Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of application context on privacy and performance of keystroke authentication systems

In this paper, we show that keystroke latencies used in continuous user authentication systems disclose application context, i.e., in which application user is entering text. Using keystroke data collected from 62 subjects, we show that an adversary can infer application context from keystroke latencies with 95.15% accuracy. To prevent leakage from keystroke latencies, and prevent exposure of application context, we develop privacy-preserving authentication protocols in the outsourced authentication model. Our protocols implement two popular matching algorithms designed for keystroke authentication, called Absolute (“A”) and Relative (“R”). With our protocols, the client reveals no information to the server during authentication, besides the authentication result. Our experiments show that these protocols are fast in practice: with 100 keystroke features, authentication was completed in about one second with the “A” protocol, and in 595 ms with the “R” protocol. Further, because the asymptotic cost of our protocols is linear, they can scale to a large number of features. On the other hand, by leveraging application context we were able to reduce HTER from 14.7% with application-agnostic templates, to as low as 5.8% with application-specific templates.
A Sink Node Assisted Lightweight Intrusion Detection Mechanism for WBAN

Relying on mini wearable or implantable biosensors, the wireless body area network (WBAN) is capable of efficiently collecting as well as of analyzing human physiological information. It has shown great potential in terms of beneficially improving healthcare quality. However, due to stringent resource constraints of biosensors, traditional security schemes, i.e. the encryption and the authentication, may not do well in countering security threats. Moreover, they are not competent in protecting the network from inside attacks and deny of service (DoS) attacks. In this paper, we propose a sink node assisted lightweight intrusion detection mechanism for WBAN, where the sink node can periodically monitor the packet transmission and record the abnormality for further analysis. Our lightweight mechanism results in a very high true positive rate and an ultra-low false positive rate. Extensive analysis and simulations based on Castalia are conducted and verify the validity and efficiency of our proposed mechanism.
Dealing with Imbalanced Data Sets for Human Activity Recognition Using Mobile Phone Sensors

In the recent years, the wide spreading of smart-phones which are daily carried by humans and fit with tens of sensors triggered an intense research activity in human activity recognition (HAR). HAR in smartphones is seen as essential not only to better understand human behavior in daily life but also for context provision to other applications in the smartphone. Many statistical and logical based models for on-line or off-line HAR have been designed, however, the current trend is to use deep-learning with neural network. These models need a high amount of data and, as most discriminative models, they are very sensitive to the imbalanced class problem. In this paper, we study different ways to deal with imbalanced data sets to improve accuracy of HAR with neu-ral networks and introduce a new oversampling method, called Border Limited Link SMOTE (BLL SMOTE) which improves the classification accuracy of Multi-Layer Perceptron (MLP) performances.
Nonsmooth Variants of Powell's BFGS Convergence Theorem

The popular BFGS quasi-Newton minimization algorithm under reasonable conditions converges globally on smooth convex functions. This result was proved by Powell in a landmark 1976 paper: we consider its implications for functions that are not smooth. In particular, an analogous convergence result holds for functions (like the Euclidean norm) whose minimizers are isolated nonsmooth points.
Keyphrases Extraction from User-Generated Contents in Healthcare Domain Using Long Short-Term Memory Networks

We propose keyphrases extraction technique to extract important terms from the healthcare user-generated contents. We employ deep learning architecture, i.e. Long Short-Term Memory, and leverage word embeddings, medical concepts from a knowledge base, and linguistic components as our features. The proposed model achieves 61.37% F-1 score. Experimental results indicate that our proposed approach outperforms the baseline methods, i.e. RAKE and CRF, on the task of extracting keyphrases from Indonesian health forum posts.
Weak Rigidity Theory and Its Application to Formation Stabilization

This paper introduces the notion of weak rigidity to characterize a framework by pairwise inner products of interagent displacements. Compared to distance-based rigidity, weak rigidity requires fewer constrained edges in the graph to determine a geometric shape in an arbitrarily dimensional space. A necessary and sufficient graphical condition for infinitesimal weak rigidity of planar frameworks is derived. As an application of the proposed weak rigidity theory, a gradient-based control law and a nongradient-based control law are designed for a group of single-integrator modeled agents to stabilize a desired formation shape, respectively. Using the gradient control law, we prove that an infinitesimally weakly rigid formation is locally exponentially stable. In particular, if the number of agents is one greater than the dimension of the space, a minimally infinitesimally weakly rigid formation is almost globally asymptotically stable. In the literature of rigid formation, the sensing graph is always requir.
Reconciling value-based objectives for security and identity management

Purpose In this paper, using values of individuals in a Swedish health-care organization, electronic identity management objectives related to security are defined. Design/methodology/approach By using value-focused thinking, eliciting values from interviews of three groups of health-care staff’s objective hierarchies for three stakeholder groups are identified and defined. Objective hierarchies allow comparison across multiple stakeholder groups such that strategic objectives for identity management can be compared and contrasted. Findings This qualitative investigation, which used value-focused thinking, revealed 94 subobjectives, grouped into 12 fundamental and 14 means objectives, which are essential for developing measures that address potential value conflicts in a health-care organization around electronic identity management. The objectives developed in this study are grounded socioorganizationally and provide a way forward in developing measures aimed to reducing potential conflicts at a policy level. Originality/value In a final synthesis, congruence (or lack thereof) in the electronic identity management approach for a Swedish health organization is suggested. This also creates a foundation to evaluate and weight different objectives for strategic decision management.
Memetic Algorithm with Hungarian Matching Based Crossover and Diversity Preservation

The Graph Partitioning Problem (GPP) is a well-known NP-hard combinatorial problem that involves the finding of a partition of vertexes that minimizes the number of cut edges while fulfilling a set of constraints. This paper presents a newly designed optimizer for the GPP: the Memetic Algorithm with Hungarian Matching Based Crossover and Diversity Preservation (MAHMBCDP). MAHMBCDP is a population-based scheme that incorporates an explicit mechanism to control the diversity with the aim of making a proper use of resources when dealing with long-termexecutions. Among the novelties of our proposal, the design of a crossover operator that is based on the Hungarian Algorithm to calculate a maximum matching is particularly important. Experimental validation with a set of well-known instances of the graph partitioning archive shows the proper performance of our proposal. In fact, new best-known solutions could be attained inten test cases.
Mineral and Bone Disorders After Kidney Transplantation

The risk of mineral and bone disorders among patients with chronic kidney disease is substantially elevated, owing largely to alterations in calcium, phosphorus, vitamin D, parathyroid hormone, and fibroblast growth factor 23. The interwoven relationship among these minerals and hormones results in maladaptive responses that are differentially affected by the process of kidney transplantation. Interpretation of conventional labs, imaging, and other fracture risk assessment tools are not standardized in the post-transplant setting. Post-transplant bone disease is not uniformly improved and considerable variation exists in monitoring and treatment practices. A spectrum of abnormalities such as hypophosphatemia, hypercalcemia, hyperparathyroidism, osteomalacia, osteopenia, and osteoporosis are commonly encountered in the post-transplant period. Thus, reducing fracture risk and other bone-related complications requires recognition of these abnormalities along with the risk incurred by concomitant immunosuppression use. As kidney transplant recipients continue to age, the drivers of bone disease vary throughout the post-transplant period among persistent hyperparathyroidism, de novo hyperparathyroidism, and osteoporosis. The use of anti-resorptive therapies require understanding of different options and the clinical scenarios that warrant their use. With limited studies underscoring clinical events such as fractures, expert understanding of MBD physiology, and surrogate marker interpretation is needed to determine ideal and individualized therapy.
Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.
Phylogenetic Lineages of Francisella tularensis in Animals

Tularemia is a zoonotic disease caused by the facultative intracellular bacterium Francisella tularensis. This microorganism can infect a plethora of animal species and its ecology is particularly complex. Much research was performed to understand its biology but many questions are still open, especially concerning the life cycle of this bacterium in the environment related to physical and biological parameters. Numerous animals are major hosts of F. tularensis but precise reservoir species are not yet well defined. Moreover, the exact range of species susceptible to tularemia is not clear and is complicated by the differences in virulence and ecology observed among the subspecies of F. tularensis. Indeed, different life cycles in nature, including the animal species concerned, were previously described for F. tularensis subsp. tularensis and F. tularensis subsp. holarctica. Recently, molecular techniques showing adequate discrimination between strains were developed, leading to the possibility to investigate links between phylogenetic lineages and infection in animals. New perspectives in research are now possible thanks to the information available and the simplicity of the molecular procedures. Current studies are unfolding the evolution of F. tularensis and these developments will lead to the elucidation of geographical and ecological differences observed by veterinarians, microbiologists and conservation biologists. However, systematic, coordinated collection of data and extensive sampling are important to efficiently assemble the findings of future research.
Comparison of Assorted Models for Transliteration

We report the results of our experiments in the context of the NEWS 2018 Shared Task on Transliteration. We focus on the comparison of several diverse systems, including three neural MT models. A combination of discriminative, generative, and neural models obtains the best results on the development sets. We also put forward ideas for improving the shared task.
Efficient Formal Concept Analysis through Recursive Context Partitioning

Formal Concept Analysis takes as input a bigraph known as a formal context. It produces a partially-ordered set of formal concepts which constitutes a complete lattice. This lattice can be represented as a directed acyclic graph, whose vertices are formal concepts and whose arcs connect neighbours in the ordering relation between them. This paper describes a divide-and-conquer technique for discovering and exploiting hierarchical structure in a formal context. Simultaneous hierarchical partitioning of both the context bigraph and the resultant lattice digraph is used to achieve efficient computation and, elsewhere, interactive visualisation of the concept lattice.
3D Annotation Transfer

In the last few years, there has been an increase in digitalization efforts within the Cultural Heritage ﬁeld, which boosted the interest for new strategies to improve documentation standards. While these concepts have been largely studied for most of the CH content types, 3D data still need to be fully worked out as document types. One of the most innovative methods to glue the documentation (i.e. the semantics) of the artifacts to their geometry is to exploit the technology of the semantic web and implement the semantic annotation pipeline for 3D data. Since the 3D representation of artifacts is not a standard, and in the particular case of triangular meshes there are differences of resolutions and vertices position, there is the strong need for tools which could allow for annotation persistence between representation switch. In this paper, we present the ﬁrst results in the design of an automatic algorithm for annotation transfer between triangular meshes with different resolutions, provided that they represent the same artifact.
ClickNF: a Modular Stack for Custom Network Functions

Network function virtualization has recently allowed specialized equipment to be replaced with equivalent software implementation. The Click router was a first step in this direction, defining a modular platform for generalized packet processing. Despite its major impact, however, Click does not provides native L4 implementation and only uses nonblocking I/O, limiting its scope to L2-L3 network functions. To overcome these limitations we introduce ClickNF, which provides modular transport and application-layer building blocks for the development of middleboxes and server-side network functions. We evaluate ClickNF to highlight its state-of-theart performance and showcase its modularity by composing complex functions from simple elements. ClickNF is open source and publicly available.
Unpacking the Social Media Bot: A Typology to Guide Research and Policy

Amidst widespread reports of digital influence operations during major elections, policymakers, scholars, and journalists have become increasingly interested in the political impact of social media 'bots. ' Most recently, platform companies like Facebook and Twitter have been summoned to testify about bots as part of investigations into digitally-enabled foreign manipulation during the 2016 US Presidential election. Facing mounting pressure from both the public and from legislators, these companies have been instructed to crack down on apparently malicious bot accounts. But as this article demonstrates, since the earliest writings on bots in the 1990s, there has been substantial confusion as to exactly what a 'bot' is and what exactly a bot does. We argue that multiple forms of ambiguity are responsible for much of the complexity underlying contemporary bot-related policy, and that before successful policy interventions can be formulated, a more comprehensive understanding of bots --- especially how they are defined and measured --- will be needed. In this article, we provide a history and typology of different types of bots, provide clear guidelines to better categorize political automation and unpack the impact that it can have on contemporary technology policy, and outline the main challenges and ambiguities that will face both researchers and legislators concerned with bots in the future.
Automatic Detection of Internal Copy-Move Forgeries in Images

This article presents an implementation and discussion of the recently proposed ‘Efficient DenseField Copy-Move Forgery Detection’ by Cozzolino et al. This method is a forgery detection based on a dense field of descriptors chosen to be invariant by rotation. Zernike moments were suggested in the original article. An efficient matching of the descriptors is then performed using PatchMatch, which is extremely efficient to find duplicate regions. Regions matched by PatchMatch are processed to find the final detections. This allows a precise and accurate detection of copy-move forgeries inside a single suspicious image. We also extend successfully the method to the use of dense SIFT descriptors and show that they are better at detecting forgeries using Poisson editing. Source Code The reviewed source code and documentation for this algorithm are available from the web page of this article1. Compilation and usage instruction are included in the README.txt file of the archive.
Seeking reproducibility: Assessing a multimodal study of the testing effect

This work has been partially supported by RecerCaixa (CoT project) and the Spanish Ministry of Economy and Competitiveness under MDM‐2015‐0502, TIN2014‐53199‐C3‐3‐R, TIN2017‐85179‐C3‐3‐R.
Building a Unified Code-Switching ASR System for South African Languages

We present our first efforts towards building a single multilingual automatic speech recognition (ASR) system that can process code-switching (CS) speech in five languages spoken within the same population. This contrasts with related prior work which focuses on the recognition of CS speech in bilingual scenarios. Recently, we have compiled a small five-language corpus of South African soap opera speech which contains examples of CS between 5 languages occurring in various contexts such as using English as the matrix language and switching to other indigenous languages. The ASR system presented in this work is trained on 4 corpora containing English-isiZulu, English-isiXhosa, English-Setswana and English-Sesotho CS speech. The interpolation of multiple language models trained on these language pairs enables the ASR system to hypothesize mixed word sequences from these 5 languages. We evaluate various state-of-the-art acoustic models trained on this 5-lingual training data and report ASR accuracy and language recognition performance on the development and test sets of the South African multilingual soap opera corpus.
Visual Computing for Archaeological Artifacts with Integral Invariant Filters in 3D

3D-artifacts from ancient civilizations contain many different kinds of information in form of forensic trace evidence, e.g., tool marks from styli or ﬁngerprints on wax sealings. These very ﬁne structures are increasingly captured by various 3D-acquisiton techniques and stored as irregular meshes. We introduce ﬁlter algorithms for the processing of these datasets to ﬁnally extract meaningful information at predeﬁned scales. Therefore, Multiscale-Integral Invariants (MSII) are introduced as robust ﬁlter methods with their four different variants, using volume, patch, surface and line integrals for their speciﬁc sensitivity on mean curvature, Gaussian curvature or noise detection. Smoothing as known from 2D-raster image processing cannot be applied directly. It needs adaptation to the irregular structure of the triangular grids describing 2D-manifolds in 3D-space. We introduce a fast 1-ring smoothing with a skillful weighting by distance and area of the neighboring points and triangles. Finally, we apply our technique to the various motivating examples for showing the results as false color images with isolines, indicating the respective ﬁeld of function values, e.g., curvature in various norms or correlations in the feature space. Smooth isolines are indicators for the successful removal of noise. We ﬁnally compare the fully automated results with a manual graphic rendering of a faded handwriting found in the tomb of the empress Gisela of Swabia.
A Case for Biased Programming in Flash

The voltage level of flash cells is directly correlated with the wear they experience. Previous studies showed that increasing the ratio of ones to zeroes within a flash page can reduce the amount of bit errors in this page as well as the long-term wear of its cells. Biased programming ensures more ones are programmed than zeroes by employing specialized codes which, in turn, incur nonnegligible storage overhead. We propose a novel approach to utilize the page spare area for biased programming, introducing a new tradeoff: while using the spare area for a stronger ECC can correct more errors, biased programming can reduce the number of those errors. We show that as long as the bit error rate is below a pre-determined threshold, biased programming can be applied without compromising the data's durability. When the threshold is reached, we revert to normal programming, but we can use the chip for as much as 24% additional writes, thanks to its reduced wear. We demonstrate the applicability of our approach on real MLC chips. We also perform an initial evaluation on a TLC chip, which exposes the challenges in applying any type of biased programming to TLC flash.
Ras-ERK-ETS inhibition alleviates neuronal mitochondrial dysfunction by reprogramming mitochondrial retrograde signaling

Mitochondrial dysfunction activates the mitochondrial retrograde signaling pathway, resulting in large scale changes in gene expression. Mitochondrial retrograde signaling in neurons is poorly understood and whether retrograde signaling contributes to cellular dysfunction or is protective is unknown. We show that inhibition of Ras-ERK-ETS signaling partially reverses the retrograde transcriptional response to alleviate neuronal mitochondrial dysfunction. We have developed a novel genetic screen to identify genes that modify mitochondrial dysfunction in Drosophila. Knock-down of one of the genes identified in this screen, the Ras-ERK-ETS pathway transcription factor Aop, alleviates the damaging effects of mitochondrial dysfunction in the nervous system. Inhibition of Ras-ERK-ETS signaling also restores function in Drosophila models of human diseases associated with mitochondrial dysfunction. Importantly, Ras-ERK-ETS pathway inhibition partially reverses the mitochondrial retrograde transcriptional response. Therefore, mitochondrial retrograde signaling likely contributes to neuronal dysfunction through mis-regulation of gene expression.