nils-herrmann/clean_scirepeval huggingface dataset:
SVM based forest fire detection using static and dynamic features

A novel approach is proposed in this paper for automatic forest fire detection from video. Based on 3D point cloud of the collected sample fire pixels, Gaussian mixture model is built and helps segment some possible flame regions in single image. Then the new specific flame pattern is defined for forest, and three types of fire colors are labeled accordingly. With 11 static features including color distributions, texture parameters and shape roundness, the static SVM classifier is trained and filters the segmented results. Using defined overlapping degree and varying degree, the remained candidate regions are matched among consecutive frames. Subsequently the variations of color, texture, roundness, area, contour are computed, then the average and the mean square deviation of them are obtained. Together with the flickering frequency from temporal wavelet based Fourier descriptors analysis of flame contour, 27 dynamic features are used to train the dynamic SVM classifier, which is applied for final decision. Our approach has been tested with dozens of video clips, and it can detect forest fire while recognize the fire like objects, such as red house, bright light and flying flag. Except for the acceptable accuracy, our detection algorithm performs in real time, which proves its value for computer vision based forest fire surveillance.
Programmable Reverse Engineering

Program understanding can be enhanced using reverse engineering technologies. The understanding process is heavily dependent on both individuals and their speci c cognitive abilities, and on the set of facilities provided by the program understanding environment. Unfortunately, most reverse engineering tools provide a xed palette of extraction, selection, and organization techniques. This paper describes a programmable approach to reverse engineering. The approach uses a scripting language that enables users to write their own routines for common reverse engineering activities such as graph layout, metrics, and subsystem decomposition, thereby extending the capabilities of the reverse engineering toolset to better suit their needs. A programmable environment supported by this approach subsumes existing reverse engineering systems by being able to simulate facets of each one.
A Design Space for Self-Adaptive Systems

Self-adaptive systems research is expanding as systems professionals recognize the importance of automation for managing the growing complexity, scale, and scope of software systems. The current approach to designing such systems is ad hoc, varied, and fractured, often resulting in systems with parts of multiple, sometimes poorly compatible designs. In addition to the challenges inherent to all software, this makes evaluating, understanding, comparing, maintaining, and even using such systems more difficult. This paper discusses the importance of systematic design and identifies the dimensions of the self-adaptive system design space. It identifies key design decisions, questions, and possible answers relevant to the design space, and organizes these into five clusters: observation, representation, control, identification, and enacting adaptation. This characterization can serve as a standard lexicon, that, in turn, can aid in describing and evaluating the behavior of existing and new self-adaptive systems. The paper also outlines the future challenges for improving the design of self-adaptive
A survey of multi-source energy harvesting systems

Energy harvesting allows low-power embedded devices to be powered from naturally-ocurring or unwanted environmental energy (e.g. light, vibration, or temperature difference). While a number of systems incorporating energy harvesters are now available commercially, they are specific to certain types of energy source. Energy availability can be a temporal as well as spatial effect. To address this issue, 'hybrid' energy harvesting systems combine multiple harvesters on the same platform, but the design of these systems is not straightforward. This paper surveys their design, including trade-offs affecting their efficiency, applicability, and ease of deployment. This survey, and the taxonomy of multi-source energy harvesting systems that it presents, will be of benefit to designers of future systems. Furthermore, we identify and comment upon the current and future research directions in this field.
On cyclic codes and quasi-cyclic codes over Z q + u Z q

Let R = Zq + uZq, where q = p s and u = 0. In this paper, some structural properties of cyclic codes and quasi-cyclic (QC) codes over the ring R are considered. A QC code of length ln with index l over R is viewed both as in the conventional row circulant form and also as an R[x]/(xn − 1)-submodule of GR(R, l)[x]/(xn − 1), where GR(R, l) is the Galois extension ring of degree l over R. A necessary and sufficient condition for cyclic codes over the ring R to be free is obtained and a BCH-type bound for them is also given. A sufficient condition for 1-generator QC codes to be R-free is given. Some distance bounds for 1-generator QC codes are also discussed. The decomposition and dual of QC codes over R are also briefly discussed.
Personal Information in Passwords and Its Security Implications

While it is not recommended, Internet users tend to include personal information in their passwords for easy memorization. However, the use of personal information in passwords and its security implications have yet to be studied. In this paper, we dissect user passwords from several leaked data sets to investigate the extent to which a user’s personal information resides in a password. Then, we introduce a new metric called coverage to quantify the correlation between passwords and personal information. Afterward, based on our analysis, we extend the probabilistic context-free grammars (PCFGs) method to be semantics-rich and propose personal-PCFG to crack passwords by generating personalized guesses. Through offline and online attack scenarios, we demonstrate that personal-PCFG cracks passwords much faster than PCFG and makes online attacks much more likely to succeed. To defend against such semantics-aware attacks, we examine the use of simple distortion functions that are chosen by users to mitigate unwanted correlation between personal information and passwords.
User Behavior Aware Cell Association in Heterogeneous Cellular Networks

In heterogeneous cellular networks (HetNets), cell association of User Equipment (UE) affects UE transmit rate and network throughput. Conventional cell association rules are usually based on UE received Signal-to-Interference-and-Noise-Ratio (SINR) without taking into account user behaviors, which can indeed be exploited for improving network performance. In this paper, we investigate UE cell association in HetNets based on individual user behavior characteristics with aim to maximize long- term expected system throughput. We model the problem as a stochastic optimization model Restless Multi-Armed Bandit (RMAB). As it is a PSPACE-hard problem, we develop a primal-dual heuristic index algorithm and the solution specifies the rule that determines which arms in the RMAB model to be selected at each decision time. According to the solution of RMAB, we propose a new cell association strategy called Index Enabled Association (IDEA). We also conduct simulation experiments to compare IDEA with conventional max-SINR cell association strategy and an existing game-based RAT selection scheme. Numerical results demonstrate the advantages of IDEA in typical scenarios.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples

We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimizationbased attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining noncertified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers.
Uniform access to NoSQL systems

Non-relational databases (often termed as NoSQL) have recently emerged and have generated both interest and criticism. Interest because they address requirements that are very important in large-scale applications, criticism because of the comparison with well known relational achievements. One of the major problems often mentioned is the heterogeneity of the languages and of the interfaces they offer to developers and users. Different platforms and languages have been proposed, and applications developed for one system require significant effort to be migrated to another one. Here we propose a common programming interface to NoSQL systems called SOS (Save Our Systems). Its goal is to support application development by hiding the specific details of the various systems. It is based on a metamodelling approach, in the sense that the specific interfaces of the individual systems are mapped to a common one. The tool provides interoperability as well, since a single application can interact with several systems at the same time. & 2013 Elsevier Ltd. All rights reserved.
MentorNet: Regularizing Very Deep Neural Networks on Corrupted Labels

Recent studies have discovered that deep networks are capable of memorizing the entire data even when the labels are completely random. Since deep models are trained on big data where labels are often noisy, the ability to overfit noise can lead to poor performance. To overcome the overfitting on corrupted training data, we propose a novel technique to regularize deep networks in the data dimension. This is achieved by learning a neural network called MentorNet to supervise the training of the base network, namely, StudentNet. Our work is inspired by curriculum learning and advances the theory by learning a curriculum from data by neural networks. We demonstrate the efficacy of MentorNet on several benchmarks. Comprehensive experiments show that it is able to significantly improve the generalization performance of the state-of-the-art deep networks on corrupted training data.
Enhanced 3GPP system for Machine Type Communications and Internet of Things

The Third Generation Partnership Project (3GPP) has been working on developing specifications on Machine Type Communications (MTC), also known as Machine to Machine Communications (M2M), which is all part of Internet of Things (IoT), a technology that enables machines and devices to be inter-connected via the internet. This paper presents recent M2M/IoT feature enhancements in 3GPP. These features include architectural enhancements for inclusion of a service exposure framework, group based IoT management, efficient monitoring mechanisms for IoT devices, optimization of IoT device power consumption, and high latency communications. These M2M/IoT related functionalities and enhancements are a continuing effort within 3GPP to make the mobile network fulfil the ever changing M2M/IoT requirements, and in so doing allow operators/service providers to offer unique services.
DoS & DDoS in Named Data Networking

With the growing realization that current Internet protocols are reaching the limits of their senescence, several ongoing research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denialof-Service (DoS) attacks that plague today’s Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) – a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN’s resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking.
A Study on the Customer-Based Brand Equity of Online Retailers

Different from traditional firms, online retailers do not often produce common goods on their own. On the contrary, they sell merchandises that traditional firms produce. Furthermore, online retailers are also different from traditional retailers because the former do not often have frontline employees, physical outlets, or displaying layouts. Therefore, when building a strong online retailer brand, it is not enough to only consider the traditional dimensions of customer-based brand equity, namely brand awareness, perceived quality, brand associations, and brand loyalty. Some dimensions, i.e. trust associations and emotional connection need to be distinguished or included in the context of online business. Besides, the willingness to pay a price premium is always being viewed as a good indicator to measure brand equity, but for online retailers, it is conditional on some premises. By structural equation modeling, it was verified that for online retailers, the importance of brand awareness and trust associations decreased but perceived quality remained its strong influence. Brand loyalty was the better indicator for measuring brand equity, and emotional connection became so critical that if without it, customers would lack willingness to pay a price premium.
Comparing Similarity Measures for Original WSD Lesk Algorithm

There are many similarity measures to determine the similarity relatedness between two words. Measures of similarity or relatedness are used in such applications as word sense disambiguation. One of the methods used to resolve WSD is the Lesk algorithm. The performance of this algorithm is connected with the similarity relatedness between all words in the text, i.e the success rate of WSD should increase as the similarity measure’s performance gets better. This paper presents a comparison of several similarity measures applied to WSD using the original Lesk Algorithm.
Example-Based Object Detection in Images by Components

In this paper we present a general example based framework for detecting objects in static images by components. The technique is demonstrated by developing a system that locates people in cluttered scenes. The system is structured with four distinct example based detectors that are trained to nd separately the four components of the human body: the head, legs, left arm, right arm. After ensuring that these components are present in the proper geometric con guration, a second example based classi er combines the results of the component detectors to classify a pattern as either a \person" or a \nonperson." We call this type of hierarchical architecture in which learning occurs at multiple stages an Adaptive Combination of Classi ers (ACC). We present results that show that this system performs signi cantly better than a similar full body person detector. This suggests that the improvement in performance is due to the component based approach and the ACC data classi cation architecture. The algorithm is also more robust than the full body person detection method in that it is capable of locating partially occluded views of people and people whose body parts have little contrast with the background.
Making B+-Trees Cache Conscious in Main Memory

Previous research has shown that cache behavior is important for main memory index structures. Cache conscious index structures such as Cache Sensitive Search Trees (CSS-Trees) perform lookups much faster than binary search and T-Trees. However, CSS-Trees are designed for decision support workloads with relatively static data. Although B+-Trees are more cache conscious than binary search and T-Trees, their utilization of a cache line is low since half of the space is used to store child pointers. Nevertheless, for applications that require incremental updates, traditional B+-Trees perform well.
Our goal is to make B+-Trees as cache conscious as CSS-Trees without increasing their update cost too much. We propose a new indexing technique called “Cache Sensitive B+-Trees” (CSB+-Trees). It is a variant of B+-Trees that stores all the child nodes of any given node contiguously, and keeps only the address of the first child in each node. The rest of the children can be found by adding an offset to that address. Since only one child pointer is stored explicitly, the utilization of a cache line is high. CSB+-Trees support incremental updates in a way similar to B+-Trees.
We also introduce two variants of CSB+-Trees. Segmented CSB+-Trees divide the child nodes into segments. Nodes within the same segment are stored contiguously and only pointers to the beginning of each segment are stored explicitly in each node. Segmented CSB+-Trees can reduce the copying cost when there is a split since only one segment needs to be moved. Full CSB+-Trees preallocate space for the full node group and thus reduce the split cost. Our performance studies show that CSB+-Trees are useful for a wide range of applications.
Six-Port Radar Sensor for Remote Respiration Rate and Heartbeat Vital-Sign Monitoring

A novel remote respiration and heartbeat monitoring sensor is presented. The device is a monostatic radar based on a six-port interferometer operating a continuous-wave signal at 24 GHz and a radiated power of less than 3 $\mu\hbox{W}$ . Minor mechanical movements of the patient's body caused by the respiration as well as hearbeat can be tracked by analyzing the phase modulation of the backscattered signal by means of microwave interferometry with the six-port network. High-distance measurement accuracy in the micrometer scale as well as low system complexity are the benefits of the six-port receiver. To verify the performance of the system, different body areas have been observed by the six-port radar. The proposed system has been tested and validated by measurement results.
The PageRank Citation Ranking: Bringing Order to the Web.

The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a method for rating Web pages objectively and mechanically, e ectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to e ciently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.
Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results

Given a (combinatorial) optimization problem and a feasible solution to it, the corresponding inverse optimization problem is to find a minimal adjustment of the cost function such that the given solution becomes optimum. Several such problems have been studied in the last twelve years. After formalizing the notion of an inverse problem and its variants, we present various methods for solving them. Then we discuss the problems considered in the literature and the results that have been obtained. Finally, we formulate some open problems.
AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

While demands on video traffic over mobile networks have been souring, the wireless link capacity cannot keep up with the traffic demand. The gap between the traffic demand and the link capacity, along with time-varying link conditions, results in poor service quality of video streaming over mobile networks such as long buffering time and intermittent disruptions. Leveraging the cloud computing technology, we propose a new mobile video streaming framework, dubbed AMES-Cloud, which has two main parts: adaptive mobile video streaming (AMoV) and efficient social video sharing (ESoV). AMoV and ESoV construct a private agent to provide video streaming services efficiently for each mobile user. For a given user, AMoV lets her private agent adaptively adjust her streaming flow with a scalable video coding technique based on the feedback of link quality. Likewise, ESoV monitors the social network interactions among mobile users, and their private agents try to prefetch video content in advance. We implement a prototype of the AMES-Cloud framework to demonstrate its performance. It is shown that the private agents in the clouds can effectively provide the adaptive streaming, and perform video sharing (i.e., prefetching) based on the social network analysis.
Moral emotions and moral behavior.

Moral emotions represent a key element of our human moral apparatus, influencing the link between moral standards and moral behavior. This chapter reviews current theory and research on moral emotions. We first focus on a triad of negatively valenced "self-conscious" emotions-shame, guilt, and embarrassment. As in previous decades, much research remains focused on shame and guilt. We review current thinking on the distinction between shame and guilt, and the relative advantages and disadvantages of these two moral emotions. Several new areas of research are highlighted: research on the domain-specific phenomenon of body shame, styles of coping with shame, psychobiological aspects of shame, the link between childhood abuse and later proneness to shame, and the phenomena of vicarious or "collective" experiences of shame and guilt. In recent years, the concept of moral emotions has been expanded to include several positive emotions-elevation, gratitude, and the sometimes morally relevant experience of pride. Finally, we discuss briefly a morally relevant emotional process-other-oriented empathy.