Reviewerly/exHarmony huggingface dataset:
A Survey on Machine Learning and Deep Learning Based Approaches for Sarcasm Identification in Social Media

Today, sentiment analysis is a basic way through which one can get an idea regarding opinion, attitude and emotion towards person or aspects or products or services, etc. In the last several years, researchers are working on a technique to analyse social media data and social chain to identify the undisclosed information from it and to make meaningful patterns and decisions through it. In sentiment analysis, sarcasm is one kind of emotion which contains the contents that are opposite of what you really want to say. People use it to show disrespect or to taunt someone. Sarcasm is useful to show silliness and to be entertaining. Sarcasm can be expressed verbally or through certain gestural clues like rolling of the eyes or raising the eyebrows, etc. A number of ways are implemented to detect sarcasm. In this paper, we try to explain current and trending ways which are used to detect sarcasm.
The Virtual Memory Palace as an Interactive Mnemonic Learning Platform

We introduce a demo application of a memory palace in virtual reality (VR) and describe findings from an initial usability and observational pilot evaluation with six users. We further identify the conceptual elements of memory palaces, based on ancient guidelines and in relation to prior literature, and show how we used them to shape the design and experience of our application, specifically its interfaces and interactions.
Usability testing mozita application based on use questionnaire model

Abstract Mozita is an application used by midwives to record and report the toddler nutritional status in the local health center (Puskesmas). In addition to nutritional status information, Mozita can present information that is used by heads of puskesmas, district health offices and provincial health offices for monitoring and decision making in terms of health policy based on nutritional status. To be able to increase application usage, feedback from users is needed. With USE Questionnaire-based usability testing covering aspects of Usefulness, Satisfaction and Easy of Use. Usability testing is focused on the interaction part of the user interface (midwife), as the main source of data collection. This study used 15 midwife respondents who were randomly drawn from puskesmas in the city area of Semarang. The technique used to measure usability testing is to provide an explanation and guidelines for using the Mozita application before respondents fill in the questionnaire using a Likert scale from 1 to 7. The questionnaire used is equipped with in-depth interviews for question items whose Likert scale value is less than equal to 3 The indepth interview aims to provide a focus on improving the Mozita application interface to suit user needs. The results of usability testing that has been done on the Mozita application shows that the value of the Usefulness component is 84.52%, Easy of Use is 83.53%, Easy of Learning is 80.95%, and Satisfaction is 85.03%. Based on the value of each component measured shows that the Mozita application has a high usability value that is equal to 83.52%, and makes it easy for users to carry out work more efficiently. Some user feedback related to satisfaction aspects, especially in the Mozita application layout section is still simple needs to be done for a better layout design.
Analysis of Detection Preference to CNN Based SAR Ship Detectors

In recent years, convolutional neural network (CNN) has been studied extensively in synthetic aperture radar (SAR) ship detection for its powerful feature extraction and classification capability, and has significantly improved the accuracy and robustness in condition of large scenes, multi-resolutions, and complex backgrounds. At present, a lot of related research focuses on design more efficient net structures. There have not been studies on how the detection performance varies for images with different complexity, backgrounds, surroundings, and quality. Taking two open-access SAR datasets, i.e. SSDD and UCAS_SARShip, as our source datasets, this paper first divides all the images into four classes according to their surroundings and backgrounds, then, dividing each class of images to training subset, validating subset, and test subset based on their features (image quality, noise level, complexity, etc.). Secondly, a large number of SAR ship detection experiments are conducted with five representative CNN based detectors on the four datasets we made. The experimental results show that detection preference is serious for each of the five detectors when they were trained with one of the four datasets. The research in this paper is a benefit for peers to understand and analyze detection preference, besides provide some valuable reference for the collection and division of SAR datasets for ship detection in the following research.
Hypergraph Basic Categorial Grammars

This work is an attempt to generalize categorial grammars, which deal with string languages, to hypergraphs. We consider a particular approach called basic categorial grammar (BCG) and introduce its natural extension to hypergraphs — hypergraph basic categorial grammar (HBCG). We show that BCGs can be naturally embedded in HBCGs. It turns out that, as BCGs are equivalent to context-free grammars, HBCGs are equivalent to hyperedge replacement grammars in generalized Greibach normal form. We also present several structural properties of HBCGs. From practical point of view, we show that HBCGs can be used to describe semantics of sentences of natural languages. We incorporate the lambda semantics into the new mechanism in the same way as it is done for BCGs and show that such an embedding allows one to describe semantics of sentences with cross-serial dependencies.
Scalable Teacher-Forcing Networks under Spark Environments for Large-Scale Streaming Problems

Large-scale data streams remains an open issue in the existing literature. It features a never ending information flow, mostly going beyond the capacity of a single processing node. Nonetheless, algorithmic development of large-scale streaming algorithms under distributed platforms faces major challenge due to the scalability issue. The network complexity exponentially grows with the increase of data batches, leading to an accuracy loss if the model fusion phase is not properly designed. A largescale streaming algorithm, namely Scalable Teacher Forcing Network (ScatterNet), is proposed here. ScatterNet has an elastic structure to handle the concept drift in the local scale within the data batch or in the global scale across batches. It is built upon the teacher forcing concept providing a short-term memory aptitude. ScatterNet features the data-free model fusion approach which consists of the zero-shot merging mechanism and the online model selection. Our numerical study demonstrates the moderate improvement of prediction accuracy by ScatterNet while gaining competitive advantage in terms of the execution time compared to its counterpart.
Cryptocurrencies in the Regulatory Field of International Organizations

The widespread use of virtual currencies has raised a question of the perimeter and ways for regulating transactions with these assets. First of all, there was a problem of determining the subject of regulated transactions, as well as a problem of minimizing risks of laundering criminal proceeds, consumer fraud, and negative impact on the financial stability. The relevant agenda has been mastered by international financial organizations, which have developed a number of recommendations, reports and analytical references using a soft legal method. These materials can be used by states to develop a model for regulating transactions with crypto assets. The recipients of these approaches are not only states, but also integration associations that have, like the EU, or seek to have, like the Eurasian Economic Union, the appropriate competence. It is obvious that the application of legal regulation of operations with virtual assets under the influence of international organizations will lead to the loss of advantages of such assets that cause their popularity at present.
Motion Compensated Virtual View Synthesis Using Novel Particle Cell

Due to the wide interest in advanced multimedia experience, free-viewpoint communication is being greatly developed in recent years. In the free-viewpoint communication, viewers can perceive a view from any angle and any position of a scene. Even though the preferred views are not captured, we can generate the views through virtual view synthesis that synthesizes an arbitrary view from captured reference view(s). For daily use, only one or few cameras in baseline distance are given to capture the scene that makes the virtual view synthesis challenging. The task is more difficult when the camera is continuously moving. In this paper, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">particle cell</i> to model a reference view sequence to a set of moving particles for virtual view synthesis. Using our novel hybrid motion estimation scheme, the projected coordinates of particles in each frame are obtained even they are occluded. The particles are warped to a virtual view and synthesized as a virtual view sequence. Our method is applicable for both dynamic camera setting and static camera setting. The experimental results show our method outperforms the state-of-the-art algorithms in dynamic camera datasets and presents improvement in static camera datasets in general.
Survey of Blockchain Applications in Database Security

Blockchain is an implementationSingh, Vedant of distributed computing which provides decentralized solutions for systems that workYadav, Vrinda in a centrally governed manner. It provides services such as smart contracts to establish consensus, immutability of data and decentralization of data. Due to these services that blockchain puts to the floor, it becomes a viable option for the data storage systems that thrive for security, privacy and confidentiality in an automated and decentralized manner. This paper investigates the current state of research in the field of blockchain as a service in providing security to databases. Five major types of data storage implementations which include blockchain as a tool to provide security solutions are discussed in this paper. Comparison studies of these implementations are done with respect to the security solutions provided. Further, some research questions are discussed which need to be addressed.
Interleaving vs True Concurrency: Some Instructive Security Examples

Information flow security properties were defined some years ago in terms of suitable equivalence checking problems. These definitions were provided by using sequential models of computations (e.g., labeled transition systems [17, 26]), and interleaving behavioral equivalences (e.g., bisimulation equivalence [27]). More recently, the distributed model of Petri nets has been used to study non-interference in [1, 5, 6], but also in these papers an interleaving semantics was used. By exploiting a simple process algebra, called CFM [18] and equipped with a Petri net semantics, we provide some examples showing that team equivalence, a truly-concurrent behavioral equivalence proposed in [19, 20], is much more suitable to define information flow security properties. The distributed non-interference property we propose, called DNI, is very easily checkable on CFM processes, as it is compositional, so that it does not suffer from the state-space explosion problem. Moreover, DNI is characterized syntactically on CFM by means of a type system.
A Novel Vehicle Tracking ID Switches Algorithm for Driving Recording Sensors

The main task for real-time vehicle tracking is establishing associations with objects in consecutive frames. After occlusion occurs between vehicles during the tracking process, the vehicle is given a new ID when it is tracked again. In this study, a novel method to track vehicles between video frames was constructed. This method was applied on driving recorder sensors. The neural network model was trained by YOLO v3 and the system collects video of vehicles on the road using a driving data recorder (DDR). We used the modified Deep SORT algorithm with a Kalman filter to predict the position of the vehicles and to calculate the Mahalanobis, cosine, and Euclidean distances. Appearance metrics were incorporated into the cosine distances. The experiments proved that our algorithm can effectively reduce the number of ID switches by 29.95% on the model trained on the BDD100K dataset, and it can reduce the number of ID switches by 32.16% on the model trained on the COCO dataset.
HADES: a Hybrid Anomaly Detection System for Large-Scale Cyber-Physical Systems

Smart cities rely on large-scale heterogeneous distributed systems known as Cyber-Physical Systems (CPS). Information systems based on CPS typically analyse a massive amount of data collected from various data sources that operate under noisy and dynamic conditions. How to determine the quality and reliability of such data is an open research problem that concerns the overall system safety, reliability and security. Our research goal is to tackle the challenge of real-time data quality assessment for large-scale CPS applications with a hybrid anomaly detection system. In this paper we describe the architecture of HADES, our Hybrid Anomaly DEtection System for sensors data monitoring, storage, processing, analysis, and management. Such data will be filtered with correlation-based outlier detection techniques, and then processed by predictive analytics for anomaly detection.
A Multi-CBR Algorithm Based on Comprehensive Evaluation for Operation Planning of Helicopter

Case-based reasoning (CBR) is an effective reasoning technology.The core idea of CBR is using past experience and expert knowledge to solve new problems.Operation planning of helicopter mainly relies on the experiences of the decision-maker, so CBR is an effective reasoning technology which can be used in the operation planning.Two key issues, evaluation method and case adaptation, are widely focused in CBR method.However, the existing CBR is not suitable for non-numeric optimization problem such as operation planning, because it belongs to small sample size problem.To solve this problem, the comprehensive evaluation based multi-CBR (CEB-mCBR) method is proposed in this paper, which can be used for operation planning of helicopter.According to the comprehensive evaluation theory, similar historical case set of the target case is established, and the historical operation plans are ranked.On this basis, the optimum initial plan for the target case is generated by the case screening CBR model.Then the solution element screening CBR model is further put forward to solve the issue of case adaptation.The CEB-mCBR method comprises multi-CBR (case screening CBR and solution element screening CBR) and combination evaluation method.It can integrate expert experience and human thinking better, especially when solution space is limited.Finally, through a case study analysis, the multi-CBR model can generate a feasible solution for the target case, and the solution elements can be adjusted according to the situation in the actual operation, which can improve the efficiency of operation planning while keeping the safety, economy and operating efficiency.
A Study of the Impact of Internet-Based Instruction Integrated Innovation Education on University Student Entrepreneurial Team Collaboration and Strategic Innovation

With the advent of knowledge economy, the competition of comprehensive national power gradually has shifted to talent competition. The cultivation of innovative talents and the incubation of student entrepreneurial teams are the primary educational goals of universities, which is an important part of the national innovation system. University student entrepreneurial teams present the attribute of high technology and unique knowledge advantage. The persistent innovation of student entrepreneurial teams is attributed to knowledge playing a critical role. In particular, in the knowledge-based economic era, student entrepreneurial teams with speed, connection, and intangible value creation have transformed the partly labor-intensive model into a knowledge-intensive competition model. Taking university student entrepreneurial teams as the research object, a total of 500 copies of questionnaire were distributed and 386 valid copies were retrieved, with a retrieval rate of 77%. Through the questionnaire survey, students' awareness, emotion, and will of innovation in innovation education are understood, and their cognition and intention of team cooperation and strategic innovation are investigated. The research results are concluded as follows: (1) Innovation education presents significantly positive correlations with collaboration. (2) Collaboration shows remarkably positive correlations with strategic innovation. (3) Innovation education reveals notably positive correlations with strategic innovation. According to the research result, suggestions are proposed, expecting that China's university student entrepreneurial teams could acquire the advantage of technological innovation by applying the opportunities of broadband and wireless network infrastructure and developing innovative applications and entrepreneurial models.
Enhancing Model Parallelism in Neural Architecture Search for Multidevice System

Neural architecture search (NAS) finds favorable network topologies for better task performance. Existing hardware-aware NAS techniques only target to reduce inference latency on single CPU/GPU systems and the searched model can hardly be parallelized. To address this issue, we propose ColocNAS, the first synchronization-aware, end-to-end NAS framework that automates the design of parallelizable neural networks for multidevice systems while maintaining a high task accuracy. ColocNAS defines a new search space with elaborated connectivity to reduce device communication and synchronization. ColocNAS consists of three phases: 1) offline latency profiling that constructs a lookup table of inference latency of various networks for online runtime approximation; 2) differentiable latency-aware NAS that simultaneously minimizes inference latency and task error; and 3) reinforcement-learning-based device placement fine-tuning to further reduce the latency of the deployed model. Extensive evaluation corroborates ColocNAS's effectiveness to reduce inference latency while preserving task accuracy.
X-Ray Diffractometer (XRD) and Scanning Electron Microscopy (SEM) of Silica Extracted from Banana Stems via Acid Leaching Treatment

Abstract Silica is known as the natural resources where it has the second most abundant mineral in Earth’s crust. In order to help the expanding research more on agricultural waste for silica extraction, banana stems has been used for this experiment studies. The main aim of the study is to know the percentage of silica in banana stems via leaching process. Hydrochloric acid (HCI) and citric acid (C 6 H 8 O 7 ) was used as a leaching reagent for removing the inorganic impurities from the banana stems and the effect of leaching time, reagent concentration and the effect of acid on the banana stems are investigated. Both acids results used same concentration of 0.1, 0.5 and 1.0 mol while leaching time were 30, 60 and 90minute respectively. The results of hydrochloric acid (HCI) and citric acid (C 6 H 8 O 7 ) leaching treatment test using the Energy Disperse X-Ray Spectroscopy (EDX) spots shows silica can be produced at 85.5% and 99%, respectively. Lastly, the silica obtained from the experiment was crystalline silica using the analysis of X-Ray Diffraction (XRD) and the morphological surface of the banana stems were obtained using the Scanning Electron Microscopy (SEM).
RePersian:An Efficient Open Information Extraction Tool in Persian

Relation extraction is the task of extracting semantic information from raw data. One of the key points in the area of open information extraction systems is the ability to extract relation information automatically for any domains, especially in web mining and web research. Many researches have been done in this field for relation extraction in different languages. Many relation extraction algorithms work based on parsing trees. The Persian language, as a low-resource language, has a dependency grammar and lexical structure which makes the dependency parsing difficult or time-consuming, and it affects the speed of relation extraction in many cases. In this paper, we will introduce RePersian which is a fast method for relation extraction in Persian. Our proposed work is based on part-of-speech (POS) tags of a sentence and particular relation patterns. To achieve these patterns, we have analyzed sentence structures in the Persian language. RePersian searches through the POS-tags for finding the relation patterns, which are given in regular expression forms. In this way, RePersian finds semantic relations by matching the correct POS pattern to a relation pattern. We test and evaluate our method on the Dadegan, Persian dependency tree dataset, with two different POS tag-sets. Our approach had on average a precision of 78.05% on finding the first argument of a relation, a precision of 80.4% in finding the second argument and precision of 54.85% on finding the right relation between the arguments.
Optimal Coverage Path Planning Algorithm of the Tractor-formation Based on Probabilistic Roadmaps

The loss of farm labor and rising wages have forced farmers to use heavy tractors, which have helped boost productivity and saved labor costs. However, driving heavy tractors on the soil can increase soil erosion and reduce crop yields. To reduce damage to soil fertility, multiple light tractors could be used instead of a single heavy tractor for field operations. However, multiple tractors require multiple operators, which increases labor costs. In order to solve the above problems, this paper proposes an optimal coverage path planning algorithm based on probabilistic roadmaps. Firstly, three light tractors are formed into a reverse-v formation, which not only reduces the damage to soil fertility, but also ensures the working efficiency. In addition, the algorithm can be applied to a wide range of scenarios, including convex region, concave region, region with obstacles and region without obstacles. In addition, the coverage time is relatively small, the coverage can reach 98% at the minimum, and the repetition rate is no more than 2% at the maximum.
An informant-based approach to argument strength in Defeasible Logic Programming

This work formalizes an informant-based structured argumentation approach in a multi-agent setting, where the knowledge base of an agent may include information provided by other agents, and each piece of knowledge comes attached with its informant. In that way, arguments are associated with the set of informants corresponding to the information they are built upon. Our approach proposes an informant-based notion of argument strength, where the strength of an argument is determined by the credibility of its informant agents. Moreover, we consider that the strength of an argument is not absolute, but it is relative to the resolution of the conflicts the argument is involved in. In other words, the strength of an argument may vary from one context to another, as it will be determined by comparison to its attacking arguments (respectively, the arguments it attacks). Finally, we equip agents with the means to express reasons for or against the consideration of any piece of information provided by a given informant agent. Consequently, we allow agents to argue about the arguments’ strength through the construction of arguments that challenge (respectively, defeat) or are in favour of their informant agents.