nils-herrmann/clean_scirepeval huggingface dataset:
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction

In this paper, we consider the problem of open information extraction (OIE) for extracting entity and relation level intermediate structures from sentences in open-domain. We focus on four types of valuable intermediate structures (Relation, Attribute, Description, and Concept), and propose a unified knowledge expression form, SAOKE, to express them. We publicly release a data set which contains 48,248 sentences and the corresponding facts in the SAOKE format labeled by crowdsourcing. To our knowledge, this is the largest publicly available human labeled data set for open information extraction tasks. Using this labeled SAOKE data set, we train an end-to-end neural model using the sequence-to-sequence paradigm, called Logician, to transform sentences into facts. For each sentence, different to existing algorithms which generally focus on extracting each single fact without concerning other possible facts, Logician performs a global optimization over all possible involved facts, in which facts not only compete with each other to attract the attention of words, but also cooperate to share words. An experimental study on various types of open domain relation extraction tasks reveals the consistent superiority of Logician to other states-of-the-art algorithms. The experiments verify the reasonableness of SAOKE format, the valuableness of SAOKE data set, the effectiveness of the proposed Logician model, and the feasibility of the methodology to apply end-to-end learning paradigm on supervised data sets for the challenging tasks of open information extraction.
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining

Single image rain streaks removal has recently witnessed substantial progress due to the development of deep convolutional neural networks. However, existing deep learning based methods either focus on the entrance and exit of the network by decomposing the input image into high and low frequency information and employing residual learning to reduce the mapping range, or focus on the introduction of cascaded learning scheme to decompose the task of rain streaks removal into multi-stages. These methods treat the convolutional neural network as an encapsulated end-to-end mapping module without deepening into the rationality and superiority of neural network design. In this paper, we delve into an effective end-to-end neural network structure for stronger feature expression and spatial correlation learning. Specifically, we propose a non-locally enhanced encoder-decoder network framework, which consists of a pooling indices embedded encoder-decoder network to efficiently learn increasingly abstract feature representation for more accurate rain streaks modeling while perfectly preserving the image detail. The proposed encoder-decoder framework is composed of a series of non-locally enhanced dense blocks that are designed to not only fully exploit hierarchical features from all the convolutional layers but also well capture the long-distance dependencies and structural information. Extensive experiments on synthetic and real datasets demonstrate that the proposed method can effectively remove rain-streaks on rainy image of various densities while well preserving the image details, which achieves significant improvements over the recent state-of-the-art methods.
Constructing Unrestricted Adversarial Examples with Generative Models

Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this type of attack. In this paper, we propose a new class of adversarial examples that are synthesized entirely from scratch using a conditional generative model, without being restricted to norm-bounded perturbations. We first train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to model the class-conditional distribution over data samples. Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier. We demonstrate through human evaluation that these new kind of adversarial images, which we call Generative Adversarial Examples, are legitimate and belong to the desired class. Our empirical results on the MNIST, SVHN, and CelebA datasets show that generative adversarial examples can bypass strong adversarial training and certified defense methods designed for traditional adversarial attacks.
Evaluate and Propose a Novel Technique to Check Genuineness of the Currency Using Image Processing

Currency recognition is an image processing technology that is used to check whether the currency under analysis is genuine or not. Counterfeiting of whatever kind has been occurring ever since humans realized the concept of valuable items. Since then there has been an ongoing race between the certifiers and counterfeiters. Methods such as checking currency genuiness helps in easing human life. The growing improvement in computers has made it easier for illegal people to produce counterfeit money using advancements in technology. Various methodologies are applied on the image under analysis. The processing is carried out on MATLAB. It’s a high performance language for technical computing. It’s user friendly and easy to use.
Discovering Malware with Time Series Shapelets

Malicious software (‘malware’) detection systems are usually signature-based and cannot stop attacks by malicious files they have never encountered. To stop these attacks, we need statistical learning approaches to identify root patterns behind execution of malware. We propose a machine learning approach for detection of malware from portable executable (PE) files. We create an ‘entropy time series’ representation of the content of each file, and then apply a unique time series classification method (called ‘shapelets’) for identifying malware. The shapelet-based approach picks up local discriminative features from the entropy signals. Our approach is file format agnostic, can deal with varying lengths in input instances, and provides fast classification. We evaluate our method on an industrial dataset containing thousands of executable files, and comparison with state-of-the-art methods illustrates the performance of our approach. This work is the first to use time series shapelets for malware detection and information security applications.
Mining criminal networks from unstructured text documents

Digital data collected for forensics analysis often contain valuable information about the suspects’ social networks. However, most collected records are in the form of unstructured textual data, such as e-mails, chat messages, and text documents. An investigator often has to manually extract the useful information from the text and then enter the important pieces into a structured database for further investigation by using various criminal network analysis tools. Obviously, this information extraction process is tedious and errorprone. Moreover, the quality of the analysis varies by the experience and expertise of the investigator. In this paper, we propose a systematic method to discover criminal networks from a collection of text documents obtained from a suspect’s machine, extract useful information for investigation, and then visualize the suspect’s criminal network. Furthermore, we present a hypothesis generation approach to identify potential indirect relationships among the members in the identified networks. We evaluated the effectiveness and performance of the method on a real-life cybercrimine case and some other datasets. The proposed method, together with the implemented software tool, has received positive feedback from the digital forensics team of a law enforcement unit in Canada. a 2012 Elsevier Ltd. All rights reserved.
Scene Parsing through ADE20K Dataset

Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the communitys efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A scene parsing benchmark is built upon the ADE20K with 150 object and stuff classes included. Several segmentation baseline models are evaluated on the benchmark. A novel network design called Cascade Segmentation Module is proposed to parse a scene into stuff, objects, and object parts in a cascade and improve over the baselines. We further show that the trained scene parsing networks can lead to applications such as image content removal and scene synthesis1.
Graph HyperNetworks for Neural Architecture Search

Neural architecture search (NAS) automatically finds the best task-specific neural network topology, outperforming many manual architecture designs. However, it can be prohibitively expensive as the search requires training thousands of different networks, while each can last for hours. In this work, we propose the Graph HyperNetwork (GHN) to amortize the search cost: given an architecture, it directly generates the weights by running inference on a graph neural network. GHNs model the topology of an architecture and therefore can predict network performance more accurately than regular hypernetworks and premature early stopping. To perform NAS, we randomly sample architectures and use the validation accuracy of networks with GHN generated weights as the surrogate search signal. GHNs are fast – they can search nearly 10× faster than other random search methods on CIFAR-10 and ImageNet. GHNs can be further extended to the anytime prediction setting, where they have found networks with better speed-accuracy tradeoff than the state-of-the-art manual designs.
Escaping affect: how motivated emotion regulation creates insensitivity to mass suffering.

As the number of people in need of help increases, the degree of compassion people feel for them ironically tends to decrease. This phenomenon is termed the collapse of compassion. Some researchers have suggested that this effect happens because emotions are not triggered by aggregates. We provide evidence for an alternative account. People expect the needs of large groups to be potentially overwhelming, and, as a result, they engage in emotion regulation to prevent themselves from experiencing overwhelming levels of emotion. Because groups are more likely than individuals to elicit emotion regulation, people feel less for groups than for individuals. In Experiment 1, participants displayed the collapse of compassion only when they expected to be asked to donate money to the victims. This suggests that the effect is motivated by self-interest. Experiment 2 showed that the collapse of compassion emerged only for people who were skilled at emotion regulation. In Experiment 3, we manipulated emotion regulation. Participants who were told to down-regulate their emotions showed the collapse of compassion, but participants who were told to experience their emotions did not. We examined the time course of these effects using a dynamic rating to measure affective responses in real time. The time course data suggested that participants regulate emotion toward groups proactively, by preventing themselves from ever experiencing as much emotion toward groups as toward individuals. These findings provide initial evidence that motivated emotion regulation drives insensitivity to mass suffering.
The Rise of Social Botnets: Attacks and Countermeasures

Online social networks (OSNs) are increasingly threatened by social bots which are software-controlled OSN accounts that mimic human users with malicious intentions. A social botnet refers to a group of social bots under the control of a single botmaster, which collaborate to conduct malicious behavior while mimicking the interactions among normal OSN users to reduce their individual risk of being detected. We demonstrate the effectiveness and advantages of exploiting a social botnet for spam distribution and digital-influence manipulation through real experiments on Twitter and also trace-driven simulations. We also propose the corresponding countermeasures and evaluate their effectiveness. Our results can help understand the potentially detrimental effects of social botnets and help OSNs improve their bot(net) detection systems.
Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents

The objects of the Internet of Things will be empowered by embedded devices whose constrained resources will need to be managed efficiently. It is envisioned that these devices will be able to form ad-hoc networks, and that the connection from these networks to the Internet of Things infrastructure will not always be possible. In this chapter we propose the use of clustering, software agents and synchronisation techniques in order to overcome the challenges of managing the resources of the Internet of Things objects. We argue that clustering will be beneficial to reduce the energy expenditure and improve the scalability and robustness of the object networks. Software agents will aide in the automation of task, both for the objects and the Internet of Things users. Finally, synchronisations techniques will be necessary to address the various challenges of harmonising plenty of copies of object data with potentially partially disconnected Internet of Things architecture components. BIBA Bremer Institut für Produktion und Logistik, University of Bremen
The Heisenberg Defense: Proactively Defending SGX Enclaves against Page-Table-Based Side-Channel Attacks

Protected-module architectures (PMAs) have been proposed to provide strong isolation guarantees, even on top of a compromised system. Unfortunately, Intel SGX – the only publicly available highend PMA – has been shown to only provide limited isolation. An attacker controlling the untrusted page tables, can learn enclave secrets by observing its page access patterns. Fortifying existing protected-module architectures in a realworld setting against side-channel attacks is an extremely difficult task as system software (hypervisor, operating system, . . . ) needs to remain in full control over the underlying hardware. Most stateof-the-art solutions propose a reactive defense that monitors for signs of an attack. Such approaches unfortunately cannot detect the most novel attacks, suffer from false-positives, and place an extraordinary heavy burden on enclave-developers when an attack is detected. We present Heisenberg, a proactive defense that provides complete protection against page table based side channels. We guarantee that any attack will either be prevented or detected automatically before any sensitive information leaks. Consequently, Heisenberg can always securely resume enclave execution – even when the attacker is still present in the system. We present two implementations. Heisenberg-HW relies on very limited hardware features to defend against page-table-based attacks. We use the x86/SGX platform as an example, but the same approach can be applied when protected-module architectures are ported to different platforms as well. Heisenberg-SW avoids these hardware modifications and can readily be applied. Unfortunately, it’s reliance on Intel Transactional Synchronization Extensions (TSX) may lead to significant performance overhead under real-life conditions.
Emergent Coordination Through Competition

We study the emergence of cooperative behaviors in reinforcement learning agents by introducing a challenging competitive multi-agent soccer environment with continuous simulated physics. We demonstrate that decentralized, populationbased training with co-play can lead to a progression in agents’ behaviors: from random, to simple ball chasing, and finally showing evidence of cooperation. Our study highlights several of the challenges encountered in large scale multi-agent training in continuous control. In particular, we demonstrate that the automatic optimization of simple shaping rewards, not themselves conducive to co-operative behavior, can lead to long-horizon team behavior. We further apply an evaluation scheme, grounded by game theoretic principals, that can assess agent performance in the absence of pre-defined evaluation tasks or human baselines.
A cyber-physical experimentation environment for the security analysis of networked industrial control systems

Although many studies address the security of Networked Industrial Control Systems (NICS), today we still lack an efficient way to conduct scientific experiments that measure the impact of attacks against both the physical and the cyber parts of these systems. This paper presents an innovative framework for an experimentation environment that can reproduce concurrently physical and cyber systems. The proposed approach uses an emulation testbed based on Emulab to recreate cyber components and a real-time simulator, based on Simulink, to recreate physical processes. The main novelty of the proposed framework is that it provides a set of experimental capabilities that are missing from other approaches, e.g. safe experimentation with real malware, flexibility to use different physical processes. The feasibility of the approach is confirmed by the development of a fully functional prototype, while its applicability is proven through two case studies of industrial systems from the electrical and chemical domain.
Zero-Shot Object Detection by Hybrid Region Embedding

Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images. In this study, we define a more difficult scenario, namely zero-shot object detection (ZSD) where no visual training data is available for some of the target object classes. We present a novel approach to tackle this ZSD problem, where a convex combination of embeddings are used in conjunction with a detection framework. For evaluation of ZSD methods, we propose a simple dataset constructed from Fashion-MNIST images and also a custom zero-shot split for the Pascal VOC detection challenge. The experimental results suggest that our method yields promising results for ZSD.
A Variational Analysis of Stochastic Gradient Algorithms

Stochastic Gradient Descent (SGD) is an important algorithm in machine learning. With constant learning rates, it is a stochastic process that, after an initial phase of convergence, generates samples from a stationary distribution. We show that SGD with constant rates can be effectively used as an approximate posterior inference algorithm for probabilistic modeling. Specifically, we show how to adjust the tuning parameters of SGD such as to match the resulting stationary distribution to the posterior. This analysis rests on interpreting SGD as a continuoustime stochastic process and then minimizing the Kullback-Leibler divergence between its stationary distribution and the target posterior.  In more detail, we model SGD as a multivariate Ornstein-Uhlenbeck process and then use properties of this process to derive the optimal parameters. This theoretical framework also connects SGD to modern scalable inference algorithms; we analyze the recently proposed stochastic gradient Fisher scoring under this perspective. We demonstrate that SGD with properly chosen constant rates gives a new way to optimize hyperparameters in probabilistic models.
PTSD symptom reports of patients evaluated for the New Mexico Medical Cannabis Program.

New Mexico was the first state to list post-traumatic stress disorder (PTSD) as a condition for the use of medical cannabis. There are no published studies, other than case reports, of the effects of cannabis on PTSD symptoms. The purpose of the study was to report and statistically analyze psychometric data on PTSD symptoms collected during 80 psychiatric evaluations of patients applying to the New Mexico Medical Cannabis Program from 2009 to 2011.



The Clinician Administered Posttraumatic Scale for DSM-IV (CAPS) was administered retrospectively and symptom scores were then collected and compared in a retrospective chart review of the first 80 patients evaluated.



Greater than 75% reduction in CAPS symptom scores were reported when patients were using cannabis compared to when they were not.



Cannabis is associated with reductions in PTSD symptoms in some patients, and prospective, placebo-controlled study is needed to determine efficacy of cannabis and its constituents in treating PTSD.
A Blockchain-based Approach for Data Accountability and Provenance Tracking

The recent approval of the General Data Protection Regulation (GDPR) imposes new data protection requirements on data controllers and processors with respect to the processing of European Union (EU) residents' data. These requirements consist of a single set of rules that have binding legal status and should be enforced in all EU member states. In light of these requirements, we propose in this paper the use of a blockchain-based approach to support data accountability and provenance tracking. Our approach relies on the use of publicly auditable contracts deployed in a blockchain that increase the transparency with respect to the access and usage of data. We identify and discuss three models for our approach with different granularity and scalability requirements where contracts can be used to encode data usage policies and provenance tracking information in a privacy-friendly way. From these three models we designed, implemented, and evaluated a model where contracts are deployed by data subjects for each data controller, and a model where subjects join contracts deployed by data controllers in case they accept the data handling conditions. Our implementations show in practice the feasibility and limitations of contracts for the purposes identified in this paper.
Modularity and community structure in networks.

Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as "modularity" over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.