Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships

Context is important for accurate visual recognition. In this work we propose an object detection algorithm that not only considers object visual appearance, but also makes use of two kinds of context including scene contextual information and object relationships within a single image. Therefore, object detection is regarded as both a cognition problem and a reasoning problem when leveraging these structured information. Specifically, this paper formulates object detection as a problem of graph structure inference, where given an image the objects are treated as nodes in a graph and relationships between the objects are modeled as edges in such graph. To this end, we present a so-called Structure Inference Network (SIN), a detector that incorporates into a typical detection framework (e.g. Faster R-CNN) with a graphical model which aims to infer object state. Comprehensive experiments on PASCAL VOC and MS COCO datasets indicate that scene context and object relationships truly improve the performance of object detection with more desirable and reasonable outputs.
Cardiac Motion Correction for Helical CT Scan With an Ordinary Pitch

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts.
Implementation of Multipath Network Virtualization With SDN and NFV

Multipath networks have been extensively studied to improve throughput and alleviate network congestion. However, multipath networks, with the exception of equal-cost multi-path networks, are difficult to implement because they schedule network resources at cross layers and require complex signaling mechanisms to monitor network status (e.g., round trip time, bandwidths, and link state). Virtualization, in contrast, provides network isolation with protocols and resources and simplifies the process of coupling the interaction between signaling mechanisms, which makes multipath implementation practical. This paper proposes a multipath network virtualization scheme that implements software-defined networking (SDN) and network function virtualization (NFV). Multipath networks can be deployed in the virtualized environment. The proposed scheme attains a summary of network resources, such as network topology and link bandwidth, and can schedule these resources for selecting and spreading flow over multiple paths. In addition, virtualization provides computing and storage resources for flow splitting, tag adding, and packet reordering, among other applications. Specifically, the tags are designed for forwarding and packet reordering. Our experiment builds the platform based on open platform for NFV and SDN on the practice testbed. To verify its functionalities, we first evaluate the overhead of each component and then implement and compare five state-of-the-art multipath models on the proposed platform. Our scheme is compatible with these multipath models. Moreover, the experimental results show that the model with flow splitting has superior performance with respect to load balancing.
5G & SLAs: Automated proposition and management of agreements towards QoS enforcement

Efficient Service Level Agreements (SLA) management and anticipation of Service Level Objectives (SLO) breaches become mandatory to guarantee the required service quality in software- defined and 5G networks. To create an operational Network Service, it is highly envisaged to associate it with their network-related parameters that reflect the corresponding quality levels. These are included in policies but while SLAs target usually business users, there is a challenge for mechanisms that bridge this abstraction gap. In this paper, a generic black box approach is used to map high-level requirements expressed by users in SLAs to low-level network parameters included in policies, enabling Quality of Service (QoS) enforcement by triggering the required policies and manage the infrastructure accordingly. In addition, a mechanism for determining the importance of different QoS parameters is presented, mainly used for “relevant” QoS metrics recommendation in the SLA templates.
A usability evaluation of a mobile exergame for ankle joint exercises

Despite the benefits of exercise programs in reha-bilitation, patients' adherence is known to be low. Especially, repetitive and monotonous exercises compromise adherence to the rehabilitation program. Exergames have been suggested as one way to support regular training which includes such mundane exercises. Within this contribution we present a mobile exergame for ankle joint exercises, focusing on dorsi-extension and plantar-flexion, that can be used in different contexts. Our system consists of two wireless inertial measurement units (IMU) and a mobile device. A between-subject user study was carried out with age-group as independent variable and its levels young adults (18-29 years old) and older adults (50-67 years old). 20 young and 21 older participants evaluated the usability. Additionally, we used the constructs perceived ease of use, perceived usefulness, and perceived enjoyment of the technology acceptance model (TAM) to predict the intention towards using our system. We present further results of the conducted user study and provide an outlook for future research directions.
Learning to Organize Knowledge and Answer Questions with N-Gram Machines

Though deep neural networks have great success in natural language processing, they are limited at more knowledge intensive AI tasks, such as open-domain Question Answering (QA). Existing end-to-end deep QA models need to process the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size. This is prohibitive for practical tasks such as QA from Wikipedia, a novel, or the Web. We propose to solve this scalability issue by using symbolic meaning representations, which can be indexed and retrieved efficiently with complexity that is independent of the text size. We apply our approach, called the N-Gram Machine (NGM), to three representative tasks. First as proof-of-concept, we demonstrate that NGM successfully solves the bAbI tasks of synthetic text. Second, we show that NGM scales to large corpus by experimenting on "life-long bAbI", a special version of bAbI that contains millions of sentences. Lastly on the WikiMovies dataset, we use NGM to induce latent structure (i.e. schema) and answer questions from natural language Wikipedia text, with only QA pairs as weak supervision.
Genome Plasticity in Cultured Leishmania donovani: Comparison of Early and Late Passages

Leishmania donovani possesses a complex heteroxenic life cycle where infective metacyclic promastigotes are pre-adapted to infect their host and cope up with intracellular stress. Exploiting the similarities between cultured and sandfly derived promastigotes, we used early and late passage cultured promastigotes to show specific changes at genome level which compromise pathogen fitness reflected in gene expression and infection studies. The pathogen loses virulence mostly via transcriptional and translational regulations and long-time cultivation makes them struggle to convert to virulent metacyclics. At the genomic level very subtle plasticity was observed between the early and the late passages mostly in defense-related, nutrient acquisition and signal transduction genes. Chromosome Copy number variation is seen in the early and late passages involving several genes that may be playing a role in pathogenicity. Our study highlights the importance of ABC transporters and calpain like cysteine proteases in parasite virulence in cultured promastigotes. Interestingly, these proteins are emerging as important patho-adaptive factors in clinical isolates of Leishmania. We found that the currently available genome of Leishmania in the NCBI database are from late passages. Our early passage genome can act as a reference for future studies on virulent isolates of Leishmania. The annotated leads from this study can be used for virulence surveillance and therapeutic studies in the Indian subcontinent.
EnFuzz: From Ensemble Learning to Ensemble Fuzzing

Fuzzing is widely used for software vulnerability detection. There are various kinds of fuzzers with different fuzzing strategies, and most of them perform well on their targets. However, in industry practice and empirical study, the performance and generalization ability of those well-designed fuzzing strategies are challenged by the complexity and diversity of real-world applications. In this paper, inspired by the idea of ensemble learning, we first propose an ensemble fuzzing approach EnFuzz, that integrates multiple fuzzing strategies to obtain better performance and generalization ability than that of any constituent fuzzer alone. First, we define the diversity of the base fuzzers and choose those most recent and well-designed fuzzers as base fuzzers. Then, EnFuzz ensembles those base fuzzers with seed synchronization and result integration mechanisms. For evaluation, we implement EnFuzz , a prototype basing on four strong open-source fuzzers (AFL, AFLFast, AFLGo, FairFuzz), and test them on Google's fuzzing test suite, which consists of widely used real-world applications. The 24-hour experiment indicates that, with the same resources usage, these four base fuzzers perform variously on different applications, while EnFuzz shows better generalization ability and always outperforms others in terms of path coverage, branch coverage and crash discovery. Even compared with the best cases of AFL, AFLFast, AFLGo and FairFuzz, EnFuzz discovers 26.8%, 117%, 38.8% and 39.5% more unique crashes, executes 9.16%, 39.2%, 19.9% and 20.0% more paths and covers 5.96%, 12.0%, 21.4% and 11.1% more branches respectively.
A globally optimised multipath routing algorithm using SDN

The Software Defined Network (SDN) model promises the delivery of a network architecture capable of handling the demands of current and future applications more efficiently than the current distributed network architecture. SDN gains this ability by moving to a logically centralised control plane. The centralised network controller has up to date information on both the network topology and the flows using the network. In this work we use the information already available at the network controller to create a globally optimised Per-Packet multipath routing algorithm. An optimal Per-Packet multipath routing algorithm may split a flow unequally between different paths. Current SDN capable hardware is not able to split flows in an unequal fashion while still remaining scalable. To overcome this limitation, we also propose a stochastic flow splitting mechanism capable of dividing a flow into any number of fractional components while offering better scalability than current methods. To analyse the performance of our proposed system, the GEANT network topology is used. Under heavy traffic load, our system shows a 90% improvement in a flow's received throughput when compared to OSPF and ECMP.
Towards multi-SDN services: Dangers of concurrent resource allocation from multiple providers

One of the Software-Defined Networking (SDN) promises is the programmability of networks through Application Programming Interfaces (APIs). Those APIs allow different users to access the network concurrently. Thus leading to the allocation of dedicated critical resources by a given Service Provider. In this paper we formalize the problem of deadlocks, and present a case where they occur.
Hard Shadow Anti-Aliasing for Spot Lights in a Game Engine

Real-time shadow rendering is desirable in several computer graphics applications, such as games. The most popular game engines typically provide support to this feature through the traditional shadow mapping algorithm. Unfortunately, shadow mapping is well known to generate aliasing artifacts along the shadow boundary, decreasing the realism of the rendered virtual scenes. In this paper, we aim to minimize these artifacts by implementing an anti-aliasing hard shadow technique in a game engine. Even with the limited source code access provided by some game engines, we demonstrate how to implement an improvement of the shadow mapping technique for shadow anti-aliasing. We validate our approach by implementing the revectorization-based shadow mapping technique for spot lights in a commercial and popular game engine, the Unity 3D. We show that, in this case, we are able to improve the shadow visual quality at the cost of only ~1.2% of loss, in average, in the frame rate. Finally, the results indicate that the proposed implementation is ready to be integrated into Unity projects which use spot lights as a source of direct illumination.
Implementation of the Constant Current and Constant Voltage Charge of Inductive Power Transfer Systems With the Double-Sided LCC Compensation Topology for Electric Vehicle Battery Charge Applications

When compared to plugged-in chargers, inductive power transfer (IPT) methods for electric vehicle (EV) battery chargers have several benefits, such as greater convenience and higher safety. In an EV, the battery is an indispensable component, and lithium-ion batteries are identified as the most competitive candidate to be used in EVs due to their high power density, long cycle life, and better safety. In order to charge lithium-ion batteries, constant current/constant voltage (CC/CV) is often adopted for high-efficiency charging and sufficient protection. However, it is not easy to design an IPT battery charger that can charge the batteries with a CC/CV charge due to the wide range of load variations, because it requires a wide range of variation in its operating frequency, duty, or phase-shift. Furthermore, zero phase angle (ZPA) condition for the primary inverter cannot be achieved over the entire charge process without the help of additional switches and related driver circuits to transform the topology. This paper proposes a design method that makes it possible to implement the CC/CV mode charge with minimum frequency variation during the entire charge process by using the load-independent characteristics of an IPT system under the ZPA condition without any additional switches. A theoretical analysis is presented to provide the appropriate procedure to design the double-sided LCC compensation tank which can achieve both CC and CV mode charge under ZPA condition at two different resonant frequencies. As a consequence, the proposed method is advantageous in that the efficiency of compensation tank is very high due to achieving the perfect resonant operation during the entire charge process. A 6.6-kW prototype charger has been implemented to demonstrate the feasibility and validity of the proposed method. A maximum efficiency of 96.1% has been achieved with a 200-mm airgap at 6.6 kW during the CC mode charge.
Adversarial Examples in Deep Learning: Characterization and Divergence

The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learning have emerged as one of the dominating security threat to a range of mission-critical deep learning systems and applications. This paper takes a holistic and principled approach to perform statistical characterization of adversarial examples in deep learning. We provide a general formulation of adversarial examples and elaborate on the basic principle for adversarial attack algorithm design. We introduce easy and hard categorization of adversarial attacks to analyze the effectiveness of adversarial examples in terms of attack success rate, degree of change in adversarial perturbation, average entropy of prediction qualities, and fraction of adversarial examples that lead to successful attacks. We conduct extensive experimental study on adversarial behavior in easy and hard attacks under deep learning models with different hyperparameters and different deep learning frameworks. We show that the same adversarial attack behaves differently under different hyperparameters and across different frameworks due to the different features learned under different deep learning model training process. Our statistical characterization with strong empirical evidence provides a transformative enlightenment on mitigation strategies towards effective countermeasures against present and future adversarial attacks.
Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ. AUTHOR SUMMARY Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.
Modeling, comprehending and summarizing textual content by graphs

Automatic Text Summarization strategies have been successfully employed to digest text collections and extract its essential content. Usually, summaries are generated using textual corpora that belongs to the same domain area where the summary will be used. Nonetheless, there are special cases where it is not found enough textual sources, and one possible alternative is to generate a summary from a different domain. One manner to summarize texts consists of using a graph model. This model allows giving more importance to words corresponding to the main concepts from the target domain found in the summarized text. This gives the reader an overview of the main text concepts as well as their relationships. However, this kind of summarization presents a significant number of repeated terms when compared to human-generated summaries. In this paper, we present an approach to produce graph-model extractive summaries of texts, meeting the target domain exigences and treating the terms repetition problem. To evaluate the proposition, we performed a series of experiments showing that the proposed approach statistically improves the performance of a model based on Graph Centrality, achieving better coverage, accuracy, and recall.
Automatic Fabric Defect Detection Using Learning-Based Local Textural Distributions in the Contourlet Domain

We propose a learning-based approach for automatic detection of fabric defects. Our approach is based on a statistical representation of fabric patterns using the redundant contourlet transform (RCT). The distribution of the RCT coefficients are modeled using a finite mixture of generalized Gaussians (MoGG), which constitute statistical signatures distinguishing between defective and defect-free fabrics. In addition to being compact and fast to compute, these signatures enable accurate localization of defects. Our defect detection system is based on three main steps. In the first step, a preprocessing is applied for detecting basic pattern size for image decomposition and signature calculation. In the second step, labeled fabric samples are used to train a Bayes classifier (BC) to discriminate between defect-free and defective fabrics. Finally, defects are detected during image inspection by testing local patches using the learned BC. Our approach can deal with multiple types of textile fabrics, from simple to more complex ones. Experiments on the TILDA database have demonstrated that our method yields better results compared with recent state-of-the-art methods.Note to Practitioners—Fabric defect detection is central to automated visual inspection and quality control in textile manufacturing. This paper deals with this problem through a learning-based approach. By opposite to several existing approaches for fabric defect detection, which are effective in only some types of fabrics and/or defects, our method can deal with almost all types of patterned fabric and defects. To enable both detection and localization of defects, a fabric image is first divided into local blocks, which are representative of the repetitive pattern structure of the fabric. Then, statistical signatures are calculated by modeling the distribution of coefficients of an RCT using the finite MoGG. The discrimination between defect-free and defective fabrics is then achieved through supervised classification of RCT-MoGG signatures based on expert-labeled examples of defective fabric images. Experiments have shown that our method yields very good performance in terms of defect detection and localization. In addition to its accuracy, inspection of images can be performed in a fully automatic fashion, whereas only labeled examples are initially required. Finally, our method can be easily adapted to a real-time scenario since defect detection on inspected images is performed at the block level, which can be easily parallelized through hardware implementation.