nils-herrmann/clean_scirepeval huggingface dataset:
Multi-Agent Imitation Learning for Driving Simulation

Simulation is an appealing option for validating the safety of autonomous vehicles. Generative Adversarial Imitation Learning (GAIL) has recently been shown to learn representative human driver models. These human driver models were learned through training in single-agent environments, but they have difficulty in generalizing to multi-agent driving scenarios. We argue these difficulties arise because observations at training and test time are sampled from different distributions. This difference makes such models unsuitable for the simulation of driving scenes, where multiple agents must interact realistically over long time horizons. We extend GAIL to address these shortcomings through a parameter-sharing approach grounded in curriculum learning. Compared with single-agent GAIL policies, policies generated by our PS-GAIL method prove superior at interacting stably in a multi-agent setting and capturing the emergent behavior of human drivers.
A Novel Method for Fingerprint Core Point Detection

Fingerprint recognition is a method of biometric authentication that uses pattern recognition techniques based on highresolution fingerprints images of the individual. Fingerprints have been used in forensic as well as commercial applications for identification as well as verification. Singular point detection is the most important task of fingerprint image classification operation. Two types of singular points called core and delta points are claimed to be enough to classify the fingerprints. The classification can act as an important indexing mechanism for large fingerprint databases which can reduce the query time and the computational complexity. Usually fingerprint images have noisy background and the local orientation field also changes very rapidly in the singular point area. It is difficult to locate the singular point precisely. There already exists many singular point detection algorithms, Most of them can efficiently detect the core point when the image quality is fine, but when the image quality is poor, the efficiency of the algorithm degrades rapidly. In the present work, a new method of detection and localization of core points in a fingerprint image is proposed.
Music and Art in Conflict Transformation : A Review

Since the early 1990s, there has been an increase in the use of music and the arts within a conflict transformation context. This guest editorial discusses the developments in this research and practical area. The current status of the field, and challenges it faces, are then examined within the context of this issue's theme of the arts and conflict transformation/peace building. *HUSS, University of Exeter, Rennes Drive, Exeter, EX4 4RJ, UK © Music and Arts in Action/Bergh and Sloboda | ISSN: 1754-7105 | Page 2  Music and Arts in Action | Volume 2 | Issue 2
Security, privacy and trust in Internet of Things: The road ahead

Internet of Things (IoT) is characterized by heterogeneous technologies, which concur to the provisioning of innovative services in various application domains. In this scenario, the satisfaction of security and privacy requirements plays a fundamental role. Such requirements include data confidentiality and authentication, access control within the IoT network, privacy and trust among users and things, and the enforcement of security and privacy policies. Traditional security countermeasures cannot be directly applied to IoT technologies due to the different standards and communication stacks involved. Moreover, the high number of interconnected devices arises scalability issues; therefore a flexible infrastructure is needed able to deal with security threats in such a dynamic environment. In this survey we present the main research challenges and the existing solutions in the field of IoT security, identifying open issues, and suggesting some hints for future research. During the last decade, Internet of Things (IoT) approached our lives silently and gradually, thanks to the availability of wireless communication systems (e.g., RFID, WiFi, 4G, IEEE 802.15.x), which have been increasingly employed as technology driver for crucial smart monitoring and control applications [1–3]. Nowadays, the concept of IoT is many-folded, it embraces many different technologies, services, and standards and it is widely perceived as the angular stone of the ICT market in the next ten years, at least [4–6]. From a logical viewpoint, an IoT system can be depicted as a collection of smart devices that interact on a collabo-rative basis to fulfill a common goal. At the technological floor, IoT deployments may adopt different processing and communication architectures, technologies, and design methodologies, based on their target. For instance, the same IoT system could leverage the capabilities of a wireless sensor network (WSN) that collects the environmental information in a given area and a set of smartphones on top of which monitoring applications run. In the middle, a standardized or proprietary middle-ware could be employed to ease the access to virtualized resources and services. The middleware, in turn, might be implemented using cloud technologies, centralized overlays , or peer to peer systems [7]. Of course, this high level of heterogeneity, coupled to the wide scale of IoT systems, is expected to magnify security threats of the current Internet, which is being increasingly used to let interact humans, machines, and robots, in any combination. More in details, traditional security countermeasures and privacy enforcement cannot be directly applied to IoT technologies due to …
Target-dependent Twitter Sentiment Classification

Sentiment analysis on Twitter data has attracted much attention recently. In this paper, we focus on target-dependent Twitter sentiment classification; namely, given a query, we classify the sentiments of the tweets as positive, negative or neutral according to whether they contain positive, negative or neutral sentiments about that query. Here the query serves as the target of the sentiments. The state-ofthe-art approaches for solving this problem always adopt the target-independent strategy, which may assign irrelevant sentiments to the given target. Moreover, the state-of-the-art approaches only take the tweet to be classified into consideration when classifying the sentiment; they ignore its context (i.e., related tweets). However, because tweets are usually short and more ambiguous, sometimes it is not enough to consider only the current tweet for sentiment classification. In this paper, we propose to improve target-dependent Twitter sentiment classification by 1) incorporating target-dependent features; and 2) taking related tweets into consideration. According to the experimental results, our approach greatly improves the performance of target-dependent sentiment classification.
Object Detection via a Multi-region and Semantic Segmentation-Aware CNN Model

We propose an object detection system that relies on a multi-region deep convolutional neural network (CNN) that also encodes semantic segmentation-aware features. The resulting CNN-based representation aims at capturing a diverse set of discriminative appearance factors and exhibits localization sensitivity that is essential for accurate object localization. We exploit the above properties of our recognition module by integrating it on an iterative localization mechanism that alternates between scoring a box proposal and refining its location with a deep CNN regression model. Thanks to the efficient use of our modules, we detect objects with very high localization accuracy. On the detection challenges of PASCAL VOC2007 and PASCAL VOC2012 we achieve mAP of 78.2% and 73.9% correspondingly, surpassing any other published work by a significant margin.
Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry

Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another.We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include logit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47,000 U.S. domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.
RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D and 3D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, 3D networks have some drawbacks due to their high cost on computational resources. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, named RA-UNet, to precisely extract the liver volume of interests (VOI) and segment tumors from the liver VOI. The proposed network has a basic architecture as a 3D U-Net which extracts contextual information combining lowlevel feature maps with high-level ones. Attention modules are stacked so that the attention-aware features change adaptively as the network goes “very deep” and this is made possible by residual learning. This is the first work that an attention residual mechanism is used to process medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and the 3DIRCADb dataset. The results show that our architecture outperforms other state-ofthe-art methods. We also extend our RA-UNet to brain tumor segmentation on the BraTS2018 and BraTS2017 datasets, and the results indicate that RA-UNet achieves good performance on a brain tumor segmentation task as well.
THE RENAISSANCE OF CANCER IMMUNOSURVEILLANCE IFN-γ , Perforin , and Lymphocytes in Tumor

■ Abstract After a century of controversy, the notion that the immune system regulates cancer development is experiencing a new resurgence. An overwhelming amount of data from animal models—together with compelling data from human patients— indicate that a functional cancer immunosurveillance process indeed exists that acts as an extrinsic tumor suppressor. However, it has also become clear that the immune system can facilitate tumor progression, at least in part, by sculpting the immunogenic phenotype of tumors as they develop. The recognition that immunity plays a dual role in the complex interactions between tumors and the host prompted a refinement of the cancer immunosurveillance hypothesis into one termed “cancer immunoediting.” In this review, we summarize the history of the cancer immunosurveillance controversy and discuss its resolution and evolution into the three Es of cancer immunoediting— elimination, equilibrium, and escape.
Query-Regression Networks for Machine Comprehension

We present Query-Regression Network (QRN), a variant of Recurrent Neural Network (RNN) that is suitable for end-to-end machine comprehension. While previous work [18, 22] largely relied on external memory and global softmax attention mechanism, QRN is a single recurrent unit with internal memory and local sigmoid attention. Unlike most RNN-based models, QRN is able to effectively handle long-term dependencies and is highly parallelizable. In our experiments we show that QRN obtains the state-of-the-art result in end-to-end bAbI QA tasks [21].
Computational Theories of Curiosity-Driven Learning

What are the functions of curiosity? What are the mechanisms of curiosity-driven learning? We approach these questions about the living using concepts and tools from machine learning and developmental robotics. We argue that curiosity-driven learning enables organisms to make discoveries to solve complex problems with rare or deceptive rewards. By fostering exploration and discovery of a diversity of behavioural skills, and ignoring these rewards, curiosity can be efficient to bootstrap learning when there is no information, or deceptive information, about local improvement towards these problems. We also explain the key role of curiosity for efficient learning of world models. We review both normative and heuristic computational frameworks used to understand the mechanisms of curiosity in humans, conceptualizing the child as a sense-making organism. These frameworks enable us to discuss the bi-directional causal links between curiosity and learning, and to provide new hypotheses about the fundamental role of curiosity in self-organizing developmental structures through curriculum learning. We present various developmental robotics experiments that study these mechanisms in action, both supporting these hypotheses to understand better curiosity in humans and opening new research avenues in machine learning and artificial intelligence. Finally, we discuss challenges for the design of experimental paradigms for studying curiosity in psychology and cognitive neuroscience.
Macro Malware Detection using Machine Learning Techniques - A New Approach

A malware macro (also called "macro virus") is the code that exploits the macro functionality of office documents (especially Microsoft Office’s Excel and Word) to carry out malicious action against the systems of the victims that open the file. This type of malware was very popular during the late 90s and early 2000s. After its rise when it was created as a propagation method of other malware in 2014, macro viruses continue posing a threat to the user that is far from being controlled. This paper studies the possibility of improving macro malware detection via machine learning techniques applied to the properties of the code.
Grammatical gender effects on cognition: implications for language learning and language use.

In 4 experiments, the authors addressed the mechanisms by which grammatical gender (in Italian and German) may come to affect meaning. In Experiments 1 (similarity judgments) and 2 (semantic substitution errors), the authors found Italian gender effects for animals but not for artifacts; Experiment 3 revealed no comparable effects in German. These results suggest that gender effects arise as a generalization from an established association between gender of nouns and sex of human referents, extending to nouns referring to sexuated entities. Across languages, such effects are found when the language allows for easy mapping between gender of nouns and sex of human referents (Italian) but not when the mapping is less transparent (German). A final experiment provided further constraints: These effects during processing arise at a lexical-semantic level rather than at a conceptual level.
Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

At the core of interpretable machine learning is the question of whether humans are able to make accurate predictions about a model’s behavior. Assumed in this question are three properties of the interpretable output: coverage, precision, and effort. Coverage refers to how often humans think they can predict the model’s behavior, precision to how accurate humans are in those predictions, and effort is either the up-front effort required in interpreting the model, or the effort required to make predictions about a model’s behavior.
Enhancing Compassion : A Randomized Controlled Trial of a Compassion Cultivation Training Program

Psychosocial interventions often aim to alleviate negative emotional states. However, there is growing interest in cultivating positive emotional states and qualities. One particular target is compassion, but it is not yet clear whether compassion can be trained. A community sample of 100 adults were randomly assigned to a 9-week compassion cultivation training (CCT) program (n = 60) or a waitlist control condition (n = 40). Before and after this 9-week period, participants completed self-report inventories that measured compassion for others, receiving compassion from others, and selfcompassion. Compared to the waitlist control condition, CCT resulted in significant improvements in all three domains of compassion—compassion for others, receiving compassion from others, and self-compassion. The amount of formal meditation practiced during CCT was associated with increased compassion for others. Specific domains of compassion can be intentionally cultivated in a training program. These findings may have important implications for mental health and well-being.
Virtual Network Function placement for resilient Service Chain provisioning

Virtualization technologies are changing the way network operators deploy and manage Internet services. In particular in this study we focus on the new Network Function Virtualization (NFV) paradigm, which consists in instantiating Virtual Network Function (VNFs) in Commercial-Off-The-Shelf (COSTS) hardware. Adopting NFV network operators can dynamically instantiate Network Functions (NFs) based on current demands and network conditions, allowing to save capital and operational costs. Typically, VNFs are concatenated together in a sequential order to form Service Chains (SCs) that provide specific Internet Services to the users. In this paper we study different approaches to provide the resiliency of SCs against single-link and single-node failures. We propose three Integer Linear Programming (ILP) models to solve the VNF placement problem with the VNF service chaining while guaranteeing resiliency against single-node/link, single-link and single-node failures. Moreover we evaluate the impact of latency of SCs on the VNFs distribution. We show that providing resiliency against both single-link and single-node failures necessitates the activation of twice the amount of resources in terms of nodes, and that for latency critical services providing resiliency against single-node failures comes at the same cost with respect to resiliency against single-link and single-nodes failures.
Single-Photon Avalanche Diode with Enhanced NIR-Sensitivity for Automotive LIDAR Systems

A single-photon avalanche diode (SPAD) with enhanced near-infrared (NIR) sensitivity has been developed, based on 0.18 μm CMOS technology, for use in future automotive light detection and ranging (LIDAR) systems. The newly proposed SPAD operating in Geiger mode achieves a high NIR photon detection efficiency (PDE) without compromising the fill factor (FF) and a low breakdown voltage of approximately 20.5 V. These properties are obtained by employing two custom layers that are designed to provide a full-depletion layer with a high electric field profile. Experimental evaluation of the proposed SPAD reveals an FF of 33.1% and a PDE of 19.4% at 870 nm, which is the laser wavelength of our LIDAR system. The dark count rate (DCR) measurements shows that DCR levels of the proposed SPAD have a small effect on the ranging performance, even if the worst DCR (12.7 kcps) SPAD among the test samples is used. Furthermore, with an eye toward vehicle installations, the DCR is measured over a wide temperature range of 25-132 °C. The ranging experiment demonstrates that target distances are successfully measured in the distance range of 50-180 cm.
Rule Extraction Algorithm for Deep Neural Networks: A Review

Despite the highest classification accuracy in wide varieties of application areas, artificial neural network has one disadvantage. The way this Network comes to a decision is not easily comprehensible. The lack of explanation ability reduces the acceptability of neural network in data mining and decision system. This drawback is the reason why researchers have proposed many rule extraction algorithms to solve the problem. Recently, Deep Neural Network (DNN) is achieving a profound result over the standard neural network for classification and recognition problems. It is a hot machine learning area proven both useful and innovative. This paper has thoroughly reviewed various rule extraction algorithms, considering the classification scheme: decompositional, pedagogical, and eclectics. It also presents the evaluation of these algorithms based on the neural network structure with which the algorithm is intended to work. The main contribution of this review is to show that there is a limited study of rule extraction algorithm from DNN. KeywordsArtificial neural network; Deep neural network; Rule extraction; Decompositional; Pedagogical; Eclectic.
Learning to Answer Questions From Image using Convolutional Neural Network

In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA) task. Our proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer. More specifically, our model consists of three CNNs: one image CNN to encode the image content, one sentence CNN to compose the words of the question, and one multimodal convolution layer to learn their joint representation for the classification in the space of candidate answer words. We demonstrate the efficacy of our proposed model on the DAQUAR and COCO-QA datasets, which are two benchmark datasets for image QA, with the performances significantly outperforming the state-of-the-art.
Two-Stream Convolutional Networks for Dynamic Texture Synthesis

We introduce a two-stream model for dynamic texture synthesis. Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction. Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics. To generate a novel texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture. Inspired by recent work on image style transfer and enabled by the two-stream model, we also apply the synthesis approach to combine the texture appearance from one texture with the dynamics of another to generate entirely novel dynamic textures. We show that our approach generates novel, high quality samples that match both the framewise appearance and temporal evolution of input texture. Finally, we quantitatively evaluate our texture synthesis approach with a thorough user study.
Does Venting Anger Feed or Extinguish the Flame ? Catharsis , Rumination , Distraction , Anger , and Aggressive Responding

Does distraction or rumination work better to diffuse anger? Catharsis theory predicts that rumination works best, but empirical evidence is lacking. In this study, angered participants hit a punching bag and thought about the person who had angered them (rumination group) or thought about becoming physically fit (distraction group). After hitting the punching bag, they reported how angry they felt. Next, they were given the chance to administer loud blasts of noise to the person who had angered them. There also was a no punching bag control group. People in the rumination group felt angrier than did people in the distraction or control groups. People in the rumination group were also most aggressive, followed respectively by people in the distraction and control groups. Rumination increased rather than decreased anger and aggression. Doing nothing at all was more effective than venting anger. These results directly contradict catharsis theory.