nils-herrmann/clean_scirepeval huggingface dataset:
Mobile Edge Computing Potential in Making Cities Smarter

This article proposes an approach to enhance users' experience of video streaming in the context of smart cities. The proposed approach relies on the concept of MEC as a key factor in enhancing QoS. It sustains QoS by ensuring that applications/services follow the mobility of users, realizing the "Follow Me Edge" concept. The proposed scheme enforces an autonomic creation of MEC services to allow anywhere anytime data access with optimum QoE and reduced latency. Considering its application in smart city scenarios, the proposed scheme represents an important solution for reducing core network traffic and ensuring ultra-short latency through a smart MEC architecture capable of achieving the 1 ms latency dream for the upcoming 5G mobile systems.
The challenge of real-time multi-agent systems for enabling IoT and CPS

Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application domains particularly include three major characteristics: intelligence, autonomy and real-time behavior. Multi-Agent Systems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to comply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from current theories, standards, and technological implementations. In particular, internal agent schedulers, communication middlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms. In this paper, we perform the first systematic study of poisoning attacks and their countermeasures for linear regression models. In poisoning attacks, attackers deliberately influence the training data to manipulate the results of a predictive model. We propose a theoretically-grounded optimization framework specifically designed for linear regression and demonstrate its effectiveness on a range of datasets and models. We also introduce a fast statistical attack that requires limited knowledge of the training process. Finally, we design a new principled defense method that is highly resilient against all poisoning attacks. We provide formal guarantees about its convergence and an upper bound on the effect of poisoning attacks when the defense is deployed. We evaluate extensively our attacks and defenses on three realistic datasets from health care, loan assessment, and real estate domains.
N-gram Opcode Analysis for Android Malware Detection

Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed achieved an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.  Android Malware, Malware Detection, Malware Categorization, Dalvik Bytecode, N-gram, Opcode, Feature Selection, Machine Learning.
Optical character recognition of the Orthodox Hellenic Byzantine Music notation

In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the Orthodox Hellenic Byzantine Music Notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a Nearest Neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4 % was achieved, in a database of about 18,000 byzantine character patterns that have been developed for the needs of the system. Optical music recognition Off-line character recognition, Byzantine Music, Byzantine Music Notation, Wavelets, Projections, Neural Networks Contour processing, Nearest Neighbor Classifier Byzantine Music Data Base
Consumers ’ Trust in Electronic Commerce Transactions : The Role of Perceived Privacy and Perceived Security 1 Introduction

Acknowledgement I am greatly indebted to Omar El Sawy and Ann Majchrzak for their guidance and suggestion, and I would like to thank Ricky Lim and Raymond for their help with the data analysis.  Consumers' trust in their online transactions is vital for the sustained progress and development of electronic commerce. Our paper proposes that in addition to known factors of trust such a vendor's reputation, consumers' perception of privacy and security influence their trust in online transactions. Our research shows that consumers exhibit variability in their perceptions of privacy, security and trust between online and offline transactions even if it is conducted with the same store. We build upon this finding to develop and validate measures of consumers' perceived privacy and perceived security of their online transactions which are then theorized to influence their trust in EC transactions. We propose that the perceptions of privacy and security are factors that affect the consumers' trust in the institutional governance mechanisms underlying the Internet. We conduct two distinct empirical studies and through successive refinement and analysis using the Partial Least Squares technique, we test our hypothesized relationships while verifying the excellent measurement properties associated with our instrument. Our study finds that the consumers' perceived privacy and perceived security are indeed distinct constructs but the effect of perceived privacy on trust in EC transactions is strongly mediated by perceived security. A major implication of our findings is that while the much studied determinants of trust such as reputation of the transacting firm should not be neglected, vendors should also engage in efforts to positively influence consumer perceptions of privacy and security. We discuss the significance of this observation in the context of increasing importance in acquiring customer information for personalization and other online strategies.
A Survey of Text Summarization Extractive Techniques

Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.
A GA based approach for task scheduling in multi-cloud environment

In multi-cloud environment, task scheduling has attracted a lot of attention due to NP-Complete nature of the problem. Moreover, it is very challenging due to heterogeneity of the cloud resources with varying capacities and functionalities. Therefore, minimizing the makespan for task scheduling is a challenging issue. In this paper, we propose a genetic algorithm (GA) based approach for solving task scheduling problem. The algorithm is described with innovative idea of fitness function derivation and mutation. The proposed algorithm is exposed to rigorous testing using various benchmark datasets and its performance is evaluated in terms of total makespan.
3D Kinematic Simulation for PA10-7C Robot Arm Based on VRML

In this paper, a graphical, flexible, interactive, and systematic 3D simulation helps facilitate analyzing and previewing kinematics of PA10-7C robot arm in terms of forward kinematics, inverse kinematics, and the Denavit-Hartenberg convention. Modeling and control are of critical importance when the robot arm is used for practical applications. In the paper, the D-H model of PA10-7C robot arm is given first to describe the relationship between two consecutive frames of joints. Based on this D-H model, forward kinematics is calculated efficiently. To describe the relationship between joint angular velocities in the joint space and the end effector's velocities in Cartesian space, Jacobian matrix for PA10-7C robot arm is derived. By Jacobian matrix, inverse kinematics approach is obtained in the paper. VRML-based model of PA10-7C is built in the paper for the simulation. Finally, simulation results are discussed and the paper is concluded.
Cluster based Outlier Detection

Outlier detection is a fundamental issue in data mining, specifically it has been used to detect and remove anomalous objects from data. mining. The proposed approach to detect outlier includes three methods which are clustering, pruning and computing outlier score. For clustering k-means algorithm is used which partition the dataset into given number of clusters. In pruning, based on some distance measure, points which are closed to centroid of each cluster are pruned. For the unpruned points, local distance based outlier factor (LDOF) measure is calculated. A measure called LDOF, tells how much a point is deviating from its neighbors. The high LDOF value of a point indicates that the point is deviating more from its neighbors and probably it may be an outlier.
Linear-Time Algorithms for Testing the Satisfiability of Propositional Horn Formulae

D New algorithms for deciding whether a (propositional) Horn formula is satisfiable are presented. If the Horn formula A contains K distinct propositional letters and if it is assumed that they are exactly Pi,. . . , PK, the two algorithms presented in this paper run in time O(N), where N is the total number of occurrences of literals in A. By representing a Horn proposition as a graph, the satisfiability problem can be formulated as a data flow problem, a certain type of pebbling. The difference between the two algorithms presented here is the strategy used for pebbling the graph. The first algorithm is based on the principle used for finding the set of nonterminals of a context-free grammar from which the empty string can be derived. The second algorithm is a graph traversal and uses a “call-by-need” strategy. This algorithm uses an attribute grammar to translate a propositional Horn formula to its corresponding graph in linear time. Our formulation of the satisfiability problem as a data flow problem appears to be new and suggests the possibility of improving efficiency using parallel processors.
Faster discovery of faster system configurations with spectral learning

Despite the huge spread and economical importance of configurable software systems, there is unsatisfactory support in utilizing the full potential of these systems with respect to finding performance-optimal configurations. Prior work on predicting the performance of software configurations suffered from either (a) requiring far too many sample configurations or (b) large variances in their predictions. Both these problems can be avoided using the WHAT spectral learner. WHAT’s innovation is the use of the spectrum (eigenvalues) of the distance matrix between the configurations of a configurable software system, to perform dimensionality reduction. Within that reduced configuration space, many closely associated configurations can be studied by executing only a few sample configurations. For the subject systems studied here, a few dozen samples yield accurate and stable predictors—less than 10% prediction error, with a standard deviation of less than 2%. When compared to the state of the art, WHAT (a) requires 2–10 times fewer samples to achieve similar prediction accuracies, and (b) its predictions are more stable (i.e., have lower standard deviation). Furthermore, we demonstrate that predictive models generated by WHAT can be used by optimizers to discover system configurations that closely approach the optimal performance.
Intervening Sequences of Regularly Spaced Prokaryotic Repeats Derive from Foreign Genetic Elements

Prokaryotes contain short DNA repeats known as CRISPR, recognizable by the regular spacing existing between the recurring units. They represent the most widely distributed family of repeats among prokaryotic genomes, suggesting a biological function. The origin of the intervening sequences, at present unknown, could provide clues about their biological activities. Here we show that CRISPR spacers derive from preexisting sequences, either chromosomal or within transmissible genetic elements such as bacteriophages and conjugative plasmids. Remarkably, these extrachromosomal elements fail to infect the specific spacer-carrier strain, implying a relationship between CRISPR and immunity against targeted DNA. Bacteriophages and conjugative plasmids are involved in prokaryotic population control, evolution, and pathogenicity. All these biological traits could be influenced by the presence of specific spacers. CRISPR loci can be visualized as mosaics of a repeated unit, separated by sequences at some time present elsewhere in the cell.
Efficient tracing of failed nodes in sensor networks

In sensor networks, nodes commonly rely on each other to route messages to a base station. Although this practice conserves power it can obscure the cause of a measurement outage in a portion of the network. For example, when a base station ceases to receive measurements from a region of nodes it can't immediately determine whether this is because of the destruction of all the nodes in that region (due to an enemy attack, for example) or merely the result of the failure of a few nodes bearing much of the routing load. Previous solutions to this problem typically consist of re-running the route-discovery protocol, a process that can be quite expensive in terms of the number of messages that must be exchanged. We demonstrate that the topology of the network can be efficiently conveyed to the base station allowing for the quick tracing of the identities of the failed nodes with moderate communication overhead. Our algorithms work in conjunction with the existing functions of the network, requiring the nodes to send no additional messages.
Learning Deep Structured Models

Many problems in real-world applications involve predicting several random variables that are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such dependencies. The goal of this paper is to combine MRFs with deep learning to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as tagging of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.
On-Demand Service Platforms

An on-demand service platform connects waiting-time-sensitive customers with independent service providers (agents). This paper examines how two defining features of an on-demand service platform—delay sensitivity and agent independence— impact the platform’s optimal per-service price and wage. Delay sensitivity reduces expected utility for customers and agents, which suggests that the platform should respond by decreasing the price (to encourage participation of customers) and increasing the wage (to encourage participation of agents). These intuitive price and wage prescriptions are valid in a benchmark setting without uncertainty in the customers’ valuation or the agents’ opportunity costs. However, uncertainty in either dimension can reverse the prescriptions: Delay sensitivity increases the optimal price when customer valuation uncertainty is moderate. Delay sensitivity decreases the optimal wage when agent opportunity cost uncertainty is high and expected opportunity cost is moderate. Under agent opportunity cost uncertainty, agent independence decreases the price. Under customer valuation uncertainty, agent independence increases the price if and only if valuation uncertainty is sufficiently high.
Estimating software based on use case points

It is well documented that software product cost estimates are notoriously inaccurate across the software industry. Creating accurate cost estimates for software product development projects early in the product development lifecycle has always been a challenge for the industry. This article describes how a large multi-team software engineering organization (over 450 engineers) estimates project cost accurately and early in the software development lifecycle using Use Case Points, and the process of evaluating metrics to ensure the accuracy of the model.The engineering teams of Agilis Solutions in partnership with FPT Software, provide our customers with accurate estimates for software product projects early in the product lifecycle. The bases for these estimates are initial definitions of Use Cases, given point factors and modified for technical and environmental factors according to the Use Case Point method defined within the Rational Unified Process. After applying the process across hundreds of sizable (60 man-months average) software projects, we have demonstrated metrics that prove an estimating accuracy of less than 9% deviation from actual to estimated cost on 95% of our projects. Our process and this success factor is documented over a period of five years, and across more than 200 projects.
Recurrent Neural Networks for Word Alignment Model

This study proposes a word alignment model based on a recurrent neural network (RNN), in which an unlimited alignment history is represented by recurrently connected hidden layers. We perform unsupervised learning using noise-contrastive estimation (Gutmann and Hyvärinen, 2010; Mnih and Teh, 2012), which utilizes artificially generated negative samples. Our alignment model is directional, similar to the generative IBM models (Brown et al., 1993). To overcome this limitation, we encourage agreement between the two directional models by introducing a penalty function that ensures word embedding consistency across two directional models during training. The RNN-based model outperforms the feed-forward neural network-based model (Yang et al., 2013) as well as the IBM Model 4 under Japanese-English and French-English word alignment tasks, and achieves comparable translation performance to those baselines for Japanese-English and Chinese-English translation tasks.
On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption

We propose a new notion of secure multiparty computation aided by a computationally-powerful but untrusted "cloud" server. In this notion that we call on-the-fly multiparty computation (MPC), the cloud can non-interactively perform arbitrary,  dynamically chosen computations on data belonging to arbitrary sets of users chosen  on-the-fly. All user's input data and intermediate results are protected from snooping by the cloud as well as other users. This extends the standard notion of fully homomorphic encryption (FHE), where users can only enlist the cloud's help in evaluating functions on their own encrypted data.
 In on-the-fly MPC, each user is involved only when initially uploading his (encrypted) data to the cloud, and in a final output decryption phase when outputs are revealed; the complexity of both is independent of the function being computed and the total number of users in the system. When users upload their data, they need not decide in advance which function will be computed, nor who they will compute with; they need only retroactively approve the eventually-chosen functions and on whose data the functions were evaluated.
 This notion is qualitatively the best possible in minimizing interaction, since the users' interaction in the decryption stage is inevitable: we show that removing it would imply generic program obfuscation and is thus impossible.
 Our contributions are two-fold:- We show how on-the-fly MPC can be achieved using a new type of encryption scheme that we call multikey FHE, which is capable of operating on inputs encrypted under multiple, unrelated keys. A ciphertext resulting from a multikey evaluation can be jointly decrypted using the secret keys of all the users involved in the computation. - We construct a multikey FHE scheme based on NTRU, a very efficient public-key encryption scheme proposed in the 1990s. It was previously not known how to make NTRU fully homomorphic even for a single party. We view the construction of (multikey) FHE from NTRU encryption as a main contribution of independent interest. Although the transformation to a fully homomorphic system deteriorates the efficiency of NTRU somewhat, we believe that this system is a leading candidate for a practical FHE scheme.
Automatic license plate recognition using extracted features

Automatic License Plate Recognition (ALPR) systems are employed for detection and recognition of license plate/number plate of vehicles. The performance of existing systems is well below the desired level. In this perspective, there is a definite need to propose a system to overcome the limitations of currently available systems. A new approach is being introduced in this paper for fast and efficient implementation of ALPR system. In this approach, the vertical edge detection algorithm is applied and removes unwanted edges by image normalization technique. The LP region is extracted by incorporating statistical and morphological image processing techniques. For character recognition, the template matching is employed for optical character recognition (OCR). The algorithm is tested on 500 real time images, which are acquired under different illumination conditions and from different scenes. Overall efficiency of the proposed method is 84.8% and the execution time is less than 0.5sec.