Miracle-dz/UniFAR-FTUs huggingface dataset:
EndBox: Scalable Middlebox Functions Using Client-Side Trusted Execution

Many organisations enhance the performance, security, and functionality of their managed networks by deploying middleboxes centrally as part of their core network. While this simplifies maintenance, it also increases cost because middlebox hardware must scale with the number of clients. A promising alternative is to outsource middlebox functions to the clients themselves, thus leveraging their CPU resources. Such an approach, however, raises security challenges for critical middlebox functions such as firewalls and intrusion detection systems. We describe EndBox, a system that securely executes middlebox functions on client machines at the network edge. Its design combines a virtual private network (VPN) with middlebox functions that are hardware-protected by a trusted execution environment (TEE), as offered by Intel's Software Guard Extensions (SGX). By maintaining VPN connection endpoints inside SGX enclaves, EndBox ensures that all client traffic, including encrypted communication, is processed by the middlebox. Despite its decentralised model, EndBox's middlebox functions remain maintainable: they are centrally controlled and can be updated efficiently. We demonstrate EndBox with two scenarios involving (i) a large company; and (ii) an Internet service provider that both need to protect their network and connected clients. We evaluate EndBox by comparing it to centralised deployments of common middlebox functions, such as load balancing, intrusion detection, firewalling, and DDoS prevention. We show that EndBox achieves up to 3.8x higher throughput and scales linearly with the number of clients.
Mobility Management Architecture in Different RATs Based Network Slicing

Network slicing is an architectural solution that enables the future 5G network to offer a high data traffic capacity and efficient network connectivity. Moreover, software defined network (SDN) and network functions virtualization (NFV) empower this architecture to visualize the physical network resources. The network slicing identified as a multiple logical network, where each network slice dedicates as an end-to-end network and works independently with other slices on a common physical network resources. Most user devices have more than one smart wireless interfaces to connect to different radio access technologies (RATs) such as WiFi and LTE, thereby network operators utilize this facility to offload mobile data traffic. Therefore, it is important to enable a network slicing to manage different RATs on the same logical network as a way to mitigate the spectrum scarcity problem and enables a slice to control its users' mobility across different access networks. In this paper, we propose a mobility management architecture based network slicing where each slice manages its users across heterogeneous radio access technologies such as WiFi, LTE and 5G networks. In this architecture, each slice has a different mobility demands and these demands are governed by a network slice configuration and service characteristics. Therefore, our mobility management architecture follows a modular approach where each slice has individual module to handle the mobility demands and enforce the slice policy for mobility management. The advantages of applying our proposed architecture include: i) Sharing network resources between different network slices; ii) creating logical platform to unify different RATs resources and allowing all slices to share them; iii) satisfying slice mobility demands.
Non-Atomic Refactoring and Software Sustainability

Sustainability is the ability of a project / codebase / organization to react to necessary changes over its expected lifespan. At a large enough scale, or with enough disconnect between dependencies, sustainability comes from application of both technical and non-technical approaches. On the technical side, I advocate for restraint among API providers on making arbitrary changes, and use of non-atomic refactoring techniques when more invasive changes are required; such techniques are employed in many Google projects, and in programming languages like Go and C++, to allow more flexible changes to language standards over time. On the non-technical side, I argue for a clear separation of responsibilities (providers need to do the bulk of the work for the update), as well as a growing need to document acceptable usage of an API, be it a library or programming language. In many languages, there are very few changes to an API that are provably safe without this idea: just because a user’s code currently works does not mean that it is supported and can be expected to continue to work indefinitely under maintenance. Taken together, these two approaches form what I believe to be a minimum set of requirements when approaching software sustainability.
Random Mining Group Selection to Prevent 51% Attacks on Bitcoin

Bitcoin is a cryptocurrency based on blockchain technology that enables peer-to-peer transactions without a central authority. Bitcoin is known for resolving double-spending problems. When two or more miners generate a block that includes transaction information at nearly the same time, an accidental fork occurs. In this case, the longest chain of blocks is selected to avoid the double-spending problem. However, if there is an attacker node whose hash power is greater than half of the total hash power, that node can perform a double-spending attack, i.e., a 51% or majority attack. We propose a random mining group selection technique to reduce the probability of successful double-spending attacks. The analysis results demonstrate that if the number of groups is greater than or equal to two, the probability that the attacker will find the next block is less than 50%.
Exploratory Radiomics for Predicting Adaptive Radiotherapy in Non-Small Cell Lung Cancer

The possibility of planning a therapy minimizing side effects and optimizing efficacy of cancer treatments is one of the main challenges tackled by precision medicine research in oncology. In this context, radiomics is revealing itself as a promising path for better understanding the correct approach to personalized cures. Its primary aim is to go beyond basic medical images analysis, which only leverages on direct measurements on the tumor mass, i.e. dimension and shape. On the contrary, radiomics approach is oriented to the extraction of heterogeneous and quantitative data from the images to characterize the disease from a wider perspective, in order to provide the physician a valid support for the therapy decision and survival prediction. This manuscript presents an application of radiomics to Non-Small Cell Lung Cancer, dealing with the novel task of predicting the possibility to carry out an adaptive therapy. We achieved promising performance, reporting a radiomics signature for predicting tumor reduction during therapy.
Improving the Search for Architecture Knowledge in Online Developer Communities

To make good architecture design decisions, developers need to know suitable architecture solution alternatives. However, with the rapid and continuous increase of solution alternatives (e.g. technologies, patterns, tactics), it is challenging to acquire architecture knowledge and to ensure that this knowledge is up to date. Our goal is to improve how architects search for architecturally relevant information in online developer communities. We developed a new search approach for architecturally relevant information using Stack Overflow as an example of an online developer community. Our search approach differs from a conventional keyword-based search in that it considers semantic information of architecturally relevant concepts in Stack Overflow. We also implemented the search approach as a web-based search engine. To show the effectiveness of the search approach compared to a conventional keyword-based search, we conducted an experiment with 16 practitioners. To ensure realism of the experiment, tasks given to practitioners are based on real scenarios identified in interviews with a different set of practitioners. The experiment showed that the new search approach outperforms a conventional keyword based search.
A Load Identification Algorithm of Frequency Domain Filtering Under Current Underdetermined Separation

Non-intrusive load identification is important to load monitoring and smart power utilization. In order to effectively identify load status and obtain detailed power data, this paper explained a load identification algorithm based on load decomposition and frequency domain filtering. It realized load decomposition through solving an underdetermined problem of a single current signal under non-intrusive collection mode and then further achieved load identification. The underdetermined problem was optimized into a 1-D underdetermined problem. The solving model was constructed as separating the collecting current into two channels. According to the sparseness of the current frequency domain signal, the optimal solution was obtained by a two-step iterative shrinkage threshold algorithm. Therefore, each operation load can be decomposed independently. The frequency-domain filter group was conducted to filter the decomposition current, and load identification was realized based on the decision of the frequency component after filtering. The algorithm simplified the identification complexity, with a fast convergence rate and high identification accuracy. The performance was validated by the actually measured data. The load decomposition and identification was effective, which could accurately determine the load status, and obtain the current waveform of the independent load.
Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images

Dimensionality reduction is an essential task in hyperspectral image processing. How to preserve the original intrinsic structure information and enhance the discriminant ability is still a challenge in this area. Recently, with the advantage of preserving global intrinsic structure information, low-rank representation has been applied to dimensionality reduction and achieved promising performance. By exploiting the submanifold information of the original data set, multimanifold learning is effective in enhancing the discriminant ability of the processed data set. In addition, due to the ability of preserving the spatial neighborhood structure information, the tensor analysis has become a popular technique for hyperspectral image processing. Motivated by the above-mentioned analysis, a novel tensor-based low-rank graph with multimanifold regularization (T-LGMR) for dimensionality reduction of hyperspectral images is proposed in this paper. In the T-LGMR, a low-rank constraint is employed to preserve the global data structure while multimanifold information is utilized to enhance the discriminant ability, and tensor representation is used to preserve the spatial neighborhood information. Finally, dimensionality reduction is achieved in the graph embedding framework. Experimental results on three real hyperspectral data sets demonstrate the superiority of the proposed method over several state-of-the-art approaches.
A Study of Robotic Cooperation in Cloud Robotics: Architecture and Challenges

Networked robotics involves a collection of robots working together to perform complex tasks, such as search and rescue task in disaster management. Because such tasks are beyond the capacity of a single powerful robot, networked robotics has been widely researched. However, the modes of cooperation in traditional networked robotics have been restricted by the inherent physical constraint that all computations are performed in the robotic network, with knowledge sharing being limited to the collective storage in the network. Cloud robotics, which allows robots to benefit from the rich storage, computation, and communication resources of modern data centers, is widely accepted as a promising approach to efficient robot cooperation in applications, such as disaster management. In this paper, we study robotic cooperation in cloud robotics. We first give a conceptual view of the nature of this cooperation. We then propose three novel robotic cooperation frameworks for cloud robotics: robotic knowledge sharing cooperation, robotic physical-task cooperation, and robotic computation task cooperation. Finally, we identify several critical challenges, and illustrate the potential benefits of robotic cooperation in cloud robotics.
fMRI-based decoding of reward effects in binocular rivalry

Abstract Binocular rivalry is a phenomenon where the simultaneous presentation of two different stimuli to the two eyes leads to alternating perception of the two stimuli. The temporary dominance of one stimulus over the other is influenced by several factors. Here, we studied the influence of reward on binocular rivalry dynamics and its neural representation in visual cortex. Orthogonal rotating grating stimuli were shown continuously, while monetary reward was given during the conscious perception of one stimulus but not the other. Periods of perceptual dominance were assessed both through participants’ subjective report and objectively using functional magnetic resonance imaging and multi-voxel pattern analysis. Results did not confirm previous evidence for an effect of reward on perceptual dominance durations. Exploratory post-hoc analyses indicated that knowledge regarding both the reward contingency and the subjective nature of perceptual alternations may have interfered with potential reward effects on perceptual phase durations, suggesting a moderating role of meta-cognitive awareness in reward-based perceptual inference. Future studies of top-down influences on bistable perception should carefully consider the methodological challenges related to meta-cognitive awareness.
PME: Projected Metric Embedding on Heterogeneous Networks for Link Prediction

Heterogenous information network embedding aims to embed heterogenous information networks (HINs) into low dimensional spaces, in which each vertex is represented as a low-dimensional vector, and both global and local network structures in the original space are preserved. However, most of existing heterogenous information network embedding models adopt the dot product to measure the proximity in the low dimensional space, and thus they can only preserve the first-order proximity and are insufficient to capture the global structure. Compared with homogenous information networks, there are multiple types of links (i.e., multiple relations) in HINs, and the link distribution w.r. t relations is highly skewed. To address the above challenging issues, we propose a novel heterogenous information network embedding model PME based on the metric learning to capture both first-order and second-order proximities in a unified way. To alleviate the potential geometrical inflexibility of existing metric learning approaches, we propose to build object and relation embeddings in separate object space and relation spaces rather than in a common space. Afterwards, we learn embeddings by firstly projecting vertices from object space to corresponding relation space and then calculate the proximity between projected vertices. To overcome the heavy skewness of the link distribution w.r. t relations and avoid "over-sampling'' or "under-sampling'' for each relation, we propose a novel loss-aware adaptive sampling approach for the model optimization. Extensive experiments have been conducted on a large-scale HIN dataset, and the experimental results show superiority of our proposed PME model in terms of prediction accuracy and scalability.
Preeclampsia: Novel Mechanisms and Potential Therapeutic Approaches

Preeclampsia is a serious complication of pregnancy where it affects 5–8% of all pregnancies. It increases the morbidity and mortality of both the fetus and pregnant woman, especially in developing countries. It deleteriously affects several vital organs, including the kidneys, liver, brain, and lung. Although, the pathogenesis of preeclampsia has not yet been fully understood, growing evidence suggests that aberrations in the angiogenic factors levels and coagulopathy are responsible for the clinical manifestations of the disease. The common nominator of tissue damage of all these target organs is endothelial injury, which impedes their normal function. At the renal level, glomerular endothelial injury leads to the development of maternal proteinuria. Actually, peripheral vasoconstriction secondary to maternal systemic inflammation and endothelial cell activation is sufficient for the development of preeclampsia-induced hypertension. Similarly, preeclampsia can cause hepatic and neurologic dysfunction due to vascular damage and/or hypertension. Obviously, preeclampsia adversely affects various organs, however it is not yet clear whether pre-eclampsia per se adversely affects various organs or whether it exposes underlying genetic predispositions to cardiovascular disease that manifest in later life. The current review summarizes recent development in the pathogenesis of preeclampsia with special focus on novel diagnostic biomarkers and their relevance to potential therapeutic options for this disease state. Specifically, the review highlights the renal manifestations of the disease with emphasis on the involvement of angiogenic factors in vascular injury and on how restoration of the angiogenic balance affects renal and cardiovascular outcome of Preeclamptic women.
Stitch: Fusible Heterogeneous Accelerators Enmeshed with Many-Core Architecture for Wearables

Wearable devices are now leveraging multi-core processors to cater to the increasing computational demands of the applications via multi-threading. However, the power, performance constraints of many wearable applications can only be satisfied when the thread-level parallelism is coupled with hardware acceleration of common computational kernels. The ASIC accelerators with high performance/watt suffer from high non-recurring engineering costs. Configurable accelerators that can be reused across applications present a promising alternative. Autonomous configurable accelerators loosely-coupled to the processor occupy additional silicon area for local data and control and incur data communication overhead. In contrast, configurable instruction set extension (ISE) accelerators tightly integrated into the processor pipeline eliminate such overheads by sharing the existing processor resources. Yet, naively adding full-blown ISE accelerators to each core in a many-core architecture will lead to huge area and power overheads, which is clearly infeasible in resource-constrained wearables. In this paper, we propose Stitch, a many-core architecture where tiny, heterogeneous, configurable and fusible ISE accelerators, called polymorphic patches are effectively enmeshed with the cores. The novelty of our architecture lies in the ability to stitch together multiple polymorphic patches, where each can handle very simple ISEs, across the chip to create large, virtual accelerators that can execute complex ISEs. The virtual connections are realized efficiently with a very lightweight compiler-scheduled network-on-chip (NoC) with no buffers or control logic. Our evaluations across representative wearable applications show an average 2.3X improvement in runtime for Stitch compared to a baseline many-core processor without ISEs, at a modest area and power overhead.
Classifying Data Heterogeneity within Budget and Spending Open Data

Open data has gained momentum for the past few years, but not much consumption was done over published open budget and spending datasets. Many challenges to consume open budget and spending data are still open. One of the challenges is the heterogeneity of these datasets. We analyze more than 75 different budget and spending datasets released by different public administrations from various levels of administrations and locations. We select five datasets, then present and illustrate several types of budget and spending heterogeneities. We compare these heterogeneities with state of the art fiscal data models, the OpenBudgets.eu (OBEU) data model and Fiscal Data Package (FDP) which are designed specifically for representing budget and spending datasets. The comparison provides hints for both datasets publishers and technical/research communities that deal with open data in budget and spending domain.
DTaint: Detecting the Taint-Style Vulnerability in Embedded Device Firmware

A rising number of embedded devices are reachable in the cyberspace, such as routers, cameras, printers, etc. Those devices usually run firmware whose code is proprietary with few public documents. Furthermore, most of the firmware images cannot be analyzed in dynamic analysis due to various hardware-specific peripherals. As a result, it hinders traditional static analysis and dynamic analysis techniques. In this paper, we propose a static binary analysis approach, DTaint, to detect taint-style vulnerabilities in the firmware. The taint-style vulnerability is a typical class of weakness, where the input data reaches a sensitive sink through an unsafe path. Specifically, we generate data dependency in a bottom-up manner through traversing callees before callers. To reduce the influence of the binary firmware, DTaint identifies pointer aliasing, interprocedural data flow, and similarity of the data structure layout. We have implemented a prototype of DTaint and conducted experiments to evaluate its performance. Our results show that DTaint discovers more vulnerabilities in less time, compared with the existing techniques. Furthermore, we illustrate the effectiveness of DTaint through applying it over six firmware images from four manufacturers. We have found 21 vulnerabilities, where 13 of them are previously-unknown and zero-day vulnerabilities.
A Case Report on Refractory Moschcowitz Syndrome

Although completely identified later on, Thrombotic thrombocytopenic purpura was first described by Dr. Moschcowitz in 1924, therefore the name 'Moschcowitz syndrome'. It is a microangiopathy associated with thrombocytopenia and hemolysis which causes organ dysfunction secondary to widespread microvascular thrombosis. The spectrum of the organ dysfunction in thrombotic thrombocytopenic purpura is quite diverse and not limited to nervous system and kidneys as classically described thus making the diagnosis more difficult. Use of plasmapheresis has led to improvement in mortality rates associated with this disease. However, multiple organ failure and presence of shock indicates likelihood of severe disease activity or refractoriness which can be avoided with earlier initiation of immunosuppressive therapy such as Rituximab.
PRESEnCE: A Framework for Monitoring, Modelling and Evaluating the Performance of Cloud SaaS Web Services

Cloud Services Providers (CSPs) provide cloud services to the cloud customers in the pay-per-use model and use the Service Level Agreements (SLAs) to define the quality of the provided services. SLAs are just a contract which characterizing the performance and quality of the CSPs' services. Unfortunately, there is not an automatic and standard mechanism to verify and assure that delivered services satisfy the signed SLA agreement. In this context, this work aim at developing an automatic framework called PRESENCE, to evaluate the Quality of Service (QoS) for the deployment of the Software-as-a-Service (SaaS) Web Services (WSs) offered across several CSPs. This performance evaluation will be used to verify and check on the SLA contraventions. PRESENCE aims at quantifying in a fair and by stealth way the performance and scalability of the delivered WS. By stealthiness, we refer to the capacity of evaluating a given Cloud service through multiple workload patterns that makes them indistinguishable from a regular user traffic from the provider point of view. PRESENCE introduces a definition for the set of Common performance metrics for measuring the behaviour of cloud applications on top of a given CSP. This framework opens a novel perspectives for SLA contraventions assessment, monitoring, modelling the performance metrics for SaaS WSs.