Reviewerly/exHarmony huggingface dataset:
Investigations on collaborative remote control of virtual robotic manipulators by using a Kinect v2 sensor

The present work deals with design aspects and testing results of a novel solution regarding the remote control of two virtual robotic manipulators by using hands motion tracking and palms gesture recognition. The spatial position and orientation of the user's left and right hands' wrists are interpreted by a custom built software application and then forwarded as positioning data and gripper status to a V-Rep server running the simulation scene. The goal of the presented setup is for the user to be able to manipulate objects in the scene in a collaborative way, i.e. pick one object with the first robot and give it to the second one for elsewhere depositing. The real-life applications for this approach are to provide a framework for simplified operator training and programming of robots used in pick-and-place or assembly operations, and also in healthcare for assisting patients in hand -coordination and mobility recovery.
VLX-Stories: Building an Online Event Knowledge Base with Emerging Entity Detection

We present an online multilingual system for event detection and comprehension from media feeds. The system retrieves information from news sites, aggregates them into events (event detection), and summarizes them by extracting semantic labels of its most relevant entities (event representation) in order to answer the journalism Ws: who, what, when and where. The generated events populate VLX-Stories -an event ontology- transforming unstructured text data to a structured knowledge base representation. Our system exploits an external entity Knowledge Graph (VKG) to help populate VLX-Stories. At the same time, this external knowledge graph can also be extended with a Dynamic Entity Linking (DEL) module, which detects emerging entities (EE) on unstructured data. The system is currently deployed in production and used by media producers in the editorial process, providing real-time access to breaking news. Each month, VLX-Stories detects over 9000 events from over 4000 news feeds from seven different countries and in three different languages. At the same time, it detects over 1300 EE per month, which populate VKG.
Efficient and Robust Non-Local Means Denoising Methods for Biomedical Images

Denoising is an important step to improve image quality and to increase the performance of image analysis. However, conventional partial differential equation based image denoising methods, especially total variation functional minimization techniques, do not work very well on biomedical images such as magnetic resonance images (MRI), ultrasound, and X-ray images. These images present small structures with signals barely detectable above the noise level which involve more complex noise and unclear edges. The recently developed non-local means (NLM) filtering method can treat these types of images better. The standard NLM filter uses the weighted averages of similar patches present in the image. Since the NLM filter is anon-local averaging method, it is very accurate in removing noise but has computational complexity. We develop efficient and optimized NLM based methods and their associate numerical algorithms. The new methods are still accurate enough and moreeffi-cient than the original NLM filter. Numerical results show that the new methods compare favorably to the conventional denoising methods.
Separable Reversible Data Hiding in Encrypted Image Based on Two-Dimensional Permutation and Exploiting Modification Direction

In this paper, we propose a separable reversible data hiding method in encrypted image (RDHEI) based on two-dimensional permutation and exploiting modification direction (EMD). The content owner uses two-dimensional permutation to encrypt original image through encryption key, which provides confidentiality for the original image. Then the data hider divides the encrypted image into a series of non-overlapping blocks and constructs histogram of adjacent encrypted pixel errors. Secret bits are embedded into a series of peak points of the histogram through EMD. Direct decryption, data extraction and image recovery can be performed separately by the receiver according to the availability of encryption key and data-hiding key. Different from some state-of-the-art RDHEI methods, visual quality of the directly decrypted image can be further improved by the receiver holding the encryption key. Experimental results demonstrate that the proposed method outperforms some state-of-the-art methods in embedding capacity and visual quality.
Reverse derivative categories

The reverse derivative is a fundamental operation in machine learning and automatic differentiation. This paper gives a direct axiomatization of a category with a reverse derivative operation, in a similar style to that given by Cartesian differential categories for a forward derivative. Intriguingly, a category with a reverse derivative also has a forward derivative, but the converse is not true. In fact, we show explicitly what a forward derivative is missing: a reverse derivative is equivalent to a forward derivative with a dagger structure on its subcategory of linear maps. Furthermore, we show that these linear maps form an additively enriched category with dagger biproducts.
Adaptive Stream-based Shifting Bottleneck Detection in IoT-based Computing Architectures

Cloud computing is revolutionizing the backbone of data analysis applications, including industrial ones. One of its main pillars is the separation of the logic with which data is accessed (e.g., to study the efficiency of a manufacturing system) from the actual hardware (e.g., server) that maintains and analyses the data. Large distributed cyber-physical systems enabled by, among other technologies, the Internet of Things (IoT), made nonetheless clear that “what to do” with the data and “where to do it” are not disjoint problems; i.e., cloud computing on its own is not enough. Fog and edge computing have emerged as complementary options, to distribute the analysis, helping with challenges by means of close-to-the-source data analysis.We show for a key problem for industrial processes, that of shifting bottleneck detection, how to take advantage of such multi-tier computing architectures, to perform continuous and configurable analysis of data from Manufacturing Execution Systems. We propose a processing framework, STRATUM, and an algorithm, AMBLE, for continuous, data stream processing. STRATUM seamlessly distributes and parallelizes the processing across the tiers and AMBLE guarantees consistent analysis in spite of timing fluctuations, which are commonly introduced due to e.g. the communication system; it also achieves efficiency through appropriate data structures for in-memory processing. The experimental study on a real-world dataset, taken from a production line over two years and including 8.5 million entries, shows the benefits of the proposed solution in enabling configurable and efficient analysis.
Popularity-Driven Ontology Ranking Using Qualitative Features

Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies’ occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies’ popularity, resulting in ineffective ranking scores. This motivated us to investigate – based on the problematic WoT case – whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature. We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies’ popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.
RTFM: Generalising to Novel Environment Dynamics via Reading

Obtaining policies that can generalise to new environments in reinforcement learning is challenging. In this work, we demonstrate that language understanding via a reading policy learner is a promising vehicle for generalisation to new environments. We propose a grounded policy learning problem, Read to Fight Monsters (RTFM), in which the agent must jointly reason over a language goal, relevant dynamics described in a document, and environment observations. We procedurally generate environment dynamics and corresponding language descriptions of the dynamics, such that agents must read to understand new environment dynamics instead of memorising any particular information. In addition, we propose txt2$π$, a model that captures three-way interactions between the goal, document, and observations. On RTFM, txt2$π$ generalises to new environments with dynamics not seen during training via reading. Furthermore, our model outperforms baselines such as FiLM and language-conditioned CNNs on RTFM. Through curriculum learning, txt2$π$ produces policies that excel on complex RTFM tasks requiring several reasoning and coreference steps.
Segmentation, Detection, and Classification of Liver Tumors for Designing a CAD System

Globally cancer is the foremost threat to public health. Out of the world population, the deaths caused by liver cancer are increasing by 3% every year. Liver tumors are the pathological disorders which can be detected with the help of various image processing methods. A Computer-Aided Diagnosis (CAD) system use image processing tools and techniques for detecting liver tumors which acts as an assistance to the radiologists, oncologists, and hepatologists for effective diagnosis. The main objective of this survey is to analyze the available techniques that can aid in developing or designing a CAD system for liver tumors. Various methods and outcome of available techniques for segmentation, detection and classification of liver tumors from Computed Tomography (CT) or Dynamic Contrast-Enhanced Magnetic Resonance (DCE-MR) images are discussed and compared in detail.
Library as «the third place»: realization of the concept

The concept of «third place», formulated by R. Oldenburg, can be used as one of the tools for a library transformation. «The third place» is a multifunctional public space, that provides opportunities for intellectual and cultural communication, creativity, self-development and self-expression, a place where one wants to be and wants to return («the first place» according to this concept is home, «the second place» – work or study). The purpose of this article is to consider the concept of «the third place» in the context of its realization in the library and information sphere in Tumen (Russia). Various relevant documents regulating the work of libraries (first of all – public libraries) contain positions confirming the interrelation of library and information activity with the concept of «the third place». Many characteristics of «the third places» («humanitarian», technical and technological, etc.) determine the direction of work of libraries at the modern stage. For the study several similar characteristics were chosen: accessibility, locality; openness to all social categories; the possibility for leisure, entertainment; comfort, ease of interaction. The method of survey with elements of the semantic differential was used. The respondents were the users of the Information and library center for youth (Tyumen, Russia). The research was to identify the degree of severity of these characteristics in library. The results of survey analysis allow the confirmation that the activity of the Information and library center for youth corresponds to the characteristics of «the third place». Librarians work on their integration and development constantly. Also they preserve the major, document-information, content of activity of libraries. The concept of «the third place» is not the only option for the development of each library, but it provides the possibility of using a certain set of conceptual characteristics as a working tool for library and information activity.
Deploy a Containerized Application with Amazon EKS

In Chapter 3 , I covered AWS ECS and ECS Fargate and showed how to deploy both Linux and Windows containers.
Mining Significant Maximum Cardinalities in Knowledge Bases

Semantic Web connects huge knowledge bases whose content has been generated from collaborative platforms and by integration of heterogeneous databases. Naturally, these knowledge bases are incomplete and contain erroneous data. Knowing their data quality is an essential long-term goal to guarantee that querying them returns reliable results. Having cardinality constraints for roles would be an important advance to distinguish correctly and completely described individuals from those having data either incorrect or insufficiently informed. In this paper, we propose a method for automatically discovering from the knowledge base’s content the maximum cardinality of roles for each concept, when it exists. This method is robust thanks to the use of Hoeffding’s inequality. We also design an algorithm, named C3M, for an exhaustive search of such constraints in a knowledge base benefiting from pruning properties that drastically reduce the search space. Experiments conducted on DBpedia demonstrate the scaling up of C3M, and also highlight the robustness of our method, with a precision higher than 95%.
Functional Tensors for Probabilistic Programming

It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework. Noting that the versatility of modern automatic differentiation frameworks is based in large part on the unifying concept of tensors, we describe a software abstraction for integration --functional tensors-- that captures many of the benefits of tensors, while also being able to describe continuous probability distributions. Moreover, functional tensors are a natural candidate for generalized variable elimination and parallel-scan filtering algorithms that enable parallel exact inference for a large family of tractable modeling motifs. We demonstrate the versatility of functional tensors by integrating them into the modeling frontend and inference backend of the Pyro programming language. In experiments we show that the resulting framework enables a large variety of inference strategies, including those that mix exact and approximate inference.
Evolution of transfer learning in natural language processing.

In this paper, we present a study of the recent advancements which have helped bring Transfer Learning to NLP through the use of semi-supervised training. We discuss cutting-edge methods and architectures such as BERT, GPT, ELMo, ULMFit among others. Classically, tasks in natural language processing have been performed through rule-based and statistical methodologies. However, owing to the vast nature of natural languages these methods do not generalise well and failed to learn the nuances of language. Thus machine learning algorithms such as Naive Bayes and decision trees coupled with traditional models such as Bag-of-Words and N-grams were used to usurp this problem. Eventually, with the advent of advanced recurrent neural network architectures such as the LSTM, we were able to achieve state-of-the-art performance in several natural language processing tasks such as text classification and machine translation. We talk about how Transfer Learning has brought about the well-known ImageNet moment for NLP. Several advanced architectures such as the Transformer and its variants have allowed practitioners to leverage knowledge gained from unrelated task to drastically fasten convergence and provide better performance on the target task. This survey represents an effort at providing a succinct yet complete understanding of the recent advances in natural language processing using deep learning in with a special focus on detailing transfer learning and its potential advantages.
A new design paradigm for provably secure keyless hash function with subsets and two variables polynomial function

Provably secure keyless hash function uses Random Oracle (RO) or Sponge principles for the design and construction of security-centric hash algorithms. It capitalizes the aforesaid principles to produce outcomes like MD2, MD5, SHA-160, SHA-224/256, SHA-256, SHA-224/512, SHA-256/512, SHA-384/512, SHA-512, and SHA-3. These functions use bitwise AND, OR, XOR, and MOD operators to foresee randomness in their hash outputs. However, the partial breaking of SHA2 and SHA3 families and the breaking of MD5 and SHA-160 algorithms raise concerns on the use of bitwise operators at the block level. The proposed design tries to address this structural flaw through a polynomial function. A polynomial function of degree 128 demands arduous effort to be decoded in the opposing direction. The application of a polynomial on the blocks produces an unpredictable random response. It is a fact that the new design exhibits the merits of the polynomial function on subsets to achieve the avalanche response to a significant level. The output from experiments with more than 24 Million hash searches proves the proposed system is a provably secure hash function. The experiments on avalanche response and confusion and diffusion analysis prove it is an apt choice for security-centric cryptographic applications.
Spectral Discrimination of Archaeological Sites Previously Occupied by Farming Communities Using In Situ Hyperspectral Data

This study investigates the ability of field spectra measurements to discriminate between soils from non-sites (natural soils) and from archaeological sites, such as middens (rubbish-dumping areas) and animal byres. First, we tested whether there is a difference in the concentration of elements between different soil types using analysis of variance while random forest (RF) and forward variable selection (FVS) methods were used to select important soil elements for the classification of the archaeological sites. In the second approach, we evaluated the ability of field spectroscopy reflectance measurements to discriminate among nonsites, middens, vitrified dung, and nonvitrified dung byres. The guided regularised random forest (GRRF) was used to identify important wavelengths for the discrimination of abovementioned archaeological and nonarchaeological soils. Thereafter, the selected soil elements and wavelengths were used as input variables in the RF classification algorithm to classify the nonsites, middens, vitrified dung, and nonvitrified dung. The findings reveal that there is a significant difference in the composition of chemical elements and spectral signatures of nonsites, middens, vitrified dung, and nonvitrified dung. In summary, high classification accuracies achieved when using field spectroscopy data prove that remote sensing techniques can be used to exploit the spectral differences among the abovementioned soil types in mapping archaeological feature characteristics of farming communities’ settlements.
Development of a Real-time Bionic Voice Generation System based on Statistical Excitation Prediction

Despite the emergent progress in many fields of bionics, larynx amputees still lack a functional Bionic Voice source to overcome their voice disability. We have established the Pneumatic Bionic Voice (PBV) as a promising technology to generate a voice for larynx amputees. This summary provides an overview of our efforts to implement the PBV system in real-time together with a demonstration of its results. The PBV is an electronic adaptation of an old school mechanical voice source called the Pneumatic Artificial Larynx (PAL). The PAL is a non-invasive voice prosthesis, with an exceptionally high-quality voice, driven exclusively by respiration. This work proposes an approach to statistically model the PAL's voice generation mechanism from the respiration and to implement it in a real-time PBV system that larynx amputees can use to generate voice in continuous speech.
Towards Millimeter-Wave Based Quantum Networks

Realization of quantum networks composed of superconducting microwave circuits acting as qubits recently gained tremendous attention as an effective way of scaling quantum computers. Here we present an experimental approach to fabricate mm-wave superconducting resonators that could be combined with transmon qubits and used in future microwave-mm-wave converters that distribute entanglement at a high rate in low-loss quantum networks. We propose a method that facilitates a long-range spread of quantum information via direct coupling of such a device into the W-band (75-110 GHz) waveguide.
Reproducible Experiments on Adaptive Discriminative Region Discovery for Scene Recognition

This companion paper supports the replication of scene image recognition experiments using Adaptive Discriminative Region Discovery (Adi-Red), an approach presented at ACM Multimedia 2018. We provide a set of artifacts that allow the replication of the experiments using a Python implementation. All the experiments are covered in a single shell script, which requires the installation of an environment, following our instructions, or using ReproZip.The data sets (images and labels) are automatically downloaded, and the train-test splits used in the experiments are created. The first experiment is from the original paper, and the second supports exploration of the resolution of the scale-specific input image, an interesting additional parameter. For both experiments, five other parameters can be adjusted: the threshold used to select the number of discriminative patches, the number of scales used, the type of patch selection (Adi-Red, dense or random), the architecture and pre-training data set of the pre-trained CNN feature extractor. The final output includes four tables (original Table 1, Table 2 and Table 4, and a table for the resolution experiment) and two plots (original Figure 3 and Figure 4).
Recognizing long-form speech using streaming end-to-end models

All-neural end-to-end (E2E) automatic speech recognition (ASR) systems that use a single neural network to transduce audio to word sequences have been shown to achieve state-of-the-art results on several tasks. In this work, we examine the ability of E2E models to generalize to unseen domains, where we find that models trained on short utterances fail to generalize to long-form speech. We propose two complementary solutions to address this: training on diverse acoustic data, and LSTM state manipulation to simulate long-form audio when training using short utterances. On a synthesized long-form test set, adding data diversity improves word error rate (WER) by 90% relative, while simulating long-form training improves it by 67% relative, though the combination doesn't improve over data diversity alone. On a real long-form call-center test set, adding data diversity improves WER by 40% relative. Simulating long-form training on top of data diversity improves performance by an additional 27% relative.