nils-herrmann/clean_scirepeval huggingface dataset:
Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

Contextual information plays an important role in solving vision problems such as image segmentation. However, extracting contextual information and using it in an effective way remains a difficult problem. To address this challenge, we propose a multi-resolution contextual framework, called cascaded hierarchical model (CHM), which learns contextual information in a hierarchical framework for image segmentation. At each level of the hierarchy, a classifier is trained based on down sampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. We repeat this procedure by cascading the hierarchical framework to improve the segmentation accuracy. Multiple classifiers are learned in the CHM, therefore, a fast and accurate classifier is required to make the training tractable. The classifier also needs to be robust against over fitting due to the large number of parameters learned during training. We introduce a novel classification scheme, called logistic disjunctive normal networks (LDNN), which consists of one adaptive layer of feature detectors implemented by logistic sigmoid functions followed by two fixed layers of logical units that compute conjunctions and disjunctions, respectively. We demonstrate that LDNN outperforms state-of-the-art classifiers and can be used in the CHM to improve object segmentation performance.
Provable defenses against adversarial examples via the convex outer adversarial polytope

We propose a method to learn deep ReLU-based classifiers that are provably robust against normbounded adversarial perturbations on the training data. For previously unseen examples, the approach is guaranteed to detect all adversarial examples, though it may flag some non-adversarial examples as well. The basic idea is to consider a convex outer approximation of the set of activations reachable through a norm-bounded perturbation, and we develop a robust optimization procedure that minimizes the worst case loss over this outer region (via a linear program). Crucially, we show that the dual problem to this linear program can be represented itself as a deep network similar to the backpropagation network, leading to very efficient optimization approaches that produce guaranteed bounds on the robust loss. The end result is that by executing a few more forward and backward passes through a slightly modified version of the original network (though possibly with much larger batch sizes), we can learn a classifier that is provably robust to any norm-bounded adversarial attack. We illustrate the approach on a number of tasks to train classifiers with robust adversarial guarantees (e.g. for MNIST, we produce a convolutional classifier that provably has less than 5.8% test error for any adversarial attack with bounded `∞ norm less than = 0.1), and code for all experiments is available at http://github.com/ locuslab/convex_adversarial. Machine Learning Department, Carnegie Mellon University, Pittsburgh PA, 15213, USA Computer Science Department, Carnegie Mellon University, Pittsburgh PA, 15213, USA. Correspondence to: Eric Wong <ericwong@cs.cmu.edu>, J. Zico Kolter <zkolter@cs.cmu.edu>. Proceedings of the 35 th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018.
Improving K-Modes Algorithm Considering Frequencies of Attribute Values in Mode

The original k-means algorithm is designed to work primarily on numeric data sets. This prohibits the algorithm from being applied to categorical data clustering, which is an integral part of data mining and has attracted much attention recently. The k-modes algorithm extended the k-means paradigm to cluster categorical data by using a frequency-based method to update the cluster modes versus the k-means fashion of minimizing a numerically valued cost. However, the dissimilarity measure used in k-modes doesn’t consider the relative frequencies of attribute values in each cluster mode, this will result in a weaker intra-cluster similarity by allocating less similar objects to the cluster. In this paper, we present an experimental study on applying a new dissimilarity measure to the k-modes clustering to improve its clustering accuracy. The measure is based on the idea that the similarity between a data object and cluster mode, is directly proportional to the sum of relative frequencies of the common values in mode. Experimental results on real life datasets show that, the modified algorithm is superior to the original kmodes algorithm with respect to clustering accuracy.
Distributed representation learning for knowledge graphs with entity descriptions

Recent studies of knowledge representation attempt to project both entities and relations, which originally compose a high-dimensional and sparse knowledge graph, into a continuous low-dimensional space. One canonical approach TransE (Bordes et al., 2013) which represents entities and relations with vectors (embeddings), achieves leading performances solely with triplets, i.e. (head entity, relation, tail entity), in a knowledge base. The cutting-edge method DKRL (Xie et al., 2016) extends TransE via enhancing the embeddings with entity descriptions by means of deep neural network models. However, DKRL requires extra space to store parameters of inner layers, and relies on more hyperparameters to be tuned. Therefore, we create a single-layer model which requests much fewer parameters. The model measures the probability of each triplet along with corresponding entity descriptions, and learns contextual embeddings of entities, relations and words in descriptions simultaneously, via maximizing the loglikelihood of the observed knowledge. We evaluate our model in the tasks of knowledge graph completion and entity type classification with two benchmark datasets: FB500K and EN15K, respectively. Experimental results demonstrate that the proposed model outperforms both TransE and DKRL, indicating that it is both efficient and effective in learning better distributed representations for knowledge bases.
Adaptive Computation Time for Recurrent Neural Networks

This paper introduces Adaptive Computation Time (ACT), an algorithm that allows recurrent neural networks to learn how many computational steps to take between receiving an input and emitting an output. ACT requires minimal changes to the network architecture, is deterministic and differentiable, and does not add any noise to the parameter gradients. Experimental results are provided for four synthetic problems: determining the parity of binary vectors, applying sequences of binary logic operations, adding sequences of integers, and sorting sequences of real numbers. Overall performance is dramatically improved by the use of ACT, which successfully adapts the number of computational steps to the requirements of the problem. When applied to character-level language modelling on the Hutter prize Wikipedia dataset, ACT yields intriguing insight into the structure of the data, with more computation allocated to harder-to-predict transitions, such as spaces between words and ends of sentences. This suggests that ACT or other adaptive computation methods could be used to infer segment boundaries in sequence data.
Language-model-based ranking for queries on RDF-graphs

The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large "knowledge repositories" such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users' needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed.
 In this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graph pattern queries over RDF graphs such as ER graphs. Our method estimates a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. We demonstrate the effectiveness of our ranking model by a comprehensive user study.
Android Botnets: What URLs are Telling Us

Botnets have traditionally been seen as a threat to personal computers; however, the recent shift to mobile platforms resulted in a wave of new botnets. Due to its popularity, Android mobile Operating System became the most targeted platform. In spite of rising numbers, there is a significant gap in understanding the nature of mobile botnets and their communication characteristics. In this paper, we address this gap and provide a deep analysis of Command and Control (C&C) and built-in URLs of Android botnets detected since the first appearance of the Android platform. By combining both static and dynamic analyses with visualization, we uncover the relationships between the majority of the analyzed botnet families and offer an insight into each malicious infrastructure. As a part of this study we compile and offer to the research community a dataset containing 1929 samples representing 14 Android botnet families.
An HIS-Based Spiral Antenna for Pattern Reconfigurable Applications

Single arm rectangular spiral antenna with four open circuit switches over a high-impedance surface (HIS) is proposed for pattern reconfigurable applications. The HIS plane without vias is utilized to achieve a low-profile antenna design with a net thickness of 5.08 mm. This is equivalent to ~ lambdao/17 for the intended operating frequency of 3.3 GHz. By using the possible sixteen switching combinations a near 360deg beam steering is achieved, and the switched beams do not have a polarization variation from one pattern to another. The realized pattern reconfigurable antenna has both the tilted (thetasmax ges 25deg) and axial (5deg < thetasmax < 10deg) beams, which have an average directivity of 6.9 dBi.
The Social Amplification of Risk A Conceptual Framework

One of the most perplexing problems in risk analysis is why some relatively minor risks or risk events, as assessed by technical experts, often elicit strong public concerns and result in substantial impacts upon society and economy. This article sets forth a conceptual framework that seeks to link systematically the technical assessment of risk with psychological, sociological, and cultural perspectives of risk perception and risk-related behavior. The main thesis is that hazards interact with psychological, social, institutional, and cultural processes in ways that may amplify or attenuate public responses to the risk or risk event. A structural description of the social amplification of risk is now possible. Amplification occurs at two stages: in the transfer of information about the risk, and in the response mechanisms of society. Signals about risk are processed by individual and social amplification stations, including the scientist who communicates the risk assessment, the news media, cultural groups, interpersonal networks, and others. Key steps of amplifications can be identified at each stage. The amplified risk leads to behavioral responses, which, in turn, result in secondary impacts. Models are presented that portray the elements and linkages in the proposed conceptual framework.
A multi-tenant RBAC model for collaborative cloud services

Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures. In this setting, a tenant temporarily uses a piece of virtually dedicated software, platform, or infrastructure. To fully benefit from the cloud, tenants are seeking to build controlled and secure collaboration with each other. In this paper, we propose a Multi-Tenant Role-Based Access Control (MT-RBAC) model family which aims to provide fine-grained authorization in collaborative cloud environments by building trust relations among tenants. With an established trust relation in MT-RBAC, the trustee can precisely authorize cross-tenant accesses to the truster's resources consistent with constraints over the trust relation and other components designated by the truster. The users in the trustee may restrictively inherit permissions from the truster so that multi-tenant collaboration is securely enabled. Using SUN's XACML library, we prototype MT-RBAC models on a novel Authorization as a Service (AaaS) platform with the Joyent commercial cloud system. The performance and scalability metrics are evaluated with respect to an open source cloud storage system. The results show that our prototype incurs only 0.016 second authorization delay for end users on average and is scalable in cloud environments.
Driver behavior profiling using smartphones

The proliferation of smartphones and mobile devices embedding different types of sensors sets up a prodigious and distributed sensing platform. In particular, in the last years there has been an increasing necessity to monitor drivers to identify bad driving habits in order to optimize fuel consumption, to reduce CO2 emissions or, indeed, to design new reliable and fair pricing schemes for the insurance market. In this paper, we analyze the driver sensing capacity of smartphones. We propose a mobile tool that makes use of the most common sensors embedded in current smartphones and implement a Fuzzy Inference System that scores the overall driving behavior by combining different fuzzy sensing data.
Impact of Automatic Feature Extraction in Deep Learning Architecture

This paper presents the impact of automatic feature extraction used in a deep learning architecture such as Convolutional Neural Network (CNN). Recently CNN has become a very popular tool for image classification which can automatically extract features, learn and classify them. It is a common belief that CNN can always perform better than other well-known classifiers. However, there is no systematic study which shows that automatic feature extraction in CNN is any better than other simple feature extraction techniques, and there is no study which shows that other simple neural network architectures cannot achieve same accuracy as CNN. In this paper, a systematic study to investigate CNN's feature extraction is presented. CNN with automatic feature extraction is firstly evaluated on a number of benchmark datasets and then a simple traditional Multi-Layer Perceptron (MLP) with full image, and manual feature extraction are evaluated on the same benchmark datasets. The purpose is to see whether feature extraction in CNN performs any better than a simple feature with MLP and full image with MLP. Many experiments were systematically conducted by varying number of epochs and hidden neurons. The experimental results revealed that traditional MLP with suitable parameters can perform as good as CNN or better in certain cases.
Neural Generative Question Answering

This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, both in natural language. More specifically, the model is built on the encoder-decoder framework for sequence-to-sequence learning, while equipped with the ability to access an embedded knowledge-base through an attention-like mechanism. The model is trained on a corpus of question-answer pairs, with their associated triples in the given knowledge-base. Empirical study shows the proposed model can effectively deal with the language variation of the question and generate a right answer by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform the embedding-based QA model as well as the neural dialogue models trained on the same data.
Eye tracking in advanced interface design

The problem of human-computer interaction can be viewed as two powerful information processors (human and computer) attempting to communicate with each other via a narrowbandwidth, highly constrained interface [23]. To address it, we seek faster, more natural, and more convenient means for users and computers to exchange information. The user’s side is constrained by the nature of human communication organs and abilities; the computer’s is constrained only by input/output devices and interaction techniques that we can invent. Current technology has been stronger in the computer-to-user direction than user-to-computer, hence today’s user-computer dialogues are rather one-sided, with the bandwidth from the computer to the user far greater than that from user to computer. Using eye movements as a user-tocomputer communication medium can help redress this imbalance. This chapter describes the relevant characteristics of the human eye, eye tracking technology, how to design interaction techniques that incorporate eye movements into the user-computer dialogue in a convenient and natural way, and the relationship between eye movement interfaces and virtual environments.
Assessment of Smartphone Addiction in Indian Adolescents: A Mixed Method Study by Systematic-review and Meta-analysis Approach

There is a considerable debate on addiction and abuse to Smartphone among adolescents and its consequent impact on their health; not only in a global context, but also specifically in the Indian population; considering that Smartphone's, globally occupy more than 50% of mobile phones market and more precise quantification of the associated problems is important to facilitate understanding in this field. As per PRISMA (2009) guidelines, extensive search of various studies in any form from a global scale to the more narrow Indian context using two key search words: "Smartphone's addiction" and "Indian adolescents" was done using websites of EMBASE, MEDLINE, PubMed, Global Health, Psyc-INFO, Biomed-Central, Web of Science, Cochrane Library, world library - World-Cat, Indian libraries such as National Medical Library of India from 1 January, 1995 to March 31, 2014 first for systematic-review. Finally, meta-analysis on only Indian studies was done using Med-Calc online software capable of doing meta-analysis of proportions. A total of 45 articles were considered in systematic-review from whole world; later on 6 studies out of these 45 related to Smartphone's addiction in India were extracted to perform meta-analysis, in which total 1304 participants (range: 165-335) were enrolled. The smartphone addiction magnitude in India ranged from 39% to 44% as per fixed effects calculated (P < 0.0001). Smartphone addiction among Indian teens can not only damage interpersonal skills, but also it can lead to significant negative health risks and harmful psychological effects on Indian adolescents.
Moving Vehicle Registration and Super-Resolution

We describe a method for registering and super-resolving moving vehicles from aerial surveillance video. The challenge of vehicle super-resolution lies in the fact that vehicles may be very small and thus frame-to-frame registration does not offer enough constraints to yield registration with sub-pixel accuracy. To overcome this, we first register the large-scale image backgrounds and then, relative to the background registration, register the small-scale moving vehicle over all frames simultaneously using a vehicle motion model. To solve for the vehicle motion parameters we optimize a cost function that incorporates both vehicle appearance and background appearance consistency. Once this process accurately registers a moving vehicle, it is super-resolved. We apply both a frequency domain and a spatial domain approach. The frequency domain approach can be used when the final registered vehicle motion is well approximated by shifts in the image plane. The robust regularized spatial domain approach handles all cases of vehicle motion.
Psycho-physiological measures for assessing cognitive load

With a focus on presenting information at the right time, the ubicomp community can benefit greatly from learning the most salient human measures of cognitive load. Cognitive load can be used as a metric to determine when or whether to interrupt a user. In this paper, we collected data from multiple sensors and compared their ability to assess cognitive load. Our focus is on visual perception and cognitive speed-focused tasks that leverage cognitive abilities common in ubicomp applications. We found that across all participants, the electrocardiogram median absolute deviation and median heat flux measurements were the most accurate at distinguishing between low and high levels of cognitive load, providing a classification accuracy of over 80% when used together. Our contribution is a real-time, objective, and generalizable method for assessing cognitive load in cognitive tasks commonly found in ubicomp systems and situations of divided attention.
Combining information extraction and human computing for crowdsourced knowledge acquisition

Automatic information extraction (IE) enables the construction of very large knowledge bases (KBs), with relational facts on millions of entities from text corpora and Web sources. However, such KBs contain errors and they are far from being complete. This motivates the need for exploiting human intelligence and knowledge using crowd-based human computing (HC) for assessing the validity of facts and for gathering additional knowledge. This paper presents a novel system architecture, called Higgins, which shows how to effectively integrate an IE engine and a HC engine. Higgins generates game questions where players choose or fill in missing relations for subject-relation-object triples. For generating multiple-choice answer candidates, we have constructed a large dictionary of entity names and relational phrases, and have developed specifically designed statistical language models for phrase relatedness. To this end, we combine semantic resources like WordNet, ConceptNet, and others with statistics derived from a large Web corpus. We demonstrate the effectiveness of Higgins for knowledge acquisition by crowdsourced gathering of relationships between characters in narrative descriptions of movies and books.
Toward Open-Set Face Recognition

Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled in the gallery, cannot make this closed-set assumption. Instead, they must assume an open set of probe samples and be able to reject/ignore those that correspond to unknown identities. In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity. Our open-set identification protocol is based on the canonical labeled faces in the wild (LFW) dataset. Additionally to the known identities, we introduce the concepts of known unknowns (known, but uninteresting persons) and unknown unknowns (people never seen before) to the biometric community. We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol: thresholded verification-like scores, linear discriminant analysis (LDA) scores, and an extreme value machine (EVM) probabilities. Our findings suggest that thresholding EVM probabilities, which are open-set by design, outperforms thresholding verification-like scores.