nils-herrmann/clean_scirepeval huggingface dataset:
Enabling device-to-device communications in millimeter-wave 5G cellular networks

Millimeter-wave communication is a promising technology for future 5G cellular networks to provide very high data rate (multi-gigabits-per-second) for mobile devices. Enabling D2D communications over directional mmWave networks is of critical importance to efficiently use the large bandwidth to increase network capacity. In this article, the propagation features of mmWave communication and the associated impacts on 5G cellular networks are discussed. We introduce an mmWave+4G system architecture with TDMA-based MAC structure as a candidate for 5G cellular networks. We propose an effective resource sharing scheme by allowing non-interfering D2D links to operate concurrently. We also discuss neighbor discovery for frequent handoffs in 5G cellular networks.
Partial Evaluation of Computation Process - An Approach to a Compiler-Compiler

This paper reports the relationship between formal description of semantics (i.e., interpreter) of a programming language and an actual compiler. The paper also describes a method to automatically generate an actual compiler from a formal description which is, in some sense, the partial evaluation of a computation process. The compiler-compiler inspired by this method differs from conventional ones in that the compilercompiler based on our method can describe an evaluation procedure (interpreter) in defining the semantics of a programming language, while the conventional one describes a translation process.
FPGA Implementation of a Switching Frequency Modulation Circuit for EMI Reduction in Resonant Inverters for Induction Heating Appliances

This paper presents the use of frequency modulation as a spread spectrum technique to reduce conducted electromagnetic interference (EMI) in the A frequency band (9-150 kHz) caused by resonant inverters used in induction heating home appliances. For sinusoidal, triangular, and sawtooth modulation profiles, the influence of peak period deviation in EMI reduction and in the power delivered to the load is analyzed. A digital circuit that generates the best of the analyzed modulation profiles is implemented in a field programmable gate array. The design is modeled in a very-high-speed integrated circuits hardware description language (VHDL). The digital circuit, the power converter, and the spectrum analyzer are simulated all together using a mixed-signal simulation tool to verify the functionality of the VHDL description. The spectrum analyzer is modeled in VHDL-analog and mixed-signal extension language (VHDL-AMS) and takes into account the resolution bandwidth stipulated by the EMI measurement standard. Finally, the simulations are experimentally verified on a 3.5 kW resonant inverter operating at 35 kHz.
SoK: Applying Machine Learning in Security - A Survey

The idea of applying machine learning(ML) to solve problems in security domains is almost 3 decades old. As information and communications grow more ubiquitous and more data become available, many security risks arise as well as appetite to manage and mitigate such risks. Consequently, research on applying and designing ML algorithms and systems for security has grown fast, ranging from intrusion detection systems(IDS) and malware classification to security policy management(SPM) and information leak checking. In this paper, we systematically study the methods, algorithms, and system designs in academic publications from 2008-2015 that applied ML in security domains. 98% of the surveyed papers appeared in the 6 highest-ranked academic security conferences and 1 conference known for pioneering ML applications in security. We examine the generalized system designs, underlying assumptions, measurements, and use cases in active research. Our examinations lead to 1) a taxonomy on ML paradigms and security domains for future exploration and exploitation, and 2) an agenda detailing open and upcoming challenges. Based on our survey, we also suggest a point of view that treats security as a game theory problem instead of a batch-trained ML problem.
Predictive Business Process Monitoring Framework with Hyperparameter Optimization

Predictive business process monitoring exploits event logs to predict how ongoing (uncompleted) cases will unfold up to their completion. A predictive process monitoring framework collects a range of techniques that allow users to get accurate predictions about the achievement of a goal or about the time required for such an achievement for a given ongoing case. These techniques can be combined and their parameters configured in different framework instances. Unfortunately, a unique framework instance that is general enough to outperform others for every dataset, goal or type of prediction is elusive. Thus, the selection and configuration of a framework instance needs to be done for a given dataset. This paper presents a predictive process monitoring framework armed with a hyperparameter optimization method to select a suitable framework instance for a given dataset.
Secure Computer Systems: Mathematical Foundations

This paper reports the first results of an investigation into solutions to problems of security in computer systems; it establishes the basis for rigorous investigation by providing a general descriptive model of a computer system. Borrowing basic concepts and constructs from general systems theory, we present a basic result concerning security in computer systems, using precise notions of "security" and "compromise". We also demonstrate how a change in requirements can be reflected in the resulting mathematical model. A lengthy introductory section is included in order to bridge the gap between general systems theory and practical problem solving. ii PREFACE General systems theory is a relatively new and rapidly growing mathematical discipline which shows great promise for application in the computer sciences. The discipline includes both "general systems-theory" and "general-systems-theory": that is, one may properly read the phrase "general systems theory" in both ways. In this paper, we have borrowed from the works of general systems theorists, principally from the basic work of Mesarovic´, to formulate a mathematical framework within which to deal with the problems of secure computer systems. At the present time we feel that the mathematical representation developed herein is adequate to deal with most if not all of the security problems one may wish to pose. In Section III we have given a result which deals with the most trivial of the secure computer systems one might find viable in actual use. In the concluding section we review the application of our mathematical methodology and suggest major areas of concern in the design of a secure system. The results reported in this paper lay the groundwork for further, more specific investigation into secure computer systems. The investigation will proceed by specializing the elements of the model to represent particular aspects of system design and operation. Such an investigation will be reported in the second volume of this series where we assume a system with centralized access control. A preliminary investigation of distributed access is just beginning; the results of that investigation would be reported in a third volume of the series.
A Distributed Publisher-Driven Secure Data Sharing Scheme for Information-Centric IoT

In Information-Centric Internet of Things (ICIoT), Internet of Things (IoT) data can be cached throughout a network for close data copy retrievals. Such a distributed data caching environment, however, poses a challenge to flexible authorization in the network. To address this challenge, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been identified as a promising approach. However, in the existing CP-ABE scheme, publishers need to retrieve attributes from a centralized server for encrypting data, which leads to high communication overhead. To solve this problem, we incorporate CP-ABE and propose a novel Distributed Publisher-Driven secure data sharing for ICIoT (DPD-ICIoT) to enable only authorized users to retrieve IoT data from distributed cache. In DPD-ICIoT, newly introduced attribute manifest is cached in the network, through which publishers can retrieve the attributes from nearby copy holders instead of a centralized attribute server. In addition, a key chain mechanism is utilized for efficient cryptographic operations, and an automatic attribute self-update mechanism is proposed to enable fast updates of attributes without querying centralized servers. According to the performance evaluation, DPD-ICIoT achieves lower bandwidth cost compared to the existing CP-ABE scheme.
Directional Prediction of Stock Prices Using Breaking News on Twitter

Stock market news and investing tips are popular topics in Twitter. In this paper, first we utilize a 5-year financial news corpus comprising over 50,000 articles collected from the NASDAQ website for the 30 stock symbols in Dow Jones Index (DJI) to train a directional stock price prediction system based on news content. Then we proceed to prove that information in articles indicated by breaking Tweet volumes leads to a statistically significant boost in the hourly directional prediction accuracies for the prices of DJI stocks mentioned in these articles. Secondly, we show that using document-level sentiment extraction does not yield to a statistically significant boost in the directional predictive accuracies in the presence of other 1-gram keyword features.
Real-time 3D segmentation of cluttered scenes for robot grasping

We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and partially occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. The real-time capabilities and the quality of the algorithm are evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of objects given by our algorithm in a task in which it cleans a table.
Optimization based IMU camera calibration

Inertia-visual sensor fusion has become popular due to the complementary characteristics of cameras and IMUs. Once the spatial and temporal alignment between the sensors is known, the fusion of measurements of these devices is straightforward. Determining the alignment, however, is a challenging problem. Especially the spatial translation estimation has turned out to be difficult, mainly due to limitations of camera dynamics and noisy accelerometer measurements. Up to now, filtering-based approaches for this calibration problem are largely prevalent. However, we are not convinced that calibration, as an offline step, is necessarily a filtering issue, and we explore the benefits of interpreting it as a batch-optimization problem. To this end, we show how to model the IMU-camera calibration problem in a nonlinear optimization framework by modeling the sensors' trajectory, and we present experiments comparing this approach to filtering and system identification techniques. The results are based both on simulated and real data, showing that our approach compares favorably to conventional methods.
A Brief Survey of Bandwidth Selection for Density Estimation

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Generative Adversarial Networks as Variational Training of Energy Based Models

In this paper, we study deep generative models for effective unsupervised learning. We propose VGAN, which works by minimizing a variational lower bound of the negative log likelihood (NLL) of an energy based model (EBM), where the model density p(x) is approximated by a variational distribution q(x) that is easy to sample from. The training of VGAN takes a two step procedure: given p(x), q(x) is updated to maximize the lower bound; p(x) is then updated one step with samples drawn from q(x) to decrease the lower bound. VGAN is inspired by the generative adversarial networks (GANs), where p(x) corresponds to the discriminator and q(x) corresponds to the generator, but with several notable differences. We hence name our model variational GANs (VGANs). VGAN provides a practical solution to training deep EBMs in high dimensional space, by eliminating the need of MCMC sampling. From this view, we are also able to identify causes to the difficulty of training GANs and propose viable solutions.
Problematic Use of the Mobile Phone : A Literature Review and a Pathways Model

Despite its unambiguous advantages, cellular phone use has been associated with harmful or potentially disturbing behaviors. Problematic use of the mobile phone is considered as an inability to regulate one’s use of the mobile phone, which eventually involves negative consequences in daily life (e.g., financial problems). The current article describes what can be considered dysfunctional use of the mobile phone and emphasizes its multifactorial nature. Validated assessment instruments to measure problematic use of the mobile phone are described. The available literature on risk factors for dysfunctional mobile phone use is then reviewed, and a pathways model that integrates the existing literature is proposed. Finally, the assumption is made that dysfunctional use of the mobile phone is part of a spectrum of cyber addictions that encompasses a variety of dysfunctional behaviors and implies involvement in specific online activities (e.g., video games, gambling, social networks, sex-related websites).
Unsupervised cross-lingual knowledge transfer in DNN-based LVCSR

We investigate the use of cross-lingual acoustic data to initialise deep neural network (DNN) acoustic models by means of unsupervised restricted Boltzmann machine (RBM) pre-training. DNNs for German are pretrained using one or all of German, Portuguese, Spanish and Swedish. The DNNs are used in a tandem configuration, where the network outputs are used as features for a hidden Markov model (HMM) whose emission densities are modeled by Gaussian mixture models (GMMs), as well as in a hybrid configuration, where the network outputs are used as the HMM state likelihoods. The experiments show that unsupervised pretraining is more crucial for the hybrid setups, particularly with limited amounts of transcribed training data. More importantly, unsupervised pretraining is shown to be language-independent.
Network Slicing to Enable Scalability and Flexibility in 5G Mobile Networks

We argue for network slicing as an efficient solution that addresses the diverse requirements of 5G mobile networks, thus providing the necessary flexibility and scalability associated with future network implementations. We elaborate on the challenges that emerge when designing 5G networks based on network slicing. We focus on the architectural aspects associated with the coexistence of dedicated as well as shared slices in the network. In particular, we analyze the realization options of a flexible radio access network with focus on network slicing and their impact on the design of 5G mobile networks. In addition to the technical study, this article provides an investigation of the revenue potential of network slicing, where the applications that originate from this concept and the profit capabilities from the network operator�s perspective are put forward.
Natural Language Arguments: A Combined Approach

With the growing use of the Social Web, an increasing number of applications for exchanging opinions with other people are becoming available online. These applications are widely adopted with the consequence that the number of opinions about the debated issues increases. In order to cut in on a debate, the participants need first to evaluate the opinions in favour or against the debated issue. Argumentation theory proposes algorithms and semantics to evaluate the set of accepted arguments, given the conflicts among them. The main problem is how to automatically generate the arguments from the natural language formulation of the opinions used in these applications. Our paper addresses this problem by proposing and evaluating the use of natural language techniques to generate the arguments. In particular, we adopt the textual entailment approach, a generic framework for applied semantics, where linguistic objects are mapped by means of semantic inferences at a textual level. We couple textual entailment together with a Dung-like argumentation system which allows us to identify the arguments that are accepted in the considered online debate. The originality of the proposed framework lies in the following point: natural language debates are analyzed and the arguments are automatically extracted.
Improving Facial Attribute Prediction Using Semantic Segmentation

Attributes are semantically meaningful characteristics whose applicability widely crosses category boundaries. They are particularly important in describing and recognizing concepts where no explicit training example is given, e.g., zero-shot learning. Additionally, since attributes are human describable, they can be used for efficient human-computer interaction. In this paper, we propose to employ semantic segmentation to improve facial attribute prediction. The core idea lies in the fact that many facial attributes describe local properties. In other words, the probability of an attribute to appear in a face image is far from being uniform in the spatial domain. We build our facial attribute prediction model jointly with a deep semantic segmentation network. This harnesses the localization cues learned by the semantic segmentation to guide the attention of the attribute prediction to the regions where different attributes naturally show up. As a result of this approach, in addition to recognition, we are able to localize the attributes, despite merely having access to image level labels (weak supervision) during training. We evaluate our proposed method on CelebA and LFWA datasets and achieve superior results to the prior arts. Furthermore, we show that in the reverse problem, semantic face parsing improves when facial attributes are available. That reaffirms the need to jointly model these two interconnected tasks.
Game-theory-based batch identification of invalid signatures in wireless mobile networks

Digital signature has been widely employed in wireless mobile networks to ensure the authenticity of messages and identity of nodes. A paramount concern in signature verification is reducing the verification delay to ensure the network QoS. To address this issue, researchers have proposed the batch cryptography technology. However, most of the existing works focus on designing batch verification algorithms without sufficiently considering the impact of invalid signatures. The performance of batch verification could dramatically drop, if there are verification failures caused by invalid signatures. In this paper, we propose a Game-theory-based Batch Identification Model (GBIM) for wireless mobile networks, enabling nodes to find invalid signatures with the optimal delay under heterogeneous and dynamic attack scenarios. Specifically, we design an incomplete information game model between a verifier and its attackers, and prove the existence of Nash Equilibrium, to select the dominant algorithm for identifying invalid signatures. Moreover, we propose an auto-match protocol to optimize the identification algorithm selection, when the attack strategies can be estimated based on history information. Comprehensive simulation results demonstrate that GBIM can identify invalid signatures more efficiently than existing algorithms.
TDMA-ASAP: Sensor Network TDMA Scheduling with Adaptive Slot-Stealing and Parallelism

TDMA has been proposed as a MAC protocol for wireless sensor networks (WSNs) due to its efficiency in high WSN load. However, TDMA is plagued with shortcomings; we present modifications to TDMA that will allow for the same efficiency of TDMA, while allowing the network to conserve energy during times of low load (when there is no activity being detected). Recognizing that aggregation plays an essential role in WSNs, TDMA-ASAP adds to TDMA: (a) transmission parallelism based on a level-by-level localized graph-coloring, (b) appropriate sleeping between transmissions ("napping"), (c) judicious and controlled TDMA slot stealing to avoid empty slots to be unused and (d) intelligent scheduling/ordering transmissions. Our results show that TDMA-ASAP's unique combination of TDMA, slot-stealing, napping, and message aggregation significantly outperforms other hybrid WSN MAC algorithms and has a performance that is close to optimal in terms of energy consumption and overall delay.
A Multilevel Inverter Topology for Inductively Coupled Power Transfer

This paper describes a multilevel inverter that can synthesize quantized approximations of arbitrary ac waveforms. This converter could be used to deliver power over multiple frequencies simultaneously. Unlike traditional multilevel inverters, this topology does not require an external voltage balancing circuit, a complicated control scheme, or isolated dc sources to maintain its voltage levels while delivering sustained real power. In this paper, we use this circuit for heating frequency selectable induction targets designed to stimulate temperature sensitive polymer gel actuators. For this application our multilevel inverter offers higher efficiency than a pulse width modulated full-bridge inverter (a more conventional power supply solution) at comparable levels of total harmonic distortion
Credit Card Fraud Detection Using AdaBoost and Majority Voting

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.
Teaching computational thinking through musical live coding in scratch

This paper discusses our ongoing experiences in developing an interdisciplinary general education course called Sound Thinking that is offered jointly by our Dept. of Computer Science and Dept. of Music. It focuses on the student outcomes we are trying to achieve and the projects we are using to help students realize those outcomes. It explains why we are moving from a web-based environment using HTML and JavaScript to Scratch and discusses the potential for Scratch's "musical live coding" capability to reinforce those concepts even more strongly.