nils-herrmann/clean_scirepeval huggingface dataset:
Salient object detection via fast R-CNN and low-level cues

Recent advances in salient object detection have exploited the deep Convolutional Neural Network (CNN) to represent high-level semantic, however, due to the presence of convolutional and pooling layers, it is difficult for CNN to generate saliency map with sharp boundaries. In this paper, we propose multi-scale mask-based Fast R-CNN framework which generate saliency score of each region. Since the regions are segmented using edge-preserved methods, the results are naturally with sharp boundaries. To consider context information, we also propose low-level contrast and backgroundness prior which are complementary with high-level semantic. Finally, an edge-based propagation method which takes advantages of edge information is proposed to refine the saliency map. Experiments on three benchmark datasets demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.
TOSS: Traffic offloading by social network service-based opportunistic sharing in mobile social networks

The ever increasing traffic demand becomes a serious concern of mobile network operators. To solve this traffic explosion problem, there have been many efforts to offload the traffic from cellular links to direct communications among users. In this paper, we propose the framework of Traffic Offloading assisted by Social network services (SNS) via opportunistic Sharing in mobile social networks, TOSS, to offload SNS-based cellular traffic by user-to-user sharing. First we select a subset of users who are to receive the same content as initial seeds depending on their content spreading impacts in online SNSs and their mobility patterns in offline mobile social networks (MSNs). Then users share the content via opportunistic local connectivity (e.g., Bluetooth, Wi-Fi Direct, Device-to-device in LTE) with each other. The observation of SNS user activities reveals that individual users have distinct access patterns, which allows TOSS to exploit the user-dependent access delay between the content generation time and each user's access time for traffic offloading purposes. We model and analyze the traffic offloading and content spreading among users by taking into account various options in linking SNS and MSN trace data. The trace-driven evaluation demonstrates that TOSS can reduce up to 86.5% of the cellular traffic while satisfying the access delay requirements of all users.
Convolutional Recurrent Neural Networks for Posture Analysis in Fall Detection

Existing methods have extensively addressed the issue of detecting abnormal events in a smart home environment through wearable sensors in the past years. However, the limitations of wearable sensors include the limited battery power as well as the use and adoption challenges of wearable activities on a daily basis. The use of nonwearable and non-intrusive sensors is necessary for providing better user experiences and achieving a sustainable and reliable detection model. However, it is still very challenging to analyze such non-wearable sensor data with a high level of accuracy. In this paper, we present a continuous deep learning model which receives a set of consecutive images for classifying posture types using a Microsoft Kinect as our nonwearable sensor. We adopt a deep learning technique called the recurrent neural network (RNN) using the long short-term memory (LSTM) architecture to construct our detection model by identifying human postures in fall detection. Furthermore, we investigate the inputs for our model by extracting the features from the pre-processed high-resolution RGB images, including body shape, depth and optical flow. As a result, the body shape with genuine motion and depth information are considered. Finally, we present the experimental results to demonstrate the performance and novelty of our approach.
Measuring Post Traumatic Stress Disorder in Twitter

Traditional mental health studies rely on information primarily collected through personal contact with a health care professional. Recent work has shown the utility of social media data for studying depression, but there have been limited evaluations of other mental health conditions. We consider post traumatic stress disorder (PTSD), a serious condition that affects millions worldwide, with especially high rates in military veterans. We also present a novel method to obtain a PTSD classifier for social media using simple searches of available Twitter data, a significant reduction in training data cost compared to previous work. We demonstrate its utility by examining differences in language use between PTSD and random individuals, building classifiers to separate these two groups and by detecting elevated rates of PTSD at and around U.S. military bases using our classifiers.
Pixel Based Off-line Signature Verification System

The verification of handwritten signatures is one of the oldest and the most popular authentication methods all around the world. As technology improved, different ways of comparing and analyzing signatures become more and more sophisticated. Since the early seventies, people have been exploring how computers can fully take over the task of signature verification and tried different methods. However, none of them is satisfactory enough and time consuming too. Therefore, our proposed pixel based offline signature verification system is one of the fastest and easiest ways to authenticate any handwritten signature we have ever found. For signature acquisition, we have used scanner. Then we have divided the signature image into 2D array and calculated the hexadecimal RGB value of each pixel. After that, we have calculated the total percentage of matching. If the percentage of matching is more than 90, the signature is considered as valid otherwise invalid. We have experimented on more than 35 signatures and the result of our experiment is quite impressive. We have made the whole system web based so that the signature can be verified from anywhere. The average execution time for signature verification is only 0.00003545 second only. Keywords– off-line signature, signature verification, pixel, execution time, pre-processing
Explaining the Rate Spread on Corporate Bonds

The purpose of this article is to explain the spread between rates on corporate and government bonds. We show that expected default accounts for a surprisingly small fraction of the premium in corporate rates over treasuries. While state taxes explain a substantial portion of the difference, the remaining portion of the spread is closely related to the factors that we commonly accept as explaining risk premiums for common stocks. Both our time series and cross-sectional tests support the existence of a risk premium on corporate bonds. THE PURPOSE OF THIS ARTICLE is to examine and explain the differences in the rates offered on corporate bonds and those offered on government bonds ~spreads!, and, in particular, to examine whether there is a risk premium in corporate bond spreads and, if so, why it exists. Spreads in rates between corporate and government bonds differ across rating classes and should be positive for each rating class for the following reasons: 1. Expected default loss—some corporate bonds will default and investors require a higher promised payment to compensate for the expected loss from defaults. 2. Tax premium—interest payments on corporate bonds are taxed at the state level whereas interest payments on government bonds are not. 3. Risk premium—The return on corporate bonds is riskier than the return on government bonds, and investors should require a premium for the higher risk. As we will show, this occurs because a large part of the risk on corporate bonds is systematic rather than diversifiable. The only controversial part of the above analyses is the third point. Some authors in their analyses assume that the risk premium is zero in the corporate bond market.1 * Edwin J. Elton and Martin J. Gruber are Nomura Professors of Finance, Stern School of Business, New York University. Deepak Agrawal and Christopher Mann are Doctoral Students, Stern School of Business, New York University. We would like to thank the Editor, René Stulz, and the Associate Editor for helpful comments and suggestions. 1 Many authors assume a zero risk premium. Bodie, Kane, and Marcus ~1993! assume the spread is all default premium. See also Fons ~1994! and Cumby and Evans ~1995!. On the other hand, rating-based pricing models like Jarrow, Lando, and Turnbull ~1997! and Das-Tufano ~1996! assume that any risk premium impounded in corporate spreads is captured by adjusting transition probabilities. THE JOURNAL OF FINANCE • VOL. LVI, NO. 1 • FEBRUARY 2001
Closed Form Solution for Minimum Conduction Loss Modulation of DAB Converters

An optimal modulation scheme that enables minimum conduction and copper losses is presented for a bidirectional dual active bridge (DAB) dc-dc converter. The considered converter system is employed for an automotive application and comprises of a high voltage (HV) port with port voltage <i>V</i><sub>1</sub>, 240V ≤ <i>V</i><sub>1</sub> ≤ 450 V, and a low voltage (LV) port with port voltage <i>V</i><sub>2</sub>, 11 V ≤ <i>V</i><sub>2</sub> ≤ 16 V; the rated output power is 2 kW. The closed-form expressions for the optimal control parameters are derived and implementation details are presented in order to facilitate the direct application to a given DAB converter. The paper further details the properties of the presented modulation scheme with respect to switching losses. Experimental results confirm a considerable increase of the converter efficiency achieved with the proposed optimal modulation scheme, compared to the efficiency obtained with conventional phase shift modulation. The efficiency increase is most distinct at <i>V</i><sub>1</sub>=450V and <i>V</i><sub>2</sub> = 11V with an increase from 78.6% to 90.6% at 1 kW output power and from 85.9% to 90.7% at rated output power as compared to conventional phase shift modulation.
Online Detection of Stealthy False Data Injection Attacks in Power System State Estimation

State estimation is one of the fundamental functions in modern power grid operations that provide operators with situational awareness and is used by several applications like contingency analysis and power markets. Several research in the recent past have highlighted the vulnerability of state estimators to stealthy false data injection attacks that bypass bad data detection mechanisms. They primarily focused on identifying stealthy attack vectors and characterizing their impacts on state estimates. Existing mitigation measures either focus on masking the effect of attacks through redundant measurements or prevent attacks by increasing the cyber security of associated sensors and communication channels. The solutions based on these offline approaches make specific assumptions about the nature of attacks and of the system, which are often restrictive and grossly inadequate to deal with dynamically evolving cyber threats and changing system configurations. In this paper, we propose an online anomaly detection algorithm that utilizes load forecasts, generation schedules, and synchrophasor data to detect measurement anomalies. We provide some insight into the factors that affect the performance of the proposed algorithm. We also describe an empirical method to obtain the minimum attack magnitudes and the detection thresholds for meeting specified false positive and true positive rates. Finally, we evaluated the performance of the proposed algorithm using the IEEE 14 bus power system model for several measures (false positive, false negative, and thresholds). We observed that the best performance of the proposed algorithm relies on finding the right balance between the minimum attack magnitude and detection thresholds. We also observed that the minimum attack magnitudes and detection thresholds could be further improved through the use of a combination of more accurate forecasts and PMU measurements.
Towards a top-down and bottom-up bidirectional approach to joint information extraction

Most high-level information extraction (IE) consists of compound and aggregated subtasks. Such IE problems are generally challenging and they have generated increasing interest recently. We investigate two representative IE tasks: (1) entity identification and relation extraction from Wikipedia, and (2) citation matching, and we formally define joint optimization of information extraction. We propose a joint paradigm integrating three factors -- segmentation, relation, and segmentation-relation joint factors, to solve all relevant subtasks simultaneously. This modeling offers a natural formalism for exploiting bidirectional rich dependencies and interactions between relevant subtasks to capture mutual benefits. Since exact parameter estimation is prohibitively intractable, we present a general, highly-coupled learning algorithm based on variational expectation maximization (VEM) to perform parameter estimation approximately in a top-down and bottom-up manner, such that information can flow bidirectionally and mutual benefits from different subtasks can be well exploited. In this algorithm, both segmentation and relation are optimized iteratively and collaboratively using hypotheses from each other. We conducted extensive experiments using two real-world datasets to demonstrate the promise of our approach.
Temporal Ontology and Temporal Reference

"Two weeks later, Bonadea had already been his lover for a fortnight."-Robert Musil, Der Mann ohne Eigenschaften. A semantics of temporal categories in language and a theory of their use in defining the temporal relations between events both require a more complex structure on the domain underlying the meaning representations than is commonly assumed. This paper proposes an ontology based on such notions as causation and consequence, rather than on purely temporal primitives. A central notion in the ontology is that of an elementary event-complex called a "nucleus." A nucleus can be thought of as an association of a goal event, or "culmination," with a "preparatory process" by which it is accomplished, and a "consequent state," which ensues. Natural-language categories like aspects, futurates, adverbials, and when-clauses are argued to change the temporal/aspectual category of propositions under the control of such a nucleic knowledge representation structure. The same concept of a nucleus plays a central role in a theory of temporal reference, and of the semantics of tense, which we follow McCawley, Partee, and Isard in regarding as an anaphoric category. We claim that any manageable formalism for natural-language temporal descriptions will have to embody such an ontology, as will any usable temporal database for knowledge about events which is to be interrogated using natural language.
Performance evaluation of subthreshold Schmitt trigger using body bias techniques

The technique of body biasing has achieved great success in modern IC design as in it, is possible to alter the threshold voltage of MOS transistor so that device leakage and timing performance can be improved. In this paper a Schmitt trigger circuit has been designed using four types of body bias techniques. The proposed circuits were simulated in SPICE 180nm CMOS technology parameters. A comparative study of all the proposed Schmitt trigger circuits in terms of lower and upper threshold voltages, noise margins and power dissipation has been done and specific applications for each circuit has been suggested.
Mesh Adaptive Direct Search Algorithms for Constrained Optimization

This paper addresses the problem of minimization of a nonsmooth function under general nonsmooth constraints when no derivatives of the objective or constraint functions are available. We introduce the mesh adaptive direct search (MADS) class of algorithms which extends the generalized pattern search (GPS) class by allowing local exploration, called polling, in an asymptotically dense set of directions in the space of optimization variables. This means that under certain hypotheses, including a weak constraint qualification due to Rockafellar, MADS can treat constraints by the extreme barrier approach of setting the objective to infinity for infeasible points and treating the problem as unconstrained. The main GPS convergence result is to identify limit points x̂, where the Clarke generalized derivatives are nonnegative in a finite set of directions, called refining directions. Although in the unconstrained case, nonnegative combinations of these directions span the whole space, the fact that there can only be finitely many GPS refining directions limits rigorous justification of the barrier approach to finitely many linear constraints for GPS. The main result of this paper is that the general MADS framework is flexible enough to allow the generation of an asymptotically dense set of refining directions along which the Clarke derivatives are nonnegative. We propose an instance of MADS for which the refining directions are dense in the hypertangent cone at x̂ with probability 1 whenever the iterates associated with the refining directions converge to a single x̂. The instance of MADS is compared to versions of GPS on some test problems. We also illustrate the limitation of our results with examples.
Self-Organisation and Emergence in MAS: An Overview

The spread of the Internet and the evolution of mobile communication, have created new possibilities for software applications such as ubiquitous computing, dynamic supply chains and medical home care. Such systems need to operate in dynamic, heterogeneous environments and face the challenge of handling frequently changing requirements; therefore they must be flexible, robust and capable of adapting to the circumstances. It is widely believed that multi-agent systems coordinated by selforganisation and emergence mechanisms are an effective way to design these systems. This paper aims to define the concepts of self-organisation and emergence and to provide a state of the art survey about the different classes of self-organisation mechanisms applied in the multi-agent systems domain. Furthermore, the strengths and limits of these approaches are examined and research issues are provided.  Članek opisuje pregled samoorganizacije v MAS.
Motivation in a language MOOC : issues for course designers Book Section

Whilst several existing studies on foreign language learning have explored motivation in more traditional settings (Dörnyei, 2003), this paper presents one of the first studies on the motivation of participants in a MOOC. The MOOC, Travailler en français (https://sites.google.com/site/mooctravaillerenfrancais/home), was a 5-week open online course for learners of French at level B1 of the CEFR, and aimed to develop language and employability skills for working in a francophone country. It took place in early 2014 and attracted more than 1000 participants. Intrinsic motivation (Wigfield  2000) is directly linked to one’s enjoyment of accomplishing a task. We conducted a study based on the cognitive variables of the Self-Determination Theory (Deci & Ryan, 1985), and adapted the Intrinsic Motivation Inventory to the context of a MOOC in order to understand the expectancy beliefs and task values of participants engaging with the MOOC. Participants answered a 40 Likert-type questions on enjoyment/ interest (i.e. I will enjoy doing this MOOC very much), perceived competence (i.e. I think I will be able to perform successfully in the MOOC), effort (i.e. I will put a lot of effort in this MOOC), value/usefulness (i.e. I think that doing this MOOC will be useful for developing my skills), felt pressure and tension (i.e. I think I might feel pressured while doing the MOOC) and relatedness (i.e. I think I will feel like I can really trust the other participants). Results highlight significant factors that could directly influence intrinsic motivation for learning in a MOOC environment. The chapter makes recommendations for LMOOC designers based on the emerging profile of MOOC participants, on their motivation and self-determination, as well as on the pressures they might feel, including time pressures. Finally, the extent to which participants relate to each other, and are able to engage in social learning and interaction, is a real challenge for LMOOC designers.
Multi-scale object candidates for generic object tracking in street scenes

Most vision based systems for object tracking in urban environments focus on a limited number of important object categories such as cars or pedestrians, for which powerful detectors are available. However, practical driving scenarios contain many additional objects of interest, for which suitable detectors either do not yet exist or would be cumbersome to obtain. In this paper we propose a more general tracking approach which does not follow the often used tracking-by-detection principle. Instead, we investigate how far we can get by tracking unknown, generic objects in challenging street scenes. As such, we do not restrict ourselves to only tracking the most common categories, but are able to handle a large variety of static and moving objects. We evaluate our approach on the KITTI dataset and show competitive results for the annotated classes, even though we are not restricted to them.
Synthetic Aperture Radar Signal Processing with MATLAB Algorithms

Why should wait for some days to get or receive the synthetic aperture radar signal processing with matlab algorithms book that you order? Why should you take it if you can get the faster one? You can find the same book that you order right here. This is it the book that you can receive directly after purchasing. This synthetic aperture radar signal processing with matlab algorithms is well known book in the world, of course many people will try to own it. Why don't you become the first? Still confused with the way?
Flora-2: A Rule-Based Knowledge Representation and Inference Infrastructure for the Semantic Web

Flora-2 is a rule-based object-oriented knowledge base system designed for a variety of automated tasks on the Semantic Web, ranging from meta-data management to information integration to intelligent agents. The Flora-2 system integrates F-logic, HiLog, and Transaction Logic into a coherent knowledge representation and inference language. The result is a flexible and natural framework that combines rule-based and object-oriented paradigms. This paper discusses the principles underlying the design of the Flora-2 system and describes its salient features, including meta-programming, reification, logical database updates, encapsulation, and support for dynamic modules.
Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey

Swarm systems consist of large numbers of robots that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from search-and-rescue situations to Cyber defence. The two decision making cycles of swarms and humans operate on two different time-scales, where the former is normally orders of magnitude faster than the latter. Closing the loop at the intersection of these two cycles will create fast and adaptive humanswarm teaming networks. This paper brings desperate pieces of the ground work in this research area together to review this multidisciplinary literature. We conclude with a framework to synthesize the findings and summarize the multi-modal indicators needed for closed-loop human-swarm adaptive systems.