nils-herrmann/clean_scirepeval huggingface dataset:
Prediction as a Rule for Unsupervised Learning in Deep Neural Networks a dissertation presented by

While machine learning systems have recently achieved impressive, (super)human-level performance in several tasks, they have often relied on unnatural amounts of supervision – e.g. large numbers of labeled images or continuous scores in video games. In contrast, human learning is largely unsupervised, driven by observation and interaction with the world. Emulating this type of learning in machines is an open challenge, and one that is critical for general artificial intelligence. Here, we explore prediction of future frames in video sequences as an unsupervised learning rule. A key insight here is that in order to be able to predict how the visual world will change over time, an agent must have at least some implicit model of object structure and the possible transformations objects can undergo. To this end, we have designed several models capable of accurate prediction in complex sequences. Our first model consists of a recurrent extension to the standard autoencoder framework. Trained end-to-end to predict the movement of synthetic stimuli, we find that the model learns a representation of the underlying latent parameters of the 3D objects themselves. Importantly, we find that this representation is naturally tolerant to object transformations, and generalizes well to new tasks, such as classification of static images. Similar models trained solely with a reconstruction loss fail to generalize as effectively. In addition, we explore the use of an adversarial loss, as in a Generative Adversarial Network, illustrating its complementary effects to traditional pixel losses for the task of next-frame prediction.
Modern JavaScript frameworks: A Survey Study

With the increasing popularity of the web, some new web technologies emerged and introduced dynamics to web applications, in comparison to HTML, as a static programming language. JavaScript is the language that provided a dynamic web site which actively communicates with users. JavaScript is used in today's web applications as a client script language and on the server side. The JavaScript language supports the Model View Controller (MVC) architecture that maintains a readable code and clearly separates parts of the program code. The topic of this research is to compare the popular JavaScript frameworks: AngularJS, Ember, Knockout, Backbone. All four frameworks are based on MVC or similar architecture. In this paper, the advantages and disadvantages of each framework, the impact on application speed, the ways of testing such JS applications and ways to improve code security are presented.
Implementation of an energy monitoring and control device based on IoT

Energy monitoring and conservation holds prime importance in today's world because of the imbalance between power generation and demand. The current scenario says that the power generated, which is primarily contributed by fossil fuels may get exhausted within the next 20 years. Currently, there are very accurate electronic energy monitoring systems available in the market. Most of these monitor the power consumed in a domestic household, in case of residential applications. Many a times, consumers are dissatisfied with the power bill as it does not show the power consumed at the device level. This paper presents the design and implementation of an energy meter using Arduino microcontroller which can be used to measure the power consumed by any individual electrical appliance. Internet of Things (IoT) is an emerging field and IoT based devices have created a revolution in electronics and IT. The main intention of the proposed energy meter is to monitor the power consumption at the device level, upload it to the server and establish remote control of any appliance. The energy monitoring system precisely calculates the power consumed by various electrical devices and displays it through a home energy monitoring website. The advantage of this device is that a user can understand the power consumed by any electrical appliance from the website and can take further steps to control them and thus help in energy conservation. Further on, the users can monitor the power consumption as well as the bill on daily basis.
DAPASA: Detecting Android Piggybacked Apps Through Sensitive Subgraph Analysis

With the exponential growth of smartphone adoption, malware attacks on smartphones have resulted in serious threats to users, especially those on popular platforms, such as Android. Most Android malware is generated by piggybacking malicious payloads into benign applications (apps), which are called piggybacked apps. In this paper, we propose DAPASA, an approach to detect Android piggybacked apps through sensitive subgraph analysis. Two assumptions are established to reflect the different invocation patterns of sensitive APIs in the injected malicious payloads (rider) of a piggybacked app and in its host app (carrier). With these two assumptions, DAPASA generates a sensitive subgraph (SSG) to profile the most suspicious behavior of an app. Five features are constructed from SSG to depict the invocation patterns. The five features are fed into the machine learning algorithms to detect whether the app is piggybacked or benign. DAPASA is evaluated on a large real-world data set consisting of 2551 piggybacked apps and 44 921 popular benign apps. Extensive evaluation results demonstrate that the proposed approach exhibits an impressive detection performance compared with that of three baseline approaches even with only five numeric features. Furthermore, the proposed approach can complement permission-based approaches and API-based approaches with the combination of our five features from a new perspective of the invocation structure.
GridDS: a hybrid data structure for residue computation in point set matching

Registration of 3D point clouds is a problem that arises in a variety of research areas such as computer vision, computer graphics and computational geometry. This situation causes most papers in the area to focus on solving practical problems by using data structures often developed in theoretical contexts. Consequently, discrepancies arise between asymptotic cost and experimental performance. The point cloud registration or matching problem encompasses many different steps. Among them, the computation of the distance between two point sets (often refereed to as residue computation) is crucial and can be seen as an aggregate of range searching or nearest neighbor searching. In this paper, we aim at providing theoretical analysis and experimental performance of range searching and nearest neighbor data structures applied to 3D point cloud registration. Performance of widely used data structures such as compressed octrees, KDtrees, BDtrees and regular grids is reported. Additionally, we present a new hybrid data structure named GridDS, which combines a regular grid with some preexisting “inner” data structure in order to meet the best asymptotic bounds while also obtaining the best performance. The experimental evaluation in both synthetic and real data demonstrates that the hybrid data structures built using GridDS improve the running times of the single data structures. Thus, as we have studied the performances of the state-of-the-art techniques managing to improve their respective running times thanks to GridDS, this paper presents the best running time for point cloud residue computation up to date.
THE BUSINESS CYCLE

A growth model with shocks to technology is studied. Labor is indivisible, so all variability in hours worked is due to fluctuations in the number employed. We find that, unlike previous equilibrium models of the business cycle, this economy displays large fluctuations in hours worked and relatively small fluctuations in productivity. This finding is independent of individuals’ willingness to substitute leisure across time. This and other findings are the result of studying and comparing summary statistics describing this economy, an economy with divisible labor, and post-war U.S. time series.
An improved TF-IDF approach for text classification

This paper presents a new improved term frequency/inverse document frequency (TF-IDF) approach which uses confidence, support and characteristic words to enhance the recall and precision of text classification. Synonyms defined by a lexicon are processed in the improved TF-IDF approach. We detailedly discuss and analyze the relationship among confidence, recall and precision. The experiments based on science and technology gave promising results that the new TF-IDF approach improves the precision and recall of text classification compared with the conventional TF-IDF approach.
Vulnerability : A generally applicable conceptual framework for climate change research

The term ‘vulnerability’ is used in many different ways by various scholarly communities. The resulting disagreement about the appropriate definition of vulnerability is a frequent cause for misunderstanding in interdisciplinary research on climate change and a challenge for attempts to develop formal models of vulnerability. Earlier attempts at reconciling the various conceptualizations of vulnerability were, at best, partly successful. This paper presents a generally applicable conceptual framework of vulnerability that combines a nomenclature of vulnerable situations and a terminology of vulnerability concepts based on the distinction of four fundamental groups of vulnerability factors. This conceptual framework is applied to characterize the vulnerability concepts employed by the main schools of vulnerability research and to review earlier attempts at classifying vulnerability concepts. None of these onedimensional classification schemes reflects the diversity of vulnerability concepts identified in this review. The wide range of policy responses available to address the risks from global climate change suggests that climate impact, vulnerability, and adaptation assessments will continue to apply a variety of vulnerability concepts. The framework presented here provides the much-needed conceptual clarity and facilitates bridging the various approaches to researching vulnerability to climate change. r 2006
Alexa, Siri, Cortana, and More: An Introduction to Voice Assistants.

Voice assistants are software agents that can interpret human speech and respond via synthesized voices. Apple's Siri, Amazon's Alexa, Microsoft's Cortana, and Google's Assistant are the most popular voice assistants and are embedded in smartphones or dedicated home speakers. Users can ask their assistants questions, control home automation devices and media playback via voice, and manage other basic tasks such as email, to-do lists, and calendars with verbal commands. This column will explore the basic workings and common features of today's voice assistants. It will also discuss some of the privacy and security issues inherent to voice assistants and some potential future uses for these devices. As voice assistants become more widely used, librarians will want to be familiar with their operation and perhaps consider them as a means to deliver library services and materials.
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks

The discriminative power of modern deep learning models for 3D human action recognition is growing ever so potent. In conjunction with the recent resurgence of 3D human action representation with 3D skeletons, the quality and the pace of recent progress have been significant. However, the inner workings of state-of-the-art learning based methods in 3D human action recognition still remain mostly black-box. In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition. TCN provides us a way to explicitly learn readily interpretable spatio-temporal representations for 3D human action recognition. Through this work, we wish to take a step towards a spatio-temporal model that is easier to understand, explain and interpret. The resulting model, Res-TCN, achieves state-of-the-art results on the largest 3D human action recognition dataset, NTU-RGBD.
GAIA Map: A Tool for Visual Ranking Analysis in Spatial Multicriteria Problems

Spatial multicriteria decision problems are decision problems where one needs to take multiple conflicting criteria as well as geographical knowledge into account. In such a context, exploratory spatial analysis is known to provide tools to visualize as much data as possible on maps but does not integrate multicriteria aspects. Also, none of the tools provided by multicriteria analysis were initially destined to be used in a geographical context.In this paper, we propose an application of the PROMETHEE and GAIA ranking methods to Geographical Information Systems (GIS). The aim is to help decision makers obtain rankings of geographical entities and understand why such rankings have been obtained. To do that, we make use of the visual approach of the GAIA method and adapt it to display the results on geographical maps. This approach is then extended to cover several weaknesses of the adaptation. Finally, it is applied to a study of the region of Brussels as well as an evaluation of the Human Development Index (HDI) in Europe.
Racial Discrimination in the Sharing Economy : Evidence from a Field Experiment

In an experiment on Airbnb, we find that applications from guests with distinctively African-American names are 16% less likely to be accepted relative to identical guests with distinctively White names. Discrimination occurs among landlords of all sizes, including small landlords sharing the property and larger landlords with multiple properties. It is most pronounced among hosts who have never had an African-American guest, suggesting only a subset of hosts discriminate. While rental markets have achieved significant reductions in discrimination in recent decades, our results suggest that Airbnb’s current design choices facilitate discrimination and raise the possibility of erasing some of these civil rights gains.
Using Online Conversations to Study Word-of-Mouth Communication

M are very interested in word-of-mouth communication because they believe that a product’s success is related to the word of mouth that it generates. However, there are at least three significant challenges associated with measuring word of mouth. First, how does one gather the data? Because the information is exchanged in private conversations, direct observation traditionally has been difficult. Second, what aspect of these conversations should one measure? The third challenge comes from the fact that word of mouth is not exogenous. While the mapping from word of mouth to future sales is of great interest to the firm, we must also recognize that word of mouth is an outcome of past sales. Our primary objective is to address these challenges. As a context for our study, we have chosen new television (TV) shows during the 1999–2000 seasons. Our source of word-of-mouth conversations is Usenet, a collection of thousands of newsgroups with diverse topics. We find that online conversations may offer an easy and cost-effective opportunity to measure word of mouth. We show that a measure of the dispersion of conversations across communities has explanatory power in a dynamic model of TV ratings.
Effects of Different Combinations of Strength, Power, and Plyometric Training on the Physical Performance of Elite Young Soccer Players.

The combination of strength (ST) and plyometric training (PT) has been shown to be effective for improving sport-specific performance. However, there is no consensus about the most effective way to combine these methods in the same training session to produce greater improvements in neuromuscular performance of soccer players. Thus, the purpose of this study was to compare the effects of different combinations of ST and PT sequences on strength, jump, speed, and agility capacities of elite young soccer players. Twenty-seven soccer players (age: 18.9 ± 0.6 years) participated in an 8-week resistance training program and were divided into 3 groups: complex training (CP) (ST before PT), traditional training (TD) (PT before ST), and contrast training (CT) (ST and PT performed alternately, set by set). The experimental design took place during the competitive period of the season. The ST composed of half-squat exercises performed at 60-80% of 1 repetition maximum (1RM); the PT composed of drop jump exercises executed in a range from 30 to 45 cm. After the experimental period, the maximum dynamic strength (half-squat 1RM) and vertical jump ability (countermovement jump height) increased similarly and significantly in the CP, TD, and CT (48.6, 46.3, and 53% and 13, 14.2, and 14.7%, respectively). Importantly, whereas the TD group presented a significant decrease in sprinting speed in 10 (7%) and 20 m (6%), the other groups did not show this response. Furthermore, no significant alterations were observed in agility performance in any experimental group. In conclusion, in young soccer players, different combinations and sequences of ST and PT sets result in similar performance improvements in muscle strength and jump ability. However, it is suggested that the use of the CP and CT methods is more indicated to maintain/maximize the sprint performance of these athletes.
A review on streptococcus mutans with its diseases dental caries, dental plaque and endocarditis

Microorganisms present in our oral cavity which are called the human micro flora attach to our tooth surfaces and develop biofilms. In maximum organic habitats microorganisms generally prevail as multispecies biolfilms with the help of intercellular interactions and communications among them which are the main keys for their endurance. These biofilms are formed by initial attachment of bacteria to a surface, development of a multi –dimensional complex structure and detachment to progress other site. The best example of biofilm formation is dental plaque. Plaque formation can lead to dental caries and other associated diseases causing tooth loss. Many different bacteria are involved in these processes and one among them is Streptococcus mutans which is the principle and the most important agent. When these infections become severe, during the treatment the bacterium can enter the bloodstream from the oral cavity and cause endocarditis. The oral bacterium S. mutans is greatly skilled in its mechanical modes of carbohydrate absorption. It also synthesizes polysaccharides that are present in dental plaque causing caries. As dental caries is a preventable disease major distinct approaches for its prevention are: carbohydrate diet, sugar substitutes, mechanical cleaning techniques, use of fluorides, antimicrobial agents, fissure sealants, vaccines, probiotics, replacement theory and dairy products and at the same time for tooth remineralization fluorides and casein phosphopeptides are extensively employed. The aim of this review article is to put forth the general features of the bacterium S.mutans and how it is involved in certain diseases like: dental plaque, dental caries and endocarditis.
Secure virtualization for cloud computing

Cloud computing adoption and diffusion are threatened by unresolved security issues that affect both the cloud provider and the cloud user. In this paper, we show how virtualization can increase the security of cloud computing, by protecting both the integrity of guest virtual machines and the cloud infrastructure components. In particular, we propose a novel architecture, Advanced Cloud Protection System (ACPS), aimed at guaranteeing increased security to cloud resources. ACPS can be deployed on several cloud solutions and can effectively monitor the integrity of guest and infrastructure components while remaining fully transparent to virtual machines and to cloud users. ACPS can locally react to security breaches as well as notify a further security management layer of such events. A prototype of our ACPS proposal is fully implemented on two current open source solutions: Eucalyptus and OpenECP. The prototype is tested against effectiveness and performance. In particular: (a) effectiveness is shown testing our prototype against attacks known in the literature; (b) performance evaluation of the ACPS prototype is carried out under different types of workload. Results show that our proposal is resilient against attacks and that the introduced overhead is small when compared to the provided
An Automatic Hole Filling Method of Point Cloud for 3 D Scanning

In recent years, due to the development of three-dimensional scanning technology, the opportunities for real objects to be three-dimensionally measured, taken into the PC as point cloud data, and used for various contents are increasing. However, the point cloud data obtained by three-dimensional scanning has many problems such as data loss due to occlusion or the material of the object to be measured, and occurrence of noise. Therefore, it is necessary to edit the point cloud data obtained by scanning. Particularly, since the point cloud data obtained by scanning contains many data missing, it takes much time to fill holes. Therefore, we propose a method to automatically filling hole obtained by three-dimensional scanning. In our method, a surface is generated from a point in the vicinity of a hole, and a hole region is filled by generating a point sequence on the surface. This method is suitable for processing to fill a large number of holes because point sequence interpolation can be performed automatically for hole regions without requiring user input.
Toward a Lightweight Authentication and Authorization Framework for Smart Objects

The Internet of Things (IoT) represents the current technology revolution that is intended to transform the current environment into a more pervasive and ubiquitous world. In this emerging ecosystem, the application of standard security technologies has to cope with the inherent nature of constrained physical devices, which are seamlessly integrated into the Internet infrastructure. This work proposes a set of lightweight authentication and authorization mechanisms in order to support smart objects during their life cycle. Furthermore, such mechanisms are framed within a proposed security framework, which is compliant with the Architectural Reference Model, recently presented by the EU FP7 IoT-A project. The resulting architecture is intended to provide a holistic security approach to be leveraged in the design of novel and lightweight security protocols for IoT constrained environments.
Modeling Contagious Merger and Acquisition via Point Processes with a Profile Regression Prior

Merger and Acquisition (M&A) has been a critical practice about corporate restructuring. Previous studies are mostly devoted to evaluating the suitability of M&A between a pair of investor and target company, or a target company for its propensity of being acquired. This paper focuses on the dual problem of predicting an investor’s prospective M&A based on its activities and firmographics. We propose to use a mutually-exciting point process with a regression prior to quantify the investor’s M&A behavior. Our model is motivated by the so-called contagious ‘wave-like’ M&A phenomenon, which has been well-recognized by the economics and management communities. A tailored model learning algorithm is devised that incorporates both static profile covariates and past M&A activities. Results on CrunchBase suggest the superiority of our model. The collected dataset and code will be released together with the paper.