Miracle-dz/UniFAR-FTUs huggingface dataset:
xLED: Covert Data Exfiltration from Air-Gapped Networks via Switch and Router LEDs

An air-gapped network is a type of IT network that is separated from the Internet - physically – due to the sensitive information it stores. Even if such a network is compromised with a malware, the hermetic isolation from the Internet prevents an attacker from leaking out any data - thanks to the lack of connectivity. In this paper we show how attackers can covertly leak sensitive data from air-gapped networks via the row of status LEDs on networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device (‘side-channel’), malware controlling the status LEDs to carry any type of data (‘covert-channel’) has never studied before. Sensitive data can be covertly encoded over the blinking of the LEDs and received by remote cameras and optical sensors. A malicious code is executed in a compromised LAN switch or router allowing the attacker direct, low-level control of the LEDs. We provide the technical background on the internal architecture of switches and routers at both the hardware and software level which enables these attacks. We present different modulation and encoding schemas, along with a transmission protocol. We implement prototypes of the malware and discuss its design and implementation. We tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and discuss detection and prevention countermeasures. Our experiments show that sensitive data can be covertly leaked via the status LEDs of switches and routers at bit rates of 1 bit/sec to more than 2000 bit/sec per LED.
Training Motor Sequences: Effects of Speed and Accuracy Instructions

Abstract Participants practiced a fixed 3- and a fixed 6-key press sequence for 144 times each. In the speed group, they were instructed to execute their sequences fast without bothering much about errors while the accurate group was instructed to be careful and prevent errors. In the test phase, participants executed series of 3 and 6 responses (a) when all element-specific stimuli were displayed in the familiar order, (b) in response to just the familiar first stimulus, and (c) by responding to random stimuli. The speed instruction yielded stronger sequencing skill while the accuracy instruction developed stronger reaction skill.
Coming of Age: A Longitudinal Study of TLS Deployment

The Transport Layer Security (TLS) protocol is the de-facto standard for encrypted communication on the Internet. However, it has been plagued by a number of different attacks and security issues over the last years. Addressing these attacks requires changes to the protocol, to server- or client-software, or to all of them. In this paper we conduct the first large-scale longitudinal study examining the evolution of the TLS ecosystem over the last six years. We place a special focus on the ecosystem's evolution in response to high-profile attacks. For our analysis, we use a passive measurement dataset with more than 319.3B connections since February 2012, and an active dataset that contains TLS and SSL scans of the entire IPv4 address space since August 2015. To identify the evolution of specific clients we also create the---to our knowledge---largest TLS client fingerprint database to date, consisting of 1,684 fingerprints. We observe that the ecosystem has shifted significantly since 2012, with major changes in which cipher suites and TLS extensions are offered by clients and accepted by servers having taken place. Where possible, we correlate these with the timing of specific attacks on TLS. At the same time, our results show that while clients, especially browsers, are quick to adopt new algorithms, they are also slow to drop support for older ones. We also encounter significant amounts of client software that probably unwittingly offer unsafe ciphers. We discuss these findings in the context of long tail effects in the TLS ecosystem.
Surface based motion retargeting by preserving spatial relationship

Retargeting motion from one character to another is a key process in computer animation. It enables to reuse animations designed for a character to animate another one, or to make performance-driven be faithful to what has been performed by the user. Previous work mainly focused on retargeting skeleton animations whereas the contextual meaning of the motion is mainly linked to the relationship between body surfaces, such as the contact of the palm with the belly. In this paper we propose a new context-aware motion retargeting framework, based on deforming a target character to mimic a source character poses using harmonic mapping. We also introduce the idea of Context Graph: modeling local interactions between surfaces of the source character, to be preserved in the target character, in order to ensure fidelity of the pose. In this approach, no rigging is required as we directly manipulate the surfaces, which makes the process totally automatic. Our results demonstrate the relevance of this automatic rigging-less approach on motions with complex contacts and interactions between the character's surface.
Learning-Based Resource Allocation for Data-Intensive and Immersive Tactile Applications

The immersive tactile applications that are emerging in the entertainment, education and health industries are anticipated to be available for mobile users in the close future. These applications are data-intensive and delay-sensitive due to the nature of information that is being exchanged. With today’s mobile networks, the throughput and latency challenges are the major roadblocks for mobile users. In this paper, we propose a resource allocation technique with the aim of increasing throughput and reducing latency of Data Intensive Devices (DIDs). We consider the coexistence of DIDs with traditional User Equipments (UEs) on a two-tier, densely deployed network of Small cell Base Stations (SBSs) and eNBs. We propose a Q-learning-based resource allocation scheme, namely, Throughput Maximizing Q-Learning (TMQ) that learns the efficient resource allocation of both SBSs and eNB. The proposed technique is compared with well-known Proportional Fairness (PF) algorithm in terms of average throughput, delay, and fairness. Simulation results show significant improvement in throughput, 80% reduction in delay, and 6% increase in fairness.
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation

This paper introduces a gamified framework for fine-grained sentiment analysis and emotion detection. We present a flexible tool, Sentimentator, that can be used for efficient annotation based on crowd sourcing and a self-perpetuating gold standard. We also present a novel dataset with multi-dimensional annotations of emotions and sentiments in movie subtitles that enables research on sentiment preservation across languages and the creation of robust multilingual emotion detection tools. The tools and datasets are public and open-source and can easily be extended and applied for various purposes.
Adoption issues for cloud computing

Cloud computing allows users to use only a Web browser to receive computing services via the Internet. Users only need to pay for the services they actually use. It appears that a wide adoption of cloud computing in the foreseeable future is inevitable, and its adoption will bring about a sea change in the pricing and distribution practices for both software and hardware. There are, however, various issues that will impede adoption of cloud computing. Most of them can be solved. We discuss the status of cloud computing today and various adoption issues. We also provide a market prognosis.
Automated conflict resolution between multiple clinical pathways: a technology report

Background The number of people in the UK with three or more long-term conditions continues to grow and the management of patients with co-morbidities is complex. In treating patients with multimorbidities, a fundamental problem is understanding and detecting points of conflict between different guidelines which to date has relied on individual clinicians collating disparate information. Objective We will develop a framework for modelling a diverse set of care pathways, and investigate how conflicts can be detected and resolved automatically. We will use this knowledge to develop a software tool for use by clinicians that can map guidelines, highlight root causes of conflict between these guidelines and suggest ways they might be resolved. Method Our work consists of three phases. First, we will accurately model clinical pathways for six of the most common chronic diseases; second, we will automatically identify and detect sources of conflict across the pathways and how they might be resolved. Third, we will present a case study to prove the validity of our approach using a team of clinicians to detect and resolve the conflicts in the treatment of a fictional patient with multiple common morbidities and compare their findings and recommendations with those derived automatically using our novel software. Discussion This paper describes the development of an important software-based method for identifying a conflict between clinical guidelines. Our findings will support clinicians treating patients with multimorbidity in both primary and secondary care settings.
Analytical Techniques to Characterize the Structure, Properties, and Assembly of Virus Capsids.

Virus capsids, the protective protein shell of a virus with typical dimensions in the nanometer range, are a challenge to analyze. Ideal analytical techniques need to characterize the morphological, chemical, physical, and mechanical properties of the capsids. Measurements require excellent size resolution, precise temporal control, label-free detection, compatibility with typical buffers, and execution under biologically relevant conditions. Ensemble techniques often obscure infrequent events in a complex mixture; consequently, single particle techniques with sufficient throughput provide both information about the heterogeneity of individual particles and statistics of the population itself. Because no existing instrumentation can examine all these characteristics, several techniques are often needed to develop an understanding of virus capsids and their fundamental properties. Here, we highlight recent advances in virus capsid characterization and review both ensemble and single-particle methods, including light scattering techniques, fluorescence spectroscopy, mass spectrometry, resistive-pulse sensing, electron microscopy, atomic force microscopy, size exclusion chromatography, and field-flow fractionation.
Modern Generative Programming for Optimizing Small Matrix-Vector Multiplication

BLAS-level functions are the cornerstone of a large subset of applications. If a large body of work surrounding efficient and large-scale implementation of some routines such as gemv exists, the interest for small-sized, highly-optimized versions of those routines emerged. In this paper, we propose to show how a modern C++ approach based on generative programming techniques such as vectorization and loop unrolling in the framework of meta-programming can deliver efficient automatically generated codes for such routines, that are competitive with existing, hand-tuned library kernels with a very low programming effort compared to writing assembly code. In particular, we analyze the performance of automatically generated small-sized gemv kernels for both Intel x86 and ARM processors. We show through a performance comparison with the OpenBLAS gemv kernel on small matrices of sizes ranging from 4 to 32 that our C++ kernels are very efficient and may have a performance that is up to 3 times better than that of OpenBLAS gemv.
On The Advanced Services That 5G May Provide To IoT Applications

The advent of the 5G network is a key enabler to the growth of IoT, with the promise to innovate and revolutionize contemporary architectures by enabling new IoT-optimized services. Far from being just a bandwidth and latency improvement, the real potential of 5G lies in the intelligent management of network resources, and in the possibility of offering new services at the network level. Developers of IoT applications will no longer be forced to adopt a cloud-centric approach, where all storage and computation is centralized, but will be able to exploit network-provided resources, adopting Edge or Fog computing approaches, with numerous advantages such as higher locality, increased computation power and reliability, reduced latency and power consumption. Network operators, on the other hand, need to offer a compelling set of services while designing the intelligent components of their 5G networks, which would drive IoT developers to prefer their network-hosted services to cloud-based ones managed by over-the-top players. This paper aims at identifying which sets of services may be offered by a 5G network, by analyzing the computing, storage, and communication services that are currently offered by 11 major IoT platform providers, as well as those that are currently not being provided due to limitations of the cloud computing paradigm.
Robust Protocols for Securely Expanding Randomness and Distributing Keys Using Untrusted Quantum Devices

Randomness is a vital resource for modern-day information processing, especially for cryptography. A wide range of applications critically rely on abundant, high-quality random numbers generated securely. Here, we show how to expand a random seed at an exponential rate without trusting the underlying quantum devices. Our approach is secure against the most general adversaries, and has the following new features: cryptographic level of security, tolerating a constant level of imprecision in devices, requiring only unit size quantum memory (for each device component) in an honest implementation, and allowing a large natural class of constructions for the protocol. In conjunction with a recent work by Chung et al. [2014], it also leads to robust unbounded expansion using just 2 multipart devices. When adapted for distributing cryptographic keys, our method achieves, for the first time, exponential expansion combined with cryptographic security and noise tolerance. The proof proceeds by showing that the Rényi divergence of the outputs of the protocol (for a specific bounding operator) decreases linearly as the protocol iterates. At the heart of the proof are a new uncertainty principle on quantum measurements and a method for simulating trusted measurements with untrusted devices.
E-StopMotion: digitizing stop motion for enhanced animation and games

Stop Motion Animation is the traditional craft of giving life to handmade models. The unique look and feel of this art form is hard to reproduce with 3D computer generated techniques. This is due to the unexpected details that appear from frame to frame and to the sometimes choppy appearance of the character movement. The artist's task can be overwhelming as he has to reshape a character into hundreds of poses to obtain just a few seconds of animation. The results of the animation are usually applied in 2D mediums like films or platform games. Character features that took a lot of effort to create thus remain unseen. We propose a novel system that allows the creation of 3D stop motion-like animations from 3D character shapes reconstructed from multi-view images. Given two or more reconstructed shapes from key frames, our method uses a combination of non-rigid registration and as-rigid-as-possible interpolation to generate plausible in-between shapes. This significantly reduces the artist's workload since much fewer poses are required. The reconstructed and interpolated shapes with complete 3D geometry can be manipulated even further through deformation techniques. The resulting shapes can then be used as animated characters in games or fused with 2D animation frames for enhanced stop motion films.
Video information retrieval using objects and ostensive relevance feedback

In this paper, we present a brief overview of current approaches to video information retrieval (IR) and we highlight its limitations and drawbacks in terms of satisfying user needs. We then describe a method of incorporating object-based relevance feedback into video IR which we believe opens up new possibilities for helping users find information in video archives. Following this we describe our own work on shot retrieval from video archives which uses object detection, object-based relevance feedback and a variation of relevance feedback called ostensive RF which is particularly appropriate for this type of retrieval.
A new furan derivative from an endophytic Aspergillus tubingensis of Decaisnea insignis (Griff.) Hook.f. & Thomson

Abstract A new furan derivative named 3-(5-oxo-2,5-dihydrofuran-3-yl) propanoic acid (1) was isolated for the first time. Its structure was elucidated by UV, IR, NMR, HR-ESI-MS and the single-crystal X-ray diffraction spectroscopic data. Meanwhile, the antifungal and antibacterial activities of compound 1 was tested, it exhibited potent antifungal activity against Fusarium graminearum with MIC value of 16 µg/mL and medium antibacterial activity against Streptococcus lactis with MIC value of 32 µg/mL. Graphical Abstract
Findings of the WMT 2018 Biomedical Translation Shared Task: Evaluation on Medline test sets

Machine translation enables the automatic translation of textual documents between languages and can facilitate access to information only available in a given language for non-speakers of this language, e.g. research results presented in scientific publications. In this paper, we provide an overview of the Biomedical Translation shared task in the Workshop on Machine Translation (WMT) 2018, which specifically examined the performance of machine translation systems for biomedical texts. This year, we provided test sets of scientific publications from two sources (EDP and Medline) and for six language pairs (English with each of Chinese, French, German, Portuguese, Romanian and Spanish). We describe the development of the various test sets, the submissions that we received and the evaluations that we carried out. We obtained a total of 39 runs from six teams and some of this year’s BLEU scores were somewhat higher that last year’s, especially for teams that made use of biomedical resources or state-of-the-art MT algorithms (e.g. Transformer). Finally, our manual evaluation scored automatic translations higher than the reference translations for German and Spanish.
An analysis of operating system behavior on a simultaneous multithreaded architecture

This paper presents the first analysis of operating system execution on a simultaneous multithreaded (SMT) processor. While SMT has been studied extensively over the past 6 years, previous research has focused entirely on user-mode execution. However, many of the applications most amenable to multithreading technologies spend a significant fraction of their time in kernel code. A full understanding of the behavior of such workloads therefore requires execution and measurement of the operating system, as well as the application itself.To carry out this study, we (1) modified the Digital Unix 4.0d operating system to run on an SMT CPU, and (2) integrated our SMT Alpha instruction set simulator into the SimOS simulator to provide an execution environment. For an OS-intensive workload, we ran the multithreaded Apache Web server on an 8-context SMT. We compared Apache's user- and kernel-mode behavior to a standard multiprogrammed SPECInt workload, and compared the SMT processor to an out-of-order superscalar running both workloads. Overall, our results demonstrate the micro-architectural impact of an OS-intensive workload on an SMT processor and provide insight into the OS demands of the Apache Web server. The synergy between the SMT processor and Web and OS software produced a greater throughput gain over superscalar execution than seen on any previously examined workloads, including commercial databases and explicitly parallel programs.
Shadow Attacks on MEDA Biochips

The Micro-electrode-dot-array (MEDA) is a next-generation digital microfluidic biochip (DMFB) platform that supports fine-grained control and real-time sensing of droplet movements. These capabilities permit continuous monitoring and checkpoint-based validation of assay execution on MEDA. This paper presents a class of “shadow attacks” that abuse the timing slack in the assay execution. State-of-the-art checkpoint-based validation techniques cannot expose the shadow operations. We develop a defense that introduces extra checkpoints in the assay execution at time instances when the assay is prone to shadow attacks. Experiments confirm the effectiveness and practicality of the defense.
Disney at IEST 2018: Predicting Emotions using an Ensemble

This paper describes our participating system in the WASSA 2018 shared task on emotion prediction. The task focuses on implicit emotion prediction in a tweet. In this task, keywords corresponding to the six emotion labels used (anger, fear, disgust, joy, sad, and surprise) have been removed from the tweet text, making emotion prediction implicit and the task challenging. We propose a model based on an ensemble of classifiers for prediction. Each classifier uses a sequence of Convolutional Neural Network (CNN) architecture blocks and uses ELMo (Embeddings from Language Model) as an input. Our system achieves a 66.2% F1 score on the test set. The best performing system in the shared task has reported a 71.4% F1 score.
Interestingness Prediction & its Application to Immersive Content

Which parts or objects are interesting in a content? In this paper we first propose three computational models to automatically predict interestingness rankings of areas/objects inside a 2D picture. We based our modeling on previous experimental findings to ensure reliability of the prediction when compared to the human assessement of interestingness. Our two first models are based on low level features, extracted from image regions, which have been stated as useful in the human interest process. A baseline model is built by estimating a linear regression from a small dataset of 49 images. The second model estimates a rewarding term based on additional experimental observations. By adding image semantics, we then construct a last model, which more generally benefits from a better understanding of the content. It also integrates notions such that unusualness or human beings' presence that have proven to play key roles in the interestingness process. Finally, targeting VR applications, we extend our models to immersive content, both images and videos, and propose an innovative application to guide the viewer in his/her navigation based on intuitive visual or audio cues.
Examining Energy Efficiency of Vectorization Techniques Using a Gaussian Elimination

Modern computer environments are limited by energy and power constraints during the execution of programs. These limits can be due to power lines, budgeting, ecology, battery life or many other reasons. To bypass these limits, hardware and software development strive to reduce the energy and power consumption of the execution of algorithms. This article investigates the capabilities and limitations of vectorization with respect to energy efficiency. Vectorization is a technique to instrument on-chip SIMD execution that increases the performance of programs. The capability of vectorization to reduce energy consumption of programs has to be shown. As applicative algorithm, the well-known Gaussian elimination is vectorized and investigated. Several implementations, including automatic and manual vectorization techniques, have been developed and their execution time and energy consumption have been measured and compared.