Reviewerly/exHarmony huggingface dataset:
Drawing on the iPad to input fuzzy sets with an application to linguistic data science

Large amounts of collected and stored data require specialized processing skills. However, a human-computer interaction is crude and far from natural. Users are forced to interact with databases using languages understood by machines. A simple way to learn more about phenomena represented by data can be done via representing the data as human-perceived concepts and enabling a human-like interpretation of it. The users should be able to use linguistic terms – for example LARGE, SLOW, MOST – as their representations of concepts in order to gain a better understanding of data. Yet, other issues arise: how to enter definitions of such terms, how to incorporate individual's understanding of their meanings, how to ensure their proper interpretation, and of course, how to do all this in an easy and simple way. In this paper, we present and describe an iPad-based software that enables an easy procedure of defining linguistic terms – such as LARGE, MEDIUM, SMALL – and linguistic qualifiers – ALL, MOSTLY – that are suitable for data analysis purposes. We state, that linguistic terms and qualifiers represented as fuzzy sets embody human defined concepts and allow users to better understand data. This understating is achieved via 'mapping' data into models built with definitions of imprecise concepts familiar to users. Further, fuzzy-based definitions of concepts and fuzzy operations on them facilitate a human-like analysis of data as an important aspect of data science. The main contribution is a tablet application – called Tablet input of Fuzzy Sets (TiFS) – that allows users to define terms in a simple way via drawing their 'shapes' using fingers. We provide a detailed description of the developed iPad application for that purpose and show its benefits when used in defining linguistic terms for data summarization processes.
Cybersecurity Risks, Vulnerabilities, and Countermeasures to Prevent Social Engineering Attacks

The broad objective of this study is to evaluate the vulnerabilities of an organization's information technology infrastructure, which include hardware and software systems, transmission media, local area networks, wide area networks, enterprise networks, intranets, and its use of the internet to cyber intrusions. To achieve this objective, the chapter explains the importance of social engineering in network intrusions and cyber-theft and the reasons for the rapid expansion of cybercrime. The chapter also includes a complete description and definition of social engineering, the role it plays in network intrusion and cyber identity theft, a discussion of the reasons for the rise in cybercrimes, and their impact on organizations. In closing the authors recommend some preventive measures and possible solutions to the threats and vulnerabilities of social engineering. The chapter concludes that while technology has a role to play in reducing the impact of social engineering attacks, the vulnerability resides with human behavior, human impulses, and psychological predispositions.
Traffic scene awareness for intelligent vehicles using ConvNets and stereo vision

In this paper, we propose an efficient approach to perform recognition and 3D localization of dynamic objects on images from a stereo camera, with the goal of gaining insight into traffic scenes in urban and road environments. We rely on a deep learning framework able to simultaneously identify a broad range of entities, such as vehicles, pedestrians or cyclists, with a frame rate compatible with the strict requirements of onboard automotive applications. Stereo information is later introduced to enrich the knowledge about the objects with geometrical information. The results demonstrate the capabilities of the perception system for a wide variety of situations, thus providing valuable information for a higher-level understanding of the traffic situation.
Multi-label learning method based on ML-RBF and laplacian ELM

Multi-label data widely exist in the real world, and the multi-label learning deals with the problem in which samples contain many labels. The main task of the multi-label learning is to train a model which adapts to the multi-label data such that the label of unknown label data can be predicted. Multi-label radial basis function neural network (ML-RBF) is an effective multi-label learning model, which combines K-means clustering and RBF neural network. The laplacian extreme learning machine (Lap-ELM) improved the traditional ELM by considering the structural relationship between low-dimension data and high-dimension data. As a kind of single-hidden layer feed-forward neural network (SLFN), ELM has the characteristics of fast training and good generalization ability compared to RBF. Affinity Propagation (AP) clustering algorithm can automatically determine the number of clusters. In this paper, a novel multi-label learning method named ML-AP-RBF-Lap-ELM is proposed which integrates AP clustering algorithm, ML-RBF and Lap-ELM. In this new model, the ML-RBF is used to map in the input layer. The number of hidden nodes and the center of the RBF function can be automatically determined by the AP clustering algorithm. The weights from the hidden layer to the output layer are solved by Lap-ELM. The simulation results show that ML-AP-RBF-Lap-ELM performs well on the three common data sets, including Natural Scene, Yeast Gene and 20NG (20 New Groups).
Continuous-variable entanglement distillation over a pure loss channel with multiple quantum scissors

Entanglement distillation is a key primitive for distributing high-quality entanglement between remote locations. Probabilistic noiseless linear amplification based on the quantum scissors is a candidate for entanglement distillation from noisy continuous-variable (CV) entangled states. Being a non-Gaussian operation, quantum scissors is challenging to analyze. We present a derivation of the non-Gaussian state heralded by multiple quantum scissors in a pure loss channel with two-mode squeezed vacuum input. We choose the reverse coherent information (RCI)---a proven lower bound on the distillable entanglement of a quantum state under one-way local operations and classical communication (LOCC), as our figure of merit. We evaluate a Gaussian lower bound on the RCI of the heralded state. We show that it can exceed the unlimited two-way LOCCassisted direct transmission entanglement distillation capacity of the pure loss channel. The optimal heralded Gaussian RCI with two quantum scissors is found to be significantly more than that with a single quantum scissors, albeit at the cost of decreased success probability. Our results fortify the possibility of a quantum repeater scheme for CV quantum states using the quantum scissors.
StarCraft AI Competitions, Bots, and Tournament Manager Software

Real-time strategy games have become an increasingly popular test bed for modern artificial intelligence (AI) techniques. With this rise in popularity has come the creation of several annual competitions, in which AI agents (bots) play the full game of StarCraft: Broodwar by Blizzard Entertainment. The three major annual StarCraft AI Competitions are the Student StarCraft AI Tournament, the Computational Intelligence in Games competition, and the Artificial Intelligence and Interactive Digital Entertainment competition. In this paper, we will give an overview of the current state of these competitions, describe the bots that compete in them, and describe the underlying open-source Tournament Manager software that runs them.
Learning Object Detectors With Semi-Annotated Weak Labels

For alleviating the human labor associated with annotating the training data for learning object detectors, recent research has focused on semi-supervised object detection (SSOD) and weakly supervised object detection (WSOD) approaches. In SSOD, instead of annotating all the instances in the whole training set, people only need to annotate the part of the training instances using bounding boxes. In WSOD, people need to annotate the image-level tags on all training images to indicate the object categories contained by the corresponding images since more detailed bounding box annotations are no longer needed. Along this line of research, this paper makes a further step to alleviate the human labor in annotating training data, leading to the problem of object detection with semi-annotated weak labels (ODSAWLs). Instead of labeling image-level tags on all training images, ODSAWL only needs the image-level tags for a small portion of the training images, and then, the object detectors can be learned from a small portion of the weakly-labeled training images and from the remaining unlabeled training images. To address such a challenging problem, this paper proposes a cross model co-training framework that collaborates an object localizer and a tag generator in an alternative optimization procedure. Specifically, during the learning procedure, these two (deep) models can transfer the needed knowledge (including labels and visual patterns) between each other. The whole learning procedure is accomplished in a few stages under the guidance of a progressive learning curriculum. To demonstrate the effectiveness of the proposed approach, we implement the comprehensive experiments on three benchmark datasets, where the obtained experimental results are quite encouraging. Notably, by using only about 15% weakly labeled training images, the proposed approach can effectively approach, or even outperform, the state-of-the-art WSOD methods.
Ciphertext-Policy Attribute-Based Encrypted Data Equality Test and Classification

Abstract Thanks to the ease of access and low expenses, it is now popular for people to store data in cloud servers. To protect sensitive data from being leaked to the outside, people usually encrypt the data in the cloud. However, management of these encrypted data becomes a challenging problem, e.g. data classification. Besides, how to selectively share data with other users is also an important and interesting problem in cloud storage. In this paper, we focus on ciphertext-policy attribute based encryption with equality test (CP-ABEET). People can use CP-ABEET to implement not only flexible authorization for the access to encrypted data, but also efficient data label classification, i.e. test of whether two encrypted data contain the same message. We construct an efficient CP-ABEET scheme, and prove its security based on a reasonable number-theoretic assumption. Compared with the only existing CP-ABEET scheme, our construction is more efficient in key generation, and has shorter attribute-related secret keys and better security.
Unscented Kalman filters for range-only cooperative localization of swarms of munitions in three-dimensional flight

Range-only cooperative localization for formations of projectiles is investigated using extended and unscented Kalman filters. Observability for range only cooperative localization is reviewed. Consequences of observability allow the user to choose many features of the localization scheme including number of measurements, ground references, and required on-board sensing. Several Kalman filters are designed including centralized filters using 3 degree of freedom plant models using both extended and unscented schemes, and decentralized unscented Kalman filters using 3 degree of freedom plant models. Results are shown for several scenarios to illustrate the effectiveness of each estimation scheme.
Universal designated verifier signature scheme with non-delegatability in the standard model

The notion of a Designated Verifier Signature (DVS) scheme allows a signer to create a signature which is only verifiable by an intended verifier. DVS is a very useful scheme for authenticating a signer without interfering with her privacy. In 2003, Steinfeld et al. extended this notion to enable a Universal Designated Verifier Signature (UDVS) scheme. In UDVS, everyone who holds Alice’s traditional signature on a message (the signature holder), can transform it into a DVS for a specific verifier. Non-delegatability is a critical property of a DVS scheme in applications where responsibility of a signer is important and can not be delegated to another entity. Shim (Information Science, 2014) posed an open problem on how to construct a non-delegatable UDVS scheme. Since then, it has been well acknowledged that constructing a UDVS scheme which is non-delegatable remains as an elusive research problem. Furthermore, gaining a construction which is based on the standard model (without random oracles) is most desirable in practice. In this work, we present an affirmative answer to the aforementioned open research problem. We present the first non-delegatable UDVS scheme and prove its security requirements in the standard model. To the best of our knowledge, our work is the first non-delegatable UDVS scheme, which fills the gap in the existing literature. Furthermore, when the signer is considered as the signature holder, our scheme is also considered as the first non-delegatable DVS scheme in the standard model.
Application of remotely sensed ASTER data in detecting alteration hosting Cu, Ag and Au bearing mineralized zones in Taghdout area, Central Anti-Atlas of Morocco

We conduct a remote sensing investigation to identify alteration zones hosting Cu, Au and Ag mineralization in the Taghdout area, Anti-Atlas. Mineralization occurs in Neoproterozoic rocks exposed along east-trending faults associated with the Major Accident Fault. Remote sensing is applied to delineate Neoproterozoic rocks including ophiolitic suites of the ophiolitic Complex and volcanosedimentary units of the Ouarzazate Group. Band ratios, principle component analysis and filter algorithms identify hydrothermal alteration zones containing ferric iron oxide, ferrous iron oxide, chlorite and carbonate. Phyllic, argillic, propylitic and silicic hypogene alteration zones host Cu, Ag, Au, Mn and Fe ore deposits. Remote sensing analysis identifies the spatial distribution of these ore zones and highlights regional structures that concentrate mineralizing hydrothermal alteration fluid flow. Ground truth verification demonstrates that ASTER provides reliable identification of hydrothermally altered rock units. Image analysis is a robust tool for targeting ore deposits in similar desert terrain ophiolitic complexes.
A Main/Subsidiary Network Framework for Simplifying Binary Neural Networks

To reduce memory footprint and run-time latency, techniques such as neural net-work pruning and binarization have been explored separately. However, it is un-clear how to combine the best of the two worlds to get extremely small and efficient models. In this paper, we, for the first time, define the filter-level pruning problem for binary neural networks, which cannot be solved by simply migrating existing structural pruning methods for full-precision models. A novel learning-based approach is proposed to prune filters in our main/subsidiary network frame-work, where the main network is responsible for learning representative features to optimize the prediction performance, and the subsidiary component works as a filter selector on the main network. To avoid gradient mismatch when training the subsidiary component, we propose a layer-wise and bottom-up scheme. We also provide the theoretical and experimental comparison between our learning-based and greedy rule-based methods. Finally, we empirically demonstrate the effectiveness of our approach applied on several binary models, including binarizedNIN, VGG-11, and ResNet-18, on various image classification datasets. For bi-nary ResNet-18 on ImageNet, we use 78.6% filters but can achieve slightly better test error 49.87% (50.02%-0.15%) than the original model
Recurrent Conditional Generative Adversarial Network for Image Deblurring

Nowadays, there is an increasing demand for images with high definition and fine textures, but images captured in natural scenes usually suffer from complicated blurry artifacts, caused mostly by object motion or camera shaking. Since these annoying artifacts greatly decrease image visual quality, deblurring algorithms have been proposed from various aspects. However, most energy-optimization-based algorithms rely heavily on blur kernel priors, and some learning-based methods either adopt pixel-wise loss function or ignore global structural information. Therefore, we propose an image deblurring algorithm based on a recurrent conditional generative adversarial network (RCGAN), in which the scale-recurrent generator extracts sequence spatio–temporal features and reconstructs sharp images in a coarse-to-fine scheme. To thoroughly evaluate the global and local generator performance, we further propose a receptive field recurrent discriminator. Besides, the discriminator takes blurry images as conditions, which helps to differentiate reconstructed images from real sharp ones. Last but not least, since the gradients are vanishing when training the generator with the output of the discriminator, a progressive loss function is proposed to enhance the gradients in back propagation and to take full advantage of discriminative features. Extensive experiments prove the superiority of RCGAN over state-of-the-art algorithms both qualitatively and quantitatively.
Efficient on-line anomaly detection for ship systems in operation

We propose novel modifications to an anomaly detection methodology based on multivariate signal reconstruction followed by residuals analysis. The reconstructions are made using Auto Associative Kernel Regression (AAKR), where the query observations are compared to historical observations called memory vectors, representing normal operation. When the data set with historical observations grows large, the naive approach where all observations are used as memory vectors will lead to unacceptable large computational loads, hence a reduced set of memory vectors should be intelligently selected. The residuals between the observed and the reconstructed signals are analysed using standard Sequential Probability Ratio Tests (SPRT), where appropriate alarms are raised based on the sequential behaviour of the residuals. The modifications we introduce include: a novel cluster based method to select memory vectors to be considered by the AAKR, which gives an extensive reduction in computation time; a generalization of the distance measure, which makes it possible to distinguish between explanatory and response variables; and a regional credibility estimation used in the residuals analysis, to let the time used to identify if a sequence of query vectors represents an anomalous state or not, depend on the amount of data situated close to or surrounding the query vector. We demonstrate how the anomaly detection method and the proposed modifications can be successfully applied for anomaly detection on a set of imbalanced benchmark data sets, as well as on recent data from a marine diesel engine in operation.
Multimodal Analysis of Video Collections: Visual Exploration of Presentation Techniques in TED Talks

While much research in the educational field has revealed many presentation techniques, they often overlap and are even occasionally contradictory. Exploring presentation techniques used in TED Talks could provide evidence for a practical guideline. This study aims to explore the verbal and non-verbal presentation techniques from a collection of TED Talks. However, such analysis is challenging due to the difficulties of analyzing multimodal video collections consisted of frame images, text, and metadata. This paper proposes a visual analytic system to analyze multimodal content in video collections. The system features three views at different levels: the Projection view with novel glyphs to facilitate cluster analysis regarding presentation styles; the Comparison View to present temporal distribution and concurrences of presentation techniques and support intra-cluster analysis; and the Video View to enable contextualized exploration of a video. We conduct a case study with language education experts and university students to provide anecdotal evidence about the effectiveness of our approach, and report new findings about presentation techniques in TED Talks. Quantitative feedback from a user study confirms the usefulness of our visual system for multimodal analysis of video collections.
Machine Learning Based Optimized Pruning Approach for Decoding in Statistical Machine Translation

A conventional decoding algorithm is critical to the success of any statistical machine translation system. Providing an enormous amount of space leads to inappropriate slow decoding. There is a trade-off between the translation accuracy and the decoding speed. Pruning algorithms (like histogram pruning, threshold pruning) are trying to optimize this. The pruning algorithm has a pre-defined limit on the supplemental parameters (i.e. stack size, beam threshold) that helps to improve the translation quality and speed up the decoder. However, the same parameter value cannot provide the qualitative translation in optimum time. These stack size and beam threshold values should be changed based on texts’ structures. In this paper, we identify the best stack size and beam threshold values runtime based on the text structure and characteristics using a machine learning-based approach. Then, the values of these parameters are applied into the beam search algorithm for decoding. Finally, our experiments on low-resourced Asian languages show significant performance improvements in terms of their translation accuracy and decoding time. The HindEnCorp and ILCI datasets are used as the benchmark datasets with English-Hindi, Hindi-Marathi, Hindi-Konkani, Bengali-Hindi language pair, for our various experiments. Moreover, we incorporate the proposed technique in cube pruning algorithm for faster decoding. We notice more improvement in this approach.
Landscaping systematic mapping studies in software engineering: A tertiary study

A number of Systematic Mapping Studies (SMSs) that cover Software Engineering (SE) are reported in literature. Tertiary studies synthesize the secondary studies to provide a holistic view of an area. We synthesize SMSs in SE to provide insights into existing SE areas and to investigate the trends and quality of SMSs. We use Systematic Literature Review protocol to analyze and map the SMSs in SE, till August 2017, to SE Body of Knowledge (SWEBOK). We analyze 210 SMSs and results show that: (1) Software design and construction are most active areas in SE; (2) Some areas lack SMSs, including mathematical foundations, software configuration management, and SE tools; (3) The quality of SMSs is improving with time; (4) SMSs in journals have higher quality than SMSs in conferences and are cited more often; (5) Low quality in SMSs can be attributed to a lack of quality assessment in SMSs and not reporting information about the primary studies. There is a potential for more SMSs in some SE areas. A number of SMSs do not provide the required information for an SMS, which leads to a low quality score.