nils-herrmann/clean_scirepeval huggingface dataset:
ViewDroid: towards obfuscation-resilient mobile application repackaging detection

In recent years, as mobile smart device sales grow quickly, the development of mobile applications (apps) keeps accelerating, so does mobile app repackaging. Attackers can easily repackage an app under their own names or embed advertisements to earn pecuniary profits. They can also modify a popular app by inserting malicious payloads into the original app and leverage its popularity to accelerate malware propagation. In this paper, we propose ViewDroid, a user interface based approach to mobile app repackaging detection. Android apps are user interaction intensive and event dominated, and the interactions between users and apps are performed through user interface, or views. This observation inspires the design of our new birthmark for Android apps, namely, feature view graph, which captures users' navigation behavior across app views. Our experimental results demonstrate that this birthmark can characterize Android apps from a higher level abstraction, making it resilient to code obfuscation. ViewDroid can detect repackaged apps at a large scale, both effectively and efficiently. Our experiments also show that the false positive and false negative rates of ViewDroid are both very low.
Semantic Models for Scalable Search in the Internet of Things

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.
Hierarchical Relational Attention for Video Question Answering

Video Question Answering (VideoQA) tasks require understanding of the connection of context specific video parts which are temporally distributed. Humans are capable of focusing on temporally distributed video scenes and also to find correspondence or relationships among these segments. To achieve similar capability, a hierarchical relational attention mechanism is proposed in this paper. The proposed VideoQA model derives attention on temporal segments i.e. video features based on each of the question words. Also, contextual relevance of these temporal segments are captured to derive the final video representation which leads to a better reasoning capability. We evaluate the performance of the proposed approach on the MSRVTT-QA and the MSVD-QA datasets to establish its superior performance over the state of the art.
Trend Following Trading under a Regime Switching Model

This paper is concerned with the optimality of a trend following trading rule. The idea is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. We characterize the bull and bear phases of the markets mathematically using the conditional probabilities of the bull market given the up to date stock prices. The optimal buying and selling times are given in terms of a sequence of stopping times determined by two threshold curves. Numerical experiments are conducted to validate the theoretical results and demonstrate how they perform in a marketplace.
A Practical Approach to Recognizing Physical Activities

We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.
ICrafter: A Service Framework for Ubiquitous Computing Environments

In this paper, we propose ICrafter, a framework for services and their user interfaces in a class of ubiquitous computing environments. The chief objective of ICrafter is to let users flexibly interact with the services in their environment using a variety of modalities and input devices. We extend existing service frameworks in three ways. First, to offload services and user input devices, ICrafter provides infrastructure support for UI selection, generation, and adaptation. Second, ICrafter allows UIs to be associated with service patterns for on-the-fly aggregation of services. Finally, ICrafter facilitates the design of service UIs that are portable but still reflect the context of the local environment. In addition, we also focus on the system properties such as incremental deployability and robustness that are critical for ubiquitous computing environments. We describe the goals and architecture of ICrafter, a prototype implementation that validates its design, and the key lessons learnt from our
Using Ontological Engineering to Overcome Common AI-ED Problems

This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.
Two-Stage Method for Large-Scale Acquisition of Contradiction Pattern Pairs using Entailment

In this paper we propose a two-stage method to acquire contradiction relations between typed lexico-syntactic patterns such as Xdrug prevents Ydisease and Ydisease caused by Xdrug . In the first stage, we train an SVM classifier to detect contradiction pattern pairs in a large web archive by exploiting the excitation polarity (Hashimoto et al., 2012) of the patterns. In the second stage, we enlarge the first stage classifier’s training data with new contradiction pairs obtained by combining the output of the first stage’s classifier and that of an entailment classifier. We acquired this way 750,000 typed Japanese contradiction pattern pairs with an estimated precision of 80%. We plan to release this resource to the NLP community.
A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation

In this paper, a multilevel thresholding method which allows the determination of the appropriate number of thresholds as well as the adequate threshold values is proposed. This method combines a genetic algorithm with a wavelet transform. First, the length of the original histogram is reduced by using the wavelet transform. Based on this lower resolution version of the histogram, the number of thresholds and the threshold values are determined by using a genetic algorithm. The thresholds are then projected onto the original space. In this step, a refinement procedure may be added to detect accurate threshold values. Experiments and comparative results with multilevel thresholding methods over a synthetic histogram and real images show the efficiency of the proposed method.
The valuation of risk assets and the selection of risky investments in stock portfolios and capital budgets

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Design of a General Purpose 8-bit RISC Processor for Computer Architecture Learning

Computers are becoming indispensable for manipulating most everyday consumer products, ranging from communications and domestic electronics to industrial processes monitoring and control. High performance computer design is not only subject to the technology used for its implementation, it is also a matter of efficient training. The skills that must prevail in a good computer designer come from the type of courses taken and the tools employed during them. This work shows the design of an 8-bit RISC soft-core processor dedicated to a complete understanding of computer architecture. We consider this Processor an effective hands-on training solution for the comprehension of a computer from its lowest level up to testing.
Security as a Service Model for Cloud Environment

Cloud computing is becoming increasingly important for provision of services and storage of data in the Internet. However there are several significant challenges in securing cloud infrastructures from different types of attacks. The focus of this paper is on the security services that a cloud provider can offer as part of its infrastructure to its customers (tenants) to counteract these attacks. Our main contribution is a security architecture that provides a flexible security as a service model that a cloud provider can offer to its tenants and customers of its tenants. Our security as a service model while offering a baseline security to the provider to protect its own cloud infrastructure also provides flexibility to tenants to have additional security functionalities that suit their security requirements. The paper describes the design of the security architecture and discusses how different types of attacks are counteracted by the proposed architecture. We have implemented the security architecture and the paper discusses analysis and performance evaluation results.
Five Facts About Prices : A Reevaluation of Menu Cost Models

We establish five facts about prices in the U.S. economy: 1) The median duration of consumer prices when sales are excluded at the product level is 11 months. The median duration of finished goods producer prices is 8.7 months. 2) Two-thirds of regular price changes are price increases. 3) The frequency of price increases responds strongly to inflation while the frequency of price decreases and the size of price increases and price decreases do not. 4) The frequency of price change is highly seasonal: It is highest in the 1st quarter and lowest in the 4th quarter. 5) The hazard function of price changes for individual consumer and producer goods is downward sloping for the first few months and then flat (except for a large spike at 12 months in consumer services and all producer prices). These facts are based on CPI microdata and a new comprehensive data set of microdata on producer prices that we construct from raw production files underlying the PPI. We show that the 1st, 2nd and 3rd facts are consistent with a benchmark menu-cost model, while the 4th and 5th facts are not.
Hierarchical Classification of Ten Skin Lesion Classes

This paper presents a hierarchical classification system based on the kNearest Neighbors (kNN) classifier for classification of ten different classes of Malignant and Benign skin lesions from color image data. Our key contribution is to focus on the ten most common classes of skin lesions. There are five malignant: Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Melanoma (MEL), Intraepithelial Carcinoma (IEC) and five benign: Melanocytic Nevus / Mole (ML), Seborrhoeic Keratosis (SK), Dermatofibroma (DF), Haemangioma (VASC), Pyogenic Granuloma (PYO). Moreover, we use only high resolution color images acquired using a standard camera (nondermoscopy). Our image dataset contains 1300 lesions belonging to ten classes (45 AK, 239 BCC, 331 ML, 88 SCC, 257 SK, 76 MEL, 65 DF, 97 VASC, 24 PYO and 78 IEC).
A compact field weakening controller implementation

This paper proposes a compact field weakening controller (FWC). The FWC was developed during the development of an electrically driven rear axle for a hybrid electric car. The overall control system of the vehicle, the hybrid controller (HC), delivers a reference torque for the rear drive unit (RDU). Depending on the torque reference, instantaneous vehicle speed and battery voltage the task of the FWC is to produce current references for the current controller. The references are chosen to give an optimal current, optimal in the sense that the torque-per-current ratio is maximized, under the restriction of keeping the stator voltage and current limited. The FWC is based on comprehensive measurements of the machine. An algorithm is used to process the data from the measurements. The output from the algorithm is look-up tables. Implementation of the look-up tables is also discussed in the paper
English Language Learners and Academic Achievement : Revisiting the Threshold Hypothesis

This nonexperimental study explored the predictive strength of English proficiency levels on academic achievement of middle school students in a sample of 17,470 native English-speaking (NES) students, 558 English language learners (current ELLs), and 500 redesignated fluent English proficient students (former ELLs). Results of multilevel analyses indicated that after controlling for relevant studentand school-level characteristics, former ELLs significantly outperformed current ELL and NES students in reading (effect sizes: 1.07 and 0.52) and mathematics (effect sizes: 0.86 and 0.42). The results support Cummins’s (1979, 2000) lower level threshold hypothesis predicting that upon reaching adequate proficiency in the language of schooling and testing, ELLs would no longer experience academic disadvantages. Refinements for the theory and directions for future research are discussed.
Speed-Security Tradeo s in Blockchain Protocols

Transaction processing speed is one of the major considerations in cryptocurrencies that are based on proof of work (POW) such as Bitcoin. At an intuitive level it is widely understood that processing speed is at odds with the security aspects of the underlying POW based consensus mechanism of such protocols, nevertheless the tradeo between the two properties is still not well understood. In this work, motivated by recent work  in the formal analysis of the Bitcoin backbone protocol, we investigate the tradeo between provable security and transaction processing speed viewing the latter as a function of the block generation rate. We introduce a new formal property of blockchain protocols, called chain growth, and we show it is fundamental for arguing the security of a robust transaction ledger. We strengthen the results of  showing for the rst time that reasonable security bounds hold even for the faster (than Bitcoin's) block generation rates that have been adopted by several major alt-coins (including Litecoin, Dogecoin etc.). We then provide a rst formal security proof of the GHOST rule for blockchain protocols. The GHOST rule was put forth in  as a mechanism to improve transaction processing speed and a variant of the rule is adopted by Ethereum. Our security analysis of the GHOST backbone matches our new analysis for Bitcoin in terms of the common pre x property but falls short in terms of chain growth where we provide an attack that substantially reduces the chain speed compared to Bitcoin. While our results establish the GHOST variant as a provably secure alternative to standard Bitcoin-like transaction ledgers they also highlight potential shortcomings in terms of processing speed compared to Bitcoin. We nally present attacks and simulation results against blockchain protocols (both for Bitcoin and GHOST) that present natural upper barriers for the speed-security tradeo . By combining our positive and negative results we map the speed/security domain for blockchain protocols and list open problems for future work.
Efficient location-aware influence maximization

users in a social network to maximize the expected number of users influenced by the selected users (called influence spread), has been extensively studied, existing works neglected the fact that the location information can play an important role in influence maximization. Many real-world applications such as location-aware word-of-mouth marketing have location-aware requirement. In this paper we study the location-aware influence maximization problem. One big challenge in location-aware influence maximization is to develop an efficient scheme that offers wide influence spread. To address this challenge, we propose two greedy algorithms with 1-1/e approximation ratio. To meet the instant-speed requirement, we propose two efficient algorithms with ε· (1-1/e) approximation ratio for any ε ∈ (0,1]. Experimental results on real datasets show our method achieves high performance while keeping large influence spread and significantly outperforms state-of-the-art algorithms.
Argumentation Theory: A Very Short Introduction

Since the time of the ancient Greek philosophers and rhetoricians, argumentation theorists have searched for the requirements that make an argument correct, by some appropriate standard of proof, by examining the errors of reasoning we make when we try to use arguments. These errors have long been called fallacies, and the logic textbooks have for over 2000 years tried to help students to identify these fallacies, and to deal with them when they are encountered. The problem was that deductive logic did not seem to be much use for this purpose, and there seemed to be no other obvious formal structure that could usefully be applied to them. The radical approach taken by Hamblin (1970) was to refashion the concept of an argument to think of it not just as an arbitrarily designated set of propositions, but as a move one party makes in a dialog to offer premises that may be acceptable to another party who doubts the conclusion of the argument. Just after Hamblin's time a school of thought called informal logic grew up that wanted to take a new practical approach to teaching students skills of critical thinking by going beyond deductive logic to seek other methods for analyzing and evaluating arguments. Around the same time, an interdisciplinary group of scholars associated with the term 'argumentation', coming from fields like speech communication, joined with the informal logic group to help build up such practical methods and apply them to real examples of argumentation (Johnson and Blair, 1987). The methods that have been developed so far are still in a process of rapid evolution. More recently, improvements in them have been due to some computer scientists joining the group, and to collaborative research efforts between argumentation theorists and computer scientists. Another recent development has been the adaption of argumentation models and techniques to fields in artificial intelligence, like multi-agent systems and artificial intelligence for legal reasoning. In a short paper, it is not possible to survey all these developments. The best that can be done is to offer an introduction to some of the basic concepts and methods of argumentation theory as they have evolved to the present point, and to briefly indicate some problems and limitations in them. 1. Arguments and Argumentation There are four tasks undertaken by argumentation, or informal logic, as it is also often called: identification, analysis, evaluation and invention. The task of identification …
Semi-supervised machine learning for textual anomaly detection

Anomaly detection comprises the identification of observations which do not follow the expected patterns of the assumed data set. We attempt to simplify the problem of textual anomaly detection by constructing a Multinomial Naïve Bayes classifier and enhancing it with an augmented Expectation Maximization (EM) algorithm. By doing so, we utilize large amounts of unlabelled data and show how the EM algorithm could increase the accuracy of the Naïve Bayes classifier. The process is applied to a binary classification environment in order to detect anomalies in text.
2-Point-based outlier rejection for camera-IMU systems with applications to micro aerial vehicles

This paper presents a novel method to perform the outlier rejection task between two different views of a camera rigidly attached to an Inertial Measurement Unit (IMU). Only two feature correspondences and gyroscopic data from IMU measurerments are used to compute the motion hypothesis. By exploiting this 2-point motion parametrization, we propose two algorithms to remove wrong data associations in the feature-matching process for case of a 6DoF motion. We show that in the case of a monocular camera mounted on a quadrotor vehicle, motion priors from IMU can be used to discard wrong estimations in the framework of a 2-point-RANSAC based approach. The proposed methods are evaluated on both synthetic and real data.
Adjustable dead-time control scheme for single-phase rectifier with auxiliary resonant snubber circuit

Auxiliary resonant snubber inverters (RSIs) have been proposed as a solution of demands for inverter, which are high efficiency and low EMI. In RSI, zero voltage switching (ZVS) on the inverter main switches is achieved by use of the auxiliary circuit; however, additional conduction loss increases simultaneously. The operating frequency of the auxiliary circuit in RSI can be reduced to a half of switching transition of a main switch when lossless snubber commutation, in which ZVS can be achieved without using auxiliary circuit by using output current, is used. As a result, additional conduction loss can be suppressed. However, in this scheme, ZVS turn-on on main switch is failed near zero output current when dead-time was constant. As a solution for this problem, adjustable dead-time control scheme for RSI has been proposed. Adjustable dead-time control has not been applied to a rectifier with auxiliary resonant snubber circuit — auxiliary resonant snubber rectifier (RSR)in the previous work. Thus, adjustable dead-time control is applied to RSR in this paper. This paper shows a control scheme for RSR. Moreover, the effectiveness of adjustable dead-time control by comparing between the adjustable and the fixed dead-time control is described.