nils-herrmann/clean_scirepeval huggingface dataset:
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks

Deep neural networks have become widely used, obtaining remarkable results in domains such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bio-informatics, where they have produced results comparable to human experts. However, these networks can be easily “fooled” by adversarial perturbations: minimal changes to correctly-classified inputs, that cause the network to misclassify them. This phenomenon represents a concern for both safety and security, but it is currently unclear how to measure a network’s robustness against such perturbations. Existing techniques are limited to checking robustness around a few individual input points, providing only very limited guarantees. We propose a novel approach for automatically identifying safe regions of the input space, within which the network is robust against adversarial perturbations. The approach is data-guided, relying on clustering to identify well-defined geometric regions as candidate safe regions. We then utilize verification techniques to confirm that these regions are safe or to provide counter-examples showing that they are not safe. We also introduce the notion of targeted robustness which, for a given target label and region, ensures that a NN does not map any input in the region to the target label. We evaluated our technique on the MNIST dataset and on a neural network implementation of a controller for the next-generation Airborne Collision Avoidance System for unmanned aircraft (ACAS Xu). For these networks, our approach identified multiple regions which were completely safe as well as some which were only safe for specific labels. It also discovered several adversarial perturbations of interest.
A spatiotemporal model with visual attention for video classification

High level understanding of sequential visual input is important for safe and stable autonomy, especially in localization and object detection. While traditional object classification and tracking approaches are specifically designed to handle variations in rotation and scale, current state-of-the-art approaches based on deep learning achieve better performance. This paper focuses on developing a spatiotemporal model to handle videos containing moving objects with rotation and scale changes. Built on models that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify sequential data, this work investigates the effectiveness of incorporating attention modules in the CNN stage for video classification. The superiority of the proposed spatiotemporal model is demonstrated on the Moving MNIST dataset augmented with rotation and scaling.
Associations of Physical Activity, Screen Time with Depression, Anxiety and Sleep Quality among Chinese College Freshmen

To investigate the independent and interactive associations of physical activity (PA) and screen time (ST) with depression, anxiety and sleep quality among Chinese college students.



A cross-sectional study was conducted in Wuhan University, China from November to December 2011. The students reported their PA, ST and socio-economic characteristics using self-administered questionnaires. Sleep quality was measured by the Pittsburgh Sleep Quality Index (PSQI). Depression and anxiety were assessed using the Self-rating Depression Scale (SDS) and Self-rating Anxiety Scale (SAS), respectively. Multivariate logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) of the independent and interactive relationships of PA and ST with depression, anxiety and sleep quality.



A total of 1106 freshmen (471 females and 635 males) aged 18.9±0.9 years were included in the study. After adjustment for potential confounders, high PA and low ST were independently associated with significantly lower risks for poor sleep quality (OR: 0.48, 95% CI: 0.30-0.78) and depression (OR: 0.67, 95%CI: 0.44-0.89), respectively. An interactive inverse association was observed for combined effects of PA and low ST on depression (OR: 0.62, 95%CI: 0.40-0.92) and sleep quality (OR: 0.51, 95%CI: 0.27-0.91). No statistically significant associations were found between PA, ST and anxiety among the participants.



These findings suggest an independent and interactive relationship of high PA and low ST with significantly reduced prevalence of depressive problems and favorable sleep quality among Chinese college freshmen.
Coverage and Capacity Analysis of LTE-M and NB-IoT in a Rural Area

The 3GPP has introduced the LTE-M and NB-IoT User Equipment categories and made amendments to LTE release 13 to support the cellular Internet of Things. The contribution of this paper is to analyze the coverage probability, the number of supported devices, and the device battery life in networks equipped with either of the newly standardized technologies. The study is made for a site specific network deployment of a Danish operator, and the simulation is calibrated using drive test measurements. The results show that LTE-M can provide coverage for 99.9 % of outdoor and indoor devices, if the latter is experiencing 10 dB additional loss. However, for deep indoor users NB-IoT is required and provides coverage for about 95 % of the users. The cost is support for more than 10 times fewer devices and a 2-6 times higher device power consumption. Thus both LTE-M and NB- IoT provide extended support for the cellular Internet of Things, but with different trade- offs.
Seven principles of goal activation: a systematic approach to distinguishing goal priming from priming of non-goal constructs.

Countless studies have recently purported to demonstrate effects of goal priming; however, it is difficult to muster unambiguous support for the claims of these studies because of the lack of clear criteria for determining whether goals, as opposed to alternative varieties of mental representations, have indeed been activated. Therefore, the authors offer theoretical guidelines that may help distinguish between semantic, procedural, and goal priming. Seven principles that are hallmarks of self-regulatory processes are proposed: Goal-priming effects (a) involve value, (b) involve postattainment decrements in motivation, (c) involve gradients as a function of distance to the goal, (d) are proportional to the product of expectancy and value, (e) involve inhibition of conflicting goals, (f) involve self-control, and (g) are moderated by equifinality and multifinality. How these principles might help distinguish between automatic activation of goals and priming effects that do not involve goals is discussed.
Online Gaming Addiction: The Role of Sensation Seeking, Self-Control, Neuroticism, Aggression, State Anxiety, and Trait Anxiety

Research into online gaming has steadily increased over the last decade, although relatively little research has examined the relationship between online gaming addiction and personality factors. This study examined the relationship between a number of personality traits (sensation seeking, self-control, aggression, neuroticism, state anxiety, and trait anxiety) and online gaming addiction. Data were collected over a 1-month period using an opportunity sample of 123 university students at an East Midlands university in the United Kingdom. Gamers completed all the online questionnaires. Results of a multiple linear regression indicated that five traits (neuroticism, sensation seeking, trait anxiety, state anxiety, and aggression) displayed significant associations with online gaming addiction. The study suggests that certain personality traits may be important in the acquisition, development, and maintenance of online gaming addiction, although further research is needed to replicate the findings of the present study.
Class label autoencoder for zero-shot learning

Existing zero-shot learning (ZSL) methods usually learn a projection function between a feature space and a semantic embedding space(text or attribute space) in the training seen classes or testing unseen classes. However, the projection function cannot be used between the feature space and multi-semantic embedding spaces, which have the diversity characteristic for describing the different semantic information of the same class. To deal with this issue, we present a novel method to ZSL based on learning class label autoencoder (CLA). CLA can not only build a uniform framework for adapting to multi-semantic embedding spaces, but also construct the encoder-decoder mechanism for constraining the bidirectional projection between the feature space and the class label space. Moreover, CLA can jointly consider the relationship of feature classes and the relevance of the semantic classes for improving zero-shot classification. The CLA solution can provide both unseen class labels and the relation of the different classes representation(feature or semantic information) that can encode the intrinsic structure of classes. Extensive experiments demonstrate the CLA outperforms stateof-art methods on four benchmark datasets, which are AwA, CUB, Dogs and ImNet-2.
The residual cognitive effects of heavy marijuana use in college students.

To assess whether frequent marijuana use is associated with residual neuropsychological effects.



Single-blind comparison of regular users vs infrequent users of marijuana.



Two samples of college undergraduates: 65 heavy users, who had smoked marijuana a median of 29 days in the last 30 days (range, 22 to 30 days) and who also displayed cannabinoids in their urine, and 64 light users, who had smoked a median of 1 day in the last 30 days (range, 0 to 9 days) and who displayed no urinary cannabinoids.



Subjects arrived at 2 PM on day 1 of their study visit, then remained at our center overnight under supervision. Neuropsychological tests were administered to all subjects starting at 9 AM on day 2. Thus, all subjects were abstinent from marijuana and other drugs for a minimum of 19 hours before testing.



Subjects received a battery of standard neuropsychological tests to assess general intellectual functioning, abstraction ability, sustained attention, verbal fluency, and ability to learn and recall new verbal and visuospatial information.



Heavy users displayed significantly greater impairment than light users on attention/executive functions, as evidenced particularly by greater perseverations on card sorting and reduced learning of word lists. These differences remained after controlling for potential confounding variables, such as estimated levels of premorbid cognitive functioning, and for use of alcohol and other substances in the two groups.



Heavy marijuana use is associated with residual neuropsychological effects even after a day of supervised abstinence from the drug. However, the question remains open as to whether this impairment is due to a residue of drug in the brain, a withdrawal effect from the drug, or a frank neurotoxic effect of the drug. from marijuana
MOOCBuddy: a Chatbot for personalized learning with MOOCs

With the proliferation of MOOCs (Massive Open Online Courses) providers, like Coursera, edX, FutureLearn, UniCampus.ro, NOVAMOOC.uvt.ro or MOOC.ro, it’s a real challenge to find the best learning resource. MOOCBuddy – a MOOC recommender system as a chatbot for Facebook Messenger, based on user’s social media profile and interests, could be a solution. MOOCBuddy is looking like the big trend of 2016, based on the Messenger Platform launched by Facebook in the mid of April 2016.
Analysis of ECG records using ECG Chaos Extractor platform and Weka system

Clustering and classification of ECG records for four patient classes from the Internet databases by using the Weka system. Patient classes include normal, atrial arrhythmia, supraventricular arrhythmia and CHF. Chaos features are extracted automatically by using the ECG Chaos Extractor platform and recorded in Arff files. The list of features includes: correlation dimension, central tendency measure, spatial filling index and approximate entropy. Both ECG signal files and ECG annotations files are analyzed. The results show that chaos features can successfully cluster and classify the ECG annotations records by using standard and efficient algorithms such as EM and C4.5.
Cytokine cascade in dengue hemorrhagic fever: implications for pathogenesis.

Dengue virus produces a mild acute febrile illness, dengue fever (DF) and a severe illness, dengue hemorrhagic fever (DHF). The characteristic feature of DHF is increased capillary permeability leading to extensive plasma leakage in serous cavities resulting in shock. The pathogenesis of DHF is not fully understood. This paper presents a cascade of cytokines, that in our view, may lead to DHF. The main feature is the early generation of a unique cytokine, human cytotoxic factor (hCF) that initiates a series of events leading to a shift from Th1-type response in mild illness to a Th2-type response resulting in severe DHF. The shift from Th1 to Th2 is regulated by the relative levels of interferon-gamma and interleukin (IL)-10 and between IL-12 and transforming growth factor-beta, which showed an inverse relationship in patients with DF.
Emotional Beasts: Visually Expressing Emotions through Avatars in VR

With the advances in Virtual Reality (VR) and physiological sensing technology, even more immersive computer-mediated communication through life-like characteristics is now possible. In response to the current lack of culture, expression and emotions in VR avatars, we propose a two-fold solution. First, integration of bio-signal sensors into the HMD and techniques to detect aspects of the emotional state of the user. Second, the use of this data to generate expressive avatars which we refer to as Emotional Beasts. The creation of Emotional Beasts will allow us to experiment with the manipulation of a user's self-expression in VR space and as well as the perception of others in it, providing some valuable tools to evoke a desired emotional reaction. As this medium moves forward, this and other tools are what will help the field of virtual reality expand from a medium of surface-level experience to one of deep, emotionally compelling human-to-human connection.
Power Electronic Traction Transformer-Low Voltage Prototype

Recently, a world's first ever power electronic traction transformer (PETT) for 15 kV, 16 2/3Hz railway grid, has been newly developed, commissioned, and installed on the locomotive, where it is presently in use. This marks an important milestone in the traction world. The design and development of the PETT are described in this paper, where a low-voltage (LV) PETT prototype is presented. It has been designed for the purposes of control hardware and software commissioning, thus serving a role of an analogue simulator. In this paper, emphasis is placed on the overall system requirements, from where control system has been developed, implemented, and successfully commissioned. The development of a 1.2MVA medium-voltage PETT prototype will be reported separately in accompanying paper.
Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding

Voice interfaces are becoming accepted widely as input methods for a diverse set of devices. This development is driven by rapid improvements in automatic speech recognition (ASR), which now performs on par with human listening in many tasks. These improvements base on an ongoing evolution of deep neural networks (DNNs) as the computational core of ASR. However, recent research results show that DNNs are vulnerable to adversarial perturbations, which allow attackers to force the transcription into a malicious output. In this paper, we introduce a new type of adversarial examples based on psychoacoustic hiding. Our attack exploits the characteristics of DNN-based ASR systems, where we extend the original analysis procedure by an additional backpropagation step. We use this backpropagation to learn the degrees of freedom for the adversarial perturbation of the input signal, i.e., we apply a psychoacoustic model and manipulate the acoustic signal below the thresholds of human perception. To further minimize the perceptibility of the perturbations, we use forced alignment to find the best fitting temporal alignment between the original audio sample and the malicious target transcription. These extensions allow us to embed an arbitrary audio input with a malicious voice command that is then transcribed by the ASR system, with the audio signal remaining barely distinguishable from the original signal. In an experimental evaluation, we attack the state-of-the-art speech recognition system Kaldi and determine the best performing parameter and analysis setup for different types of input. Our results show that we are successful in up to 98% of cases with a computational effort of fewer than two minutes for a ten-second audio file. Based on user studies, we found that none of our target transcriptions were audible to human listeners, who still understand the original speech content with unchanged accuracy.
Understanding customer experience.

Anyone who has signed up for cell phone service, attempted to claim a rebate, or navigated a call center has probably suffered from a company's apparent indifference to what should be its first concern: the customer experiences that culminate in either satisfaction or disappointment and defection. Customer experience is the subjective response customers have to direct or indirect contact with a company. It encompasses every aspect of an offering: customer care, advertising, packaging, features, ease of use, reliability. Customer experience is shaped by customers' expectations, which largely reflect previous experiences. Few CEOs would argue against the significance of customer experience or against measuring and analyzing it. But many don't appreciate how those activities differ from CRM or just how illuminating the data can be. For instance, the majority of the companies in a recent survey believed they have been providing "superior" experiences to customers, but most customers disagreed. The authors describe a customer experience management (CEM) process that involves three kinds of monitoring: past patterns (evaluating completed transactions), present patterns (tracking current relationships), and potential patterns (conducting inquiries in the hope of unveiling future opportunities). Data are collected at or about touch points through such methods as surveys, interviews, focus groups, and online forums. Companies need to involve every function in the effort, not just a single customer-facing group. The authors go on to illustrate how a cross-functional CEM system is created. With such a system, companies can discover which customers are prospects for growth and which require immediate intervention.
An Automatic System for Unconstrained Video-Based Face Recognition

Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to be processed and intra/inter-video variations on pose, illumination, occlusion, scene, blur, video quality, etc. In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames. To handle these problems, we propose a robust and efficient system for unconstrained video-based face recognition, which is composed of face/fiducial detection, face association, and face recognition. First, we use multi-scale single-shot face detectors to efficiently localize faces in videos. The detected faces are then grouped respectively through carefully designed face association methods, especially for multi-shot videos. Finally, the faces are recognized by the proposed face matcher based on an unsupervised subspace learning approach and a subspace-tosubspace similarity metric. Extensive experiments on challenging video datasets, such as Multiple Biometric Grand Challenge (MBGC), Face and Ocular Challenge Series (FOCS), JANUS Challenge Set 6 (CS6) for low-quality surveillance videos and IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the proposed system can accurately detect and associate faces from unconstrained videos and effectively learn robust and discriminative features for recognition.
(How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising

This paper examines microblogging information diffusion activity during the 2011 Egyptian political uprisings. Specifically, we examine the use of the retweet mechanism on Twitter, using empirical evidence of information propagation to reveal aspects of work that the crowd conducts. Analysis of the widespread contagion of a popular meme reveals interaction between those who were "on the ground" in Cairo and those who were not. However, differences between information that appeals to the larger crowd and those who were doing on-the-ground work reveal important interplay between the two realms. Through both qualitative and statistical description, we show how the crowd expresses solidarity and does the work of information processing through recommendation and filtering. We discuss how these aspects of work mutually sustain crowd interaction in a politically sensitive context. In addition, we show how features of this retweet-recommendation behavior could be used in combination with other indicators to identify information that is new and likely coming from the ground.
SimaticScan: Towards A Specialised Vulnerability Scanner for Industrial Control Systems

Rob Antrobus Security Lancaster Research Centre Lancaster University Lancaster LA1 4WA UK security-centre.lancs.ac.uk r.antrobus1@lancaster.ac.uk Sylvain Frey Security Lancaster Research Centre Lancaster University Lancaster LA1 4WA UK security-centre.lancs.ac.uk s.frey@lancaster.ac.uk Benjamin Green Security Lancaster Research Centre Lancaster University Lancaster LA1 4WA UK security-centre.lancs.ac.uk b.green2@lancaster.ac.uk
Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (eg. whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias xspace, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias xspace on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias xspace are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task.