Reviewerly/exHarmony huggingface dataset:
Lightweight Video Object Segmentation Based on ConvGRU

As one of the key tasks of video processing, video object segmentation technology is the foundation of high-level computer vision application. The spatio-temporal context information in the video is of great significance for video object segmentation. Existing algorithms usually introduce spatio-temporal context information with pre-trained models such as optical flow for segmentation, which will result in sub-optimal solution and huge computational resource consumption. To address the above problem, this paper proposes an end-to-end lightweight video object segmentation model based on ConvGRU. A convolutional neural network is used to extract the visual features of each frame, and recursive neural network is used to extract the spatio-temporal context information of the whole video. The ConvGRU is used to achieve the deep fusion of visual features and spatial-temporal context information. The MobileNet-based lightweight algorithm can meet the demand for practical application and solve the problem of high consumption for computing resources. Experiments on DAVIS2016 dataset show that our method is competitive with similar state-of-the-art methods.
A Study on Partial Replacement of Plastics in Paver Blocks

The use of plastics in the current busy world has increased multiple times in the past few years. Many forthcoming industries and enterprises (business companies and organizations) produce more varieties of plastic. The inappropriate way of disposing these plastics will result these detrimental compounds to pollute and degrade the environment by getting mixed with it. The intermolecular bonds that constitute plastics are designed in such a way that it can neither get corroded nor decomposed. In order to succeed this issue we have to find an effective way to dispose plastics. In this project we have used recycled plastic wastes to construct paver blocks. This material also has few advantages like they are extremely durable and hard to reject (them last for a long period of time), it doesn’t have rusting properties and are non-soluble in water. These properties together makes plastic highly durable. The main aim of this project is to reduce the dependency level of construction materials on natural resources which are on verge of getting exhausted and also to diminish the environmental degradation caused by the utilization of plastics. We also used IOT sensors in this project to detect the temperature and alert the users regarding the condition of plastics used inside the paver blocks. This will allow the users to take proper measures and repair it accordingly.
In-plane deformation monitoring of a cylindriscal specimen using a simple approximate method combined with two-dimensional digital image correlation

This paper presents a quick-to-implement and simple approximate method that uses a single camera system to measure the relatively small surface deformation of a cylindrical specimen in the context of triaxial tests in geotechnical engineering. Digital images of a speckled cylindrical specimen before and after loading were obtained from only one perspective view. For each acquired image, there are geometric distortions and speckle losses, which are mainly caused by both perspective projection and column shape. Based on particular prior knowledge, we attempted to back-project the raw perspective images onto a cylindrical curved surface to reconstruct the speckle feature scene. These back-projected images were subsequently analyzed by the two-dimensional digital image correlation method to reveal the surface deformation information of the region of interest on the surface of the test specimen. Finally, a test case and a finite element study were, respectively, given to validate the effectiveness of the method. This feasibility study demonstrates the potential of extending this method to handle a wide range of regular cylindrical specimens with a relatively small deformation which are analyzed without taking out-of-plane motion into consideration.
Evaluating a Cloud Service using Scheduling Security Model (SSM)

The development in technology makes cloud computing widely used in different sectors such as academic and business or for a private purposes.Also, it can provide a convenient services via the Internet allowing stakeholders get all the benefits that the cloud can facilitate.With all the benefits of cloud computing still there are some risks such as security.This brings into consideration the need to improve the Quality of Service (QoS).A Scheduling Security Model (SSM) for Cloud Computing has been developed to address these issues.This paper will discuss the evaluation of the SSM model on some examples with different scenarios to investigate the cost and the effect on the service requested by customers.
An Automatic Question Generation System Using Rule-Based Approach in Bloom’s Taxonomy

Background: In this competing era, education has become part of everyday life. The process of imparting the knowledge to the learner through education is the core idea in the Teaching- Learning Process (TLP). An assessment is one way to identify the learner’s weak spot in the area under discussion. An assessment question has higher preferences in judging the learner's skill. In manual preparation, the questions are not assured in excellence and fairness to assess the learner’s cognitive skill. To generate questions is the most important part of the teaching-learning process. It is clearly understood that generating the test question is the toughest part. Objective: The proposed system is to generate the test questions that are mapped with bloom’s taxonomy to determine the learner’s cognitive level. The cloze type questions are generated using the tag part-of-speech and random function. Rule-based approaches and Natural Language Processing (NLP) techniques are implemented to generate the procedural question of the lowest bloom’s cognitive levels. Methods: Proposed an Automatic Question Generation (AQG) system which autom Proposed an Automatic Question Generation (AQG) system which, automatically generates the assessment questions from the input file Dynamically.atically generates the assessment questions dynamically from the input file. Results and Conclusion: The outputs are dynamic to create a different set of questions at each execution. Here, the input paragraph is selected from the computer science domain and their output efficiency is measured using precision and recall.
Efficient Identity-based Signature Scheme with Message Recovery

Abstract Digital signature is one of the important primitive of public-key cryptography and has become an essential technique in providing security services in modern communications. Due to the limitations imposed by both the communication bandwidth and computational power of wireless communication devices, signature schemes with less bandwidth and less computational cost are desirable for practical applications. Signature schemes with message recovery provide a feature that the message is recoverable from the signature and hence does not need to be transmitted separately for signature verification. Recently many signature schemes with message recovery have been designed in traditional as well as Identity based settings and most of the schemes are constructed using bilinear pairings over elliptic curves. Nevertheless, the computational cost of a pairing is more expensive and is higher than the scalar multiplication. Thus, signature schemes without pairing would be more appealing in terms of efficiency. In this paper, we propose an efficient identity-based message recovery scheme without pairings. In our scheme the message itself is not required to be transmitted together with the signature and so it turns out to have the least data size of communication cost. Also, we compare our scheme with the existing ID-based signature schemes with message recovery in terms of computational and communicational point of view. With the pairing-free realization, the proposed scheme is efficient and applicable for resource constrained devices.
Vertex markers: Modification of grid methods as markers to reproduce large size augmented reality objects to afford hands

The weakness of the research on developing Marker Based Augmented Reality applications found is using small digital content. Weakness is caused by the area of view of Augmented Reality cameras is limited by the placement of markers that are affordable to the user's hand. This study reproducing large digital objects by cutting large digital objects into many pieces. But to keep continuity accuracy between digital object pieces, this study aims to modify the grid method to become a vertex marker. Vertex marker is produced by removing the edge and using the vertex to place the marker. Vertex markers inherit the advantages of the Grid method. Therefore, vertex markers can be used to reproduce large digital objects accurately and can be reached by hand when displayed. The aim of this study is to measure the accuracy of the grid method that has been modified into a marker. The fundamental contributions and advantages of the vertex marker are innovations in the field of Marker Based Augmented Reality research.
Improvement of Rule Generation Methods for Fuzzy Controller

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to improvement the above challenge. A zero order Sugeno fuzzy model used as fuzzy system structure. At first by using clustering obtains the number of rules and input membership functions. A set of candidate amounts for consequence parts of fuzzy rules is considered. Considering each pair of training data, according which rules fires and what is the output in the pair, the amount of probability of consequences candidates are change. In the next step, eligibility probability of each consequence candidate for all rules is determined. Finally, using these obtained probability, two probable outputs is generate for each input. The experimental results show superiority of the proposed approach rather than some available well-known approaches that makes reduce the number of rule and reduce system complexity.
An Application of Sentiment Analysis Techniques to Determine Public Opinion in Social Media

This paper describes a prototype application that gathers textual data from the microblogging platform Twitter and carries out sentiment analysis to determine the polarity and subjectivity in relation to Brexit, the UK´ s exit from the European Union. The design, implementation and testing of the developed prototype will be discussed and an experimental evaluation of the product described. Specifically we provide insight into how events affect public opinion and how sentiment and public mood may be gathered from textual twitter data and propose this as an alternative to opinion polls. Traditional approaches to opinion polling face growing challenges in capturing the public mood. Small sample response and the time it takes to capture swings in public opinion make it difficult to provide accurate data for the political process. With over 500 million daily messages posted worldwide, the social media platform Twitter is an untapped resource of information. Users post short real time messages views and opinions on many topics, often signed with a ‘#hashtag’ to classify and document the subject matter in discussion. In this paper we apply automated sentiment analysis methods to tweets giving a measure of public support or hostility to a topic (‘Brexit’). The data were collected during several periods to determine changes in opinion. Using machine learning techniques we show that changes in opinion were also related to external events. Limitations of the method are that age, location and education are confounding factors where Twitter users over represent a young, urban public. However, the economic advantage of the method over real-time telephone polling are considerable.
EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks

Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth.Lexical semantic relations among words or concepts are important parts of knowledge encoded in wordnets.As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention.Manual construction and extension of lexical semantic relations for WordNets or knowledge graphs are very time consuming.Using automatic relation extraction methods can speedup this process.In this study, we exploit an ensemble of LSTM and convolutional neural networks in a supervised manner to capture lexical semantic relations which can either be used directly in NLP applications or compose the edges of wordnets.The whole procedure of learning vector space representation of relations is language independent.We used Princeton WordNet 3.1, and FarsNet 3.0 (the Persian wordnet), as gold standards to evaluate the predictive performance of our model and the results are comparable on the two languages.Empirical results demonstrate that our model outperforms the state-ofthe-art models.
DEVELOPING INSTRUCTIONAL GAME "ADVENTURE OF WORD" TO IMPROVE MORPHOLOGICAL AWARENESS ON VOCATIONAL HIGHER STUDENTS

Morphological awareness (MA) has relation to language development. Most of Informatics students class of 2017 in Politeknik Kota Malang are lack of MA especially in vocabulary and reading comprehension. Therefore, this present study is aimed at developing instructional game “Adventure of Word” to improve morphological awareness. This is Research &amp; Development study which used Lee &amp; Owens’ development model. From the result of expert validation, from media expert it got 96.8% and from media/design expert it got 78.7%. As those results, the product is categorized as ‘valid’. After the product has been validated, the next step is try-out to the students. It got 96.6% which meant the product is classified as ‘excellent’. It can be concluded that the product was well-developed as instructional media to teach morphology explicitly so that can help students to improve their morphological awareness especially in root word, prefix, suffix, and circumfix.
Creative Media for Cyberbullying Education

Social media is a place to share, express, and convey ideas. Adults to children can have social media accounts. The minimum age limit for having a social media account is 13 years, but there are many children under the age of 13 who already have social media. This makes children vulnerable to cybercrime, one of which is cyberbullying. This is often considered to be joking and short-term but can be fatal, such as suicide. Therefore, an interesting medium is needed to provide cyberbullying education for children aged 9-13 years in the form of video explainer. The existence of this video explainer children can understand the dynamics of cyberbullying with an understanding level of 80.95%. This video explainer visual determination process involves children so that this video is considered interesting by 90% by children. It is expected that in the future this video explainer can be a form of prevention of cyberbullying in children.
Leveraging syntactic parsing to improve event annotation matching

Detecting event mentions is the first step in event extraction from text and annotating them is a notoriously difficult task.Evaluating annotator consistency is crucial when building datasets for mention detection.When event mentions are allowed to cover many tokens, annotators may disagree on their span, which means that overlapping annotations may then refer to the same event or to different events.This paper explores different fuzzy matching functions which aim to resolve this ambiguity.The functions extract the sets of syntactic heads present in the annotations, use the Dice coefficient to measure the similarity between sets and return a judgment based on a given threshold.The functions are tested against the judgments of a human evaluator and a comparison is made between sets of tokens and sets of syntactic heads.The best-performing function is a head-based function that is found to agree with the human evaluator in 89% of cases.
Scrum: An Agile Software Development Process and Metrics

In a traditional software development process such as the Waterfall Model, works best in a stable environment. But, it is not flexible when it comes to change. There is a gap in the interaction between the users and the development team which leads to incomplete and misunderstood specification. Because of this, the end product is sometimes a surprise to users and this gap accelerates incorrect development of the software product. Once requirements are frozen there is no scope of accepting changes. There is a need for a framework which holds the solution for all these situations. With this premise, the agile development methodology came into existence. Scrum, an agile approach supports continuous collaboration among the customer, team members, and other stakeholders. Its time-boxed approach and continuous feedback from the product owner ensures the development of working product with essential features at all the time. This paper explains the agile software development approach, its proclamation and different frameworks of agile approach. Further illustrate most widely used framework: Scrum. This research paper covers the implementation and application of Scrum. It focuses on why Scrum is preferred over the Waterfall Model with the help of some survey results and later a discussion on some Scrum Metrics which will be helpful and accounting for the best Scrum Practices in achieving goals set by the software development team, the product owner and the customers. The outcome of this study shows that Scrum Metrics is critical and highly valuable for successful product development. The quantitative insight that these metrics provide for the Scrum Team, Product Owner and Stakeholders is necessary for achieving strong project dynamics and optimal results.
Construction of quasi-twisted codes and enumeration of defining polynomials

Let $d_{q}(n,k)$ be the maximum possible minimum Hamming distance of a linear [$n,k$] code over $\mathbb{F}_{q}$. Tables of best known linear codes exist for small fields and some results are known for larger fields. Quasi-twisted codes are constructed using $m \times m$ twistulant matrices and many of these are the best known codes. In this paper, the number of $m \times m$ twistulant matrices over $\mathbb{F}_q$ is enumerated and linear codes over $\mathbb{F}_{17}$ and $\mathbb{F}_{19}$ are constructed for $k$ up to $5$.
Character-based recurrent neural networks for morphological relational reasoning

We present a model for predicting inflected word forms based on morphological analogies. Previous work includes rule-based algorithms that determine and copy affixes from one word to another, with limited support for varying inflectional patterns. In related tasks such as morphological reinflection, the algorithm is provided with an explicit enumeration of morphological features which may not be available in all cases. In contrast, our model is feature-free: instead of explicitly representing morphological features, the model is given a demo pair that implicitly specifies a morphological relation (such as write:writes specifying infinitive:present). Given this demo relation and a query word (e.g. watch), the model predicts the target word (e.g. watches). To address this task, we devise a character-based recurrent neural network architecture using three separate encoders and one decoder.Our experimental evaluation on five different languages shows tha the exact form can be predicted with high accuracy, consistently beating the baseline methods. Particularly, for English the prediction accuracy is 95.60%. The solution is not limited to copying affixes from the demo relation, but generalizes to words with varying inflectional patterns, and can abstract away from the orthographic level to the level of morphological forms.
Flicker’s sources identification using a case-based reasoning prototype

Flicker is an issue of power quality that affects both the utility companies and consumers. To mitigate and eliminate flicker, the first stage is to identify the source. This paper presents an application of artificial intelligence techniques, namely a knowledge-based system, dedicated for detecting the flicker source. The application is a prototype that was built using case-based reasoning. The main elements of the prototype are the cases that were obtained from in-situ measurements at a power substation over an observation period of a year. The prototype was tested using data from a different power substation. The results show that the prototype detects properly the sources, confirming the utility of artificial intelligence in this field ofpower quality.
A novel intelligent model for classify and evaluating non-functional security requirements form scenarios

Software requirements with its functional and non-functional methods are the first important phase in producing a software system with free errors. The functional requirements are the visual actions that may easily evaluated from the developer and from the user, but non-functional requirements are not visual and need a lot of efforts to be evaluated. One of the main important non-functional requirements is security, which focuses on generating secure systems from strangers. Evaluating the security of the system in earlier steps will help to reduce the efforts of reveals critical system threats. Security threats found because of leaking of security scenarios in requirement phase. In this paper, we purpose an intelligent model to extract and evaluate security features from scenarios based on set of security system goals and a set of security requirements saved on rich story scenarios dataset (RSSD). This model will used a support vector machine (SVM) classifier to classify the security requirement based on RSS dataset. The using of SVM will enhance the overall process of evaluating the security requirements. The results show a significant enhancement in security improvements.
Modelling the Palaeozoic tectonic evolution of the Lachlan Orogen

SummarySummaryThe Lachlan Orogen’s mineral wealth is a direct result of tectonic processes that took place in the early Palaeozoic, but the exact nature and timing of events is widely contested. Here, we apply new methods of deforming tectonic reconstruction modelling to the area. The resulting reconstructions enable us to consistently compare alternative, previously-proposed models and test them against new and old data. This approach highlights model self-inconsistencies and incompatibilities with available data. We adopted an approach where the most valid components of individual tectonic reconstructions were combined to produce a new reconstruction model constrained by the most recent data. The new model invokes two concurrent subduction zones from the Early Cambrian to the Late Ordovician. It includes a consistent continent-dipping subduction at the Eastern Gondwanan margin, and an outboard subduction complex, which experiences multiple reversals. These are responsible for an unnamed Cambrian Arc and its obduction in Tasmania, which is part of the microcontinent VanDieland before accretion to Gondwana in the late Cambrian. The Macquarie Arc later develops in the Ordovician over the Cambrian Arc. A single continent-dipping system then resumes following the Benambran Orogeny, when oroclinal folding occurs across south-eastern Australia followed by east-west shortening of the Tabberabberan Orogeny.Lachlan OrogenOroclineTectonic ReconstructionsMacquarie ArcGPlates