Reviewerly/exHarmony huggingface dataset:
If space, time are necessary conditions, then, mass, energy are sufficient conditions for universe. Any one can be smoothly transformed into any other condition

Our universe has full of mysteries. The universe is actually an encoded form of information. The universe is a universal language expressing itself. The misery is that the mysteries cannot be easily decoded to understand the laws governing the behavior of the universe. Some parameters are explicit in the universe and they can decode the whole universe. For example, space and time can be treated as the necessary conditions of the universe while mass and energy can be regarded as the sufficient conditions of the universe. All we need to find how to relate all four parameters. This paper will discuss that we can transform any of the four conditions into the rest of the other conditions. The speed is a parameter that relates space with time. The Einstein’s mass – energy equivalence condition can help in the transformation. I will show a space – time – mass – energy matrix can be regarded as mathematical formulation of the transformation.
progViz: Visualizing Patient Journeys Based on Finite State Models

Patient journeys are the key for treatment development, quality insurance and better understanding of disease progression. Therefore, many visualization toolkits exist to monitor individual patient lives. However, there is no visualization that relies on the overall patterns of patient journeys. We propose a new framework progViz that visualizes patterns of patient journeys, which rely on finite state models. These models are able to illustrate progression of diseases, treatment and outcomes of whole populations as well as very specific analyses. Finally, we propose a method to evaluate how treatment affects disease progression.
The E-Commerce Implementation to Improve the Agricultural Product by using User Centered Design Method

Abstract Nowadays, information technology has altered the people’s life in many aspects. Simply stated, there are lots of benefits of buying things online. The people only use their gadget simply of choosing the product they need. Likewise, for the field of agriculture. The farmers at Sukatani, Indramayu, West Java, are able to market their products in digital. In this context, the role of e-commerce is the solution to be done of selling process. The objective of this research is to create e-commerce of selling the agricultural products of farmers. Therefore, it is needed the method of User Centered Design in e-commerce designing by utilizing the income of user and behavior as well.
Students' Perception on SQ3R Method in Reading Comprehension with the Help of Technology

This study is aimed to know the students respond the use SQ3R method in reading comprehension with the help of technology. This traditional method combined with some activities using technology in the pre-, during-, and post-reading activities. All of them were the steps to increase students' understanding. The survey was conducted to 105 Indonesian EFL learners who enrolled a reading classroom. The result of the survey indicates that the majority of students stated that the method of SQ3R combined with technology is beneficial for them to achieve their goal in reading.
Auditable Compressed Storage

Outsourcing data to the cloud for personal use is becoming an everyday trend rather than an extreme scenario. The frequent outsourcing of data increases the possible attack window because users do not fully control their personal files. Typically, once there are established secure channels between two endpoints, communication is considered secure. However, in the cloud model the receiver–the cloud–cannot be fully trusted, either because it has been under adversarial control, or because it acts maliciously to increase its revenue by deleting infrequent accessed file blocks. One approach used by current literature to address the aforementioned security concerns is via Remote Data Integrity Checking (RDIC) protocols, whereby a data owner can challenge an untrusted cloud service provider (CSP) to prove faithful storage of its data. Current RDIC protocols assume that the original data format remains unchanged. However, users may wish to compress their data in order to enjoy less charges. In that case, current RDIC protocols become impractical because, each time compression happens on a file, the user has to run a new RDIC protocol. In this work we initiate the study for Auditable Compressed Storage (ACS). After defining the new model we instantiate two protocols for different widely used compression techniques: run length encoding and Huffman encoding. In contrast with conventional RDIC, our protocols allow a user to delegate the compression to the cloud in a provably secure way: The client can verify correctness of compression without having to download the entire uncompressed file and check it against the compressed one.
Adversarial Representation Learning for Dynamic Scene Deblurring: A Simple, Fast and Robust Approach

In this paper, we investigate a novel learning-based method for dynamic scene deblurring. Since the inference model is formulated as an encoder-decoder, the core task has turned to learning blur-invariant hidden features from the blurred images to a great degree. To achieve state-of-the-art results in terms of both deblurring accuracy and efficiency, a simple, robust and computationally efficient deep auto-encoder is developed tailored specifically for blind deblurring, which is learned in an adversarial fashion based on use of the recent Wasserstein generative adversarial networks. Thanks to the designed framework, the new model has shown comparable or superior performance both qualitatively and quantitatively to existing state-of-the-art methods. What is more important, instead of exploiting the multi-scale strategy as previous methods, our model is just single-scale capable of achieving 6 times more efficiency than the closest competitor by Nah et al. [1], which is a more complex multi-scale deep method.
A Semi-automatic Structure Learning Method for Language Modeling

This paper presents a semi-automatic method for statistical language modeling. The method addresses the structure learning problem of the linguistic classes prediction model (LCPM) in class-dependent N-grams supporting multiple linguistic classes per word. The structure of the LCPM is designed, within the Factorial Language Model framework, combining a knowledge-based approach with a data-driven technique. First, simple linguistic knowledge is used to define a set with linguistic features appropriate to the application, and to sketch the LCPM main structure. Next an automatic algorithm selects, based on Information Theory solid concepts, the relevant factors associated to the selected features and establishes the LCPM definitive structure. This approach is based on the so called Buried Markov Models [1]. Although only preliminary results were obtained, they afford great confidence on the method's ability to learn from the data, LCPM structures that represent accurately the application's real dependencies and also favor the training robustness.
Light Field Synthesis Using Inexpensive Surveillance Camera Systems

We present a light field synthesis technique that achieves accurate reconstruction given a low-cost, wide-baseline camera rig. Our system integrates optical flow with methods for rectification, disparity estimation, and feature extraction, which we then feed to a neural network view synthesis solver with wide-baseline capability. We propose two novel warping methods that improve the accuracy of disparity estimation and view synthesis. The methods enable the use of off-the-shelf surveillance camera hardware in a simplified and expedited capture workflow. A thorough analysis of the process and resulting view synthesis accuracy over state of the art is provided.
A New Paradigm of Addressing the Complexity of Entrepreneurial Community Design Leveraging Augmented Reality

Confronting the complex community design challenges, we demonstrated a novel paradigm of addressing the complexity by leveraging Augmented Reality incorporating information visualizations and the CityScope tangible interface. We next deployed CityScope Shenzhen Bay (CSB) as a practical case to quantify the entrepreneurial vitality of the Bay Area community. CSB incorporates information visualization toolkits, tangible interface for the rapid community design together with the simulation matrix of the community performance, which creates a crowd-sourcing platform to engage diverse stakeholders into the design and decision-making process towards the complexity of establishing an entrepreneurial community.
Design of Advertisement Tax Calculation Application Based on Android using the Extreme Programming at Dispenda Tasikmalaya

Advertise tax is one of the important sources of Regional Revenue to finance the implementation of Development and Government in the Region, so it needs to be regulated separately. So far the Regional Office of Financial and Asset Management in Tasikmalaya during the calculation of advertisement tax has used Microsoft Excel and calculators (calculators). But the obstacles that are often experienced when using Microsoft Excel are frequent advertisement tax calculation errors, when officers are in the field then the calculating machine (calculator) sometimes occurs errors that cause advertisement tax calculation errors and billboard price standard tables must always be taken because the officers do not memorize all advertisement standard price. Then the use of smartphone technology is needed to help the problems that exist in the calculation of advertisement tax in the field. The use of smartphones in the field of government cannot be maximized so that new breakthroughs are needed that can support work, especially work that requires precision in work. Therefore given a solution in this study is to create an advertisement tax calculation application that applies the advertisement tax rules to optimize the use of smartphones, especially Android in the field of government and facilitate the management of local revenue in the city of Tasikmalaya. The results of this study are in the form of prototypes of the advertisement tax calculation application.
Thick Wall Pipe Ultrasonic Signals Processing Based on Wavelet Packet and Singular Value Decomposition

The defect echo signals are often suffered from scan system and background noise when detecting internal and external flaws of thick wall pipe through ultrasonic testing method. Especially for detecting tiny flaws, the lower amplitude echo signals are easily submerged in noise. Wavelet packet threshold method can restrain the noise interference, however, the limitation and disadvantages of this method still exist. In response to this issue, a new approach of denoising based on wavelet packet and singular value decomposition (SVD) was advanced in this paper. The echo signals were decomposed with wavelet packets by utilizing the optimal wavelet functions. The high frequency noise was removed according to the energy distribution ratio of different frequency band and then the extracted energy distribution band was decomposed by singular value. Eventually an adaptive filter was realized after determining the effective echo signal area through singular entropy analysis. Experimentally analysis shows that the algorithm of denoising based on wavelet packet and singular value decomposition can not only remove the high frequency noise, but also effectively remove the low frequency noise, echo signal-to-noise ratio (SNR) was improved greatly.
Learning A Recurrent Neural Network for State Estimation using Filtered Sensory Data

State estimation is one of the essential tasks for autonomous systems, which is required in multiple applications, such as localization, object tracking, mapping, and many more. Various solution paradigms have been proposed by the scientific community to solve this well established problem. Some of the commonly used techniques are based on Bayesian inference, specifically the particle filter as it has the advantage of modeling arbitrary distributions without the unimodal assumption limitation. The particle filter has proven to be successful in many applications, however, a large set of particles is required in order to have robust estimates against noisy measurements and erroneous data association, which impedes the runtime performance of the filter. This study shows that a learning framework that trains a recurrent neural network with labeled data generated from the probabilistic estimation of a particle filter is capable of exploiting the expressive power of neural networks in order to capture the behavior of the filter and handle the noisy sensory information. The trained model is capable of performing the estimation with lower runtime complexity, making it applicable to numerous autonomous systems. This approach also proves to be helpful in situations when the ground truth data can not be accessed. We train and evaluate our strategy using raw GPS sensor measurements from the Oxford RobotCar dataset. The results show that the performance of the recurrent network closely matches that of the particle filter without exhaustive tuning and that the network is able to generalize effectively on test datasets as well.
Effects of Software Modifications and Development After an Organizational Change on Software Metrics Value

Deterioration of software quality developed by multiple organizations has become a serious problem. To predict software degradation after an organizational change, this paper investigates the influence of quality deterioration on software metrics by analyzing three software projects. To detect factors indicating a low evolvability, we focus on the relationships between the change in software metric values and refactoring tendencies. Refactoring after an organization change impacts the quality.
Cascading Outage Assessment using Thévenin Equivalent Static Contingency Assessment

This paper presents a new method for cascading outage assessment (BECCOM) that exploits Thévenin equivalent static contingency assessment (TESCA) for fast assessment of a post contingency state. Using this method the distance to cascading outages is assessed quickly, and the level of system stability after an initial disturbance and possible cascades is assessed. Hence, the method provides accurate information about the operational security and which combination of contingencies will lead to instability. The method's performance is tested and verified using he Nordic32 test system. Investigations of the algorithm's applicability to extensive palatalization reveal a good potential for real-time operation.
Extending a meta-tracing compiler to mix method and tracing compilation

Meta-interpreter-based just-in-time compiler frameworks provide a convenient way for language designers to implement efficient virtual machines. Those frameworks either employ tracing-based or method- (or partial evaluation) based strategies, which have their own pros and cons. This paper proposes an approach to enable both tracing- and method-based compilation so that the runtime can selectively apply an appropriate strategy to different parts of a program. The proposal basically extends a meta-tracing compiler to method-based compilation by roll backing at conditional branches, trace-splitting at loop entries, and not following at function calls. As a proof-of-concept, we implemented a tiny meta-tracing compiler in MinCaml by following the RPython's architecture and extended it to support both tracing- and method-based compilation.
A Study of Three Different Approaches to Point Placement on a Line in an Inexact Model

The point placement problem is to determine the locations of n distinct points on a line uniquely (up to translation and reflection) by making the fewest possible pairwise distance queries of an adversary (an adversary is just a source of true distances). A number of deterministic and randomized algorithms are available when distances are known exactly. In this paper, we discuss the problem in an inexact model. This is when distances returned by the adversary are not exact; instead, only upper and lower bounds on the distances are provided. We explore three different approaches to this problem. The first is based on an adaption of a distance geometry approach that Havel and Crippen [6] used to solve the molecular conformation problem. The second is based on a linear programming solution to a set of difference constraints that was used by Mumey [7] to solve a probe location problem arsing in DNA sequence analysis. The third is based on a heuristic called Stochastic Proximity Embedding, proposed by Agrafiotis [8]. Extensive experiments were carried out to determine the most promising approach vis-a-vis two parameters: run-time and quality of the embedding, measured by computing a certain stress function.
Contribution to a New Approach to Analyzing Arabic Words

The richness of the morphology of the Arabic language raises the problem of ambiguities at the different levels of Automatic Natural Language Processing. By addressing this problem at root level, which has not yet experienced a standard approach. In addition, the old techniques of rooting have limits that weaken the process of root extraction. In this paper, we propose a new approach to rooting based on two finite state automata.
Optimal Scheduling of Generators and BESS using Forecasting in Power System with Extremely Large Photovoltaic Generation

Large scale integration of renewable energy sources (RES) can cause supply demand imbalances due to its uncertainty. In Japanese power systems the photovoltaic (PV) generation is growing rapidly. PV forecasting with energy storage systems can be used in Unit Commitment (UC) to reduce these imbalances. In this study Battery Energy Storage systems (BESS) and day-ahead PV forecasting with prediction intervals have been used to examine the imbalances. The day-ahead UC of thermal generators and day-ahead optimal BESS charging and discharging is calculated with different BESS inverter capacities and BESS energy capacities. Then the power shortfall and surplus of PV power in the target day are calculated. The simulation is run for 3 months from April to June 2010 for Kanto area power system of Japan.
Moving Image Scene Recognition and Its Application to Highly-Safe Intelligent Systems

High-accuracy recognition of dangerous scenes from the input image information, is essential to realize a highly-safe intelligent system which autonomously makes danger avoidance in the real-world environment where human lives. There are many kinds of dangerous scenes which can be recognized by using moving images such as a violence scene. In this paper, optical flow between adjacent frames for an input moving image is represented by an RGB image. A still image obtained by converting a frame at a certain time into grayscale is superposed with the RGB optical flow image. For the superposed image at each time, a scene recognition result can be obtained by combining a convolutional neural network for extracting features and a support vector machine for classification. By taking majority of these scene recognition results, high-accuracy scene recognition for a moving image can be achieved.
Data Mining Based on Technical Demand of Power Enterprises

This paper briefly introduces the application of data mining in the technical requirements of power enterprises and the flow of K-Means algorithm. The data of an evaluation index is given. The K-Means algorithm is used for data mining of clustering analysis. The experimental results show that the algorithm has good clustering effect and the effective analysis conclusion is obtained.
Automatic Infogram Generation for Online Journalism

Infographics is a tool for data visualization. It makes data easy to understand and interpret. An infographic is defined as a visual representation for data like a chart or a diagram. Infographics can help in many fields including education. This is due to the fact that information can be easily memorized and understood if was given in a visual form. Infographics are found everywhere and used in many different fields. Facebook timeline is considered as an infographic. On another hand, online journalism is increasingly gaining popularity. It is also considered as a source of big data that is rapidly expanding. Online newspapers and magazines provide a large population with daily important information. The aim of the work is to use data visualization, infographics and Natural Language Processing (NLP) techniques in online journalism. The aim of the work is to automatically visualize the information in an article in the form of infographics.
A Study on Word Vector Dimensions for Sentence Classifications Using Convolutional Neural Networks

Recently, convolutional neural networks (CNNs) have achieved remarkable results on sentence classification problems. In these approaches, each word in the sentences is transformed to real number vectors (called word vectors) and the sentences as input data to the CNN are represented by the sequences of the word vectors. A dataset for training and testing for the CNN includes the large number of words, therefore the word vectors are embedded so high-dimentional space. As a result of this, the input data space of the CNN becomes very high. When the input data have high dimension, much training data are required for enough training of the CNN. It is not always possible, however, to get enough number of data for training. If the enough data cannot prepare for learning, it is desirable to decrease the dimension of input data. This paper shows the results that the smaller dimensional word vectors are applied to sentence classifications by CNNs. The results have shown that some dimensionality reduction does not effect too much to the accuracy of the sentence classifications by CNNs.
Domain-Agnostic Video Prediction from Motion Selective Kernels

Existing conditional video prediction approaches train a network from large databases and generalise to previously unseen data. We take the opposite stance, and introduce a model that learns from the first frames of a given video and extends its content and motion, to, e.g., double its length. To this end, we propose a dual network that can use in a flexible way both dynamic and static convolutional motion kernels, to predict future frames. We demonstrate experimentally the robustness of our approach on challenging videos in-the-wild and show that it is competitive w.r.t related baselines.
Educational activity of students as a key element of the educational process in a higher educational institution

The article is devoted to the study of the peculiarities of the organization of educational activity in a higher educational institution. It is prepared on the basis of monitoring results of students of higher educational institutions in Penza. More than 410 students from 6 regional universities took part in the monitoring.&#x0D; Elements of educational activity of students, such as participation in the educational process, building relationships in the university, independent work of students are considered.&#x0D; The main problems of students in the organization of the educational process, the features of the activity of students in training classes, during the passing of tests and examinations are revealed. The psychological atmosphere in higher education institutions in which students are trained, the level of building relationships in the student environment is analyzed. The answers to many questions on the educational activities of students are answered.&#x0D; Based on the results of the study, recommendations were developed to improve the effectiveness of student learning activities.