nils-herrmann/clean_scirepeval huggingface dataset:
How can machine learning help stock investment ?

The million-dollar question for stock investors is if the price of a stock will rise or not. The fluctuation of stock market is violent and there are many complicated financial indicators. Only people with extensive experience and knowledge can understand the meaning of the indicators, use them to make good prediction to get fortune. Most of other people can only rely on lucky to earn money from stock trading. Machine learning is an opportunity for ordinary people to gain steady fortune from stock market and also can help experts to dig out the most informative indicators and make better prediction.
Coverage-Guided Fuzzing for Deep Neural Networks

In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving. Despite great success achieved in various human intelligence tasks, similar to traditional software, DNNs could also exhibit incorrect behaviors caused by hidden defects causing severe accidents and losses. In this paper, we propose DeepHunter, an automated fuzz testing framework for hunting potential defects of general-purpose DNNs. DeepHunter performs metamorphic mutation to generate new semantically preserved tests, and leverages multiple plugable coverage criteria as feedback to guide the test generation from different perspectives. To be scalable towards practical-sized DNNs, DeepHunter maintains multiple tests in a batch, and prioritizes the tests selection based on active feedback. The effectiveness of DeepHunter is extensively investigated on 3 popular datasets (MNIST, CIFAR-10, ImageNet) and 7 DNNs with diverse complexities, under large set of 6 coverage criteria as feedback. The large-scale experiments demonstrate that DeepHunter can (1) significantly boost the coverage with guidance; (2) generate useful tests to detect erroneous behaviors and facilitate the DNN model quality evaluation; (3) accurately capture potential defects during DNN quantization for platform migration.
DynaMat: A Dynamic View Management System for Data Warehouses

Pre-computation and materialization of views with aggregate functions is a common technique in Data Warehouses. Due to the complex structure of the warehouse and the different profiles of the users who submit queries, there is need for tools that will automate the selection and management of the materialized data. In this paper we present DynaMat, a system that dynamically materializes information at multiple levels of granularity in order to match the demand (workload) but also takes into account the maintenance restrictions for the warehouse, such as down time to update the views and space availability. DynaMat unifies the view selection and the view maintenance problems under a single framework using a novel “goodness” measure for the materialized views. DynaMat constantly monitors incoming queries and materializes the best set of views subject to the space constraints. During updates, DynaMat reconciles the current materialized view selection and refreshes the most beneficial subset of it within a given maintenance window. We compare DynaMat against a system that is given all queries in advance and the pre-computed optimal static view selection. The comparison is made based on a new metric, the Detailed Cost Savings Ratio introduced for quantifying the benefits of view materialization against incoming queries. These experiments show that DynaMat's dynamic view selection outperforms the optimal static view selection and thus, any sub-optimal static algorithm that has appeared in the literature.
An urban metabolism and ecological footprint assessment of Metro Vancouver.

As the world urbanizes, the role of cities in determining sustainability outcomes grows in importance. Cities are the dominant form of human habitat, and most of the world's resources are either directly or indirectly consumed in cities. Sustainable city analysis and management requires understanding the demands a city places on a wider geographical area and its ecological resource base. We present a detailed, integrated urban metabolism of residential consumption and ecological footprint analysis of the Vancouver metropolitan region for the year 2006. Our overall goal is to demonstrate the application of a bottom-up ecological footprint analysis using an urban metabolism framework at a metropolitan, regional scale. Our specific objectives are: a) to quantify energy and material consumption using locally generated data and b) to relate these data to global ecological carrying capacity. Although water is the largest material flow through Metro Vancouver (424,860,000 m(3)), it has the smallest ecological footprint (23,100 gha). Food (2,636,850 tonnes) contributes the largest component to the ecological footprint (4,514,400 gha) which includes crop and grazing land as well as carbon sinks required to sequester emissions from food production and distribution. Transportation fuels (3,339,000 m(3)) associated with motor vehicle operation and passenger air travel comprises the second largest material flow through the region and the largest source of carbon dioxide emissions (7,577,000 tonnes). Transportation also accounts for the second largest component of the EF (2,323,200 gha). Buildings account for the largest electricity flow (17,515,150 MWh) and constitute the third largest component of the EF (1,779,240 gha). Consumables (2,400,000 tonnes) comprise the fourth largest component of the EF (1,414,440 gha). Metro Vancouver's total Ecological Footprint in 2006 was 10,071,670 gha, an area approximately 36 times larger than the region itself. The EFA reveals that cropland and carbon sinks (forested land required to sequester carbon dioxide emissions) account for 90% of Metro Vancouver's overall demand for biocapacity. The per capita ecological footprint is 4.76 gha, nearly three times the per capita global supply of biocapacity. Note that this value excludes national government services that operate outside the region and could account for up to an additional 2 gha/ca.
Facial Expression Recognition with Convolutional Neural Networks

Facial expression recognition systems have attracted much research interest within the field of artificial intelligence. Many established facial expression recognition (FER) systems apply standard machine learning to extracted image features, and these methods generalize poorly to previously unseen data. This project builds upon recent research to classify images of human faces into discrete emotion categories using convolutional neural networks (CNNs). We experimented with different architectures and methods such as fractional max-pooling and finetuning, ultimately achieving an accuracy of 0.48 in a sevenclass classification task.
Security Architecture of the Internet of Things Oriented to Perceptual Layer

The Internet of Things (IoT) is a ubiquitous Internet-based network. However, the IoT exhibits characteristics that pose considerable risks: inherent openness, heterogeneity, and terminal vulnerability. Thus, a new type of architecture must be established to ensure security. The new architecture should aim to improve the efficiency, reliability, and controllability of the entire security system. It should also cover most security technologies and ensure thorough compatibility of various security mechanisms. Therefore, this study investigates all possible attacks and security threats to the IoT and proposes related security technologies. This study presents a design of a highly refined security subject and universal two-dimensional security architecture integrated with related safety technologies.
A Space Vector PWM Scheme for Multilevel Inverters Based on Two-Level Space Vector PWM

Multilevel inverters are increasingly being used in high-power medium voltage applications due to their superior performance compared to two-level inverters. Among various modulation techniques for a multilevel inverter, the space vector pulsewidth modulation (SVPWM) is widely used. However, the implementation of the SVPWM for a multilevel inverter is complicated. The complexity is due to the difficulty in determining the location of the reference vector, the calculation of on-times, and the determination and selection of switching states. This paper proposes a general SVPWM algorithm for multilevel inverters based on standard two-level SVPWM. Since the proposed multilevel SVPWM method uses two-level modulation to calculate the on-times, the computation of on-times for an n-level inverter becomes easier. The proposed method uses a simple mapping to achieve the SVPWM for a multilevel inverter. A general n-level implementation is explained, and experimental results are given for three-level and five-level inverters
EmotiGO: Bluetooth-enabled eyewear for unobtrusive physiology-based emotion recognition

While machines (e.g., computers, tablets, cellphones, robots) have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition have great potential for addressing this issue. This paper presents a novel wearable sensor - EmotiGO - for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users' usual activities. EmotiGO's open software and communication protocol provide an easy way for developers to interact with the device and design custom applications. We present the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement.
Analyzing the Performance of Mutation Operators to Solve the Travelling Salesman Problem

The genetic algorithm includes some parameters that should be adjusted, so as to get reliable results. Choosing a representation of the problem addressed, an initial population, a method of selection, a crossover operator, mutation operator, the probabilities of crossover and mutation, and the insertion method creates a variant of genetic algorithms. Our work is part of the answer to this perspective to find a solution for this combinatorial problem. What are the best parameters to select for a genetic algorithm that creates a variety efficient to solve the Traveling Salesman Problem (TSP)? In this paper, we present a comparative analysis of different mutation operators, surrounded by a dilated discussion that justifying the relevance of genetic operators chosen to solving the TSP problem.
Semi-supervised Learning of Naive Bayes Classifier with feature constraints

Semi-supervised learning methods address the problem of building classifiers when labeled data is scarce. Text classification is often augmented by rich set of labeled features representing a particular class. As tuple level labling is resource consuming, semi-supervised and weakly supervised learning methods are explored recently. Compared to labeling data instances (documents), feature labeling takes much less effort and time. Posterior regularization (PR) is a framework recently proposed for incorporating bias in the form prior knowledge into posterior for the label. Our work focuses on incorporating labeled features into a naive bayes classifier in a semi-supervised setting using PR. Generative learning approaches utilize the unlabeled data more effectively compared to discriminative approaches in a semi-supervised setup. In the current study we formulate a classification method which uses the labeled features as constraints for the posterior in a semi-supervised generative learning setting. Our empirical study shows that performance gains are significant compared to an approach solely based on Generelized Expectation(GE) or limited amount of labeled data alone. We also show an application of our framework in a transfer learning setup for text classification. As we allow labeled data as well as labeled features to be used, our setup allows the presence of limited amount of labeled data on the target side of transfer learning where feature constraints are used for transferring knowledge from source domain to target domain.
Rate control for communication networks: shadow prices, proportional fairness and stability

Rate Control for Communication Networks: Shadow Prices, Proportional Fairness and Stability Author(s): F. P. Kelly, A. K. Maulloo and D. K. H. Tan Source:  The Journal of the Operational Research Society, Vol. 49, No. 3 (Mar., 1998), pp. 237-252 Published by: on behalf of the Palgrave Macmillan Journals Operational Research Society Stable URL: http://www.jstor.org/stable/3010473 Accessed: 07-10-2015 21:10 UTC
A Review on the ESD Robustness of Drain-Extended MOS Devices

This paper reviews electrostatic discharge (ESD) investigations on laterally diffused MOS (LDMOS) and drain-extended MOS (DeMOS) devices. The limits of the safe operating area of LDMOS/DeMOS devices and device physics under ESD stress are discussed under various biasing conditions and layout schemes. Specifically, the root cause of early filament formation is highlighted. Differences in filamentary nature among various LDMOS/DeMOS devices are shown. Based on the physical understanding, device optimization guidelines are given. Finally, an outlook on technology scaling is presented.
Conclusions of Ghaheri's Study That Laser Surgery for Posterior Tongue and Lip Ties Improves Breastfeeding Are Not Substantiated.

The diagnosis and treatment of tongue and lip ties remains a topic of much and at times heated debate. The following correspondences, which take opposite sides of the argument as to the value of laser surgery, are symptomatic of this still unresolved and hotly contested subject. Breastfeeding Medicine is of the opinion that it should serve as a platform for the presentation of both sides of the issue so as to provide information that will assist clinicians in their management decision.
The Past, Present, and Future of Silicon Photonics

The pace of the development of silicon photonics has quickened since 2004 due to investment by industry and government. Commercial state-of-the-art CMOS silicon-on-insulator (SOI) foundries are now being utilized in a crucial test of 1.55-mum monolithic optoelectronic (OE) integration, a test sponsored by the Defense Advanced Research Projects Agency (DARPA). The preliminary results indicate that the silicon photonics are truly CMOS compatible. R&D groups have now developed 10-100-Gb/s electro-optic modulators, ultrafast Ge-on-Si photodetectors, efficient fiber-to-waveguide couplers, and Si Raman lasers. Electrically pumped silicon lasers are under intense investigation, with several approaches being tried; however, lasing has not yet been attained. The new paradigm for the Si-based photonic and optoelectric integrated circuits is that these chip-scale networks, when suitably designed, will operate at a wavelength anywhere within the broad spectral range of 1.2-100 mum, with cryocooling needed in some cases
The Role of Flow Experience in Cyber-Game Addiction

Consumer habit, an important key to repetitive consumption, is an interesting yet puzzling phenomenon. Sometimes this consumption becomes obsessive--consumers will continue to act a certain way even when they feel it is not in their best interests. However, not all consumers develop such addictions. This study uses cyber-game addiction syndrome as an analogue to trace the possible causes of consumer addiction. Results from structure equation modeling show that repetition of favorite activities has a moderate effect upon addiction, which is in line with the assertion of rational addiction theory. However, flow experience--the emotional state embracing perceptional distortion and enjoyment--shows a much stronger impact on addiction. This suggests that consumers who have experienced flow are more likely to be addicted.
Remote Heart Rate Measurement from RGB-NIR Video Based on Spatial and Spectral Face Patch Selection

In this paper, we propose a novel heart rate (HR) estimation method using simultaneously recorded RGB and near-infrared (NIR) face videos. The key idea of our method is to automatically select suitable face patches for HR estimation in both spatial and spectral domains. The spatial and spectral face patch selection enables us to robustly estimate HR under various situations, including scenes under which existing RGB camera-based methods fail to accurately estimate HR. For a challenging scene in low light and with light fluctuations, our method can successfully estimate HR for all 20 subjects $( \pm 3$ beats per minute), while the RGB camera-based methods succeed only for 25% of the subjects.
FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs

Sparse matrix matrix (SpMM) multiplication is involved in a wide range of scientific and technical applications. The computational requirements for this kind of operation are enormous, especially for large matrices. This paper analyzes and evaluates a method to efficiently compute the SpMM product in a computing environment that includes graphics processing units (GPUs). Some libraries to compute this matricial operation can be found in the literature. However, our strategy (FastSpMM) outperforms the existing approaches because it combines the use of the ELLPACK-R storage format with the exploitation of the high ratio computation/memory access of the SpMM operation and the overlapping of CPU–GPU communications/computations by Compute Unified Device Architecture streaming computation. In this work, FastSpMM is described and its performance evaluated with regard to the CUSPARSE library (supplied by NVIDIA), which also includes routines to compute SpMM on GPUs. Experimental evaluations based on a representative set of test matrices show that, in terms of performance, FastSpMM outperforms the CUSPARSE routine as well as the implementation of the SpMM as a set of sparse matrix vector products.
Domain-specific iterative readability computation

We present a new algorithm to measure domain-specific readability. It iteratively computes the readability of domain-specific resources based on the difficulty of domain-specific concepts and vice versa, in a style reminiscent of other bipartite graph algorithms such as Hyperlink-Induced Topic Search (HITS) and the Stochastic Approach for Link-Structure Analysis (SALSA). While simple, our algorithm outperforms standard heuristic measures and remains competitive among supervised-learning approaches. Moreover, it is less domain-dependent and portable across domains as it does not rely on an annotated corpus or expensive expert knowledge that supervised or domain-specific methods require.
Bottom-Up Generalization : A Data Mining Solution to Privacy Protection

In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. This paper investigates data mining as a technique for masking data; therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The following privacy problem is considered in this paper: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. An iterative bottom-up generalization is adapted from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration.
A Novel Broadband Dual-Polarization Antenna Utilizing Strong Mutual Coupling

A novel broadband dual-polarization antenna utilizing strong mutual coupling with high isolation is presented in this communication. The antenna is composed of two orthogonally situated dipoles which have square-loop shape arms (SLSAs). And to shape the radiation pattern of the antenna, a metal reflector is placed under the antenna. By elaborately utilizing the strong mutual coupling between the two dipoles, very good impedance match and sufficiently high isolation can be achieved simultaneously, the reasons for which are analyzed in this communication. Both the simulated and the measured results show that a more than 57.5% impedance bandwidth with SWR<;1.5 and port-to-port isolation>31 dB can be achieved for the proposed antenna. As an important advantage, the input impedance of the antenna can be controlled within certain limits by adjusting the configurations, thus the antenna can be fed directly by coaxial cables without using impedance transformers. Besides, the antenna structure is compact and simple.
DeltaIoT: A Self-Adaptive Internet of Things Exemplar

Internet of Things (IoT) consists of networked tiny embedded computers (motes) that are capable of monitoring and controlling the physical world. Examples range from building security monitoring to smart factories. A central problem of IoT is minimising the energy consumption of the motes, while guaranteeing high packet delivery performance, regardless of uncertainties such as sudden changes in traffic load and communication interference. Traditionally, to deal with uncertainties the network settings are either hand-tuned or over-provisioned, resulting in continuous network maintenance or inefficiencies. Enhancing the IoT network with self-adaptation can automate these tasks. This paper presents DeltaIoT, an exemplar that enables researchers to evaluate and compare new methods, techniques and tools for self-adaptation in IoT. DeltaIoT is the first exemplar for research on self-adaptation that provides both a simulator for offline experimentation and a physical setup that can be accessed remotely for real-world experimentation.
Comparison of current driven Class-D and Class-E half-wave rectifiers for 6.78 MHz high power IPT applications

Two current driven half wave rectifier topologies, Class-D and -E, have been characterised for IPT applications. Both rectifiers utilised SiC Schottky diodes for high power capability. The Class-D topology has reached efficiencies up to 95% and the Class-E up to 90%. Both topologies achieved their highest efficiencies at high voltage operation as the parasitic capacitances of the SiC diodes were affecting the circuit's operation at low voltages.
Feature Selection and Kernel Learning for Local Learning-Based Clustering

The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.