Reviewerly/exHarmony huggingface dataset:
A Clinical Decision Support System to Help the Interpretation of Laboratory Results and to Elaborate a Clinical Diagnosis in Blood Coagulation Domain

Hemophilia is a rare hemorrhagic disorder caused by clotting factor deficiencies that leads to a less efficient coagulation system. Treatments of this pathology rely on a patient’s subjective assessment which reflects a need for a laboratory assay able to predict the clinical patient phenotype. According to the literature, global assays such as thrombin generation (TG), are good predictors of bleeding episodes and therefore seem to be good candidates to fit this need. Nevertheless, the result of the TG assay, known as thrombogram, is difficult to interpret for non-expert clinicians. In this paper, we present a machine learning-based clinical decision support system which goal is to help clinical decision making. In doing so, we have adopted several approaches in order to evaluate well-known machine learning algorithms, in terms of accuracy and robustness, on a thrombogram database generated using numerical simulations. Obtained results, 95.57% of accuracy using a cascade of a SVM and MLPs to classify all categories and 98.10% of accuracy for the binary case hemophilia A/B, prove that our proposal can efficiently diagnose hemophilia.
One-Shot Learning Using Triplet Network with kNN Classifier

This is an extension from a selected paper from JSAI2019. Humans have the ability to learn new things correctly without requiring large amount of data, while it is a challenging task in AI, which is called few-shot Learning or one-shot learning. Our key insight is using data augmentation technique to enlarge our dataset, then feeding them into a Triplet Network which is to collect same categories and separate the different. We have compared different augmentation methods, and we confirm that CVAE(Conditional VAE) can make sense as data augmentation method to slove one-shot classification problems.
Low-Shot Learning from Imaginary 3D Model

Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits. However, the state-of-the-art approaches are largely unsuitable in scarce data regimes. To address this shortcoming, this paper proposes employing a 3D model, which is derived from training images. Such a model can then be used to hallucinate novel viewpoints and poses for the scarce samples of the few-shot learning scenario. A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the training of a classifier. The performance of the proposed approach is showcased on the fine-grained CUB-200-2011 dataset in a few-shot setting and significantly improves our baseline accuracy.
3D Face Dynamic Expression Synthesis System Based on DFFD

Synthesis of highly realistic facial expressions with speech communication function is one of the most challenging problems in computer graphics. In recent years, virtual characters have been widely used in the game industry, e-commerce, film and television production, medical treatment, military and other fields. How to build realistic 3d face model and synthesize real facial expression is the main problem of 3d facial expression animation. In this paper, a 3d face dynamic expression synthesis algorithm driven by DFFD data is proposed. By collecting real facial expressions to drive a universal neutral 3d virtual face model, a realistic 3d facial expression animation consistent with the facial movements of real performers is synthesized. In addition, the 3d virtual face synthesized by this method presents the consistency between visual, auditory and the content to be expressed in the process of speech.
Investigating 3D Printer Residual Data

The continued adoption of Additive Manufacturing technologies is raising concerns in the security, forensics, and intelligence gathering communities. These concerns range from identifying and mitigating compromised devices, to theft of intellectual property, to sabotage, to the production of prohibited objects. Previous research has provided insight into the retrieval of configuration information maintained on the devices, but this work shows that the devices can additionally maintain information about the print process. Comparisons between before and after images taken from an AM device reveal details about the device's activities, including printed designs, menu interactions, and the print history. Patterns in the storage of that information also may be useful for reducing the amount of data that needs to be examined during an investigation. These results provide a foundation for future investigations regarding the tools and processes suitable for examining these devices.
Hardness and Ease of Curing the Sign Problem for Two-Local Qubit Hamiltonians

We examine the problem of determining whether a multi-qubit two-local Hamiltonian can be made stoquastic by single-qubit unitary transformations. We prove that when such a Hamiltonian contains one-local terms, then this task can be NP-hard. This is shown by constructing a class of Hamiltonians for which performing this task is equivalent to deciding $3$-SAT. In contrast, we show that when such a Hamiltonian contains no one-local terms then this task is easy, namely we present an algorithm which decides, in a number of arithmetic operations over $\mathbb{R}$ which is polynomial in the number of qubits, whether the sign problem of the Hamiltonian can be cured by single-qubit rotations.
Pembelajaran Matematika Anak Usia Dini Melalui Media Dadu dan Pengaruhnya ditinjau dari Perkembangan Kognitif

Learning mathematics for early childhood requires ways and media that are suitable for children's development. Mathematics is a scientific discipline that covers almost all levels of education including early childhood education. The characteristic of early childhood learning is learning while playing as embodied in its curriculum at the level of the National Education System. Media dice games can be used in mathematics learning numerical recognition material that is directly related to aspects of children's cognitive development. This study examines the effect of dice media on cognitive development in the mathematics learning of early childhood education number recognition material. This type of research is quantitative causal with statistical tests used including normality test and homogeneity test and hypothesis testing using T test (T-test) or partial test using SPSS version 22 program. The conclusion of this study is that there are influences of media dice game on cognitive aspects learning material for number recognition in learning early childhood mathematics in the 2018/2019 academic year.
Acoustically Grounded Word Embeddings for Improved Acoustics-to-Word Speech Recognition

Direct acoustics-to-word (A2W) systems for end-to-end automatic speech recognition are simpler to train, and more efficient to decode with, than sub-word systems. However, A2W systems can have difficulties at training time when data is limited, and at decoding time when recognizing words outside the training vocabulary. To address these shortcomings, we investigate the use of recently proposed acoustic and acoustically grounded word embedding techniques in A2W systems. The idea is based on treating the final pre-softmax weight matrix of an AWE recognizer as a matrix of word embedding vectors, and using an externally trained set of word embeddings to improve the quality of this matrix. In particular we introduce two ideas: (1) Enforcing similarity at training time between the external embeddings and the recognizer weights, and (2) using the word embeddings at test time for predicting out-of-vocabulary words. Our word embedding model is acoustically grounded, that is it is learned jointly with acoustic embeddings so as to encode the words' acoustic-phonetic content; and it is parametric, so that it can embed any arbitrary (potentially out-of-vocabulary) sequence of characters. We find that both techniques improve the performance of an A2W recognizer on conversational telephone speech.
In Fleeting Visions

We describe a series of short music pieces that is generated in a semi-improvised manner by a computer, using deep representations and temporal memory model constructed from learning a corpus of piano works by Sergei Prokofiev. Inspired by Prokofiev's piece of a similar name, this work explores imaginative capabilities of generative music machine through a series of passing-by sonic visions triggered by fleeting activations of the underlying musical network from a musician performer input. Unlike most other common machine learning and neural music compositions that explore stylistic imitation, the impetus here is to provide a rainbow of unimagined possibilities enabling creative human intervention and interaction with a complex system, realized in a series of improvisations, each with a different form, texture and character.
Performance of Password Guessing Enumerators Under Cracking Conditions

In this work, we aim to measure the impact of hash functions on the password cracking process. This brings us to measure the performance of password enumerators, how many passwords they find in a given period of time. We propose a performance measurement methodology for enumerators, which integrates the success rate and the speed of the whole password cracking process. This performance measurement required us to develop advanced techniques to solve measurement challenges that were not mentioned before. The experiments we conduct show that software-optimized enumerators like John The Ripper-Markov and the bruteforce perform well when attacking fast hash functions like SHA-1. Whereas enumerators like OMEN and PCFG-based algorithm perform the best when attacking slow hash functions like bcrypt or Argon2. Using this approach, we realize a more in-depth measurement of the enumerators performance, considering quantitatively the trade-off between the enumerator choice and the speed of the hash function. We conclude that software-optimized enumerators and tools must implement academic methods in the future.
Modern subword-based models for automatic speech recognition

In today's society, speech recognition systems have reached a mass audience, especially in the field of personal assistants such as Amazon Alexa or Google Home. Yet, this does not mean that speech recognition has been solved. On the contrary, for many domains, tasks, and languages such systems do not exist. Subword-based automatic speech recognition has been studied in the past for many reasons, often to overcome limitations on the size of the vocabulary. Specifically for agglutinative languages, where new words can be created on the fly, handling these limitations is possible using a subword-based automatic speech recognition (ASR) system. Though, over time subword-based systems lost a bit of popularity as system resources increased and word-based models with large vocabularies became possible. Still, subword-based models in modern ASR systems can predict words that have never been seen before and better use the available language modeling resources. Furthermore, subword models have smaller vocabularies, which makes neural network language models (NNLMs) easier to train and use. Hence, in this thesis, we study subword models for ASR and make two major contributions. First, this thesis reintroduces subword-based modeling in a modern framework based on weighted finite-state transducers and describe the necessary tools for making a sound and effective system. It does this through careful modification of the lexicon FST part of a WFST-based recognizer. Secondly, extensive experiments using are done using subwords, with different types of language models including n-gram models and NNLMs. These experiments are performed on six different languages setting the new best-published result for any of these datasets. Overall, we show that subword-based models can outperform word-based models in terms of ASR performance for many different types of languages. This thesis also details design choices needed when building modern subword ASR systems, including the choices of the segmentation algorithm, vocabulary size and subword marking style. In addition, it includes techniques to combine speech recognition models trained on different units through system combination. Lastly, it evaluates the use of the smallest possible subword unit; characters and shows that these models can be smaller and yet be competitive to word-based models.
Asset information management using linked data for the life-cycle of roads

From September 2016 to August 2018 a CEDR (Conference of European Directors of Roads) research has been conducted by the consortium INTERLINK into asset information management for European Roads life-cycle management using Linked Data. This research aims to improve interoperability between the European National Road Authorities (NRAs) and their stakeholders, mainly to explore the procurement of road asset information by NRAs for better operations, asset management and project management, to investigate suitable data structures for asset information, including BIM and GIS, to develop common principles for a European Road object type library (OTL) and to design and test a basic European Road OTL connected to open BIM/GIS standards. The CEDR INTERLINK project will design a managed European Road OTL as the basis of a common language to be used for developing high quality national Road OTLs for exchanging and sharing road asset data. This European Road OTL is supported by three pillars to optimize the success of subsequent implementation: 1. a quality open available standardized Technical Specification, 2. a suitable and sustainable Standardization Body, with a plan for development beyond this project and 3. acceptance in practice by the industry through engagement and dissemination. First the business and data needs have been investigated by INTERLINK through a literature review, interviews with stakeholders (NRAs, contractors, consultants, suppliers, academics, software developers and standardization initiatives in Europe) and an industry survey, which resulted in a approach for the European Road OTL. The common principles, based on the requirements, describe a hybrid solution, with semantically-rich data referencing more traditional document-based information, combining the strengths of currently applied BIM/GIS standards with Linked Data / Semantic Web technology. The proof-of-concept European Road OTL is tested via three trial cases, which demonstrated data transfers representing typical processes during assets’ life-cycles in three European countries: Germany, Sweden and The Netherlands. A road map is delivered for development and implementation of the Road OTL from document based to object based road information management based on the European Road OTL open standard using Linked Data. © 2019 Taylor & Francis Group, London.
Open Problems in Stream Processing

In the last four years, stream processing has gone from niche to mainstream, with real-time data processing systems gaining traction in not only fast-moving startups, but also their more skeptical and cautious enterprise brethren. In light of such pervasive adoption, is it safe to say we've we finally reached the point where stream processing is a solved commodity? Are we done and ready to move on to the next big thing?
Fast Spatially-Varying Indoor Lighting Estimation

We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image. Given an image and a 2D location in that image, our CNN estimates a 5th order spherical harmonic representation of the lighting at the given location in less than 20ms on a laptop mobile graphics card. While existing approaches estimate a single, global lighting representation or require depth as input, our method reasons about local lighting without requiring any geometry information. We demonstrate, through quantitative experiments including a user study, that our results achieve lower lighting estimation errors and are preferred by users over the state-of-the-art. Our approach can be used directly for augmented reality applications, where a virtual object is relit realistically at any position in the scene in real-time.
Data And Content Analysis For Social Network Using LDA Text Model

Abstract WhatsApp is a popular cross-platform application for communicating and connecting people. In this paper, the author analyzes massive data from WhatsApp public groups and obtains so-called tacit knowledge and social complex knowledge. During the processing, the author uses LDA(Latent Dirichlet Allocation) model to analyze the content which helps us to be aware of many aspects from public groups such as social trend, sentiment analysis and other hidden information. In the end, author get the results about topic for each group and the occurrence of words for each topic.
Performance Comparison of MPICH and MPI4py on Raspberry Pi-3B Beowulf Cluster

Moore's Law is running out. Instead of making powerful computer by increasing number of transistor now we are moving toward Parallelism. Beowulf cluster means cluster of any Commodity hardware. Our Cluster works exactly similar to current day's supercomputers. The motivation is to create a small sized, cheap device on which students and researchers can get hands on experience. There is a master node, which interacts with user and all other nodes are slave nodes. Load is equally divided among all nodes and they send their results to master. Master combines those results and show the final output to the user. For communication between nodes we have created a network over Ethernet. We are using MPI4py, which a Python based implantation of Message Passing Interface (MPI) and MPICH which also an open source implementation of MPI and allows us to code in C, C++ and Fortran. MPI is a standard for writing codes for such clusters. We have written parallel programs of Monte Carlo's Simulation for finding value of pi and prime number generator in Python and C++ making use of MPI4py and MPICH respectively. We have also written sequential programs applying same algorithms in Python. Then we compared the time it takes to run them on cluster in parallel and in sequential on a computer having 6500 core i7 Intel processor. It is found that making use of parallelism; we were able to outperform an expensive computer which costs much more than our cluster.
Training Variational Autoencoders with Buffered Stochastic Variational Inference

The recognition network in deep latent variable models such as variational autoencoders (VAEs) relies on amortized inference for efficient posterior approximation that can scale up to large datasets. However, this technique has also been demonstrated to select suboptimal variational parameters, often resulting in considerable additional error called the amortization gap. To close the amortization gap and improve the training of the generative model, recent works have introduced an additional refinement step that applies stochastic variational inference (SVI) to improve upon the variational parameters returned by the amortized inference model. In this paper, we propose the Buffered Stochastic Variational Inference (BSVI), a new refinement procedure that makes use of SVI's sequence of intermediate variational proposal distributions and their corresponding importance weights to construct a new generalized importance-weighted lower bound. We demonstrate empirically that training the variational autoencoders with BSVI consistently out-performs SVI, yielding an improved training procedure for VAEs.
Incremental Reasoning for Fleet Management

We propose RTECinc, an incremental version of RTEC, a composite event recognition engine with formal, declarative semantics, that has been shown to scale to several real-world data streams. RTEC deals with delayed arrival of events by computing at each query time everything from scratch. This is often not efficient since it results to redundant computations. Instead, RTECinc deals with delays in a more efficient way, by updating only the affected events. We compare RTECinc and RTEC experimentally using a real-world dataset with position signals of vehicles.
Haptic Feedback-Based Reactive Navigation for Aerial Robots Subject to Localization Failure

This paper considers the problem of resilient and collision–tolerant navigation of aerial robots when the estimation of the full robot pose and the map of the environment is not possible due to sensor failure or severe degradation. Typical examples relate to GPS–denied operation in visually–degraded environments (e.g. smoke–filled tunnels, dust–filled mines) where exteroceptive sensing such as visual cameras and LiDAR becomes degraded and therefore reliable localization and mapping is not possible. A bioinspired alternative approach for resilient and collision–tolerant flight is proposed and only relies on inertial sensors, barometric readings, and force sensing on antennae integrated perimetrically to the robot's body. A reactive control scheme is implemented and enables operation within the collision–free space, as well as safe interaction with the environment objects and surfaces. Experimental results demonstrate the potential of the proposed approach especially in relation to operation within confined environments. Video-https://youtu.be/lInqtgIst8E
Data Prefetching for Heterogeneous Hadoop Cluster

Hadoop is an open source implementation of MapReduce. Performance of Hadoop is affected by the overhead of communication during the transmission of large datasets to the computing node. In a heterogeneous cluster if a map task wants to process the data, which is not present in the local disk then the data transmission overhead occurs. To overcome this issue, Data Prefetching for Heterogeneous Hadoop cluster for resource optimization is proposed. data prefetching is used to fetch the input data in advance from the remote node to a particular computing node. Hence transmission of data occurs in parallel with data processing and the job execution time is reduced. Different MapReduce jobs are used to conduct the experiment. The results demonstrate that the time taken to transmit the data is reduced. The job execution time is reduced by 15% for the input data size greater than or equal to 2GB and performance improvement of 25% is obtained for 64MB block size.
Penggunaan Media Kartu Angka Bergambar dalam Pembelajaran Anak Usia Dini Materi Pengenalan Angka dan Pengaruhnya Terhadap Motivasi Belajar

The role of learning media is very large in achieving learning messages, namely teaching material. Learning media can provide reinforcement of the explanations delivered by the teacher. Picture numeric card media is a visual media that can be used in learning or function as a learning media. The use of pictorial number card media in the Early Childhood Education (PAUD) learning is very suitable because it is relevant to the style of learning that is learning while playing. One kind of kindergarten learning material is the introduction of numbers and simple counting operations that are part of elementary level mathematics learning. Submission of numerical recognition learning materials and simple counting operations can be done using specially designed numeric card media. The use of multiple number card media in learning in this study is to see the effect on children's learning motivation. This type of research is quantitative causality, which is to see the effect of using numeric card media on children's learning motivation. Because of the quantitative type, this study uses statistical calculations through the SPSS version 22.0 program which includes a prerequisite test for normality and homogeneity, hypothesis testing with partial tests or different tests (T test). The results of this study indicate the influence of multiple number card media on early childhood learning motivation in numerical recognition learning materials and simple count operations Al-Mahrus Integrated Kindergarten, Siti Khotijah 1 PAUD and TK Labschool IKIP PGRI Jember even semester 2018/2019 academic year.