Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversary A3C for Robust Reinforcement Learning

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement Learning (RL) algorithm for a wide range of tasks, such as Atari games and robot control. The agent learns policies and value function through trial-and-error interactions with the environment until converging to an optimal policy. Robustness and stability are critical in RL; however, neural network can be vulnerable to noise from unexpected sources and is not likely to withstand very slight disturbances. We note that agents generated from mild environment using A3C are not able to handle challenging environments. Learning from adversarial examples, we proposed an algorithm called Adversary Robust A3C (AR-A3C) to improve the agent's performance under noisy environments. In this algorithm, an adversarial agent is introduced to the learning process to make it more robust against adversarial disturbances, thereby making it more adaptive to noisy environments. Both simulations and real-world experiments are carried out to illustrate the stability of the proposed algorithm. The AR-A3C algorithm outperforms A3C in both clean and noisy environments.
Breast Cancer Detection In Data Mining : A Review

The purpose of this paper was during the literature review of journals and publications in the field of data mining in computer science and engineering. The research focused on more recent publications in breast cancer. In this paper we have discussed various evaluation and technological articles on breast cancer analysis and prediction. Also we focus on current research being carried out using the data mining algorithms and techniques, to enhance the breast cancer identification and diagnosis.
Question Answering with Deep Learning

In this report, we describe our approach to the question answering problem by first reducing it to a reading comprehension task. Our models parse the question as well as a document likely to contain the answer we are looking for and generate their predictions from both of them. We combine common neural networks techniques with more advanced deep learning stemming from recent natural language processing research, and we provide the first publicly available implementation of an attention model applied to reading comprehension.
Schur Complement Based Method of Moments Solution for 2 D Scattering Problem from Mixed Dielectric-conductor Targets

In this paper, we deal with the two dimensional scattering problem from mixed dielectric-conductor targets. In particular, we analyze a Schur-complement based Method of Moments technique as the solution procedure. In fact, different solutions to the mixed dielectricconductor scatterer problem [1–3] and several works on Schur preconditioners can be found in the literature [4, 5]. Here, what we propose is simply that the currents on different kinds of scatterer must be appended in different vectors. For this purpose, we use a rectangular mesh to discretize the dielectric scatterers. Pulse functions are used as basis functions and the integral of the Green’s function on the discretized rectangles is analytically calculated by using the wellknown circle approximation [6]. For the conductor objects the surface is discretized into points and the integrals of Green’s function is numerically calculated on the lines which connect the discretized points. Finally, dirac-delta functions are used as weighting functions and an equation system for the currents on the scatterers are obtained. As stressed above, the currents on the different kind of materials (dielectric-conductor) are casted into two different vectors. Then the inversion of “the object equation” are done by means of the Schur complement. By doing so, the inversion of the large Green matrix turns into the inversion of two subblocks of this matrix. The accuracy of this Schur complement based Method of Moments solution is tested against the analytical solution of the scattering from a dielectric coated conductor. Moreover, an error rate analysis is made by comparing the theoretical expressions with the simulated fields. Obtained results show that the Schur complement based inversion of object equation increases the validity range of the solution obtained from Method of Moments.
The Technological Elements of Artificial Intelligence

We have seen in the past decade a sharp increase in the extent that companies use data to optimize their businesses. Variously called the `Big Data' or `Data Science' revolution, this has been characterized by massive amounts of data, including unstructured and nontraditional data like text and images, and the use of fast and flexible Machine Learning (ML) algorithms in analysis. With recent improvements in Deep Neural Networks (DNNs) and related methods, application of high-performance ML algorithms has become more automatic and robust to different data scenarios. That has led to the rapid rise of an Artificial Intelligence (AI) that works by combining many ML algorithms together – each targeting a straightforward prediction task – to solve complex problems. We will define a framework for thinking about the ingredients of this new ML-driven AI. Having an understanding of the pieces that make up these systems and how they fit together is important for those who will be building businesses around this technology. Those studying the economics of AI can use these definitions to remove ambiguity from the conversation on AI's projected productivity impacts and data requirements. Finally, this framework should help clarify the role for AI in the practice of modern business analytics and economic measurement.
Log-period dipole array optimization

The log periodic dipole array is a commonly used broadband antenna. Design equations exist for calculating the size and spacings of the wires that make up the antenna. These equations fail to take into account the electromagnetic interactions between the wires, though. The Numerical Electromagnetics Code is an excellent wire modeling code for analyzing the full electromagnetic interactions of a wire antenna. In this paper we use the Nelder-Mead downhill simplex algorithm and a genetic algorithm to optimize the design of a log periodic dipole array. Modeling results show that a hybrid genetic algorithm/Nelder-Mead downhill simplex algorithm produce superior antenna designs to traditional analytical approaches or using the genetic algorithm or Nelder-Mead algorithm alone.
ClusCo: clustering and comparison of protein

Background: The development, optimization and validation of protein modeling methods require efficient tools for structural comparison. Frequently, a large number of models need to be compared with the target native structure. The main reason for the development of Clusco software was to create a high-throughput tool for all-versus-all comparison, because calculating similarity matrix is the one of the bottlenecks in the protein modeling pipeline. Results: Clusco is fast and easy-to-use software for high-throughput comparison of protein models with different similarity measures (cRMSD, dRMSD, GDT TS, TM-Score, MaxSub, Contact Map Overlap) and clustering of the comparison results with standard methods: K-means Clustering or Hierarchical Agglomerative Clustering. Conclusions: The application was highly optimized and written in C/C++, including the code for parallel execution on CPU and GPU, which resulted in a significant speedup over similar clustering and scoring computation programs.
Chapter 2 Survey of Current Developments

Heterogeneous information network (HIN) provides a new paradigm to manage networked data. Meanwhile, it also introduces new challenges for many data mining tasks. Here, we give a brief survey on recent developments of this ﬁeld. Concretely, we have analyzed more than 100 referred papers published in the referred conferences and journals in recent years and divided them into seven categories according to their data mining tasks. In this chapter, we will summarize the developments on these seven main data mining tasks. Moreover, these data mining tasks are coarsely ordered from basic task to advanced task.
A Decision-Making Technique for Financial Grant Allocation to Research Projects

The importance of financial support to scientific research is increasing due to international competition. Consequently, National, State and institutional foundations that provide financial grant to research projects face the responsibility of selecting projects efficiently. Hence, this paper proposes a technique to support the decision-making process of grant allocation to research projects. The selection process aims maximizing the overall cost-benefit relation. The data used in this study were from financial grant bids of the Research Foundation of the State of Tocantins – FAPT, in Brazil. The selection problem in question is a variation of the classic combinatorial optimization problem, which can be solved by the Tabu Search algorithm. Results have showed that this optimization technique provides a quick and accurate support to the selection process considering cost-effective criteria.
DASH over CCN : A CCN Use-Case for a Social Media based Collaborative Project

Social networks have become an integral part of modern life, driving more and faster communication than ever before. For politics, business, and pleasure these new connections are shaping our world. SocialSensor is a European funded project (FP7) that is developing a new framework for enabling realtime multimedia indexing, search, and access across multiple social media sources1. For this reason, efficient content distribution and maintaining Quality of Service (QoS) under difficult network conditions is essential. Content Centric Networking (CCN) [1] has been designed for efficient content delivery and is also well suited for multimedia applications. To ensure QoS in difficult network conditions, i.e., large round trip times and tremendous bandwidth fluctuations, it is necessary to adapt the content dynamically according to these conditions. Dynamic Adaptive Streaming over HTTP (DASH) is an emerging standard for adaptive streaming over HTTP, recently ratified by ISO/IEC MPEG [2, 3]. In this paper, we report on our findings of applying DASH over CCN in the context of a larger collaborative project. The Alpen-Adria-Universität (AAU) Klagenfurt has contributed significantly to the standardtization of MPEG-DASH and provides a borad range of tools 2 such as the DASH VLC Plugin, DASHEncoder, libdash, MPD Validator, and DASH-JS (JavaScript client for instant Web integration) [4]. Like JCPConsult, the AAU is a partner in the SocialSensor project. DASH supports different media formats, e.g., segmented ISO Base Media File Format (ISOBMFF) [5], which divide the media file into segments of different bit rates, resolutions, etc. These segments are represented by their URL in a so-called Media Presentation Description (MPD) file. At runtime the DASH client automatically downloads the most appropriate segment based on the users’ context, i.e., bandwidth conditions, preferences, etc. Although DASH is primarily designed for HTTP, its modular design allows HTTP to be substituted by CCN (or any other delivery protocol). In contrast to the native DASH approach, segments can be cached more efficiently in the network which reduces latency and congestion. Figure 1 presents the architec-
Improved 1 D and 2 D barcode detection with morphological operations ?

Barcode technology is the pillar of automatic identification, that is used in a wide range of real-time applications with various types of codes. The different types of codes and applications impose special problems, so there is a continuous need for solutions with improved effectiveness. There are several methods for barcode localization, that are well characterized by accuracy and speed. Particularly, high-speed processing places need automatic barcode localization, e.g. conveyor belts, automated production, where missed detections cause loss of profit. In this paper, we mainly deal with segmentation of images with 1D barcode, but also analyze the operation of different methods for 2D barcode images as well. Our goal is to detect automatically, rapidly and accurately the barcode location by the help of extracted features. We compare some published method from the literature, which basically rely on the contrast between the background and the shape that represent the code. We also propose a novel algorithm, that outperforms the others in both accuracy and efficiency, and also improves on the detection performance of our previous algorithm in detecting 2D codes.
2018-0101 Suggesting Missing Information in Text Documents

Suggesting Missing Information in Text Documents Grant Michael Hodgson Department of Computer Science, BYU Master of Science A key part of contract drafting involves thinking of issues that have not been addressed and adding language that will address the missing issues. To assist attorneys with this task, we present a pipeline approach for identifying missing information within a contract section. The pipeline takes a contract section as input and includes 1) identifying sections that are similar to the input section from a corpus of contract sections; and 2) identifying and suggesting information from the similar sections that are missing from the input section. By taking advantage of sentence embedding and principal component analysis, this approach suggests sentences that are helpful for finishing a contract. We show that sentence suggestions are more useful than the state of the art topic suggestion algorithm by synthetic experiments and a user study.
RECOGNITION USING SUPPORT VECTOR MACHINE

The Sign language is very important for people who have hearing and speaking deficiency generally called Deaf and Mute. It is the only mode of communication for such people to convey their messages and it becomes very important for people to understand their language. In this paper, we have implemented the algorithm of extracting Histogram of Gradient Orientation (HOG) features and these features are used to pass in an Support Vector Machine (SVM) to construct a training model which will further classify the test images given by the user depending on the respective feature vector. We have developed a system to recognize alphabets characters using two feature vectors namely HOG and Scale Invariant Feature Transform (SIFT) features and tested both of them such that it will give the optimal result . The real time images will be captured first by means of webcam and then stored in directory and on recently captured image, feature extraction will take place to identify which sign has been articulated by the user through algorithm in MATLAB. After the comparison, the result will be produced in accordance with the input image to the image stored for a specific letter already in the database. KeywordsHOG, libSVM, SIFT, SVM, Vision based hand Gesture.
Content Provider Server for Wireless Mobile Device

This paper described the process of building a content provider server to give a service for mobile user wirelessly. That service could be accessed from a personal digital assistant (PDA). The system is built from ASP.NET technology combined with the Content Management System from DotNetNuke. This will make administrator’s managing the system; such as adding content, creating new voucher, viewing user billing and viewing statistic, easy. The system is also using the SQL Server technology, which hopefully compatible to most PDA; which use Windows Mobile Operating System. The system has been tested by using a PDA to access contents via wireless LAN. The result is server is able to provide the data that was requested by the user. Furthermore, all of the features that have been developed could also work well. Keyword: Content Provider, Mobile Device, Wireless Access
PTEN / PI 3 K / Akt pathway is involved is involved in the protection of hydrogen sulfide against myocardium ischemic / reperfusion injury

Myocardial ischemia-reperfusion injury (MIRI) is one of the important reasons for the high mortality of ischemic heart disease in patients who have undergone a blood supply reconstruction. Hydrogen sulfide (H2S) has been thought of to be just a toxic gas with a strong odor of rotten eggs for hundreds of years, but it has been demonstrated that H2S plays a protective role in the pathogenesis and development of heart diseases, especially in MIRI. In the present study, we treated rats with H2S after MIRI and then observed its effects on heart function parameters following in situ ischemia/reperfusion. Multiple oxidative products, myeloperoxidase and proinflammatory cytokines in the myocardium were measured to evaluate the anti-oxidative and anti-inflammatory effect of H2S. The role of phosphatase and tensin homologue (PTEN)/PI3K/Akt pathway was examined to explore the underlying mechanism. The treatment of H2S for seven consecutive days exhibited dramatic improvement in cardiac function, as manifested by increased LVSP and ± (dP/dt) max, and decreased LVDP. PTEN mRNA and expression were decreased by H2S. PI3K/Akt pathway was activated by H2S and PTEN inhibitor bisperoxovanadium (Phen) but de-activated by PI3K inhibitor LY294002. The heart function was enhanced by Phen but worsened by LY294002, suggesting the involvement of PTEN/PI3K/Akt pathway. These results revealed that H2S may exert its cardioprotection by inactivation of PTEN, which in turn activates the PI3K/Akt signaling and increases survival in the MIRI model.
“ By the people , for the people ” : Can People Really Design Their Own Information Systems ?

This paper describes a year in a design project; the objective of this project was to create a concept for a next generation Enterprise Resource Planning (ERP) system. Over the course of the project, the existing system’s users were enticed to specify their needs and requirements, to create new designs to remove unsolved problems, and to evaluate and enhance a prototype of the new design. The goal was to enable people who were unfamiliar with information system (IS) design to improve their current work processes and tools and to produce new creative design solutions for the future releases of their information system. Traditionally, user-centered design (UCD) processes involve studying and analyzing users in order to enable the designer or researcher to create a new improved design. In this project, however, we encouraged the users to become active participants in creating the contents for the new design artifacts, and our roles (the designer and researcher roles) became more editorial. The seven guidelines for design science in information systems research, from the seminal article by Hevner et al., are used as an analysis framework to recount the various aspects of the user-generated artifacts and user involvement. In the end we argue the potential and observed success of information systems designed by their users.
Fault-Tolerant Storage and Implications for the Cloud

Fault-tolerance is an essential aspect of any storage system – data must be correctly preserved and transmitted in order to be useful. The emergence of cloud computing as a service requires storage systems to be fault-tolerant in the same sense as traditional computing systems, as well as in new ways to accommodate a distributed system. This paper looks at the current state of reliability in storage systems and how things change in the cloud.
Draft Soil microbial communities of three grassland ecosystems in the Bayinbuluke

Journal: Canadian Journal of Microbiology Manuscript ID cjm-2017-0585.R1 Manuscript Type: Note Date Submitted by the Author: 21-Nov-2017 Complete List of Authors: Shao, Keqiang; Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Gao, Guang; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Is the invited manuscript for consideration in a Special Issue? : N/A
Improving Processing Data Skills on Econometrics. Could Cybernetic Learning Theory Practice Do it

Implementation of cybernetic practice in econometric courses to improve student learning outcomes and data processing skills. This study is a classroom action research with two cycles. Cybernetic learning theory practice of econometric courses is learning that collaborates on theoretical knowledge and practice with Eviews support, an information technology software. By results: 1) Lecturer activity in cybernetic learning theory practice increases from cycle 1 to cycle 2; 2) Student activity during cybernetic learning theory practice also increases; 3) Student learning outcomes after applying cybernetic learning theory improved practice; 4) data processing of student data during cybernetic learning theory-increased practice. Theory of cybernetic learning-practice on information technology and information should be processed, specifically to build a strong memory of information received by students. Thus, the impact of using Cybernetic-practice learning techniques can improve student learning outcomes and data processing skills. Keywords— Cybernetic learning theory-practice, student learning outcomes, data processing skills
cXcr 3 is a prognostic marker and a potential target for patients with solid tumors : a meta-analysis

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Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users

: With the ubiquitous use of location-based services, large-scale individual-level location data has been widely collected through location-awareness devices. Geoprivacy concerns arise on the issues of user identity de-anonymization and location exposure. In this work, we investigate the effectiveness of geomasking techniques for protecting the geo-privacy of active Twitter users who frequently share geotagged tweets in their home and work locations. By analyzing over 38,000 geotagged tweets of 93 active Twitter users in three U.S. cities, the two-dimensional Gaussian masking technique with proper standard deviation settings is found to be more effective to protect user’s location privacy while sacriﬁcing geospatial analytical resolution than the random perturbation masking method and the aggregation on trafﬁc analysis zones. Furthermore, a three-dimensional theoretical framework considering privacy, analytics, and uncertainty factors simultaneously is proposed to assess geomasking techniques. Our research offers insights into geoprivacy concerns of social media users’ georeferenced data sharing for future development of location-based applications and services.
The One-Time Methodology : Encapsulating Application Data

This presentation will introduce a simple but powerful concept called the “One-Time Methodology” for encapsulating data in applications. Extensive SAS/AF® development experience is not required, though it will help to have had exposure to the basics of SAS/AF and SCL (SAS Component Language). The methodology can be applied in other application languages.