Reviewerly/exHarmony huggingface dataset:
One wheel vehicle real world control based on interval type 2 fuzzy controller

In this study, a novel adaptive interval type-2 fuzzy controller (AIT2FC) is used for a single-wheel vehicle (SWV) control problem. A control technique for use by a person riding on an SWV that enables real-world balance control of electric unicycles is proposed. The proposed SWV can keep balance and move around. Moreover, it can also stand up stably in any position when a person rides on it without touching the ground. AIT2FC is an advanced version of the traditional Mamdani-like fuzzy controller. The AIT2FC parameters are adjusted online using an intelligent dynamic tuning method. Moreover, the parameter learning rates of the adaptive laws are determined using a stability algorithm to guarantee the system error convergence. Furthermore, an SWV based on a microcontroller with some handmade hardware circuits is implemented. Finally, the efficiency of the AIT2FC is proved by real-time control of SWVs.
A <scp>co‐training</scp>‐based approach for the hierarchical multi‐label classification of research papers

Abstract This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.
Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction

Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features learned to generate relation triples from the open information extraction (OIE) task. First, we studied how transferable these features are from one OIE domain to another, such as from a news domain to a bio-medical domain. Second, we analyzed their transferability to a semantically related NLP task, namely, relation extraction (RE). We thereby contribute to answering the question: can OIE help us achieve adequate NLP performance without labelled data? Our results showed comparable performance when using inductive transfer learning in both experiments by relying on a very small amount of the target data, wherein promising results were achieved. When transferring to the OIE bio-medical domain, we achieved an F-measure of 78.0%, only 1% lower when compared to traditional learning. Additionally, transferring to RE using an inductive approach scored an F-measure of 67.2%, which was 3.8% lower than training and testing on the same task. Hereby, our analysis shows that OIE can act as a reliable source task.
Digital Forensic Analysis of Fitbit Wearable Technology: An Investigator’s Guide

Wearable technology, such as Fitbit devices, log a user’s daily activities, heart rate, calories burned, step count, and sleep activity. This information is valuable to digital forensic investigators as it may serve as evidence to a crime, to either support a suspect’s innocence or guilt. It is important for an investigator to find and analyze every piece of data for accuracy and integrity; however, there is no standard for conducting a forensic investigation for wearable technology. In this paper, we conduct a forensic analysis of two different Fitbit devices using open-source tools. It is the responsibility of the investigator to show how the data was obtained and to ensure that the data was not modified during the analysis. This paper will guide investigators in understanding what data is collected by a Fitbit device (specifically the Ionic smartwatch and Alta tracker), how to handle Fitbit devices, and how to extract and forensically analyze said devices using open-source tools, Autopsy Sleuth Kit and Bulk Extractor Viewer.
Variational approach to time-dependent fluorescence of a driven qubit

We employ the Dirac-Frenkel variational principle and the multiple Davydov ansatz to study time-dependent fluorescence spectra of a driven qubit in the weak to strong qubit-reservoir coupling regimes, where both the Rabi frequency and the spontaneous decay rate are comparable to the transition frequency of the qubit. Our method agrees well with the time-local master-equation approach in the weak coupling regime, and offers a flexible way to compute the spectra from the bosonic dynamics instead of two-time correlation functions. While the perturbative master equation breaks down in the strong coupling regime, our method actually becomes more accurate due to the use of bosonic coherent states under certain conditions. We show that the counter-rotating coupling between the qubit and the reservoir has considerable contributions to the photon number dynamics and the spectra under strong driving conditions even when the coupling is moderately weak. The time-dependent spectra are found to be generally asymmetric, a feature that is derived from photon number dynamics. In addition, it is shown that the spectral profiles can be dramatically different from the Mollow triplet due to strong dissipation and/or multiphoton processes associated with the strong driving. Our formalism provides a unique perspective to interpret time-dependent spectra.
Pairwise Link Prediction Model for Out of Vocabulary Knowledge Base Entities

Real-world knowledge bases such as DBPedia, Yago, and Freebase contain sparse linkage connectivity, which poses a severe challenge to link prediction between entities. To cope with such data scarcity issues, recent models have focused on learning interactions between entity pairs by means of relations that exist between them. However promising, some relations are associated with very few tail entities or head entities, resulting in poor estimation of the relation interaction between entities. In this article, we break the sole dependency of modeling relation interactions between entity pairs by associating a triple with pairwise embeddings, i.e., distributed vector representations for pairs of word-based entities and relation of a triple. We capture the interactions that exist between pairwise embeddings by means of a Pairwise Factorization Model that employs a factorization machine with relation attention. This approach allows parameters for related interactions to be estimated efficiently, ensuring that the pairwise embeddings are discriminative, providing strong supervisory signals for the decoding task of link prediction. The Pairwise Factorization Model we propose exploits a neural bag-of-words model as the encoder, which effectively encodes word-based entities into distributed vector representations for the decoder. The proposed model is simple and enjoys efficiency and capability, showing superior link prediction performance over state-of-the-art complex models on benchmark datasets DBPedia50K and FB15K-237.
ResDUnet: Residual Dilated UNet for Left Ventricle Segmentation from Echocardiographic Images

Echocardiography is the modality of choice for the assessment of left ventricle function. Left ventricle is responsible for pumping blood rich in oxygen to all body parts. Segmentation of this chamber from echocardiographic images is a challenging task, due to the ambiguous boundary and inhomogeneous intensity distribution. In this paper we propose a novel deep learning model named ResDUnet. The model is based on U-net incorporated with dilated convolution, where residual blocks are employed instead of the basic U-net units to ease the training process. Each block is enriched with squeeze and excitation unit for channel-wise attention and adaptive feature re-calibration. To tackle the problem of left ventricle shape and size variability, we chose to enrich the process of feature concatenation in U-net by integrating feature maps generated by cascaded dilation. Cascaded dilation broadens the receptive field size in comparison with traditional convolution, which allows the generation of multi-scale information which in turn results in a more robust segmentation. Performance measures were evaluated on a publicly available dataset of 500 patients with large variability in terms of quality and patients pathology. The proposed model shows a dice similarity increase of 8.4% when compared to deeplabv3 and 1.2% when compared to the basic U-net architecture. Experimental results demonstrate the potential use in clinical domain.
Suppressing excess noise for atmospheric continuous-variable quantum key distribution via adaptive optics approach

Abstract The excess noise inducing in the process of the quantum communication procedure is the major obstacle restricting the performance of continuous-variable quantum key distribution (CVQKD). In order to effectively suppress the excess noise through correcting the propagation-induced distortions on the quality of the propagated quantum signal, we propose a general scheme of suppressing excess noise for CVQKD via adaptive optics (AO) approach. The analysis shows that phase-only AO compensation exhibits excellent performance in controlling the excess noise, which is embodied in substantially extending the secure propagation distance and improving the secret key rate of the system. And thereby the development and improvement of AO has the potential advantage to break the distance constraints due to the excess noise results from propagation-dominated factors. Our scheme provides a feasible method for further implementation of practical large-scale CVQKD.
Video Fraud Detection using Blockchain

This paper has considered the problem of Video Fraudulence, i.e., attackers can tamper with the original video and can create a fake video of their own. This problem is of great practical importance given the massive volume of online videos available through the World Wide Web, Internet news feeds, electronic mail, corporate databases, and digital libraries. To the best of our knowledge, no such video fraud detection algorithm has been proposed in the literature that can detect, using Blockchain, whether the video has been tampered with. This paper is a comparative study and provides a prototype of how it applies Blockchain to detect video fraudulence. The focus is on the usability of Blockchain and how to implement it to get the desired result. Three features of Blockchain that are Decentralization, Data transparency, and Security and privacy are being used to provide a reliable solution. Some cryptographic algorithms are used to find unique feature in all of the videos that can act as the hash value of the video because, in Blockchains, every node stores the data in the form of the hash value. If the video is tampered with, then the hash value changes and hence any fraud in the video can be detected.
Matching logic explained

Matching logic was recently proposed as a unifying logic for specifying and reasoning about static structure and dynamic behavior of programs. In matching logic, patterns and specifications are used to uniformly represent mathematical domains (such as numbers and Boolean values), datatypes, and transition systems, whose properties can be reasoned about using one fixed matching logic proof system. In this paper we give a tutorial of matching logic. We use a suite of examples to explain the basic concepts of matching logic and show how to capture many important mathematical domains, datatypes, and transition systems using patterns and specifications. We put emphasis on the general principles of induction and coinduction in matching logic and show how to do inductive and coinductive reasoning about datatypes and codatatypes. To encourage the future tools development for matching logic, we propose and use throughout the paper a human-readable formal syntax to write specifications in a modular and compact way.
UCMH: Unpaired cross-modal hashing with matrix factorization

In recent years, hashing based cross-modal retrieval methods have attracted considerable attention, due to the significant reduction in computational cost and storage consumption. Most previous cross-modal hashing methods usually assume that data examples in different modalities are fully-paired. However, they neglect the fact that the data are often unpaired without one-to-one corresponding relationships in practical applications. Though several methods have noted the semi-paired or partial paired scenario, they ignore the completely unpaired scenario. In this paper, we propose a novel cross-modal hashing method, named Unpaired Cross-Modal Hashing (UCMH) for cross-modal retrieval to address the data with completely unpaired relationships. It leverages matrix factorization, similarity preservation, and semantic information to map data of different modalities to their respective semantic spaces. Moreover, different from most previous approaches, we construct an affinity matrix to bridge the semantic gap of data in different semantic spaces, which allows our method to handle single-label and multi-label unpaired cases simultaneously. Extensive experiments on one single-label dataset Wiki and two multi-label datasets namely MIR Flickr and NUS-WIDE, demonstrate that UCMH outperforms the state-of-the-art cross-modal hashing methods.
Conceptualising, extracting and analysing requirements arguments in users' forums: The CrowdRE‐Arg framework

Abstract Due to the pervasive use of online forums and social media, users' feedback are more accessible today and can be used within a requirements engineering context. However, such information is often fragmented, with multiple perspectives from multiple parties involved during on‐going interactions. In this paper, the authors propose a Crowd‐based Requirements Engineering approach by Argumentation (CrowdRE‐Arg). The framework is based on the analysis of the textual conversations found in user forums, identification of features, issues and the arguments that are in favour or opposing a given requirements statement. The analysis is to generate an argumentation model of the involved user statements, retrieve the conflicting‐viewpoints, reason about the winning‐arguments and present that to systems analysts to make informed‐requirements decisions. For this purpose, the authors adopted a bipolar argumentation framework and a coalition‐based meta‐argumentation framework as well as user voting techniques. The CrowdRE‐Arg approach and its algorithms are illustrated through two sample conversations threads taken from the Reddit forum. Additionally, the authors devised algorithms that can identify conflict‐free features or issues based on their supporting and attacking arguments. The authors tested these machine learning algorithms on a set of 3,051 user comments, preprocessed using the content analysis technique. The results show that the proposed algorithms correctly and efficiently identify conflict‐free features and issues along with their winning arguments.
Efficient Protocols for Oblivious Linear Function Evaluation from Ring-LWE

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function $$f(x) = ax + b$$ , where a sender inputs the field elements a, b, and a receiver inputs x and learns f(x). OLE can be used to build secret-shared multiplication, and is an essential component of many secure computation applications including general-purpose multi-party computation, private set intersection and more. In this work, we present several efficient OLE protocols from the ring learning with errors (RLWE) assumption. Technically, we build two new passively secure protocols, which build upon recent advances in homomorphic secret sharing from (R)LWE (Boyle et al., Eurocrypt 2019), with optimizations tailored to the setting of OLE. We upgrade these to active security using efficient amortized zero-knowledge techniques for lattice relations (Baum et al., Crypto 2018), and design new variants of zero-knowledge arguments that are necessary for some of our constructions. Our protocols offer several advantages over existing constructions. Firstly, they have the lowest communication complexity amongst previous, practical protocols from RLWE and other assumptions; secondly, they are conceptually very simple, and have just one round of interaction for the case of OLE where b is randomly chosen. We demonstrate this with an implementation of one of our passively secure protocols, which can perform more than 1 million OLEs per second over the ring $$\mathbb {Z}_m$$ , for a 120-bit modulus m, on standard hardware.
ZyConChain: A Scalable Blockchain for General Applications

Blockchain's popularity has seen a historic rise over the last decade. However, existing blockchain systems have a major issue with scalability, which has become one of the main obstacles in technology's adoption in mainstream. There have been several attempts to address this limitation by identifying Blockchain's scalability/performance bottlenecks (e.g. those mainly related to consensus algorithms), and thus proposed different solutions (e.g., new consensus protocols) to address such limitations. Other works applied sharding to tackle the issue. All solutions however have mainly focused on Cryptocurrency applications, and thus addressing the scalability of blockchain systems for general applications remains a concern. This work proposes a scalable blockchain protocol for general applications (i.e., not restricted to Cryptocurrencies). To improve the two major factors affecting transaction scalability, namely throughput and latency, we needed to modify both the blockchain structure as well as the block generation process. ZyConChain, the proposed Blockchain system, introduces three types of blocks that form three separate chains: parentBlock, sideBlock and state block. These blocks are generated based on different consensus algorithms, as each algorithm has specific properties that make it suitable for each type of block. To improve the overall performance, ZyConChain generates sideBlocks (that carry transactions) at a high rate and keep them in a pool. To generate parentBlock, miners, instead of packing transactions into a block as they do in conventional blockchains, pack sideBlocks into a parentBlock. SideBlocks are generated based on an adapted Zyzzyva consensus protocol, with O(\log n) complexity. This has reduced the final consensus complexity per transaction, in comparison to previous work. To enable the protocol to scale out with the increase in the number of nodes, ZyConChain applied sharding technique. Parallel state chains have also been introduced to address cross-shard transactions.
One-dimension hierarchical local receptive fields based extreme learning machine for radar target HRRP recognition

Radar automatic target recognition (RATR) aims at extracting meaningful target features from the electromagnetic echo signal and utilizing the features to automatically recognize the target types. The high-resolution range profile (HRRP) plays an important role in RATR field, HRRP is the amplitude of the echo summation for target scattering centers in each range cell of wideband radar. Using deep neural networks for HRRP radar target recognition encounters the problem of storage overhead and slow convergence rate, to resolve those issues, we propose a one-dimension local receptive fields based extreme learning auto-encoder (1D ELM-LRF-AE) network for HRRP local structures and meaningful representations learning. ELM-LRF-AE consists of an input layer, a random convolution layer, a pooling layer, several local connected layers and an output layer, it reconstructs the input with a greedy strategy that the input feature vectors are divided into several subgroups and the i-th pooling feature vector is used to reconstruct the i-th grouping input feature vector. Then we use the learned pooling feature vectors to replace the random pooling feature vectors as the learned representations. We also stack several 1D ELM-LRF-AEs to build 1D hierarchical local receptive fields based extreme learning machine (1D H-ELM-LRF) for high level HRRP abstract representations learning and recognition. Experimental results on simulated HRRP data set demonstrate the superior high recognition performance and high computational efficiency of our algorithm.
Fuzzy Crow Search Algorithm-Based Deep LSTM for Bitcoin Prediction

Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data and thus, the prediction becomes challenging among the investors to invest the money for making profits. Initially, the blockchain network is fed to the blockchain network bridge from which the bitcoin data is acquired that is followed with the bitcoin prediction. Bitcoin prediction is performed using the proposed FuzzyCSA-based Deep Long short-term memory (LSTM). At first, the flow strength indicators are extracted based on Double exponential moving average (DEMA), Rate of Change (ROCR), Average True Range (ATR), Simple Moving Average (SMA), and Moving Average Convergence Divergence (MACD) from the blockchain data. Based on the extracted features, the prediction is done using FuzzyCSA-based Deep LSTM, which is the combination of FuzzyCSA with Deep LSTM. Then, the CSA is modified using the fuzzy operator for determining the optimal weights in Deep LSTM. The experimentation of the proposed method is performed from the openly available dataset. The analysis of the method in terms of Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) reveals that the proposed FuzzyCSA-based Deep LSTM acquired a minimal MAE of 0.4811, and the minimal RMSE of 0.3905, respectively.
Exploring Neural Network Hidden Layer Activity Using Vector Fields

Deep Neural Networks are known for impressive results in a wide range of applications, being responsible for many advances in technology over the past few years. However, debugging and understanding neural networks models’ inner workings is a complex task, as there are several parameters and variables involved in every decision. Multidimensional projection techniques have been successfully adopted to display neural network hidden layer outputs in an explainable manner, but comparing different outputs often means overlapping projections or observing them side-by-side, presenting hurdles for users in properly conveying data flow. In this paper, we introduce a novel approach for comparing projections obtained from multiple stages in a neural network model and visualizing differences in data perception. Changes among projections are transformed into trajectories that, in turn, generate vector fields used to represent the general flow of information. This representation can then be used to create layouts that highlight new information about abstract structures identified by neural networks.