nils-herrmann/clean_scirepeval huggingface dataset:
Improving Stock Market Prediction via Heterogeneous Information Fusion

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsic ∗Corresponding author Email addresses: zhangx@bupt.edu.cn (Xi Zhang), 2011213120@bupt.edu.cn (Yunjia Zhang), szwang@nuaa.edu.cn (Senzhang Wang), yaoyuntao@bupt.edu.cn (Yuntao Yao), fangbx@bupt.edu.cn (Binxing Fang), psyu@uic.edu (Philip S. Yu)  Templates September 2, 2018 ar X iv :1 80 1. 00 58 8v 1 [ cs .S I] 2 J an 2 01 8 relations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model.
Quantized Memory-Augmented Neural Networks

Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks). These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intriguing AI tasks that would otherwise be hard to address. This paper concerns the problem of quantizing MANNs. Quantization is known to be effective when we deploy deep models on embedded systems with limited resources. Furthermore, quantization can substantially reduce the energy consumption of the inference procedure. These benefits justify recent developments of quantized multilayer perceptrons, convolutional networks, and RNNs. However, no prior work has reported the successful quantization of MANNs. The in-depth analysis presented here reveals various challenges that do not appear in the quantization of the other networks. Without addressing them properly, quantized MANNs would normally suffer from excessive quantization error which leads to degraded performance. In this paper, we identify memory addressing (specifically, content-based addressing) as the main reason for the performance degradation and propose a robust quantization method for MANNs to address the challenge. In our experiments, we achieved a computation-energy gain of 22× with 8-bit fixed-point and binary quantization compared to the floating-point implementation. Measured on the bAbI dataset, the resulting model, named the quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit fixed-point and binary quantization, respectively, compared to the MANN quantized using conventional techniques.
Feature analysis and selection for training an end-to-end autonomous vehicle controller using deep learning approach

Deep learning-based approaches have been widely used for training controllers for autonomous vehicles due to their powerful ability to approximate nonlinear functions or policies. However, the training process usually requires large labeled data sets and takes a lot of time. In this paper, we analyze the influences of features on the performance of controllers trained using the convolutional neural networks (CNNs), which gives a guideline of feature selection to reduce computation cost. We collect a large set of data using The Open Racing Car Simulator (TORCS) and classify the image features into three categories (sky-related, roadside-related, and road-related features). We then design two experimental frameworks to investigate the importance of each single feature for training a CNN controller. The first framework uses the training data with all three features included to train a controller, which is then tested with data that has one feature removed to evaluate the feature's effects. The second framework is trained with the data that has one feature excluded, while all three features are included in the test data. Different driving scenarios are selected to test and analyze the trained controllers using the two experimental frameworks. The experiment results show that (1) the road-related features are indispensable for training the controller, (2) the roadside-related features are useful to improve the generalizability of the controller to scenarios with complicated roadside information, and (3) the sky-related features have limited contribution to train an end-to-end autonomous vehicle controller.
Long-axis specialization of the human hippocampus

Investigation of the hippocampus has historically focused on computations within the trisynaptic circuit. However, discovery of important anatomical and functional variability along its long axis has inspired recent proposals of long-axis functional specialization in both the animal and human literatures. Here, we review and evaluate these proposals. We suggest that various long-axis specializations arise out of differences between the anterior (aHPC) and posterior hippocampus (pHPC) in large-scale network connectivity, the organization of entorhinal grid cells, and subfield compositions that bias the aHPC and pHPC towards pattern completion and separation, respectively. The latter two differences give rise to a property, reflected in the expression of multiple other functional specializations, of coarse, global representations in anterior hippocampus and fine-grained, local representations in posterior hippocampus.
Error Correcting Codes in Quantum Theory.

A new type of uncertainty relation is presented, concerning the information-bearing properti a discrete quantum system. A natural link is then revealed between basic quantum theory a linear error correcting codes of classical information theory. A subset of the known codes is desc having properties which are important for error correction in quantum communication. It is shown a pair of states which are, in a certain sense, “macroscopically different,” can form a superposit which the interference phase between the two parts is measurable. This provides a highly sta “Schrödinger cat” state.
Matthan: Drone Presence Detection by Identifying Physical Signatures in the Drone's RF Communication

Drones are increasingly flying in sensitive airspace where their presence may cause harm, such as near airports, forest fires, large crowded events, secure buildings, and even jails. This problem is likely to expand given the rapid proliferation of drones for commerce, monitoring, recreation, and other applications. A cost-effective detection system is needed to warn of the presence of drones in such cases. In this paper, we explore the feasibility of inexpensive RF-based detection of the presence of drones. We examine whether physical characteristics of the drone, such as body vibration and body shifting, can be detected in the wireless signal transmitted by drones during communication. We consider whether the received drone signals are uniquely differentiated from other mobile wireless phenomena such as cars equipped with Wi- Fi or humans carrying a mobile phone. The sensitivity of detection at distances of hundreds of meters as well as the accuracy of the overall detection system are evaluated using software defined radio (SDR) implementation.
Text recognition using deep BLSTM networks

This paper presents a Deep Bidirectional Long Short Term Memory (LSTM) based Recurrent Neural Network architecture for text recognition. This architecture uses Connectionist Temporal Classification (CTC) for training to learn the labels of an unsegmented sequence with unknown alignment. This work is motivated by the results of Deep Neural Networks for isolated numeral recognition and improved speech recognition using Deep BLSTM based approaches. Deep BLSTM architecture is chosen due to its ability to access long range context, learn sequence alignment and work without the need of segmented data. Due to the use of CTC and forward backward algorithms for alignment of output labels, there are no unicode re-ordering issues, thus no need of lexicon or postprocessing schemes. This is a script independent and segmentation free approach. This system has been implemented for the recognition of unsegmented words of printed Oriya text. This system achieves 4.18% character level error and 12.11% word error rate on printed Oriya text.
Generating High Fidelity 3-D Thermograms With a Handheld Real-Time Thermal Imaging System

Infrared thermography is a widely used technique to measure and portray the surface temperature of an object in form of thermal images. Two-dimensional images, however, have some inherent limitations with regard to the fidelity with which they can depict the surface temperature of a three dimensional object. In the past two decades, there have been several works describing different techniques to generate 3-D models textured with thermal information using various combinations of sensors in order to address some of these limitations. Most of these approaches generate 3-D thermograms of an object from a single perspective with bulky measurement systems and therefore do not address problems that arise when scanning objects in a continuous manner from multiple perspectives. But reductions in cost, size, and weight of infrared and depth-sensing cameras as well as a significant increase in computational power of personal computers have enabled the development of low cost, handheld, real-time 3-D thermal imaging systems. This paper elaborates through a series of experiments on the main factors that affect the real-time generation of 3-D thermograms with such a system and demonstrates how taking these factors into consideration significantly improves the appearance and fidelity of the generated 3-D thermogram. Most of the insight gained in this paper can be transferred to 3-D thermal imaging systems based on other combination of sensors.
Tokenization As The Initial Phase In NLP

In this paper, the authors address the significance and complexity of tokenization, the beginning step of NLP. Notions of word and token are discussed and defined from the viewpoints of lexicography and pragmatic implementation, respectively. Automatic segmentation of Chinese words is presented as an illustration of tokenization. Practical approaches to identification of compound tokens in English, such as idioms, phrasal verbs and fixed expressions, are developed.
An evaluation of the consequences of using short measures of the Big Five personality traits.

Researchers often use very abbreviated (e.g., 1-item, 2-item) measures of personality traits due to their convenience and ease of use as well as the belief that such measures can adequately capture an individual's personality. Using data from 2 samples (N = 437 employees, N = 355 college students), we show that this practice, particularly the use of single-item measures, can lead researchers to substantially underestimate the role that personality traits play in influencing important behaviors and thereby overestimate the role played by new constructs. That is, the use of very short measures of personality may substantially increase both the Type 1 and Type 2 error rates. We argue that even slightly longer measures can substantially increase the validity of research findings without significant inconvenience to the researcher or research participants.
EduCTX: A blockchain-based higher education credit platform

Blockchain technology enables the creation of a decentralized environment where transactions and data are not under the control of any third party organization. Any transaction ever completed is recorded in a public ledger in a verifiable and permanent way. Based on blockchain technology, we propose a global higher education credit platform, named EduCTX. This platform is based on the concept of the European Credit Transfer and Accumulation System (ECTS). It constitutes a globally trusted, decentralized higher education credit and grading system that can offer a globally unified viewpoint for students and higher education institutions (HEIs), as well as for other potential stakeholders such as companies, institutions and organizations. As a proof of concept, we present a prototype implementation of the environment, based on the open-source Ark Blockchain Platform. Based on a globally distributed peer-to-peer network, EduCTX will process, manage and control ECTX tokens, which represent credits that students gain for completed courses such as ECTS. HEIs are the peers of the blockchain network. The platform is a first step towards a more transparent and technologically advanced form of higher education systems. The EduCTX platform represents the basis of the EduCTX initiative which anticipates that various HEIs would join forces in order to create a globally efficient, simplified and ubiquitous environment in order to avoid language and administrative barriers. Therefore we invite and encourage HEIs to join the EduCTX initiative and the EduCTX blockchain network.
Incorporating simulation and implementation into teaching computer organization and architecture

This paper describes the motivation, the realization, and the experience of incorporating simulation and hardware implementation into teaching computer organization and architecture to computer science students. It demonstrates that learning by doing has helped students to truly understand how a computer is constructed and how it really works in practice. Correlated with textbook material, a set of simulation and implementation projects were created on the basis of the work that students had done in previous homework and laboratory activities. Students can thus use these designs as building blocks for completing more complex projects at a later time. The projects cover a wide range of topics from simple adders up to ALU's and CPU's. These processors operate in a virtual manner on certain short assembly-language programs. Specifically, this paper shares the experience of using simulation tools (Alterareg Quartus II) and reconfigurable hardware prototyping platforms (Alterareg UP2 development boards)
Traffic-Aware Placement of NFV Middleboxes

Network Function Virtualization (NFV) enables flex- ible deployment of middleboxes as Virtual Machines (VMs) running on general hardware. Different types of middleboxes have the potential to either increase or decrease the volume of processed traffic. In this paper, we investigate the traffic changing effects of middleboxes, and study efficient deployment of NFV middleboxes in Software-Defined Networks (SDNs). To begin with, we formulate the Traffic-Aware Middlebox Placement (TAMP) problem as a graph optimization problem, and show that it is NP-hard when there are multiple flows to consider. Next, by observing that in reality flows arrive one at a time, we leverage the SDN central control mechanism, and propose an optimal solution for the TAMP problem with a single flow. We develop the solution in two steps. First, when the flow path has been determined, we present the Least-First-Greatest- Last (LFGL) rule to place middleboxes. Second, we integrate the LFGL rule with widest-path routing to propose the LFGL based MinMax routing algorithm. Further, we have implemented the proposed algorithm as a module running on top of the Floodlight SDN controller, and conducted experiments in the Mininet emulation system. The experiment results fully demonstrate the superiority of our algorithm over other benchmark solutions.
Principal component analysis: a review and recent developments.

Large datasets are increasingly common and are often difficult to interpret. Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. Finding such new variables, the principal components, reduces to solving an eigenvalue/eigenvector problem, and the new variables are defined by the dataset at hand, not a priori, hence making PCA an adaptive data analysis technique. It is adaptive in another sense too, since variants of the technique have been developed that are tailored to various different data types and structures. This article will begin by introducing the basic ideas of PCA, discussing what it can and cannot do. It will then describe some variants of PCA and their application.
MIMO Precoding and Combining Solutions for Millimeter-Wave Systems

Millimeter-wave communication is one way to alleviate the spectrum gridlock at lower frequencies while simultaneously providing high-bandwidth communication channels. MmWave makes use of MIMO through large antenna arrays at both the base station and the mobile station to provide sufficient received signal power. This article explains how beamforming and precoding are different in MIMO mmWave systems than in their lower-frequency counterparts, due to different hardware constraints and channel characteristics. Two potential architectures are reviewed: hybrid analog/digital precoding/combining and combining with low-resolution analog- to-digital converters. The potential gains and design challenges for these strategies are discussed, and future research directions are highlighted.
Computer science. Accessible reproducible research.

C R E D IT : J O E S U T L IF F /W W W .C D A D .C O M /J O E S cientifi c publications have at least two goals: (i) to announce a result and (ii) to convince readers that the result is correct. Mathematics papers are expected to contain a proof complete enough to allow knowledgeable readers to fi ll in any details. Papers in experimental science should describe the results and provide a clear enough protocol to allow successful repetition and extension. Over the past ~35 years, computational science has posed challenges to this traditional paradigm—from the publication of the four-color theorem in mathematics ( 1), in which the proof was partially performed by a computer program, to results depending on computer simulation in chemistry, materials science, astrophysics, geophysics, and climate modeling. In these settings, the scientists are often sophisticated, skilled, and innovative programmers who develop large, robust software packages. More recently, scientists who are not themselves computational experts are conducting data analysis with a wide range of modular software tools and packages. Users may often combine these tools in unusual or novel ways. In biology, scientists are now routinely able to acquire and explore data sets far beyond the scope of manual analysis, including billions of DNA bases, millions of genotypes, and hundreds of thousands of RNA measurements. Similar issues may arise in other fi elds, such as astronomy, seismology, and meteorology. While propelling enormous progress, this increasing and sometimes “indirect” use of computation poses new challenges for scientifi c publication and replication. Large data sets are often analyzed many times, with modifi cations to the methods and parameters, and sometimes even updates of the data, until the fi nal results are produced. The resulting publication often gives only scant attention to the computational details. Some have suggested these papers are “merely the advertisement of scholarship whereas the computer programs, input data, parameter values, etc. embody the scholarship itself ” ( 2). However, the actual code or software “mashup” that gave rise to the fi nal analysis may be lost or unrecoverable. For example, colleagues and I published a computational method for distinguishing between two types of acute leukemia, based on large-scale gene expression profi les obtained from DNA microarrays ( 3). This paper generated hundreds of requests from scientists interested in replicating and extending the results. The method involved a complex pipeline of steps, including (i) preprocessing of the data, to eliminate likely artifacts; (ii) selection of genes to be used in the model; (iii) building the actual model and setting the appropriate parameters for it from the training data; (iv) preprocessing independent test data; and fi nally (v) applying the model to test its effi cacy. The result was robust and replicable, and the original data were available online, but there was no standardized form in which to make available the various software components and the precise details of their use.
Quantization index modulation: A class of provably good methods for digital watermarking and information embedding

We consider the problem of embedding one signal (e.g., a digital watermark), within another “host” signal to form a third, “composite” signal. The embedding is designed to achieve efficient trade-offs among the three conflicting goals of maximizing information-embedding rate, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is “provably good” against arbitrary bounded and fully-informed attacks, which arise in several copyright applications, and in particular it achieves provably better rate-distortion-robustness trade-offs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error constrained attack channels that model private-key watermarking applications.
A Review of Charging Algorithms for Nickel and Lithium Battery Chargers

Battery-charging algorithms can be used for either single- or multiple-battery chemistries. In general, single-chemistry chargers have the advantages of simplicity and reliability. On the other hand, multichemistry chargers, or “universal battery chargers,” provide a practical option for multichemistry battery systems, particularly for portable appliances, but they have some limitations. This paper presents a review of some charging algorithms for major batteries, i.e., nickel-cadmium, nickel-metal-hydride, and lithium-ion batteries for single- and multiple-chemistry chargers. A comparison between these algorithms in terms of their charging schemes and charge termination techniques is included. In addition, some trends of recent chargers development are presented.