nils-herrmann/clean_scirepeval huggingface dataset:
A Probabilistic Exclusion Principle for Tracking Multiple Objects

Tracking multiple targets is a challenging problem, especially when the targets are “identical”, in the sense that the same model is used to describe each target. In this case, simply instantiating several independent 1-body trackers is not an adequate solution, because the independent trackers tend to coalesce onto the best-fitting target. This paper presents an observation density for tracking which solves this problem by exhibiting a probabilistic exclusion principle. Exclusion arises naturally from a systematic derivation of the observation density, without relying on heuristics. Another important contribution of the paper is the presentation of partitioned sampling, a new sampling method for multiple object tracking. Partitioned sampling avoids the high computational load associated with fully coupled trackers, while retaining the desirable properties of coupling.
The ball-pivoting algorithm for surface reconstruction

The Ball-Pivoting Algorithm (BPA) computes a triangle mesh interpolating a given point cloud. Typically, the points are surface samples acquired with multiple range scans of an object. The principle of the BPA is very simple: Three points form a triangle if a ball of a user-specified radius p touches them without containing any other point. Starting with a seed triangle, the ball pivots around an edge (i.e., it revolves around the edge while keeping in contact with the edge's endpoints) until it touches another point, forming another triangle. The process continues until all reachable edges have been tried, and then starts from another seed triangle, until all points have been considered. The process can then be repeated with a ball of larger radius to handle uneven sampling densities. We applied the BPA to datasets of millions of points representing actual scans of complex 3D objects. The relatively small amount of memory required by the BPA, its time efficiency, and the quality of the results obtained compare favorably with existing techniques.
Predicting Stock Market Behavior using Data Mining Technique and News Sentiment Analysis

Stock market prediction has become an attractive investigation topic due to its important role in economy and beneficial offers. There is an imminent need to uncover the stock market future behavior in order to avoid investment risks. The large amount of data generated by the stock market is considered a treasure of knowledge for investors. This study aims at constructing an effective model to predict stock market future trends with small error ratio and improve the accuracy of prediction. This prediction model is based on sentiment analysis of financial news and historical stock market prices. This model provides better accuracy results than all previous studies by considering multiple types of news related to market and company with historical stock prices. A dataset containing stock prices from three companies is used. The first step is to analyze news sentiment to get the text polarity using naïve Bayes algorithm. This step achieved prediction accuracy results ranging from 72.73% to 86.21%. The second step combines news polarities and historical stock prices together to predict future stock prices. This improved the prediction accuracy up to 89.80%.
Advanced illumination techniques for GPU-based volume raycasting

Volume raycasting techniques are important for both visual arts and visualization. They allow efficient generation of visual effects and visualization of scientific data obtained by tomography or numerical simulation. Volume-rendering techniques are also effective for direct rendering of implicit surfaces used for soft-body animation and constructive solid geometry. The focus of this course is on volumetric illumination techniques that approximate physically based light transport in participating media. Such techniques include interactive implementation of soft and hard shadows, ambient occlusion, and simple Monte Carlo-based approaches to global illumination, including translucency and scattering.
Welding robots

U sing robots in industrial welding operations is common but far from being a streamlined technological process. The problems are with the robots, still in their early design stages and difficult to use and program by regular operators; the welding process, which is complex and not really well known; and the human-machine interfaces, which are nonnatural and not really working. In this article, these problems are discussed, and a system designed with the double objective of serving R&D efforts on welding applications and to assist industrial partners working with welding setups is presented. The system is explained in some detail and demonstrated using two test cases that reproduce two situations common in industry: multilayer butt welding, used on big structures requiring very strong welds, and multipoint fillet welding, used, for example, on structural pieces in the construction industry.
Sensing the future of HCI: touch, taste, and smell user interfaces

The senses we call upon when interacting with technology are restricted. We mostly rely on vision and hearing, and increasingly touch, but taste and smell remain largely unused. Although our knowledge about sensory systems and devices has grown rapidly over the past few decades, there is still an unmet challenge in understanding people's multisensory experiences in HCI. The goal is that by understanding the ways in which our senses process information and how they relate to one another, it will be possible to create richer experiences for human-­‐ technology interactions. To meet this challenge, we need specific actions within the HCI community. First, we must determine which tactile, gustatory, and olfactory experiences we can design for, and how to meaningfully stimulate them when people interact with technology. Second, we need to build on previous frameworks for multisensory design while also creating new ones. Third, we need to design interfaces that allow the stimulation of unexplored sensory inputs (e.g., digital smell), as well as interfaces that take into account the relationships between the senses (e.g., integration of taste and smell into flavor). Finally, it is vital to understand what limitations come into play when users need to monitor information from more than one sense simultaneously. Though much development is needed, in recent years we have witnessed progress in multisensory experiences involving touch. It is key for HCI to leverage the full range of tactile sensations (vibrations, pressure, force, balance, heat, coolness/wetness, electric shocks, pain and itch, etc.), taking into account the active and passive modes of touch and its integration with the other senses. This will undoubtedly provide new tools for interactive experience design, and will help to uncover the fine granularity of sensory stimulation and emotional responses.
V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

Most of the existing deep learning-based methods for 3D hand and human pose estimation from a single depth map are based on a common framework that takes a 2D depth map and directly regresses the 3D coordinates of keypoints, such as hand or human body joints, via 2D convolutional neural networks (CNNs). The first weakness of this approach is the presence of perspective distortion in the 2D depth map. While the depth map is intrinsically 3D data, many previous methods treat depth maps as 2D images that can distort the shape of the actual object through projection from 3D to 2D space. This compels the network to perform perspective distortion-invariant estimation. The second weakness of the conventional approach is that directly regressing 3D coordinates from a 2D image is a highly nonlinear mapping, which causes difficulty in the learning procedure. To overcome these weaknesses, we firstly cast the 3D hand and human pose estimation problem from a single depth map into a voxel-to-voxel prediction that uses a 3D voxelized grid and estimates the per-voxel likelihood for each keypoint. We design our model as a 3D CNN that provides accurate estimates while running in real-time. Our system outperforms previous methods in almost all publicly available 3D hand and human pose estimation datasets and placed first in the HANDS 2017 frame-based 3D hand pose estimation challenge. The code is available in1.
A visual framework for clustering memes in social media

The spread of "rumours" in Online Social Networks (OSNs) has grown at an alarming rate. Consequently, there is an increasing need to improve understanding of the social and technological processes behind this trend. The first step in detecting rumours is to identify and extract memes, a unit of information that can be spread from person to person in OSNs. This paper proposes four similarity scores and two novel strategies to combine those similarity scores for detecting the spread of memes in OSNs, with the end goal of helping researchers as well as members of various OSNs to study the phenomenon. The two proposed strategies include: (1) automatically computing the similarity score weighting factors for four elements of a submission and (2) allowing users to engage in the clustering process and filter out outlier submissions, modify submission class labels, or assign different similarity score weight factors for various elements of a submission using a visualization prototype. To validate our approach, we collect submissions on Reddit about five controversial topics and demonstrate that the proposed strategies outperform the baseline.
Hiding Text In Audio Using Multiple LSB Steganography And Provide Security Using Cryptography

: Steganography is an art and science of writing hidden messages in such a way that no one apart from the intended recipient knows the existence of the message.The maximum number of bits that can be used for LSB audio steganography without causing noticeable perceptual distortion to the host audio signal is 4 LSBs, if 16 bits per sample audio sequences are used.We propose two novel approaches of substit ution technique of audio steganography that improves the capacity of cover audio for embedding additional data. Using these methods, message bits are embedded into multiple and variable LSBs. These methods utilize upto 7 LSBs for embedding data.Results show that both these methods improve capacity of data hiding of cover audio by 35% to 70% as compared to the standerd LSB algorithm with 4 LSBs used for data embedding. And using encryption and decryption techniques performing cryptography. So for this RSA algorithm used. KeywordsInformation hiding,Audio steganography,Least significant bit(LSB),Most significant bit(MSB)
The Banff 97 working classification of renal allograft pathology.

Standardization of renal allograft biopsy interpretation is necessary to guide therapy and to establish an objective end point for clinical trials. This manuscript describes a classification, Banff 97, developed by investigators using the Banff Schema and the Collaborative Clinical Trials in Transplantation (CCTT) modification for diagnosis of renal allograft pathology.



Banff 97 grew from an international consensus discussion begun at Banff and continued via the Internet. This schema developed from (a) analysis of data using the Banff classification, (b) publication of and experience with the CCTT modification, (c) international conferences, and (d) data from recent studies on impact of vasculitis on transplant outcome.



Semiquantitative lesion scoring continues to focus on tubulitis and arteritis but includes a minimum threshold for interstitial inflammation. Banff 97 defines "types" of acute/active rejection. Type I is tubulointerstitial rejection without arteritis. Type II is vascular rejection with intimal arteritis, and type III is severe rejection with transmural arterial changes. Biopsies with only mild inflammation are graded as "borderline/suspicious for rejection." Chronic/sclerosing allograft changes are graded based on severity of tubular atrophy and interstitial fibrosis. Antibody-mediated rejection, hyperacute or accelerated acute in presentation, is also categorized, as are other significant allograft findings.



The Banff 97 working classification refines earlier schemas and represents input from two classifications most widely used in clinical rejection trials and in clinical practice worldwide. Major changes include the following: rejection with vasculitis is separated from tubulointerstitial rejection; severe rejection requires transmural changes in arteries; "borderline" rejection can only be interpreted in a clinical context; antibody-mediated rejection is further defined, and lesion scoring focuses on most severely involved structures. Criteria for specimen adequacy have also been modified. Banff 97 represents a significant refinement of allograft assessment, developed via international consensus discussions.
Modeling and Dynamic Simulation of Electric Power Steering System of Automobile Using Bond Graph Technique

Power Steering (EPS) systems have many merits over traditional hydraulic power steering systems in fuel economy, variable assist capabilities and favorable road feeling by virtue of adjustment assist characteristic with adopting assist motor in replacement of hydraulic pump. This paper aims at modeling the mechanical parts, the assist motor and electronic control unit by bond graph technique and simulating system dynamic performance regarding vehicle maneuverability and stability and driver’s steering feel by Matlab/Simulink. The simulation results provide that the bond graph EPS model can reflect stability and dynamic steering characteristic in effect.
Reference pricing , competition , and pharmaceutical expenditures : theory and evidence from a natural experiment

We study the impact of regulation on competition between brand-names and generics and pharmaceutical expenditures using a unique policy experiment in Norway, where reference pricing (RP) replaced price cap regulation in 2003 for a sub-sample of o¤-patent products. First, we construct a vertical di¤erentiation model to analyze the impact of regulation on prices and market shares of brand-names and generics. Then, we exploit a detailed panel data set at product level covering several o¤-patent molecules before and after the policy reform. O¤-patent drugs not subject to RP serve as our control group. We nd that RP signicantly reduces both brand-name and generic prices, and results in signicantly lower brand-name market shares. Finally, we show that RP has a strong negative e¤ect on average molecule prices, suggesting signicant cost-savings, and that patients copayments decrease despite the extra surcharges under RP.  Pharmaceuticals; Regulation; Generic Competition JEL Classications: I11; I18; L13; L65 We thank David Bardey, Øivind Anti Nilsen, Frode Steen, and two anonymous referees for valuable comments and suggestions. We also thank the Norwegian Research Council, Health Economics Bergen (HEB) for nancial support. Corresponding author. Department of Economics and Health Economics Bergen, Norwegian School of Economics and Business Administration, Helleveien 30, N-5045 Bergen, Norway. E-mail: kurt.brekke@nhh.no. Uni Rokkan Centre, Health Economics Bergen, Nygårdsgaten 5, N-5015 Bergen, Norway. E-mail: tor.holmas@uni.no. Department of Economics/NIPE, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal; and University of Bergen (Economics), Norway. E-mail: o.r.straume@eeg.uminho.pt.
Plant genotypic diversity predicts community structure and governs an ecosystem process.

Theory predicts, and recent empirical studies have shown, that the diversity of plant species determines the diversity of associated herbivores and mediates ecosystem processes, such as aboveground net primary productivity (ANPP). However, an often-overlooked component of plant diversity, namely population genotypic diversity, may also have wide-ranging effects on community structure and ecosystem processes. We showed experimentally that increasing population genotypic diversity in a dominant old-field plant species, Solidago altissima, determined arthropod diversity and community structure and increased ANPP. The effects of genotypic diversity on arthropod diversity and ANPP were comparable to the effects of plant species diversity measured in other studies.
Exploring the System Dynamics of Innovation Systems

The innovation system is perceived as an ensemble of resources committed to interrelated innovation activities. The question arising is how the innovation system becomes one (a system), i.e., what are the mechanisms that govern the alignment of resources and activities in innovative directions. A system dynamics framework of analysis is presented where alignment stems from institutional arrangements and strategic intent. These operate as mechanisms of stretch and leverage within an environment of co-opetition and connexity. Central amongst them is the evolution of cognitive-cultural elements that, in the form of mental models (worldviews) and intangible assets (such as trust and co-opeting competence), drive economic action and interaction. A system dynamics modelling approach is proposed that facilitates the quantification and exploration of policy alternatives.
Individual differences in executive control relate to metaphor processing: an eye movement study of sentence reading

Metaphors are common elements of language that allow us to creatively stretch the limits of word meaning. However, metaphors vary in their degree of novelty, which determines whether people must create new meanings on-line or retrieve previously known metaphorical meanings from memory. Such variations affect the degree to which general cognitive capacities such as executive control are required for successful comprehension. We investigated whether individual differences in executive control relate to metaphor processing using eye movement measures of reading. Thirty-nine participants read sentences including metaphors or idioms, another form of figurative language that is more likely to rely on meaning retrieval. They also completed the AX-CPT, a domain-general executive control task. In Experiment 1, we examined sentences containing metaphorical or literal uses of verbs, presented with or without prior context. In Experiment 2, we examined sentences containing idioms or literal phrases for the same participants to determine whether the link to executive control was qualitatively similar or different to Experiment 1. When metaphors were low familiar, all people read verbs used as metaphors more slowly than verbs used literally (this difference was smaller for high familiar metaphors). Executive control capacity modulated this pattern in that high executive control readers spent more time reading verbs when a prior context forced a particular interpretation (metaphorical or literal), and they had faster total metaphor reading times when there was a prior context. Interestingly, executive control did not relate to idiom processing for the same readers. Here, all readers had faster total reading times for high familiar idioms than literal phrases. Thus, executive control relates to metaphor but not idiom processing for these readers, and for the particular metaphor and idiom reading manipulations presented.
Computer Vision for Skin Cancer Diagnosis and Recognition using RBF and SOM

Human skin is the largest organ in our body which provides protection against heat, light, infections and injury. It also stores water, fat, and vitamin. Cancer is the leading cause of death in economically developed countries and the second leading cause of death in developing countries. Skin cancer is the most commonly diagnosed type of cancer among men and women. Exposure to UV rays, modernize diets, smoking, alcohol and nicotine are the main cause. Cancer is increasingly recognized as a critical public health problem in Ethiopia. There are three type of skin cancer and they are recognized based on their own properties. In view of this, a digital image processing technique is proposed to recognize and predict the different types of skin cancers using digital image processing techniques. Sample skin cancer image were taken from American cancer society research center and DERMOFIT which are popular and widely focuses on skin cancer research. The classification system was supervised corresponding to the predefined classes of the type of skin cancer. Combining Self organizing map (SOM) and radial basis function (RBF) for recognition and diagnosis of skin cancer is by far better than KNN, Naïve Bayes and ANN classifier. It was also showed that the discrimination power of morphology and color features was better than texture features but when morphology, texture and color features were used together the classification accuracy was increased. The best classification accuracy (88%, 96.15% and 95.45% for Basal cell carcinoma, Melanoma and Squamous cell carcinoma respectively) were obtained using combining SOM and RBF. The overall classification accuracy was 93.15%.
Privacy Risk Assessment on Email Tracking

Today's online marketing industry has widely employed email tracking techniques, such as embedding a tiny tracking pixel, to track email opens of potential customers and measure marketing effectiveness. However, email tracking could allow miscreants to collect metadata information associated with email reading without user awareness and then leverage the information for stealthy surveillance, which has raised serious privacy concerns. In this paper, we present an in-depth and comprehensive study on the privacy implications of email tracking. First, we develop an email tracking system and perform realworld tracking on hundreds of solicited crowdsourcing participants. We estimate the amount of privacy-sensitive information available from email reading, assess privacy risks of information leakage, and demonstrate how easy it is to launch a long-term targeted surveillance attack in real scenarios by simply sending an email with tracking capability. Second, we investigate the prevalence of email tracking through a large-scale measurement, which includes more than 44,000 email samples obtained over a period of seven years. Third, we conduct a user study to understand users' perception of privacy infringement caused by email tracking. Finally, we evaluate existing countermeasures against email tracking and propose guidelines for developing more comprehensive and fine-grained prevention solutions.
Securing the Internet of Things: A Meta-Study of Challenges, Approaches, and Open Problems

The Internet of Things (IoT) is becoming a key infrastructure for the development of smart ecosystems. However, the increased deployment of IoT devices with poor security has already rendered them increasingly vulnerable to cyber attacks. In some cases, they can be used as a tool for committing serious crimes. Although some researchers have already explored such issues in the IoT domain and provided solutions for them, there remains the need for a thorough analysis of the challenges, solutions, and open problems in this domain. In this paper, we consider this research gap and provide a systematic analysis of security issues of IoT-based systems. Then, we discuss certain existing research projects to resolve the security issues. Finally, we highlight a set of open problems and provide a detailed description for each. We posit that our systematic approach for understanding the nature and challenges in IoT security will motivate researchers to addressing and solving these problems.