nils-herrmann/clean_scirepeval huggingface dataset:
Smartwatch-Based Keystroke Inference Attacks and Context-Aware Protection Mechanisms

Wearable devices, such as smartwatches, are furnished with state-of-the-art sensors that enable a range of context-aware applications. However, malicious applications can misuse these sensors, if access is left unaudited. In this paper, we demonstrate how applications that have access to motion or inertial sensor data on a modern smartwatch can recover text typed on an external QWERTY keyboard. Due to the distinct nature of the perceptible motion sensor data, earlier research efforts on emanation based keystroke inference attacks are not readily applicable in this scenario. The proposed novel attack framework characterizes wrist movements (captured by the inertial sensors of the smartwatch worn on the wrist) observed during typing, based on the relative physical position of keys and the direction of transition between pairs of keys. Eavesdropped keystroke characteristics are then matched to candidate words in a dictionary. Multiple evaluations show that our keystroke inference framework has an alarmingly high classification accuracy and word recovery rate. With the information recovered from the wrist movements perceptible by a smartwatch, we exemplify the risks associated with unaudited access to seemingly innocuous sensors (e.g., accelerometers and gyroscopes) of wearable devices. As part of our efforts towards preventing such side-channel attacks, we also develop and evaluate a novel context-aware protection framework which can be used to automatically disable (or downgrade) access to motion sensors, whenever typing activity is detected.
Convolutional Neural Networks for Fashion Classification and Object Detection

Fashion classification encompasses the identification of clothing items in an image. The field has applications in social media, e-commerce, and criminal law. In our work, we focus on four tasks within the fashion classification umbrella: (1) multiclass classification of clothing type; (2) clothing attribute classification; (3) clothing retrieval of nearest neighbors; and (4) clothing object detection. We report accuracy measurements for clothing style classification (50.2%) and clothing attribute classification (74.5%) that outperform baselines in the literature for the associated datasets. We additionally report promising qualitative results for our clothing retrieval and clothing object detection tasks.
CiteRivers: Visual Analytics of Citation Patterns

The exploration and analysis of scientific literature collections is an important task for effective knowledge management. Past interest in such document sets has spurred the development of numerous visualization approaches for their interactive analysis. They either focus on the textual content of publications, or on document metadata including authors and citations. Previously presented approaches for citation analysis aim primarily at the visualization of the structure of citation networks and their exploration. We extend the state-of-the-art by presenting an approach for the interactive visual analysis of the contents of scientific documents, and combine it with a new and flexible technique to analyze their citations. This technique facilitates user-steered aggregation of citations which are linked to the content of the citing publications using a highly interactive visualization approach. Through enriching the approach with additional interactive views of other important aspects of the data, we support the exploration of the dataset over time and enable users to analyze citation patterns, spot trends, and track long-term developments. We demonstrate the strengths of our approach through a use case and discuss it based on expert user feedback.
Using CPU System Management Mode to Circumvent Operating System Security Functions

In this paper we show how hardware functionalities can be misused by an attacker to extend her control over a system. The originality of our approach is that it exploits seldom used processor and chipset functionalities, such as switching to system management mode, to escalate local privileges in spite of security restrictions imposed by the operating system. As an example we present a new attack scheme against OpenBSD on x86-based architectures. On such a system the superuser is only granted limited privileges. The attack allows her to get full privileges over the system, including unrestricted access to physical memory. Our sample code shows how the superuser can lower the “secure level” from highly secure to permanently insecure mode. To the best of our knowledge, it is the first time that documented processor and chipset functionalities have been used to circumvent operating system security functions.
The LDBC Social Network Benchmark: Interactive Workload

The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.
Deep learning with geodesic moments for 3D shape classification

In this paper, we present a deep learning framework for efficient 3D shape classification using geodesic moments. Our approach inherits many useful properties from the geodesic distance, most notably the capture of the intrinsic geometric structure of 3D shapes and the invariance to isometric deformations. Moreover, we show the similarity between the convergent series of the geodesic moments and the inverse-square eigenvalues of the Laplace–Beltrami operator in the continuous setting. The proposed algorithm uses a two-layer stacked sparse autoencoder to learn deep features from geodesic moments by training the hidden layers individually in an unsupervised fashion, followed by a softmax classifier. Experimental results on three standard 3D shape benchmarks demonstrate superior performance of the proposed approach compared to existing methods.
Color image database TID2013: Peculiarities and preliminary results

Visual quality of color images is an important aspect in various applications of digital image processing and multimedia. A large number of visual quality metrics (indices) has been proposed recently. In order to assess their reliability, several databases of color images with various sets of distortions have been exploited. Here we present a new database called TID2013 that contains a larger number of images. Compared to its predecessor TID2008, seven new types and one more level of distortions are included. The need for considering these new types of distortions is briefly described. Besides, preliminary results of experiments with a large number of volunteers for determining the mean opinion score (MOS) are presented. Spearman and Kendall rank order correlation factors between MOS and a set of popular metrics are calculated and presented. Their analysis shows that adequateness of the existing metrics is worth improving. Special attention is to be paid to accounting for color information and observers focus of attention to locally active areas in images.
Government 2.0: Making connections between citizens, data and government

The revolution in information and communication technologies (ICT) has been changing not only the daily lives of people but also the interactions between governments and citizens. The digital government or electronic government (e-government) has started as a new form of public organization that supports and redefines the existing and new information, communication and transaction-related interactions with stakeholders (e.g., citizens and businesses) through ICT, especially through the Internet and Web technologies, with the purpose of improving government performance and processes .
TaskGenies: Automatically Providing Action Plans Helps People Complete Tasks

People complete tasks more quickly when they have concrete plans. However, they often fail to create such action plans. (How) can systems provide these concrete steps automatically? This article demonstrates that these benefits can also be realized when these plans are created by others or reused from similar tasks. Four experiments test these approaches, finding that people indeed complete more tasks when they receive externally-created action plans. To automatically provide plans, we introduce the Genies workflow that combines benefits of crowd wisdom, collaborative refinement, and automation. We demonstrate and evaluate this approach through the TaskGenies system, and introduce an NLP similarity algorithm for reusing plans. We demonstrate that it is possible for people to create action plans for others, and we show that it can be cost effective.
Detection of Malicious Portable Executables Using Evidence Combinational Theory with Fuzzy Hashing

Fuzzy hashing is a known technique that has been adopted to speed up malware analysis processes. However, Hashing has not been fully implemented for malware detection because it can easily be evaded by applying a simple obfuscation technique such as packing. This challenge has limited the usage of hashing to triaging of the samples based on the percentage of similarity between the known and unknown. In this paper, we explore the different ways fuzzy hashing can be used to detect similarities in a file by investigating particular hashes of interest. Each hashing method produces independent but related interesting results which are presented herein. We further investigate combination techniques that can be used to improve the detection rates in hashing methods. Two such evidence combination theory based methods are applied in this work in order propose a novel way of combining the results achieved from different hashing algorithms. This study focuses on file and section Ssdeep hashing, PeHash and Imphash techniques to calculate the similarity of the Portable Executable files. Our results show that the detection rates are improved when evidence combination techniques are used.
A deterministic global optimization algorithm

In this paper, we consider a deterministic global optimization algorithm for solving a general linear sum of ratios (LFP). First, an equivalent optimization problem (LFP1) of LFP is derived by exploiting the characteristics of the constraints of LFP. By a new linearizing method the linearization relaxation function of the objective function of LFP1 is derived, then the linear relaxation programming (RLP) of LFP1 is constructed and the proposed branch and bound algorithm is convergent to the global minimum through the successive refinement of the linear relaxation of the feasible region of the objection function and the solutions of a series of RLP. And finally the numerical experiments are given to illustrate the feasibility of the proposed algorithm.
Smart People or Smart Contexts ? Cognition , Ability , and Talent Development in an Age of Situated Approaches to Knowing and Learning

Intelligence, expertise, ability and talent, as these terms have traditionally been used in education and psychology, are socially agreed upon labels that minimize the dynamic, evolving, and contextual nature of individual–environment relations. These hypothesized constructs can instead be described as functional relations distributed across whole persons and particular contexts through which individuals appear knowledgeably skillful. The purpose of this article is to support a concept of ability and talent development that is theoretically grounded in 5 distinct, yet interrelated, notions: ecological psychology, situated cognition, distributed cognition, activity theory, and legitimate peripheral participation. Although talent may be reserved by some to describe individuals possessing exceptional ability and ability may be described as an internal trait, in our description neither ability nor talent are possessed. Instead, they are treated as equivalent terms that can be used to describe functional transactions that are situated across person-in-situation. Further, and more important, by arguing that ability is part of the individual–environment transaction, we take the potential to appear talented out of the hands (or heads) of the few and instead treat it as an opportunity that is available to all although it may be actualized more frequently by some.
Knowledge Engineering and Knowledge Management Methods, Models, and Tools

Currently computers are changing from single isolated devices into entry points into a worldwide network of information exchange and business transactions. Support in data, information, and knowledge exchange is becoming the key issue in current computer technology. Ontologies will play a major role in supporting information exchange processes in various areas. A prerequisite for such a role is the development of a joint standard for specifying and exchanging ontologies. The purpose of the paper is precisely concerned with this necessity. We will present OIL, which is a proposal for such a standard. It is based on existing proposals such as OKBC, XOL and RDF schema, enriching them with necessary features for expressing ontologies. The paper sketches the main ideas of OIL.
Securing Video Integrity Using Decentralized Trusted Timestamping on the Bitcoin Blockchain

The ability to verify the integrity of video files is important for consumer and business applications alike. Especially if video files are to be used as evidence in court, the ability to prove that a file existed in a certain state at a specific time and was not altered since is crucial. This paper proposes the use of blockchain technology to secure and verify the integrity of video files. To demonstrate a specific use case for this concept, we present an application that converts a videocamera enabled smartphone into a cost-effective tamperproof dashboard camera (dash cam). If the phone’s built-in sensors detect a collision, the application automatically creates a hash of the relevant video recording. This video file’s hash is immediately transmitted to the OriginStamp service, which includes the hash in a transaction made to the Bitcoin network. Once the Bitcoin network confirms the transaction, the video file’s hash is permanently secured in the tamperproof decentralized public ledger that is the blockchain. Any subsequent attempt to manipulate the video is futile, because the hash of the manipulated footage will not match the hash that was secured in the blockchain. Using this approach, the integrity of video evidence cannot be contested. The footage of dashboard cameras could become a valid form of evidence in court. In the future, the approach could be extended to automatically secure the integrity of digitally recorded data in other scenarios, including: surveillance systems, drone footage, body cameras of law enforcement, log data from industrial machines, measurements recorded by lab equipment, and the activities of weapon systems. We have made the source code of the demonstrated application available under an MIT License and encourage anyone to contribute: www.gipp.com/dtt
A Graph Summarization: A Survey

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Thus, efficient computational methods for condensing and simplifying data are becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.
E-Learning standards and learning analytics. Can data collection be improved by using standard data models?

The Learning Analytics (LA) discipline analyzes educational data obtained from student interaction with online resources. Most of the data is collected from Learning Management Systems deployed at established educational institutions. In addition, other learning platforms, most notably Massive Open Online Courses such as Udacity and Coursera or other educational initiatives such as Khan Academy, generate large amounts of data. However, there is no generally agreedupon data model for student interactions. Thus, analysis tools must be tailored to each system's particular data structure, reducing their interoperability and increasing development costs. Some e-Learning standards designed for content interoperability include data models for gathering student performance information. In this paper, we describe how well-known LA tools collect data, which we link to how two e-Learning standards - IEEE Standard for Learning Technology and Experience API - define their data models. From this analysis, we identify the advantages of using these e-Learning standards from the point of view of Learning Analytics.
A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) λ -coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The λ-coverage problem is concerned with finding a set of key nodes having minimal size that can influence a given percentage λ of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the λ -coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient.
Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling

We propose two dynamic indexing schemes for shortest-path and distance queries on large time-evolving graphs, which are useful in a wide range of important applications such as real-time network-aware search and network evolution analysis. To the best of our knowledge, these methods are the first practical exact indexing methods to efficiently process distance queries and dynamic graph updates. We first propose a dynamic indexing scheme for queries on the last snapshot. The scalability and efficiency of its offline indexing algorithm and query algorithm are competitive even with previous static methods. Meanwhile, the method is dynamic, that is, it can incrementally update indices as the graph changes over time. Then, we further design another dynamic indexing scheme that can also answer two kinds of historical queries with regard to not only the latest snapshot but also previous snapshots.
 Through extensive experiments on real and synthetic evolving networks, we show the scalability and efficiency of our methods. Specifically, they can construct indices from large graphs with millions of vertices, answer queries in microseconds, and update indices in milliseconds.
Multi-DOF Counterbalance Mechanism for a Service Robot Arm

Low-cost but high-performance robot arms are required for widespread use of service robots. Most robot arms use expensive motors and speed reducers to provide torques sufficient to support the robot mass and payload. If the gravitational torques due to the robot mass, which is usually much greater than the payload, can be compensated for by some means; the robot would need much smaller torques, which can be delivered by cheap actuator modules. To this end, we propose a novel counterbalance mechanism which can completely counterbalance the gravitational torques due to the robot mass. Since most 6-DOF robots have three pitch joints, which are subject to gravitational torques, we propose a 3-DOF counterbalance mechanism based on the double parallelogram mechanism, in which reference planes are provided to each joint for proper counterbalancing. A 5-DOF counterbalance robot arm was built to demonstrate the performance of the proposed mechanism. Simulation and experimental results showed that the proposed mechanism had effectively decreased the torque required to support the robot mass, thus allowing the prospective use of low-cost motors and speed reducers for high-performance robot arms.
An Airborne Radar Power Supply With Contactless Transfer of Energy—Part I: Rotating Transformer

Reliability and precision are key requirements for electronic systems in aerospace applications. Transferring electrical energy from a stationary to a moving device involves wearable parts such as slip rings and brushes. This paper examines the possibility of using a rotating transformer for contactless transfer of energy from the base to the revolving platform of an airborne radar system. The first part of the series focuses on the magnetic interface, investigating its electrical properties and their association with the core and windings geometry. The reader will gain an understanding of the merits and limitations of this technology and will be able to assess its suitability for other applications. The effects of the increased leakage and reduced magnetizing inductances of the transformer are investigated, and two winding layouts are proposed and characterized by measurements and finite-element analysis. Some equations are presented along with practical guidelines on designing a rotating transformer with a 0.25-2-mm air gap. The transformer voltage gain and efficiency plots are introduced as performance-assessment tools. The impact of the air-gap stray flux on the winding conduction losses is shown, and some electromagnetic-compatibility considerations are presented. Finally, a mechanical layout for a 1-kW rotating transformer is proposed.