Reviewerly/exHarmony huggingface dataset:
CNN-on-AWS: Efficient Allocation of Multikernel Applications on Multi-FPGA Platforms

Multi-FPGA platforms, like Amazon AWS F1, can run in the cloud multi-kernel pipelined applications, like Convolutional Neural Networks (CNNs), with excellent performance and lower energy consumption than CPUs or GPUs.We propose a method to efficiently map these applications on multi-FPGA platforms to maximize the application throughput.Our methodology finds, for the given resources, the optimal number of parallel instances of each kernel in the pipeline and their allocation to one or more among the available FPGAs.We obtain this by formulating and solving a mixed-integer, nonlinear optimization problem, in which we model the performance of each component and the duration of the phases in which the accelerated computation can be split into, namely: 1) data transfer from a host CPU to the DDR memory of each FPGA, 2) data transfer from FPGA DDR to FPGA on-chip memory, 3) kernel computation on the FPGA, 4) data transfer from FPGA on-chip memory to FPGA DDR, 5) data transfer from FPGA DDR to host.Finding the optimal solution using a Mixed-Integer Non-Linear Programming (MINLP) solver is often highly inefficient.Hence, we provide a fast heuristic method that according to our experiments can be much more efficient than the MINLP solver and finds comparable results.For larger problems (more CNN layers), our heuristic method can quickly find (several thousand times faster) much better solutions than the MINLP solver, even if we run the latter for a very long time.
Domain Ontology for Requirements Classification in Requirements Engineering Context

Research in recent years has shown a merge between the areas of requirements engineering and semantic technologies.With the release of the semantic concept and the progress of semantic technologies, the opportunities for applying ontologies as a means to define information and knowledge semantics have become increasingly accepted in different domains.Concurrently, the implementation of most requirements classification techniques does not handle the semantic aspects of requirements.If the meaning of requirements and their relations can be handled, software developers can obtain more effective requirement classifications to produce requirements specifications of higher quality.In this study, a domain ontology is proposed to present a requirements classification technique that can be used to share and describe different classifications.The proposed ontology is built using a systematic method based on Methontology and it is implemented using Protégé.The developed ontology was successfully evaluated using validation and verification tests.The validation test included the evaluation of content and competency questions, while the verification test included the evaluation of taxonomy and the implementation of the FOCA method.The proposed ontology may represent a significant contribution to ontology libraries.In addition, this ontology can be used in several ways to increase the quality of software requirements specification documents.It could also ensure consistency between requirements, and facilitate communication between requirements engineers owing to the use of same terminologies for various software applications.
An Interactive Real-Time Cutting Technique for 3D Models in Mixed Reality

This work describes a Mixed Reality application useful to modify and cut virtual objects. A digital simulation of surgical operations is presented. Following this approach, surgeons can test all the designed solutions of the preoperative stage in a Mixed Reality environment. High precision in surgery applications can be achieved thanks to the new methodology. The presented solution is hands free and does not need the use of a mouse or computer’s keyboard: it is based on HoloLens, Leap Motion device and Unity. A new cutting algorithm has been developed in order to handle multiple objects and speed up the cut with complex meshes and preserve geometry quality. A case study presents the cut of several bones in order to simulate surgeon’s operations. A reduction in cut time compared to the original method is noticed, together with a high flexibility of the tool and a good fidelity of the geometry. Moreover, all the object fragments generated from the algorithm are available for manipulation and new cuts.
Design and Initial Evaluation of a VR based Immersive and Interactive Architectural Design Discussion System

Design discussion is a very important course in architecture education. In this paper, we developed a VR based architecture design discussion system that allows members to visualize and discuss the architectural models and to modify the models during discussion. Since the system is designed to work on top of Rhinoceros and Grasshopper, the object database is updated right after the object modification. Members communicate via voice, object manipulations, and mid-air sketching as well as on-surface sketching in the virtual environment. Several tools have been designed to enhance the sense of presence and to make the discussion more effective. We also developed a rollback mechanism to help users intuitively and quickly revert to a previous state of discussion to make some changes or to start a new direction of discussion. To evaluate the system, we conducted an initial user study with 14 participants to assess the user experience, user impression and effectiveness of the system. The feedback from participants were positive and suggested that the system could be effective and useful for supporting architecture design discussion.
A Fuzzy Word Similarity Measure for Selecting Top-$k$ Similar Words in Query Expansion

Top-k words selection is a technique used to detect and return the k most similar words to a given word from a candidate set. This is a crucial and widely used tool in various tasks. The key issue in top-k words selection is how to measure the similarity between words. One popular and effective solution is to use a word embedding-based similarity measure, which represents words as low-dimensional vectors and measures the similarities between words according to the similarity of the vectors, using a metric. However, most word embedding methods only consider the local proximity properties of two words in a corpus. To mitigate this issue. In this article, we propose to use association rules for measuring word similarity at a global level, and a fuzzy similarity measure for top-k words selection that jointly encodes the local and the global similarities. Experiments on a real-world query task with three benchmark datasets, i.e., TREC-disk 4&5, WT10G, and RCV1, demonstrate the efficiency of the proposed method compared to several state-of-the-art baselines.
Stochastic Dynamics for Video Infilling

In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation which aims to produce transitional frames for a short interval between every two frames and increase the temporal resolution. Our task, namely video infilling, however, aims to infill long intervals with plausible frame sequences. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation module to guarantee the spatial-temporal coherence among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear frame sequences with varying contents. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the given start frame to the terminal frame.
Context-Dependent Sequence-to-Sequence Turkish Spelling Correction

In this article, we make use of sequence-to-sequence (seq2seq) models for spelling correction in the agglutinative Turkish language. In the baseline system, misspelled and target words are split into their letters and the letter sequences are fed into the seq2seq model. We prefer letters as the unit of the model due to the agglutinative nature of Turkish, which results in an impractical dictionary size when words are used as a dictionary unit. In order to improve the baseline performance, we incorporate right and left context of the misspelled words. All context words are represented with their first three consonants in the context-dependent model. We train the seq2seq models using a large text corpus collected automatically from the Internet. The corpus contains approximately 4 million sentences. We randomly introduce substitution, deletion, and insertion spelling errors to the words in the corpus. We test the performance of the proposed context-dependent seq2seq model using synthetic and realistic test sets. The synthetic test set is constructed similar to the training set. The realistic test set contains human-made misspellings from Twitter messages. In the experiments, we observed that the proposed context-dependent model performs significantly better than the baseline system. Its correction accuracy reaches 94% on the synthetic dataset. Additionally, the proposed method provides 2.1% absolute improvement over a state-of-the-art Turkish spelling correction system on the Twitter test set.
Hand-Priming in Object Localization for Assistive Egocentric Vision

Egocentric vision holds great promises for increasing access to visual information and improving the quality of life for people with visual impairments, with object recognition being one of the daily challenges for this population. While we strive to improve recognition performance, it remains difficult to identify which object is of interest to the user; the object may not even be included in the frame due to challenges in camera aiming without visual feedback. Also, gaze information, commonly used to infer the area of interest in egocentric vision, is often not dependable. However, blind users often tend to include their hand either interacting with the object that they wish to recognize or simply placing it in proximity for better camera aiming. We propose localization models that leverage the presence of the hand as the contextual information for priming the center area of the object of interest. In our approach, hand segmentation is fed to either the entire localization network or its last convolutional layers. Using egocentric datasets from sighted and blind individuals, we show that the handpriming achieves higher precision than other approaches, such as fine-tuning, multi-class, and multi-task learning, which also encode hand-object interactions in localization.
Deep Parametric Active Contour Model for Neurofibromatosis Segmentation

Locating the neurofibromatosis boundaries is one of the most challenging tasks due to the properties of low contrast and irregular blurred boundaries. To handle this problem, we propose Deep Parametric Active Contours (DPAC), a novel end-to-end framework that integrates the precise boundary localization abilities of the Active Contour Models (ACMs) with the robust nonlinear priori feature extraction abilities of the Convolutional Neural Networks (CNN). DPAC employs CNN to predict the parameter maps of the ACM per instance and incorporates them into the energy functional of the ACM to locate the neurofibromatosis boundaries, which makes DPAC end-to-end trainable. We evaluate the proposed method on three neurofibromatosis datasets that we created, and experimental results show that our DPAC method can improve the boundary similarity and precision of the classic ACMs. Even in the case with a small amount of data, our method can achieve good performance outperforming similar methods.
Gaze Analysis and Prediction in Virtual Reality

In virtual reality (VR) systems, users’ gaze information has gained importance in recent years. It can be applied to many aspects, including VR content design, eye-movement based interaction, gaze-contingent rendering, etc. In this context, it becomes increasingly important to understand users’ gaze behaviors in virtual reality and to predict users’ gaze positions. This paper presents research in gaze behavior analysis and gaze position prediction in virtual reality. Specifically, this paper focuses on static virtual scenes and dynamic virtual scenes under free-viewing conditions. Users’ gaze data in virtual scenes are collected and statistical analysis is performed on the recorded data. The analysis reveals that users’ gaze positions are correlated with their head rotation velocities and the salient regions of the content. In dynamic scenes, users’ gaze positions also have strong correlations with the positions of dynamic objects. A data-driven eye-head coordination model is proposed for realtime gaze prediction in static scenes and a CNN-based model is derived for predicting gaze positions in dynamic scenes.
Novel Models of Image Permutation and Diffusion Based on Perturbed Digital Chaos

Most of chaos-based cryptosystems utilize stationary dynamics of chaos for the permutation and diffusion, and many of those are successfully attacked. In this paper, novel models of the image permutation and diffusion are proposed, in which chaotic map is perturbed at bit level on state variables, on control parameters or on both. Amounts of perturbation are initially the coordinate of pixels in the permutation, the value of ciphered word in the diffusion, and then a value extracted from state variables in every iteration. Under the persistent perturbation, dynamics of chaotic map is nonstationary and dependent on the image content. The simulation results and analyses demonstrate the effectiveness of the proposed models by means of the good statistical properties of transformed image obtained after just only a single round.
Image Hashing via Linear Discriminant Learning

Hashing has attracted attention in recent years due to the rapid growth of image and video data on the web. Benefiting from recent advances in deep learning, deep supervised hashing has achieved promising results for image retrieval. However, existing methods are either less efficient in data usage or incapable of learning linearly discriminative binary codes. In this paper, we revisit linear discriminative analysis and propose a linear discriminative hashing (LDH) objective that is efficient in training and achieves better accuracy in retrieval. With the joint supervision of a classification loss, we design a robust deep network to obtain binary codes that are inter-class separable and intra-class compact, which provides better representations for image retrieval. We conduct extensive experiments on three benchmark datasets, and our LDH algorithm performs favorably against existing state-of-the-art deep supervised hashing methods.
Robust detection of median filtering based on data-pair histogram feature and local configuration pattern

Median filtering is a widely used method for removing noise, preserving edges and smoothing regions within an image. Detection of median filtering has drawn much attention from researchers of image forensic. A new robust detection scheme of median filtering based on combined features of difference image is proposed in this paper. In the proposed approach, the combined features consist of data-pair histogram features (DPHFs) and Local configuration pattern (LCP) of first-order difference image (DI). A large number of experiments on single dataset and compound datasets show that, the proposed scheme not only achieves super performance on the uncompressed image datasets, but also achieves better detection performance compared with state-of-the-art methods, especially for strongly JPEG compressed and low resolution images.
Interpretable multi-scale graph descriptors via structural compression

Graph representations that preserve relevant topological information allow the use of a rich machine learning toolset for data-driven network analytics. Some notable graph representations in the literature are fruitful in their respective applications but they either lack interpretability or are unable to effectively encode a graph’s structure at both local and global scale. In this work, we propose the Higher-Order Structure Descriptor (HOSD): an interpretable graph descriptor that captures information about the patterns in a graph at multiple scales. Scaling is achieved using a novel graph compression technique that reveals successive higher-order structures. The proposed descriptor is invariant to node permutations due to its graph-theoretic nature. We analyze the HOSD algorithm for time complexity and also prove the NP-completeness of three interesting graph compression problems. A faster version, HOSD-Lite, is also presented to approximate HOSD on dense graphs. We showcase the interpretability of our model by discussing structural patterns found within real-world datasets using HOSD. HOSD and HOSD-Lite are evaluated on benchmark datasets for applicability to classification problems; results demonstrate that a simple random forest setup based on our representations competes well with the current state-of-the-art graph embeddings.
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation

We present a domain adaptation based generative framework for zero-shot learning. Our framework addresses the problem of domain shift between the seen and unseen class distributions in zero-shot learning and minimizes the shift by developing a generative model trained via adversarial domain adaptation. Our approach is based on end-to-end learning of the class distributions of seen classes and unseen classes. To enable the model to learn the class distributions of unseen classes, we parameterize these class distributions in terms of the class attribute information (which is available for both seen and unseen classes). This provides a very simple way to learn the class distribution of any unseen class, given only its class attribute information, and no labeled training data. Training this model with adversarial domain adaptation further provides robustness against the distribution mismatch between the data from seen and unseen classes. Our approach also provides a novel way for training neural net based classifiers to overcome the hubness problem in zero-shot learning. Through a comprehensive set of experiments, we show that our model yields superior accuracies as compared to various state-of-the-art zero shot learning models, on a variety of benchmark datasets. Code for the experiments is available at github.com/vkkhare/ZSL-ADA.
Multi-branch guided attention network for irregular text recognition

Reading irregular text of arbitrary shape or low quality in natural scene images is a challenging task. Existing irregular scene text recognition methods mainly focus on irregular text with arbitrary shape, but rarely focus on irregular text of low quality. In this work, we propose a simple but effective method for recognizing irregular texts with arbitrary shape and low quality simultaneously. The proposed Multi-Branch guided Attention Network (MBAN) makes mutual guidance among multi-branch data in training, so as to learn invariant semantic representation between regular text images and the corresponding irregular images. Compared with the standard attention framework for text recognition, MBAN can significantly improve the performance of irregular text recognition while preserving similar performance for regular text recognition. In addition, regarding the attention drift problem encountered in standard attention network, MBAN can significantly improve the accuracy of alignment factors at each time step. We verify the effectiveness of MBAN in irregular text recognition and attention drift problem through extensive experiments. The performance of MBAN is shown to be comparable on regular datasets and superior on some irregular datasets with state-of-the-art methods.
BiGe-Onto: An ontology-based system for managing biodiversity and biogeography data1

Great progress to digitize the world’s available Biodiversity and Biogeography data have been made recently, but managing data from many different providers and research domains still remains a challenge. A review of the current landscape of metadata standards and ontologies in Biodiversity sciences suggests that existing standards, such as the Darwin Core terminology, are inadequate for describing Biodiversity data in a semantically meaningful and computationally useful way. As a contribution to fill this gap, we present an ontology-based system, called BiGe-Onto, designed to manage data together from Biodiversity and Biogeography. As data sources, we use two internationally recognized repositories: the Global Biodiversity Information Facility (GBIF) and the Ocean Biogeographic Information System (OBIS). BiGe-Onto system is composed of (i) BiGe-Onto Architecture (ii) a conceptual model called BiGe-Onto specified in OntoUML, (iii) an operational version of BiGe-Onto encoded in OWL 2, and (iv) an integrated dataset for its exploitation through a SPARQL endpoint. We will show use cases that allow researchers to answer questions that manage information from both domains.
MUSE: A Multi-Tierd and SLA-Driven Deduplication Framework for Cloud Storage Systems

For cloud storage service vendors, balancing the client-perceived IO performance and the self-perceived space cost is always one of the standing challenges. When applying deduplication techniques for the cloud storage systems, the demand for optimizing such tradeoff becomes more pressing. Enabling deduplication decreases the storage space cost, whereas the IO performance will be somewhat affected due to extra processing overhead and data fragmentation. In this article, we address this challenge by proposing MUSE, a MUti-tiered and SLA-drivEn deduplication framework for cloud storage systems. First, we propose a novel notation of Dedup-SLA (deduplication-oriented service level agreement). With different levels of quantified performance/space-cost combinations, the Dedup-SLA serves as a refined service quality protocol between service vendor and customer. Second, MUSE adopts multi-tiered deduplication that orchestrates several combinational forms of deduplication into multiple tiers with varied “deduplication strength”. Third, we implement a mechanism called dynamic deduplication regulation (DDR) to adjust the deduplication behavior during runtime. MUSE's deduplication behavior is periodically switched between tiers according to the predefined Dedup-SLA and instant system status. We conduct comprehensive experiments to compare MUSE with several other types of deduplication schemes. The results demonstrate that MUSE significantly optimizes the IO-performance/space-cost balance compared to other schemes, hence delivering higher deduplication service quality for deduplication-enabled cloud storage systems.