Reviewerly/exHarmony huggingface dataset:
The use of mobile learning at SMP Negeri 3 Karawang Barat in improving students’ mathematical representation ability

Abstract The mathematical representation ability is a mathematically visual image either in a person’s image, symbol, or expression, which is a mental picture of himself in learning mathematics. Many concepts of abstract mathematics lessons sometimes make students do not understand what they are learning, therefore the ability of mathematical representation is needed when students learn math. In growing the ability of mathematical representation, it is necessary an appropriate learning so that students can visualize it, namely using mobile learning. Mobile learning is a teaching learning activity that utilizes information and communication technology that refers to mobile handheld devices through mobile devices such as mobile phones, smartphones, tablets, personal digital assistance (PDAs), notebooks and netbooks, with availability teaching materials that can be accessed at any time as well as interesting and interactive material visualization in it. The results show that students using mobile learning achievement of representational ability is better than students who use conventional learning.
Touch Interactive 3D Surfaces

This paper presents the design and implementation of a touch interactive 3D surface using frustrated total internal reflection (FTIR). Using a simple and scalable approach, the touch sensing is enabled all around a transparent acrylic pyramid surface. The developed system steers away from the conventional flat touch surfaces thus enabling touch-based control of applications such as volumetric holographic displays. The potential of presented system also lies in gesture-based control of holographic displays.
Research on Isolated Word Recognition Algorithm Based on Machine Learning

Natural language communication between humans and machines is a popular direction of artificial intelligence. As a technology for realizing direct dialogue between humans and machines, speech recognition technology can convert human speech signals into language information, providing technical support for human-machine language communication. In this paper, we conduct a speech recognition study. First, we collect more than 1,000 pieces of speech data, preprocess the speech data, extract three different characteristics of speech such as LPC, LPCC, and MFCC, and divide the speech data into a training set and a test set. Two naive Bayesian and KNN classifiers are used for classification, and the classification accuracy is obtained. This paper mainly studies the accuracy of isolated word recognition in three different features and two different classification algorithms.
A Hybrid Approach to the Analysis of a Collection of Research Papers

We define and find a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. This generalization lifts the set to a "head subject" in the higher ranks of the taxonomy, that is supposed to "tightly" cover the query set, possibly bringing in some errors, both "gaps" and "offshoots". Our hybrid method involves two more automated analysis techniques: a fuzzy clustering method, FADDIS, involving both additive and spectral properties, and a purely structural string-to-text relevance measure based on suffix trees annotated by frequencies. We apply this to extract research tendencies from two collections of research papers: (a) about 18000 research papers published in Springer journals on data science for 20 years, and (b) about 27000 research papers retrieved from Springer and Elsevier journals in response to data science related queries. We consider a taxonomy of Data Science based on the Association for Computing Machinery Classification of Computing System (ACM-CCS 2012). Our findings allow us to make some comments on the tendencies of research that cannot be derived by using more conventional techniques.
The Summary of Deep Learning in the Field of Weather Forecast Research

Abstract This article investigates application of deep learning in the field of meteorology at home and abroad. summarized of deep learning and several deep learning network models widely used in meteorological field, It focuses on research and summary application of deep learning in Meteorological Department of China, and also summarized the future development trend of deep learning in weather forecast application.
Development and Evaluation of a Pen Type Thermal Sensation Presentation Device for SPIDAR-Tablet

It is thought that anyone can get high learning effect anywhere by VR learning with tablet device. In this study, we focused on the field of thermodynamics having difficulties in understanding the basic logical structure for high school students. The purpose is to assist understanding of thermodynamic principles by simulating and visualizing the invisible phenomena with VR. We developed a system for controlling the temperature of a pen type thermal sensation presentation device and implemented it on SPIDAR-tablet using iPad. And we constructed an experimental simulation content of thermodynamics with a piston and a cylinder. It presents the pressure and the temperature of the gas in the cylinder as the force and the thermal sensation respectively. Then, we conducted an experiment to investigate the effects of presenting the sensations on adiabatic change simulation of thermodynamics. Many participants felt easier to imagine the principle of it when presenting both the force and the thermal sensation than when presenting only the force sensation. Therefore, it is considered that the understanding of the principle was improved by presenting the thermal sense. However, there were many opinions that they could not feel the enough change in the force.
A method for predicting the solar photovoltaic (PV) potential in China

Abstract A reliable assessment of energy efficiency for photovoltaic system is of importance for the solar energy policy planning and industrial development. Despite extensive advances over past years, almost all economic analyses overlooked the effect of local environment on the photoelectric conversion efficiency of solar cells, therefore leading to a non-negligible error in the prediction of photovoltaic potential. In this work, we developed a unified model to evaluate the regional economics of photovoltaic system by incorporating the heat transfer in solar cell and associated environmental influences. This model significantly improves the prediction accuracy of photovoltaic potential by establishing a correlation between the regional meteorological data and solar energy generation. In particular, with a meteorological data reduction, we dramatically decrease the computational complexity for predicting the photovoltaic potential. Through a theoretical analysis with the unified model, we demonstrate that the existing price mechanism of PV electricity in China cannot fully eliminate the economic disparities in various areas owing to the neglect of environmental effect. This work not only offers new insight into the research of solar resource and related policy making, but also provides guidelines for the further development of solar power industry.
Accelerating Metric Filtering by Improving Bounds on Estimated Distances

Filtering is a fundamental strategy of metric similarity indexes to minimise the number of computed distances. Given a triple of objects for which distances of two pairs are known, the lower and upper bounds on the third distance can be set as the difference and the sum of these two already known distances, due to the triangle inequality rule of the metric space. For efficiency reasons, the tightness of bounds is crucial, but as angles within triangles of distances can be arbitrary, the worst case with zero and straight angles must also be considered for correctness. However, in data of real-life applications, the distribution of possible angles is skewed and extremes are very unlikely to occur. In this paper, we enhance the existing definition of bounds on the unknown distance with information about possible angles within triangles. We show that two lower bounds and one upper bound on each distance exist in case of limited angles. We analyse their filtering power and confirm high improvements of efficiency by experiments on several real-life datasets.
Group-based Password Characteristics Analysis

In this article, we analyze password characteristics from the perspective of user groups in different countries and web-based services. We collect a dataset from the Chinese railway website www.12306.cn. which contains data from four provinces, Hubei, Zhejiang, Inner Mongolia and Xinjiang. Additionally, we select datasets from two English based Internet applications, Faithwrit-er and Facebook. We analyze these six datasets based on several common indicators, including popular passwords, password structure and letter distribution. The analysis results show that there are remarkable differences in different user groups. The experiments show that geographical factors (embodied in the native language) and types of website services play a significant role in password creation. We further evaluate the security of these passwords by employing two state-of-the-art password cracking techniques. The attack results show that datasets of different provinces and different types of website services have different password strength. To the best of our knowledge, this is the first time passwords are analyzed based on different user groups.
PViMat

We present PViMat, a portable photovoltaic mat that can recognise six unique useful dynamic hover gestures up to 30 cm above the surface over a large area with more than 97% accuracy. We utilised an off-the-shelf portable and rollable outdoor solar tape and employed it to harvest indoor natural and artificial light energy to trickle charge a LiPo battery for self-powering. We demonstrate a low-power operation technique of continuous charging with DC photocurrent and simultaneous event-driven gesture recognition with AC photocurrent. The PViMat prototype harvests 30 mW in a general livingroom light level of 300 lux and consumes 0.8 mW per gesture. Lastly, we propose applications of PViMat with its large-area, flexible and rollable form-factors and the gesture set.
Linguistic Analysis of Tweets – Using Data Mining to study usage of English on Twitter

The recent evolution in the language used by people in their day to day life has shown a major shift in trend in how they like to express themselves in public. With the increased use of abbreviations, shorthand notations, mnemonics and an introduction of a new set of hybrid words, the vocabulary used in communications happening between people has started to deviate from the original vocabulary that is traditionally used for a language. In this study, we have analyzed the data collected through a set of Tweets on Twitter to observe the shift of used vocabulary from the traditional vocabulary. Twitter is a popular social media platform where a variety of people express themselves openly and this makes it a good choice to extract samples of data without any bias. The tweets were extracted and analyzed by data mining techniques using the functions of the Twitter API for python. Each word was categorized into one of the two categories: words part of the traditional vocabulary of a language, what we are calling here as Proper English Words, and words not part of the traditional vocabulary, The Improper English Words, and then analysis was done over the observations. For this study, we have restricted ourselves to the tweets made in English only. The British English spellings were preferred.
Impact of Tools on the Acquisition of RAM Memory

When responding to a security incident in a system, several basic principles must be followed regarding the collection of pieces of evidence from the system. The capture of these pieces of evidence has to be done according to its order of volatility. In this sense, RAM memory constitute the most important element to capture, given its extreme volatility. RAM memory must be acquired and analyzed because the data it holds, which may belong to the system itself or to any other device connected to it, can survive a certain amount of time in it. Since RAM is a constantly changing element, it must be stood out that any action carried on the system under analysis will modify the contents of the RAM. In this article a comparative and an objective analysis has been carried out, showing the impact that the execution of some tools for the capture of RAM has on the system. This comparative study details both the private shared workspaces, for each of the processes executed by each of the tools used.
Energy Trading Using Ethereum Blockchain

In the recent past, blockchain has gained more popularity and dragged the attention of both industry and academia. Blockchain is always misunderstood as Bitcoin by many people. Blockchain has the potential to implement many applications ranging from banking to digital Ids, e-commerce to immutable data backups. This paper implements energy transactions for smart grid using Ethereum blockchain. A smart contract is used to execute trading if necessary conditions are met. Blockchain is used to fuel the energy trading for peer-to-peer exchange of electricity. Blockchain enables the application more secure through its default feature immutability. Smart grid and its components are necessary to implement this trading platform. In this platform, people can buy/sell electricity whenever they require.
Computer Testing and Maintenance and Data Recovery Technology

Abstract With the high frequency application of computers, various network information security problems are common. Among them, data loss to a certain extent has a certain impact on people’s normal work. In this paper, computer detection and maintenance technology is discussed and analyzed from two aspects of hardware and software, and then some specific data recovery technologies are put forward.
Ensemble active imputation for incomplete data

Real data is often incomplete, which hinders its usability and learnability. A reasonable machine learning scenario is to obtain some values and labels at cost upon request. In this paper, we propose a new ensemble active missing imputation (EAMI) algorithm to handle the learning task. First, we design five missing imputation methods, including mean filling, cubic spline interpolation filling, sample-based collaborative filtering weighed filling, attribute-based collaborative filtering weighted filling and k-nearest neighbor (KNN) filling. Second, we propose an ensemble imputation model through the linear weighting of attribute prediction values. Third, We propose a three-way decisions model that uses the variance of the predicted values to fill in missing values by querying true label or using predicted values. We conduct experiments on University of California Irvine(UCI) datasets. The results of significance test verify the effectiveness of EAMI and its superiority over KNN missing data imputation algorithms.
Lessons Learned in Network and Memory-Based Moving Target Defenses

Moving-Target Defenses seek to introduce dynamism, randomness, and diversity into computer systems in an effort to make these systems harder to explore, predict, and exploit. Over the past decade a variety of work has explored applying these kinds of defenses to applications? runtime environments, to the operating systems and architectures running the applications, and to networks.
GTCN: Dynamic Network Embedding Based on Graph Temporal Convolution Neural Network

Network embedding aims to learn the low-dimensional node representations from high-dimensional network structures of complex systems. Embedding in dynamic networks is a very difficult but important problem due to the dynamics of network structures in real-world systems and the high computational complexity. In this paper, we propose a novel Graph Temporal Convolution Network (short for GTCN) for the dynamic network embedding. In GTCN, a graph convolution network is used to learn the embedding representations of nodes in each snapshot, while a temporal convolutional network is adopted to parallelly reveal the evolution of node structures in dynamic networks. Extensive experiments on six real dynamic networks show that GTCN has a better performance than the state-art-of-art dynamic network embedding methods in tackling prediction tasks.
Trusted Sharing of IOT Data Using an Efficient Re-encryption Scheme and Blockchain

The major unit of Internet of things or IoT is data, and it is being used by IoT sharing system. It is necessary to develop a trust between sensor owner and sensor user. This research paper aims to provide solution to the following burning problems by providing the “dual” re-encryption methods: (i) scalability, (ii) trust, and (iii) digital payments. This method saves the data in a distributed cloud, when the encryption process is finished. A bridge has been developed in this work between the sensor owner and sensor user without any intervention.
Szegő Quadrature Kalman Filter for Oscillatory Systems

Filtering problems with oscillatory system dynamics commonly appear in real-life. However, the existing Gaussian filters, like the unscented Kalman filter (UKF), cubature Kalman filter CKF, Gauss-Hermite filter (GHF) and cubature quadrature Kalman filter (CQKF), are accurate for the nonlinear systems with a particular order of polynomials only. This manuscript introduces a new Gaussian filter, which is accurate for oscillatory systems with 2π-period of oscillation. The proposed method is named as Szegö Quadrature Kalman Filter (SQKF). The SQKF transforms the intractable integrals that appear during the filtering over a unit circle. The transformed integral is approximated using the univariate Szegö quadrature rule. The univariate quadrature rule is extended in a multivariate domain using the product rule. Simulation results reveal an improved estimation accuracy for the SQKF in an oscillatory environment. The computational burden of the SQKF is similar to the GHF and higher than the UKF, CKF and CQKF.
Mesh Learning: A Cloud and Edge–based Computing Network Providing Data–Driven Solutions to the Oil and Gas Industry

Abstract Over the past decade, the application of machine learning has become one of the most studied topics in the oil and gas industry. The recent success of some small-scale pilot projects has resulted in more attention being paid to deploying machine learning models at scale for operations. While cloud-based deployment (data backhauled for analysis in the cloud) and edge-based deployment (data being analyzed locally at a remote site without persistent Internet connection) are the two major approaches, each strategy has specific advantages and drawbacks. In this paper, we proposed "mesh learning", a new approach that optimally balances cloud computing and edge computing for deploying machine learning solutions in an industrial internet of things (IIoT) environment. We present how the system enables some new strategies to manage machine learning model lifecycles and how it helps with relevant use cases for the oil and gas industry.
Human-AI Collaboration

Continuous increase in computing power and more available data contribute to AI maturing, enabling an efficient and powerful human-AI collaboration. As a result, the nature of work will change, and more and more AI will be involved in joint work. In this study, the authors introduce the so-called Virtual Collaborator (VC), an equal partner in digital collaboration, and report the results of a conducted online study. Based on the results of our study and current research, the concept of a VC was constructed, which consists of potential roles, tasks, level of autonomy, and behavior. This construct can be used as a guideline to design and implement a VC in collaboration scenarios.
Axial Data Modeling with Collapsed Nonparametric Watson Mixture Models and Its Application to Depth Image Analysis

Recently, axial data (i.e. the observations are axes of direction) have been involved with various fields ranging from blind speech separation to gene expression data clustering. In this paper, axial data modeling is performed by proposing a nonparametric infinite Watson mixture model which is constructed in a collapsed space (denoted by Co-InWMM) where the mixing coefficients are integrated out. Then, an effective collapsed variational Bayes (CVB) inference method is theoretically developed to learn the Co-InWMM with closed-from solutions. The proposed Co-InWMM with CVB inference for modeling axial data is validated through both synthetical data sets and a challenging application regarding depth image analysis.
Architecture information system for zakat, infaq and sadaqah management institutions

Abstract Indonesia had potential zakat as much as 217 Trillion Rupiah in 2017, but can only be collected 6 trillion or 0.2%, one of the factors is Muzakki’s (Zakat Giver) lack of trust in zakat management institutions so that they prefer to distribute their zakat without going through zakat management institutions directly. To encourage improving management of zakat management institutions requires planning, designing, and building systems that can assist in managing zakat. This study aims to design an architectural enterprise for zakat management institutions in achieving the collection, management, utilization, and distribution of zakat and alms. The methodology used in this study uses the TOGAF ADM approach by collecting data through observation and interviews with one of the private zakat management institutions. The results of this study are in the form of a system architecture design that can be used as a reference for zakat management institutions with a prototype of the zakat reporting system.
Conditional Image Repainting via Semantic Bridge and Piecewise Value Function

We study conditional image repainting where a model is trained to generate visual content conditioned on user inputs, and composite the generated content seamlessly onto a user provided image while preserving the semantics of users' inputs. The content generation community has been pursuing to lower the skill barriers. The usage of human language is the rose among thorns for this purpose, because the language is friendly to users but poses great difficulties for the model in associating relevant words with the semantically ambiguous regions. To resolve this issue, we propose a delicate mechanism which bridges the semantic chasm between the language input and the generated visual content. The state-of-the-art image compositing techniques pose a latent ceiling of fidelity for the composited content during the adversarial training process. In this work, we improve the compositing by breaking through the latent ceiling using a novel piecewise value function. We demonstrate on two datasets that the proposed techniques can better assist tackling conditional image repainting compared to the existing ones.