nils-herrmann/clean_scirepeval huggingface dataset:
Towards Understanding and Harnessing the Potential of Clause Learning

Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems, such as verification, planning and design. Despite its importance, little is known of the ultimate strengths and limitations of the technique. This paper presents the first precise characterization of clause learning as a proof system (CL), and begins the task of understanding its power by relating it to the well-studied resolution proof system. In particular, we show that with a new learning scheme, CL can provide exponentially shorter proofs than many proper refinements of general resolution (RES) satisfying a natural property. These include regular and Davis-Putnam resolution, which are already known to be much stronger than ordinary DPLL. We also show that a slight variant of CL with unlimited restarts is as powerful as RES itself. Translating these analytical results to practice, however, presents a challenge because of the nondeterministic nature of clause learning algorithms. We propose a novel way of exploiting the underlying problem structure, in the form of a high level problem description such as a graph or PDDL specification, to guide clause learning algorithms toward faster solutions. We show that this leads to exponential speed-ups on grid and randomized pebbling problems, as well as substantial improvements on certain ordering formulas.
An experimental comparison of reading techniques for defect detection in UML design documents

The basic motivation for software inspections is to detect and remove defects before they propagate to subsequent development phases where their detection and removal becomes more expensive. To attain this potential, the examination of the artefact under inspection must be as thorough and detailed as possible. This implies the need for systematic reading techniques that tell inspection participants what to look for and, more importantly, how to scrutinise a software document. Recent research efforts investigated the benefits of scenario-based reading techniques for defect detection in functional requirements and functional code documents. A major finding has been that these techniques help inspection teams find more defects than existing state-of-the-practice approaches, such as, ad-hoc or checklist-based reading (CBR). In this paper we describe and experimentally compare one scenariobased reading technique, namely perspective-based reading (PBR), for defect detection in objectoriented design documents using the notation of the Unified Modelling Language (UML) with the more traditional CBR approach. The comparison was performed in a controlled experiment with 18 practitioners as subjects. Our results indicate that PBR is more effective than CBR (i.e., it resulted in inspection teams detecting on average 41% more unique defects than CBR). Moreover the cost of defect detection using PBR is significantly lower than CBR (i.e., PBR exhibits on average a 58% cost per defect improvement over CBR). This study therefore provides evidence demonstrating the efficacy of PBR scenarios for defect detection in UML design documents. In addition, it demonstrates that a PBR inspection is a promising approach for improving the quality of models developed using the UML notation.
ARP-P4: A Hybrid ARP-Path/P4Runtime Switch

This paper presents ARP-P4, a hybrid switch based on a local ARP-Path control plane written in P4, but maintaining legacy P4 Runtime control plane capabilities. Its main purpose is to study the readiness of P4 to perform directly local control actions at the data plane, such as the required ones for the ARP-Path protocol. Thus, ARP-P4 defines a data plane that forwards any ingress packet locally via ARP-Path or remotely via P4 Runtime installed rules through an SDN controller. Finally, we discuss the current limitations of P4 non-standard externs to interact with P4 Runtime to develop autonomous capabilities.
POLYGLOT-NER: Massive Multilingual Named Entity Recognition

The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.
An evaluation framework for 360-degree video compression

360-degree video is emerging as a new way of offering immersive visual experience. 360-degree video can be viewed on dedicated head mounted devices as well as on conventional 2D displays. Due to increased resolution to support wide field of view, efficient compression of 360-degree video becomes crucial. Whereas it is of significant interest to evaluate how different projection formats impact the compression efficiency of 360-degree video, a main technical challenge is that the input video is captured in a given native projection format, and that the native format has an obvious edge over other projection formats. In this paper, an evaluation framework is proposed to reduce the bias towards the native projection format when comparing different projection formats and their impact on 360-degree video compression. Additionally, a quality metric called area weighted spherical PSNR (AW-SPSNR) is proposed for objective 360-degree video quality evaluation. The proposed evaluation framework was included in the common test conditions defined for the exploration work of 360-degree video compression technologies under the Joint Video Exploration Team (JVET).
A novel broadband dual-polarized low-profile antenna for 4G LTE applications

A novel broadband dual-polarized low-profile antenna is presented for the 4G Long Term Evolution (LTE) network. The antenna structure contains a metallic low-profile cone for vertical polarization (VP) and eight pairs planar dipole elements for horizontal polarization (HP). The profile of the dual-polarized omnidirectional antenna is approximately 36.8mm. The broad frequency bands with the S11 less than −10 dB from 0.93 GHz to 3.5 GHz for VP and with the S22 less than −9 dB from 2 GHz to 2.65 GHz for HP can be achieved with the compact volume. Owing to the noticeable low profile, broadband and omnidirectional radiation characteristics, the proposed antenna can be widely used in indoor roof antenna networking applications.
On Detecting Adversarial Perturbations

Machine learning and deep learning in particular has advanced tremendously on perceptual tasks in recent years. However, it remains vulnerable against adversarial perturbations of the input that have been crafted specifically to fool the system while being quasi-imperceptible to a human. In this work, we propose to augment deep neural networks with a small “detector” subnetwork which is trained on the binary classification task of distinguishing genuine data from data containing adversarial perturbations. Our method is orthogonal to prior work on addressing adversarial perturbations, which has mostly focused on making the classification network itself more robust. We show empirically that adversarial perturbations can be detected surprisingly well even though they are quasi-imperceptible to humans. Moreover, while the detectors have been trained to detect only a specific adversary, they generalize to similar and weaker adversaries. In addition, we propose an adversarial attack that fools both the classifier and the detector and a novel training procedure for the detector that counteracts this attack.
Electronic word of mouth effects on consumers ’ brand attitudes , brand image and purchase intention : an empirical study in Egypt

Word of mouth communication has changed massively over the years, due to sophisticated technologies and new techniques, to a more ubiquitous form of communication called electronic word of mouth. The purpose of this paper is to examine the influence of e-WOM on purchase intention, as well as examining the mediating effect of brand image and consumer's attitude towards the brand on the relationship between e-WOM and purchase intention. To empirically test the hypothesized relationships between variables a model consisted of those variables was developed based on literature review. This model was analyzed using Structural Equation Modeling (SEM) using AMOS program 22. A quantitative, descriptive analysis a self-administered structured questionnaire was designed to investigate model relationships and was distributed on.469 undergraduates students from two large Business Schools affiliated to public and private universities operating in Cairo the capital of Egypt using the convenience sample technique. As most studies can hardly be implemented and applied to countries like Egypt. Therefore, Egypt is worthwhile to be studied on the topic of e-WOM and its influence on purchase intention. Empirical results indicated that eWOM had a significantly positive impact on brand image, brand attitudes and consumers’ purchasing intention also findings revealed that brand image has a strong effect on the consumers’ attitude toward a specific brand. The results could be useful for the organizations to better serve their consumers through the online buzz marketing strategies.
A Case for Standard Non-blocking Collective Operations

In this paper we make the case for adding standard nonblocking collective operations to the MPI standard. The non-blocking point-to-point and blocking collective operations currently defined by MPI provide important performance and abstraction benefits. To allow these benefits to be simultaneously realized, we present an application programming interface for non-blocking collective operations in MPI. Microbenchmark and application-based performance results demonstrate that non-blocking collective operations offer not only improved convenience, but improved performance as well, when compared to manual use of threads with blocking collectives.
Recurrent Recommender Networks

Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a user's taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.
Learning deep representations by mutual information estimation and maximization

This work investigates unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality in the input into the objective can significantly improve a representation’s suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and compares favorably with fully-supervised learning on several classification tasks in with some standard architectures. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation learning objectives for specific end-goals.
Investigations on Impact of Feature Normalization Techniques on Classifier's Performance in Breast Tumor Classification

Feature extraction and feature normalization is an important preprocessing technique, usually employed before classification. Feature normalization is a useful step to restrict the values of all features within predetermined ranges. However, appropriate choice of normalization technique and normalization range is an important issue, since, applying normalization on the input could change the structure of data and thereby affecting the outcome of multivariate analysis and calibration used in data mining and pattern recognition problems. This paper investigates and evaluates some popular feature normalization techniques and studies their impact on performance of classifier with application to breast tumor classification using ultrasound images. For evaluating the feature normalization techniques, back-propagation artificial neural network [BPANN] and support vector machine [SVM] classifier models are used. Results show that that normalization of features has significant effect on the classification accuracy. General Terms Pattern Recognition, Medical Image Processing.
3D feature extraction from uncalibrated video clips

This paper explores the idea of extracting a dense 3D point cloud corresponding to salient features in a video. The goal is to generate the dense point cloud efficiently, in order to use the information in various other video processing tasks. We present a method that is capable of extracting 3D information of videos with no previous knowledge of the scene, while keeping computational costs low. Our method exploits the movement of the camera while robustly tracking features over time, in order to obtain multiple views of a scene and perform 3D reconstruction. Additionally, our system is able to cope with individually moving people seen in the videos, and can estimate each person's pose and fit a 3D model to it. This 3D model is inserted into the dense point cloud in order to visualize the reconstructed scenes, and does not affect the tracking of the rest of the scene
Factors Affecting Intent to Purchase Virtual Goods in Online Games

Online games increasingly sell virtual goods to generate real income. As a result, it is increasingly important to identify factors and theory of consumption values that affect intent to purchase virtual goods in online games. However, very little research has been devoted to the topic. This study is an empirical investigation of the factors and theory of consumption values that affect intent to purchase virtual goods in online games. The study determines the effects of game type, satisfaction with the game, identification with the character, and theory of consumption values on intent to purchase virtual goods. The study used a survey to collect information from 523 virtual game users. Study results showed that game type is a moderating variable that affects intent to purchase virtual goods. And it demonstrated that role-playing game users are affected by theory of consumption values: functional quality, playfulness, and social relationship support. Moreover, war-strategy game users are affected by satisfaction with the game, identification with the character, and theory of consumption values: price, utility, and playfulness. The study also presents conclusions, proposes applications, and describes opportunities for further research.
Plans and situated actions - the problem of human-machine communication

This thesis considers two alternative views of purposeful action and shared understanding. The first, adopted by researchers in Cognitive Science, views the organization and significance of action as derived from plans, which are prerequisite to and prescribe action at whatever level of detail one might imagine. Mutual intelligibility on this view is a matter of the recognizability of plans, due to common conventions for the expression of intent, and common knowledge about typical situations and appropriate actions. The second view, drawn from recent work in social science, treats plans as derivative from situated action. Situated action as such comprises necessarily ad hoc responses to the actions of others and to the contingencies of particular situations. Rather than depend upon the reliable recognition of intent, successful interaction consists in the collaborative production of intelligibility through mutual access to situation resources, and through the detection, repair or exploitation of differences in understanding. As common sense formulations designed to accomodate the unforseeable contingences of situated action, plans are inherently vague. Researchers interested in machine intelligence attempt to remedy the vagueness of plans, to make them the basis for artifacts intended to embody intelligent behavior, including the ability to interact with their human users. The idea that computational artifacts might interact with their users is supported by their reactive, linguistic, and internally opaque properties. Those properties suggest the possibility that computers might 'explain themselves: thereby providing a solution to the problem of conveying the designer's purposes to the user, and a means of establishing the intelligence of the artifact itself. I examine the problem of human-machine communication through a case study of people using a machine designed on the planning model, and intended to be intelligent and interactive~ A conversation analysis of "interactions" between users and the machine reveals that the machine's insensitivity to particular circumstances is a central design resource, and a fundamental limitation. I conclude that problems in Cognitive Science's theorizing about purposeful action as a basis for machine intelligence are due to the project of substituting plans for actions, and representations of the situation of action, for action's actual circumstances. XEROX PARe. ISL-6.
Efficiently solving dynamic Markov random fields using graph cuts

In this paper, we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP estimates for dynamically changing MRF models of labeling problems in computer vision, such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, we show how to efficiently compute the maximum flow in a modified version of the graph. Our experiments showed that the time taken by our algorithm is roughly proportional to the number of edges whose weights were different in the two graphs. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video and compare it with the best known st-mincut algorithm. The results show that the dynamic graph cut algorithm is much faster than its static counterpart and enables real time image segmentation. It should be noted that our method is generic and can be used to yield similar improvements in many other cases that involve dynamic change in the graph
A 0.3 pJ/access 8T data-aware SRAM utilizing column-based data encoding for ultra-low power applications

This paper proposes an 8T SRAMs utilizing a column-based data encoding scheme to reduce read and write power when there are similarities between consecutive data. It is useful in image processing applications where nearby pixels tend to have similar values. The proposed design has two modes of operation: normal and sequential modes. In the normal mode, it operates as a normal SRAM. In the sequential mode, bit-wise differences between consecutive data are written instead of the actual data. This leads to a much higher number of zeros in the array. Accordingly, a new data-aware bitline pre-charge scheme is proposed to minimize write power when writing a zero. A PVT-tracking reference voltage generator is also employed to compensate the read-bitline leakage for ultra-low voltage operation. On average, it offers more than 30% WBL switching power reduction. A 32Kb SRAM implemented in 65 nm CMOS process demonstrated successful operation down to 0.36 V. The total power consumption is 0.37 μW, corresponding to the maximum frequency of 0.25 MHz. Its minimum energy is 0.3 pJ/access achieved at 0.5 V.
G-Distillation: Reducing Overconfident Errors on Novel Samples

Counter to the intuition that unfamiliarity should lead to lack of confidence, current algorithms often make highly confident yet wrong predictions when faced with unexpected test samples from an unknown distribution different from training. Unlike all domain adaptation methods, we cannot gather an “unexpected dataset” prior to test. We propose a simple solution that reduces overconfident errors of samples from an unknown novel distribution without increasing evaluation time: train an ensemble of classifiers and then distill into a single model using both labeled and unlabeled examples. Experimentally, we investigate the overconfidence problem and evaluate our solution by creating “familiar” and “novel” test splits, where “familiar” are identically distributed with training and “novel” are not. We show that our solution yields more appropriate prediction confidences, on familiar and novel data, compared to single models and ensembles distilled on training data only. For example, we reduce confident errors in gender recognition by 94% on demographic groups different from the training data.
Improving classification with latent variable models by sequential constraint optimization

In this paper we propose a method to use multiple generative models with latent variables for classi cation tasks. The standard approach to use generative models for classi cation is to train a separate model for each class. A novel data point is then classi ed by the model that attributes the highest probability. The algorithm we propose modi es the parameters of the models to improve the classi cation accuracy. Our approach is made computationally tractable by assuming that each of the models is deterministic, by which we mean that a data-point is associated to only a single latent state. The resulting algorithm is a variant of the support vector machine learning algorithm and in a limiting case the method is similar to the standard perceptron learning algorithm. We apply the method to two types of latent variable models. The rst has a discrete latent state space and the second, principal component analysis, has a continuous latent state space. We compare the e6ectiveness of both approaches on a handwritten digit recognition problem and on a satellite image recognition problem.