Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing CNN-based Segmentation with Deeply Customized Convolutional and Deconvolutional Architectures on FPGA

Convolutional Neural Networks-- (CNNs) based algorithms have been successful in solving image recognition problems, showing very large accuracy improvement. In recent years, deconvolution layers are widely used as key components in the state-of-the-art CNNs for end-to-end training and models to support tasks such as image segmentation and super resolution. However, the deconvolution algorithms are computationally intensive, which limits their applicability to real-time applications. Particularly, there has been little research on the efficient implementations of deconvolution algorithms on FPGA platforms that have been widely used to accelerate CNN algorithms by practitioners and researchers due to their high performance and power efficiency. In this work, we propose and develop deconvolution architecture for efficient FPGA implementation. FPGA-based accelerators are proposed for both deconvolution and CNN algorithms. Besides, memory sharing between the computation modules is proposed for the FPGA-based CNN accelerator as well as for other optimization techniques. A non-linear optimization model based on the performance model is introduced to efficiently explore the design space to achieve optimal processing speed of the system and improve power efficiency. Furthermore, a hardware mapping framework is developed to automatically generate the low-latency hardware design for any given CNN model on the target device. Finally, we implement our designs on Xilinx Zynq ZC706 board and the deconvolution accelerator achieves a performance of 90.1 giga operations per second (GOPS) under 200MHz working frequency and a performance density of 0.10 GOPS/DSP using 32-bit quantization, which significantly outperforms previous designs on FPGAs. A real-time application of scene segmentation on Cityscapes Dataset is used to evaluate our CNN accelerator on Zynq ZC706 board, and the system achieves a performance of 107 GOPS and 0.12 GOPS/DSP using 16-bit quantization and supports up to 17 frames per second for 512 × 512 image inputs with a power consumption of only 9.6W.
Financial Complexity and Trade

What are the implications on trading activity if investors are not sophisticated enough to understand and evaluate trades that have a complex payoff structure? Can frictions generated by this type of financial complexity be so severe that they lead to a complete market freeze, like that of the recent financial crisis? Starting from an allocation that is not Pareto optimal, we find that whether complexity impedes trade depends on how investors perceive risk and uncertainty. For smooth convex preferences, such as subjective expected utility, complexity cannot halt trade, even in the extreme case where each investor is so unsophisticated that he can only trade up to one Arrow–Debreu security, without being able to combine two or more in order to construct a complex trade. However, for non-smooth preferences, which allow for kinked indifference curves, such as maxmin expected utility, complexity can completely shut down trade.
Efficient routing and reconfiguration in virtualized HPC environments with vSwitch‐enabled lossless networks

To meet the demands of communication‐intensive workloads in the cloud, virtual machines (VMs) should utilize low overhead network communication paradigms. In general, such paradigms enable VMs to directly communicate with the hardware by means of a passthrough technology like Single‐Root I/O Virtualization (SR‐IOV). However, when passthrough‐based virtualization is coupled with lossless interconnection networks, live migrations introduce scalability challenges due to the substantial network reconfiguration overhead. With these challenges in mind, we proposed a virtual switch (vSwitch) SR‐IOV architecture for InfiniBand in our previous work titled “Towards the InfiniBand SR‐IOV vSwitch Architecture”. In this paper, we first suggest solutions to rectify the space‐domain scalability issues that are present in vSwitch‐enabled subnets as a result of the VMs using dedicated layer‐two addresses. Then, we discuss routing strategies for virtualized environments using vSwitches and present a routing algorithm for Fat‐Trees. We also present a reconfiguration method that minimizes imposed reconfiguration overhead on Fat‐Trees. We perform an extensive evaluation of our prototype algorithms, and as vSwitch‐enabled hardware does not yet exist, we deduce from empirical observations by emulating vSwitches with existing hardware, as well as large‐scale simulations. Our results show significant reduction in the reconfiguration times as route recalculations can be eliminated, and for certain scenarios, the number of reconfiguration subnet management packets sent to switches is reduced from several hundred thousand down to a single one without degrading the routing quality.
Healthcare Interoperability and Pervasive Intelligent Systems ( HiPIS 2018 ) Deep Learning Based Pipeline for Fingerprinting Using Brain Functional MRI Connectivity Data

In this work we describe an appropriate pipeline for using deep-learning as a form of improving the brain functional connectivitybased fingerprinting process which is based in functional Magnetic Resonance Imaging (fMRI) data-processing results. This pipeline approach is mostly intended for neuroscientists, biomedical engineers, and physicists that are looking for an easy form of using fMRI-based Deep-Learning in identifying people, drastic brain alterations in those same people, and/or pathologic consequences to people’s brains. Computer scientists and engineers can also gain by noticing the data-processing improvements obtained by using the here-proposed pipeline. With our best approach, we obtained an average accuracy of 0.3132 ± 0.0129 and an average validation cost of 3.1422 ± 0.0668, which clearly outperformed the published Pearson correlation approach performance with a 50 Nodes parcellation which had an accuracy of 0.237. © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
The Perceived Emotion of Isolated Synthetic Audio: The EmoSynth Dataset and Results

The ability of sound to enhance human wellbeing has been known since ancient civilisations, and methods can be found today across domains of health and within a variety of cultures. There are an abundance of sound-based methods which show benefits for both physical and mental-states of wellbeing. Current methods vary from low frequency vibrations to high frequency distractions, and from drone-like sustain to rhythmical pulsing, with limited knowledge of a listeners psycho-physical perception of this. In this regard, for the presented study 40 listeners were asked to evaluate the perceived emotional dimensions of Valence and Arousal from a dataset of 144 isolated synthetic periodic waveforms. Results show that Arousal does correlate moderately to fundamental frequency, and that the sine waveform is perceived as significantly different to square and sawtooth waveforms when evaluating perceived Arousal. The general results suggest that isolated synthetic audio can be modelled as a means of evoking affective states of emotion.
CacheSCDefender: VMM-based Comprehensive Framework against Cache-based Side-channel Attacks

Cache-based side-channel attacks have achieved more attention along with the development of cloud computing technologies. However, current host-based mitigation methods either provide bad compatibility with current cloud infrastructure, or turn out too application-specific. Besides, they are defending blindly without any knowledge of on-going attacks. In this work, we present CacheSCDefender, a framework that provides a (Virtual Machine Monitor) VMM-based comprehensive defense framework against all levels of cache attacks. In designing CacheSCDefender, we make three key contributions: (1) an attack-aware framework combining our novel dynamic remapping and traditional cache cleansing, which provides a comprehensive defense against all three cases of cache attacks that we identify in this paper; (2) a new defense method called dynamic remapping which is a developed version of random permutation and is able to deal with two cases of cache attacks; (3) formalization and quantification of security improvement and performance overhead of our defense, which can be applicable to other defense methods. We show that CacheSCDefender is practical for deployment in normal virtualized environment, while providing favorable security guarantee for virtual machines.
Semantic approach for multi‐objective optimisation of the ENTICE distributed Virtual Machine and container images repository

New software engineering technologies facilitate development of applications from reusable software components, such as Virtual Machine and container images (VMI/CIs). Key requirements for the storage of VMI/CIs in public or private repositories are their fast delivery and cloud deployment times. ENTICE is a federated storage facility for VMI/CIs that provides optimisation mechanisms through the use of fragmentation and replication of images and a Pareto Multi‐Objective Optimisation (MO) solver. The operation of the MO solver is, however, time‐consuming due to the size and complexity of the metadata, specifying various non‐functional requirements for the management of VMI/CIs, such as geolocation, operational cost, and delivery time. In this work, we address this problem with a new semantic approach, which uses an ontology of the federated ENTICE repository, knowledge base, and constraint‐based reasoning mechanism. Open Source technologies such as Protégé, Jena Fuseki, and Pellet were used to develop a solution. Two specific use cases, (1) repository optimisation with offline and (2) online redistribution of VMI/CIs, are presented in detail. In both use cases, data from the knowledge base are provided to the MO solver. It is shown that Pellet‐based reasoning can be used to reduce the input metadata size used in the optimisation process by taking into consideration the geographic location of the VMI/CIs and the provenance of the VMI fragments. It is shown that this process leads to reduction of the input metadata size for the MO solver by up to 60% and reduction of the total optimisation time of the MO solver by up to 68%, while fully preserving the quality of the solution, which is significant.
Communication with Evidence in the Lab (cid:3)

We study a class of sender-receiver disclosure games in the lab. Our experiment relies on a graphical representation of sender’s incentives in these games, and permits partial disclosure. We use local and global properties of the incentive graph to explain behavior and performance of players across diﬀerent games. Sender types whose interests are aligned with those of the receiver fully disclose, while other types use vague messages. Receivers take the evidence disclosed by senders into account, and perform better in games with an acyclic graph. Senders perform better in games with a cyclic graph. The data is largely consistent with a non-equilibrium model of strategic thinking based on the iterated elimination of obviously dominated strategies.
AutoCloud+, a "Universal" Physical and Statistical Model-Based 2D Spatial Topology-Preserving Software for Cloud/Cloud-Shadow Detection in Multi-Sensor Single-Date Earth Observation Multi-Spectral Imagery - Part 1: Systematic ESA EO Level 2 Product Generation at the Ground Segment as Broad Context

The European Space Agency (ESA) defines Earth observation (EO) Level 2 information product the stack of: (i) a single-date multi-spectral (MS) image, radiometrically corrected for atmospheric, adjacency and topographic effects, with (ii) its data-derived scene classification map (SCM), whose thematic map legend includes quality layers cloud and cloud–shadow. Never accomplished to date in an operating mode by any EO data provider at the ground segment, systematic ESA EO Level 2 product generation is an inherently ill-posed computer vision (CV) problem (chicken-and-egg dilemma) in the multi-disciplinary domain of cognitive science, encompassing CV as subset-of artificial general intelligence (AI). In such a broad context, the goal of our work is the research and technological development (RTD) of a “universal” AutoCloud+ software system in operating mode, capable of systematic cloud and cloud–shadow quality layers detection in multi-sensor, multi-temporal and multi-angular EO big data cubes characterized by the five Vs, namely, volume, variety, veracity, velocity and value. For the sake of readability, this paper is divided in two. Part 1 highlights why AutoCloud+ is important in a broad context of systematic ESA EO Level 2 product generation at the ground segment. The main conclusions of Part 1 are that ESA EO Level 2 information product is regarded as: (I) necessary-but-not-sufficient pre-condition for the yet-unaccomplished dependent problems of semantic content-based image retrieval (SCBIR) and semantics-enabled information/knowledge discovery (SEIKD) in multi-source EO big data cubes, where SCBIR and SEIKD are part-of the GEO-CEOS visionary goal of a yet-unaccomplished Global EO System of Systems (GEOSS). (II) State-of-the-art definition of EO Analysis Ready Data (ARD) format. (III) Horizontal policy, the goal of which is background developments, in a “seamless chain of innovation” needed for a new era of Space Economy 4.0. In the subsequent Part 2, the AutoCloud+ software system requirements specification, information/knowledge representation, system design, algorithm, implementation and preliminary experimental results are presented and discussed.
Staging sonic atmospheres as the new aesthetic work

Our primary concern in this paper is to bring attention to the promising, yet largely unexplored concept of atmosphere in sound design. Although this notion is not new, we approach it from a novel perspective i.e., New Phenomenology and New Aesthetics. Accordingly, we review some basic theoretical results in these fields and try to explore their possible application in the sonic context. In particular, the paper: i) compares the concept of sonic atmosphere with the notions of acoustic environment and soundscape by articulating salient elements that constitute each concept, ii) discusses some consequences of the above distinction with respect to the understanding of emotion and immersion, and, finally, iii) provides some initial suggestions about how to design for emotions.
Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems

Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between run-time and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the Item2Item model to improve the prediction accuracy. As a case study, this paper focuses on user behaviour prediction in restaurant recommender systems and uses a public dataset including restaurant information and user sessions. Within this dataset, user behaviour can be discovered for the collaborative filtering, and restaurant information is extracted for the content-based filtering. The idea of the pre-trained word embedding in Natural Language Processing is utilized in the item-based collaborative filtering to find the similarity between restaurants based on user sessions. Experimental results have shown that the combination of these techniques makes valuable recommendations.
A Method for Filtering Pages by Similarity Degree based on Dynamic Programming

To obtain the target webpages from many webpages, we proposed a Method for Filtering Pages by Similarity Degree based on Dynamic Programming (MFPSDDP). The method needs to use one of three same relationships proposed between two nodes, so we give the definition of the three same relationships. The biggest innovation of MFPSDDP is that it does not need to know the structures of webpages in advance. First, we address the design ideas with queue and double threads. Then, a dynamic programming algorithm for calculating the length of the longest common subsequence and a formula for calculating similarity are proposed. Further, for obtaining detailed information webpages from 200,000 webpages downloaded from the famous website “www.jd. com”, we choose the same relationship Completely Same Relationship (CSR) and set the similarity threshold to 0.2. The Recall Ratio (RR) of MFPSDDP is in the middle in the four filtering methods compared. When the number of webpages filtered is nearly 200,000, the PR of MFPSDDP is highest in the four filtering methods compared, which can reach 85.1%. The PR of MFPSDDP is 13.3 percentage points higher than the PR of a Method for Filtering Pages by Containing Strings (MFPCS).
Spatial Multilevel Optical Flow Architecture-based Dynamic Motion Estimation in Vehicular Traffic Scenarios

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified.
A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams

Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes.
Private Blockchain-Based Secure Access Control for Smart Home Systems

Smart home systems provide a safe, comfortable, and convenient living environment for users, whereby users enjoy featured home services supported by the data collected and generated by smart devices in smart home systems. However, existing smart devices lack sufficient protection in terms of data security and privacy, and challenging security and privacy issues inevitably emerge when using these data. This article aims to address these challenging issues by proposing a private blockchain-based access control (PBAC) scheme. PBAC involves employing a private blockchain to provide an unforgeable and auditable foundation for smart home systems, that can thwart illegal data access, and ensure the accuracy, integrity, and timeliness of access records. A detailed security analysis shows that PBAC could preserve data security against various attacks. In addition, we conduct a comprehensive performance evaluation to demonstrate that PBAC is feasible and efficient.
Fraud detection within bankcard enrollment on mobile device based payment using machine learning

The rapid growth of mobile Internet technologies has induced a dramatic increase in mobile payments as well as concomitant mobile transaction fraud. As the first step of mobile transactions, bankcard enrollment on mobile devices has become the primary target of fraud attempts. Although no immediate financial loss is incurred after a fraud attempt, subsequent fraudulent transactions can be quickly executed and could easily deceive the fraud detection systems if the fraud attempt succeeds at the bankcard enrollment step. In recent years, financial institutions and service providers have implemented rule-based expert systems and adopted short message service (SMS) user authentication to address this problem. However, the above solution is inadequate to face the challenges of data loss and social engineering. In this study, we introduce several traditional machine learning algorithms and finally choose the improved gradient boosting decision tree (GBDT) algorithm software library for use in a real system, namely, XGBoost. We further expand multiple features based on analysis of the enrollment behavior and plan to add historical transactions in future studies. Subsequently, we use a real card enrollment dataset covering the year 2017, provided by a worldwide payment processor. The results and framework are adopted and absorbed into a new design for a mobile payment fraud detection system within the Chinese payment processor.
Survey of design and security evaluation of authenticated encryption algorithms in the CAESAR competition

The Competition for Authenticated Encryption: Security, Applicability, and Robustness (CAESAR) supported by the National Institute of Standards and Technology (NIST) is an ongoing project calling for submissions of authenticated encryption (AE) schemes. The competition itself aims at enhancing both the design of AE schemes and related analysis. The design goal is to pursue new AE schemes that are more secure than advanced encryption standard with Galois/counter mode (AES-GCM) and can simultaneously achieve three design aspects: security, applicability, and robustness. The competition has a total of three rounds and the last round is approaching the end in 2018. In this survey paper, we first introduce the requirements of the proposed design and the progress of candidate screening in the CAESAR competition. Second, the candidate AE schemes in the final round are classified according to their design structures and encryption modes. Third, comprehensive performance and security evaluations are conducted on these candidates. Finally, the research trends of design and analysis of AE for the future are discussed.