nils-herrmann/clean_scirepeval huggingface dataset:
Using CoreSight PTM to Integrate CRA Monitoring IPs in an ARM-Based SoC

The ARM CoreSight Program Trace Macrocell (PTM) has been widely deployed in recent ARM processors for real-time debugging and tracing of software. Using PTM, the external debugger can extract execution behaviors of applications running on an ARM processor. Recently, some researchers have been using this feature for other purposes, such as fault-tolerant computation and security monitoring. This motivated us to develop an external security monitor that can detect control hijacking attacks, of which the goal is to maliciously manipulate the control flow of victim applications at an attacker’s disposal. This article focuses on detecting a special type of attack called code reuse attacks (CRA), which use a recently introduced technique that allows attackers to perform arbitrary computation without injecting their code by reusing only existing code fragments. Our external monitor is attached to the outside of the host system via the system bus and ARM CoreSight PTM, and is fed with execution traces of a victim application running on the host. As a majority of CRAs violates the normal execution behaviors of a program, our monitor constantly watches and analyzes the execution traces of the victim application and detects a symptom of attacks when the execution behaviors violate certain rules that normal applications are known to adhere. We present two different implementations for this purpose: a hardware-based solution in which all CRA detection components are implemented in hardware, and a hardware/software mixed solution that can be employed in a more resource-constrained environment where the deployment of full hardware-level CRA detection is burdensome.
Effects of brief and sham mindfulness meditation on mood and cardiovascular variables.

Although long-term meditation has been found to reduce negative mood and cardiovascular variables, the effects of a brief mindfulness meditation intervention when compared to a sham mindfulness meditation intervention are relatively unknown. This experiment examined whether a 3-day (1-hour total) mindfulness or sham mindfulness meditation intervention would improve mood and cardiovascular variables when compared to a control group.



Eighty-two (82) undergraduate students (34 males, 48 females), with no prior meditation experience, participated in three sessions that involved training in either mindfulness meditation, sham mindfulness meditation, or a control group. Heart rate, blood pressure, and psychologic variables (Profile of Mood States, State Anxiety Inventory) were assessed before and after the intervention.



The meditation intervention was more effective at reducing negative mood, depression, fatigue, confusion, and heart rate, when compared to the sham and control groups.



These results indicate that brief meditation training has beneficial effects on mood and cardiovascular variables that go beyond the demand characteristics of a sham meditation intervention.
Sustainable Retailing in the Fashion Industry : A Systematic Literature Review

Although sustainability in the fashion industry has gained prominence from both business practices and academic research, retailing, a vital part of the supply chain, has not yet been fairly explored in academia. The interest in this area has increased lately, mainly due to the growing complexity within this dynamic context. Therefore, it is meaningful to conduct a systematic review of the relevant published literature in this field. This study aims to identify the main perspectives of research on sustainable retailing in the fashion industry. The content analysis results indicate that the most prominent areas in the field are sustainable retailing in disposable fashion, fast fashion, slow fashion, green branding and eco-labeling; retailing of secondhand fashion; reverse logistics in fashion retailing; and emerging retailing opportunities in e-commerce. The results from this review also indicate that there is a lack of research on sustainable retailing in the fashion industry in the developing market.
Graph-Based Statistical Language Model for Code

n-gram statistical language model has been successfully applied to capture programming patterns to support code completion and suggestion. However, the approaches using n-gram face challenges in capturing the patterns at higher levels of abstraction due to the mismatch between the sequence nature in n-grams and the structure nature of syntax and semantics in source code. This paper presents GraLan, a graph-based statistical language model and its application in code suggestion. GraLan can learn from a source code corpus and compute the appearance probabilities of any graphs given the observed (sub)graphs. We use GraLan to develop an API suggestion engine and an AST-based language model, ASTLan. ASTLan supports the suggestion of the next valid syntactic template and the detection of common syntactic templates. Our empirical evaluation on a large corpus of open-source projects has shown that our engine is more accurate in API code suggestion than the state-of-the-art approaches, and in 75% of the cases, it can correctly suggest the API with only five candidates. ASTLan also has high accuracy in suggesting the next syntactic template and is able to detect many useful and common syntactic templates.
Data and Information Quality Research: Its Evolution and Future

Awareness of data and information quality issues has grown rapidly in light of the critical role played by the quality of information in our data-intensive, knowledge-based economy. Research in the past two decades has produced a large body of data quality knowledge and has expanded our ability to solve many data and information quality problems. We present an overview of the evolution and current landscape of data and information quality research. We introduce an integrative framework to characterize the research along two dimensions: topics and methods. Representative papers are cited for purposes of illustrating the issues addressed and the methods used. We also identify and discuss challenges to be addressed in future research.
An INS Monitor to Detect GNSS Spoofers Capable of Tracking Vehicle Position

In this paper, we describe and evaluate a new monitor that uses inertial navigation system (INS) measurements to detect spoofing attacks on global navigation satellite system (GNSS) receivers. Spoofing detection is accomplished by monitoring the Kalman filter innovations in a tightly coupled INS/GNSS mechanization. Monitor performance is evaluated against worst case spoofing attacks, including spoofers capable of tracking vehicle position. There are two main contributions of this paper. The first is a mathematical framework to quantify postmonitor spoofing integrity risk. The second is an analytical expression of the worst case sequence of spoofed GNSS signals. We then apply these to an example spoofing attack on a Boeing 747 on final approach. The results show that GNSS spoofing is easily detected, with high integrity, unless the spoofer's position-tracking devices have unrealistic, near-perfect accuracy, and no delays.
Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations.
On perspective of security and privacy-preserving solutions in the internet of things

The Internet of Things (IoT) brings together a large variety of devices of different platforms, computational capacities and functionalities. The network heterogeneity and the ubiquity of IoT devices introduce increased demands on both security and privacy protection. Therefore, the cryptographic mechanisms must be strong enough to meet these increased requirements but, at the same time, they must be efficient enough for the implementation on constrained devices. In this paper, we present a detailed assessment of the performance of the most used cryptographic algorithms on constrained devices that often appear in IoT networks. We evaluate the performance of symmetric primitives, such as block ciphers, hash functions, random number generators, asymmetric primitives, such as digital signature schemes, and privacyenhancing schemes on various microcontrollers, smart-cards and mobile devices. Furthermore, we provide the analysis of the usability of upcoming schemes, such as the homomorphic encryption schemes, group signatures and attribute-based schemes.
Evaluating the descriptive power of Instagram hashtags

Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. However, manual image annotation, even for creating training sets for machine learning algorithms, requires hard effort and contains human judgment errors and subjectivity. Thus, alternative ways for automatically creating training examples, i.e., pairs of images and tags, are pursued. In this work, we investigate whether tags accompanying photos in the Instagram can be considered as image annotation metadata. If such a claim is proved then Instagram could be used as a very rich, easy to collect automatically, source of training data for the development of AIA techniques. Our hypothesis is that Instagram hashtags, and especially those provided by the photo owner/creator, express more accurately the content of a photo compared to the tags assigned to a photo during explicit image annotation processes like crowdsourcing. In this context, we explore the descriptive power of hashtags by examining whether other users would use the same, with the owner, hashtags to annotate an image. For this purpose 1000 Instagram images were collected and one to four hashtags, considered as the most descriptive ones for the image in question, were chosen among the hashtags used by the photo owner. An online database was constructed to generate online questionnaires containing 20 images each, which were distributed to experiment participants so they can choose the best suitable hashtag for every image according to their interpretation. Results show that an average of 66% of the participants hashtag choices coincide with those suggested by the photo owners; thus, an initial evidence towards our hypothesis confirmation can be claimed. c ⃝ 2016 Qassim University. Production and Hosting by Elsevier B.V.  Peer review under responsibility of Qassim University. ∗ Corresponding author. E-mail addresses: s.giannoulakis@cut.ac.cy (S. Giannoulakis), nic http://dx.doi.org/10.1016/j.jides.2016.10.001 2352-6645/ c ⃝ 2016 Qassim University. Production and Hosting by E license (http://creativecommons.org/licenses/by-nc-nd/4.0/). olas.tsapatsoulis@cut.ac.cy (N. Tsapatsoulis). lsevier B.V.  1 1 4 – 1 2 9 115
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.
Human-aware task planning for mobile robots

Robots that share their workspace with people, like household or service robots, need to take into account the presence of humans when planning their actions. In this paper, we present a framework for human-aware planning that would make the robots capable of performing their tasks without interfering with the user in his every day life. We focus in particular on the core module of the framework, a human-aware planner that generates a sequence of actions for a robot, taking into account the state of the environment and the goals of the robot, together with a set of forecasted possible plans of the human. We describe the planner and its relations to other system components like a plan recognizer, and present a series of experiments performed with a household robot in a small apartment.
From gulf to bridge: when do moral arguments facilitate political influence?

Much of contemporary American political rhetoric is characterized by liberals and conservatives advancing arguments for the morality of their respective political positions. However, research suggests such moral rhetoric is largely ineffective for persuading those who do not already hold one's position because advocates advancing these arguments fail to account for the divergent moral commitments that undergird America's political divisions. Building on this, we hypothesize that (a) political advocates spontaneously make arguments grounded in their own moral values, not the values of those targeted for persuasion, and (b) political arguments reframed to appeal to the moral values of those holding the opposing political position are typically more effective. We find support for these claims across six studies involving diverse political issues, including same-sex marriage, universal health care, military spending, and adopting English as the nation's official langauge. Mediation and moderation analyses further indicated that reframed moral appeals were persuasive because they increased the apparent agreement between the political position and the targeted individuals' moral values.
ITU Turkish NLP Web Service

We present a natural language processing (NLP) platform, namely the “ITU Turkish NLP Web Service” by the natural language processing group of Istanbul Technical University. The platform (available at tools.nlp.itu.edu.tr) operates as a SaaS (Software as a Service) and provides the researchers and the students the state of the art NLP tools in many layers: preprocessing, morphology, syntax and entity recognition. The users may communicate with the platform via three channels: 1. via a user friendly web interface, 2. by file uploads and 3. by using the provided Web APIs within their own codes for constructing higher level applications.
Light Field Stereo Matching Using Bilateral Statistics of Surface Cameras

In this paper, we introduce a bilateral consistency metric on the surface camera (SCam) [26] for light field stereo matching to handle significant occlusions. The concept of SCam is used to model angular radiance distribution with respect to a 3D point. Our bilateral consistency metric is used to indicate the probability of occlusions by analyzing the SCams. We further show how to distinguish between on-surface and free space, textured and non-textured, and Lambertian and specular through bilateral SCam analysis. To speed up the matching process, we apply the edge preserving guided filter  on the consistency-disparity curves. Experimental results show that our technique outperforms both the state-of-the-art and the recent light field stereo matching methods, especially near occlusion boundaries.
Image-Based Proxy Accumulation for Real-Time Soft Global Illumination

We present a new, general, and real-time technique for soft global illumination in low-frequency environmental lighting. It accumulates over relatively few spherical proxies which approximate the light blocking and re-radiating effect of dynamic geometry. Soft shadows are computed by accumulating log visibility vectors for each sphere proxy as seen by each receiver point. Inter-reflections are computed by accumulating vectors representing the proxy's unshadowed radiance when illuminated by the environment. Both vectors capture low-frequency directional dependence using the spherical harmonic basis. We also present a new proxy accumulation strategy that splats each proxy to receiver pixels in image space to collect its shadowing and indirect lighting contribution. Our soft GI rendering pipeline unifies direct and indirect soft effects with a simple accumulation strategy that maps entirely to the GPU and outperforms previous vertex-based methods.
Approximation Schemes for Covering and Packing Problems in Image Processing and VLSI

A unified and powerful approach is presented for devising polynomial approximation schemes for many strongly NP-complete problems. Such schemes consist of families of approximation algorithms for each desired performance bound on the relative error ε > &Ogr;, with running time that is polynomial when ε is fixed. Though the polynomiality of these algorithms depends on the degree of approximation ε being fixed, they cannot be improved, owing to a negative result stating that there are no fully polynomial approximation schemes for strongly NP-complete problems unless NP = P.
The unified technique that is introduced here, referred to as the shifting strategy, is applicable to numerous geometric covering and packing problems. The method of using the technique and how it varies with problem parameters are illustrated. A similar technique, independently devised by B. S. Baker, was shown to be applicable for covering and packing problems on planar graphs.
Communication-efficient distributed monitoring of thresholded counts

Monitoring is an issue of primary concern in current and next generation networked systems. For ex, the objective of sensor networks is to monitor their surroundings for a variety of different applications like atmospheric conditions, wildlife behavior, and troop movements among others. Similarly, monitoring in data networks is critical not only for accounting and management, but also for detecting anomalies and attacks. Such monitoring applications are inherently continuous and distributed, and must be designed to minimize the communication overhead that they introduce. In this context we introduce and study a fundamental class of problems called "thresholded counts" where we must return the aggregate frequency count of an event that is continuously monitored by distributed nodes with a user-specified accuracy whenever the actual count exceeds a given threshold value.In this paper we propose to address the problem of thresholded counts by setting local thresholds at each monitoring node and initiating communication only when the locally observed data exceeds these local thresholds. We explore algorithms in two categories: static and adaptive thresholds. In the static case, we consider thresholds based on a linear combination of two alternate strategies, and show that there exists an optimal blend of the two strategies that results in minimum communication overhead. We further show that this optimal blend can be found using a steepest descent search. In the adaptive case, we propose algorithms that adjust the local thresholds based on the observed distributions of updated information. We use extensive simulations not only to verify the accuracy of our algorithms and validate our theoretical results, but also to evaluate the performance of our algorithms. We find that both approaches yield significant savings over the naive approach of centralized processing.
Deep Metric Learning via Facility Location

Learning image similarity metrics in an end-to-end fashion with deep networks has demonstrated excellent results on tasks such as clustering and retrieval. However, current methods, all focus on a very local view of the data. In this paper, we propose a new metric learning scheme, based on structured prediction, that is aware of the global structure of the embedding space, and which is designed to optimize a clustering quality metric (NMI). We show state of the art performance on standard datasets, such as CUB200-2011 , Cars196 [18], and Stanford online products [30] on NMI and R@K evaluation metrics.
Seq2Sick: Evaluating the Robustness of Sequence-to-Sequence Models with Adversarial Examples

Crafting adversarial examples has become an important technique to evaluate the robustness of deep neural networks (DNNs). However, most existing works focus on attacking the image classification problem since its input space is continuous and output space is finite. In this paper, we study the much more challenging problem of crafting adversarial examples for sequence-to-sequence (seq2seq) models, whose inputs are discrete text strings and outputs have an almost infinite number of possibilities. To address the challenges caused by the discrete input space, we propose a projected gradient method combined with group lasso and gradient regularization. To handle the almost infinite output space, we design some novel loss functions to conduct non-overlapping attack and targeted keyword attack. We apply our algorithm to machine translation and text summarization tasks, and verify the effectiveness of the proposed algorithm: by changing less than 3 words, we can make seq2seq model to produce desired outputs with high success rates. On the other hand, we recognize that, compared with the well-evaluated CNN-based classifiers, seq2seq models are intrinsically more robust to adversarial attacks.
Light field video capture using a learning-based hybrid imaging system

Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings after capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time.