nils-herrmann/clean_scirepeval huggingface dataset:
InfoGather+: semantic matching and annotation of numeric and time-varying attributes in web tables

Users often need to gather information about "entities" of interest. Recent efforts try to automate this task by leveraging the vast corpus of HTML tables; this is referred to as "entity augmentation". The accuracy of entity augmentation critically depends on semantic relationships between web tables as well as semantic labels of those tables. Current techniques work well for string-valued and static attributes but perform poorly for numeric and time-varying attributes.
 In this paper, we first build a semantic graph that (i) labels columns with unit, scale and timestamp information and (ii) computes semantic matches between columns even when the same numeric attribute is expressed in different units or scales. Second, we develop a novel entity augmentation API suited for numeric and time-varying attributes that leverages the semantic graph. Building the graph is challenging as such label information is often missing from the column headers. Our key insight is to leverage the wealth of tables on the web and infer label information from semantically matching columns of other web tables; this complements "local" extraction from column headers. However, this creates an interdependence between labels and semantic matches; we address this challenge by representing the task as a probabilistic graphical model that jointly discovers labels and semantic matches over all columns. Our experiments on real-life datasets show that (i) our semantic graph contains higher quality labels and semantic matches and (ii) entity augmentation based on the above graph has significantly higher precision and recall compared with the state-of-the-art.
aIR-Jumper: Covert air-gap exfiltration/infiltration via security cameras & infrared (IR)

Infrared (IR) light is invisible to humans, but cameras are optically sensitive to this type of light. In this paper, we show how attackers can use surveillance cameras and infrared light to establish bi-directional covert communication between the internal networks of organizations and remote attackers. We present two scenarios: exfiltration (leaking data out of the network) and infiltration (sending data into the network). Exfiltration. Surveillance and security cameras are equipped with IR LEDs, which are used for night vision. In the exfiltration scenario, malware within the organization access the surveillance cameras across the local network and controls the IR illumination. Sensitive data such as PIN codes, passwords, and encryption keys are then modulated, encoded, and transmitted over the IR signals. Infiltration. In an infiltration scenario, an attacker standing in a public area (e.g., in the street) uses IR LEDs to transmit hidden signals to the surveillance camera(s). Binary data such as command and control (C&C) and beacon messages are encoded on top of the IR signals. The exfiltration and infiltration can be combined to establish bidirectional, 'air-gap' communication between the compromised network and the attacker. We discuss related work and provide scientific background about this optical channel. We implement a malware prototype and present data modulation schemas and a basic transmission protocol. Our evaluation of the covert channel shows that data can be covertly exfiltrated from an organization at a rate of 20 bit/sec per surveillance camera to a distance of tens of meters away. Data can be covertly infiltrated into an organization at a rate of over 100 bit/sec per surveillance camera from a distance of hundreds of meters to kilometers away.
Opportunistic beamforming using dumb antennas

Multiuser diversity is a form of diversity inherent in a wireless network, provided by independent time varying channels across the different users. The diversity benefit is exploited by tracking the channel fluctuations of the users and scheduling transmissions to users when their instantaneous channel quality is near the peak. The diversity gain increases with the dynamic range of the fluctuations and is thus limited in environments with little scattering and/or slow fading. In such environments, we propose the use of multiple transmit antennas to artificially induce large and fast channel fluctuations so that multiuser diversity can still be exploited. The scheme can be interpreted as opportunistic beamforming and we show that true beamforming gains can be achieved when there are sufficient users, even though very limited channel feedback is needed. Furthermore, in a cellular system, the scheme plays an important and dual role of opportunistic nulling of the interference created on users of adjacent cells. We discuss the design implications of implementing this scheme in a complete wireless system.
Netbait: a Distributed Worm Detection Service

This paper presents Netbait, a planetary-scale service for distributed detection of Internet worms. Netbait allows users to pose queries that identify which machines on a given network have been compromised based on the collective view of a geographically distributed set of machines. It is based on a distributed query processing architecture that evaluates queries expressed using a subset of SQL against a single logical database table. This single logical table is realized using a distributed set of relational databases, each populated by local intrusion detection systems running on Netbait server nodes. For speed, queries in Netbait are processed in parallel by distributing them over dynamically constructed query processing trees built over Tapestry, a distributed object and location routing (DOLR) layer. For efficiency, query results are compressed using application-specific aggregation and compact encodings. We have implemented a prototype system based on a simplified version of the architecture and have deployed it on 90 nodes of the PlanetLab testbed at 42 sites spread across three continents. The system has been continuously running for over a month now and has been collecting probe information from machines compromised by both the Code Red and Nimda worms. Early results based on this data are promising. First, we observe that by having multiple machines sharing probe information from infected machines, we can identify a substantially larger set of infected hosts that would be possible otherwise. Second, we also observe that by having multiple viewpoints of the network, Netbait is able to identify compromised machines that otherwise would have been difficult to detect in cases where worms have an affinity to certain regions of the IP address space.
A miniaturized power combiner for compact design of CMOS phase shifter at K-Band

This paper proposes a ultra compact Wilkinson power combiner (WPC) incorporating synthetic transmission lines at K-band in CMOS technology. The 50 % improvement on the size reduction can be achieved by increasing the slow-wave factor of synthetic transmission line. The presented Wilkinson power combiner design is analyzed and fabricated by using standard 0.18 µm 1P6M CMOS technology. The prototype has only a chip size of 480 µm × 90 µm, corresponding to 0.0002λ02 at 21.5 GHz. The measured insertion losses and return losses are less and higher than 4 dB and 17.5 dB from 16 GHz to 27 GHz, respectively. Furthermore, the proposed WPC is also integrated into the phase shifter to confirm its feasibility. The prototype of phase shifter shows 15 % size reduction and on-wafer measurements show good linearity of full 360-degree phase shifting from 21 GHz to 27 GHz.
A Scalable Data Chunk Similarity Based Compression Approach for Efficient Big Sensing Data Processing on Cloud

Big sensing data is prevalent in both industry and scientific research applications where the data is generated with high volume and velocity. Cloud computing provides a promising platform for big sensing data processing and storage as it provides a flexible stack of massive computing, storage, and software services in a scalable manner. Current big sensing data processing on Cloud have adopted some data compression techniques. However, due to the high volume and velocity of big sensing data, traditional data compression techniques lack sufficient efficiency and scalability for data processing. Based on specific on-Cloud data compression requirements, we propose a novel scalable data compression approach based on calculating similarity among the partitioned data chunks. Instead of compressing basic data units, the compression will be conducted over partitioned data chunks. To restore original data sets, some restoration functions and predictions will be designed. MapReduce is used for algorithm implementation to achieve extra scalability on Cloud. With real world meteorological big sensing data experiments on U-Cloud platform, we demonstrate that the proposed scalable compression approach based on data chunk similarity can significantly improve data compression efficiency with affordable data accuracy loss.
SLA-oriented resource provisioning for cloud computing: Challenges, architecture, and solutions

Cloud computing systems promise to offer subscription-oriented, enterprise-quality computing services to users worldwide. With the increased demand for delivering services to a large number of users, they need to offer differentiated services to users and meet their quality expectations. Existing resource management systems in data centers are yet to support Service Level Agreement (SLA)-oriented resource allocation, and thus need to be enhanced to realize cloud computing and utility computing. In addition, no work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. This paper presents vision, challenges, and architectural elements of SLA-oriented resource management. The proposed architecture supports integration of market-based provisioning policies and virtualisation technologies for flexible allocation of resources to applications. The performance results obtained from our working prototype system shows the feasibility and effectiveness of SLA-based resource provisioning in Clouds.
Automated Classification and Analysis of Internet Malware

Numerous attacks, such as worms, phishing, and botnets, threaten the availability of the Internet, the integrity of its hosts, and the privacy of its users. A core element of defense against these attacks is anti-virus(AV)–a service that detects, removes, and characterizes these threats. The ability of these products to successfully characterize these threats has far-reaching effects—from facilitating sharing across organizations, to detecting the emergence of new threats, and assessing risk in quarantine and cleanup. In this paper, we examine the ability of existing host-based anti-virus products to provide semantically meaningful information about the malicious software and tools (or malware) used by attackers. Using a large, recent collection of malware that spans a variety of attack vectors (e.g., spyware, worms, spam), we show that different AV products characterize malware in ways that are inconsistent across AV products, incomplete across malware, and that fail to be concise in their semantics. To address these limitations, we propose a new classification technique that describes malware behavior in terms of system state changes (e.g., files written, processes created) rather than in sequences or patterns of system calls. To address the sheer volume of malware and diversity of its behavior, we provide a method for automatically categorizing these profiles of malware into groups that reflect similar classes of behaviors and demonstrate how behavior-based clustering provides a more direct and effective way of classifying and analyzing Internet malware.
Coresets for Discrete Integration and Clustering

Given a set P of n points on the real line and a (potentially in nite) family of functions, we investigate the problem of nding a small (weighted) subset S ⊆ P , such that for any f ∈ F, we have that f(P ) is a (1 ± ε)-approximation to f(S). Here, f(Q) = ∑ q∈Q w(q)f(q) denotes the weighted discrete integral of f over the point set Q, where w(q) is the weight assigned to the point q. We study this problem, and provide tight bounds on the size S for several families of functions. As an application, we present some coreset constructions for clustering.
Problematic mobile phone use in adolescents: derivation of a short scale MPPUS-10

Our aim was to derive a short version of the Mobile Phone Problem Use Scale (MPPUS) using data from 412 adolescents of the Swiss HERMES (Health Effects Related to Mobile phonE use in adolescentS) cohort. A German version of the original MPPUS consisting of 27 items was shortened by principal component analysis (PCA) using baseline data collected in 2012. For confirmation, the PCA was carried out again with follow-up data 1 year later. PCA revealed four factors related to symptoms of addiction (Loss of Control, Withdrawal, Negative Life Consequences and Craving) and a fifth factor reflecting the social component of mobile phone use (Peer Dependence). The shortened scale (MPPUS-10) highly reflects the original MPPUS (Kendalls’ Tau: 0.80 with 90% concordant pairs). Internal consistency of MPPUS-10 was good with Cronbach’s alpha: 0.85. The results were confirmed using the follow-up data. The MPPUS-10 is a suitable instrument for research in adolescents. It will help to further clarify the definition of problematic mobile phone use in adolescents and explore similarities and differences to other technological addictions.
VerSum: Verifiable Computations over Large Public Logs

VerSum allows lightweight clients to outsource expensive computations over large and frequently changing data structures, such as the Bitcoin or Namecoin blockchains, or a Certificate Transparency log. VerSum clients ensure that the output is correct by comparing the outputs from multiple servers. VerSum assumes that at least one server is honest, and crucially, when servers disagree, VerSum uses an efficient conflict resolution protocol to determine which server(s) made a mistake and thus obtain the correct output.
 VerSum's contribution lies in achieving low server-side overhead for both incremental re-computation and conflict resolution, using three key ideas: (1) representing the computation as a functional program, which allows memoization of previous results; (2) recording the evaluation trace of the functional program in a carefully designed computation history to help clients determine which server made a mistake; and (3) introducing a new authenticated data structure for sequences, called SeqHash, that makes it efficient for servers to construct summaries of computation histories in the presence of incremental re-computation. Experimental results with an implementation of VerSum show that VerSum can be used for a variety of computations, that it can support many clients, and that it can easily keep up with Bitcoin's rate of new blocks with transactions.
Random Key Predistribution Schemes for Sensor Networks

Key establishment in sensor networks is a challenging problem because asymmetric key cryptosystems are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. We present three new mechanisms for key establishment using the framework of pre-distributing a random set of keys to each node. First, in the q-composite keys scheme, we trade off the unlikeliness of a large-scale network attack in order to significantly strengthen random key predistribution’s strength against smaller-scale attacks. Second, in the multipath-reinforcement scheme, we show how to strengthen the security between any two nodes by leveraging the security of other links. Finally, we present the random-pairwise keys scheme, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication and quorum-based revocation.
A hybrid technique for License Plate Recognition based on feature selection of wavelet transform and artificial neural network

A number of vehicles may be controlled and supervised by traffic security and its management. The License Plate Recognition is broadly employed in traffic management to recognize a vehicle whose owner has despoiled traffic laws or to find stolen vehicles. Vehicle License Plate Detection and Recognition is a key technique in most of the traffic related applications such as searching of stolen vehicles, road traffic monitoring, airport gate monitoring, speed monitoring and automatic parking lots access control. It is simply the ability of automatically extract and recognition of the vehicle license number plate's character from a captured image. Number Plate Recognition method suffered from problem of feature selection process. The current method of number plate recognition system only focus on local, global and Neural Network process of Feature Extraction and process for detection. The Optimized Feature Selection process improves the detection ratio of number plate recognition. In this paper, it is proposed a new methodology for `License Plate Recognition' based on wavelet transform function. This proposed methodology compare with Correlation based method for detection of number plate. Empirical result shows that better performance in comparison of correlation based technique for number plate recognition. Here, it is modified the Matching Technique for numberplate recognition by using Multi-Class RBF Neural Network Optimization.
Human Swarms, a real-time method for collective intelligence

Although substantial research has explored the emergence of collective intelligence in real-time human-based collaborative systems, much of this work has focused on rigid scenarios such as the Prisoner’s Dilemma (PD). (Pinheiro et al., 2012; Santos et al., 2012). While such work is of great research value, there’s a growing need for a flexible real-world platform that fosters collective intelligence in authentic decision-making situations. This paper introduces a new platform called UNUM that allows groups of online users to collectively answer questions, make decisions, and resolve dilemmas by working together in unified dynamic systems. Modeled after biological swarms, the UNUM platform enables online groups to work in real-time synchrony, collaboratively exploring a decision-space and converging on preferred solutions in a matter of seconds. We call the process “social swarming” and early real-world testing suggests it has great potential for harnessing collective intelligence.
Frequency-Modulation Control of a DC/DC Current-Source Parallel-Resonant Converter

This paper proposes a frequency-modulation control scheme for a dc/dc current-source parallel-resonant converter with two possible configurations. The basic configuration comprises an external voltage loop, an internal current loop, and a frequency modulator: the voltage loop is responsible for regulating the output voltage, the current loop makes the system controllable and limits the input current, and the modulator provides robustness against variations in resonant component values. The enhanced configuration introduces the output inductor current as a feed-forward term and clearly improves the transient response to fast load changes. The theoretical design of these control schemes is performed systematically by first deriving their small-signal models and second using Bode diagram analysis. The actual performance of the proposed control schemes is experimentally validated by testing on a laboratory prototype.
Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition

Visual emotion recognition aims to associate images with appropriate emotions. There are different visual stimuli that can affect human emotion from low-level to high-level, such as color, texture, part, object, etc. However, most existing methods treat different levels of features as independent entity without having effective method for feature fusion. In this paper, we propose a unified CNNRNN model to predict the emotion based on the fused features from different levels by exploiting the dependency among them. Our proposed architecture leverages convolutional neural network (CNN) with multiple layers to extract different levels of features within a multi-task learning framework, in which two related loss functions are introduced to learn the feature representation. Considering the dependencies within the low-level and high-level features, a bidirectional recurrent neural network (RNN) is proposed to integrate the learned features from different layers in the CNN model. Extensive experiments on both Internet images and art photo datasets demonstrate that our method outperforms the state-of-the-art methods with at least 7% performance improvement.
The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or 'ontologies'. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium is pursuing a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing coordinated reform, and new ontologies are being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable and logically well formed and to incorporate accurate representations of biological reality. We describe this OBO Foundry initiative and provide guidelines for those who might wish to become involved.
Autonomous Fruit Picking Machine: A Robotic Apple Harvester

HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt età la diffusion de documents scientifiques de niveau recherche, publiés ou non, ´ emanant desétablissements d'enseignement et de recherche français oú etrangers, des laboratoires publics ou privés.  This paper describes the construction and functionality of an Autonomous Fruit Picking Machine (AFPM) for robotic apple harvesting. The key element for the success of the AFPM is the integrated approach which combines state of the art industrial components with the newly designed flexible gripper. The gripper consist of a silicone funnel with a camera mounted inside. The proposed concepts guarantee adequate control of the autonomous fruit harvesting operation globally and of the fruit picking cycle particularly. Extensive experiments in the field validate the functionality of the AFPM.