nils-herrmann/clean_scirepeval huggingface dataset:
A Numerical PDE Approach For Pricing Callable Bonds Y . d

Many debt issues contain an embedded call option that allows the issuer to redeem the bond at specified dates for a specified price. The issuer is typically required to provide advance notice of a decision to exercise this call option. The valuation of these contracts is an interesting numerical exercise because discontinuities may arise in the bond value or its derivative at call and/or notice dates. Recently, it has been suggested that finite difference methods cannot be used to price callable bonds requiring notice (Büttler, 1995; Büttler and Waldvogel, 1996). Poor accuracy was attributed to discontinuities and difficulties in handling boundary conditions. As an alternative, a semi-analytical method using Green’s functions for valuing callable bonds with notice was proposed (Büttler and Waldvogel, 1996). Unfortunately, the Green’s function method is limited to special cases. Consequently, it is desirable to develop a more general approach. We provide this by using more advanced techniques such as flux limiters to obtain an accurate numerical partial differential equation method. Finally, in a typical pricing model (Cox et al., 1985) an inappropriate financial condition is required in order to properly specify boundary conditions for the associated PDE. We show that a small perturbation of such a model is free from such artificial conditions.
A novel hybrid powered RFID sensor tag

In recent years we have seen a tremendous growth in applications of passive sensor-enabled RFID technology by researchers; however, their usability in applications such as activity recognition is limited by a key issue associated with their incapability to handle unintentional brownout events leading to missing significant sensed events such as a fall from a chair. Furthermore, due to the need to power and sample a sensor the practical operating range of passive-sensor enabled RFID tags are also limited with respect to passive RFID tags. Although using active or semi-passive tags can provide alternative solutions, they are not without the often undesirable maintenance and limited lifespan issues due to the need for batteries. In this article we propose a new hybrid powered sensor-enabled RFID tag concept which can sustain the supply voltage to the tag circuitry during brownouts and increase the operating range of the tag by combining the concepts from passive RFID tags and semipassive RFID tags, while potentially eliminating shortcomings of electric batteries. We have designed and built our concept, evaluated its desirable properties through extensive experiments and demonstrate its significance in the context of a human activity recognition application.
Implementation of LSB Steganography and its Evaluation for Various File Formats

----------------------------------------------------------------------ABSTRACT-------------------------------------------------------------Steganography is derived from the Greek word steganos which literally means “Covered” and graphy means “Writing”, i.e. covered writing. Steganography refers to the science of “invisible” communication. For hiding secret information in various file formats, there exists a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. The Least Significant Bit (LSB) embedding technique suggests that data can be hidden in the least significant bits of the cover image and the human eye would be unable to notice the hidden image in the cover file. This technique can be used for hiding images in 24-Bit, 8-Bit, Gray scale format. This paper explains the LSB Embedding technique and Presents the evaluation for various file formats.
Sensor based healthcare information system

Over the last few years, the convincing forward steps in the development of Internet of Things (IoT)-enabling solutions are spurring the advent of novel and fascinating applications. Today's healthcare system is the lack of security and real time monitoring. In the wake of this tendency, this paper proposes a novel, IoT-aware, smart architecture for automatic monitoring and tracking of patients from their home itself. Staying true to the IoT vision, we propose a Automation Healthcare System (AHS). The proposed AHS is to investigate advanced home health care services. Data produced in AHS shared with doctors and patients through IoT. The system utilizes IoT telemetry to transmit data from sensors to a remote monitor. This paper discusses recent advances in wearable sensors healthcare system to monitor temperature, heart rate and the energy efficient routing. The system provides the security and real time monitoring.
Questions programmers ask during software evolution tasks

Though many tools are available to help programmers working on change tasks, and several studies have been conducted to understand how programmers comprehend systems, little is known about the specific kinds of questions programmers ask when evolving a code base. To fill this gap we conducted two qualitative studies of programmers performing change tasks to medium to large sized programs. One study involved newcomers working on assigned change tasks to a medium-sized code base. The other study involved industrial programmers working on their own change tasks on code with which they had experience. The focus of our analysis has been on what information a programmer needs to know about a code base while performing a change task and also on howthey go about discovering that information. Based on this analysis we catalog and categorize 44 different kinds of questions asked by our participants. We also describe important context for how those questions were answered by our participants, including their use of tools.
Biped walking control based on hybrid position/force control

This paper describes a real-time walking control system developed for the biped robots JOHNNIE and LOLA. Walking trajectories are planned on-line using a simplified robot model and modified by a stabilizing controller. The controller uses hybrid position/force control in task space based on a resolved motion rate scheme. Inertial stabilization is achieved by modifying the contact force trajectories. The paper includes an analysis of the dynamics of controlled bipeds, which is the basis for the proposed control system. The system was tested both in forward dynamics simulations and in experiments with JOHNNIE.
DevNet: A Deep Event Network for multimedia event detection and evidence recounting

In this paper, we focus on complex event detection in internet videos while also providing the key evidences of the detection results. Convolutional Neural Networks (CNNs) have achieved promising performance in image classification and action recognition tasks. However, it remains an open problem how to use CNNs for video event detection and recounting, mainly due to the complexity and diversity of video events. In this work, we propose a flexible deep CNN infrastructure, namely Deep Event Network (DevNet), that simultaneously detects pre-defined events and provides key spatial-temporal evidences. Taking key frames of videos as input, we first detect the event of interest at the video level by aggregating the CNN features of the key frames. The pieces of evidences which recount the detection results, are also automatically localized, both temporally and spatially. The challenge is that we only have video level labels, while the key evidences usually take place at the frame levels. Based on the intrinsic property of CNNs, we first generate a spatial-temporal saliency map by back passing through DevNet, which then can be used to find the key frames which are most indicative to the event, as well as to localize the specific spatial position, usually an object, in the frame of the highly indicative area. Experiments on the large scale TRECVID 2014 MEDTest dataset demonstrate the promising performance of our method, both for event detection and evidence recounting.
CBE: Corpus-based of emotion for emotion detection in text document

Emotion Detection is a part of Natural Language Processing (NLP) that still evolve. Emotional Corpus that had been widely used are Wordnet Affect Emotion (WNA) and ANEW (Affective Norms for English Words). There are two ways to analyze the text based Emotion Detection: Categorical and Dimensional Model. Each model has different advantages and disadvantages. And each model has a different concept to predict emotion. The contribution of this research is forming automatic emotional corpus with merging two computational model. It called Corpus-Based of Emotion (CBE). CBE developed from ANEW and WNA with term similarity measure and distance of node approach. Latent Dirichlet Allocation (LDA) is used too for automatically expand CBE. The CBE attributes are a score of Valence (V), Arousal (A), Dominance (D) and categorical label emotion. Categorical label emotion based on six basic emotion of Ekman. Based on experiment results, it is known that CBE is able to improve the accuracy in detection of emotions. F-Measure using WNA+ANEW is 0.50 and F-Measure using CBE with expanding is 0.61.
The Problematic Internet Entertainment Use Scale for Adolescents: Prevalence of Problem Internet Use in Spanish High School Students

Many researchers and professionals have reported nonsubstance addiction to online entertainments in adolescents. However, very few scales have been designed to assess problem Internet use in this population, in spite of their high exposure and obvious vulnerability. The aim of this study was to review the currently available scales for assessing problematic Internet use and to validate a new scale of this kind for use, specifically in this age group, the Problematic Internet Entertainment Use Scale for Adolescents. The research was carried out in Spain in a gender-balanced sample of 1131 high school students aged between 12 and 18 years. Psychometric analyses showed the scale to be unidimensional, with excellent internal consistency (Cronbach's alpha of 0.92), good construct validity, and positive associations with alternative measures of maladaptive Internet use. This self-administered scale can rapidly measure the presence of symptoms of behavioral addiction to online videogames and social networking sites, as well as their degree of severity. The results estimate the prevalence of this problematic behavior in Spanish adolescents to be around 5 percent.
Comparative evaluation of machines for electric and hybrid vehicles based on dynamic operation and loss minimization

This paper proposes a method to evaluate induction machines for electric vehicles (EVs) and hybrid electric vehicles (HEVs). Some performance aspects of induction machines are also compared to permanent magnet synchronous machines (PMSMs). An overview of static efficiency maps is presented, but efficiency maps miss dynamic effects and under-predict induction machine efficiencies. The proposed evaluation method is based on dynamic efficiency under loss minimization and overall energy consumption over standard driving cycles that are provided by the U.S. Environmental Protection Agency. Over each of these cycles, the dynamic efficiency and drive-cycle energy are determined based on experimental motor data in combination with a dynamic HEV simulator. Results show that efficiency in the fast-changing dynamic environment of a vehicle can be higher than inferred from static efficiency maps. Overall machine efficiency is compared for rated flux, and for dynamic loss-minimizing flux control. The energy efficiency given optimum flux is typically five points higher than for rated flux. This result is comparable to published PMSM results. A PMSM is also used for comparisons, and results show that both machines can perform well in HEV and EV applications.
Leadership, followership, and evolution: some lessons from the past.

This article analyzes the topic of leadership from an evolutionary perspective and proposes three conclusions that are not part of mainstream theory. First, leading and following are strategies that evolved for solving social coordination problems in ancestral environments, including in particular the problems of group movement, intragroup peacekeeping, and intergroup competition. Second, the relationship between leaders and followers is inherently ambivalent because of the potential for exploitation of followers by leaders. Third, modern organizational structures are sometimes inconsistent with aspects of our evolved leadership psychology, which might explain the alienation and frustration of many citizens and employees. The authors draw several implications of this evolutionary analysis for leadership theory, research, and practice.
Dual Band and Dual Polarized Microstrip Patch Antenna

In today’s modern communication industry, antennas are the most important components required to create a communication link. Microstrip antennas are the most suited for aerospace and mobile applications because of their low profile, light weight and low power handling capacity. They can be designed in a variety of shapes in order to obtain enhanced gain and bandwidth, dual band and circular polarization to even ultra wideband operation. The thesis provides a detailed study of the design of probe-fed Rectangular Microstrip Patch Antenna to facilitate dual polarized, dual band operation. The design parameters of the antenna have been calculated using the transmission line model and the cavity model. For the simulation process IE3D electromagnetic software which is based on method of moment (MOM) has been used. The effect of antenna dimensions and substrate parameters on the performance of antenna have been discussed. The antenna has been designed with embedded spur lines and integrated reactive loading for dual band operation with better impedance matching. The designed antenna can be operated at two frequency band with center frequencies 7.62 (with a bandwidth of 11.68%) and 9.37 GHz (with a bandwidth of 9.83%). A cross slot of unequal length has been inserted so as to have dual polarization. This results in a minor shift in the central frequencies of the two bands to 7.81 and 9.28 GHz. At a frequency of 9.16 GHz, circular polarization has been obtained. So the dual band and dual frequency operation has successfully incorporated into a single patch.
A Survey on Truth Discovery

Thanks to information explosion, data for the objects of interest can be collected from increasingly more sources. However, for the same object, there usually exist conflicts among the collected multi-source information. To tackle this challenge, truth discovery, which integrates multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this survey, we focus on providing a comprehensive overview of truth discovery methods, and summarizing them from different aspects. We also discuss some future directions of truth discovery research. We hope that this survey will promote a better understanding of the current progress on truth discovery, and offer some guidelines on how to apply these approaches in application domains.
Generating emotionally relevant musical scores for audio stories

Highly-produced audio stories often include musical scores that reflect the emotions of the speech. Yet, creating effective musical scores requires deep expertise in sound production and is time-consuming even for experts. We present a system and algorithm for re-sequencing music tracks to generate emotionally relevant music scores for audio stories. The user provides a speech track and music tracks and our system gathers emotion labels on the speech through hand-labeling, crowdsourcing, and automatic methods. We develop a constraint-based dynamic programming algorithm that uses these emotion labels to generate emotionally relevant musical scores. We demonstrate the effectiveness of our algorithm by generating 20 musical scores for audio stories and showing that crowd workers rank their overall quality significantly higher than stories without music.
One-time cookies: Preventing session hijacking attacks with stateless authentication tokens

HTTP cookies are the de facto mechanism for session authentication in Web applications. However, their inherent security weaknesses allow attacks against the integrity of Web sessions. HTTPS is often recommended to protect cookies, but deploying full HTTPS support can be challenging due to performance and financial concerns, especially for highly distributed applications. Moreover, cookies can be exposed in a variety of ways even when HTTPS is enabled. In this article, we propose one-time cookies (OTC), a more robust alternative for session authentication. OTC prevents attacks such as session hijacking by signing each user request with a session secret securely stored in the browser. Unlike other proposed solutions, OTC does not require expensive state synchronization in the Web application, making it easily deployable in highly distributed systems. We implemented OTC as a plug-in for the popular WordPress platform and as an extension for Firefox and Firefox for mobile browsers. Our extensive experimental analysis shows that OTC introduces a latency of less than 6 ms when compared to cookies—a negligible overhead for most Web applications. Moreover, we show that OTC can be combined with HTTPS to effectively add another layer of security to Web applications. In so doing, we demonstrate that one-time cookies can significantly improve the security of Web applications with minimal impact on performance and scalability.
An ontology of suspicious software behavior

–Malicious programs have been the main actors in complex, sophisticated attacks against nations, governments, diplomatic agencies, private institutions and people. Knowledge about malicious program behavior forms the basis for constructing more secure information systems. In this article, we introduce MBO, a Malicious Behavior Ontology that represents complex behaviors of suspicious executions, and through inference rules calculates their associated threat level for analytical proposals. We evaluate MBO using over two thousand unique known malware and 385 unique known benign software. Results highlight the representativeness of the MBO for expressing typical malicious activities. Security ontologyMalware behaviorThreat analysis
Assessing Threat of Adversarial Examples on Deep Neural Networks

Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in of acquiring the image in a real world application, such as using a scanner to acquire digits for a check amount or using a camera in an autonomous car. These small transformations negate the effect of the carefully crafted perturbations of adversarial examples, resulting in a correct classification by the deep neural network. Thus just acquiring the image decreases the potential impact of the proposed security threat. We also show that the already widely used process of averaging over multiple crops neutralizes most adversarial examples. Normal preprocessing, such as text binarization, almost completely neutralizes adversarial examples. This is the first paper to show that for text driven classification, adversarial examples are an academic curiosity, not a security threat.
Twitter-Based User Modeling for News Recommendations

In this paper, we study user modeling on Twitter. We investigate different strategies for mining user interest profiles from microblogging activities ranging from strategies that analyze the semantic meaning of Twitter messages to strategies that adapt to temporal patterns that can be observed in the microblogging behavior. We evaluate the quality of the user modeling methods in the context of a personalized news recommendation system. Our results reveals that an understanding of the semantic meaning of microposts is key for generating high-quality user profiles.
Wideband Fabry–Perot Resonator Antenna With Two Layers of Dielectric Superstrates

This letter presents a new design of a Fabry-Perot resonator antenna (FPRA) with a wide gain bandwidth. A double-layered dielectric superstrate, which produces a reflection phase curve versus frequency with a positive slope, is used as a partially reflective surface (PRS) to enhance the bandwidth of the FPRA. For a physical insight, the PRS is analyzed by using the transmission line theory and Smith Chart. Experimental results demonstrate that the antenna has a 3-dB gain bandwidth over 13.5-17.5 GHz, relatively 25.8%, with a peak gain of 15 dBi. Furthermore, the gain band is overlapped well by the impedance band for the reflection coefficient ( S11) less than -10 dB.
PicAlert!: a system for privacy-aware image classification and retrieval

Photo publishing in Social Networks and other Web2.0 applications has become very popular due to the pervasive availability of cheap digital cameras, powerful batch upload tools and a huge amount of storage space. A portion of uploaded images are of a highly sensitive nature, disclosing many details of the users' private life. We have developed a web service which can detect private images within a user's photo stream and provide support in making privacy decisions in the sharing context. In addition, we present a privacy-oriented image search application which automatically identifies potentially sensitive images in the result set and separates them from the remaining pictures.
Big data challenge: a data management perspective

There is a trend that, virtually everyone, ranging from big Web companies to traditional enterprisers to physical science researchers to social scientists, is either already experiencing or anticipating unprecedented growth in the amount of data available in their world, as well as new opportunities and great untapped value. This paper reviews big data challenges from a data management respective. In particular, we discuss big data diversity, big data reduction, big data integration and cleaning, big data indexing and query, and finally big data analysis and mining. Our survey gives a brief overview about big-data-oriented research and problems.
Fast and reliable anomaly detection in categorical data

Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. We introduce COMPREX, a new approach for identifying anomalies using pattern-based compression. Informally, our method finds a collection of dictionaries that describe the norm of a database succinctly, and subsequently flags those points dissimilar to the norm---with high compression cost---as anomalies.
 Our approach exhibits four key features: 1) it is parameter-free; it builds dictionaries directly from data, and requires no user-specified parameters such as distance functions or density and similarity thresholds, 2) it is general; we show it works for a broad range of complex databases, including graph, image and relational databases that may contain both categorical and numerical features, 3) it is scalable; its running time grows linearly with respect to both database size as well as number of dimensions, and 4) it is effective; experiments on a broad range of datasets show large improvements in both compression, as well as precision in anomaly detection, outperforming its state-of-the-art competitors.