nils-herrmann/clean_scirepeval huggingface dataset:
A literature survey of low-rank tensor approximation techniques

An entry of X is denoted by Xi1,...,id where each index iμ ∈ {1, . . . , nμ} refers to the μth mode of the tensor for μ = 1, . . . , d. For simplicity, we will assume that X has real entries, but it is of course possible to define complex tensors or, more generally, tensors over arbitrary fields. A wide variety of applications lead to problems where the data or the desired solution can be represented by a tensor. In this survey, we will focus on tensors that are induced by the discretization of a multivariate function; we refer to the survey [169] and to the books [175, 241] for the treatment of tensors containing observed data. The simplest way a given multivariate function f(x1, x2, . . . , xd) on a tensor product domain Ω = [0, 1] leads to a tensor is by sampling f on a tensor grid. In this case, each entry of the tensor contains the function value at the corresponding position in the grid. The function f itself may, for example, represent the solution to a high-dimensional partial differential equation (PDE). As the order d increases, the number of entries in X increases exponentially for constant n = n1 = · · · = nd. This so called curse of dimensionality prevents the explicit storage of the entries except for very small values of d. Even for n = 2, storing a tensor of order d = 50 would require 9 petabyte! It is therefore essential to approximate tensors of higher order in a compressed scheme, for example, a low-rank tensor decomposition. Various such decompositions have been developed, see Section 2. An important difference to tensors containing observed data, a tensor X induced by a function is usually not given directly but only as the solution of some algebraic equation, e.g., a linear system or eigenvalue problem. This requires the development of solvers for such equations working within the compressed storage scheme. Such algorithms are discussed in Section 3. The range of applications of low-rank tensor techniques is quickly expanding. For example, they have been used for addressing:
Feedback control for adaptive live video streaming

Multimedia content feeds an ever increasing fraction of the Internet traffic. Video streaming is one of the most important applications driving this trend. Adaptive video streaming is a relevant advancement with respect to classic progressive download streaming such as the one employed by YouTube. It consists in dynamically adapting the content bitrate in order to provide the maximum Quality of Experience, given the current available bandwidth, while ensuring a continuous reproduction. In this paper we propose a Quality Adaptation Controller (QAC) for live adaptive video streaming designed by employing feedback control theory. An experimental comparison with Akamai adaptive video streaming has been carried out. We have found the following main results: 1) QAC is able to throttle the video quality to match the available bandwidth with a transient of less than 30s while ensuring a continuous video reproduction; 2) QAC fairly shares the available bandwidth both in the cases of a concurrent TCP greedy connection or a concurrent video streaming flow; 3) Akamai underutilizes the available bandwidth due to the conservativeness of its heuristic algorithm; moreover, when abrupt available bandwidth reductions occur, the video reproduction is affected by interruptions.
Noisy Sparse Subspace Clustering

This paper considers the problem of subspace clustering under noise. Specifically, we study the behavior of Sparse Subspace Clustering (SSC) when either adversarial or random noise is added to the unlabelled input data points, which are assumed to lie in a union of low-dimensional subspaces. We show that a modified version of SSC is provably effective in correctly identifying the underlying subspaces, even with noisy data. This extends theoretical guarantee of this algorithm to the practical setting and provides justification to the success of SSC in a class of real applications.
TOSThreads: thread-safe and non-invasive preemption in TinyOS

Many threads packages have been proposed for programming wireless sensor platforms. However, many sensor network operating systems still choose to provide an event-driven model, due to efficiency concerns. We present TOS-Threads, a threads package for TinyOS that combines the ease of a threaded programming model with the efficiency of an event-based kernel. TOSThreads is backwards compatible with existing TinyOS code, supports an evolvable, thread-safe kernel API, and enables flexible application development through dynamic linking and loading. In TOS-Threads, TinyOS code runs at a higher priority than application threads and all kernel operations are invoked only via message passing, never directly, ensuring thread-safety while enabling maximal concurrency. The TOSThreads package is non-invasive; it does not require any large-scale changes to existing TinyOS code.
 We demonstrate that TOSThreads context switches and system calls introduce an overhead of less than 0.92% and that dynamic linking and loading takes as little as 90 ms for a representative sensing application. We compare different programming models built using TOSThreads, including standard C with blocking system calls and a reimplementation of Tenet. Additionally, we demonstrate that TOSThreads is able to run computationally intensive tasks without adversely affecting the timing of critical OS services.
On Controller Performance in Software-Defined Networks

Network architectures in which the control plane is decoupled from the data plane have been growing in popularity. Among the main arguments for this approach is that it provides a more structured software environment for developing network-wide abstractions while potentially simplifying the data plane. As has been adopted elsewhere [11], we refer to this split architecture as Software-Defined Networking (SDN). While it has been argued that SDN is suitable for some deployment environments (such as homes [17, 13], data centers , and the enterprise [5]), delegating control to a remote system has raised a number of questions on control-plane scaling implications of such an approach. Two of the most often voiced concerns are: (a) how fast can the controller respond to data path requests?; and (b) how many data path requests can it handle per second? There are some references to the performance of SDN systems in the literature [16, 5, 3]. For example, an oft-cited study shows that a popular network controller (NOX) handles around 30k flow initiation events1 per second while maintaining a sub-10ms flow install time [14]. Unfortunately, recent measurements of some deployment environments suggests that these numbers are far from sufficient. For example, Kandula et al.  found that a 1500-server cluster has a median flow arrival rate of 100k flows per second. Also, Benson et al.  show that a network with 100 switches can have spikes of 10M flows arrivals per second in the worst case. In addition, the 10ms flow setup delay of an SDN controller would add a 10% delay to the majority of flows (short-lived) in such a network. This disconnect between relatively poor controller performance and high network demands has motivated a
Open Networks, Closed Regimes: The Impact of the Internet on Authoritarian Rule

for Open Networks, Closed Regimes " Many hope that information technology will generate new opportunities for global communications, breaking down national barriers even in dictatorial regimes with minimal freedom of the press. Kalathil and Boas provide a path-breaking and thoughtful analysis of this issue. A fascinating study, this should be widely read by all concerned with understanding and promoting democratization, regime change, and new information technology. " " Through a country-by-country analysis, Kalathil and Boas shed light on practices formerly known only by anecdote, and their findings chip away at the apocryphal notion that going digital necessarily means going democratic. Their work answers a number of important questions, and frames a worthy challenge to those who wish to deploy technology for the cause of political openness. " The Carnegie Endowment normally does not take institutional positions on public policy issues; the views and recommendations presented in this publication do not necessarily represent the views of the Carnegie Endowment, its officers, staff, or trustees.
DEVELOPMENT OF A STEP-CLIMBING OMNI-DIRECTIONAL MOBILE ROBOT

In this paper, we purpose a new holonomic omni-directional mobile robot that can move on not only flat floors but also uneven environment. A prototype robot can move in omni-direction, run on the uneven floors and slopes, and pass over large steps. The robot has seven universal wheels that have twelve cylindrical free rollers. We adopt a passive suspension system that enable the robot to change the shape of the robot body in proportion to ground states without using actuators and sensors. We construct the prototype robot and analyse the kinematics of the robot. The performance of the prototype robot is verified through experiments.
The Impact of Mergers & Acquisitions on IT Governance Structures: A Case Study

Developing information technology (IT) gov-ernance structures within an organization has always been challenging. This is particularly the case in organizations that have achieved growth through mergers and acquisitions. When the acquired organizations are geographically located in different regions than the host enterprise, the factors affecting this integration and the choice of IT governance structures are quite different than when this situation does not exist. This study performs an exploratory examination of the factors that affect the choice of IT governance structures in organizations that grow through mergers and acquisitions in developing countries using the results of a case study of an international telecommunications company. We find that in addition to the commonly recognized factors such as government regulation, competition and market stability, organizational culture, and IT competence, top management's predisposition toward a specific business strategy and governance structure can profoundly influence the choice of IT governance in organizations. Managerial implications are discussed.
Learning about problem based learning: Student teachers integrating technology, pedagogy and content knowledge

What should constitute knowledge bases that we expect our future teachers to gain related to pedagogically sound technology integration? Employing the Shulman’s teacher knowledge base as a theoretical lens, this study examined the complexity of pre-service teachers’ technological pedagogical content knowledge (TPCK) in the context of integrating problem based learning (PBL) and information and communications technology (ICT). Ninety-seven pre-service teachers in this study engaged in a collaborative lesson design project where they applied pedagogical knowledge about PBL to design a technology integrated lesson in their subject area of teaching. Data were collected from two sources: survey and lesson design artifacts. Data analyses revealed that while participants had theoretical understandings of pedagogical knowledge about PBL, their lesson designs showed a mismatch among technology tools, content representations, and pedagogical strategies, indicating conflicts in translating pedagogical content knowledge into designing pedagogically sound, technology integrated lessons. The areas that students perceived to be particularly challenging and difficult include: a) generating authentic and ill-structured problems for a chosen content topic, b) finding and integrating ICT tools and resources relevant for the target students and learning activities, and c) designing tasks with a balance between teacher guidance and student independence. The present study suggests the potential of two explanations for such difficulties: lack of intimate connection among beliefs, knowledge, and actions, and insufficient repertoires for teaching with technology for problem based learning.
One Million Posts: A Data Set of German Online Discussions

In this paper we introduce a new data set consisting of user comments posted to the website of a German-language Austrian newspaper. Professional forum moderators have annotated 11,773 posts according to seven categories they considered crucial for the efficient moderation of online discussions in the context of news articles. In addition to this taxonomy and annotated posts, the data set contains one million unlabeled posts. Our experimental results using six methods establish a first baseline for predicting these categories. The data and our code are available for research purposes from https://ofai.github.io/million-post-corpus.
Phone-level pronunciation scoring and assessment for interactive language learning

This paper investigates a method of automatic pronunciation scoring for use in computer-assisted language learning (CALL) systems. The method utilises a likelihood-based `Goodness of Pronunciation' (GOP) measure which is extended to include individual thresholds for each phone based on both averaged native con®dence scores and on rejection statistics provided by human judges. Further improvements are obtained by incorporating models of the subjectÕs native language and by augmenting the recognition networks to include expected pronunciation errors. The various GOP measures are assessed using a specially recorded database of non-native speakers which has been annotated to mark phone-level pronunciation errors. Since pronunciation assessment is highly subjective, a set of four performance measures has been designed, each of them measuring dierent aspects of how well computer-derived phone-level scores agree with human scores. These performance measures are used to cross-validate the reference annotations and to assess the basic GOP algorithm and its re®nements. The experimental results suggest that a likelihood-based pronunciation scoring metric can achieve usable performance, especially after applying the various enhancements. Ó 2000 Elsevier Science B.V. All rights reserved.
Hymen : facts and conceptions

The hymen is a membrane, partially closing the external vaginal opening. Until now, many myths regarding its anatomy exist but are based primarily on dogma and lack of scientific  In conservative cultures, its presence without rupture in a premarital girl represents an indication of her pride and honor. According to Arab customs, woman who is not found to be virgin on her wedding night will bring a great shame on her family. She might be killed by her brothers, uncles or even her father, thereby "cleansing the shame". Although such murders are illegal, the perpetrators escape prosecution because the tribal customs that underpin these actions are so strong.2 Also, the husband might kill his bride, if he does not notice a bleeding as a result of defloration. In such cultures, the traditional proof of virginity is the occurrence of bleeding from ruptured hymen on wedding night. Therefore, the ignorance about structure of the hymen can lead to violence, such as 'honor killing' as well as psychological problems including suicidal feelings. Meanwhile, the physician’s testimony about the appearance of the genitalia may be one of the key factors which determine the outcome of such cases.3 Therefore, the clinician should be familiar with normal prepubertal genital anatomy and hymenal configuration.4 The current review aimed to highlight anatomy of the hymen for medical practitioners and to eliminate misconceptions of some population about virginity that might represent a matter of life and death.
Playing FPS Games with Deep Reinforcement Learning

Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions. However, most of these games take place in 2D environments that are fully observable to the agent. In this paper, we present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observable states. Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature information such as the presence of enemies or items, during the training phase. Our model is trained to simultaneously learn these features along with minimizing a Q-learning objective, which is shown to dramatically improve the training speed and performance of our agent. Our architecture is also modularized to allow different models to be independently trained for different phases of the game. We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as average humans in deathmatch scenarios.
Effects of LO Phase and Amplitude Imbalances and Phase Noise on $M$-QAM Transceiver Performance

This paper presents a rigorous analytical model for analyzing the effects of local oscillator output imperfections such as phase/amplitude imbalances and phase noise on M -ary quadrature amplitude modulation (M-QAM) transceiver performance. A closed-form expression of the error vector magnitude (EVM) and an analytic expression of the symbol error rate (SER) are derived considering a single-carrier linear transceiver link with additive white Gaussian noise channel. The proposed analytical model achieves a good agreement with the simulation results based on the Monte Carlo method. The proposed QAM imperfection analysis model provides an efficient means for system and circuit designers to analyze the wireless transceiver performance and specify the transceiver block specifications.
Self-Managed Systems: an Architectural Challenge

Self-management is put forward as one of the means by which we could provide systems that are scalable, support dynamic composition and rigorous analysis, and are flexible and robust in the presence of change. In this paper, we focus on architectural approaches to self-management, not because the language-level or network-level approaches are uninteresting or less promising, but because we believe that the architectural level seems to provide the required level of abstraction and generality to deal with the challenges posed. A self-managed software architecture is one in which components automatically configure their interaction in a way that is compatible with an overall architectural specification and achieves the goals of the system. The objective is to minimise the degree of explicit management necessary for construction and subsequent evolution whilst preserving the architectural properties implied by its specification. This paper discusses some of the current promising work and presents an outline three-layer reference model as a context in which to articulate some of the main outstanding research challenges.
Potential Friend Recommendation in Online Social Network

As the wide popularization of online social networks, online users are not content only with keeping online friendship with social friends in real life any more. They hope the system designers can help them exploring new friends with common interest. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this paper, by leveraging interest-based features, we design a general friend recommendation framework, which can characterize user interest in two dimensions: context (location, time) and content, as well as combining domain knowledge to improve recommending quality. We also design a potential friend recommender system in a real online social network of biology field to show the effectiveness of our proposed framework.
Sentiment analysis using Support Vector Machine

Sentiment analysis is treated as a classification task as it classifies the orientation of a text into either positive or negative. This paper describes experimental results that applied Support Vector Machine (SVM) on benchmark datasets to train a sentiment classifier. N-grams and different weighting scheme were used to extract the most classical features. It also explores Chi-Square weight features to select informative features for the classification. Experimental analysis reveals that by using Chi-Square feature selection may provide significant improvement on classification accuracy.
Fashion-Gen: The Generative Fashion Dataset and Challenge

We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists. Each item is photographed from a variety of angles. We provide baseline results on 1) high-resolution image generation, and 2) image generation conditioned on the given text descriptions. We invite the community to improve upon these baselines. In this paper we also outline the details of a challenge that we are launching based upon this dataset.
A Data Mining methodology for cross-sales

In this paper we discuss the use of Data Mining to provide a solution to the problem of cross-sales. We define and analyse the cross-sales problem and develop a hybrid methodology to solve it, using characteristic rule discovery and deviation detection. Deviation detection is used as a measure of interest to filter out the less interesting characteristic roles and only retain the best characteristic rules discovered. The effect of domain knowledge on the interestingness value of the discovered rules is discussed and techniques for relining the knowledge to increase this interestingness measure are studied. We also investigate the use of externally procured lifestyle and other survey data for data enrichment and discuss its use as additional domain knowledge. The developed methodology has been applied to a real world cross-sales problem within the financial sector, and the results are also presented in this paper. Although the application described is in the financial sector, the methodology is generic in nature and can be applied to other sectors.   Data Mining; Characteristic rule discovery: Deviation detection
Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems

Social sensing is a new big data application paradigm for Cyber-Physical Systems (CPS), where a group of individuals volunteer (or are recruited) to report measurements or observations about the physical world at scale. A fundamental challenge in social sensing applications lies in discovering the correctness of reported observations and reliability of data sources without prior knowledge on either of them. We refer to this problem as truth discovery. While prior studies have made progress on addressing this challenge, two important limitations exist: (i) current solutions did not fully explore the uncertainty aspect of human reported data, which leads to sub-optimal truth discovery results; (ii) current truth discovery solutions are mostly designed as sequential algorithms that do not scale well to large-scale social sensing events. In this paper, we develop a Scalable Uncertainty-Aware Truth Discovery (SUTD) scheme to address the above limitations. The SUTD scheme solves a constraint estimation problem to jointly estimate the correctness of reported data and the reliability of data sources while explicitly considering the uncertainty on the reported data. To address the scalability challenge, the SUTD is designed to run a Graphic Processing Unit (GPU) with thousands of cores, which is shown to run two to three orders of magnitude faster than the sequential truth discovery solutions. In evaluation, we compare our SUTD scheme to the state-of-the-art solutions using three real world datasets collected from Twitter: Paris Attack, Oregon Shooting, and Baltimore Riots, all in 2015. The evaluation results show that our new scheme significantly outperforms the baselines in terms of both truth discovery accuracy and execution time.