nils-herrmann/clean_scirepeval huggingface dataset:
A new infrared image enhancement algorithm

In order to solve the problem of fuzzy details, low contrast and poor visual effect of infrared image. A novel scheme for infrared image enhancement is proposed in this paper, which solved drawbacks of the histogram equalization algorithm that loss detail information of image and some areas are bleached, greatly improved the visual effect of the infrared image. Firstly, bilateral filter is performed on the original infrared image, we can obtain a base layer and a detail layer. At base layer, most value normalizing and gray linear mapping is applied to enhance the global contrast. At detail layer, S-curve algorithm is applied to enhance detail information. Finally linear weighted synthesis is used to achieve the integration of base layer and detail layer, to improve the visual effect of the infrared image. This algorithm could also be applied to 14 bit infrared image, compressing 14bit image to 8 bit image and achieving good detail enhancement. Through simulation on MATLAB, the results show that the proposed scheme could noticeably enhance image contrast, and obtain a good enhanced infrared image, at end.
The Impact of Agile Methodology ( DSDM ) on Software Project

Software project management leads to success and failure of software project. Software project management include planning, managing and controlling different knowledge areas such as scope, time, cost, quality, risk, human resource, stakeholders, and procurement management. The key issue of software project management is to manage scope, time and cost for a project. Requirement of user changes throughout life of project, and those effect time and cost of project and other knowledge areas as well. Agile methodology is framework for software development with reduced risk. Agile is iterative software development methodology that focuses on frequent and faster delivery, and entertain customer changes. There is positive impact on development cost, time and productivity by switching from traditional waterfall model to agile model. This paper examines that how agile methodology affect different aspect of software project management. Our literature review proposes that agile methodology helps in software project management that leads to the success of software.
A Probabilistic On-Line Mapping Algorithm for Teams of Mobile Robots

An efficient probabilistic algorithm for the concurrent mapping and localization problem that arises in mobile robotics is presented. The algorithm addresses the problem in which a team of robots builds a map on-line while simultaneously accommodating errors in the robots’ odometry. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an on-line algorithm that can cope with large odometric errors typically found when mapping environments with cycles. The algorithm can be implemented in a distributed manner on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring three-dimensional maps, which capture the structure and visual appearance of indoor environments in three dimensions. mobile robotics, map acquisition, localization, robotic exploration, multi-robot systems, threedimensional modeling
Intelligence : Knowns and Unknowns

Ulric Neisser (Chair) Gwyneth Boodoo Thomas J. Bouchard, Jr. A. Wade Boykin Nathan Brody Stephen J. Ceci Diane E Halpern John C. Loehlin Robert Perloff Robert J. Sternberg Susana Urbina Emory University Educational Testing Service, Princeton, New Jersey University of Minnesota, Minneapolis Howard University Wesleyan University Cornell University California State University, San Bernardino University of Texas, Austin University of Pittsburgh Yale University University of North Florida
Translating Videos to Natural Language Using Deep Recurrent Neural Networks

Solving the visual symbol grounding problem has long been a goal of artificial intelligence. The field appears to be advancing closer to this goal with recent breakthroughs in deep learning for natural language grounding in static images. In this paper, we propose to translate videos directly to sentences using a unified deep neural network with both convolutional and recurrent structure. Described video datasets are scarce, and most existing methods have been applied to toy domains with a small vocabulary of possible words. By transferring knowledge from 1.2M+ images with category labels and 100,000+ images with captions, our method is able to create sentence descriptions of open-domain videos with large vocabularies. We compare our approach with recent work using language generation metrics, subject, verb, and object prediction accuracy, and a human evaluation.
Internet of Things (IoT): Security challenges, business opportunities & reference architecture for E-commerce

The Internet of Things (IoT) represents a diverse technology and usage with unprecedented business opportunities and risks. The Internet of Things is changing the dynamics of security industry & reshaping it. It allows data to be transferred seamlessly among physical devices to the Internet. The growth of number of intelligent devices will create a network rich with information that allows supply chains to assemble and communicate in new ways. The technology research firm Gartner predicts that there will be 26 billion installed units on the Internet of Things (IoT) by 2020[1]. This paper explains the concept of Internet of Things (IoT), its characteristics, explain security challenges, technology adoption trends & suggests a reference architecture for E-commerce enterprise.
Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers - Comparison of Two Cross-Entropy Optimization Approaches

In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities in the GPS-denied environments using Monocular Visual-Inertial SLAM-based strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate localization information. In the Matlab Simulink-based training framework, the Scaling Factor (SF) is adjusted according to the collision avoidance task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the control performances. After obtained the optimal SF and MF, 64 % of rules has been reduced (from 125 rules to 45 rules), and a large number of real see-and-avoid tests with a quadcopter have done. The simulation and experiment results show that this new proposed FLC can precisely navigates the Fail-Safe UAV to avoid the obstacle, obtaining better performances compared to only SF optimization-based FLC. To our best knowledge, this is the first work to present the optimized FLC using Cross-Entropy method in both SF and MF optimization, and apply it in the UAV.
State-Frequency Memory Recurrent Neural Networks

Modeling temporal sequences plays a fundamental role in various modern applications and has drawn more and more attentions in the machine learning community. Among those efforts on improving the capability to represent temporal data, the Long Short-Term Memory (LSTM) has achieved great success in many areas. Although the LSTM can capture long-range dependency in the time domain, it does not explicitly model the pattern occurrences in the frequency domain that plays an important role in tracking and predicting data points over various time cycles. We propose the State-Frequency Memory (SFM), a novel recurrent architecture that allows to separate dynamic patterns across different frequency components and their impacts on modeling the temporal contexts of input sequences. By jointly decomposing memorized dynamics into statefrequency components, the SFM is able to offer a fine-grained analysis of temporal sequences by capturing the dependency of uncovered patterns in both time and frequency domains. Evaluations on several temporal modeling tasks demonstrate the SFM can yield competitive performances, in particular as compared with the state-of-the-art LSTM models.
Immersive interfaces for engagement and learning.

Immersion is the subjective impression that one is participating in a comprehensive, realistic experience. Interactive media now enable various degrees of digital immersion. The more a virtual immersive experience is based on design strategies that combine actional, symbolic, and sensory factors, the greater the participant's suspension of disbelief that she or he is "inside" a digitally enhanced setting. Studies have shown that immersion in a digital environment can enhance education in at least three ways: by allowing multiple perspectives, situated learning, and transfer. Further studies are needed on the capabilities of immersive media for learning, on the instructional designs best suited to each type of immersive medium, and on the learning strengths and preferences these media develop in users.
The Rise of Consumer Health Wearables: Promises and Barriers

Will consumer wearable technology ever be adopted or accepted by the medical community? Patients and practitioners regularly use digital technology (e.g., thermometers and glucose monitors) to identify and discuss symptoms. In addition, a third of general practitioners in the United Kingdom report that patients arrive with suggestions for treatment based on online search results . However, consumer health wearables are predicted to become the next “Dr Google.”One in six (15%) consumers in the United States currently uses wearable technology, including smartwatches or fitness bands. While 19 million fitness devices are likely to be sold this year, that number is predicted to grow to 110 million in 2018 . As the line between consumer health wearables and medical devices begins to blur, it is now possible for a single wearable device to monitor a range of medical risk factors . Potentially, these devices could give patients direct access to personal analytics that can contribute to their health, facilitate preventive care, and aid in the management of ongoing illness. However, how this new wearable technology might best serve medicine remains unclear.
Learning to communicate: Channel auto-encoders, domain specific regularizers, and attention

We address the problem of learning an efficient and adaptive physical layer encoding to communicate binary information over an impaired channel. In contrast to traditional work, we treat the problem an unsupervised machine learning problem focusing on optimizing reconstruction loss through artificial impairment layers in an autoencoder (we term this a channel autoencoder) and introduce several new regularizing layers which emulate common wireless channel impairments. We also discuss the role of attention models in the form of the radio transformer network for helping to recover canonical signal representations before decoding. We demonstrate some promising initial capacity results from this approach and address remaining challenges before such a system could become practical.
A visual approach for driver inattention detection

Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the driver’s visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.
Effects of increasing concentrations of glycerol in concentrate diets on nutrient digestibility, methane emissions, growth, fatty acid profiles, and carcass traits of lambs.

Two experiments were conducted to evaluate the effects of increasing concentrations of glycerol in concentrate diets on total tract digestibility, methane (CH4) emissions, growth, fatty acid profiles, and carcass traits of lambs. In both experiments, the control diet contained 57% barley grain, 14.5% wheat dried distillers grain with solubles (WDDGS), 13% sunflower hulls, 6.5% beet pulp, 6.3% alfalfa, and 3% mineral-vitamin mix. Increasing concentrations (7, 14, and 21% dietary DM) of glycerol in the dietary DM were replaced for barley grain. As glycerol was added, alfalfa meal and WDDGS were increased to maintain similar concentrations of CP and NDF among diets. In Exp.1, nutrient digestibility and CH4 emissions from 12 ram lambs were measured in a replicated 4 × 4 Latin square experiment. In Exp. 2, lamb performance was evaluated in 60 weaned lambs that were blocked by BW and randomly assigned to 1 of the 4 dietary treatments and fed to slaughter weight. In Exp. 1, nutrient digestibility and CH4 emissions were not altered (P = 0.15) by inclusion of glycerol in the diets. In Exp.2, increasing glycerol in the diet linearly decreased DMI (P < 0.01) and tended (P = 0.06) to reduce ADG, resulting in a linearly decreased final BW. Feed efficiency was not affected by glycerol inclusion in the diets. Carcass traits and total SFA or total MUFA proportions of subcutaneous fat were not affected (P = 0.77) by inclusion of glycerol, but PUFA were linearly decreased (P < 0.01).  10t-18:1, linoleic acid (18:2 n-6) and the n-6/n-3 ratio were linearly reduced (P < 0.01) and those of 18:0 (stearic acid), 9c-18:1 (oleic acid), linearly increased (P < 0.01) by glycerol. When included up to 21% of diet DM, glycerol did not affect nutrient digestibility or CH4 emissions of lambs fed barley based finishing diets. Glycerol may improve backfat fatty acid profiles by increasing 18:0 and 9c-18:1 and reducing 10t-18:1 and the n-6/n-3 ratio.
Parametric Classification Techniques for Theater Ballistic Missile Defense

ne of the fundamental challenges in theater ballistic missile defense (TBMD) is ascertaining which element in the threat complex is the lethal object. To classify the lethal object and other objects in the complex, it is necessary to model how these objects will appear to TBMD sensors. This article describes a generic parametric approach to building classifier models. The process is illustrated with an example of building a classifier for an infrared sensor. The formulas for probability of classification error are derived. The probability of error for a proposed classification scheme is vital to assessing its efficacy in system trade studies. (
Deaf Mute Communication Interpreter-A Review

Communication between the deaf and non-deaf has always been a very cumbersome task. This paper aims to cover the various prevailing methods of deaf-mute communication interpreter system. The two broad classification of the communication methodologies used by the deaf –mute people are Wearable Communication Device and Online Learning System. Under Wearable communication method, there are Glove based system, Keypad method and Handicom Touchscreen. All the above mentioned three sub-divided methods make use of various sensors, accelerometer, a suitable microcontroller, a text to speech conversion module, a keypad and a touch-screen. The need for an external device to interpret the message between a deaf –mute and non-deaf-mute people can be overcome by the second method i.e online learning system. The Online Learning System has different methods under it, five of which are explained in this paper. The five sub-divided methods areSLIM module, TESSA, Wi-See Technology, SWI_PELE System and Web-Sign Technology. The working of the individual components used and the operation of the whole system for the communication purpose has been explained in detail in this paper.
ChartSense: Interactive Data Extraction from Chart Images

Charts are commonly used to present data in digital documents such as web pages, research papers, or presentation slides. When the underlying data is not available, it is necessary to extract the data from a chart image to utilize the data for further analysis or improve the chart for more accurate perception. In this paper, we present ChartSense, an interactive chart data extraction system. ChartSense first determines the chart type of a given chart image using a deep learning based classifier, and then extracts underlying data from the chart image using semi-automatic, interactive extraction algorithms optimized for each chart type. To evaluate chart type classification accuracy, we compared ChartSense with ReVision, a system with the state-of-the-art chart type classifier. We found that ChartSense was more accurate than ReVision. In addition, to evaluate data extraction performance, we conducted a user study, comparing ChartSense with WebPlotDigitizer, one of the most effective chart data extraction tools among publicly accessible ones. Our results showed that ChartSense was better than WebPlotDigitizer in terms of task completion time, error rate, and subjective preference.
A data mining framework to analyze road accident data

One of the key objectives in accident data analysis to identify the main factors associated with a road and traffic accident. However, heterogeneous nature of road accident data makes the analysis task difficult. Data segmentation has been used widely to overcome this heterogeneity of the accident data. In this paper, we proposed a framework that used K-modes clustering technique as a preliminary task for segmentation of 11,574 road accidents on road network of Dehradun (India) between 2009 and 2014 (both included). Next, association rule mining are used to identify the various circumstances that are associated with the occurrence of an accident for both the entire data set (EDS) and the clusters identified by K-modes clustering algorithm. The findings of cluster based analysis and entire data set analysis are then compared. The results reveal that the combination of k mode clustering and association rule mining is very inspiring as it produces important information that would remain hidden if no segmentation has been performed prior to generate association rules. Further a trend analysis have also been performed for each clusters and EDS accidents which finds different trends in different cluster whereas a positive trend is shown by EDS. Trend analysis also shows that prior segmentation of accident data is very important before analysis.
Learning table tennis with a Mixture of Motor Primitives

Table tennis is a sufficiently complex motor task for studying complete skill learning systems. It consists of several elementary motions and requires fast movements, accurate control, and online adaptation. To represent the elementary movements needed for robot table tennis, we rely on dynamic systems motor primitives (DMP). While such DMPs have been successfully used for learning a variety of simple motor tasks, they only represent single elementary actions. In order to select and generalize among different striking movements, we present a new approach, called Mixture of Motor Primitives that uses a gating network to activate appropriate motor primitives. The resulting policy enables us to select among the appropriate motor primitives as well as to generalize between them. In order to obtain a fully learned robot table tennis setup, we also address the problem of predicting the necessary context information, i.e., the hitting point in time and space where we want to hit the ball. We show that the resulting setup was capable of playing rudimentary table tennis using an anthropomorphic robot arm.
FAMILY MEDICINE: A MISSING LINK IN HEALTH SYSTEM OF PAKISTAN

By taking a global perspective in order to look at how the field of Medicine has diversified, we believe that we can come to see how the Family Physician has, over time, disappeared from it. Prior to the idea of the Family Physician, a single, non-specialist, Physician would be responsible for the oversight, diagnosis and treatment of a number of diseases. However, due to the ever increasing number of illnesses and diseases, over time, the role of the Family Physician became minimized, as Specialist Physicians of particular illnesses and diseases began emerging at speed. We now find ourselves, particularly in Pakistan, in dire need of more Family Physicians. In this review we look at how, in other parts of the world, Family Physicians – also referred to as General Practitioners, or ‘GPs’ in parts of Europe – are responsible for the preventive and curative provision to whole families. The principles of Family Medicine, we argue, are universal, and there are few contextual factors, such as geography, the availability of material and medical resources, as well as disease prevalence, that impact the practice of Family Physicians from country to country. By looking at these factors and by summing up the countries in which they are endemic, we believe we can then establish a link between the similarities in each First World Country, and provide a contrast between those and the things that impact practice in Third World Countries. We also examine the role of regulation, qualifications and contemporary primary and family care in Pakistan.
A Requirements-Driven Methodology for the Proper Selection and Configuration of Blockchains

In recent years, the interest in blockchain has grown exponentially, and nowadays it is foreseen as a technology with the potential to revolutionize the way data is maintained and transferred around the globe. The reason of this excitement is ascribable to the ability of enabling new forms of transactions and interactions between mistrusting and decentralized entities. Indeed, it has attracted interests and huge investments from enterprises, and it is predictable that in a near future many industries will adopt it. However, it is not a panacea and in some cases may even become useless or not convenient. Moreover, even when it can really constitute an added value, selecting the proper blockchain and configuring it may not be trivial. Trying to go beyond the hype and to address this problem, this paper proposes a methodology addressing: i) whether, given a specific problem requirements, the blockchain is a proper solution for it ii) in such a case which is the blockchain category more suitable, and finally iii) guiding the designer throughout its configuration.