nils-herrmann/clean_scirepeval huggingface dataset:
Windows Registry Forensics : An Imperative Step in Tracking Data Theft via USB Devices

Owing to the increasing pace of occurrence of crimes in digital world, cyber forensic investigation is becoming a burning topic in the field of information security. Registry is an important location in Windows system that contains footprints of user activities and other configuration data, which may be valuable for forensic investigators in collecting potential evidences from the system. This work aims to point out the significance of Registry Analysis, and attempts to answer why it should be carried as a part of digital forensic investigation by demonstrating the role played by Registry in tracking data theft from system to USB external devices.  Forensics Analysis, Registry Analysis, Tracking Data Theft, USB footprints, Windows Forensics.
LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

Our model achieves the state-of-the-art performance on SUNRGBD, NYUdepth v2. It can be leveraged to improve the groundtruth annotations of newly captured 3943 RGB-D images in the SUNRGBD dataset. 2.2% improvement over previous state-of-the-art average accuracy and best on 15 categories Photometric data is vital for scene labeling while depth is an auxiliary information source. LSMT-CF model is important for fusing context features from different sources.
VM-centric snapshot deduplication for cloud data backup

Data deduplication is important for snapshot backup of virtual machines (VMs) because of excessive redundant content. Fingerprint search for source-side duplicate detection is resource intensive when the backup service for VMs is co-located with other cloud services. This paper presents the design and analysis of a fast VM-centric backup service with a tradeoff for a competitive deduplication efficiency while using small computing resources, suitable for running on a converged cloud architecture that cohosts many other services. The design consideration includes VM-centric file system block management for the increased VM snapshot availability. This paper describes an evaluation of this VM-centric scheme to assess its deduplication efficiency, resource usage, and fault tolerance.
QR Code Security: A Survey of Attacks and Challenges for Usable Security

QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.
Intent aware adaptive admittance control for physical Human-Robot Interaction

Effective physical Human-Robot Interaction (pHRI) needs to account for variable human dynamics and also predict human intent. Recently, there has been a lot of progress in adaptive impedance and admittance control for human-robot interaction. Not as many contributions have been reported on online adaptation schemes that can accommodate users with varying physical strength and skill level during interaction with a robot. The goal of this paper is to present and evaluate a novel adaptive admittance controller that can incorporate human intent, nominal task models, as well as variations in the robot dynamics. An outer-loop controller is developed using an ARMA model which is tuned using an adaptive inverse control technique. An inner-loop neuroadaptive controller linearizes the robot dynamics. Working in conjunction and online, this two-loop technique offers an elegant way to decouple the pHRI problem. Experimental results are presented comparing the performance of different types of admittance controllers. The results show that efficient online adaptation of the robot admittance model for different human subjects can be achieved. Specifically, the adaptive admittance controller reduces jerk which results in a smooth human-robot interaction.
Series WP-1207 A Source of Bias in Public Opinion Stability

A long acknowledged but seldom addressed problem with political communication experiments concerns the use of captive participants. Study participants rarely have the opportunity to choose information themselves, instead receiving whatever information the experimenter provides. Druckman and his co-authors relax this assumption in the context of an over-time framing experiment focused on opinions about health care policy. Their results dramatically deviate from extant understandings of over-time communication effects. Allowing individuals to choose information for themselves—a common situation on many political issues—leads to the preeminence of early frames and the rejection of later frames. Instead of opinion decay, they find dogmatic adherence to opinions formed in response to the first frame to which participants were exposed (i.e., staunch opinion stability). The effects match those that occur when early frames are repeated multiple times. The results suggest that opinion stability may often reflect biased information seeking. Moreover, the findings have implications for a range of topics including the micro-macro disconnect in studies of public opinion, political polarization, normative evaluations of public opinion, the role of inequality considerations in the debate about health care, and perhaps most importantly, the design of experimental studies of public opinion. The authors thank Cengiz Erisen, Larry Jacobs, Jon Krosnick, Heather Madonia, Leslie McCall, Spencer Piston, David Sterrett, and seminar participants in Northwestern’s Applied Quantitative  Workshop for helpful comments and advice.
AZR-LEACH: An Energy Efficient Routing Protocol for Wireless Sensor Networks

Reducing the energy consumption of available resources is still a problem to be solved in Wireless Sensor Networks (WSNs). Many types of existing routing protocols are developed to save power consumption. In these protocols, cluster-based routing protocols are found to be more energy efficient. A cluster head is selected to aggregate the data received from root nodes and forwards these data to the base station in cluster-based routing. The selection of cluster heads should be efficient to save energy. In our proposed protocol, we use static clustering for the efficient selection of cluster heads. The proposed routing protocol works efficiently in large as well as small areas. For an optimal number of cluster head selection we divide a large sensor field into rectangular clusters. Then these rectangular clusters are further grouped into zones for efficient communication between cluster heads and a base station. We perform MATLAB simulations to observe the network stability, throughput, energy consumption, network lifetime and the number of cluster heads. Our proposed routing protocol outperforms in large areas in comparison with the LEACH, MH-LEACH, and SEP routing protocols.
The State of Public Infrastructure-as-a-Service Cloud Security

The public Infrastructure-as-a-Service (IaaS) cloud industry has reached a critical mass in the past few years, with many cloud service providers fielding competing services. Despite the competition, we find some of the security mechanisms offered by the services to be similar, indicating that the cloud industry has established a number of “best-practices,” while other security mechanisms vary widely, indicating that there is also still room for innovation and experimentation. We investigate these differences and possible underlying reasons for it. We also contrast the security mechanisms offered by public IaaS cloud offerings and with security mechanisms proposed by academia over the same period. Finally, we speculate on how industry and academia might work together to solve the pressing security problems in public IaaS clouds going forward.
Quadtree Convolutional Neural Networks

This paper presents a Quadtree Convolutional Neural Network (QCNN) for efficiently learning from image datasets representing sparse data such as handwriting, pen strokes, freehand sketches, etc. Instead of storing the sparse sketches in regular dense tensors, our method decomposes and represents the image as a linear quadtree that is only refined in the non-empty portions of the image. The actual image data corresponding to non-zero pixels is stored in the finest nodes of the quadtree. Convolution and pooling operations are restricted to the sparse pixels, leading to better efficiency in computation time as well as memory usage. Specifically, the computational and memory costs in QCNN grow linearly in the number of non-zero pixels, as opposed to traditional CNNs where the costs are quadratic in the number of pixels. This enables QCNN to learn from sparse images much faster and process high resolution images without the memory constraints faced by traditional CNNs. We study QCNN on four sparse image datasets for sketch classification and simplification tasks. The results show that QCNN can obtain comparable accuracy with large reduction in computational and memory costs.
Facing facts: neuronal mechanisms of face perception.

The face is one of the most important stimuli carrying social meaning. Thanks to the fast analysis of faces, we are able to judge physical attractiveness and features of their owners' personality, intentions, and mood. From one's facial expression we can gain information about danger present in the environment. It is obvious that the ability to process efficiently one's face is crucial for survival. Therefore, it seems natural that in the human brain there exist structures specialized for face processing. In this article, we present recent findings from studies on the neuronal mechanisms of face perception and recognition in the light of current theoretical models. Results from brain imaging (fMRI, PET) and electrophysiology (ERP, MEG) show that in face perception particular regions (i.e. FFA, STS, IOA, AMTG, prefrontal and orbitofrontal cortex) are involved. These results are confirmed by behavioral data and clinical observations as well as by animal studies. The developmental findings reviewed in this article lead us to suppose that the ability to analyze face-like stimuli is hard-wired and improves during development. Still, experience with faces is not sufficient for an individual to become an expert in face perception. This thesis is supported by the investigation of individuals with developmental disabilities, especially with autistic spectrum disorders (ASD).
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning.

In recent years, Convolutional Neural Networks (CNNs) have shown remarkable performance in many computer vision tasks such as object recognition and detection. However, complex training issues, such as “catastrophic forgetting” and hyper-parameter tuning, make incremental learning in CNNs a difficult challenge. In this paper, we propose a hierarchical deep neural network, with CNNs at multiple levels, and a corresponding training method for incremental learning. The network grows in a tree-like manner to accommodate the new classes of data without losing the ability to identify the previously trained classes. The proposed network was tested on CIFAR-100 and reported 60.46% accuracy and 20% reduction in training effort as compared to retraining final layers of a deep network. The network organizes the incoming classes of data into feature-driven super-classes and improves upon existing hierarchical CNN models by adding the capability of self-growth.
Re-ranking for joint named-entity recognition and linking

Recognizing names and linking them to structured data is a fundamental task in text analysis. Existing approaches typically perform these two steps using a pipeline architecture: they use a Named-Entity Recognition (NER) system to find the boundaries of mentions in text, and an Entity Linking (EL) system to connect the mentions to entries in structured or semi-structured repositories like Wikipedia. However, the two tasks are tightly coupled, and each type of system can benefit significantly from the kind of information provided by the other. We present a joint model for NER and EL, called NEREL, that takes a large set of candidate mentions from typical NER systems and a large set of candidate entity links from EL systems, and ranks the candidate mention-entity pairs together to make joint predictions. In NER and EL experiments across three datasets, NEREL significantly outperforms or comes close to the performance of two state-of-the-art NER systems, and it outperforms 6 competing EL systems. On the benchmark MSNBC dataset, NEREL provides a 60% reduction in error over the next-best NER system and a 68% reduction in error over the next-best EL system.
Summer 6-15-2016 VALUE OF DECENTRALIZED CONSENSUS SYSTEMS – EVALUATION FRAMEWORK

The emergence of decentralized virtual currency systems based on cryptographic operations has been accompanied by efforts to utilize the technology as infrastructure for further applications besides cryptocurrencies such as Bitcoin. So-called Decentralized Consensus Systems (DCSs) could reshape financial and other information-based industries, where the invention, registration and transfer of assets constitutes the core of business activities. The starting point for this paper is the observation that innovative applications beyond payments already attract the attention of scholars and practitioners, whose works so far are mainly focused on explanatory issues. What is missing are approaches to evaluate the value of DCSs, taking into account the diversity of applications ranging from currencies to the decentralization of business operations. To fill this gap, this paper firstly discusses the potentials and introduces a contextualization of DCSs operating as digital infrastructures. Furthermore, the notion of economic value is examined. This forms the foundation for the subsequent development of an evaluation framework regarding the value of DCSs in order to provide a basis for the assessment of business models. The framework is then exemplarily applied to the Bitcoin system, which is evaluated according to various indicators such as venture capital investments, research or demand.
An empirical study on the impact of static typing on software maintainability

Static type systems play an essential role in contemporary programming languages. Despite their importance, whether static type systems impact human software development capabilities remains open. One frequently mentioned argument in favor of static type systems is that they improve the maintainability of software systems—an often-used claim for which there is little empirical evidence. This paper describes an experiment that tests whether static type systems improve the maintainability of software systems, in terms of understanding undocumented code, fixing type errors, and fixing semantic errors. The results show rigorous empirical evidence that static types are indeed beneficial to these activities, except when fixing semantic errors. We further conduct an exploratory analysis of the data in order to understand possible reasons for the effect of type systems on the three kinds of tasks used in this experiment. From the exploratory analysis, we conclude that developers using a dynamic type system tend to look at different files more frequently when doing programming tasks—which is a potential reason for the observed differences in time.
Large-scale frequent subgraph mining in MapReduce

Mining frequent subgraphs from a large collection of graph objects is an important problem in several application domains such as bio-informatics, social networks, computer vision, etc. The main challenge in subgraph mining is efficiency, as (i) testing for graph isomorphisms is computationally intensive, and (ii) the cardinality of the graph collection to be mined may be very large. We propose a two-step filter-and-refinement approach that is suitable to massive parallelization within the scalable MapReduce computing model. We partition the collection of graphs among worker nodes, and each worker applies the filter step to determine a set of candidate subgraphs that are locally frequent in its partition. The union of all such graphs is the input to the refinement step, where each candidate is checked against all partitions and only the globally frequent graphs are retained. We devise a statistical threshold mechanism that allows us to predict which subgraphs have a high chance to become globally frequent, and thus reduce the computational overhead in the refinement step. We also propose effective strategies to avoid redundant computation in each round when searching for candidate graphs, as well as a lightweight graph compression mechanism to reduce the communication cost between machines. Extensive experimental evaluation results on several real-world large graph datasets show that the proposed approach clearly outperforms the existing state-of-the-art and provides a practical solution to the problem of frequent subgraph mining for massive collections of graphs.
Selective harmonic elimination modulation technique applied on four-leg NPC

In this paper the selective harmonic elimination pulse width modulation (SHE-PWM) technique is proposed to control the three-level four-leg neutral point clamped (NPC) inverter in order to have both advantages of low switching frequency of SHE and neutral point of fourth leg in four wire systems. In this proposed method, the obtained switching angles of inverter phase legs are used to eliminate the non-triplen, 5th to 23th harmonics orders from the output voltage. As well, switching angles calculated for the fourth leg are considered to eliminate the triplen harmonics containing 3th, 9th, 15th, 21th and 27th orders from phase voltage. The efficiency of the proposed modulation technique is verified by simulations of a four leg NPC inverter as an UPS feeding different types of dynamic and unbalanced loads.
Application of electrostatic separation to the recycling of plastic wastes: separation of PVC, PET, and ABS.

Plastics are widely used in everyday life as a useful material, and thus their consumption is growing at a rate of about 5% per year in Korea. However, the constant generation of plastic wastes and their disposal generates environmental problems along with economic loss. In particular, mixed waste plastics are difficult to recycle because of their inferior characteristics. A laboratory-scale triboelectrostatic separator unit has been designed and assembled for this study. On the basis of the control of electrostatic charge, the separation of three kinds of mixed plastics, polyvinyl chloride (PVC), poly(ethylene terephthalate) (PET), and acrylonitrile-butadiene-styrene (ABS), in a range of similar gravities has been performed through a two-stage separation process. Polypropylene (PP) and high-impact polystyrene (HIPS) were found to be the most effective materials for a tribo-charger in the separation of PVC, PET, and ABS. The charge-to-mass ratio (nC/g) of plastics increased with increasing air velocity in the tribo charger. In the first stage, using the PP cyclone charger, the separation efficiency of particles considerably depended on the air velocity (10 m/s), the relative humidity (< 30%), the electrode potential (> 20 kV), and the splitter position (+2 cm from the center) in the triboelelctrostatic separator unit. At this time, a PVC grade of 99.40% and a recovery of 98.10% have successfully been achieved. In the second stage, using the HIPS cyclone charger, a PET grade of 97.80% and a recovery of 95.12% could be obtained under conditions of 10 m/s, over 25 kV, a central splitter position, and less than 40% relative humidity. In order to obtain 99.9% PVC grade and 99.3% PET grade, their recoveries should be sacrificed by 20.9% and 27%, respectively, with moving the splitter from the center to a (+)6 cm position.
Cannabinoid-induced effects on the nociceptive system: a neurophysiological study in patients with secondary progressive multiple sclerosis.

Although clinical studies show that cannabinoids improve central pain in patients with multiple sclerosis (MS) neurophysiological studies are lacking to investigate whether they also suppress these patients' electrophysiological responses to noxious stimulation. The flexion reflex (FR) in humans is a widely used technique for assessing the pain threshold and for studying spinal and supraspinal pain pathways and the neurotransmitter system involved in pain control. In a randomized, double-blind, placebo-controlled, cross-over study we investigated cannabinoid-induced changes in RIII reflex variables (threshold, latency and area) in a group of 18 patients with secondary progressive MS. To investigate whether cannabinoids act indirectly on the nociceptive reflex by modulating lower motoneuron excitability we also evaluated the H-reflex size after tibial nerve stimulation and calculated the H wave/M wave (H/M) ratio. Of the 18 patients recruited and randomized 17 completed the study. After patients used a commercial delta-9-tetrahydrocannabinol (THC) and cannabidiol mixture as an oromucosal spray the RIII reflex threshold increased and RIII reflex area decreased. The visual analogue scale score for pain also decreased, though not significantly. Conversely, the H/M ratio measured before patients received cannabinoids remained unchanged after therapy. In conclusion, the cannabinoid-induced changes in the RIII reflex threshold and area in patients with MS provide objective neurophysiological evidence that cannabinoids modulate the nociceptive system in patients with MS.
Soft contact lens biosensor for in situ monitoring of tear glucose as non-invasive blood sugar assessment.

A contact lens (CL) biosensor for in situ monitoring of tear glucose was fabricated and tested. Biocompatible 2-methacryloyloxyethyl phosphorylcholine (MPC) polymer and polydimethyl siloxane (PDMS) were employed as the biosensor material. The biosensor consists of a flexible Pt working electrode and a Ag/AgCl reference/counter electrode, which were formed by micro-electro-mechanical systems (MEMS) technique. The electrode at the sensing region was modified with glucose oxidase (GOD). The CL biosensor showed a good relationship between the output current and glucose concentration in a range of 0.03-5.0mM, with a correlation coefficient of 0.999. The calibration range covered the reported tear glucose concentrations in normal and diabetic patients. Also, the CL biosensor was applied to a rabbit for the purpose of tear glucose monitoring. The basal tear glucose was estimated to 0.11 mM. Also, the change of tear glucose induced by the change of blood sugar level was assessed by the oral glucose tolerance test. As a result, tear glucose level increased with a delay of 10 min from blood sugar level. The result showed that the CL biosensor is expected to provide further detailed information about the relationship between dynamics of blood glucose and tear glucose.