nils-herrmann/clean_scirepeval huggingface dataset:
Can Government Purchases Stimulate the Economy ?

O of the few positive effects of the recent financial crisis has been the revival of interest in the short-run macroeconomic effects of government spending and tax changes. Before 2008, the topic of stimulus effects of fiscal policy was a backwater compared to research on monetary policy. One reason for the lack of interest was the belief that the lags in implementing fiscal policy were typically too long to be useful for combating recessions. Perhaps another reason was that central banks sponsored many more conferences than government treasury departments. When the economy fell off the cliff in 2008 and the Fed reached the dreaded “zero lower bound” on interest rates, however, it became abundantly clear that more research was needed. Given the upsurge in research on this topic, we now have many more resources to draw upon when asked “what is the government spending multiplier?” In this essay, I will begin by briefly reviewing what theory has to say about the potential effects. As I will discuss, “the multiplier” is a nebulous concept that depends very much on the type of government spending, its persistence, and how it is financed. I will then go on to review the aggregate empirical evidence for the United States, as well as the cross-locality evidence on multipliers. I will conclude that the U.S. aggregate multiplier for a temporary, deficit-financed increase in government purchases (that enter separately in the utility function and have no direct effect on private sector production functions) is probably between 0.8 and 1.5. Reasonable people can argue, however, that the data do not reject 0.5 or 2.0. Can Government Purchases Stimulate the Economy?
Ren Ng1 Marc Levoy1 Mathieu Bredif1 Gene Duval2 Mark Horowitz1 Pat Hanrahan1

This paper presents a camera that samples the 4D light field on its sensor in a single photographic exposure. This is achieved by inserting a microlens array between the sensor and main lens, creating a plenoptic camera. Each microlens measures not just the total amount of light deposited at that location, but how much light arrives along each ray. By re-sorting the measured rays of light to where they would have terminated in slightly different, synthetic cameras, we can compute sharp photographs focused at different depths. We show that a linear increase in the resolution of images under each microlens results in a linear increase in the sharpness of the refocused photographs. This property allows us to extend the depth of field of the camera without reducing the aperture, enabling shorter exposures and lower image noise. Especially in the macrophotography regime, we demonstrate that we can also compute synthetic photographs from a range of different viewpoints. These capabilities argue for a different strategy in designing photographic imaging systems. To the photographer, the plenoptic camera operates exactly like an ordinary hand-held camera. We have used our prototype to take hundreds of light field photographs, and we present examples of portraits, high-speed action and macro close-ups.
Students' perceptions of Facebook for academic purposes

Facebook is the most popular Social Network Site (SNS) among college students. Despite the popularity and extensive use of Facebook by students, its use has not made significant inroads into classroom usage. In this study, we seek to examine why this is the case and whether it would be worthwhile for faculty to invest the time to integrate Facebook into their teaching. To this end, we decided to undertake a study with a sample of 214 undergraduate students at the University of Huelva (Spain). We applied the structural equation model specifically designed by Mazman and Usluel (2010) to identify the factors that may motivate these students to adopt and use social network tools, specifically Facebook, for educational purposes. According to our results, Social Influence is the most important factor in predicting the adoption of Facebook; students are influenced to adopt it to establish or maintain contact with other people with whom they share interests. Regarding the purposes of Facebook usage, Social Relations is perceived as the most important factor among all of the purposes collected. Our findings also revealed that the educational use of Facebook is explained directly by its purposes of usage and indirectly by its adoption.
Detecting Events in Online Social Networks: Definitions, Trends and Challenges

Event detection is a research area that attracted attention during the last years due to the widespread availability of social media data. The problem of event detection has been examined in multiple social media sources like Twitter, Flickr, YouTube and Facebook. The task comprises many challenges including the processing of large volumes of data and high levels of noise. In this article, we present a wide range of event detection algorithms, architectures and evaluation methodologies. In addition, we extensively discuss on available datasets, potential applications and open research issues. The main objective is to provide a compact representation of the recent developments in the field and aid the reader in understanding the main challenges tackled so far as well as identifying interesting future research directions.
Stock market direction prediction using deep neural networks

In this study, the daily movement directions of three frequently traded stocks (GARAN, THYAO and ISCTR) in Borsa Istanbul were predicted using deep neural networks. Technical indicators obtained from individual stock prices and dollar-gold prices were used as features in the prediction. Class labels indicating the movement direction were found using daily close prices of the stocks and they were aligned with the feature vectors. In order to perform the prediction process, the type of deep neural network, Convolutional Neural Network, was trained and the performance of the classification was evaluated by the accuracy and F-measure metrics. In the experiments performed, using both price and dollar-gold features, the movement directions in GARAN, THYAO and ISCTR stocks were predicted with the accuracy rates of 0.61, 0.578 and 0.574 respectively. Compared to using the price based features only, the use of dollar-gold features improved the classification performance.
Traffic sign detection and recognition using OpenCV

The aim of the project is to detect and recognize traffic signs in video sequences recorded by an onboard vehicle camera. Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for automated intelligent driving vehicle or driver assistance systems. This paper presents a study to recognize traffic sign patterns using openCV technique. The images are extracted, detected and recognized by pre-processing with several image processing techniques, such as, threshold techniques, Gaussian filter, canny edge detection, Contour and Fit Ellipse. Then, the stages are performed to detect and recognize the traffic sign patterns. The system is trained and validated to find the best network architecture. The experimental results show the highly accurate classifications of traffic sign patterns with complex background images and the computational cost of the proposed method.
Academia.edu: Social network or Academic Network?

Academic social network sites Academia.edu and ResearchGate and reference sharing sites Mendeley, Bibsonomy, Zotero, and CiteULike give scholars the ability to publicise their research outputs and connect to each other. With millions of users, these are a significant addition to the scholarly communication and academic information seeking eco-structure. There is thus a need to understand the role that they play and the changes, if any, that they can make to the dynamics of academic careers. This article investigates attributes of philosophy scholars on Academia.edu, introducing a median-based time-normalising method to adjust for time delays in joining the site. In comparison to students, faculty tend to attract more profile views but female philosophers did not attract more profile views than did males, suggesting that academic capital drives philosophy uses of the site more than friendship and networking. Secondary analyses of law, history and computer science confirmed the faculty advantage (in terms of higher profile views) except for females in law and females in computer science. It also found a female advantage for both faculty and students in law and computer science as well as for history students. Hence, Academia.edu overall seems to reflect a hybrid of scholarly norms (the faculty advantage) and a female advantage that is suggestive of general social networking norms. Finally, traditional bibliometric measures did not correlate with any Academia.edu metrics for philosophers, perhaps because more senior academics use the site less extensively or because of the range informal scholarly activities that cannot be measured by bibliometric methods.
A Heartbeat and Temperature Measuring System for Remote Health Monitoring using Wireless Body Area Network

This paper presents the design and development of a microcontroller based heartbeat and body temperature monitor using fingertip and temperature sensor. The device uses the optical technology to detect the flow of blood through the finger and offers the advantage of portability over conventional recording systems. However, wireless body area network based remote patient monitoring systems have been presented with numerous problems including efficient data extraction and dynamic tuning of data to preserve the quality of data transmission. Evaluation of the device on real signals shows accuracy in heartbeat measurement, even under intense physical activity. This paper presents these challenges as well as solution to these problems by proposing an architecture which allows a network to be formed between the patient and doctor in order to enable remote monitoring of patient by analyzing the data of patient. The device consists of sensors which are used to measure heartbeat as well as body temperature of a patient and it is controlled by a central unit. The readings from these sensors are further processed and sent via GSM module to a remote location where it is displayed on cell phone. The optical heartbeat sensor counts the heartbeat per minute and temperature sensor measures the temperature from the body and both the measured data are sent to a receiving end utilizing wireless technology where the data is displayed in a cell phone for further processing and patient care. Moreover, the superiority of this device is shown in comparison to traditional system.
Computational Social Science to Gauge Online Extremism

Recent terrorist attacks carried out on behalf of ISIS on American and European soil by lone wolf attackers or sleeper cells remind us of the importance of understanding the dynamics of radicalization mediated by social media communication channels. In this paper, we shed light on the social media activity of a group of twenty-five thousand users whose association with ISIS online radical propaganda has been manually verified. By using a computational tool known as dynamical activity-connectivity maps, based on network and temporal activity patterns, we investigate the dynamics of social influence within ISIS supporters. We finally quantify the effectiveness of ISIS propaganda by determining the adoption of extremist content in the general population and draw a parallel between radical propaganda and epidemics spreading, highlighting that information broadcasters and influential ISIS supporters generate highly-infectious cascades of information contagion. Our findings will help generate effective countermeasures to combat the group and other forms of online extremism.
Corporate Governance Consequences of Accounting Scandals : Evidence from Top Management , CFO and Auditor Turnover

This paper examines the consequences of accounting scandals to top management, top financial officers, and outside auditors. We examine a sample of 518 U.S. public companies that announced earnings-decreasing restatements during the 1997-2002 period and an industry-size matched sample of control firms. Using logistic regressions that control for other determinants of management turnover, we find strong evidence of greater turnover of CEOs, top management and CFOs of restating firms compared to the control sample. Over the three years surrounding the year of restatement announcement, CEOs and CFOs, respectively, face a 14% and 10% greater probability of being replaced in restating firms than in control firms, after controlling for other factors. These represent increases of about 42% and 23%, respectively, compared to the usual turnover probabilities. The magnitudes of these effects are even larger for restatements that are more serious, have worse effects on stock prices, result in negative restated earnings, are initiated by outside parties, are accompanied by AAERs, or trigger securities class action lawsuits. We find little systematic evidence that auditor turnover is higher in restating firms. Our paper provides evidence of effective functioning of internal governance mechanisms following accounting scandals.
Revisiting Semi-Supervised Learning with Graph Embeddings

We present a semi-supervised learning framework based on graph embeddings. Given a graph between instances, we train an embedding for each instance to jointly predict the class label and the neighborhood context in the graph. We develop both transductive and inductive variants of our method. In the transductive variant of our method, the class labels are determined by both the learned embeddings and input feature vectors, while in the inductive variant, the embeddings are defined as a parametric function of the feature vectors, so predictions can be made on instances not seen during training. On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, we show improved performance over many of the existing models.
Unified state estimation for a ballbot

This paper presents a method for state estimation on a ballbot; i.e., a robot balancing on a single sphere. Within the framework of an extended Kalman filter and by utilizing a complete kinematic model of the robot, sensory information from different sources is combined and fused to obtain accurate estimates of the robot's attitude, velocity, and position. This information is to be used for state feedback control of the dynamically unstable system. Three incremental encoders (attached to the omniwheels that drive the ball of the robot) as well as three rate gyroscopes and accelerometers (attached to the robot's main body) are used as sensors. For the presented method, observability is proven analytically for all essential states in the system, and the algorithm is experimentally evaluated on the Ballbot Rezero.
Knowledge Semantic Representation: A Generative Model for Interpretable Knowledge Graph Embedding

Knowledge representation is an important, long-history topic in AI, and there have been a large amount of work for knowledge graph embedding which projects symbolic entities and relations into low-dimensional, real-valued vector space. However, most embedding methods merely concentrate on data fitting and ignore the explicit semantic expression, leading to uninterpretable representations. Thus, traditional embedding methods have limited potentials for many applications such as question answering, and entity classification. To this end, this paper proposes a semantic representation method for knowledge graph (KSR), which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments justify our model outperforms other state-of-the-art baselines substantially.
Big forensic data reduction: digital forensic images and electronic evidence

An issue that continues to impact digital forensics is the increasing volume of data and the growing number of devices. One proposed method to deal with the problem of “big digital forensic data”: the volume, variety, and velocity of digital forensic data, is to reduce the volume of data at either the collection stage or the processing stage. We have developed a novel approach which significantly improves on current practice, and in this paper we outline our data volume reduction process which focuses on imaging a selection of key files and data such as: registry, documents, spreadsheets, email, internet history, communications, logs, pictures, videos, and other relevant file types. When applied to test cases, a hundredfold reduction of original media volume was observed. When applied to real world cases of an Australian Law Enforcement Agency, the data volume further reduced to a small percentage of the original media volume, whilst retaining key evidential files and data. The reduction process was applied to a range of real world cases reviewed by experienced investigators and detectives and highlighted that evidential data was present in the data reduced forensic subset files. A data reduction approach is applicable in a range of areas, including: digital forensic triage, analysis, review, intelligence analysis, presentation, and archiving. In addition, the data reduction process outlined can be applied using common digital forensic hardware and software solutions available in appropriately equipped digital forensic labs without requiring additional purchase of software or hardware. The process can be applied to a wide variety of cases, such as terrorism and organised crime investigations, and the proposed data reduction process is intended to provide a capability to rapidly process data and gain an understanding of the information and/or locate key evidence or intelligence in a timely manner.
Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud

With cloud storage services, it is commonplace for data to be not only stored in the cloud, but also shared across multiple users. However, public auditing for such shared data - while preserving identity privacy - remains to be an open challenge. In this paper, we propose the first privacy-preserving mechanism that allows public auditing on shared data stored in the cloud. In particular, we exploit ring signatures to compute the verification information needed to audit the integrity of shared data. With our mechanism, the identity of the signer on each block in shared data is kept private from a third party auditor (TPA), who is still able to verify the integrity of shared data without retrieving the entire file. Our experimental results demonstrate the effectiveness and efficiency of our proposed mechanism when auditing shared data.
Comparing Open-Source Speech Recognition Toolkits ⋆

In this paper, a large-scale evaluation of open-source speech recognition toolkits is described. Specifically, HTK in association with the decoders HDecode and Julius, CMU Sphinx with the decoders pocketsphinx and Sphinx-4, and the Kaldi toolkit are compared in terms of usability and expense of recognition accuracy. The evaluation presented in this paper was done on German and English language using respective the Verbmobil 1 and the Wall Street Journal 1 corpus. We found that, Kaldi providing the most advanced training recipes gives outstanding results out of the box, the Sphinx toolkit also containing recipes enables good results in short time. HTK provides the least support and requires much more time, knowledge and effort to obtain results of the order of the other toolkits.
Argumentation mining: the detection, classification and structure of arguments in text

Argumentation is the process by which arguments are constructed and handled. Argumentation constitutes a major component of human intelligence. The ability to engage in argumentation is essential for humans to understand new problems, to perform scientific reasoning, to express, to clarify and to defend their opinions in their daily lives. Argumentation mining aims to detect the arguments presented in a text document, the relations between them and the internal structure of each individual argument. In this paper we analyse the main research questions when dealing with argumentation mining and the different methods we have studied and developed in order to successfully confront the challenges of argumentation mining in legal texts.
Do Deep Generative Models Know What They Don't Know?

A neural network deployed in the wild may be asked to make predictions for inputs that were drawn from a different distribution than that of the training data. A plethora of work has demonstrated that it is easy to find or synthesize inputs for which a neural network is highly confident yet wrong. Generative models are widely viewed to be robust to such mistaken confidence as modeling the density of the input features can be used to detect novel, out-of-distribution inputs. In this paper we challenge this assumption. We find that the density learned by flow-based models, VAEs, and PixelCNNs cannot distinguish images of common objects such as dogs, trucks, and horses (i.e. CIFAR-10) from those of house numbers (i.e. SVHN), assigning a higher likelihood to the latter when the model is trained on the former. Moreover, we find evidence of this phenomenon when pairing several popular image data sets: FashionMNIST vs MNIST, CelebA vs SVHN, ImageNet vs CIFAR-10 / CIFAR-100 / SVHN. To investigate this curious behavior, we focus analysis on flow-based generative models in particular since they are trained and evaluated via the exact marginal likelihood. We find such behavior persists even when we restrict the flows to constant-volume transformations. These transformations admit some theoretical analysis, and we show that the difference in likelihoods can be explained by the location and variances of the data and the model curvature. Our results caution against using the density estimates from deep generative models to identify inputs similar to the training distribution until their behavior for out-of-distribution inputs is better understood.
A Simulation Model for the Waterfall Software Development Life Cycle

Software development life cycle or SDLC for short is a methodology for designing, building, and maintaining information and industrial systems. So far, there exist many SDLC models, one of which is the Waterfall model which comprises five phases to be completed sequentially in order to develop a software solution. However, SDLC of software systems has always encountered problems and limitations that resulted in significant budget overruns, late or suspended deliveries, and dissatisfied clients. The major reason for these deficiencies is that project directors are not wisely assigning the required number of workers and resources on the various activities of the SDLC. Consequently, some SDLC phases with insufficient resources may be delayed; while, others with excess resources may be idled, leading to a bottleneck between the arrival and delivery of projects and to a failure in delivering an operational product on time and within budget. This paper proposes a simulation model for the Waterfall development process using the Simphony.NET simulation tool whose role is to assist project managers in determining how to achieve the maximum productivity with the minimum number of expenses, workers, and hours. It helps maximizing the utilization of development processes by keeping all employees and resources busy all the time to keep pace with the arrival of projects and to decrease waste and idle time. As future work, other SDLC models such as spiral and incremental are to be simulated, giving project executives the choice to use a diversity of software development methodologies.