Reviewerly/exHarmony huggingface dataset:
Monitoring Wandering Behavior of Persons Suffering from Dementia Using BLE Based Localization System

With the aging of our populations, dementia will become a problem which would directly or indirectly affect a large number of people. One of the most dangerous dementia symptoms is wandering. It consists in aimless walking and spatial disorientation, which might lead to various unpleasant situations like falling down accidents at home to leaving the living place and going missing. Therefore, in order to ensure elderly people's safety it is crucial to detect and alarm the caregivers in case of such incidents. It can be done by tracking the sufferers movements and detecting signs of repetitiveness. The paper presents the results of the study, in which the wandering behavior of people suffering from dementia was monitored using a Bluetooth Low Energy based positioning system. The paper includes the description of the system used for patients localization and the results of the tests performed in a long term care facility.
Multi-label Connectionist Temporal Classification

The Connectionist Temporal Classification (CTC) loss function [1] enables end-to-end training of a neural network for sequence-to-sequence tasks without the need for prior alignments between the input and output. CTC is traditionally used for training sequential, single-label problems; each element in the sequence has only one class. In this work, we show that CTC is not suitable for multi-label tasks and we present a novel Multi-label Connectionist Temporal Classification (MCTC) loss function for multi-label, sequence-to-sequence classification. Multi-label classes can represent meaningful attributes of a single element; for example, in Optical Music Recognition (OMR), a music note can have separate duration and pitch attributes. Our approach achieves state-of-the-art results on Joint Handwritten Text Recognition and Name Entity Recognition, Asian Character Recognition, and OMR.
Exploring Resource-Efficient Acceleration Algorithm for Transposed Convolution of GANs on FPGA

In recent years, Generative Adversarial Networks (GANs) have been widely adopted for computer vision tasks such as generation/synthesis of massive images and 3D object modeling. The hardware acceleration of Transposed Convolution layers is especially essential since the Generative Model (Generator) as a critical component in GANs is computationally intensive in nature. In transposed Convolution, the zeros-inserting preprocessing causes sparsity of the feature maps and further results in many invalid operations. Most of the existing FPGA architectures cannot effectively tackle this issue. To address the challenges of implementing Transposed Convolution on FPGAs, we present an innovative dataflow design approach by applying the Winograd algorithm for fast processing with a high efficiency in terms of resource allocations. In addition, we propose an underlying Hardware Accelerator Architecture that features having PUs embedded in Parallel, Pipelined, and Buffered processing flow. In this paper, a parallelism-aware Memory Partition scheme is also exploded for bandwidth efficient data access. Implementations of several state-of-the-art GANs by our approach achieves an average performance of 639.2 GOPS on Xilinx ZCU102 FPGA device. In reference to an optimized conventional accelerator baseline, this work demonstrates an 8.6× (up to 11.7×) improvement in processing performance, compared to below 2.2× improvement by the other works in literature.
Explainable 3D convolutional neural network using GMM encoding

The aim of this paper is to propose a novel method to explain, interpret and support the decision-making process of deep Convolutional Neural Network (CNN). This is achieved by analysing neuron activations of trained 3D-CNN on selected layers via Gaussian Mixture Model (GMM) and custom binary encoding of both training and test images based on their activation’s affiliation to computed GMM components. Based on the similarity of encoded image representations, the system is able to retrieve most activation-wise similar atlas (training) images for given test image and therefore support and clarify its decision. Possible uses of this method include mainly Computer-Aided Diagnosis (CAD) systems working with medical imaging data such as magnetic resonance (MRI) or computed tomography (CT) scans. Network’s decision interpretation in the form of similar domain examples (images) is natural to the work-flow of the system’s operating medical personnel.
Exploiting polar grid structure and object shadows for fast object detection in point clouds

Based on PointPillars[1] and SECOND[2] we propose a novel LiDAR object detection system focused on speed, while not neglecting detection performance with the help of included occupancy grid features. We achieve this by replacing the voxelgrid defined in cartesian coordinates as introduced by Zhou et al. in VoxelNet[3] by a grid in a polar coordinate system. Doing this we can significantly reduce the number of required grid cells, while still keeping a good grid resolution in areas of highest point density close to the sensor. Because of this strong reduction on resolution we have a loss in performance which we try to regain with extending the feature network by adding occupancy grid and height map features. Furthermore we integrate the ground truth augmentation introduced in SECOND[2], injecting additional ground truth objects into the limited number of point clouds to increase variance. We achieve performance close to the state of the art, while reaching inference speeds around 12ms.
Nonlinear Equation Solving: A Faster Alternative to Feedforward Computation

Feedforward computations, such as evaluating a neural network or sampling from an autoregressive model, are ubiquitous in machine learning. The sequential nature of feedforward computation, however, requires a strict order of execution and cannot be easily accelerated with parallel computing. To enable parrallelization, we frame the task of feedforward computation as solving a system of nonlinear equations. We then propose to find the solution using a Jacobi or Gauss-Seidel fixed-point iteration method, as well as hybrid methods of both. Crucially, Jacobi updates operate independently on each equation and can be executed in parallel. Our method is guaranteed to give exactly the same values as the original feedforward computation with a reduced (or equal) number of parallel iterations. Experimentally, we demonstrate the effectiveness of our approach in accelerating 1) the evaluation of DenseNets on ImageNet and 2) autoregressive sampling of MADE and PixelCNN. We are able to achieve between 1.2 and 33 speedup factors under various conditions and computation models.
Leveraging Blockchain Technology to Break the Cloud Computing Market Monopoly

Cloud computing offerings traditionally originate from a handful of large and well established providers, which monopolize the market, preventing small players and individuals from having a share. As a result, the few, blindly and perforce trusted entities define the prices and manage to gain a significant competitive advantage by exploiting the knowledge derived by users’ data and computations. To tackle this monopoly and empower the democratization and full decentralization of the cloud computing market, we present CloudAgora, a platform that enables any potential resource provider, ranging from individuals to large companies, to monetize idle resources competing on equal terms, and allows any cloud consumer to enjoy access to low-cost storage and computation without having to trust any central authority. The key enabler of the platform is Blockchain technology, which is used to record commitment policies through the use of smart contracts, publicly verify off-chain services, both storage and computation related, and trigger automatic micropayments. On one hand, cloud consumers have the chance to request storage or compute resources, upload data, and outsource task processing over remote, fully distributed infrastructures. Although such infrastructures cannot be a priori trusted, CloudAgora offers mechanisms to ensure the verifiable validity of the outsourced storage and computation, discourage potential providers from behaving maliciously, and incentivize participants to play fair. On the other hand, providers are able to participate in auctions, placing bids for storage or computation tasks, serve requests, and offer validity proofs upon request. Our prototype is built as a Dapp on top of Ethereum and is available as an open source project.
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm

The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots. RNEA can be framed as a differentiable computational graph, enabling the dynamics parameters of the robot to be learned from data via modern auto-differentiation toolboxes. However, the dynamics parameters learned in this manner can be physically implausible. In this work, we incorporate physical constraints in the learning by adding structure to the learned parameters. This results in a framework that can learn physically plausible dynamics via gradient descent, improving the training speed as well as generalization of the learned dynamics models. We evaluate our method on real-time inverse dynamics control tasks on a 7 degree of freedom robot arm, both in simulation and on the real robot. Our experiments study a spectrum of structure added to the parameters of the differentiable RNEA algorithm, and compare their performance and generalization.
Guaranteed storage and stabilization of desired binary periodic orbits in three-layer dynamic binary neural networks

This paper presents three-layer dynamic binary neural networks characterized by ternary connection parameters and the signum activation function. The dynamics is described by a difference equation of binary state variables. Depending on the parameters, the network can generate various binary periodic orbits. We give two main theoretical results. First, when a desired periodic orbit is given, we can set the parameters that guarantee storage and local stability of the periodic orbit. The stability is related to error correction of various binary signals in engineering applications. Second, if a part of the connection parameters becomes zero then stability of the periodic orbit becomes very strong. In this case, all the initial states fall directly into the periodic orbit.
Object Detection as a Positive-Unlabeled Problem

As with other deep learning methods, label quality is important for learning modern convolutional object detectors. However, the potentially large number and wide diversity of object instances that can be found in complex image scenes makes constituting complete annotations a challenging task; objects missing annotations can be observed in a variety of popular object detection datasets. These missing annotations can be problematic, as the standard cross-entropy loss employed to train object detection models treats classification as a positive-negative (PN) problem: unlabeled regions are implicitly assumed to be background. As such, any object missing a bounding box results in a confusing learning signal, the effects of which we observe empirically. To remedy this, we propose treating object detection as a positive-unlabeled (PU) problem, which removes the assumption that unlabeled regions must be negative. We demonstrate that our proposed PU classification loss outperforms the standard PN loss on PASCAL VOC and MS COCO across a range of label missingness, as well as on Visual Genome and DeepLesion with full labels.
Hardware-Software Implementation of a McEliece Cryptosystem for Post-quantum Cryptography

This paper shows the implementation on FPGA of a McEliece cryptosystem, which ensures the security recommendations given by the European Telecommunications Standards Institute (ETSI) for next generation of quantum-resistant cryptosystems. The proposed implementation provides more than 128 bits of quantum security using a public key of 2,097,152 bytes. The proposed system is based on a hardware/software implementation that uses an ARM Cortex-A53 core connected to a coprocessor through an AX14 lite interface. The complete system was implemented on a Xilinx Zynq UltraScale+ and it is able to decipher texts of 8192 bit-length is 47.39 ms.
Adaptive ViBe Algorithm Based on Pearson Correlation Coefficient

The ViBe algorithm is a foreground detection algorithm based on background update. Its effect is better than most mainstream background modeling methods. It is robust to video jitter to a certain extent, but it will still affect the background when the video jitter is too large. When video jitter occurs due to external factors, we propose an algorithm based on the Pearson coefficient, which is an improvement on the traditional ViBe algorithm. Firstly, the offset of the video is calculated, and then the ViBe modeling is changed according to the offset of the video. The center point, which changes the range of ViBe pixel set, changes the sensitivity of the model, and gives a better background modeling effect when the video is jittered.
Facebook’s Libra: Big Bang or Big Crunch? A Technical Perspective and Challenges for Cryptocurrencies

Libra cryptocurrency is a new invention; as with any disruptive invention, Libra blockchain presents some risks. Facebook’s formal announcement of Libra had the “big bang” effect in the cryptocurrency markets; equally, the industry will witness “big crunch” if Libra sputters out resulting from a failure to receive all appropriate approvals from central banks, regulators, and law makers who are eager to run headlong into backlash to Libra in order to punish Facebook with a troubled past of privacy abuse and exploitation of users’ data. Libra’s unique features are distinctly different from Bitcoin and over 2,400 altcoins. Although Facebook claims Libra as a permissioned decentralized blockchain, but this is far cry from truth because Libra is a non-mineable cryptocurrency without the need of providing mathematical solutions to double spending problem. Under Bitcoin’s permissionless decentralized blockchain without a trusted third party, every new bitcoin is minted through a mining process which starts with a block, and then a ledger comprising timestamped transactions in a chronological order is distributed to all nodes (miners) in the Bitcoin network who check and validate transactions based on consensus before they are added to the end of each coin in its block. As a start, Libra blockchain will be governed by the Libra Association as a de facto central authority, which will initially comprise 28 private founding-members, each of which will act as a validator to ensure Libra’s stability. Libra is cost efficient in electricity usage and more consistent than other cryptocurrencies; Libra’s superior functionality, higher security, and vast scale due to its user base of nearly 3 billion will eventually make Libra become a viable alternative reserve crypto-currency to the dollar. Libra’s proof-of-stake algorithm supported by a new programming language “Move” plus the use of LibraBFT consensus make Libra 1/3 more secure than Bitcoin, Libra can function correctly even if 1/3 of its network fails or hacked. Libra’s PoS leads to higher transaction throughput, lower latency, and lower energy cost.
Investigation of Phase Pattern Modulation for Digital Fringe Projection Profilometry

Abstract The fringe projection profilometry with sinusoidal patterns based on phase-shifting algorithms is commonly distorted by the nonlinear intensity response of commercial projector. In order to solve this issue, sinusoidal width modulation is presented to generate binary sinusoidal patterns for defocusing the projection. However, the residual errors in the phase maps are usually notable for highly accurate three-dimensional shape measurements. In this paper, we propose the fringe patterns of the sinusoidal, square, and triangular periodic waveforms with seven-step phase-shifting algorithm to further improve the accuracy of three-dimensional profile reconstruction. The absolute phase values are calculated by using quality guided path unwrapping. We learn that by properly selecting fringe patterns according to the target shape, the undesired harmonics of the measured surface have negligible effect on the phase values. The experiments are presented to verify the imaging performances of three fringe patterns for different testing targets. The triangular fringe patterns are suitable for the shape measurements of complex targets with curved surfaces. The results provide a great possibility for high-accuracy shape measurement technique with wider measuring depth range.
Deep Learning Architectures for Named Entity Recognition: A Survey

Extracting named entities from natural language text is an important task in natural language processing, with applications in sentiment analysis, information retrieval, and answer selection in question answering. For identifying named entities, many methods have been developed ranging from knowledge-based methods to supervised machine learning methods. In recent years, deep learning models have achieved cutting-edge results in language processing tasks, particularly in Named Entity Recognition (NER). NER aims to locate and categorize proper names in natural language text into predefined classes such as people, organization, location names, etc. In this paper, we provide a comprehensive survey on existing deep learning architectures used in the CoNLL-2003 NER task. We first introduce the basic deep learning models employed in the NER task followed by reviewing the prominent deep learning NER architectures. Furthermore, the present study throws light upon top factors impacting NER performance which includes the design choices of deep-learning-based NER architecture, and the significance of incorporating character-level information, additional lexical features, external training data, etc. while training the NER model.
High-Fidelity Point-Based Rendering of Large-Scale 3-D Scan Datasets

Digitalization of three-dimensional (3-D) objects and scenes using modern depth sensors and high-resolution RGB cameras enables the preservation of human cultural artifacts at an unprecedented level of detail. Interactive visualization of these large datasets, however, is challenging without degradation in visual fidelity. A common solution is to fit the dataset into available video memory by downsampling and compression. The achievable reproduction accuracy is thereby limited for interactive scenarios, such as immersive exploration in virtual reality (VR). This degradation in visual realism ultimately hinders the effective communication of human cultural knowledge. This article presents a method to render 3-D scan datasets with minimal loss of visual fidelity. A point-based rendering approach visualizes scan data as a dense splat cloud. For improved surface approximation of thin and sparsely sampled objects, we propose oriented 3-D ellipsoids as rendering primitives. To render massive texture datasets, we present a virtual texturing system that dynamically loads required image data. It is paired with a single-pass page prediction method that minimizes visible texturing artifacts. Our system renders a challenging dataset in the order of 70 million points and a texture size of 1.2 TB consistently at 90 frames per second in stereoscopic VR.
IT Security for Measuring Instruments: Confidential Checking of Software Functionality

Legally supervised measuring instruments, like supermarket scales or utility meters for the supply of electricity, to name just a few, need to be checked in most countries. In this regard, smart meters are a fitting example for distributed systems that need to fulfill many IT security requirements. It is of utterly importance to make sure that the functionality of these measuring devices is preserved, with the goal to enhance trust in the market, protect the consumer of fraud, and preserve privacy. Normally, legally controlled measuring devices are checked before commissioning by so-called Notified Bodies, and afterwards cyclically by market surveillance officers. The hardware is scrutinized by manually testing the sensors. This paper looks more closely at the software testing aspect and highlights how current methods can be enhanced to check correct software functionality. We describe alternatives that will pave the way to a more secure and trustworthy market, which additionally, grants more flexibility to patch software bugs without the need for recertification, as long as the core functionality of the device remains the same. In our framework the functionality checking can be done automatically, while preserving confidentiality on all ends. Based on this framework, it is no problem to allow remote displays, e.g., smartphones, or, a completely distributed measuring instrument, e.g., with many sensors in different locations connected over the Internet. Our approach is of general nature, but perhaps most interesting for smart meter infrastructures.
LGBTQI+ icons between resistance and normalization: looking for mediatization of emotions in hashtags

The mediatization of emotions emerges as an affordance of social media, the study of which involves paying attention to digital practices and to the construction and expression of public affection. This happens both for the great events and for the daily demonstrations of support or its denial. In this article we work on the phenomenon of the mediatization of emotions linked to two LGBTQI+ icons and expressed in hashtags on Twitter. Placing it in a specific context – the one of well-known television characters who have declared their homosexual orientation or transgender identity. The objective is to understand if the cloud of feelings they have created on Twitter is to be attributed to a true globally mediatized emotional exchange, or just an expression of emotions on the social media, and discover which emotional dynamics, linked to the LGBTQI+ world, they express.
Federated Recommendation Systems

Despite its great progress so far, artificial intelligence (AI) is facing a serious challenge in the availability of high-quality Big Data. In many practical applications, data are in the form of isolated islands. Efforts to integrate the data are increasingly difficult partly due to serious concerns over user privacy and data security. The problem is exacerbated by strict government regulations such as Europe's General Data Privacy Regulations (GDPR). In this talk, I will review these challenges and describe efforts to address them in recommendation systems area. In particular, I will give an overview of recent advances in federated learning and then focus on developments of “federated recommendation systems”, which aims to build high-performance recommendation systems by bridging data repositories without compromising data security and privacy.
Analysis and Prediction of Customers’ Reviews with Amazon Dataset on Products

The main objective of this paper is to get a deeper knowledge of the text classification methods used in text mining. This paper describes different methods and algorithms used in text mining. Various text preprocessing steps have been performed like tokenization, case folding, stemming, stopword removal, etc. The customer reviews posted in the amazon website have been used as the training set and used with various classifiers like Naive Bayes, KNN, random forest and decision tree. The performance parameter of each method is determined with standard evaluation parameters such as precision, recall, and kappa measures. The results show that K-nearest neighbor method gives the optimal performance with the same dataset.