Reviewerly/exHarmony huggingface dataset:
Space Efficient Algorithms for Breadth-Depth Search

Continuing the recent trend, in this article we design several space-efficient algorithms for two well-known graph search methods. Both these search methods share the same name breadth-depth search (henceforth BDS), although they work entirely in different fashion. The classical implementation for these graph search methods takes $$O(m+n)$$ time and $$O(n \lg n)$$ bits of space in the standard word RAM model (with word size being $$\varTheta (\lg n)$$ bits), where m and n denotes the number of edges and vertices of the input graph respectively. Our goal here is to beat the space bound of the classical implementations, and design $$o(n \lg n)$$ space algorithms for these search methods by paying little to no penalty in the running time. Note that our space bounds (i.e., with $$o(n \lg n)$$ bits of space) do not even allow us to explicitly store the required information to implement the classical algorithms, yet our algorithms visits and reports all the vertices of the input graph in correct order.
Modular effects in Haskell through effect polymorphism and explicit dictionary applications: a new approach and the μVeriFast verifier as a case study

In applications with a complex structure of side effects, effects should be dealt with modularly: components should be programmed against abstract effect interfaces that other components can instantiate as required, and reusable effect patterns should be factored out from the rest of the application. In this paper, we study a new, general approach to achieve this in Haskell by combining effect polymorphism and the recently proposed coherent explicit dictionary applications. We demonstrate the elegance and generality of our approach in μVeriFast: a Haskell-based reimplementation of the semi-automatic separation-logic-based verification tool VeriFast. This implementation features a complex interplay of advanced side effects: a backtracking search of program paths with angelic and demonic non-determinism, interaction with an underlying off-the-shelf SMT solver, and mutable state that is either backtracked or not during the search. Our use of effect polymorphism improves over the current non-modular implementation of VeriFast, allows us to nicely factor out the backtracking search pattern as a new AssumeAssert monad, and enables advanced features involving effects, such as the non-intrusive addition of a graphical symbolic debugger based on delimited continuations.
Public Key Protocols over Twisted Dihedral Group Rings

Key management is a central problem in information security. The development of quantum computation could make the protocols we currently use unsecure. Because of that, new structures and hard problems are being proposed. In this work, we give a proposal for a key exchange in the context of NIST recommendations. Our protocol has a twisted group ring as setting, jointly with the so-called decomposition problem, and we provide a security and complexity analysis of the protocol. A computationally equivalent cryptosystem is also proposed.
An Automated Feedback System to Support Student Learning in Writing-to-Learn Activities

Formative feedback has long been recognized as a crucial scaffold for student learning. Due to the job demand of instructors, it is impossible for them to provide individual students with on-demand formative feedback based on individual students' performance. There is a growing interest in developing better approaches to provide students with automated formative feedback to assist their learning. In this research, we design and develop an automated formative feedback system to support student learning of conceptual knowledge in the course of writing assignments. In the proposed system, formative feedback can be generated automatically with the help of concept maps constructed from instructors' lecture slides and students' writing assignments. In this paper, we present the automatic approach to generate formative feedback, discuss the system architecture, and illustrate a prototype of the proposed system.
Providing a Unified Software Environment for Canada's National Advanced Computing Centers

Exploiting an advanced computing platform consisting of several clusters distributed across the second-largest country in the world is challenging. Each cluster may run a different operating system, use a different generation of CPU, GPU, or network fabric, or be managed by a different team of system administrators. Presenting a unified software environment can tremendously facilitate the task of supporting researchers, but is challenging to implement. This is nevertheless what Compute Canada set out to do in 2016, in the midst of deploying a new generation of large clusters.
DM-Free Curvelet Based Denoising for Astronomical Single Pulse Detection

Rotating radio transients (RRATs) are sporadically emitting pulsars which are detected only through single pulse search. Detecting these single pulses in RRATs observation with high detection accuracy is a challenge due to the background noise. It is better to conduct the single pulse detection directly on the raw time-frequency observation than on the de-dispersed data, because de-dispersion process takes very intensive computation. In this paper, we propose to accomplish this idea by treating two-dimensional (2D) time-frequency data as images and develop a curvelet based denoising approach after studying the characteristics of the RRATs pulses and the noise. The denoising approach estimates the range of curvature (orientations) and width (scales) that describe the RRATs pulses and reconstructs cleaner images from the selected orientations and scales. The proposed denoising approach does not require prior knowledge of exact dispersion measures (DM) value. In addition, a framework of detecting the single pulses from the time-frequency data, named HOG-SVM, is also proposed to further evaluate the curvelet based denoising approach. Compared with the other four denoising approaches, the proposed curvelet based method leads to better detection results, with detection accuracy being increased to 98.7% by HOG-SVM.
Justification for reassessing elemental analysis data of ceramics, sediments and lithics using rare earth element concentrations and ratios

The mensuration of multi‐elemental concentrations from assorted archaeological materials has always required great care and attention to detail to ensure good‐quality data and their ensuing interpretations. Although most suspect data were generated before the wide use of computers, error‐free data are not still a certainty. This paper presents the geochemical rationale for a proposed chemical data‐assessment process, using a globally dispersed collection of ceramic, sediment and lithic data. It is argued that this process can allow archaeologists and archaeometrists to investigate systematically older and current data sets and, if need be, alter them to the reliable values they were originally intended to include.
Secure Transmission of DICOM Images by comparing different cryptographic algorithms

This paper aims to implement the concept of preserving the Confidentiality and Integrity of DICOM Images Using Cryptographic Algorithms. This work uses two types of cryptographic algorithms namely AES (Advanced Encryption Standard) and Blake Hash Function. Using these algorithms, we implement and check the confidentiality and integrity of DICOM images. Our proposed system also ensures that the images are transferred from one hospital database to another in a secure manner and the system will be able to tackle any changes while the image is being transferred. The initial concern of the system lies in providing confidentiality in the communication of the images which can be done using AES algorithm. The integrity can be provided using Blake Hash Function. The comparative analysis of AES with other algorithms such as Triple DES, IDEA, Blowfish and SEED with respect to their execution time is also done. In this work, DICOM files are taken as input and then kept in the encrypted format for storage purpose and also they can be sent over the network in the encrypted format.
Data Materialisation: A New Undergraduate Course for a Data Driven Society

Abstract Traditionally, data has been presented in textual format and the interaction with the user confined to the keyboard or touch screen to input data and the screen to deliver information. However, with the advent of a data-driven society, an opportunity for more natural and efficient ways of presenting data and interacting with it has emerged. Although the area of Human-Computer Interaction has existed for a long time, its focus has always been on the interaction with an artefact (the computer). Today instead, we face the challenge of interacting with an intangible object: data. As a result, a key requirement emerges: How do we make legible the enormous amounts of data produced per day to ordinary people? Designers, able to devise natural and smooth interaction experiences, should play a relevant role in this new scenario. However, they might lack the basic technical knowledge required to understand the possibilities of these new systems. In this paper we present a brief how-to manual for an undergraduate course on data materialisation: the process of transforming an intangible object (data) in an artefact that can be interacted with in a physical way.
A Fuzzy WordNet graph based approach to find key terms for students short answer evaluation

Short answer evaluation has always been an enormously popular research topic due to its large application domain. In this paper, it is applied for students answer sheet evaluation. An automated method for students answer sheet evaluation may ensure that the assessment is impartial and free of any prejudices that the evaluator may have in the favor of or against the student. This paper proposes a method for students answer sheet evaluation for short answer type questions using a Fuzzy WordNet graph based approach. The results are obtained on a synthetic dataset and as compared to the state-of-art, they seem promising.
Bi-Directional Message Passing Based Scanet for Human Pose Estimation

Articulated human pose estimation is one of the fundamental computer vision problems. In this paper, a Bi-directional Message Passing(BDMP) module is proposed to fuse convolutional features of different scales in the up-sampling process of the hourglass model for human pose estimation. Moreover, a novel module which integrates Spatial and Channelwise Attention Network(SCANet) is proposed to refine the features obtained from the message passing stage. We design a Semantics-aware Channel-wise Attention(SACWA) module to reduce the feature redundancy and enrich the semantic information simultaneously. A Sharper Spatial Attention(SSA) module based on the Gumbel-Softmax sampling is proposed to exclude the interference from cluttered background and overcomes the gradient degradation induced by the softmax normalization. The proposed framework achieves leading position on MPII benchmark against the state-of-the-arts methods with much less parameters.
Blueprint Model: A new Approach to Scrum Agile Methodology

Coordinating software development with teams distributed across different sites can be challenging. SIDIA is an R&D institute that is responsible for implementing innovative solutions for the Brazilian and global market through research activities and development. SIDIA has Samsung Company as a strategic partner, and part of its teams work in collaboration with Samsung Mobile division, located in Korea. Both local and remote teams have different skills including software engineering, design, and quality assurance. This report focuses on the experience at designing and deploying a new software development model called Blueprint, a model based on Scrum and Kanban agile methodologies that facilitate (i) global software development, (ii) teams and tasks allocations, and (iii) effective members communication. We also report the lessons learned from adopting the Blueprint model in the development of a project.
Explaining The DLT Applications in The Context of a Customers, Facility Managements and Utility Companies Relationship

The relationship between customers, Facility managements and Utility companies can be a challenge. The persistent lack of trust leads to a fraud doubts, disputes and arbitration which results are expensive. Such kind of issues generate expenses for all participants. We have developed three models based on Hyperledger Fabric and we have revealed different difficulties in an every one. Usage of Distributed Ledger Technology based solutions will bring a real value to all actors in the system.
Multi-Stage Optimization for Photorealistic Neural Style Transfer

This work introduces a new approach toward photorealistic style transfer. When applying current style transfer techniques on real world photographs, the generated results often contain distortions and artifacts that diminish the real-world quality of the photograph. To address these issues, we propose a two-stage optimization process that transfers style globally and regionally and applies a sharpening filter after each step. As evaluated by a user study, our method is qualitatively comparable to existing state-of-the-art methods, but successfully handles previous failure cases. Our method also quantitatively outperform previous methods as evaluated by natural scene statistic metrics.
Deep Semantic Correlation with Adversarial Learning for Cross-Modal Retrieval

Correlation learning usually maps heterogeneous data into a common subspace to achieve cross-modal retrieval. Thanks to the success of deep learning in recent years, the performance of cross-modal retrieval has made a great improvement. However, how to bridge the modality gap is still the key problem. In this paper, we propose a deep semantic correlation learning method with generative adversarial network to deal with cross-modal data annotated by multi-labels. With adversarial learning, the generative network tries to produce the common semantic representations respect to image and text modalities, while discriminative model tries to point out the differences between them. Besides that, we propose a classification loss applied to one or multiple categories for semantic subspace learning to promote cross-modal retrieval. The adversarial network and the classification network are jointly optimized. Experiments verify the effectiveness of our proposed model on two widely used datasets.
Agile Product Development: Features Identification and Application in the Electricity Sector

Abstract The electricity sector is in the midst of a structural change driven by new technologies. In Brazil, the electricity sector regulation has mechanisms to foster innovation, including investments in R&amp;D. Recently, the regulatory agency and the industry have been calling for approaches to increase the rate at which R&amp;D departments generate solutions that end up being adopted. As a result, novel approaches to R&amp;D project management have entered the agenda. In this context, the objective of this paper is to characterise Agile Product Development and its application in a highly regulated sector. The paper presents a systematic literature review with the debates about Agile and new product development. Then, a case study exploring an early adoption of the Agile approach in R&amp;D project management in the Brazilian electricity sector is presented. Results include the identification of the Agile features most frequently mentioned in the literature. Moreover, the case study explores the Agile features that were more easily absorbed in early adoption, such as iterative patterns, and discusses implementation challenges in team structure, feedback loops, and communication.
Clothes Keypoints Localization and Attribute Recognition via Prior Knowledge

Rich clothes datasets and high-quality annotations have driven recent advances in fashion clothes recognition. However, the existing approaches treat clothes as common images, ignoring the prior clothing knowledge such as spatial relations, symmetry, proportions, and key characteristics of clothes. In order to combine the semantic information with the advantages of deep learning, we propose Detection+, a model using the prior symmetric constraint to refine the keypoints located by any backbone detection networks. To deal with uncertainty in labelling clothing, we introduce a new loss to utilize all available data which contain "maybe" labels. Detection+ has reduced about 2.54% Normalized Error in FashionAI dataset and improved 3.2% AP in human keypoints dataset coco2017 compared to the Mask R-CNN baseline. A large number of experimental results show the proposed approach achieves better results in different recognition datasets (resp., FashionAI, and Deepfashion) with about (resp., 2.57% mAP, and 10% recall) improvements.
Colloquial Image Captioning

Image captioning has recently attracted ever-increasing research attention in multimedia and computer vision. However, most of image captioning models focus on generating the plain description for images, neglecting its colloquialism under a potential topic, e.g., the topic Movie for a poster. In this paper, we consider colloquial image captioning a new task, which, however, is trapped in the visual-textual semantic gap, as well as the semantic and the sentiment diversities. To this end, we propose a novel topic-guided multi-discriminator captioning model, termed TGMD-Cap. In particular, a topic-guided attention module is firstly designed to compose the effective visual semantic features. Secondly, a multi-discriminator generative adversarial module is designed to generate the semantically-diverse and affectively-diverse expression. To facilitate quantitative evaluation, we further construct and release a colloquial image captioning dataset, CIC-dataset, crawled from real-world social network, serving as the first of its kind. Quantitative results show that the proposed TGMD-Cap model outperforms the state-of-the-art approaches under various standard evaluation metrics.
Real-Time Downward View Generation of a Vehicle Using Around View Monitor System

In this paper, a method to generate the downward image of current vehicle location using a commercial around view monitor (AVM) is proposed. The proposed system consists of three stages, namely feature tracking, obstacle filtering, and downward view generation. In the feature tracking stage, the Shi-Tomasi corner detection is used and feature tracking is performed with a Kanade-Lucas-Tomasi (KLT) tracker to determine the transformation between AVM images. In the obstacle filtering stage, features on obstacles are filtered by using the difference in a tracking distance between features detected on the ground and features detected on the obstacle. This is performed by using a histogram based on the tracking distance of features. Finally, in the downward view generation stage, transformation between the current AVM image and the previous AVM image is obtained by using the tracked feature pair refined via the aforementioned steps. Specifically, the random sample consensus (RANSAC) method is used to obtain transformations from which the influence of outliers is removed. The downward image of the current vehicle is generated by the obtained transformation and is synthesized to the existing AVM image. The results indicate that the proposed system synthesizes the existing AVM image and the generated downward image of a vehicle in a seamless way by determining the exact pair of matching points between current and the previous AVM images. We believe the proposed system can be utilized in efficient wireless charging system and safe driving system.
Automatic Security Classification Based on Incremental Learning and Similarity Comparison

Document security classification is the foundation of security management for the sensitive and confidential information. Different with the general document classification, the security one is more difficult and challenging, due to the diverse and changeable sensitive features and high requirement for accuracy. In this paper, we propose a new method named incremental learning and similarity comparison (ILSC), which combines two effective security labeling strategies for automatic security classification. In fact, the sensitive document dataset augments continuously. Accordingly, we exploit the use of incremental learning to capture continuously the useful information of the new classified documents and update the classifier. In addition, to utilize the identified sensitive sentences in the classified documents, we introduce a method based on similarity comparison of sentence features, as a supplement to the prediction obtained by the incremental learning. Experimental results show the proposed method can produce competitive results in terms of accuracy.
Future Networking Research Plethora for Smart Cities

In most electronic voting schemes, a voter signs his/her ballot using a digital signature algorithm before submission to ensure that the ballot is generated by himself/herself, which results that the workload of the verification process in ballot tallying increases with the number of voters and candidates. In this paper we present a new electronic voting scheme, HSE-voting, to lighten the workload of tallying. The cryptographic approach of this scheme is homomorphic signcryption, whose signature homomorphism enables that the number of signature verifications depends on the number of candidates only, and the encryption homomorphism keeps ballots from being decrypted during the tallying process. Moreover, for voters, the encryption and signature of ballots can be completed in one step using the homomorphic signcryption. Finally, this paper gives proofs for the privacy of voters and the security and verifiability of voting, and provides a comparative analysis with the latest scheme.
Design and Visualization of Guided GAN on MNIST dataset

In this paper, we propose a hybrid model aiming to map input noise vector to the label of the generated image by Generative Adversarial Network (GAN). This model mainly consists of a pre-trained Deep Convolution Generative Adversarial Network (DCGAN) and a classifier. By using the model, we visualize the distribution of two-dimensional input noise leading to specific type of generated image after each training epoch of GAN. The visualization reveals the distribution feature of the input noise vectors and the performance of the generator. With this feature, we try to build a Guided Generator which can produce a fake image which we exactly need. Two methods are proposed to build the Guided Generator. One is the most significant noise (MSN) method, and another is utilizing labeled noise. MSN method can generate images precisely but with less variations. In contrast, labeled noise method can have more variations but slightly less stable.
Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals

In this paper, several variants of two-stream architectures for temporal action proposal generation in long, untrimmed videos are presented. Inspired by the recent advances in the field of human action recognition utilizing 3D convolutions in combination with two-stream networks and based on the Single-Stream Temporal Action Proposals (SST) architecture, four different two-stream architectures utilizing sequences of images on one stream and sequences of images of optical flow on the other stream are subsequently investigated. The four architectures fuse the two separate streams at different depths in the model; for each of them, a broad range of parameters is investigated systematically as well as an optimal parametrization is empirically determined. The experiments on the THUMOS'14 dataset - containing untrimmed videos of 20 different sporting activities for temporal action proposals - show that all four two-stream architectures are able to outperform the original single-stream SST and achieve state of the art results. Additional experiments revealed that the improvements are not restricted to one method of calculating optical flow by exchanging the method of Brox with FlowNet2 and still achieving improvements.