nils-herrmann/clean_scirepeval huggingface dataset:
Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems

What makes a good recommendation or good list of recommendations?
 Research into recommender systems has traditionally focused on accuracy, in particular how closely the recommender’s predicted ratings are to the users’ true ratings. However, it has been recognized that other recommendation qualities—such as whether the list of recommendations is diverse and whether it contains novel items—may have a significant impact on the overall quality of a recommender system. Consequently, in recent years, the focus of recommender systems research has shifted to include a wider range of “beyond accuracy” objectives.
 In this article, we present a survey of the most discussed beyond-accuracy objectives in recommender systems research: diversity, serendipity, novelty, and coverage. We review the definitions of these objectives and corresponding metrics found in the literature. We also review works that propose optimization strategies for these beyond-accuracy objectives. Since the majority of works focus on one specific objective, we find that it is not clear how the different objectives relate to each other.
 Hence, we conduct a set of offline experiments aimed at comparing the performance of different optimization approaches with a view to seeing how they affect objectives other than the ones they are optimizing. We use a set of state-of-the-art recommendation algorithms optimized for recall along with a number of reranking strategies for optimizing the diversity, novelty, and serendipity of the generated recommendations. For each reranking strategy, we measure the effects on the other beyond-accuracy objectives and demonstrate important insights into the correlations between the discussed objectives. For instance, we find that rating-based diversity is positively correlated with novelty, and we demonstrate the positive influence of novelty on recommendation coverage.
CPW-Fed Circular Fractal Slot Antenna Design for Dual-Band Applications

Using configuration synthesis and design map, the CPW-fed circular fractal slot antennas are proposed for dual-band applications. In practice, the experimental results with broadband and dual-band responses (47.4% and 13.5% bandwidth) and available radiation gains (peak gain 3.58 and 7.28 dBi) at 0.98 and 1.84 GHz respectively for half-wavelength design are achieved firstly. Then, the other broadband and dual-band responses (75.9% and 16.1% bandwidth) and available radiation gains (peak gain 3.16 and 6.62 dBi) at 2.38 and 5.35 GHz for quarter-wavelength design are described herein. Contour distribution patterns are applied to figure out the omni-directional patterns. The demonstration among the design map and the EM characteristics of the antenna is presented by current distributions.
Skin Segmentation Using YUV and RGB Color Spaces

Skin detection is used in many applications, such as face recognition, hand tracking, and human-computer interaction. There are many skin color detection algorithms that are used to extract human skin color regions that are based on the thresholding technique since it is simple and fast for computation. The efficiency of each color space depends on its robustness to the change in lighting and the ability to distinguish skin color pixels in images that have a complex background. For more accurate skin detection, we are proposing a new threshold based on RGB and YUV color spaces. The proposed approach starts by converting the RGB color space to the YUV color model. Then it separates the Y channel, which represents the intensity of the color model from the U and V channels to eliminate the effects of luminance. After that the threshold values are selected based on the testing of the boundary of skin colors with the help of the color histogram. Finally, the threshold was applied to the input image to extract skin parts. The detected skin regions were quantitatively compared to the actual skin parts in the input images to measure the accuracy and to compare the results of our threshold to the results of other’s thresholds to prove the efficiency of our approach. The results of the experiment show that the proposed threshold is more robust in terms of dealing with the complex background and light conditions than others.  Skin segmentation; Thresholding technique; Skin detection; Color space
How to Write and Use the Ontology Requirements Specification Document

The goal of the ontology requirements specification activity is to state why the ontology is being built, what its intended uses are, who the endusers are, and which requirements the ontology should fulfill. The novelty of this paper lies in the systematization of the ontology requirements specification activity since the paper proposes detailed methodological guidelines for specifying ontology requirements efficiently. These guidelines will help ontology engineers to capture ontology requirements and produce the ontology requirements specification document (ORSD). The ORSD will play a key role during the ontology development process because it facilitates, among other activities, (1) the search and reuse of existing knowledge-aware resources with the aim of re-engineering them into ontologies, (2) the search and reuse of existing ontological resources (ontologies, ontology modules, ontology statements as well as ontology design patterns), and (3) the verification of the ontology along the ontology development. In parallel to the guidelines, we present the ORSD that resulted from the ontology requirements specification activity within the SEEMP project, and how this document facilitated not only the reuse of existing knowledge-aware resources but also the verification of the SEEMP ontologies. Moreover, we present some use cases in which the methodological guidelines proposed here were applied.
Modeling Textures with Total Variation Minimization and Oscillating Patterns in Image Processing

This paper is devoted to the modeling of real textured images by functional minimization and partial differential equations. Following the ideas of Yves Meyer in a total variation minimization framework of L. Rudin, S. Osher, and E. Fatemi, we decompose a given (possible textured) image f into a sum of two functions u+v, where u ¥ BV is a function of bounded variation (a cartoon or sketchy approximation of f), while v is a function representing the texture or noise. To model v we use the space of oscillating functions introduced by Yves Meyer, which is in some sense the dual of the BV space. The new algorithm is very simple, making use of differential equations and is easily solved in practice. Finally, we implement the method by finite differences, and we present various numerical results on real textured images, showing the obtained decomposition u+v, but we also show how the method can be used for texture discrimination and texture segmentation.
Progger: An Efficient, Tamper-Evident Kernel-Space Logger for Cloud Data Provenance Tracking

Cloud data provenance, or "what has happened to my data in the cloud", is a critical data security component which addresses pressing data accountability and data governance issues in cloud computing systems. In this paper, we present Progger (Provenance Logger), a kernel-space logger which potentially empowers all cloud stakeholders to trace their data. Logging from the kernel space empowers security analysts to collect provenance from the lowest possible atomic data actions, and enables several higher-level tools to be built for effective end-to-end tracking of data provenance. Within the last few years, there has been an increasing number of proposed kernel space provenance tools but they faced several critical data security and integrity problems. Some of these prior tools' limitations include (1) the inability to provide log tamper-evidence and prevention of fake/manual entries, (2) accurate and granular timestamp synchronisation across several machines, (3) log space requirements and growth, and (4) the efficient logging of root usage of the system. Progger has resolved all these critical issues, and as such, provides high assurance of data security and data activity audit. With this in mind, the paper will discuss these elements of high-assurance cloud data provenance, describe the design of Progger and its efficiency, and present compelling results which paves the way for Progger being a foundation tool used for data activity tracking across all cloud systems.
Algorithms for Large Scale Markov Blanket Discovery

This paper presents a number of new algorithms for discovering the Markov Blanket of a target variable T from training data. The Markov Blanket can be used for variable selection for classification, for causal discovery, and for Bayesian Network learning. We introduce a low-order polynomial algorithm and several variants that soundly induce the Markov Blanket under certain broad conditions in datasets with thousands of variables and compare them to other state-of-the-art local and global methods with excel-
Compositional Sequence Labeling Models for Error Detection in Learner Writing

The task: Detect errors in learner writing (spelling, grammar, word usage, etc).  I want to travel on July and because of it is more suitable for me. We don’t need to wear clothes like layer and layer. The restaurant was closed because unknown reasons.  • Immediate feedback in self-tutoring systems for language learning. •Automated exam grading for language testing. •Providing language checking in general writing applications.
Flow-Based Detection of DNS Tunnels

DNS tunnels allow circumventing access and security policies in firewalled networks. Such a security breach can be misused for activities like free web browsing, but also for command & control traffic or cyber espionage, thus motivating the search for effective automated DNS tunnel detection techniques. In this paper we develop such a technique, based on the monitoring and analysis of network flows. Our methodology combines flow information with statistical methods for anomaly detection. The contribution of our paper is twofold. Firstly, based on flow-derived variables that we identified as indicative of DNS tunnelling activities, we identify and evaluate a set of non-parametrical statistical tests that are particularly useful in this context. Secondly, the efficacy of the resulting tests is demonstrated by extensive validation experiments in an operational environment, covering many different usage scenarios.
FacePoseNet: Making a Case for Landmark-Free Face Alignment

We show how a simple convolutional neural network (CNN) can be trained to accurately and robustly regress 6 degrees of freedom (6DoF) 3D head pose, directly from image intensities. We further explain how this FacePoseNet (FPN) can be used to align faces in 2D and 3D as an alternative to explicit facial landmark detection for these tasks. We claim that in many cases the standard means of measuring landmark detector accuracy can be misleading when comparing different face alignments. Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method. Our results show that (a) better landmark detection accuracy measured on the 300W benchmark does not necessarily imply better face recognition accuracy. (b) Our FPN provides superior 2D and 3D face alignment on both benchmarks. Finally, (c), FPN aligns faces at a small fraction of the computational cost of comparably accurate landmark detectors. For many purposes, FPN is thus a far faster and far more accurate face alignment method than using facial landmark detectors.
Real-time obstacle avoidance for fact mobile robots

A new real-time obstacle avoidance approach for mobile robots has been developed and implemented. This approach permits the detection of unknown obstacles simultaneously with the steering of the mobile robot to avoid collisions and advancing toward the target. The novelty of this approach, entitled the Virtual Force Field, lies in the integration of two known concepts: Certainty Grids for obstacle representation, and Potential Fields for navigation. This combination is especially suitable for the accommodation of inaccurate sensor data (such as produced by ultrasonic sensors) as well as for sensor fusion, and enables continuous motion of the robot without stopping in front of obstacles. This navigation algorithm also takes into account the dynamic behavior of a fast mobile robot and solves the "local minimum trap" problem. Experimental results from a mobile robot running at a maximum speed of 0.78 m/sec demonstrate the power of the proposed algorithm.
Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders

A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning methods to automatically detect falls is the choice of engineered features. In this paper, we propose to use an ensemble of autoencoders to extract features from different channels of wearable sensor data trained only on normal activities. We show that the traditional approach of choosing a threshold as the maximum of the reconstruction error on the training normal data is not the right way to identify unseen falls. We propose two methods for automatic tightening of reconstruction error from only the normal activities for better identification of unseen falls. We present our results on two activity recognition datasets and show the efficacy of our proposed method against traditional autoencoder models and two standard one-class classification methods.
A Deep Visual Correspondence Embedding Model for Stereo Matching Costs

This paper presents a data-driven matching cost for stereo matching. A novel deep visual correspondence embedding model is trained via Convolutional Neural Network on a large set of stereo images with ground truth disparities. This deep embedding model leverages appearance data to learn visual similarity relationships between corresponding image patches, and explicitly maps intensity values into an embedding feature space to measure pixel dissimilarities. Experimental results on KITTI and Middlebury data sets demonstrate the effectiveness of our model. First, we prove that the new measure of pixel dissimilarity outperforms traditional matching costs. Furthermore, when integrated with a global stereo framework, our method ranks top 3 among all two-frame algorithms on the KITTI benchmark. Finally, cross-validation results show that our model is able to make correct predictions for unseen data which are outside of its labeled training set.
Mixed-initiative co-creativity

Creating and designing with a machine: do we merely create together (co-create) or can a machine truly foster our creativity as human creators? When does such co-creation foster the co-creativity of both humans and machines? This paper investigates the simultaneous and/or iterative process of human and computational creators in a mixed-initiative fashion within the context of game design and attempts to draw from both theory and praxis towards answering the above questions. For this purpose, we first discuss the strong links between mixed-initiative co-creation and theories of human and computational creativity. We then introduce an assessment methodology of mixed-initiative co-creativity and, as a proof of concept, evaluate Sentient Sketchbook as a co-creation tool for game design. Core findings suggest that tools such as Sentient Sketchbook are not mere game authoring systems or mere enablers of creation but, instead, foster human creativity and realize mixed-initiative co-creativity.
Gendered Media : The Influence of Media on Views of Gender

Of the many influences on how we view men and women, media are the most pervasive and one of the most powerful. Woven throughout our daily lives, media insinuate their messages into our consciousness at every turn. All forms of media communicate images of the sexes, many of which perpetuate unrealistic, stereotypical, and limiting perceptions. Three themes describe how media represent gender. First, women are underrepresented which falsely implies that men are the cultural standard and women are unimportant or invisible. Second, men and women are portrayed in stereotypical ways that reflect and sustain socially endorsed views of gender. Third, depictions of relationships between men and women emphasize traditional roles and normalize violence against women. We will consider each of these themes in this section.
Broadband multilayer in-phase power divider

A new broadband in-phase power divider based on multilayer technology is presented. A simple design procedure is developed for the proposed multilayer power divider. An S-band four-way multilayer power divider was designed and measured. The simulated results are compared with the measured data, and good agreement is reported. The measured 15 dB return loss bandwidth is demonstrated to be about 72%, and its phase difference between the output signals is less than 38.
A Character-level Decoder without Explicit Segmentation for Neural Machine Translation

The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental question: can neural machine translation generate a character sequence without any explicit segmentation? To answer this question, we evaluate an attention-based encoder– decoder with a subword-level encoder and a character-level decoder on four language pairs–En-Cs, En-De, En-Ru and En-Fi– using the parallel corpora from WMT’15. Our experiments show that the models with a character-level decoder outperform the ones with a subword-level decoder on all of the four language pairs. Furthermore, the ensembles of neural models with a character-level decoder outperform the state-of-the-art non-neural machine translation systems on En-Cs, En-De and En-Fi and perform comparably on En-Ru.
Polling-based MAC protocols for improving real-time performance in a wireless PROFIBUS

The idea of having a wireless PROFIBUS is appealing, since this can bring benefits like reduced cabling need and mobile stations to the factory floor. But unfortunately wireless transmission is error-prone, which affects the timeliness and reliability behavior users expect from a fieldbus system (hard real-time). In this paper we compare two different approaches for the medium access control and link layer of a wireless PROFIBUS system with respect to their so-called real-time performance in the presence of transmission errors. Specifically, we compare the existing PROFIBUS MAC and link layer protocol with a simple round-robin protocol. It shows up that round-robin delivers significantly better real-time performance than the PROFIBUS protocol under bursty error conditions. In a second step we propose three add-ons to round-robin and we show that they further increase the real-time performance of round-robin. The add-ons take certain characteristics of the wireless medium into account.  PROFIBUS, Wireless PROFIBUS, Real-Time Performance, MAC Protocols, Polling
SentiWS - A Publicly Available German-language Resource for Sentiment Analysis

University of Leipzig, Natural Language Processing Department, Johannisgasse 26, 04081 Leipzig, Germany robert.remus@googlemail.com, {quasthoff, heyer}@informatik.uni-leipzig.de Abstract SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative sentiment bearing words weighted within the interval of [−1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS (v1.8b) contains 1,650 negative and 1,818 positive words, which sum up to 16,406 positive and 16,328 negative word forms, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one. The present work describes the resource’s structure, the three sources utilised to assemble it and the semi-supervised method incorporated to weight the strength of its entries. Furthermore the resource’s contents are extensively evaluated using a German-language evaluation set we constructed. The evaluation set is verified being reliable and its shown that SentiWS provides a beneficial lexical resource for German-language sentiment analysis related tasks to build on.
Text summarization for Malayalam documents — An experience

The amount of data available in the internet is increasing at a very high speed. Text summarization has helped in making a better use of the information available online. Various methods were adopted to automate text summarization. However there is no existing system for summarizing Malayalam documents. In this paper we have investigated on developing efficient and effective methods to summarize Malayalam documents. This paper explains a statistical sentence scoring technique and a semantic graph based technique for text summarization.
ProtectMyPrivacy: detecting and mitigating privacy leaks on iOS devices using crowdsourcing

In this paper we present the design and implementation of ProtectMyPrivacy (PMP), a system for iOS devices to detect access to private data and protect users by substituting anonymized data in its place if users decide. We developed a novel crowdsourced recommendation engine driven by users who contribute their protection decisions, which provides app specific privacy recommendations. PMP has been in use for over nine months by 90,621 real users, and we present a detailed evaluation based on the data we collected for 225,685 unique apps. We show that access to the device identifer (48.4% of apps), location (13.2% of apps), address book (6.2% of apps) and music library (1.6% of apps) is indeed widespread in iOS. We show that based on the protection decisions contributed by our users we can recommend protection settings for over 97.1% of the 10,000 most popular apps. We show the effectiveness of our recommendation engine with users accepting 67.1% of all recommendations provide to them, thereby helping them make informed privacy choices. Finally, we show that as few as 1% of our users, classified as experts, make enough decisions to drive our crowdsourced privacy recommendation engine.
Taxonomy Induction Using Hypernym Subsequences

We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms. Unlike all previous approaches, which typically extract direct hypernym edges for terms, our approach utilizes a novel probabilistic framework to extract hypernym subsequences. Taxonomy induction from extracted subsequences is cast as an instance of the minimum-cost flow problem on a carefully designed directed graph. Through experiments, we demonstrate that our approach outperforms state-of-the-art taxonomy induction approaches across four languages. Importantly, we also show that our approach is robust to the presence of noise in the input vocabulary. To the best of our knowledge, this robustness has not been empirically proven in any previous approach.