nils-herrmann/clean_scirepeval huggingface dataset:
Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling

Recurrent neural networks have been very successful at pred icting sequences of words in tasks such as language modeling. However, all such m odels are based on the conventional classification framework, where model is t rained against one-hot targets, and each word is represented both as an input and as a output in isolation. This causes inefficiencies in learning both in terms of utili zing all of the information and in terms of the number of parameters needed to train. We introduce a novel theoretical framework that facilitates better learn ing in language modeling, and show that our framework leads to tying together the input embedding and the output projection matrices, greatly reducing the numbe r of trainable variables. Our LSTM model lowers the state of the art word-level perplex ity on the Penn Treebank to 68.5.
Random Walks for Knowledge-Based Word Sense Disambiguation

Word Sense Disambiguation (WSD) systems automatically choose the intended meaning of a word in context. In this article we present a WSD algorithm based on random walks over large Lexical Knowledge Bases (LKB). We show that our algorithm performs better than other graph-based methods when run on a graph built from WordNet and eXtended WordNet. Our algorithm and LKB combination compares favorably to other knowledge-based approaches in the literature that use similar knowledge on a variety of English data sets and a data set on Spanish. We include a detailed analysis of the factors that affect the algorithm. The algorithm and the LKBs used are publicly available, and the results easily reproducible.
Expression invariant 3D face recognition with a Morphable Model

We describe an expression-invariant method for face recognition by fitting an identity/expression separated 3D Morphable Model to shape data. The expression model greatly improves recognition and retrieval rates in the uncooperative setting, while achieving recognition rates on par with the best recognition algorithms in the face recognition great vendor test. The fitting is performed with a robust nonrigid ICP algorithm. It is able to perform face recognition in a fully automated scenario and on noisy data. The system was evaluated on two datasets, one with a high noise level and strong expressions, and the standard UND range scan database, showing that while expression invariance increases recognition and retrieval performance for the expression dataset, it does not decrease performance on the neutral dataset. The high recognition rates are achieved even with a purely shape based method, without taking image data into account.
SEED: Hands-On Lab Exercises for Computer Security Education

This paper presents the development of a set of hands-on exercises(labs) that covered a spectrum of security topics and could be shared with other instructors. The author developed SEED labs covering many security topics: vulnerabilities, attacks, software security, system security, network security, Web security, access control, authentication, cryptography, and so on. Most SEED labs have gone through multiple development trial cycles in university courses. Many universities have requested the instructor's manual and adopted SEED labs for their courses. Most SEED labs use Ubuntu Linux. A few labs require significant effort for kernel-level coding; for them, the author chose Minix, an instructional OS. SEED labs don't require a dedi cated physical laboratory. The author created a prebuilt VM image of Ubuntu and installed the necessary software. Various lab environment setups were tried, including using students' PCs, a general computing lab, and a cloud-computing infrastructure. And found that for convenience, students prefer to use their own computers.
Understanding Digital Transformation Strategy formation: Insights from Europe's Automotive Industry

An increasing number of firms are responding to new opportunities and risks originating from digital technologies by introducing company-wide digital transformation strategies as a means to systematically address their digital transformation. Yet, what processes and strategizing activities affect the formation of digital transformation strategies in organizations are not well understood. We adopt a phenomenon-based approach and investigate the formation of digital transformation strategies in organizations from a process perspective. Drawing on an activity-based process model that links Mintzberg’s strategy typology with the concept of IS strategizing, we conduct a multiple-case study at three European car manufacturers. Our results indicate that digital transformation strategies are predominantly shaped by a diversity of emergent strategizing activities of separate organizational subcommunities through a bottom-up process and prior to the initiation of a holistic digital transformation strategy by top management. As a result, top management’s deliberate strategies seek to accomplish the subsequent alignment of preexisting emergent strategy contents with their intentions and to simultaneously increase the share of deliberate contents. Besides providing practical implications for the formulation and implementation of a digital transformation strategy, we contribute to the literature on digital transformation and IS strategizing.
Framing Bias : Media in the Distribution of Power

This article proposes integrating the insights generated by framing, priming, and agenda-setting research through a systematic effort to conceptualize and understand their larger implications for political power and democracy. The organizing concept is bias, that curiously undertheorized staple of public discourse about the media. After showing how agenda setting, framing and priming fit together as tools of power, the article connects them to explicit definitions of news slant and the related but distinct phenomenon of bias. The article suggests improved measures of slant and bias. Properly defined and measured, slant and bias provide insight into how the media influence the distribution of power: who gets what, when, and how. Content analysis should be informed by explicit theory linking patterns of framing in the media text to predictable priming and agenda-setting effects on audiences. When unmoored by such underlying theory, measures and conclusions of media bias are suspect.
Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments

The combination of visual and inertial sensors has proved to be very popular in robot navigation and, in particular, Micro Aerial Vehicle (MAV) navigation due the flexibility in weight, power consumption and low cost it offers. At the same time, coping with the big latency between inertial and visual measurements and processing images in real-time impose great research challenges. Most modern MAV navigation systems avoid to explicitly tackle this by employing a ground station for off-board processing. In this paper, we propose a navigation algorithm for MAVs equipped with a single camera and an Inertial Measurement Unit (IMU) which is able to run onboard and in real-time. The main focus here is on the proposed speed-estimation module which converts the camera into a metric body-speed sensor using IMU data within an EKF framework. We show how this module can be used for full self-calibration of the sensor suite in real-time. The module is then used both during initialization and as a fall-back solution at tracking failures of a keyframe-based VSLAM module. The latter is based on an existing high-performance algorithm, extended such that it achieves scalable 6DoF pose estimation at constant complexity. Fast onboard speed control is ensured by sole reliance on the optical flow of at least two features in two consecutive camera frames and the corresponding IMU readings. Our nonlinear observability analysis and our real experiments demonstrate that this approach can be used to control a MAV in speed, while we also show results of operation at 40Hz on an onboard Atom computer 1.6 GHz.
Predicting Customer Churn in Telecom Industry using Multilayer Preceptron Neural Networks : Modeling and Analysis

Churn represents the problem of losing a customer to another business competitor which leads to serious profit loss. Therefore, many companies investigate different techniques that can predict churn rates and help in designing effective plans for customer retention. In this study we investigate the application of Multilayer Perceptron (MLP) neural networks with back-propagation learning for churn prediction in a telecommunication company. Different MLP topologies with different settings are used to build churn classification models. Moreover, two MLP based approaches are used and compared in order to rank the most influencing factors on churn rates. For the purpose of this research, real data of customers in a major Jordanian telecom company were provided.  . (ISSN:1097-8135). http://www.lifesciencesite.com.
Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors

The 1990s saw the emergence of cognitive models that depend on very high dimensionality and randomness. They include Holographic Reduced Representations, Spatter Code, Semantic Vectors, Latent Semantic Analysis, Context-Dependent Thinning, and Vector-Symbolic Architecture. They represent things in high-dimensional vectors that are manipulated by operations that produce new high-dimensional vectors in the style of traditional computing, in what is called here hyperdimensional computing on account of the very high dimensionality. The paper presents the main ideas behind these models, written as a tutorial essay in hopes of making the ideas accessible and even provocative. A sketch of how we have arrived at these models, with references and pointers to further reading, is given at the end. The thesis of the paper is that hyperdimensional representation has much to offer to students of cognitive science, theoretical neuroscience, computer science and engineering, and mathematics.
Student engagement in course-based social networks: The impact of instructor credibility and use of communication

Social network sites provide the opportunity for bu ilding and maintaining online social network groups around a specific interest. Despite the increasing use of social networks in higher education, little previous research has studied their impacts on stud en ’s engagement and on their perceived educational outcomes. This research investigates the impact of instructors’ self-disclosure and use of humor via course-based social networks as well as their credi bility, and the moderating impact of time spent in hese course-based social networks, on the students’ enga g ment in course-based social networks. The researc h provides a theoretical viewpoint, supported by empi rical evidence, on the impact of students’ engageme nt in course-based social networks on their perceived educational outcomes. The findings suggest that instructors who create course-based online social n etworks to communicate with their students can increase their engagement, motivation, and satisfac on. We conclude the paper by suggesting the theoretical implications for the study and by provi ding strategies for instructors to adjust their act ivities in order to succeed in improving their students’ engag ement and educational outcomes.
Personalized click prediction in sponsored search

Sponsored search is a multi-billion dollar business that generates most of the revenue for search engines. Predicting the probability that users click on ads is crucial to sponsored search because the prediction is used to influence ranking, filtering, placement, and pricing of ads. Ad ranking, filtering and placement have a direct impact on the user experience, as users expect the most useful ads to rank high and be placed in a prominent position on the page. Pricing impacts the advertisers' return on their investment and revenue for the search engine. The objective of this paper is to present a framework for the personalization of click models in sponsored search. We develop user-specific and demographic-based features that reflect the click behavior of individuals and groups. The features are based on observations of search and click behaviors of a large number of users of a commercial search engine. We add these features to a baseline non-personalized click model and perform experiments on offline test sets derived from user logs as well as on live traffic. Our results demonstrate that the personalized models significantly improve the accuracy of click prediction.
Predicting Discourse Connectives for Implicit Discourse Relation Recognition

Existing works indicate that the absence of explicit discourse connectives makes it difficult to recognize implicit discourse relations. In this paper we attempt to overcome this difficulty for implicit relation recognition by automatically inserting discourse connectives between arguments with the use of a language model. Then we propose two algorithms to leverage the information of these predicted connectives. One is to use these predicted implicit connectives as additional features in a supervised model. The other is to perform implicit relation recognition based only on these predicted connectives. Results on Penn Discourse Treebank 2.0 show that predicted discourse connectives help implicit relation recognition and the first algorithm can achieve an absolute average f-score improvement of 3% over a state of the art baseline system.
Reduction Otoplasty: Correction of the Large or Asymmetric Ear

Reduction otoplasty is an uncommon procedure performed for macrotia and ear asymmetry. Techniques described in the literature for this procedure are few. The authors present their ear reduction approach that not only achieves the desired reduction effectively and accurately, but also addresses and creates the natural anatomic proportions of the ear, leaving a scar well hidden within the fold of the helix.
The butterfly effect of caring - clinical nursing teachers' understanding of self-compassion as a source to compassionate care.

This study has its roots in a clinical application project, focusing on the development of a teaching-learning model enabling participants to understand compassion. During that project four clinical nursing teachers met for a total of 12 hours of experiential and reflective work. This study aimed at exploring participants' understanding of self-compassion as a source to compassionate care. It was carried out as a phenomenological and hermeneutic interpretation of participants' written and oral reflections on the topic. Data were interpreted in the light of Watson's Theory of Human Caring. Five themes were identified: Being there, with self and others; respect for human vulnerability; being nonjudgmental; giving voice to things needed to be said and heard; and being able to accept the gift of compassion from others. A main metaphorical theme, 'the Butterfly effect of Caring', was identified, addressing interdependency and the ethics of the face and hand when caring for Other - the ethical stance where the Other's vulnerable face elicits a call for compassionate actions. The findings reveal that the development of a compassionate self and the ability to be sensitive, nonjudgmental and respectful towards oneself contributes to a compassionate approach towards others. It is concluded that compassionate care is not only something the caregiver does, nor is compassion reduced to a way of being with another person or a feeling. Rather, it is a way of becoming and belonging together with another person where both are mutually engaged and where the caregiver compassionately is able to acknowledge both self and Other's vulnerability and dignity.
Recursive Program Synthesis

Input-output examples are a simple and accessible way of describing program behaviour. Program synthesis from input-output examples has the potential of extending the range of computational tasks achievable by end-users who have no programming knowledge, but can articulate their desired computations by describing input-output behaviour. In this paper, we present Escher, a generic and efficient algorithm that interacts with the user via input-output examples, and synthesizes recursive programs implementing intended behaviour. Escher is parameterized by the components (instructions) that can be used in the program, thus providing a generic synthesis algorithm that can be instantiated to suit different domains. To search through the space of programs, Escher adopts a novel search strategy that utilizes special data structures for inferring conditionals and synthesizing recursive procedures. Our experimental evaluation of Escher demonstrates its ability to efficiently synthesize a wide range of programs, manipulating integers, lists, and trees. Moreover, we show that Escher outperforms a state-ofthe-art SAT-based synthesis tool from the literature.
Table Extraction from Document Images using Fixed Point Model

The paper presents a novel learning-based framework to identify tables from scanned document images. The approach is designed as a structured labeling problem, which learns the layout of the document and labels its various entities as table header, table trailer, table cell and non-table region. We develop features which encode the foreground block characteristics and the contextual information. These features are provided to a fixed point model which learns the inter-relationship between the blocks. The fixed point model attains a contraction mapping and provides a unique label to each block. We compare the results with Condition Random Fields(CRFs). Unlike CRFs, the fixed point model captures the context information in terms of the neighbourhood layout more efficiently. Experiments on the images picked from UW-III (University of Washington) dataset, UNLV dataset and our dataset consisting of document images with multicolumn page layout, show the applicability of our algorithm in layout analysis and table detection.
Shock Graphs and Shape Matching

We have been developing a theory for the generic representation of 2-D shape, where structural descriptions are derived from the shocks (singularities) of a curve evolution process, acting on bounding contours. We now apply the theory to the problem of shape matching. The shocks are organized into a directed, acyclic shock graph, and complexity is managed by attending to the most significant (central) shape components first. The space of all such graphs is highly structured and can be characterized by the rules of a shock graph grammar. The grammar permits a reduction of a shock graph to a unique rooted shock tree. We introduce a novel tree matching algorithm which finds the best set of corresponding nodes between two shock trees in polynomial time. Using a diverse database of shapes, we demonstrate our system's performance under articulation, occlusion, and moderate changes in viewpoint.
A 12-bit 50-MS/s 3.3-mW SAR ADC with background digital calibration

This paper describes a background digital calibration technique based on bitwise correlation (BWC) to correct the capacitive digital-to-analog converter (DAC) mismatch error in successive-approximation-register (SAR) analog-to-digital converters (ADC's). Aided by a single-bit pseudorandom noise (PN) injected to the ADC input, the calibration engine extracts all bit weights simultaneously to facilitate a digital-domain correction. The analog overhead associated with this technique is negligible and the conversion speed is fully retained (in contrast to  in which the ADC throughput is halved). A prototype 12bit 50-MS/s SAR ADC fabricated in 90-nm CMOS measured a 66.5-dB peak SNDR and an 86.0-dB peak SFDR with calibration, while occupying 0.046 mm2 and dissipating 3.3 mW from a 1.2-V supply. The calibration logic is estimated to occupy 0.072 mm2 with a power consumption of 1.4 mW in the same process.
A Survey of Man In The Middle Attacks

The Man-In-The-Middle (MITM) attack is one of the most well known attacks in computer security, representing one of the biggest concerns for security professionals. MITM targets the actual data that flows between endpoints, and the confidentiality and integrity of the data itself. In this paper, we extensively review the literature on MITM to analyse and categorize the scope of MITM attacks, considering both a reference model, such as the open systems interconnection (OSI) model, as well as two specific widely used network technologies, i.e., GSM and UMTS. In particular, we classify MITM attacks based on several parameters, like location of an attacker in the network, nature of a communication channel, and impersonation techniques. Based on an impersonation techniques classification, we then provide execution steps for each MITM class. We survey existing countermeasures and discuss the comparison among them. Finally, based on our analysis, we propose a categorisation of MITM prevention mechanisms, and we identify some possible directions for future research.
Diagnosing Cloud Performance Anomalies Using Large Time Series Dataset Analysis

Virtualized Cloud platforms have become increasingly common and the number of online services hosted on these platforms is also increasing rapidly. A key problem faced by providers in managing these services is detecting the performance anomalies and adjusting resources accordingly. As online services generate a very large amount of monitored data in the form of time series, it becomes very difficult to process this complex data by traditional approaches. In this work, we present a novel distributed parallel approach for performance anomaly detection. We build upon Holt-Winters forecasting for automatic aberrant behavior detection in time series. First, we extend the technique to work with MapReduce paradigm. Next, we correlate the anomalous metrics with the target Service Level Objective (SLO) in order to locate the suspicious metrics. We implemented and evaluated our approach on a production Cloud encompassing IaaS and PaaS service models. Experimental results confirm that our approach is efficient and effective in capturing the metrics causing performance anomalies in large time series datasets.
A reconfigurable architecture for hybrid CMOS/Nanodevice circuits

This report describes a preliminary evaluation of performance of a cell-FPGA-like architecture for future hybrid "CMOL" circuits. Such circuits will combine a semiconduc-tor-transistor (CMOS) stack and a two-level nanowire crossbar with molecular-scale two-terminal nanodevices (program-mable diodes) formed at each crosspoint. Our cell-based architecture is based on a uniform CMOL fabric of "tiles". Each tile consists of 12 four-transistor basic cells and one (four times larger) latch cell. Due to high density of nanodevices, which may be used for both logic and routing functions, CMOL FPGA may be reconfigured around defective nanodevices to provide high defect tolerance. Using a semi-custom set of design automation tools we have evaluated CMOL FPGA performance for the Toronto 20 benchmark set, so far without optimization of several parameters including the power supply voltage and nanowire pitch. The results show that even without such optimization, CMOL FPGA circuits may provide a density advantage of more than two orders of magnitude over the traditional CMOS FPGA with the same CMOS design rules, at comparable time delay, acceptable power consumption and potentially high defect tolerance.