Miracle-dz/UniFAR-FTUs huggingface dataset:
Domain Adaptation Based on Mixture of Latent Words Language Models for Automatic Speech Recognition

SUMMARY This paper proposes a novel domain adaptation method that can utilize out-of-domain text resources and partially domain matched text resources in language modeling. A major problem in domain adaptation is that it is hard to obtain adequate adaptation e ﬀ ects from out-of-domain text resources. To tackle the problem, our idea is to carry out model merger in a latent variable space created from latent words language models (LWLMs). The latent variables in the LWLMs are represented as spe-ciﬁc words selected from the observed word space, so LWLMs can share a common latent variable space. It enables us to perform ﬂexible mixture modeling with consideration of the latent variable space. This paper presents two types of mixture modeling, i.e., LWLM mixture models and LWLM cross-mixture models. The LWLM mixture models can perform a latent word space mixture modeling to mitigate domain mismatch problem. Furthermore, in the LWLM cross-mixture models, LMs which individually constructed from partially matched text resources are split into two element models, each of which can be subjected to mixture modeling. For the approaches, this paper also describes methods to optimize mixture weights using a validation data set. Experiments show that the mixture in latent word space can achieve performance improvements for both target domain and out-of-domain compared with that in observed word space.
Differencing and Merging for 3D Animation Revision Control

Version Control System (VCS) techniques for managing the creation of 3D computer generated models exist for static 3D models, however, there are no solutions for revision control of the animation data from animated 3D models. A precondition for any type of VCS is the ability to compare two versions of an item, to identify the differences between them and to combine these. To this end, we propose a novel 3-way difference, merging and conflict resolution technique for 3D animation data.
High performance graph analytics with productivity on hybrid CPU-GPU platforms

In recent years, the rapid-growing scales of graphs have sparked a lot of parallel graph analysis frameworks to leverage the massive hardware resources on CPUs or GPUs. Existing CPU implementations are time-consuming, while GPU implementations are restricted by the memory space and the complexity of programming. In this paper, we present a high performance hybrid CPU-GPU parallel graph analytics framework with good productivity based on GraphMat. We map vertex programs to generalized sparse matrix vector multiplication on GPUs to deliver high performance, and propose a high-level abstraction for developers to implement various graph algorithms with relatively little efforts. Meanwhile, several optimizations have been adopted for reducing the communication cost and leveraging hardware resources, especially the memory hierarchy. We evaluate the proposed framework on three graph primitives (PageRank, BFS and SSSP) with large-scale graphs. The experimental results show that, our implementation achieves an average speedup of 7.0X than GraphMat on two 6-core Intel Xeon CPUs. It also has the capability to process larger datasets but achieves comparable performance than MapGraph, a state-of-the-art GPU-based framework.
A Preliminary Exploration of Floating Point Grammatical Evolution

Current GP frameworks are highly effective on a range of real and simulated benchmarks. However, due to the high dimensionality of the genotypes for GP, the task of visualising the fitness landscape for GP search can be difficult. This paper describes a new framework: Floating Point Grammatical Evolution (FP-GE) which uses a single floating point genotype to encode an individual program. This encoding permits easier visualisation of the fitness landscape arbitrary problems by providing a way to map fitness against a single dimension. The new framework also makes it trivially easy to apply continuous search algorithms, such as Differential Evolution, to the search problem. In this work, the FP-GE framework is tested against several regression problems, visualising the search landscape for these and comparing different search meta-heuristics.
Construction and validation of content of one instrument to assess falls in the elderly

ABSTRACT Objective To develop and validate the content of the online Questionnaire for Fall Risk Assessment in the Elderly. Methods The instrument was developed based on the International Classification of Functioning, Disability and Health (ICF) of the World Health Organization. Initially, the set of items was submitted to evaluation of judges (healthcare professionals with experience in elderly health), who could suggest inclusion or exclusion of questions from the instrument; they were also asked to rate each question according to the expected scope. At this stage, clarity and relevance levels for each item were evaluated, generating a total of Content Validity Coefficient (CtVC). Results Content Validity Coefficient values were satisfactory for both clarity (CtVC=0.76) and relevance (CtVC=0.82) of the questions. Next, a group of elderly volunteers participating in a socializing group evaluated the questionnaire for comprehension. The level of comprehension for each item was identified on a Likert scale, ranging from 0 to 5. The questionnaire was considered easy to understand by most participants (95%), with a mean of 4.75 (±0.11) points for each item. Conclusion The instrument showed acceptable psychometric qualities for screening fall risk among the elderly population. Future studies shall investigate different validation aspects of construct for this measure.
Efficacy of early cognitive-linguistic treatment for aphasia due to stroke: A randomised controlled trial (Rotterdam Aphasia Therapy Study-3)

Introduction One third of patients with acute stroke have aphasia. The majority receive speech and language therapy. There is evidence for a beneficial effect of speech and language therapy on restoring communication, but it is unknown whether and how efficacy of speech and language therapy is influenced by timing of treatment. We studied whether speech and language therapy early after stroke by way of intensive cognitive-linguistic treatment is more effective than no speech and language therapy in the Rotterdam Aphasia Therapy Study-3, a multicentre randomised single-blind trial. Methods and patients Stroke patients with first-ever aphasia were randomised within 2 weeks of onset to either 4 weeks of early intensive cognitive-linguistic treatment (1 h/day) or no language treatment. Hereafter, both groups received regular speech and language therapy. Primary outcome was the score on the Amsterdam-Nijmegen Everyday Language Test, measuring everyday verbal communication, 4 weeks after randomisation. Secondary outcomes were Amsterdam-Nijmegen Everyday Language Test at 3 and 6 months. The study was powered to detect a clinically relevant difference of four points on the Amsterdam-Nijmegen Everyday Language Test. Results Of the 152 included patients, 80 patients were allocated to intervention. Median treatment intensity in the intervention-group was 24.5 h. The adjusted difference between groups in mean Amsterdam-Nijmegen Everyday Language Test-scores 4 weeks after randomisation was 0.39, 95% confidence interval: [−2.70 to 3.47], p = 0.805. No statistically significant differences were found at 3 and 6 months after randomisation either. Conclusion Four weeks of intensive cognitive-linguistic treatment initiated within 2 weeks of stroke is not more effective than no language treatment for the recovery of post-stroke aphasia. Our results exclude a clinically relevant effect of very early cognitive-linguistic treatment on everyday language.
Enabling Strong Privacy Preservation and Accurate Task Allocation for Mobile Crowdsensing

Mobile crowdsensing engages a crowd of individuals to use their mobile devices to cooperatively collect data about social events and phenomena for customers with common interest. It can reduce the cost on sensor deployment and improve data quality with human intelligence. To enhance data trustworthiness, it is critical for the service provider to recruit mobile users based on their personal features, e.g., mobility pattern and reputation, but it leads to the privacy leakage of mobile users. Therefore, how to resolve the contradiction between user privacy and task allocation is challenging in mobile crowdsensing. In this paper, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation based on geographic information and credit points of mobile users. In SPOON, the service provider enables to recruit mobile users based on their locations, and select proper sensing reports according to their trust levels without invading user privacy. By utilizing proxy re-encryption and BBS+ signature, sensing tasks are protected and reports are anonymized to prevent privacy leakage. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users. Finally, we show the security properties of SPOON and demonstrate its efficiency in terms of computation and communication.
Image Reconstruction : Off-Resonance Correction and Water-Fat Separation

In most MR systems, the main magnet produces a uniform, static magnetic field, which partially aligns the nuclear spins in the subject or object to be examined and thus creates the net magnetization the MR signal is generated from. The gradient system temporarily superimposes linearly varying magnetic fields along different directions for a spatial encoding of the MR signal, exploiting the proportionality between the local magnetic field strength and the local resonance frequency of the nuclear spins.
Assessing the Threat of Untracked Changes in Software Evolution

While refactoring is extensively performed by practitioners, many Mining Software Repositories (MSR) approaches do not detect nor keep track of refactorings when performing source code evolution analysis. In the best case, keeping track of refactorings could be unnecessary work; in the worst case, these untracked changes could significantly affect the performance of MSR approaches. Since the extent of the threat is unknown, the goal of this paper is to assess whether it is significant. Based on an extensive empirical study, we answer positively: we found that between 10 and 21% of changes at the method level in 15 large Java systems are untracked. This results in a large proportion (25%) of entities that may have their histories split by these changes, and a measurable effect on at least two MSR approaches. We conclude that handling untracked changes should be systematically considered by MSR studies.
Apartment Structure Estimation Using Fully Convolutional Networks and Graph Model

Apartment searching has been a hot demand all over the world. Understanding the apartment structure will significantly contribute to simplifying the searching process. In this project, the fully convolutional networks are applied to generate semantic segmentation for the apartment floor plan images. We then study and optimize an algorithm to extract the graph model from the segmentation, and to extract the maximum common subgraph with which the structure similarity can be measured. We generate the ground truth data by using online annotation tool and experiment on half of the data. Our segmentation prediction results achieve a pixel mean accuracy of $0.89$ for the full-label model, and retrieve quite similar apartment structures by using graph modeling.
A Cost-based Storage Format Selector for Materialization in Big Data Frameworks

Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously. Typically, users deploy Data-Intensive Workflows (DIWs) for their analytical tasks. These DIWs of different users share many common parts (i.e, 50-80%), which can be materialized to reuse them in future executions. The materialization improves the overall processing time of DIWs and also saves computational resources. Current solutions for materialization store data on Distributed File Systems (DFS) by using a fixed data format. However, a fixed choice might not be the optimal one for every situation. For example, it is well-known that different data fragmentation strategies (i.e., horizontal, vertical or hybrid) behave better or worse according to the access patterns of the subsequent operations. In this paper, we present a cost-based approach which helps deciding the most appropriate storage format in every situation. A generic cost-based storage format selector framework considering the three fragmentation strategies is presented. Then, we use our framework to instantiate cost models for specific Hadoop data formats (namely SequenceFile, Avro and Parquet), and test it with realistic use cases. Our solution gives on average 33% speedup over SequenceFile, 11% speedup over Avro, 32% speedup over Parquet, and overall, it provides upto 25% performance gain.
Nonstochastic Bandits with Composite Anonymous Feedback

We investigate a nonstochastic bandit setting in which the loss of an action is not immediately charged to the player, but rather spread over at most d consecutive steps in an adversarial way. This implies that the instantaneous loss observed by the player at the end of each round is a sum of as many as d loss components of previously played actions. Hence, unlike the standard bandit setting with delayed feedback, here the player cannot observe the individual delayed losses, but only their sum. Our main contribution is a general reduction transforming a standard bandit algorithm into one that can operate in this harder setting. We also show how the regret of the transformed algorithm can be bounded in terms of the regret of the original algorithm. Our reduction cannot be improved in general: we prove a lower bound on the regret of any bandit algorithm in this setting that matches (up to log factors) the upper bound obtained via our reduction. Finally, we show how our reduction can be extended to more complex bandit settings, such as combinatorial linear bandits and online bandit convex optimization.
A Graph Solver for the Automated Generation of Consistent Domain-Specific Models

Many testing and benchmarking scenarios in software and systems engineering depend on the systematic generation of graph models. For instance, tool qualification necessitated by safety standards would require a large set of consistent (well-formed or malformed) instance models specific to a domain. However, automatically generating consistent graph models which comply with a metamodel and satisfy all well-formedness constraints of industrial domains is a significant challenge. Existing solutions which map graph models into first-order logic specification to use back-end logic solvers (like Alloy or Z3) have severe scalability issues. In the paper, we propose a graph solver framework for the automated generation of consistent domain-specific instance models which operates directly over graphs by combining advanced techniques such as refinement of partial models, shape analysis, incremental graph query evaluation, and rule-based design space exploration to provide a more efficient guidance. Our initial performance evaluation carried out in four domains demonstrates that our approach is able to generate models which are 1-2 orders of magnitude larger (with 500 to 6000 objects! ) compared to mapping-based approaches natively using Alloy.
O3-Induced Leaf Senescence in Tomato Plants Is Ethylene Signaling-Dependent and Enhances the Population Abundance of Bemisia tabaci

Elevated ozone (O3) can alter the phenotypes of host plants particularly in induction of leaf senescence, but few reports examine the involvement of phytohormone in O3-induced changes in host phenotypes that influence the foraging quality for insects. Here, we used an ethylene (ET) receptor mutant Nr and its wild-type to determine the function of the ET signaling pathway in O3-induced leaf senescence, and bottom-up effects on the performance of Bemisia tabaci in field open-top chambers (OTCs). Our results showed that elevated O3 reduced photosynthetic efficiency and chlorophyll content and induced leaf senescence of plant regardless of plant genotype. Leaf senescence in Nr plants was alleviated relative to wild-type under elevated O3. Further analyses of foliar quality showed that elevated O3 had little effect on phytohormone-mediated defenses, but significantly increased the concentration of amino acids in two plant genotypes. Furthermore, Nr plants had lower amino acid content relative to wild-type under elevated O3. These results provided an explanation of O3-induced increase in abundance of B. tabaci. We concluded that O3-induced senescence of plant was ET signal-dependent, and positive effects of O3-induced leaf senescence on the performance of B. tabaci largely resulted from changes of nutritional quality of host plants.
Streptomyces spp. From Ethiopia Producing Antimicrobial Compounds: Characterization via Bioassays, Genome Analyses, and Mass Spectrometry

A total of 416 actinomycete cultures were isolated from various unique environments in Ethiopia and tested for bioactivity. Six isolates with pronounced antimicrobial activity were chosen for taxonomic identification and further investigation. Morphological and cultural properties of the isolates were found to be consistent with those of the genus Streptomyces, which was further confirmed by phylogenetic analysis based on 16S rRNA gene sequences. One of the isolates, designated Streptomyces sp. Go-475, which displayed potent activity against both pathogenic yeasts and Gram-positive bacteria, was chosen for further investigation. Metabolite profiles and bioactivity of Go-475 incubated on wheat bran-based solid and soya flour-based liquid media were compared using high-resolution LC-MS. This allowed identification of several known compounds, and suggested the ability of Go-475 to produce new secondary metabolites. Major anti-bacterial compounds were purified from liquid cultures of Go-475, and their structures elucidated by NMR and HRMS as 8-O-methyltetrangomycin and 8-O-methyltetrangulol. In addition, many potentially novel metabolites were detected, the majority of which were produced in solid media-based fermentation. The genome sequence of Streptomyces sp. Go-475 was obtained using a hybrid assembly approach of high quality Illumina short read and low quality Oxford Nanopore long read data. The complete linear chromosome of 8,570,609 bp, featuring a G+C content of 71.96%, contains 7,571 predicted coding sequences, 83 t(m)RNA genes, and six rrn operons. Analysis of the genome for secondary metabolite biosynthesis gene clusters further confirmed potential of this isolate to synthesize chemically diverse natural products, and allowed to connect certain clusters with experimentally confirmed molecules.
Justifying the strong memory semantics of concurrent high-level programming languages for system programming

In this thesis we justify the strong memory semantics of high-level concurrent languages, such as C11 or Java, when compiled to x86-like machines. Languages such as C11 guarantee that programs that obey a specific software discipline behave as if they were executed on a simple coherent shared memory multi-processor. Real x86 machines, on the other hand, do not provide a coherent shared memory, and compilers add slow synchronizing instructions to provide the illusion of coherency. We show that one software discipline that closely matches software disciplines of languages such as C11 and Java — in a nutshell, that racing memory accesses are annotated as such by the programmer — and one particular way to add relatively few synchronization instructions — in a nutshell, between an annotated store and an annotated load in the same thread — suffice to create this illusion. The software discipline has to be obeyed by the program in the semantics of the high-level language, therefore allowing the verification effort to take place completely in the strong memory semantics of the high-level language. We treat a machine with operating system support, and accordingly our theorems can be used to verify interruptible multi-core operating systems, including those where users can run unverified programs that do not obey the software discipline.
Fast Non-stationary Deconvolution in Ultrasound Imaging

Pulse-echo ultrasound (US) aims at imaging tissue using an array of piezoelectric elements by transmitting short US pulses and receiving backscattered echoes. Conventional US imaging relies on delay-and-sum (DAS) beamforming which retrieves a radio-frequency (RF) image, a blurred estimate of the tissue reflectivity function (TRF). To address the problem of the blur induced by the DAS, deconvolution techniques have been extensively studied as a post-processing tool for improving the resolution. Most approaches assume the blur to be spatially invariant, i.e. stationary, across the imaging domain. However, due to physical effects related to the propagation, the blur is nonstationary across the imaging domain. In this work, we propose a continuous-domain formulation of a model which accounts for the diffraction effects related to the propagation. We define a PSF operator as a sequential application of the forward and adjoint operators associated with this model, under some specific assumptions that we precise. Taking into account this sequential structure, we exploit efficient formulations of the operators in the discrete domain and provide a PSF operator which exhibits linear complexity with respect to the grid size. We use the proposed model in a maximum-a-posteriori estimation algorithm, with a generalized Gaussian distribution prior for the TRF. Through simulations and in-vivo experimental data, we demonstrate its superiority against state-of-the-art deconvolution methods based on a stationary PSF.
A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets

With the world moving towards being increasingly dependent on computers and automation, one of the main challenges in the current decade has been to build secure applications, systems and networks. Alongside these challenges, the number of threats is rising exponentially due to the attack surface increasing through numerous interfaces offered for each service. To alleviate the impact of these threats, researchers have proposed numerous solutions; however, current tools often fail to adapt to ever-changing architectures, associated threats and 0-days. This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets. These taxonomies and surveys aim to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets. To this end, this manuscript also provides a taxonomy and survey or network threats and associated tools. The manuscript highlights that current IDS only cover 25% of our threat taxonomy, while current datasets demonstrate clear lack of real-network threats and attack representation, but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS. Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository.