nils-herrmann/clean_scirepeval huggingface dataset:
Deep neural networks with Elastic Rectified Linear Units for object recognition

Rectified Linear Unit (ReLU) is crucial to the recent success of deep neural networks (DNNs). In this paper, we propose a novel Elastic Rectified Linear Unit (EReLU) that focuses on processing the positive part of input. Unlike previous variants of ReLU that typically adopt linear or piecewise linear functions to represent the positive part, EReLU is characterized by that each positive value scales within a moderate range like a spring during training stage. On test time, EReLU becomes standard ReLU. EReLU improves model fitting with no extra parameters and little overfitting risk. Furthermore, we propose Elastic Parametric Rectified Linear Unit (EPReLU) by taking advantage of EReLU and parametric ReLU (PReLU). EPReLU ∗Corresponding author Email addresses: jiangxiaoheng@tju.edu.cn (Xiaoheng Jiang), pyw@tju.edu.cn (Yanwei Pang ), xuelong_li@opt.ac.cn (Xuelong Li), jingpan23@gmail.com (Jing Pan), xieyinghong@163.com (Yinghong Xie)  2017
Scale-Space Filtering

The extrema in a signal and its first few derivatives provide a useful general-purpose qualitative description for many kinds of signals. A fundamental problem in computing such descriptions is scale: a derivative must be taken over some neighborhood, but there is seldom a principled basis for choosing its size. Scale-space filtering is a method that describes signals qualitatively, managing the ambiguity of scale in an organized and natural way. The signal is first expanded by convolution with gaussian masks over a continuum of sizes. This "scale-space" image is then collapsed, using its qualitative structure, into a tree providing a concise but complete qualitative description covering all scales of observation. The description is further refined by applying a stability criterion, to identify events that persist of large changes in scale.
Fully convolutional networks for multi-modality isointense infant brain image segmentation

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development. In the isointense phase (approximately 6–8 months of age), WM and GM exhibit similar levels of intensity in both T1 and T2 MR images, resulting in extremely low tissue contrast and thus making the tissue segmentation very challenging. The existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single T1, T2 or fractional anisotropy (FA) modality or their simply-stacked combinations without fully exploring the multi-modality information. To address the challenge, in this paper, we propose to use fully convolutional networks (FCNs) for the segmentation of isointense phase brain MR images. Instead of simply stacking the three modalities, we train one network for each modality image, and then fuse their high-layer features together for final segmentation. Specifically, we conduct a convolution-pooling stream for multimodality information from T1, T2, and FA images separately, and then combine them in high-layer for finally generating the segmentation maps as the outputs. We compared the performance of our approach with that of the commonly used segmentation methods on a set of manually segmented isointense phase brain images. Results showed that our proposed model significantly outperformed previous methods in terms of accuracy. In addition, our results also indicated a better way of integrating multi-modality images, which leads to performance improvement.
Intrusion detection system for high-speed network

The increasing network throughput challenges the current Network Intrusion Detection Systems (NIDS) to have compatible highperformance data processing. In this paper, we describe an in-depth research on the related techniques of high-performance network intrusion detection and an implementation of a Rule-based High-performance Network Intrusion Detection System (RHPNIDS) for high-speed networks. By integrating several performance optimizing methods, the performance of RHPNIDS is very impressive compared with the popular open source NIDS Snort. q 2004 Elsevier B.V. All rights reserved.
IoT based autonomous percipient irrigation system using raspberry Pi

This paper propounds a design for automatic water supplying system in farmland using raspberry pi 3, Arduino microcontrollers, WiFi module, GSM shield, relay boards and couple of sensors. The components we used in our system ensures overall fecund, scalable and spirited implementation. Depending upon the moisture level of farmland and daylight intensity, the system can detect the appropriate time of water supply in the trees and can also keep track of the water level to prevent water from being accumulated around the roots of the saplings. The analog data received from the sensors are transmitted by Arduino as digital signal via Wifi Module to the Raspberry Pi 3. The system is able to notify the administrator if water shortage arises in the main water supply and an administrator can also communicate with the system by sending SMS (Short message service) of a particular keyword. This system can be applied in farmland as well as small pot plants. Using this system, a very promising outcome is found in sustaining and cherishing the plants in a more scientific way.
A unified approach for mining outliers

This paper deals with nding outliers (exceptions) in large datasets. The identiication of outliers can often lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. One contribution of this paper is to show how our proposed, intuitive notion of outliers can unify or generalize many of the existing notions of outliers provided by discordancy tests for standard statistical distributions. Thus, when mining large datasets containing many attributes, a uniied approach can replace many statistical discordancy tests, regardless of any knowledge about the underlying distribution of the attributes. A second contribution of this paper is the development of an algorithm to nd all outliers in a dataset. An important advantage of this algorithm is that its time complexity is linear with respect to the number of objects in the dataset. We include preliminary performance results.
A 2.1 MHz Crystal Oscillator Time Base with a Current Consumption under 500 nA

A micro-power circuit is encapsulated with a 2.1 MHz ZT-cut quartz in a vacuum package. The oscillator core has 2 complementary active MOSFETS and amplitude stabilization. New coupling and biasing circuits, and dynamic frequency dividers allow to achieve ±2 ppm frequency stability down to 1.8 V with a current under 0.5 ¿A.
ANANAS - A Framework for Analyzing Android Applications

Android is an open software platform for mobile devices with a large market share in the smart phone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS is an expandable and modular framework for analyzing Android applications. It takes care of common needs for dynamic malware analysis and provides an interface for the development of plugins. Adaptability and expandability have been main design goals during the development process. An abstraction layer for simple user interaction and phone event simulation is also part of the framework. It allows an analyst to script the required user simulation or phone events on demand or adjust the simulation to his needs. Six plugins have been developed for ANANAS. They represent well known techniques for malware analysis, such as system call hooking and network traffic analysis. The focus clearly lies on dynamic analysis, as five of the six plugins are dynamic analysis methods.
7-Level asymmetrical hybrid cascaded multilevel converter topology for traction system

Electrified traction system connected to the medium voltage high power achieve great attention due to its high efficiency. However the conventional topologies used in electrified traction system causes power quality issues such as high harmonics distortion, low power factor on ac line due to the non linear nature of the load. To address the said issues, this paper presents a topology of 7-level asymmetrical cascaded converter based on the series connection of cascaded module of five levels converter (CM), with H-bridge cell to meet the demand of traction application with improved power factor at minimum harmonics distortion. The proposed topology consists of a single dc source and two dc-link capacitors to obtain the desire level of output with minimum power switches. Harmonics compensation is achieved by providing H-bridge cell and cascaded module with a gating signal at fundamental frequency and high switching frequency respectively. Working principle of proposed topology, mathematical analysis, controlled PWM strategy, stability problem related to dc-link capacitor voltage, possible means of balancing and results has been fully investigated with the help of simulation.
A Dual-Polarized Dual-Band Antenna With High Gain for 2G/3G/LTE Indoor Communications

A compact dual-polarized dual-band omnidirectional antenna with high gain is presented for 2G/3G/LTE communications, which comprise two horizontal polarization (HP) and a vertical polarization (VP) elements. The upper HP element consists of four pairs of modified printed magneto-electric (ME) dipoles that are fed by a four-way power divider feeding network, and eight pieces of arc-shaped parasitic patches that are printed on both sides of the circular printed circuit board alternately. The four-way power divider feeding network together with the four pairs of ME dipoles mainly provide a stable 360° radiation pattern and high gain, while the eight pieces of patches are used to enhance the bandwidth. The lower HP element is similar to the upper one except that it do not have the parasitic patches. The VP element consists of four pairs of cone-shaped patches. Different from the HP element, the upper VP element provides the lower frequency band while the lower VP one yields the upper frequency band. The VP element and the HP element are perpendicularly arranged to obtain the compact and dual-polarized features. Measured results show that a bandwidth of 39.6% (0.77–1.15 GHz) with a gain of about 2.6 dBi and another bandwidth of 55.3% (1.66–2.93 GHz) with a gain of about 4.5 dBi can be achieved for the HP direction, while a bandwidth of 128% (0.7–3.2 GHz) with a gain of around 4.4 dBi can be acquired for the VP direction. Port isolation larger than 20 dB and low-gain variation levels within 2 dBi are also obtained. Hence, the proposed antenna is suitable for 2G/3G/LTE indoor communications.
MATLAB / SIMULINK Based Modelling Photovoltaic Array Fed T-Source Inverter

In order to utilize the solar energy for industrial, commercial and domestic applications the power conversion schemes plays an important role. The problem exists in conventional power conversion schemes are low efficiency, poor transient response, low voltage gain and more reactive components are being used .This paper proposes a single stage power conversion scheme called T-source inverter(TSI) to overcome these drawbacks. T-Source inverter (TSI) with simple boost control scheme is used as interface circuit between PV array and load. The PV array is analyzed under different irradiation and temperature value. The mathematical equations are verified with simulation and hardware. The verification shows the voltage gain of TSI was comparatively higher than ZSI. The reactive components in the circuit are less, fast transient response and low output ripple.
Scalable Distributed Stream Processing

Many stream-based applications are naturally distributed. Applications are often embedded in an environment with numerous connected computing devices with heterogeneous capabilities. As data travels from its point of origin (e.g., sensors) downstream to applications, it passes through many computing devices, each of which is a potential target of computation. Furthermore, to cope with time-varying load spikes and changing demand, many servers would be brought to bear on the problem. In both cases, distributed computation is the norm.
Multi-column deep neural network for traffic sign classification

We describe the approach that won the final phase of the German traffic sign recognition benchmark. Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. Combining various DNNs trained on differently preprocessed data into a Multi-Column DNN (MCDNN) further boosts recognition performance, making the system insensitive also to variations in contrast and illumination.
An efficient downlink packet scheduling algorithm in LTE-Advanced systems with Carrier Aggregation

Carrier Aggregation (CA) is one of the promising features to support the much wider bandwidth for packet transmissions in Long Term Evolution-Advanced (LTE-A) systems. To the best of our knowledge, works regarding the packet scheduling algorithm in LTE-A systems with the CA mechanism have not thoroughly been investigated yet till now. In this paper, we therefore aim to design an efficient packet scheduling algorithm based on the proportional fair criterion in such environment. We focus on downlink transmissions supporting both Real-Time (RT) and non-RT traffics simultaneously. Simulation results show that performance measures in terms of the system throughput, the mean delay, and the fairness are much better than those of a system in which component carriers are not aggregated but used independently.
Detection Method of DNS-based Botnet Communication Using Obtained NS Record History

To combat with botnet, early detection of the botnet communication and fast identification of the bot-infected PCs is very important for network administrators. However, in DNS protocol, which appears to have been used for botnet communication recently, it is difficult to differentiate the ordinary domain name resolution and suspicious communication. Our key idea is that the most of domain name resolutions first obtain the corresponding NS (Name Server) record from authoritative name servers in the Internet, whereas suspicious communication may omit the procedures to hide their malicious activities. Based on this observation, we propose a detection method of DNS basis botnet communication using obtained NS record history. Our proposed method checks whether the destined name server (IP address) of a DNS query is included in the obtained NS record history to detect the botnet communications.
The best clustering algorithms in data mining

In data mining, Clustering is the most popular, powerful and commonly used unsupervised learning technique. It is a way of locating similar data objects into clusters based on some similarity. Clustering algorithms can be categorized into seven groups, namely Hierarchical clustering algorithm, Density-based clustering algorithm, Partitioning clustering algorithm, Graph-based algorithm, Grid-based algorithm, Model-based clustering algorithm and Combinational clustering algorithm. These clustering algorithms give different result according to the conditions. Some clustering techniques are better for large data set and some gives good result for finding cluster with arbitrary shapes.  Algorithms which are under exploration as follows: K-Means algorithm, K-Medoids, Distributed K-Means clustering algorithm, Hierarchical clustering algorithm, Grid-based Algorithm and Density based clustering algorithm. This paper compared all these clustering algorithms according to the many factors. After comparison of these clustering algorithms I describe that which clustering algorithms should be used in different conditions for getting the best result.
Scapular dyskinesis and its relation to shoulder pain.

Scapular dyskinesis is an alteration in the normal position or motion of the scapula during coupled scapulohumeral movements. It occurs in a large number of injuries involving the shoulder joint and often is caused by injuries that result in the inhibition or disorganization of activation patterns in scapular stabilizing muscles. It may increase the functional deficit associated with shoulder injury by altering the normal scapular role during coupled scapulohumeral motions. Scapular dyskinesis appears to be a nonspecific response to shoulder dysfunction because no specific pattern of dyskinesis is associated with a specific shoulder diagnosis. It should be suspected in patients with shoulder injury and can be identified and classified by specific physical examination. Treatment of scapular dyskinesis is directed at managing underlying causes and restoring normal scapular muscle activation patterns by kinetic chain-based rehabilitation protocols.
An automatic rebar splitting system based on two-level of the chain transmission

This paper proposed an automatic rebar splitting system counting by machine vision based on two-level transmission. Compared with other existing systems, this system significantly reduces the space requirements for installation, can use the space of rebar workshop more effectively. This system uses efficient algorithm to solve counting problems caused adhesion and sliding between rebar. By improving the sub-steel machine, this system uses the two-step rebar splitting and visual verification technology to split stacked rebar exactly. This system makes little change on production line and runs fast, accurately, easily to be maintained. It not only meets the needs of production but greatly improve the efficiency of the existing counting system, which is fit for space limited rebar workshop.
Social Network Clustering and Visualization using Hierarchical Edge Bundles

The hierarchical edge bundle (HEB) method generates useful visualizations of dense graphs, such as social networks, but requires a predefined clustering hierarchy, and does not easily benefit from existing straight-line visualization improvements. This paper proposes a new clustering approach that extracts the community structure of a network and organizes it into a hierarchy that is flatter than existing community-based clustering approaches and maps better to HEB visualization. Our method not only discovers communities and generates clusters with better modularization qualities, but also creates a balanced hierarchy that allows HEB visualization of unstructured social networks without predefined hierarchies. Results on several data sets demonstrate that this approach clarifies real-world communication, collaboration and competition network structure and reveals information missed in previous visualizations. We further implemented our techniques into a social network visualization application on facebook.com and let users explore the visualization and community clustering of their own social networks.
Gratitude and depressive symptoms: the role of positive reframing and positive emotion.

Eight studies (N=2,973) tested the theory that gratitude is related to fewer depressive symptoms through positive reframing and positive emotion. Study 1 found a direct path between gratitude and depressive symptoms. Studies 2-5 demonstrated that positive reframing mediated the relationship between gratitude and depressive symptoms. Studies 6-7 showed that positive emotion mediated the relationship between gratitude and depressive symptoms. Study 8 found that positive reframing and positive emotion simultaneously mediated the relationship between gratitude and depressive symptoms. In sum, these eight studies demonstrate that gratitude is related to fewer depressive symptoms, with positive reframing and positive emotion serving as mechanisms that account for this relationship.
LEADERS USING SOCIAL NETWORK ANALYSIS , A STUDY IN AN EGYPTIAN VILLAGE

This research used four approaches to identify opinion leaders in two networks in an Egyptian Village. One approach was used for identifying single opinion leaders (network centrality measures), and three approaches were used to identify sets of key players (group centrality, network efficiency, and KPP-Pos & KPP-Neg). Social network analysis software (UCINET 6) was used for individual & group centrality measures. The study used the key player software to identify sets of key players under KPP-Pos & KPPNeg. The study identified different opinion leaders using the four different techniques. This research gives an opportunity for further work on a comprehensive approach to identify key players in social networks to overcome the problem of identifying different leaders by different techniques without an absolute answer for the question concerning which technique will capture the best leaders.
Clustering and Summarizing Protein-Protein Interaction Networks: A Survey

The increasing availability and significance of large-scale protein-protein interaction (PPI) data has resulted in a flurry of research activity to comprehend the organization, processes, and functioning of cells by analyzing these data at network level. Network clustering, that analyzes the topological and functional properties of a PPI network to identify clusters of interacting proteins, has gained significant popularity in the bioinformatics as well as data mining research communities. Many studies since the last decade have shown that clustering PPI networks is an effective approach for identifying functional modules, revealing functions of unknown proteins, etc. In this paper, we examine this issue by classifying, discussing, and comparing a wide ranging approaches proposed by the bioinformatics community to cluster PPI networks. A pervasive desire of this review is to emphasize the uniqueness of the network clustering problem in the context of PPI networks and highlight why generic network clustering algorithms proposed by the data mining community cannot be directly adopted to address this problem effectively. We also review a closely related problem to PPI network clustering, network summarization, which can enable us to make sense out of the information contained in large PPI networks by generating multi-level functional summaries.
Detection of Bare PCB Defects by Image Subtraction Method using Machine Vision

A Printed Circuit Board (PCB) consists of circuit with electronic components mounted on surface. There are three main steps involved in manufacturing process, where the inspection of PCB is necessary to reduce the defects. First step is printing, second step is the components fabrication over the PCB surface and third is the components soldering. In this paper Machine Vision PCB Inspection System is applied at the first step of manufacturing, i.e., the making of bare PCB. We first compare a standard PCB image with a PCB image to be inspected, using a simple subtraction algorithm that can detect the defected regions. Our focus is to detect defects on printed circuit boards. Typical defects that can be detected are over etchings (opens), under-etchings (shorts), holes etc.
Rectification of images of QR codes posted on cylinders by conic segmentation

Powered by the pervasive penetration of inexpensive mobile devices and relevant APP's into our daily life, QR codes have found a wide range of applications in recent years. Therefore, the processing of the images of QR codes (also referred to as QR images) is an important task. In this paper, we try to deal with the scenario wherein the QR code is printed or pasted (altogether also referred to as "posted") on the surface of a cylinder. In the scenario considered in this work, the posting need not be aligned to the axis of the cylinder. When a picture is taken on such a QR code, the picture can be seriously distorted, and thus suffers a high probability of decoding failure. In this paper, a scheme called rectification by conic segmentation (CS) is proposed to tackle this kind of problems. Experiments show that the proposed scheme is effective, in the sense that many failed trials at QR decoding become successful after the rectification.