nils-herrmann/clean_scirepeval huggingface dataset:
Single phase based on ups applied Z-source inverter by using matlab / simulink

Uninterruptible power supplies (UPSs) are widely used to supply critical loads, such as airline computers and life-support systems in hospitals, providing protection against power failure or anomalies of power-line voltage. In general, there are two types of traditional single phase UPSs. The first one couples a battery bank to a half or full-bridge inverter with a low-frequency transformer. In this type of UPSs, the ac output voltage is higher than that of the battery bank; thus, a step-up transformer is required to boost voltage. .The second one couples a battery bank to a dc/dc booster with a half or full bridge inverter. In this type of UPSs, the additional booster is needed, leading to high cost and low efficiency. The controlling of the switches in the booster also complicates the system. Due to the presence of the step-up transformer, the inverter current is much higher than the load current, causing high current stress on the switches of the inverter The dead time in the pulse width-modulation (PWM) signals to prevent the upper and lower switches at the same phase leg from shooting through has to be provided in the aforementioned two types of UPSs, and it distorts the voltage waveform of the ac output voltage. In order to overcome the above problems in the traditional UPSs, new topology of the UPS is proposed by using a voltage source inverter and Zsource inverter. With this new topology, the proposed UPS offers the advantages over the traditional UPSs such as the dc/dc booster and the inverter have been combined into one single-stage power conversion and the distortion of the ac output-voltage waveform is reduced in the absence of dead time in the PWM signals. The effectiveness of the proposed method is implemented by using MATLAB/SIMULINK software.
A new algorithm for age recognition from facial images

In this paper, a new algorithm for age-group recognition from frontal face image is presented. The algorithm classifies subjects into four different age categories in four key stages: Pre-processing, facial feature extraction by a novel geometric feature-based method, face feature analysis, and age classification. In order to apply the algorithm to the problem, a face image database focusing on people’s age information is required. Because there were no such databases, we created a database for this purpose, which is called Iranian face database (IFDB). IFDB contains digital images of people from 1 to 85 years of age. After pre-processing, then primary features of the faces in the database will be accurately detected. Finally, a neural network is used to classify the face into age groups using computed facial feature ratios and wrinkle densities. Experimental results show that the algorithm identifies the age group with accuracy of 86.64%. & 2010 Elsevier B.V. All rights reserved.
Robust Vehicle Detection under Various Environments to Realize Road Traffic Flow Surveillance Using an Infrared Thermal Camera

To realize road traffic flow surveillance under various environments which contain poor visibility conditions, we have already proposed two vehicle detection methods using thermal images taken with an infrared thermal camera. The first method uses pattern recognition for the windshields and their surroundings to detect vehicles. However, the first method decreases the vehicle detection accuracy in winter season. To maintain high vehicle detection accuracy in all seasons, we developed the second method. The second method uses tires' thermal energy reflection areas on a road as the detection targets. The second method did not achieve high detection accuracy for vehicles on left-hand and right-hand lanes except for two center-lanes. Therefore, we have developed a new method based on the second method to increase the vehicle detection accuracy. This paper proposes the new method and shows that the detection accuracy for vehicles on all lanes is 92.1%. Therefore, by combining the first method and the new method, high vehicle detection accuracies are maintained under various environments, and road traffic flow surveillance can be realized.
Binary stirring: self-randomizing instruction addresses of legacy x86 binary code

Unlike library code, whose instruction addresses can be randomized by address space layout randomization (ASLR), application binary code often has static instruction addresses. Attackers can exploit this limitation to craft robust shell codes for such applications, as demonstrated by a recent attack that reuses instruction gadgets from the static binary code of victim applications.
 This paper introduces binary stirring, a new technique that imbues x86 native code with the ability to self-randomize its instruction addresses each time it is launched. The input to STIR is only the application binary code without any source code, debug symbols, or relocation information. The output is a new binary whose basic block addresses are dynamically determined at load-time. Therefore, even if an attacker can find code gadgets in one instance of the binary, the instruction addresses in other instances are unpredictable. An array of binary transformation techniques enable STIR to transparently protect large, realistic applications that cannot be perfectly disassembled due to computed jumps, code-data interleaving, OS callbacks, dynamic linking and a variety of other difficult binary features. Evaluation of STIR for both Windows and Linux platforms shows that stirring introduces about 1.6% overhead on average to application runtimes.
Unitary Evolution Recurrent Neural Networks

Recurrent neural networks (RNNs) are notoriously difficult to train. When the eigenvalues of the hidden to hidden weight matrix deviate from absolute value 1, optimization becomes difficult due to the well studied issue of vanishing and exploding gradients, especially when trying to learn long-term dependencies. To circumvent this problem, we propose a new architecture that learns a unitary weight matrix, with eigenvalues of absolute value exactly 1. The challenge we address is that of parametrizing unitary matrices in a way that does not require expensive computations (such as eigendecomposition) after each weight update. We construct an expressive unitary weight matrix by composing several structured matrices that act as building blocks with parameters to be learned. Optimization with this parameterization becomes feasible only when considering hidden states in the complex domain. We demonstrate the potential of this architecture by achieving state of the art results in several hard tasks involving very long-term dependencies.
Analyzing the transitional region in low power wireless links

The wireless sensor networks community, has now an increased understanding of the need for realistic link layer models. Recent experimental studies have shown that real deployments have a "transitional region" with highly unreliable links, and that therefore the idealized perfect-reception-within-range models used in common network simulation tools can be very misleading. In this paper, we use mathematical techniques from communication theory to model and analyze the low power wireless links. The primary contribution of this work is the identification of the causes of the transitional region, and a quantification of their influence. Specifically, we derive expressions for the packet reception rate as a function of distance, and for the width of the transitional region. These expressions incorporate important channel and radio parameters such as the path loss exponent and shadowing variance of the channel; and the modulation and encoding of the radio. A key finding is that for radios using narrow-band modulation, the transitional region is not an artifact of the radio non-ideality, as it would exist even with perfect-threshold receivers because of multi-path fading. However, we hypothesize that radios with mechanisms to combat multi-path effects, such as spread-spectrum and diversity techniques, can reduce the transitional region.
Yet another DSL for cross-platforms mobile development

With the growing success of mobility, mobile platforms (iOS, Android, WindowsPhone, etc.) multiply, each requiring specific development skills. Given this situation, it becomes very difficult for software developers to duplicate their apps accordingly. Meanwhile, web-based applications have evolved to be "mobile-friendly" but it appears that this is not a silver bullet: the user experience and the overall quality is still better with native applications. Of course, cross-platform mobile development tools have emerged in recent years. This paper provides a survey of these tools and points out that a full-fledged language for mobile development is highly desirable. Consequently, we present a preliminary work on Xmob, a technology-neutral DSL intended to be cross-compiled to produce native code for a variety of platforms.
Fast algorithm for robust template matching with M-estimators

In this paper, we propose a fast algorithm for speeding up the process of template matching that uses M-estimators for dealing with outliers. We propose a particular image hierarchy called the p-pyramid that can be exploited to generate a list of ascending lower bounds of the minimal matching errors when a non-decreasing robust error measure is adopted. Then, the set of lower bounds can be used to prune the search of the p-pyramid, and a fast algorithm is thereby developed in this paper. This fast algorithm ensures finding the global minimum of the robust template matching problem in which a non-decreasing M-estimator serves as an error measure. Experimental results demonstrate the effectiveness of our method. Index Terms —Template matching, robust template matching, M-estimator, fast algorithm.
Bitcoin Market Return and Volatility Forecasting Using Transaction Network Flow Properties

Bit coin, as the foundation for a secure electronic payment system, has drawn broad interests from researchers in recent years. In this paper, we analyze a comprehensive Bit coin transaction dataset and investigate the interrelationship between the flow of Bit coin transactions and its price movement. Using network theory, we examine a few complexity measures of the Bit coin transaction flow networks, and we model the joint dynamic relationship between these complexity measures and Bit coin market variables such as return and volatility. We find that a particular complexity measure of the Bit coin transaction network flow is significantly correlated with the Bit coin market return and volatility. More specifically we document that the residual diversity or freedom of Bit coin network flow scaled by the total system throughput can significantly improve the predictability of Bit coin market return and volatility.
Pattern-Reconfigurable Unidirectional Dipole Antenna Array Fed by SIW Coupler for Millimeter Wave Application

A directional two-element mm-wave dipole antenna array with reconfigurable patterns is presented in this paper. An substrate integrated waveguide (SIW) 90° coupler is used to realize reconfigurable patterns of the antenna array. The two dipole elements are parallel in space, and there is a distance of <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula>/4 (<inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength of the free space) between them. When the two inputs of the 90° coupler are excited, respectively, two completely opposite radiation patterns are able to be achieved. In order to further enhance the directivity of the proposed antenna, two groups of novel zero-index metamaterials and three metallic strips are introduced. As a consequence, a 0.7–1.2 dBi gain enhancement and reduced back lobe are obtained within the working frequencies. The measured amplitudes of S<sub>11</sub> and S<sub>22</sub> are below −10 dB from 45.5 to 49.5 GHz, and the measured gain is all great than 5 dBi within this band.
Smart Health Monitoring Systems: An Overview of Design and Modeling

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.
Cyberbullying classification using text mining

Cyberbully is a misuse of technology advantage to bully a person. Cyberbully and its impact have occurred around the world and now the number of cases are increasing. Cyberbullying detection is very important because the online information is too large so it is not possible to be tracked by humans. The purpose of this research is to construct a classification model with optimal accuracy in identifying cyberbully conversation using Naive Bayes method and Support Vector Machine (SVM) then applying n-gram 1 to 5 for the number of class 2, 4, and 11 for each method. Naive Bayes yields an average accuracy of 92.81%, SVM with a poly kernel yields an average accuracy of 97.11% It can be concluded that SVM with poly kernel yields higher accuracy than SVM with other kernels, Naive Bayes, and Kelly Reynolds research method of decision tree (J48) and k-NN.
Challenges in developing opinion mining tools for social media

While much work has recently focused on the analysis of social media in order to get a feel for what people think about current topics of interest, there are, however, still many challenges to be faced. Text mining systems originally designed for more regular kinds of texts such as news articles may need to be adapted to deal with facebook posts, tweets etc. In this paper, we discuss a variety of issues related to opinion mining from social media, and the challenges they impose on a Natural Language Processing (NLP) system, along with two example applications we have developed in very different domains. In contrast with the majority of opinion mining work which uses machine learning techniques, we have developed a modular rule-based approach which performs shallow linguistic analysis and builds on a number of linguistic subcomponents to generate the final opinion polarity and score.
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation

The end-to-end nature of neural machine translation (NMT) removes many ways of manually guiding the translation process that were available in older paradigms. Recent work, however, has introduced a new capability: lexically constrained or guided decoding, a modification to beam search that forces the inclusion of pre-specified words and phrases in the output. However, while theoretically sound, existing approaches have computational complexities that are either linear (Hokamp and Liu, 2017) or exponential (Anderson et al., 2017) in the number of constraints. We present an algorithm for lexically constrained decoding with a complexity of O(1) in the number of constraints. We demonstrate the algorithm’s remarkable ability to properly place these constraints, and use it to explore the shaky relationship between model and BLEU scores. Our implementation is available as part of SOCKEYE.1
Clustering with Deep Learning: Taxonomy and New Methods

Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.
Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing

Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. The large amount of data associated with process variables monitored over time form a rich reservoir of information, which can be used for a variety of purposes, such as anomaly detection, quality control, and fault diagnostics. In particular, following the same recipe for a certain Integrated Circuit device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this article, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. Furthermore, we establish the equivalence between C-Struts and a generic optimization problem, which is able to accommodate various distance functions. Extensive experiments on synthetic, benchmark, as well as manufacturing datasets demonstrate the effectiveness of the proposed method.
A 3.6 mW, 90 nm CMOS Gated-Vernier Time-to-Digital Converter With an Equivalent Resolution of 3.2 ps

Two gated ring oscillators (GROs) act as the delay lines in an improved Vernier time-to-digital converter (TDC), where the already small quantization noise of the standard Vernier TDC is further first-order shaped by the GRO operation. The TDC has been implemented in a 90 nm CMOS process and consumes 3 mA from 1.2 V when operating at 25 MHz. The native Vernier resolution of the TDC is 5.8 ps, while the total noise integrated over a bandwidth of 800 kHz yields an equivalent TDC resolution of 3.2 ps.
CloneWorks: A Fast and Flexible Large-Scale Near-Miss Clone Detection Tool

Clone detection within large inter-project source-code repositories has numerous rich applications. CloneWorks is a fast and flexible clone detector for large-scale near-miss clone detection experiments. CloneWorks gives the user full control over the processing of the source code before clone detection, enabling the user to target any clone type or perform custom clone detection experiments. Scalable clone detection is achieved, even on commodity hardware, using our partitioned partial indexes approach. CloneWorks scales to 250MLOC in just four hours on an average workstation with good recall and precision.
HyperFlex: An SDN virtualization architecture with flexible hypervisor function allocation

Network Virtualization (NV) and Software-Defined Networking (SDN) are both expected to increase the flexibility and programmability of today's communication networks. Combining both approaches may even be a further step towards increasing the efficiency of network resource utilization. Multiple solutions for virtualizing SDN networks have already been proposed, however, they are either implemented in software or they require special network hardware. We propose HyperFlex, an SDN hypervisor architecture that relies on the decomposition of the hypervisor into functions that are essential for virtualizing SDN networks. The hypervisor functions can be flexibly executed in software or hosted on SDN network elements. Furthermore, existing hypervisor solutions focus on data-plane virtualization mechanisms and neglect the virtualization of the control-plane of SDN networks. HyperFlex provides control-plane virtualization by adding a control-plane isolation function, either in software or on network elements. The isolation function ensures that the resources of the control-plane are shared correctly between each virtual SDN network while it also protects the hypervisor resources from resource exhaustion.
vDesign: a CAVE-based virtual design environment using hand interactions

The cave automatic virtual environment (CAVE) system is one of the most fully immersive systems for virtual reality environments. By providing users with realistic perception and immersive experience, CAVE systems have been widely used in many fields, including military, education, health care, entertainment, design, and others. In this paper, we focus on the design applications in the CAVE. The design applications involve many interactions between the user and the CAVE. However, the conventional interaction tool, the wand, cannot provide fast and convenient interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using hand interactions. The hand interactions in vDesign are classified into menu navigation and object manipulations. For menu navigation, we define two interactions: activating the main menu and selecting a menu item. For object manipulations, we define three interactions: moving, rotating, and scaling an object. By using the proposed hand interactions, we develop the functions of image segmentation and image composition in vDesign. With the image segmentation function, the designer can select and cut the interested objects from different images. With the image composition function, the designer can manipulate the segmented objects and combine them as a composite image. We implemented the vDesign prototype in CAVE and conducted experiments to evaluate the interaction performance in terms of manipulation time and distortion. The experimental results demonstrated that the proposed hand interactions can provide faster and more accurate interactions compared to the traditional wand interactions.
The e V ect of level of construal on the temporal distance of activity enactment

A series of four studies demonstrates that, across diVerent operationalizations of level of construal, diVerent types of activities, and for both the self and another person as targets, construing activities in high-level terms fosters perception of the more distal future as appropriate for their enactment. SpeciWcally, the studies show that thinking about superordinate “why” (rather than subordinate “how”) aspects of actions, about the implications of actions for one’s personality (rather than the objects that would be used in performing the action), and about abstract (rather than concrete) aspects of actions leads people to expect the actions to be performed in the more distant future. These results suggest that the association of level of construal and time perspective is bi-directional, in that time perspective aVects level of construal and, in addition, level of construal aVects time perspective.