nils-herrmann/clean_scirepeval huggingface dataset:
Security vulnerabilities in DNS and DNSSEC

We present an analysis of security vulnerabilities in the domain name system (DNS) and the DNS security extensions (DNSSEC). DNS data that is provided by name servers lacks support for data origin authentication and data integrity. This makes DNS vulnerable to man in the middle (MITM) attacks, as well as a range of other attacks. To make DNS more robust, DNSSEC was proposed by the Internet Engineering Task Force (IETF). DNSSEC provides data origin authentication and integrity by using digital signatures. Although DNSSEC provides security for DNS data, it suffers from serious security and operational flaws. We discuss the DNS and DNSSEC architectures, and consider the associated security vulnerabilities
Photographic Image Synthesis with Cascaded Refinement Networks

We present an approach to synthesizing photographic images conditioned on semantic layouts. Given a semantic label map, our approach produces an image with photographic appearance that conforms to the input layout. The approach thus functions as a rendering engine that takes a two-dimensional semantic specification of the scene and produces a corresponding photographic image. Unlike recent and contemporaneous work, our approach does not rely on adversarial training. We show that photographic images can be synthesized from semantic layouts by a single feedforward network with appropriate structure, trained end-to-end with a direct regression objective. The presented approach scales seamlessly to high resolutions; we demonstrate this by synthesizing photographic images at 2-megapixel resolution, the full resolution of our training data. Extensive perceptual experiments on datasets of outdoor and indoor scenes demonstrate that images synthesized by the presented approach are considerably more realistic than alternative approaches.
Knet : beginning deep learning with 100 lines of

Knet (pronounced "kay-net") is the Koç University machine learning framework implemented in Julia, a high-level, high-performance, dynamic programming language. Unlike gradient generating compilers like Theano and TensorFlow which restrict users into a modeling mini-language, Knet allows models to be defined by just describing their forward computation in plain Julia, allowing the use of loops, conditionals, recursion, closures, tuples, dictionaries, array indexing, concatenation and other high level language features. High performance is achieved by combining automatic differentiation of most of Julia with efficient GPU kernels and memory management. Several examples and benchmarks are provided to demonstrate that GPU support and automatic differentiation of a high level language are sufficient for concise definition and efficient training of sophisticated models.
Business Process Variability Modeling: A Survey

It is common for organizations to maintain multiple variants of a given business process, such as multiple sales processes for different products or multiple bookkeeping processes for different countries. Conventional business process modeling languages do not explicitly support the representation of such families of process variants. This gap triggered significant research efforts over the past decade, leading to an array of approaches to business process variability modeling. In general, each of these approaches extends a conventional process modeling language with constructs to capture customizable process models. A customizable process model represents a family of process variants in a way that a model of each variant can be derived by adding or deleting fragments according to customization options or according to a domain model. This survey draws up a systematic inventory of approaches to customizable process modeling and provides a comparative evaluation with the aim of identifying common and differentiating modeling features, providing criteria for selecting among multiple approaches, and identifying gaps in the state of the art. The survey puts into evidence an abundance of customizable process-modeling languages, which contrasts with a relative scarcity of available tool support and empirical comparative evaluations.
A High-Throughput Energy-Efficient Implementation of Successive Cancellation Decoder for Polar Codes Using Combinational Logic

This paper proposes a high-throughput energy-efficient Successive Cancellation (SC) decoder architecture for polar codes based on combinational logic. The proposed combinational architecture operates at relatively low clock frequencies compared to sequential circuits, but takes advantage of the high degree of parallelism inherent in such architectures to provide a favorable tradeoff between throughput and energy efficiency at short to medium block lengths. At longer block lengths, the paper proposes a hybrid-logic SC decoder that combines the advantageous aspects of the combinational decoder with the low-complexity nature of sequential-logic decoders. Performance characteristics on ASIC and FPGA are presented with a detailed power consumption analysis for combinational decoders. Finally, the paper presents an analysis of the complexity and delay of combinational decoders, and of the throughput gains obtained by hybrid-logic decoders with respect to purely synchronous architectures.
LECTURE NOTES ON STABILITY THEORY

These are the notes for my quarter-long course on basic stability theory at UCLA (MATH 285D, Winter 2015). The presentation highlights some relations to set theory and cardinal arithmetic reflecting my impression about the tastes of the audience. We develop the general theory of local stability instead of specializing to the finite rank case, and touch on some generalizations of stability such as NIP and simplicity. The material in this notes is based on [Pil02, Pil96], [vdD05], [TZ12], [Cas11a, Cas07], [Sim15], [Poi01] and [Che12]. I would also like to thank the following people for their comments and suggestions: Tyler Arant, Madeline Barnicle, Allen Gehret, Omer Ben Neria, Anton Bobkov, Jesse Han, Pietro Kreitlon Carolino, Andrew Marks, Alex Mennen, Assaf Shani, John Susice, Spencer Unger. Comments and corrections are very welcome (chernikov@math.ucla.edu, http://www.math.ucla.edu/~chernikov/).
Interpreting Deep Classification Models With Bayesian Inference

In this paper, we propose a novel approach to interpret a well-trained classification model through systematically investigating effects of its hidden units on prediction making. We search for the core hidden units responsible for predicting inputs as the class of interest under the generative Bayesian inference framework. We model such a process of unit selection as an Indian Buffet Process, and derive a simplified objective function via the MAP asymptotic technique. The induced binary optimization problem is efficiently solved with a continuous relaxation method by attaching a Switch Gate layer to the hidden layers of interest. The resulted interpreter model is thus end-to-end optimized via standard gradient back-propagation. Experiments are conducted with two popular deep convolutional classifiers, respectively well-trained on the MNIST dataset and the CIFAR10 dataset. The results demonstrate that the proposed interpreter successfully finds the core hidden units most responsible for prediction making. The modified model, only with the selected units activated, can hold correct predictions at a high rate. Besides, this interpreter model is also able to extract the most informative pixels in the images by connecting a Switch Gate layer to the input layer.
3D point cloud generation from 2D depth camera images using successive triangulation

Depth cameras are low-cost, plug & play solution to generate point cloud. 3D depth camera yields depth images which do not convey the actual distance. A 3D camera driver does not support raw depth data output, these are usually filtered and calibrated as per the sensor specifications and hence a method is required to map every pixel back to its original point in 3D space. This paper demonstrates the method to triangulate a pixel from the 2D depth image back to its actual position in 3D space. Further this method illustrates the independence of this mapping operation, which facilitates parallel computing. Triangulation method and ratios between the pixel positions and camera parameters are used to estimate the true position in 3D space. The algorithm performance can be increased by 70% by the usage of TPL libraries. This performance differs from processor to processor
A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

Educational Data Mining (EDM) is an interdisciplinary ingenuous research area that handles the development of methods to explore data arising in a scholastic fields. Computational approaches used by EDM is to examine scholastic data in order to study educational questions. As a result, it provides intrinsic knowledge of teaching and learning process for effective education planning. This paper conducts a comprehensive study on the recent and relevant studies put through in this field to date. The study focuses on methods of analysing educational data to develop models for improving academic performances and improving institutional effectiveness. This paper accumulates and relegates literature, identifies consequential work and mediates it to computing educators and professional bodies. We identify research that gives well-fortified advice to amend edifying and invigorate the more impuissant segment students in the institution. The results of these studies give insight into techniques for ameliorating pedagogical process, presaging student performance, compare the precision of data mining algorithms, and demonstrate the maturity of open source implements.
Stationary dynamic texture synthesis using convolutional neural networks

In this paper, we utilize deep networks to study the problem of modeling and synthesizing stationary dynamic textures that exhibit both spatial and temporal regularity. Our work is a generalization of the generative static texture model based on convolutional neural networks (CNNs). We extend the static texture synthesis method using CNNs to the dynamical setting by adding the temporal dimension, where the multiple layers of spatial-temporal filters come from off-the-shelf deep 3-dimensional convolutional networks (3D ConvNets) trained in a discriminative fashion. Dynamic textures generation is formulated as an iterative optimization procedure which imposes the correlation statistics derived from 3D ConvNets of the input exemplar on the synthesized one. Synthesis experiments on various video texture exemplars show that such generative method can synthesize realistic dynamic textures of good visual quality.
Mobile-Assisted Language Learning

The main characteristics of mobile learning (m-learning) are recognized as the potential for learning process to be personalized, spontaneous, informal and ubiquitous. Although learning through mobile phones may take longer time compared to computers, the learners feel a greater sense of freedom of time and place, so that they can take the advantage of spare time to learn a second language when and where they are. Mobile-Assisted Language Learning (MALL) deals with the use of mobile technology in language learning. In contrast to classroom learning, in MALL there is no need for the learners to sit in a classroom or at a computer to get learning materials. In fact, MALL can be considered an ideal solution to language learning barriers in terms of time and place. In this paper by looking at some applications of m-learning as well as some examples across various aspects of it, we observe the advantages and disadvantages derived from using mobile technologies for students as well as professionals. Here, it has been tried to demonstrate the benefits of using mobile phones in learning English as a second language. Areas of mobile-based language learning discussed in this paper are vocabulary, listening, grammar, phonetics, and reading comprehension.
Feature Selection: A Data Perspective

Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity-based, information-theoretical-based, sparse-learning-based, and statistical-based methods. To facilitate and promote the research in this community, we also present an open source feature selection repository that consists of most of the popular feature selection algorithms (http://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research.
An Investigation on Wannacry Ransomware and its Detection

The technological advancement has been accompanied with many issues to the information: security, privacy, and integrity. Malware is one of the security issues that threaten computer system. Ransomware is a type of malicious software that threatens to publish the victim’s data or perpetually block access to it unless a ransom is paid. This paper investigates the intrusion of WannaCry ransomware and the possible detection of the ransomware using static and dynamic analysis. From the analysis, the features of the malware were extracted and detection has been done using those features. The intrusion detection technique used here in this study is Yara-rule based detection which involves an attempt to define a set of rules which comprises of unique strings which is decoded from the wannacry file.
Big Data , the perfect instrument to study today ’ s consumer behavior

Consumer behavior study is a new, interdisciplinary nd emerging science, developed in the 1960s. Its main sources of information come from ec onomics, psychology, sociology, anthropology and artificial intelligence. If a cent ury ago, most people were living in small towns, with limited possibilities to leave their co mmunity, and few ways to satisfy their needs, now, due to the accelerated evolution of technology and the radical change of life style, consumers begin to have increasingly diverse needs. At the same time the instruments used to study their behavior have evolved, and today databa ses are included in consumer behavior research. Throughout time many models were develope d, first in order to analyze, and later in order to predict the consumer behavior. As a res ult, the concept of Big Data developed, and by applying it now, companies are trying to und erstand and predict the behavior of their consumers.
Natural language understanding

I unstructured natural language — allow any statements, but make mistakes or failure. I controlled natural language — only allow unambiguous statements that can be interpreted (e.g., in supermarkets or for doctors). There is a vast amount of information in natural language. Understanding language to extract information or answering questions is more difficult than getting extracting gestalt properties such as topic, or choosing a help page. Many of the problems of AI are explicit in natural language understanding. “AI complete”.
The coverage problem in UAV network: A survey

UAV has been a popular piece of equipment both in military and civilian applications. Groups of UAVs can form an UAV network and accomplish complicated missions such as rescue, searching, patrolling and mapping. One of the most active areas of research in UAV networks is that of area coverage problem which is usually defined as a problem of how well the UAV networks are able to monitor the given space, and how well the UAVs inside a network are able to cooperate with each other. Area coverage problem in cooperative UAV networks is the very base of many applications. In this paper, we take a representative survey of the current work that has been done about this problem via discussion of different classifications. This study serves as an overview of area coverage problem, and give some inspiration to related researchers.
Mobile-Edge Computing Architecture: The role of MEC in the Internet of Things

In this article, we provided a tutorial on MEC technology and an overview of the MEC framework and architecture recently defined by the ETSI MEC ISG standardization group. We described some examples of MEC deployment, with special reference to IoT uses since the IoT is recognized as a main driver for 5G. After having also discussed benefits and challenges for MEC toward 5G, we can say that MEC has definitely a window of opportunity to contribute to the creation of a common layer of integration for the IoT world. One of the main questions still open is: How will this technology coexist with LTE advanced pro and the future 5G network? For this aspect, we foresee the need for very strong cooperation between 3GPP and ETSI (e.g., NFV and possibly other SDOs) to avoid unnecessary duplication in the standard. In this sense, MEC could pave the way and be natively integrated in the network of tomorrow.
On the Transitivity of Hypernym-Hyponym Relations in Data-Driven Lexical Taxonomies

Taxonomy is indispensable in understanding natural language. A variety of large scale, usage-based, data-driven lexical taxonomies have been constructed in recent years. Hypernym-hyponym relationship, which is considered as the backbone of lexical taxonomies can not only be used to categorize the data but also enables generalization. In particular, we focus on one of the most prominent properties of the hypernym-hyponym relationship, namely, transitivity, which has a significant implication for many applications. We show that, unlike human crafted ontologies and taxonomies, transitivity does not always hold in data-driven lexical taxonomies. We introduce a supervised approach to detect whether transitivity holds for any given pair of hypernym-hyponym relationships. Besides solving the inferencing problem, we also use the transitivity to derive new hypernym-hyponym relationships for data-driven lexical taxonomies. We conduct extensive experiments to show the effectiveness of our approach.
The Budgeted Multi-armed Bandit Problem

The following coins problem is a version of a multi-armed bandit problem where one has to select from among a set of objects, say classifiers, after an experimentation phase that is constrained by a time or cost budget. The question is how to spend the budget. The problem involves pure exploration only, differentiating it from typical multi-armed bandit problems involving an exploration/exploitation tradeoff [BF85]. It is an abstraction of the following scenarios: choosing from among a set of alternative treatments after a fixed number of clinical trials, determining the best parameter settings for a program given a deadline that only allows a fixed number of runs; or choosing a life partner in the bachelor/bachelorette TV show where time is limited. We are interested in the computational complexity of the coins problem and/or efficient algorithms with approximation guarantees.
Attitudes toward Facebook advertising

The purpose of this study was to provide insight on attitudes towards Facebook advertising. In order to figure out the attitudes towards Facebook advertising, a snowball survey was executed among Facebook users by spreading a link to the survey. This study was quantitative study but the results of the study were interpreted in qualitative way. This research was executed with the help of factor analysis and cluster analysis, after which Chisquare test was used. This research expected that the result of the survey would lead in to two different groups with negative and positive attitudes. Factor analysis was used to find relations between variables that the survey data generated. The factor analysis resulted in 12 factors that were put in a cluster analysis to find different kinds of groups. Surprisingly the cluster analysis enabled the finding of three groups with different interests and different attitudes towards Facebook advertising. These clusters were analyzed and compared. One group was clearly negative, tending to block and avoid advertisements. Second group was with more neutral attitude towards advertising, and more carefree internet using. They did not have blocking software in use and they like to participate in activities more often. The third group had positive attitude towards advertising. The result of this study can be used to help companies better plan their Facebook advertising according to groups. It also reminds about the complexity of people and their attitudes; not everything suits everybody.
Recovering 6D Object Pose: Multi-modal Analyses on Challenges

A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community for maintaining “automation” in robotic manipulation? What next steps should the community take for improving “autonomy” in robotics while handling objects? Direct comparison of the detectors is difficult, since they are tested on multiple datasets with different characteristics and are evaluated using widely varying evaluation protocols. To deal with these issues, we follow a threefold strategy: five representative object datasets, mainly differing from the point of challenges that they involve, are collected. Then, two classes of detectors are tested on the collected datasets. Lastly, the baselines’ performances are evaluated using two different evaluation metrics under uniform scoring criteria. Regarding the experiments conducted, we analyse our observations on the baselines along with the challenges involved in the interested datasets, and we suggest a number of insights for the next steps to be taken, for improving the autonomy in robotics.