word length. We have experiments as a feasibility study and discuss usability.
LG-Starship: A Framework for Text Analysis

In this work we present a new framework for the analysis of Italian texts that could help linguists to perform rapid text analysis. The framework, that performs both statistical and rule-based analysis, is called LG-Starship. The idea is to built a modular software that includes the basic algorithms to perform different kinds of analysis. The framework will include a Preprocessing Module a POS Tagging and Lemmatization module, a Statistic Module, a Semantic Module based on Distributional Analysis algorithms, and a Syntactic Module, which analyze syntax structures of a selected sentence and tag the verbs and its arguments with semantic labels. The objective of the Framework is to build an “all-in-one” platform for NLP which allows any kind of users to perform basic and advanced text analysis.
Nonlinear Multi-system Interactive Positioning Algorithms

The Bayesian probabilistic observation model is established by using the interactive input of multi-system observation data. The positioning information between multi-system is directly interacted. The non-linear problem of the observation system is solved by the extended Kalman filter theory. Moreover, the system probability is updated in real time by using the filtering innovation and variance of each system, and the estimated results are fused with each weight to output. The simulation results show that the proposed algorithm has better stability and adaptability than the traditional location algorithm under the same observation conditions.
Fuzzy Hierarchical Model and Particle Swarm Optimization in Gas Leakage Detector Mobile Robot

Mobile robots have been widely applied to various aspects of life. For example, a robot for detecting gas leaks. The process of finding this gas leak can be done by mobile robots automatically, but in the search process, there are three challenges to be faced. Namely vast search space, complexity in handling objects in an unstructured environment, and a certain level of intelligence is needed, so that the search process on mobile robots becomes optimal. To answer these challenges, control methods will be developed with the Fuzzy Hierarchical Model (FHM) and Particle Swarm Optimization (PSO). Where the methods are expected to be able to control mobile robots to search for gas leaks, in a complex specific virtual environment more optimally. In this research, the design of the FHM-PSO control model was carried out and then applied to an FHM-PSO software application that can control the swarm robot model until it successfully reaches the gas leak center in a specific complex virtual environment optimally.
The Uncertainty Property on Intelligent Resource Provision in Complex Cloud Environment

Resource provision is one of the key issues for multiple objective optimizations in cloud data center. This paper emphasizes the fluctuation of workload and investigates the uncertainty property in complex cloud environment especially in software-defined cloud. By leveraging the uncertainty theory, the resource demand value from workload in each dimension is defined as uncertain variable while the uncertain distribution is applied to quantify the variable. The experimental simulation in this paper shows that the uncertain property has great potential in the combinational resource provision.
UNIVERSITY AS A VALUABLE UNIT OF SELF- KNOWLEDGE

&#x0D; &#x0D; &#x0D; &#x0D; This article discusses the trend of successful university activity as an integral unit of the educational system with its proper management. Successful activities of the university are realized in the formation and maintenance of the internal atmosphere of the university itself. A manifestation of such activity is the open character of university management (transparency in making important decisions, the elective nature of governing bodies and staff appointments), as well as the tendency to provide a centralized information environment for staff (creating an extensive internal electronic information network covering all structures). This activity is essential for maintaining the cohesion of labor collectives in higher education. Elements of competitive relations are among the university staff in the field of higher education. The purpose of competition is to attract quantitative and qualitative applicants, as well as gaining public recognition. Most teachers are proud of the prestige of the university.This article analyzes socially significant and personal values. The student’s personal and professional growth is a qualitative change in his position and role in society. The source of personal and professional development are the contradictions between the achieved level of student development and the requirements that the university community makes to him. Self-educational activity is the main condition necessary for the development of professional competence among students of engineering universities at the present stage. Given the variety of definitions of self-education, we can highlight some of its essential features, such as: an independent search for additional information, the acquisition of knowledge and on-going development. Further, the article expresses the opinion that self-education can exist only on the basis of deep perspective internal motives. Individual cases of searching for answers to questions of interest to a person under the influence of any motives cannot be considered self-education.In this paper, self-education is considered as purposeful, systematic, cognitive activity managed by students themselves, necessary for improving education.&#x0D; &#x0D; &#x0D; &#x0D;
Prediction Based Compression Algorithm for Univariate and Multivariate Data in Wireless Communication Networks

In wireless sensor network, energy is primary constraint in designing and installation. WSN network possess many sensor nodes and each are equipped with batteries as its power bank. Due to the usage of many sensor nodes and batteries, there comes need to reduce the power consumption and increase the efficiency. The data sensed can be compressed before transmission to improve efficiency and reduce power ingestion. The data sensed through WSN network are time series data. Data prediction plays a crucial role in forecasting data with past values, which reduce the usage of sensor nodes in sensing information. This paper proposes the differencing based prediction algorithm and leveling based compression algorithm for both univariate and multivariate data. This system reduces the number of sources and power consumption. The algorithms were simulated using a simulation tool MATLAB 8.2- R2013a. The algorithms are quantified by calculating approximation error, compression ratio, and computation complexity on the compressed data. From the results it is evident that the proposed algorithm is better suitable for compression of univariate and multivariate signals.
Aspect and Opinion Word Extraction on Opinion Sentences in Bahasa Indonesia using Rule Based Generated from Regular Expression

Extracting aspects and opinion words is important in ABSA study. Generally, aspect extraction was conducted first then determining a pair of opinion words from the aspects. The process allows errors to occur. This study proposes an approach to obtain the appropriate pair of aspect and opinion words in opinion sentences using rule-based method generated from regular expressions. The strength of this process is that it enables to obtain a pair of aspects and opinion words simultaneously. Three methods are developed this approach (1) selection of aspect-based opinion sentences in user reviews, (2) extraction of candidate aspects and opinion words in aspect-based opinion sentences and (3) aspect categorization. This study enables to extract pairs of aspects and opinion words using rule-based aspects generated from regular expression. Determining valid aspects uses aspect extraction. Evaluation of aspect categorization shows the value of precision of 0.82, recall of 0.70 and f-measure of 0.75.
A Face and Posture Recognition Model Using CNN for Adapting Different Resolution Images

This paper presents a face and posture recognition model using convolutional neural network for adapting different resolution images. The performance of face recognition has dramatically improved by applying deep learning. However, it is known that the accuracy of face recognition would be decreased according to the resolution of face image. This problem is not negligible for some applications such as the real-time face recognition by surveillance camera, since the camera cannot always capture a face in a suitable position and distance. In order to solve this problem, our model adapts the resolution of an input image by selecting a proper model according to the resolution. In the experiment, we confirm the relationships between the recognition accuracy of face and posture and the resolution of captured image.
Fault-Tolerant Decompression Method of Compressed Chinese Text Files

Once lossless compressed data is damaged in the transmission process, a specific fault-tolerant decompression algorithm is required to correct the error. A novel fault-tolerant decompression method for English text files has been proposed for error detection and correction in previous research work, which is trained with natural language model of English. In this paper, we transfer action scope of this fault-tolerant decompression method from English compressed files to Chinese text files. In order to apply the algorithm framework to Chinese compressed files, a N-Gram-based language model is built adhering to the compression coding rules and the grammar rules of Chinese, with which the prior information of source can be fully expressed, and the character encoding adaptation problem in the process of algorithm transfer-learning is solved. The experiment results demonstrate that the proposed algorithm can meet the requirements for fault-tolerant decompression of lossless compressed Chinese text files.