nils-herrmann/clean_scirepeval huggingface dataset:
An Algorithmic Separating Hyperplane Theorem and Its Applications

We first prove a new separating hyperplane theorem characterizing when a pair of compact convex subsets K,K′ of the Euclidean space intersect, and when they are disjoint. The theorem is distinct from classical separation theorems. It generalizes the distance duality proved in our earlier work for testing the membership of a distinguished point in the convex hull of a finite point set. Next by utilizing the theorem, we develop a substantially generalized and stronger version of the Triangle Algorithm introduced in the previous work to perform any of the following three tasks: (1) To compute a pair (p, p′) ∈ K×K′, where either the Euclidean distance d(p, p′) is to within a prescribed tolerance, or the orthogonal bisecting hyperplane of the line segment pp′ separates the two sets; (2) When K and K′ are disjoint, to compute (p, p′) ∈ K×K′ so that d(p, p′) approximates d(K,K′) to within a prescribed tolerance; (3) When K and K′ are disjoint, to compute a pair of parallel supporting hyperplanes H,H ′ so that d(H,H ′) is to within a prescribed tolerance of the optimal margin. The worst-case complexity of each iteration is solving a linear objective over K or K′. The resulting algorithm is a fully polynomial-time approximation scheme for such important special cases as when K and K′ are convex hulls of finite points sets, or the intersection of a finite number of halfspaces. The results find many theoretical and practical applications, such as in machine learning, statistics, linear, quadratic and convex programming. In a forthcoming work we show how the algorithms can provide a certain approximation to NP-complete problems.
Stochastic-based Optimal Daily Energy Management of Microgrids in Distribution Systems

Microgrid (MG) with different technologies in distributed generations (DG) and different control facilities require proper management and scheduling strategies. In these strategies, in order to reach the optimal management, the stochastic nature of some decision variables should be considered. Therefore, we proposes a new stochastic-based method for optimal daily energy management (SDEM) of MGs considering economic and reliability aspects. The optimization aim is to minimize overall operating cost, power losses cost, pollutants emission cost and cost of energy not supply (ENS). The network is assumed to be supplied by renewable and dispatchable generators and energy storage systems (ESS). The system uncertainties are considered using a set of scenarios and a scenario reduction method is applied to enhance a tradeoff between the accuracy of the solution and the computational burden. Cuckoo optimization algorithm (COA) is applied to minimize the objective function as an optimization algorithm. The effectiveness and efficiency of the proposed method are validated through extensive numerical tests on PG&E 69-bus test distribution system. The results show that the proposed framework can be considered as an efficient tool in optimal daily energy management of smart distribution networks. Energy management, COA algorithm, Stochastic programming, microgrids, Reliability.
A theoretical review of social media usage by cyber-criminals

Social media plays an integral part in individual's everyday lives as well as for companies. Social media brings numerous benefits in people's lives such as to keep in touch with close ones and specially with relatives who are overseas, to make new friends, buy products, share information and much more. Unfortunately, several threats also accompany the countless advantages of social media. The rapid growth of the online social networking sites provides more scope for criminals and cyber-criminals to carry out their illegal activities. Hackers have found different ways of exploiting these platform for their malicious gains. This research englobes some of the common threats on social media such as spam, malware, Trojan horse, cross-site scripting, industry espionage, cyber-bullying, cyber-stalking, social engineering attacks. The main purpose of the study to elaborates on phishing, malware and click-jacking attacks. The main purpose of the research, there is no particular research available on the forensic investigation for Facebook. There is no particular forensic investigation methodology and forensic tools available which can follow on the Facebook. There are several tools available to extract digital data but it's not properly tested for Facebook. Forensics investigation tool is used to extract evidence to determine what, when, where, who is responsible. This information is required to ensure that the sufficient evidence to take legal action against criminals.
DART: a Dataset of Arguments and their Relations on Twitter

The problem of understanding the stream of messages exchanged on social media such as Facebook and Twitter is becoming a major challenge for automated systems. The tremendous amount of data exchanged on these platforms as well as the specific form of language adopted by social media users constitute a new challenging context for existing argument mining techniques. In this paper, we describe a resource of natural language arguments called DART (Dataset of Arguments and their Relations on Twitter) where the complete argument mining pipeline over Twitter messages is considered: (i) we identify which tweets can be considered as arguments and which cannot, and (ii) we identify what is the relation, i.e., support or attack, linking such tweets to each other.
Motivated Reinforcement Learning

The standard reinforcement learning view of the involvement of neuromodulatory systems in instrumental conditioning includes a rather straightforward conception of motivation as prediction of sum future reward. Competition between actions is based on the motivating characteristics of their consequent states in this sense. Substantial, careful, experiments reviewed in Dickinson & Balleine, 12,13 into the neurobiology and psychology of motivation shows that this view is incomplete. In many cases, animals are faced with the choice not between many different actions at a given state, but rather whether a single response is worth executing at all. Evidence suggests that the motivational process underlying this choice has different psychological and neural properties from that underlying action choice. We describe and model these motivational systems, and consider the way they interact.
Cooperative Control for A Hybrid Rehabilitation System Combining Functional Electrical Stimulation and Robotic Exoskeleton

Functional electrical stimulation (FES) and robotic exoskeletons are two important technologies widely used for physical rehabilitation of paraplegic patients. We developed a hybrid rehabilitation system (FEXO Knee) that combined FES and an exoskeleton for swinging movement control of human knee joints. This study proposed a novel cooperative control strategy, which could realize arbitrary distribution of torque generated by FES and exoskeleton, and guarantee harmonic movements. The cooperative control adopted feedfoward control for FES and feedback control for exoskeleton. A parameter regulator was designed to update key parameters in real time to coordinate FES controller and exoskeleton controller. Two muscle groups (quadriceps and hamstrings) were stimulated to generate active torque for knee joint in synchronization with torque compensation from exoskeleton. The knee joint angle and the interactive torque between exoskeleton and shank were used as feedback signals for the control system. Central pattern generator (CPG) was adopted that acted as a phase predictor to deal with phase confliction of motor patterns, and realized synchronization between the two different bodies (shank and exoskeleton). Experimental evaluation of the hybrid FES-exoskeleton system was conducted on five healthy subjects and four paraplegic patients. Experimental results and statistical analysis showed good control performance of the cooperative control on torque distribution, trajectory tracking, and phase synchronization.
Comparison of different methods to cancel offset capacitance in capacitive displacement sensors

This paper presents a comparative study of the power consumption of two methods to cancel the offset capacitance in high-resolution capacitive displacement sensors. One method is applied based on correlated-double-sampling (CDS) technique and the other method is applied using oversampling technique. The analytical comparison predicts similar power consumption, if the same resolution for the same conversion time need to be reached. Experimental results with implementations of the two methods confirmed the analytical prediction.
Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization.

An iterative algorithm, based on recent work in compressive sensing, is developed for volume image reconstruction from a circular cone-beam scan. The algorithm minimizes the total variation (TV) of the image subject to the constraint that the estimated projection data is within a specified tolerance of the available data and that the values of the volume image are non-negative. The constraints are enforced by the use of projection onto convex sets (POCS) and the TV objective is minimized by steepest descent with an adaptive step-size. The algorithm is referred to as adaptive-steepest-descent-POCS (ASD-POCS). It appears to be robust against cone-beam artifacts, and may be particularly useful when the angular range is limited or when the angular sampling rate is low. The ASD-POCS algorithm is tested with the Defrise disk and jaw computerized phantoms. Some comparisons are performed with the POCS and expectation-maximization (EM) algorithms. Although the algorithm is presented in the context of circular cone-beam image reconstruction, it can also be applied to scanning geometries involving other x-ray source trajectories.
A new taxonomy of Web attacks suitable for efficient encoding

Web attacks, i.e. attacks exclusively using the HTTP/HTTPS protocol, are rapidly becoming one of the fundamental threats for information systems connected to the Internet. When the attacks suffered by web servers through the years are analyzed, it is observed that most of them are very similar, using a reduced number of attacking techniques. It is generally agreed that classification can help designers and programmers to better understand attacks and build more secure applications. As an effort in this direction, a new taxonomy of web attacks is proposed in this paper, with the objective of obtaining a useful reference framework for security applications. The use of the taxonomy is illustrated by means of multiplatform real world web attack examples. Along with this taxonomy, important features of each attack category are discussed. A semantic-dependent web attack encoding scheme is also defined that, together with the taxonomy, can be used to process the attack information with low time and memory consumption. Applications of the taxonomy and the encoding scheme are described, such as intrusion detection systems and application firewalls.
Implementation of Integer Square Root

Square root plays a major role in applications like computer graphics, image processing. To increase the performance of computation, many algorithms have been proposed to carry out the computation task in hardware instead of software. One very common and relatively quick method for finding the square root of a number is the Newton-Raphson method which requires extensive use of division to produce results and its implementation on hardware is difficult as it requires large area. In this paper we implemented Integer square root by using square and compare, successive subtraction of odd integer’s and modified non-restoring methods. The three methods are implemented using Verilog HDL and Xilinx12.1. The results show that modified non-restoring method has less delay and area.
The graduate nurse experience.

Graduate nurses experience stress transitioning from student to practicing professional nurse, moving from a familiar educational environment into the workforce, where expectations are to rapidly function as a competent nurse. This study identified the stresses and challenges experienced by cohorts of graduate nurses working in 6 acute care hospitals, during specific timed data periods, to better understand factors that may influence graduate nurse retention. Results report graduate nurses do not feel skilled, comfortable, and confident for as long as 1 year after being hired, highlighting the need for healthcare organizations to provide extended orientation and support programs to facilitate successful entry into practice.
Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations

An increasing number of interactive visualization tools stress the integration with computational software like MATLAB and R to access a variety of proven algorithms. In many cases, however, the algorithms are used as black boxes that run to completion in isolation which contradicts the needs of interactive data exploration. This paper structures, formalizes, and discusses possibilities to enable user involvement in ongoing computations. Based on a structured characterization of needs regarding intermediate feedback and control, the main contribution is a formalization and comparison of strategies for achieving user involvement for algorithms with different characteristics. In the context of integration, we describe considerations for implementing these strategies either as part of the visualization tool or as part of the algorithm, and we identify requirements and guidelines for the design of algorithmic APIs. To assess the practical applicability, we provide a survey of frequently used algorithm implementations within R regarding the fulfillment of these guidelines. While echoing previous calls for analysis modules which support data exploration more directly, we conclude that a range of pragmatic options for enabling user involvement in ongoing computations exists on both the visualization and algorithm side and should be used.
Refining Geometry from Depth Sensors using IR Shading Images

We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.
What is principal component analysis?

Several measurement techniques used in the life sciences gather data for many more variables per sample than the typical number of samples assayed. For instance, DNA microarrays and mass spectrometers can measure levels of thousands of mRNAs or proteins in hundreds of samples. Such high-dimensionality makes visualization of samples difficult and limits simple exploration of the data. Principal component analysis (PCA) is a mathematical algorithm that reduces the dimensionality of the data while retaining most of the variation in the data set1. It accomplishes this reduction by identifying directions, called principal components, along which the variation in the data is maximal. By using a few components, each sample can be represented by relatively few numbers instead of by values for thousands of variables. Samples can then be plotted, making it possible to visually assess similarities and differences between samples and determine whether samples can be grouped. Saal et al.2 used microarrays to measure the expression of 27,648 genes in 105 breast tumor samples. I will use this gene expression data set, which is available through the Gene Expression Omnibus database (accession no. GSE5325), to illustrate how PCA can be used to represent samples with a smaller number of variables, visualize samples and genes, and detect dominant patterns of gene expression. My aim with this example is to leave you with an idea of how PCA can be used to explore data sets in which thousands of variables have been measured.
Deep Abnormality Detection in Video Data

Automated detection of anomalous events plays an important role in video surveillance systems in practice. This task, however, requires to deal with three challenging problems of the lack of annotated training data, the inexact description of what to be “abnormal” and the expensive feature engineering procedure. Most anomaly detection systems are only able to satisfy some of these challenges. In this work, we propose a deep abnormality detection system to handle all of them simultaneously. Deep abnormality detection is a deep generative network that is an unsupervised probabilistic framework to model the normality and learn feature representation automatically. Furthermore, unlike other existing methods, our system can detect abnormality at multiple levels and be used as a powerful tool for video analysis and scene understanding.
Normative rational agents-A BDI approach

This paper proposes an approach on how to accommodate norms to an already existing architecture of rational agents. Starting from the famous BDI model, an extension of the BDI execution loop will be presented; it will address such issues as norm instantiation and norm internalization, with a particular emphasis on the problem of norm consistency. A proposal for the resolution of conflicts between newly occurring norms, on one side, and already existing norms or mental states, on the other side, will be described. While it is fairly difficult to imagine an evaluation for the proposed architecture, a challenging scenario inspired form the science-fiction literature will be used to give the reader an intuition of how the proposed approach will deal with situations of normative conflicts.
Automatic Construction of Inference-Supporting Knowledge Bases

While there has been tremendous progress in automatic database population in recent years, most of human knowledge does not naturally fit into a database form. For example, knowledge that "metal objects can conduct electricity" or "animals grow fur to help them stay warm" requires a substantially different approach to both acquisition and representation. This kind of knowledge is important because it can support inference e.g., (with some associated confidence) if an object is made of metal then it can conduct electricity; if an animal grows fur then it will stay warm. If we want our AI systems to understand and reason about the world, then acquisition of this kind of inferential knowledge is essential. In this paper, we describe our work on automatically constructing an inferential knowledge base, and applying it to a question-answering task. Rather than trying to induce rules from examples, or enter them by hand, our goal is to acquire much of this knowledge directly from text. Our premise is that much inferential knowledge is written down explicitly, in particular in textbooks, and can be extracted with reasonable reliability. We describe several challenges that this approach poses, and innovative, partial solutions that we have developed. Finally we speculate on the longer-term evolution of this work.
PRISM: platform for remote sensing using smartphones

To realize the potential of opportunistic and participatory sensing using mobile smartphones, a key challenge is ensuring the ease of developing and deploying such applications, without the need for the application writer to reinvent the wheel each time. To this end, we present a Platform for Remote Sensing using Smartphones (PRISM) that balances the interconnected goals of generality, security, and scalability. PRISM allows application writers to package their applications as executable binaries, which offers efficiency and also the flexibility of reusing existing code modules. PRISM then pushes the application out automatically to an appropriate set of phones based on a specified set of predicates. This push model enables timely and scalable application deployment while still ensuring a good degree of privacy. To safely execute untrusted applications on the smartphones, while allowing them controlled access to sensitive sensor data, we augment standard software sandboxing with several PRISM-specific elements like resource metering and forced amnesia.
 We present three applications built on our implementation of PRISM on Windows Mobile: citizen journalist, party thermometer, and road bump monitor. These applications vary in the set of sensors they use and in their mode of operation (depending on human input vs. automatic). We report on our experience from a small-scale deployment of these applications. We also present a large-scale simulation-based analysis of the scalability of PRISM's push model.
Toward a Rational Choice Process Theory of Internet Scamming: The Offender's Perspective

Internet fraud scam is a crime enabled by the Internet to swindle Internet users. The global costs of these scams are in the billions of US dollars. Existing research suggests that scammers maximize their economic gain. Although this is a plausible explanation, since the idea of the scam is to fool people to send money, this explanation alone, cannot explain why individuals become Internet scammers. An equally important, albeit unexplored riddle, is the question of what strategies Internet scammers adopt to perform the act. As a first step to address these gaps, we interviewed five Internet scammers in order to develop a rational choice process theory of Internet scammers’ behavior. The initial results suggest that an interplay of socioeconomic and dynamic thinking processes explains why individuals drift into Internet scamming. Once an individual drifts into Internet scamming, a successful scam involves two processes: persuasive strategy and advance fee strategy.
CyberC3: A Prototype Cybernetic Transportation System for Urban Applications

In this paper, a prototype cybernetic transportation system called cybernetic technologies for cars in Chinese cities (CyberC3) is introduced. This system contains as many modules as necessary to evaluate the feasibility for its potential mass application in cities, including a central control room, five stations, three on-road monitoring cameras, three intelligent vehicles, and a green-energy power system. The entire system is centrally controlled and runs in two different modes - the ¿shuttle mode¿ and the ¿on-demand mode. The control algorithm is divided into three logical layers: scheduling, planning, and executing. The scheduling layer manages the entire system, the planning layer navigates the vehicle, and the executing layer controls the vehicle in real time. The vehicle is powered by supercapacitor batteries. This system is a demonstration system, as well as a research platform, and has been open to the public in Shanghai, China, since May 2007, which has helped to evaluate the entire system and spread the concept of ¿cybernetic transportation systems¿ in China.