nils-herrmann/clean_scirepeval huggingface dataset:
Malware classification with recurrent networks

Attackers often create systems that automatically rewrite and reorder their malware to avoid detection. Typical machine learning approaches, which learn a classifier based on a handcrafted feature vector, are not sufficiently robust to such reorderings. We propose a different approach, which, similar to natural language modeling, learns the language of malware spoken through the executed instructions and extracts robust, time domain features. Echo state networks (ESNs) and recurrent neural networks (RNNs) are used for the projection stage that extracts the features. These models are trained in an unsupervised fashion. A standard classifier uses these features to detect malicious files. We explore a few variants of ESNs and RNNs for the projection stage, including Max-Pooling and Half-Frame models which we propose. The best performing hybrid model uses an ESN for the recurrent model, Max-Pooling for non-linear sampling, and logistic regression for the final classification. Compared to the standard trigram of events model, it improves the true positive rate by 98.3% at a false positive rate of 0.1%.
RS-Forest: A Rapid Density Estimator for Streaming Anomaly Detection

Anomaly detection in streaming data is of high interest in numerous application domains. In this paper, we propose a novel one-class semi-supervised algorithm to detect anomalies in streaming data. Underlying the algorithm is a fast and accurate density estimator implemented by multiple fully randomized space trees (RS-Trees), named RS-Forest. The piecewise constant density estimate of each RS-tree is defined on the tree node into which an instance falls. Each incoming instance in a data stream is scored by the density estimates averaged over all trees in the forest. Two strategies, statistical attribute range estimation of high probability guarantee and dual node profiles for rapid model update, are seamlessly integrated into RS Forestto systematically address the ever-evolving nature of data streams. We derive the theoretical upper bound for the proposed algorithm and analyze its asymptotic properties via bias-variance decomposition. Empirical comparisons to the state-of-the-art methods on multiple benchmark datasets demonstrate that the proposed method features high detection rate, fast response, and insensitivity to most of the parameter settings. Algorithm implementations and datasets are available upon request.
Maximum battery life routing to support ubiquitous mobile computing in wireless ad hoc networks

Most ad hoc mobile devices today operate on batteries. Hence, power consumption becomes an important issue. To maximize the lifetime of ad hoc mobile networks, the power consumption rate of each node must be evenly distributed, and the overall transmission power for each connection request must be minimized. These two objectives cannot be satisfied simultaneously by employing routing algorithms proposed in previous work. In this article we present a new power-aware routing protocol to satisfy these two constraints simultaneously; we also compare the performance of different types of power-related routing algorithms via simulation. Simulation results confirm the need to strike a balance in attaining service availability performance of the whole network vs. the lifetime of ad hoc mobile devices.
A Simple Generic Attack on Text Captchas

Text-based Captchas have been widely deployed across the Internet to defend against undesirable or malicious bot programs. Many attacks have been proposed; these fine prior art advanced the scientific understanding of Captcha robustness, but most of them have a limited applicability. In this paper, we report a simple, low-cost but powerful attack that effectively breaks a wide range of text Captchas with distinct design features, including those deployed by Google, Microsoft, Yahoo!, Amazon and other Internet giants. For all the schemes, our attack achieved a success rate ranging from 5% to 77%, and achieved an average speed of solving a puzzle in less than 15 seconds on a standard desktop computer (with a 3.3GHz Intel Core i3 CPU and 2 GB RAM). This is to date the simplest generic attack on text Captchas. Our attack is based on Log-Gabor filters; a famed application of Gabor filters in computer security is John Daugman’s iris recognition algorithm. Our work is the first to apply Gabor filters for breaking Captchas.
Predicting the popularity of web 2.0 items based on user comments

In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible than view counts. Due to the sparsity of comments, traditional solutions that are solely based on view histories do not perform well. To deal with this sparsity, we mine comments to recover additional signal, such as social influence. By modeling comments as a time-aware bipartite graph, we propose a regularization-based ranking algorithm that accounts for temporal, social influence and current popularity factors to predict the future popularity of items. Experimental results on three real-world datasets --- crawled from YouTube, Flickr and Last.fm --- show that our method consistently outperforms competitive baselines in several evaluation tasks.
PAC Subset Selection in Stochastic Multi-armed Bandits

We consider the problem of selecting, from among the arms of a stochastic n-armed bandit, a subset of size m of those arms with the highest expected rewards, based on efficiently sampling the arms. This “subset selection” problem finds application in a variety of areas. In the authors’ previous work (Kalyanakrishnan & Stone, 2010), this problem is framed under a PAC setting (denoted “Explore-m”), and corresponding sampling algorithms are analyzed. Whereas the formal analysis therein is restricted to the worst case sample complexity of algorithms, in this paper, we design and analyze an algorithm (“LUCB”) with improved expected sample complexity. Interestingly LUCB bears a close resemblance to the wellknown UCB algorithm for regret minimization. The expected sample complexity bound we show for LUCB is novel even for singlearm selection (Explore-1). We also give a lower bound on the worst case sample complexity of PAC algorithms for Explore-m.
Substrate integrated waveguide six-port broadband front-end circuit for millimeter-wave radio and radar systems

A novel multi-purpose integrated planar six-port front-end circuit combining both substrate integrated waveguide (SIW) technology and integrated loads is presented and demonstrated. The use of SIW technology allows a very compact circuit and very low radiation loss at millimeter frequencies. An integrated load is used to simplify the fabrication process and also reduce dimensions and cost. To validate the proposed concept, an integrated broadband six-port front-end circuit prototype was fabricated and measured. Simulation and measurement results show that the proposed six-port circuit can easily operate at 24 GHz for radar systems and also over 23–29 GHz for broadband millimetre-wave radio services.
Estimation of Simultaneously Sparse and Low Rank Matrices

The paper introduces a penalized matrix estimation procedure aiming at solutions which are sparse and low-rank at the same time. Such structures arise in the context of social networks or protein interactions where underlying graphs have adjacency matrices which are block-diagonal in the appropriate basis. We introduce a convex mixed penalty which involves `1-norm and trace norm simultaneously. We obtain an oracle inequality which indicates how the two effects interact according to the nature of the target matrix. We bound generalization error in the link prediction problem. We also develop proximal descent strategies to solve the optimization problem efficiently and evaluate performance on synthetic and real data sets.
An Overview of Low-Rank Matrix Recovery From Incomplete Observations

Low-rank matrices play a fundamental role in modeling and computational methods for signal processing and machine learning. In many applications where low-rank matrices arise, these matrices cannot be fully sampled or directly observed, and one encounters the problem of recovering the matrix given only incomplete and indirect observations. This paper provides an overview of modern techniques for exploiting low-rank structure to perform matrix recovery in these settings, providing a survey of recent advances in this rapidly-developing field. Specific attention is paid to the algorithms most commonly used in practice, the existing theoretical guarantees for these algorithms, and representative practical applications of these techniques.
Automatic Forgery of Cryptographically Consistent Messages to Identify Security Vulnerabilities in Mobile Services

Most mobile apps today require access to remote services, and many of them also require users to be authenticated in order to use their services. To ensure the security between the client app and the remote service, app developers often use cryptographic mechanisms such as encryption (e.g., HTTPS), hashing (e.g., MD5, SHA1), and signing (e.g., HMAC) to ensure the confidentiality and integrity of the network messages. However, these cryptographic mechanisms can only protect the communication security, and server-side checks are still needed because malicious clients owned by attackers can generate any messages they wish. As a result, incorrect or missing server side checks can lead to severe security vulnerabilities including password brute-forcing, leaked password probing, and security access token hijacking. To demonstrate such a threat, we present AUTOFORGE, a tool that can automatically forge valid request messages from the client side to test whether the server side of an app has ensured the security of user accounts with sufficient checks. To enable these security tests, a fundamental challenge lies in how to forge a valid cryptographically consistent message such that it can be consumed by the server. We have addressed this challenge with a set of systematic techniques, and applied them to test the server side implementation of 76 popular mobile apps (each of which has over 1,000,000 installs). Our experimental results show that among these apps, 65 (86%) of their servers are vulnerable to password brute-forcing attacks, all (100%) are vulnerable to leaked password probing attacks, and 9 (12%) are vulnerable to Facebook access token hijacking attacks.
Convolutional neural networks with low-rank regularization

Large CNNs have delivered impressive performance in various computer vision applications. But the storage and computation requirements make it problematic for deploying these models on mobile devices. Recently, tensor decompositions have been used for speeding up CNNs. In this paper, we further develop the tensor decomposition technique. We propose a new algorithm for computing the low-rank tensor decomposition for removing the redundancy in the convolution kernels. The algorithm finds the exact global optimizer of the decomposition and is more effective than iterative methods. Based on the decomposition, we further propose a new method for training low-rank constrained CNNs from scratch. Interestingly, while achieving a significant speedup, sometimes the lowrank constrained CNNs delivers significantly better performance than their nonconstrained counterparts. On the CIFAR-10 dataset, the proposed low-rank NIN model achieves 91.31% accuracy (without data augmentation), which also improves upon state-of-the-art result. We evaluated the proposed method on CIFAR10 and ILSVRC12 datasets for a variety of modern CNNs, including AlexNet, NIN, VGG and GoogleNet with success. For example, the forward time of VGG16 is reduced by half while the performance is still comparable. Empirical success suggests that low-rank tensor decompositions can be a very useful tool for speeding up large CNNs.
Adaptive mixed reality stroke rehabilitation: system architecture and evaluation metrics

This paper presents a novel system architecture and evaluation metrics for an Adaptive Mixed Reality Rehabilitation (AMRR) system for stroke patient. This system provides a purposeful, engaging, hybrid (visual, auditory and physical) scene that encourages patients to improve their performance of a reaching and grasping task and promotes learning of generalizable movement strategies. This system is adaptive in that it provides assistive adaptation tools to help the rehabilitation team customize the training strategy. Our key insight is to combine the patients, rehabilitation team, multimodal hybrid environments and adaptation tools together as an adaptive experiential mixed reality system.
 There are three major contributions in this paper: (a) developing a computational deficit index for evaluating the patient's kinematic performance and a deficit-training-improvement (DTI) correlation for evaluating adaptive training strategy, (b) integrating assistive adaptation tools that help the rehabilitation team understand the relationship between the patient's performance and training and customize the training strategy, and (c) combining the interactive multimedia environment and physical environment together to encourage patients to transfer movement knowledge from media space to physical space. Our system has been used by two stroke patients for one-month mediated therapy. They have significant improvement in their reaching and grasping performance (+48.84% and +39.29%) compared to other two stroke patients who experienced traditional therapy (-18.31% and -8.06%).
Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle

We extend the arc-hybrid transition system for dependency parsing with a SWAP transition that enables reordering of the words and construction of non-projective trees. Although this extension potentially breaks the arc-decomposability of the transition system, we show that the existing dynamic oracle can be modified and combined with a static oracle for the SWAP transition. Experiments on five languages with different degrees of non-projectivity show that the new system gives competitive accuracy and is significantly better than a system trained with a purely static oracle.
RUPERT: An exoskeleton robot for assisting rehabilitation of arm functions

The design of a wearable upper extremity therapy robot RUPERT IVtrade (Robotic Upper Extremity Repetitive Trainer) device is presented. It is designed to assist in repetitive therapy tasks related to activities of daily living which has been advocated for being more effective for functional recovery. RUPERTtrade has five actuated degrees of freedom driven by compliant and safe pneumatic muscle actuators (PMA) assisting shoulder elevation, humeral external rotation, elbow extension, forearm supination and wrist/hand extension. The device is designed to extend the arm and move in a 3D space with no gravity compensation, which is a natural setting for practicing day-to-day activities. Because the device is wearable and lightweight, the device is very portable; it can be worn standing or sitting for performing therapy tasks that better mimic activities of daily living. A closed-loop controller combining a PID-based feedback controller and a iterative learning controller (ILC)-based feedforward controller is proposed for RUPERT for passive repetitive task training. This type of control aids in overcoming the highly nonlinear nature of the plant under control, and also helps in adapting easily to different subjects for performing different tasks. The system was tested on two able-bodied subjects to evaluate its performance.
Adaptive control for UAVs equipped with a robotic arm

This paper deals with the trajectory tracking control for quadrotor aerial vehicles equipped with a robotic manipulator. The proposed approach is based on a two-layer controller: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables while in the bottom layer, an adaptive motion control algorithm is in charge of tracking the motion references. A stability analysis of the closed-loop system is developed. Finally, a simulation case study is presented to prove the effectiveness of the approach.
The Verilog® Hardware Description Language

Verilog is a concurrent language Aimed at modeling hardware — optimized for it! Typical of hardware description languages (HDLs), i t: provides for the specification of concurrent activi ties stands on its head to make the activities look like they happened at the same time Why? allows for intricate timing specifications A concurrent language allows for: Multiple concurrent “elements” An event in one element to cause activity in anothe r. (An event is an output or state change at a given time) based on interconnection of the element’s ports Further execution to be delayed until a specific event occurs
Toward Macro-Insights for Suicide Prevention : Analyzing Fine-Grained Distress at Scale

Suicide is a leading cause of death in the United States. One of the major challenges to suicide prevention is that those who may be most at risk cannot be relied upon to report their conditions to clinicians. This paper takes an initial step toward the automatic detection of suicidal risk factors through social media activity, with no reliance on self-reporting. We consider the performance of annotators with various degrees of expertise in suicide prevention at annotating microblog data for the purpose of training text-based models for detecting suicide risk behaviors. Consistent with crowdsourcing literature, we found that novice-novice annotator pairs underperform expert annotators and outperform automatic lexical analysis tools, such as Linguistic Inquiry and Word Count.
A potential field approach to dexterous tactile exploration of unknown objects

Haptic exploration of unknown objects is of great importance for acquiring multi-modal object representations, which enable a humanoid robot to autonomously execute grasping and manipulation tasks. In this paper we present a tactile exploration strategy to guide an anthropomorphic five-finger hand along the surface of previously unknown objects and build a 3D object representation based on acquired tactile point clouds. The proposed strategy makes use of the dynamic potential field approach suggested in the context of mobile robot navigation. To demonstrate the capabilities of this strategy, we conduct experiments in a detailed physics simulation using a model of the five-finger hand. Exploration results of several test objects are given.
A fully electrical startup batteryless boost converter with 50mV input voltage for thermoelectric energy harvesting

A fully electrical startup boost converter is presented in this paper. With a three-stage stepping-up architecture, the proposed circuit is capable of performing thermoelectric energy harvesting at an input voltage as low as 50 mV. Due to the zero-current-switching (ZCS) operation of the boost converter and automatic shutdown of the low-voltage starter and the auxiliary converter, conversion efficiency up to 73% is demonstrated. The boost converter does not require bulky transformers or mechanical switches for kick-start, making it very attractive for body area sensor network applications.
An efficient overloaded implementation of forward mode automatic differentiation in MATLAB

The Mad package described here facilitates the evaluation of first derivatives of multidimensional functions that are defined by computer codes written in MATLAB. The underlying algorithm is the well-known forward mode of automatic differentiation implemented via operator overloading on variables of the class fmad. The main distinguishing feature of this MATLAB implementation is the separation of the linear combination of derivative vectors into a separate derivative vector class derivvec. This allows for the straightforward performance optimization of the overall package. Additionally, by internally using a matrix (two-dimensional) representation of arbitrary dimension directional derivatives, we may utilize MATLAB's sparse matrix class to propagate sparse directional derivatives for MATLAB code which uses arbitrary dimension arrays. On several examples, the package is shown to be more efficient than Verma's ADMAT package [Verma 1998a].
Specifying and placing chains of virtual network functions

Network appliances perform different functions on network flows and constitute an important part of an operator's network. Normally, a set of chained network functions process network flows. Following the trend of virtualization of networks, virtualization of the network functions has also become a topic of interest. We define a model for formalizing the chaining of network functions using a context-free language. We process deployment requests and construct virtual network function graphs that can be mapped to the network. We describe the mapping as a Mixed Integer Quadratically Constrained Program (MIQCP) for finding the placement of the network functions and chaining them together considering the limited network resources and requirements of the functions. We have performed a Pareto set analysis to investigate the possible trade-offs between different optimization objectives.
HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation

Shilling attackers apply biased rating profiles to recommender systems for manipulating online product recommendations. Although many studies have been devoted to shilling attack detection, few of them can handle the hybrid shilling attacks that usually happen in practice, and the studies for real-life applications are rarely seen. Moreover, little attention has yet been paid to modeling both labeled and unlabeled user profiles, although there are often a few labeled but numerous unlabeled users available in practice. This paper presents a Hybrid Shilling Attack Detector, or HySAD for short, to tackle these problems. In particular, HySAD introduces MC-Relief to select effective detection metrics, and Semi-supervised Naive Bayes (SNB_lambda) to precisely separate Random-Filler model attackers and Average-Filler model attackers from normal users. Thorough experiments on MovieLens and Netflix datasets demonstrate the effectiveness of HySAD in detecting hybrid shilling attacks, and its robustness for various obfuscated strategies. A real-life case study on product reviews of Amazon.cn is also provided, which further demonstrates that HySAD can effectively improve the accuracy of a collaborative-filtering based recommender system, and provide interesting opportunities for in-depth analysis of attacker behaviors. These, in turn, justify the value of HySAD for real-world applications.