nils-herrmann/clean_scirepeval huggingface dataset:
Tracking port scanners on the IP backbone

Port scanning is the usual precursor to malicious attacks on today's Internet. Although many algorithms have been proposed for different aspects of the scan detection problem, their focused designed space is enterprise gateway level Intrusion Detection. Furthermore, we find few studies that track scanner behaviors over an extended period of time. Operating from a unique vantage point, the IP backbone, we put all the pieces together in designing and implementing a fast and accurate online port scan detection and tracking system. We introduce our flexible architecture, discuss trade-offs and design choices. Specifically, we go in depth to two design choices: the distinct counter data structure and the buffer size tuning. Our choice of a probabilistic counter is proved to have a low average error of 3.5%. The buffer size is derived using Lyapunov drift techniques on an equivalent queuing model. Our initial scanner tracking results show that the scanners' timing behavior follows a 90-10 curve. That is, 90% of scanners are active for a short time, with low scanning rates, while 10% are long term and fast scanners, with a few super-scanners lasting the entire duration of monitoring.
Investigating bank failures using text mining

We extend beyond healthiness assessment of banks using quantitative financial data by applying textual sentiment analysis. Looking at public annual reports for a large sample of U.S. banks in the 2000-2014 period, we identify 52 public bank holding companies that were associated with bank failures during the global financial crisis. Utilizing sentiment dictionaries designed for financial context, we find that negative and positive sentiments discriminate between failed and non-failed banks 88% and 79%, respectively, of the time. However, we find that positive sentiment contains stronger predictive power than negative sentiment; out of ten failed banks, on average positive sentiment can identify six true events, while negative sentiment identifies five failed banks at most. While one would link financial soundness with more positive sentiment, it appears that failed banks exhausted more positive sentiment than their non-failed peers, whether ex-ante in anticipation of good news or ex-post to conceal financial distress.
Learning Music Embedding with Metadata for Context Aware Recommendation

Contextual factors can benefit music recommendation and retrieval tasks remarkably. However, how to acquire and utilize the contextual information still need to be studied. In this paper, we propose a context aware music recommendation approach, which can recommend music appropriate for users' contextual preference for music. In analogy to matrix factorization methods for collaborative filtering, the proposed approach does not require songs to be described by features beforehand, but it learns music pieces' embeddings (vectors in low-dimensional continuous space) from music playing records and corresponding metadata and infer users' general and contextual preference for music from their playing records with the learned embedding. Then, our approach can recommend appropriate music pieces. Experimental evaluations on a real world dataset show that the proposed approach outperforms baseline methods.
WWHow! Freeing Data Storage from Cages

Efficient data storage is a key component of data managing systems to achieve good performance. However, currently data storage is either heavily constrained by static decisions (e.g. fixed data stores in DBMSs) or left to be tuned and configured by users (e.g. manual data backup in File Systems). In this paper, we take a holistic view of data storage and envision a virtual storage layer. Our virtual storage layer provides a unified storage framework for several use-cases including personal, enterprise, and cloud storage.
Facial expression recognition based on local region specific features and support vector machines

Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.
TransFlow: Unsupervised Motion Flow by Joint Geometric and Pixel-level Estimation

We address unsupervised optical flow estimation for ego-centric motion. We argue that optical flow can be cast as a geometrical warping between two successive video frames and devise a deep architecture to estimate such transformation in two stages. First, a dense pixel-level flow is computed with a geometric prior imposing strong spatial constraints. Such prior is typical of driving scenes, where the point of view is coherent with the vehicle motion. We show how such global transformation can be approximated with an homography and how spatial transformer layers can be employed to compute the flow field implied by such transformation. The second stage then refines the prediction feeding a second deeper network. A final reconstruction loss compares the warping of frame Xt with the subsequent frame Xt+1 and guides both estimates. The model, which we named TransFlow, performs favorably compared to other unsupervised algorithms, and shows better generalization compared to supervised methods with a 3x reduction in error on unseen data.
Real-time wireless vibration monitoring system using LabVIEW

Vibration analysis provides relevant information about abnormal working condition of machine parts. Vibration measurement is prerequisite for vibration analysis which is used for condition monitoring of machinery. Also, wireless vibration monitoring has many advantages over wired monitoring. This Paper presents, implementation of a reliable and low cost wireless vibration monitoring system. Vibration measurement has been done using 3-Axis digital output MEMS Accelerometer sensor. This sensor can sense vibrations in the range 0.0156g to 8g where, 1g is 9.81m/s2. Accelerometer Sensor is interfaced with Arduino-derived microcontroller board having Atmel's AT-mega328p microcontroller. The implemented system uses ZigBee communication protocol i.e. standard IEEE 802.15.4, for wireless communication between Sensor Unit and Vibration Monitoring Unit. The wireless communication has been done using XBee RF modules. National Instruments's LabVIEW software has been used for development of graphical user interface, data-logging and alarm indication on the PC. Experimental results show continuous real-time monitoring of machine's vibrations on charts. These results, along with data-log file have been used for vibration analysis. This analysis is used to ensure safe working condition of machinery and used in predictive maintenance.
Mining checkins from location-sharing services for client-independent IP geolocation

Accurately determining the geographic location of an Internet host is important for location-aware applications such as location-based advertising and network diagnostics. Despite their fast response time, widely used database-driven geolocation approaches provide only inaccurate locations. Delay measurement based approaches improve the estimation accuracy but still suffer from a limited precision (about 10 km) and a long response time (tens of seconds) to localize a single PC, which cannot meet the demand of precise and real-time geolocation for location-aware applications. In this paper, we propose a new geolocation approach, Checkin-Geo, which exploits geolocation resources fundamentally different from existing database-driven (using DNS, Whois, etc.) or network delay measurement based approaches. In particular, we leverage the location data that users are willing to share in location-sharing services and logs of user logins from PCs for real-time and accurate geolocation. Experimental results show that compared to existing geolocation techniques, Checkin-Geo achieves 1) a median estimation error of 799 meters (an order of magnitude smaller than existing approaches), and 2) a negligible response time, which are promising for accurate location-aware applications.
Development and implement of an inspection robot for power substation

Along with the development demand of intelligent power substation, mobile robot is implemented to serve power substations. The robot is equipped with inspecting sensors, such as CCD camera and thermal infrared imager which are together referred to as workload. These inspecting sensors are used to detect the environment parameters e.g. reading meter, measuring temperature etc., inside the substation. However, due to the strong electromagnetic interference inside substation, accurate localization of the workload and inspection robot is still a difficult problem to be solved. Existing methods solve the problem by paving magnetic strips or rail. However, in this way, the working area of the robot is limited by the paved infrastructures. In this paper, an inspection robot system is developed for working in substation. In the proposed system, the localization of the robot is realized using adaptive Monte-Carlo localization based on the known environment model. A visual servo system is designed to realize the high accuracy localization of the workload. The proposed robot system should guarantee to obtain fine inspecting results of equipments in substation.
From Privacy Concern to Uses of Social Network Sites: A Cultural Comparison via User Survey

Individuals from different regions normally bear different cultural norms and values, and these variations would in turn affect their behavior and attitude with the computing system. However, though the cross-cultural study has been performed in other areas (e.g., e-commerce websites), few have identified its influence on users' behavior in social network sites (SNS). With the increasing popularity of SNS (e.g., Face book) worldwide, especially in Asian region, we are interested in revealing the effect of culture variables on users' privacy concern and trust in SNS, and furthermore their influence on users' usage motivation, actual uses, overall attitudes and future behavior intentions. This paper presents the in-depth analysis of results from an online survey, which indicates the significant differences between Hong Kong and French SNS users, in respect of various measures. Moreover, it shows that for predicting a user's usage pattern, her/his culture value should be considered since the predictors are different between the two cultural groups.
SmartAds: bringing contextual ads to mobile apps

A recent study showed that while US consumers spent 30% more time on mobile apps than on traditional web, advertisers spent 1600% less money on mobile ads. One key reason is that unlike most web ad providers, today's mobile ads are not contextual---they do not take into account the content of the page they are displayed on. Thus, most mobile ads are irrelevant to what the user is interested in. For example, it is not uncommon to see gambling ads being displayed in a Bible app. This irrelevance results in low clickthrough rates, and hence advertisers shy away from the mobile platform. Using data from top 1200 apps in Windows Phone marketplace, and a one-week trace of ad keywords from Microsoft's ad network, we show that content displayed by mobile apps is a potential goldmine of keywords that advertisers are interested in.
 However, unlike web pages, which can be crawled and indexed offline for contextual advertising, content shown on mobile apps is often either generated dynamically, or is embedded in the apps themselves; and hence cannot be crawled. The only solution is to scrape the content at runtime, extract keywords and fetch contextually relevant ads. The challenge is to do this without excessive overhead and without violating user privacy. In this paper, we describe a system called SmartAds to address this challenge.
 We have built a prototype of SmartAds for Windows Phone apps. In a large user study with over 5000 ad impressions, we found that SmartAds nearly doubles the relevance score, while consuming minimal additional resources and preserving user privacy.
Decentralized IoT Data Management Using BlockChain and Trusted Execution Environment

Due to the centralization of authority in the management of data generated by IoT devices, there is a lack of transparency in how user data is being shared among third party entities. With the popularity of adoption of blockchain technology, which provide decentralized management of assets such as currency as seen in Bitcoin, we propose a decentralized system of data management for IoT devices where all data access permission is en-forced using smart contracts and the audit trail of data access is stored in the blockchain. With smart contracts applications, multiple parties can specify rules to govern their interactions which is independently enforced in the blockchain without the need for a centralized system. We provide a framework that store the hash of the data in the blockchain and store the raw data in a secure storage platform using trusted execution environment (TEE). In particular, we consider Intel SGX as a part of TEE that ensure data security and privacy for sensitive part of the application (code and data).
From sunlight to phytomass: on the potential efficiency of converting solar radiation to phyto-energy.

The relationship between solar radiation capture and potential plant growth is of theoretical and practical importance. The key processes constraining the transduction of solar radiation into phyto-energy (i.e. free energy in phytomass) were reviewed to estimate potential solar-energy-use efficiency. Specifically, the out-put:input stoichiometries of photosynthesis and photorespiration in C(3) and C(4) systems, mobilization and translocation of photosynthate, and biosynthesis of major plant biochemical constituents were evaluated. The maintenance requirement, an area of important uncertainty, was also considered. For a hypothetical C(3) grain crop with a full canopy at 30°C and 350 ppm atmospheric [CO(2) ], theoretically potential efficiencies (based on extant plant metabolic reactions and pathways) were estimated at c. 0.041 J J(-1) incident total solar radiation, and c. 0.092 J J(-1) absorbed photosynthetically active radiation (PAR). At 20°C, the calculated potential efficiencies increased to 0.053 and 0.118 J J(-1) (incident total radiation and absorbed PAR, respectively). Estimates for a hypothetical C(4) cereal were c. 0.051 and c. 0.114 J J(-1), respectively. These values, which cannot be considered as precise, are less than some previous estimates, and the reasons for the differences are considered. Field-based data indicate that exceptional crops may attain a significant fraction of potential efficiency.
Hierarchical Text Categorization Using Neural Networks

This paper presents the design and evaluation of a text categorization method based on the Hierarchical Mixture of Experts model. This model uses a divide and conquer principle to define smaller categorization problems based on a predefined hierarchical structure. The final classifier is a hierarchical array of neural networks. The method is evaluated using the UMLS Metathesaurus as the underlying hierarchical structure, and the OHSUMED test set of MEDLINE records. Comparisons with an optimized version of the traditional Rocchio's algorithm adapted for text categorization, as well as flat neural network classifiers are provided. The results show that the use of the hierarchical structure improves text categorization performance with respect to an equivalent flat model. The optimized Rocchio algorithm achieves a performance comparable with that of the hierarchical neural networks.
Reinforcement learning of clothing assistance with a dual-arm robot

This study aims at robotic clothing assistance as it is yet an open field for robotics despite it is one of the basic and important assistance activities in daily life of elderly as well as disabled people. The clothing assistance is a challenging problem since robots must interact with non-rigid clothes generally represented in a high-dimensional space, and with the assisted person whose posture can vary during the assistance. Thus, the robot is required to manage two difficulties to perform the task of the clothing assistance: 1) handling of non-rigid materials and 2) adaptation of the assisting movements to the assisted person's posture. To overcome these difficulties, we propose to use reinforcement learning with the cloth's state which is low-dimensionally represented in topology coordinates, and with the reward defined in the low-dimensional coordinates. With our developed experimental system, for T-shirt clothing assistance, including an anthropomorphic dual-arm robot and a soft mannequin, we demonstrate the robot quickly learns a suitable arm motion for putting the mannequin's head into a T-shirt.
50 mV-Input Batteryless Boost Converter for Thermal Energy Harvesting

A fully electrical startup boost converter for thermal energy harvesting is presented in this paper. The converter is implemented in a 65-nm bulk CMOS technology. With the proposed 3-stage stepping-up architecture, the minimum input voltage for startup is as low as 50 mV while the input voltage required for sustained power conversion is 30 mV. Due to the use of a zero-current-switching (ZCS) converter as the last stage and an automatic shutdown mechanism for the auxiliary converter, conversion efficiency up to 73% is achieved. By incorporating the boost converter and a thermoelectric generator (TEG), a miniaturized module is demonstrated for energy harvesting applications.
Applying data mining techniques in job recommender system for considering candidate job preferences

Job recommender systems are desired to attain a high level of accuracy while making the predictions which are relevant to the customer, as it becomes a very tedious task to explore thousands of jobs, posted on the web, periodically. Although a lot of job recommender systems exist that uses different strategies , here efforts have been put to make the job recommendations on the basis of candidate's profile matching as well as preserving candidate's job behavior or preferences. Firstly, rules predicting the general preferences of the different user groups are mined. Then the job recommendations to the target candidate are made on the basis of content based matching as well as candidate preferences, which are preserved either in the form of mined rules or obtained by candidate's own applied jobs history. Through this technique a significant level of accuracy has been achieved over other basic methods of job recommendations.
An N-Bit Digitally Variable Ultra Wideband Pulse Generator for GPR and UWB Applications

This paper presents a low-cost Ultra Wideband (UWB) pulse generator that can vary the pulse duration digitally by using a Step Recovery Diode (SRD), microstrip transmission lines and PIN diodes. First, a sharp edge is generated by using a SRD circuitry. Then a pulse is formed from the sharp edge through the use of transmission lines and the PIN diodes. Based on the number of transmission lines (N), the duration of the pulse can be varied in steps. The UWB pulse generator circuits are implemented on an FR-4 substrate using microstrip line technology and UWB pulses with durations of 550 to 2400 psec are measured. N 2
AWEsome: An open-source test platform for airborne wind energy systems

In this paper we present AWEsome (Airborne Wind Energy Standardized Open-source Model Environment), a test platform for airborne wind energy systems that consists of low-cost hardware and is entirely based on open-source software. It can hence be used without the need of large financial investments, in particular by research groups and startups to acquire first experiences in their flight operations, to test novel control strategies or technical designs, or for usage in public relations. Our system consists of a modified off-the-shelf model aircraft that is controlled by the pixhawk autopilot hardware and the ardupilot software for fixed wing aircraft. The aircraft is attached to the ground by a tether. We have implemented new flight modes for the autonomous tethered flight of the aircraft along periodic patterns. We present the principal functionality of our algorithms. We report on first successful tests of these modes in real flights. ∗Author names are in alphabetical order. a Universität Bonn, bechtle@physik.uni-bonn.de b Universität Bonn, thomasgehrmann@gmx.net c Humboldt-Universität zu Berlin, csieg@physik.hu-berlin.de d Daidalos Capital, zillmann@daidalos-capital.com awesome.physik.uni-bonn.de 1 ar X iv :1 70 4. 08 69 5v 1 [ cs .S Y ] 2 7 A pr 2 01 7 Section
COMPARISON OF MAXIMUM POWER POINT TRACKING ALGORITHMS FOR PHOTOVOLTAIC SYSTEM

Photovoltaic systems normally use a maximum power point tracking (MPPT) technique to continuously deliver the highest possible power to the load when variations in the isolation and temperature occur, Photovoltaic (PV) generation is becoming increasingly important as a renewable source since it offers many advantages such as incurring no fuel costs, not being polluting, requiring little maintenance, and emitting no noise, among others. PV modules still have relatively low conversion efficiency; therefore, controlling maximum power point tracking (MPPT) for the solar array is essential in a PV system. The Maximum Power Point Tracking (MPPT) is a technique used in power electronic circuits to extract maximum energy from the Photovoltaic (PV) Systems. In the recent days, PV power generation has gained more importance due its numerous advantages such as fuel free, requires very little maintenance and environmental benefits. To improve the energy efficiency, it is important to operate PV system always at its maximum power point. Many maximum power point Tracking (MPPT) techniques are available and proposed various methods for obtaining maximum power point. But, among the available techniques sufficient comparative study particularly with variable environmental conditions is not done. This paper is an attempt to study and evaluate two main types of MPPT techniques namely, Open-circuit voltage and Short-circuit current. The detailed comparison of each technique is reported. The SIMULINK simulation results of Open-circuit voltage and Short-circuit current methods with changing radiation and temperature are presented.