Reviewerly/exHarmony huggingface dataset:
Goal Lifecycle Networks For Robotics

A Goal Lifecycle Network (GLN) is a conceptual process model that captures the progression of goals from their formulation to their completion, including planning and execution concerns. GLNs synthesize the literature on hierarchical goal networks, goal lifecycles, and plan execution. We formalize GLNs based on a state-variable representation, extend GLNs with an execution lifecycle, describe a partial reference implementation of GLNs, and show how the temporal PDDL language can be translated into GLNs for dispatchable execution. We integrate GLNs in three proof-of-concept robotics demonstrations: (1) a two-armed robot sorting items into baskets; (2) a multi-vehicle quad-rotor team surveying a region; and (3) centralized planning for a simulated disaster relief based on the Robocup Rescue League. The theory, implementation, and demonstrations highlight that GLNs are effective for goal management in the robotics systems we study.
Bok Choy Growth Prediction Model Analysis based on Smart Farm using Machine Learning

AbstractIndonesia is an agricultural country that has a dependency on the horticulture sub-sector. Bok choy is included in the mustard greens group as one of the strategic products from the horticulture. The needs for mustard greens are getting higher. Based on Indonesia’s Central Statistics Agency data in 2019, the mustard beans production rate increases only 2.63 % higher than in 2018. If it does not meet the desired supply, it opens the possibility of a lack of bok choy supply at the market, resulting in high potential price fluctuations. These conditions initiate relevant system research to help the farmer develop a bok choy crop reference guide, especially in the seeding phase. In reducing the limitations caused by the lack of science and knowledge in the farmer environment, the prediction model is the proposed outcome by considering the use of IoT mechanism that has widely developed. The model is based on a system that integrates IoT’s interest in the agriculture field, namely smart farm, for retrieving real-time data based on automatic control, MySQL database for storing data, and machine learning technique to establish the prediction model as the guide for the farmers to find appropriate parameters for planting bok choy. The prediction model performs using Python, a high-level popular programming language due to its ease and open source. Python interprets the bok choy growth dataset based on the irrigation system scenario from the integrated system with the relevant library of data preprocessing interest and the Decision Tree algorithm of the Scikit-learn library to train the model. The system conducts a series of machine learning phases to take the insight analysis needed to create a prediction model. This thesis’s expected result is an ideal prediction model that results from the global system dataset based on the previous undergraduate thesis created as a reference guide for the farmer environment in planting bok choy during the seeding phase. The model performance metrics as the consideration in deciding the outcome model, which are accuracy and precision.Keywords : IoT, smart farm, machine learning, Python, Decision Tree, Scikit-learn, dataset.
Evaluating Students Placement Performance Using Normalized K-Means Clustering Algorithm

Ensemble Cluster is verifiedas a worthy alternative in front of theanalysis of the clustering problems. Constructinga cluster for ausing similar dataset and mergingit into a distinct clustering. The mixturing process is useful toextend theclustering quality.Another name of clustering Ensemble is   consensus clustering. ClusterEnsemble providing as a promising solutions for heterogeneous or for multisource data clustering. Spectral ensemble clustering results in used todropped thedifficulty of algorithm.Now weprovide various clusteringmethods applied in same dataset and producedifferent clusteringresults. The several methods feature alldiscussed, it helped in choosing the utmostsuitable one to solve a problem at handOn the preprocessed dataset, clustering isgenerated by using clustering’s namely; normalized k-means, to predict the level of student's performance  inplacement.
A New Method in Attribution? Attempts of the Employment of Geometric Morphometrics in the Attribution of Late Archaic Attic Lekythoi

In the Late Archaic - Early Classic period, the Attic ceramic industry was characterized by a kind of duality. On the one hand, the red-figure technique was flourishing, when Euphronios’, Douris’ or the Berlin Painter’s works represented the height of Greek vase painting. On the other hand, the market was also covered by large quantities of low-quality black-figure pottery. Not only in Athens, but even in the whole Ancient Mediterranean these mass-produced vessels emerge constantly, even from modern excavations. Therefore, in contrast to most vases of more talented painters they can be attached to an archaeological feature or layer. Due to their inadequate style, relatively few characteristics can be determined while looking at the painting. Thus, the manufacturing criteria, such as the details of the shapes, are more important in the attribution. In this paper, I study the late black-figure lekythoi of the Museum of Fine Arts in Budapest with the help of geometric morphometrics using 3D reconstructions.
A Novel Approach to Communicate with Video Game Character using Cascade Classifiers

Today, video games have a special place among entertainment. In this article, we have developed an interactive video game for mobile devices. In this game, the user can control the game’s character by his face and hand gestures. Cascading classifiers along with Haar-like features and local binary patterns are used for hand gesture recognition and face detection. The game’s character moves according to the current hand and face state received from the frontal camera. Various ideas are used to achieve the appropriate accuracy and speed. Unity 3D and OpenCV for Unity are employed to design and implement the video game. The programming language is C#. This game is written in C# and developed for both Windows and Android operating systems. Experiments show an accuracy of 86.4% in the detection of five gestures. It also has an acceptable frame rate and can run at 11 fps and 8 fps in Windows and Android respectively.
PRODUKSI DAN EFEKTIVITAS MOTION GRAPHIC SEBAGAI MEDIA PROMOSI ZETIZEN BATAM POS

Nowadays, the promotional media is increasingly growing. This can be seen from the number of companies that do promotions and to be known by the public. Zetizen Batam Pos is a tool for teenagers between 13 to 20 years old that provides the information about lifestyle and daily life, through various media such as printed media, online media, and other various offline activities. The problems that were found are some people do not know much about Zetizen Batam Pos. Therefore, Zetizen Batam Pos needs more attractive, creative and effective promotional media such as motion graphics.This study uses the Villamil-Molina (1997) development method which consists of five stages: development, pre-production, production, post-production, and delivery. Softwares used in this study are Adobe Illustrator CC 2017, Adobe After Effects CC 2017, and Adobe Premiere Pro CC 2017. After the motion graphic had been completed, an EPIC model analysis was conducted to test the motion graphic’s effectiveness as a promotional media for Zetizen Batam Pos. There are four dimensions of EPIC such as Empathy, Persuasion, Impact, dan Communication. Testing the effectiveness of Zetizen Batam Pos's motion graphics by using the EPIC model is very effective because the results for the dimension of Empathy, Persuasion, Impact, and Communication are 4.38, 4.37, 4.52, and 4.59 respectively. In that case, the communication dimension is becoming the most dominant of all dimensions. This could happen because the display of motion graphics on Zetizen Batam Pos can deliver the right, clear, and understandable information to the audience.
A General Method of Realistic Avatar Modeling and Driving for Head-Mounted Display Users

The head-mounted displays (HMDs) provide immersive experiences in the virtual reality (VR). However, the face interactions are limited due to the serious occlusion of the user’s face. Existed approaches try to recover the user’s facial expression by adding additional sensors to HMD. In this article, we develop a novel framework to reconstruct the user’s 3-D face in VR only using an RGB camera. Given a reference face, a realistic full-textured avatar is created by fitting a 3-D Morphable Model (3DMM). A self-supervised UV map generative adversarial network (GAN) is proposed to make the facial texture look more realistic. Next, we propose a novel landmark detection method to locate the landmark positions under HMD occlusion since facial landmarks are commonly used for driving the avatar. To this end, we synthesize a face data set with HMD. Our method is easy to build and popularize with low cost. The experiments on the synthetic and real HMD data demonstrate that the proposed method can detect landmark accurately and restore facial expressions faithfully despite the large occlusion of HMD.
Efektifitas Pembelajaran Berbasis Daring/E-Learning dalam Pandangan Siswa SD Negeri 6 Padangsambian

The development of education today is influenced by the rapid progress of information and communication technology. One of the information technologies that play a role in the world of education is online learning. Online learning functions as a liaison between educators and students with an internet network that can be accessed anytime and anywhere. The online media used by the research subjects are various (WhatsApp, Google Classroom, Website). The purpose of this study was to analyze the effectiveness of online learning. The method collected in data collection was question and answer to the source, namely students of SD Negeri 6 Padangsambian via WhatsApp. The results of the study concluded that 2 out of 5 respondents felt online learning was more effective and 3 respondents stated that face to face learning was felt to be more effective
Finite Ring of Characteristic 2 and Cryptography

In [1] and [2] we defined the elliptic curve over the ring 2 F3d [e ],e = 0 . In this work we will give some properties of the elliptic curve over the special ideal ring of characteristic 2 and an application in cryptography. Our future work will focus on the study of the general case of these rings, which seem to be beneficial and interesting in cryptography, specially the one based on the identity (IBE) [6], [7], [8].
Digitalization Of Production At Aerospace Enterprises: What A University Graduate Should Know

The article is devoted to the problems of forming the educational policy of aerospace universities in Russia, taking into account the implementation in the country of the program for the development of the digital economy and the development of an end-to-end life cycle management system based on the Digital Enterprise platform.In addition to traditional design and technological training, university graduates must have the knowledge and skills to work with the software of a digital enterprise.Federal state educational standards do not regulate the construction of the educational process for the study of technologies and processes implemented in digital production.Therefore, universities should independently formulate educational policies in this area.For this, at the first stage, it is necessary to analyze the current state of the digitalization process of aerospace production.The article provides a brief description of the software of aviation enterprises -partners at the Komsomolsk-on-Amur State University, which trains personnel for them.The "Digital Enterprise" program is implemented at airlines as a complex of interacting informational 3 subsystems: computer-aided design, PLM lifecycle management and an electronic document management subsystem operating within an automated system on a single software and hardware platform.A brief overview of the composition of the digital enterprise software platform is given.It is noted that universities widely use computer-aided design programs (CAD), Computer Aided Engineering programs (CAE), Computer Aided Process Planning (CAPP), Computer Aided Manufacturing (CAM), which replaced the drawing board and calculator in the course of coursework and diploma projects.
Deep Learning-based Damage Mapping with InSAR Coherence Time Series

&amp;lt;p&amp;gt;Satellite remote sensing is playing an increasing role in rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth&amp;amp;#8217;s surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth&amp;amp;#8217;s surface. In this study, we propose to map damage using the full time history of SAR observations of an impacted region from a single satellite constellation in order to detect anomalous variations in the Earth&amp;amp;#8217;s surface properties due to a natural disaster. We quantify Earth surface change using time series of sequential interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local temporal behavior before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with measured damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.&amp;lt;/p&amp;gt;
An Online Cold-Chain Monitoring System Powered by Miniature Smart Tag and Blockchain

Food supply chain safety has been considered as the most crucial building block of trust with customers. This paper describes a food supply chain tracing system powered by a smart tag hardware and blockchain technology. To continually monitor environmental temperature and keep the data trustable, a miniature electronic tag with features of security is designed in the system. This temperature sensor tag supports digital signatures and verification to make sure the temperature data collected are authorized and tamper proof. Combined with the traceable characteristics of blockchain technology, a blockchain-based food safety assurance platform is built in our work.
Gender-Based Analysis of Students’ Ability in Answering Factual and Vocabulary-in-Context Questions of the TOEFL-Like Reading Comprehension Test

This study was aimed to find out whether or not there was a significant difference between male and female students’ ability in answering factual and vocabulary-in-context questions of the TOEFL-Like reading comprehension test. Secondary data analysis was utilized by taking reading comprehension test scores of twenty-one male and twenty-one female students of the English Education Program. Samples were selected using random sampling technique. Data were analyzed using independent sample t-test. Results showed that the t-values of sig. 2-tailed was higher than 0.05 in male and female students’ ability in answering both the factual questions (0.267 &gt; 0.05) and the vocabulary-in-context questions (0.837 &gt; 0.05). The results revealed that there was no significant difference between male and female students’ ability in answering factual and vocabulary-in-context questions of the TOEFL-Like reading comprehension test.
Generalized Galbraith’s Test: Characterization and Applications to Anonymous IBE Schemes

The main approaches currently used to construct identity-based encryption (IBE) schemes are based on bilinear mappings, quadratic residues and lattices. Among them, the most attractive approach is the one based on quadratic residues, due to the fact that the underlying security assumption is a well-understood hard problem. The first such IBE scheme was constructed by Cocks, and some of its deficiencies were addressed in subsequent works. In this paper, we focus on two constructions that address the anonymity problem inherent in Cocks’ scheme, and we tackle some of their incomplete theoretical claims. More precisely, we rigorously study Clear et al.’s and Zhao et al.’s schemes and give accurate probabilities of successful decryption and identity detection in the non-anonymized version of the schemes. Furthermore, in the case of Zhao et al.’s scheme, we give a proper description of the underlying security assumptions.
Study on the Deep Learning of Physics Review Course in Junior Middle School Based on CRS

Deep learning is an important research topic in the field of educational technology, the Horizon Report released by the New Media Alliance in 2017 points out that deep learning is a key direction in promoting educational development and reform.This research is based on the CRS review discussion teaching mode, applying classroom response system in interactive teaching environment, in order to promot students' deep learning.Aiming at the problems of single teaching method, simple classroom communication, fuzzy monitoring of learning situation and low efficiency of teaching goal in junior high school physics review course, this paper takes junior high school physics review course as an example, discusses the teaching efficiency of CRS-based review course qualitatively, and uses comparative experiment to analyze the difference between teaching effect, students' deep learning achievement degree and learning type.The results show that, Based on the CRS junior high school physics review class in-depth learning model can improve students' academic performance, enhance the degree of achievement of students' deep learning.Therefore, we should actively explore the deep integration path of CRS and physics teaching, give full play to the advantages of CRS in focusing on the core issues in the classroom, evaluation of innovative learning process to improve the effectiveness of physics teaching.
Modeling of an active multi-agent environment for the design of a multi-criteria group decision support system

In the present study, the research problem concerns business intelligence, more precisely collaborative decision-making. The authors propose a complete modeling of a multi-agent active environment for the design of a multicriteria group decision support system dedicated to the spatial problem of localization in territory planning. The proposed model is called ActiveGDSS (Active Group Decision Support System) which uses a coupling between a geographic information system and a multi agents system and is endowed by a new negotiation protocol based on the concession allowing reaching to a consensus which satisfies the territorial actors. The main purpose is to integrate the principle of contextual activation in the modeling of the system which makes the environment an active entity. The main advantages of contextual activation are efficiency gain in terms of execution, better flexibility and reuse of agent behaviors.
Detecting Fraud Apps Using Sentiment Analysis

Nowadays, mobile apps are considered common place because of how popular and mainstream they are with mobile technology has become.Since there are so many mobile applications in the market, app rating manipulation is the most difficult problem for both the developers and consumers to prevent .Class expansionism is when malicious or disingenuous actions have the intention of artificially rising the numbers of apps in the rating.As a result, it is increasingly popular for application developers to use questionable strategies, like inflating revenue or posting false scores, nefarious ratings, to attempt to achieve a higher rank in the App store.That's why using statistical models to calculate, we also analyse three varieties of evidences: 1) In these cases, the models rank applications, and2) product ratings and quantitative evidences, and 3) product feedback may serve as quantitative evidence.
AI for Facial Autobiographical Memory Recovering and Transmission. A Study for a Mobile and Web Application

Drawing on research conducted within the ERC Consolidator Project FACETS, this presentation aims at describing the state of art of solutions currently offered by AI for coping with loss of memory and identity in the pandemic scenario and at discussing advantages and disadvantages of these digital tools. On the one side, the presentation will focus on how the interpersonal physical distance imposed because of the risk of contagion might be dealt with by applications based on Artificial Intelligence purposed to mitigate the loss of memory in elderly people by building new bridges between them and the generation of digital natives. On the other side, the paper will concentrate on obstacles that hamper the adoption of Artificial Intelligence across the generations. Lack in digital literacy as well as inappropriate interaction design as obstacles to effective cooperation between humans and artificial intelligence will be particularly stressed.Finally, the presentation aims at presenting a concept study for a mobile and web application for autobiographical memory recovering, digitalization, preservation, recreation and transmission. The app is intended to bear on the visage as a memory and identity catalyst so as to enhance the neurophysiological functions of users, training their memory, and improving their ability to communicate.
CHATBOTS IN BANKING INDUSTRY: A CASE STUDY

Conversational Banking is a smarter way to retain the loyal customers by offering them a quick response to their queries. Technology has helped humans to evolve from the Stone Age to the modern digital era. The pace of the shift in consumerism behavior from a service seeker to a game-changer is quite quick. Gone are the days, when a business could afford to prioritize the customers’ needs at a later stage. With the customer becoming the ultimate decision-maker for a business to stay or go, it had become the priority of every business to ensure that customer satisfaction is achieved at any extent. Speaking about the banking sector, technology has gifted many exclusive ways to allow the industry to gain customer’s satisfaction to the maximum. One such smart strategy is to introduce the chatbots to its customers. Chatbots designed with AI are one of the most promising strategies of a banking business that can lead the bank to win the satisfaction vote of their loyal customers.
Count–Mass Asymmetries: The Importance of Being Count

The term count/mass distinction, despite its success as a name for a domain of research, suggests a symmetry between count and mass that is not supported by cross-linguistic data. A first asymmetry is related to the grammatical encoding of count vs. mass. A second asymmetry between count and mass is related to the sensitivity of quantity expressions to count and mass meaning and count morphology. A third asymmetry concerns possible meanings of nouns. Whereas there is strong evidence that an opposition between count and mass meanings plays an important role in the lexicon even of languages that seem at first mass-only, there do seem to exist count-only languages. The second part of the paper will consider count meanings across languages. What types of meanings are count? What are reliable diagnostics for count meaning? Are there differences in this respect between obligatory number marking languages (also commonly called ‘mass/count languages’) and languages that do not have obligatory number?