nils-herrmann/clean_scirepeval huggingface dataset:
Efficient Machine Learning Techniques for Stock Market Prediction

Stock market prediction is forever important issue for investor. Computer science plays vital role to solve this problem. From the evolution of machine learning, people from this area are busy to solve this problem effectively. Many different techniques are used to build predicting system. This research describes different state of the art techniques used for stock forecasting and compare them w.r.t. their pros and cons. We have classified different techniques categorically; Time Series, Neural Network and its different variation (RNN, ESN, MLP, LRNN etc.) and different hybrid techniques (combination of neural network with different machine learning techniques) (ANFIS, GA/ATNN, GA/TDNN, ICA-BPN). By extensive study of different techniques, it was analyzed that Neural Network is the best technique till time to predict stock prices especially when some denoising schemes are applied with neural network. We, also, have implemented and compared different neural network techniques like Layered Recurrent Neural Network (LRNN), Wsmpca-NN and Feed forward Neural Network (NN). By comparing said techniques, it was observed that LRNN performs better than feed forward NN and Wsmpca-NN performs better than LRNN and NN. We have applied said techniques on PSO (Pakistan State Oil), S&P500 data sets.
Time-Delay Compensation by Communication Disturbance Observer for Bilateral Teleoperation Under Time-Varying Delay

This paper presents the effectiveness of a time-delay compensation method based on the concept of network disturbance and communication disturbance observer for bilateral teleoperation systems under time-varying delay. The most efficient feature of the compensation method is that it works without time-delay models (model-based time-delay compensation approaches like Smith predictor usually need time-delay models). Therefore, the method is expected to be widely applied to network-based control systems, in which time delay is usually unknown and time varying. In this paper, the validity of the time-delay compensation method in the cases of both constant delay and time-varying delay is verified by experimental results compared with Smith predictor.
Python as a hardware description language: A case study

Many people may see the development of software and hardware like different disciplines. However, there are great similarities between them that have been shown due to the appearance of extensions for general purpose programming languages for its use as hardware description languages. In this contribution, the approach proposed by the MyHDL package to use Python as an HDL is analyzed by making a comparative study. This study is based on the independent application of Verilog and Python based flows to the development of a real peripheral. The use of MyHDL has revealed to be a powerful and promising tool, not only because of the surprising results, but also because it opens new horizons towards the development of new techniques for modeling and verification, using the full power of one of the most versatile programming languages nowadays.
Efficient execution of memory access phases using dataflow specialization

This paper identifies a new opportunity for improving the efficiency of a processor core: memory access phases of programs. These are dynamic regions of programs where most of the instructions are devoted to memory access or address computation. These occur naturally in programs because of workload properties, or when employing an in-core accelerator, we get induced phases where the code execution on the core is access code. We observe such code requires an OOO core's dataflow and dynamism to run fast and does not execute well on an in-order processor. However, an OOO core consumes much power, effectively increasing energy consumption and reducing the energy efficiency of in-core accelerators.
 We develop an execution model called memory access dataflow (MAD) that encodes dataflow computation, event-condition-action rules, and explicit actions. Using it we build a specialized engine that provides an OOO core's performance but at a fraction of the power. Such an engine can serve as a general way for any accelerator to execute its respective induced phase, thus providing a common interface and implementation for current and future accelerators. We have designed and implemented MAD in RTL, and we demonstrate its generality and flexibility by integration with four diverse accelerators (SSE, DySER, NPU, and C-Cores). Our quantitative results show, relative to in-order, 2-wide OOO, and 4-wide OOO, MAD provides 2.4×, 1.4× and equivalent performance respectively. It provides 0.8×, 0.6× and 0.4× lower energy.
A Wideband Transformer Balun With Center Open Stub in CMOS Process

This letter presents a wideband transformer balun with a center open stub. Since the interconnected line between two coupled-lines greatly deteriorates the performance of balun in millimeter-wave designs, the proposed center open stub provides a good solution to further optimize the balance of balun. The proposed transformer balun with center open stub has been fabricated in 90 nm CMOS technology, with a compact chip area of 0.012 mm2. The balun achieves an amplitude imbalance of less than 1 dB for a frequency band ranging from 1 to 48 GHz along with a phase imbalance of less than 5 degrees for the frequency band ranging from 2 to 47 GHz.
River plastic emissions to the world's oceans

Plastics in the marine environment have become a major concern because of their persistence at sea, and adverse consequences to marine life and potentially human health. Implementing mitigation strategies requires an understanding and quantification of marine plastic sources, taking spatial and temporal variability into account. Here we present a global model of plastic inputs from rivers into oceans based on waste management, population density and hydrological information. Our model is calibrated against measurements available in the literature. We estimate that between 1.15 and 2.41 million tonnes of plastic waste currently enters the ocean every year from rivers, with over 74% of emissions occurring between May and October. The top 20 polluting rivers, mostly located in Asia, account for 67% of the global total. The findings of this study provide baseline data for ocean plastic mass balance exercises, and assist in prioritizing future plastic debris monitoring and mitigation strategies.
Control Methods of Inverter-Interfaced Distributed Generators in a Microgrid System

Microgrids are a new concept for future energy distribution systems that enable renewable energy integration and improved energy management capability. Microgrids consist of multiple distributed generators (DGs) that are usually integrated via power electronic inverters. In order to enhance power quality and power distribution reliability, microgrids need to operate in both grid-connected and island modes. Consequently, microgrids can suffer performance degradation as the operating conditions vary due to abrupt mode changes and variations in bus voltages and system frequency. This paper presents controller design and optimization methods to stably coordinate multiple inverter-interfaced DGs and to robustly control individual interface inverters against voltage and frequency disturbances. Droop-control concepts are used as system-level multiple DG coordination controllers, and control theory is applied to device-level inverter controllers. Optimal control parameters are obtained by particle-swarm-optimization algorithms, and the control performance is verified via simulation studies.
Optical Flow-based 3D Human Motion Estimation from Monocular Video

This paper presents a method to estimate 3D human pose and body shape from monocular videos. While recent approaches infer the 3D pose from silhouettes and landmarks, we exploit properties of optical flow to temporally constrain the reconstructed motion. We estimate human motion by minimizing the difference between computed flow fields and the output of our novel flow renderer. By just using a single semi-automatic initialization step, we are able to reconstruct monocular sequences without joint annotation. Our test scenarios demonstrate that optical flow effectively regularizes the under-constrained problem of human shape and motion estimation from monocular video.  Following our main idea we compute the optical flow between two consecutive frames and match it to an optical flow field estimated by our proposed optical flow renderer. From left to right: input frame, color-coded observed flow, estimated flow, resulting pose.
Architecture of Internet of Things and Its Key Technology Integration Based-On RFID

In recent years, the IoT(Internet of Things)has been wide spreadly concerned as a new technology architecture integrated by many information technologies. However, IoT is a complex system. If there is no intensive study from the system point of view, it will be very hard to understand the key technologies of IoT. in order to better study IoT and its technologies this paper proposes an extent six-layer architecture of IoT based on network hierarchical structure firstly. then it discusses the key technologies involved in every layer of IoT, such as RFID, WSN, Internet, SOA, cloud computing and Web Service etc. According to this, an automatic recognition system is designed via integrating both RFID and WSN and a strategy is also brought forward to via integrating both RFID and Web Sevice. This result offers the feasibility of technology integration of IoT.
When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries

The ability to identify authors of computer programs based on their coding style is a direct threat to the privacy and anonymity of programmers. While recent work found that source code can be attributed to authors with high accuracy, attribution of executable binaries appears to be much more difficult. Many distinguishing features present in source code, e.g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship. We examine programmer de-anonymization from the standpoint of machine learning, using a novel set of features that include ones obtained by decompiling the executable binary to source code. We adapt a powerful set of techniques from the domain of source code authorship attribution along with stylistic representations embedded in assembly, resulting in successful deanonymization of a large set of programmers. We evaluate our approach on data from the Google Code Jam, obtaining attribution accuracy of up to 96% with 100 and 83% with 600 candidate programmers. We present an executable binary authorship attribution approach, for the first time, that is robust to basic obfuscations, a range of compiler optimization settings, and binaries that have been stripped of their symbol tables. We perform programmer de-anonymization using both obfuscated binaries, and real-world code found “in the wild” in single-author GitHub repositories and the recently leaked Nulled.IO hacker forum. We show that programmers who would like to remain anonymous need to take extreme countermeasures to protect their privacy.
A multivariate regression approach to association analysis of a quantitative trait network

Many complex disease syndromes such as asthma consist of a large number of highly related, rather than independent, clinical phenotypes, raising a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. Although a causal genetic variation may influence a group of highly correlated traits jointly, most of the previous association analyses considered each phenotype separately, or combined results from a set of single-phenotype analyses.



We propose a new statistical framework called graph-guided fused lasso to address this issue in a principled way. Our approach represents the dependency structure among the quantitative traits explicitly as a network, and leverages this trait network to encode structured regularizations in a multivariate regression model over the genotypes and traits, so that the genetic markers that jointly influence subgroups of highly correlated traits can be detected with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently, our approach analyzes all of the traits jointly in a single statistical method to discover the genetic markers that perturb a subset of correlated traits jointly rather than a single trait. Using simulated datasets based on the HapMap consortium data and an asthma dataset, we compare the performance of our method with the single-marker analysis, and other sparse regression methods that do not use any structural information in the traits. Our results show that there is a significant advantage in detecting the true causal single nucleotide polymorphisms when we incorporate the correlation pattern in traits using our proposed methods.


AVAILABILITY
Software for GFlasso is available at http://www.sailing.cs.cmu.edu/gflasso.html.
Secured and Robust Dual ImageSteganography: A Survey

In the last few years communication technology has been improved, which increase the need of secure data communication. For this, many researchers have exerted much of their time and efforts in an attempt to find suitable ways for data hiding. There is a technique used for hiding the important information imperceptibly, which is Steganography. Steganography is the art of hiding information in such a way that prevents the detection of hidden messages. The process of using steganography in conjunction with cryptography, called as Dual Steganography. This paper tries to elucidate the basic concepts of steganography, its various types and techniques, and dual steganography. There is also some of research works done in steganography field in past few years.
Investigation of Magnetizing Inrush Current in a Single-Phase Transformer

Transformer inrush currents are high-magnitude, harmonic rich currents generated when transformer cores are driven into saturation during energization. These currents have undesirable effects, including potential damage or loss-of-life of transformer, protective relay miss operation, and reduced power quality on the system. This paper explores the theoretical explanations of inrush currents and explores different factors that have influences on the shape and magnitude of those inrush currents. PSCAD/EMTDC is used to investigate inrush currents phenomena by modeling a practical power system circuit for single phase transformer
Regularizing RNNs by Stabilizing Activations

We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modelling and phoneme recognition, and outperforming weight noise. With this penalty term, IRNN can achieve similar performance to LSTM on language modelling, although adding the penalty term to the LSTM results in superior performance. Our penalty term also prevents the exponential growth of IRNNs activations outside of their training horizon, allowing them to generalize to much longer sequences.
Blockchain to Rule the Waves - Nascent Design Principles for Reducing Risk and Uncertainty in Decentralized Environments

Many decentralized, inter-organizational environments such as supply chains are characterized by high transactional uncertainty and risk. At the same time, blockchain technology promises to mitigate these issues by introducing certainty into economic transactions. This paper discusses the findings of a Design Science Research project involving the construction and evaluation of an information technology artifact in collaboration with Maersk, a leading international shipping company, where central documents in shipping, such as the Bill of Lading, are turned into a smart contract on blockchain. Based on our insights from the project, we provide first evidence for preliminary design principles for applications that aim to mitigate the transactional risk and uncertainty in decentralized environments using blockchain. Both the artifact and the first evidence for emerging design principles are novel, contributing to the discourse on the implications that the advent of blockchain technology poses for governing economic activity.
24/7 Place Recognition by View Synthesis

We address the problem of large-scale visual place recognition for situations where the scene undergoes a major change in appearance, for example, due to illumination (day/night), change of seasons, aging, or structural modifications over time such as buildings being built or destroyed. Such situations represent a major challenge for current large-scale place recognition methods. This work has the following three principal contributions. First, we demonstrate that matching across large changes in the scene appearance becomes much easier when both the query image and the database image depict the scene from approximately the same viewpoint. Second, based on this observation, we develop a new place recognition approach that combines (i) an efficient synthesis of novel views with (ii) a compact indexable image representation. Third, we introduce a new challenging dataset of 1,125 camera-phone query images of Tokyo that contain major changes in illumination (day, sunset, night) as well as structural changes in the scene. We demonstrate that the proposed approach significantly outperforms other large-scale place recognition techniques on this challenging data.
Alice in Warningland: A Large-Scale Field Study of Browser Security Warning Effectiveness

We empirically assess whether browser security warnings are as ineffective as suggested by popular opinion and previous literature. We used Mozilla Firefox and Google Chrome’s in-browser telemetry to observe over 25 million warning impressions in situ. During our field study, users continued through a tenth of Mozilla Firefox’s malware and phishing warnings, a quarter of Google Chrome’s malware and phishing warnings, and a third of Mozilla Firefox’s SSL warnings. This demonstrates that security warnings can be effective in practice; security experts and system architects should not dismiss the goal of communicating security information to end users. We also find that user behavior varies across warnings. In contrast to the other warnings, users continued through 70.2% of Google Chrome’s SSL warnings. This indicates that the user experience of a warning can have a significant impact on user behavior. Based on our findings, we make recommendations for warning designers and researchers.
Data mining tools

The development and application of data mining algorithms requires the use of powerful software tools. As the number of available tools continues to grow, the choice of the most suitable tool becomes increasingly difficult. This paper attempts to support the decision-making process by discussing the historical development and presenting a range of existing state-of-the-art data mining and related tools. Furthermore, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different types. The typical characteristics of these types are explained and a selection of the most important tools is categorized. This paper is organized as follows: the first section Historical Development and State-of-the-Art highlights the historical development of data mining software until present; the criteria to compare data mining software are explained in the second section Criteria for Comparing Data Mining Software. The last section Categorization of Data Mining Software into Different Types proposes a categorization of data mining software and introduces typical software tools for the different types.
A comparative study for content-based dynamic spam classification using four machine learning algorithms

The growth of email users has resulted in the dramatic increasing of the spam emails during the past few years. In this paper, four machine learning algorithms, which are Naı̈ve Bayesian (NB), neural network (NN), support vector machine (SVM) and relevance vector machine (RVM), are proposed for spam classification. An empirical evaluation for them on the benchmark spam filtering corpora is presented. The experiments are performed based on different training set size and extracted feature size. Experimental results show that NN classifier is unsuitable for using alone as a spam rejection tool. Generally, the performances of SVM and RVM classifiers are obviously superior to NB classifier. Compared with SVM, RVM is shown to provide the similar classification result with less relevance vectors and much faster testing time. Despite the slower learning procedure, RVM is more suitable than SVM for spam classification in terms of the applications that require low complexity.
NR-grep: a fast and flexible pattern-matching tool

We present nrgrep (\nondeterministic reverse grep"), a new pattern matching tool designed for eecient search of complex patterns. Unlike previous tools of the grep family, such as agrep and Gnu grep, nrgrep is based on a single and uniform concept: the bit-parallel simulation of a nondeterministic suux automaton. As a result, nrgrep can nd from simple patterns to regular expressions, exactly or allowing errors in the matches, with an eeciency that degrades smoothly as the complexity of the searched pattern increases. Another concept fully integrated into nrgrep and that contributes to this smoothness is the selection of adequate subpatterns for fast scanning, which is also absent in many current tools. We show that the eeciency of nrgrep is similar to that of the fastest existing string matching tools for the simplest patterns, and by far unpaired for more complex patterns.
Context-aware location recommendation by using a random walk-based approach

The location-based social networks (LBSN) enable users to check in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a context-aware location recommendation system for LBSNs using a random walk approach. Our proposed approach considers the current context (i.e., current social relations, personal preferences and current location) of the user to provide personalized recommendations. We build a graph model of LBSNs for performing a random walk approach with restart. Random walk is performed to calculate the recommendation probabilities of the nodes. A list of locations are recommended to users after ordering the nodes according to the estimated probabilities. We compare our algorithm, CLoRW, with popularity-based, friend-based and expert-based baselines, user-based collaborative filtering approach and a similar work in the literature. According to experimental results, our algorithm outperforms these approaches in all of the test cases.
Tightly-coupled monocular visual-inertial fusion for autonomous flight of rotorcraft MAVs

There have been increasing interests in the robotics community in building smaller and more agile autonomous micro aerial vehicles (MAVs). In particular, the monocular visual-inertial system (VINS) that consists of only a camera and an inertial measurement unit (IMU) forms a great minimum sensor suite due to its superior size, weight, and power (SWaP) characteristics. In this paper, we present a tightly-coupled nonlinear optimization-based monocular VINS estimator for autonomous rotorcraft MAVs. Our estimator allows the MAV to execute trajectories at 2 m/s with roll and pitch angles up to 30 degrees. We present extensive statistical analysis to verify the performance of our approach in different environments with varying flight speeds.