Reviewerly/exHarmony huggingface dataset:
Deep Multiview Learning From Sequentially Unaligned Data

Multiview learning is concerned with machine learning problems, where data are represented by distinct feature sets or views. Recently, this definition has been extended to accommodate sequential data, i.e., each view of the data is in the form of a sequence. Multiview sequential data pose major challenges for representation learning, including i) absence of sample correspondence information between the views, ii) complex relations among samples within each view, and iii) high complexity for handling multiple sequences. In this article, we first introduce a generalized deep learning model that can simultaneously discover sample correspondence and capture the cross-view relations among the data sequences. The model parameters can be optimized using a gradient descent-based algorithm. The complexity for computing the gradient is at most quadratic with respect to sequence lengths in terms of both computational time and space. Based on this model, we propose a second model by integrating the objective with reconstruction losses of autoencoders. This allows the second model to provide a better trade-off between view-specific and cross-view relations in the data. Finally, to handle multiple (more than two) data sequences, we develop a third model along with a convergence-guaranteed optimization algorithm. Extensive experiments on public datasets demonstrate the superior performances of our models over competing methods.
Arrival Picking of Acoustic Emission Signals Using a Hybrid Algorithm Based on AIC and Histogram Distance

Automatic picking of the arrival of acoustic emission (AE) and microseismic signals is very important in geophysics and process monitoring. However, because an acoustic signal undergoes many reflections and refractions before being received by an AE sensor, its signal-to-noise ratio (SNR) varies and, in some cases, can be very low. A new hybrid method for arrival-time picking based on the Akaike information criterion (AIC) picker and Manhattan distance is presented. A criterion and a standard framework for determining an arrival point are formulated, and two key parameters are discussed, namely, the length of the time window and the size of the selected time series. Experimental results show that the proposed method can detect the arrival points of signals with various SNRs while overcoming some of the drawbacks of the conventional AIC method. It could provide a better alternative for determining the arrival points of AE signals in process monitoring and other fields.
Revealing Sonic Wisdom in the Works of Cat Hope

There are many ways in which wisdom pervades artistic discourse. It is only recently, however, that a concerted effort has been made to understand the wisdom embodied in music. Drawing from the literal translation of phonosophy, this article attempts to unveil what might be described as sonic wisdom from a composer’s perspective, derived from an interview between Australian philosopher Samuel McAuliffe and composer Cat Hope. Hope’s notational practice challenges the hierarchies established by common practice notation, resulting in her contemporary art music being accessible to a wider range of performers, including those that do not read any music notations. Engagement with Hope’s notation leads to a revealing and transfer of a different kind of sonic wisdom than found in more traditionally notated works, in a process facilitated by technologies and articulated through musicianship.
Abnormal High-Density Crowd Dataset

Anomaly detection within crowded environments is a key challenge in the computer vision and crowd behaviour understanding fields. Furthermore, anomaly detection within high-density crowds remains an insufficiently explored area. In this paper, we propose a novel abnormal high-density crowd dataset. The proposed dataset adheres to the same constraints as some of the benchmark datasets such as UCSD, UMN and Avenue dataset. These constraints include occurrences of both normal and abnormal behaviour and validated abnormal behaviour annotations. Additionally, this dataset includes footage of only high-density crowds, whereas benchmark datasets include low to medium density crowds. We have taken into consideration privacy issues, the veracity of annotations and pre-processing of the dataset. We evaluate the dataset against state-of-the-art crowd anomaly detection methods. The generated results indicate that training/testing these methods on high-density crowds decreases their detection performance.
Exploring User Trust and Reliability for Recommendation: A Hypergraph Ranking Approach

Recent recommendation strategies attempt to explore relations among both users and items, applying techniques of graph learning and reasoning for solving the so-called information isolated island limitations. However, the graph-based ranking algorithms model the interactions between the user and item either as a user-user (item-item) graph or a bipartite graph that capture pairwise relations. Such modeling cannot capture the complex relationship shared among multiple interactions that can be useful for item ranking. In this paper, we propose to leverage hypergraph random walk into the ranking process. We develop a new recommendation framework Hypergraph Rank (HGRank), which exploits the weighting methods for hypergraph on both hyperedges and vertices. This leads to the expressive modeling of high-order interactions instead of pairwise relations. Specifically, we take social trust and reliability into the hypergraph weighting process to improve the accuracy of the algorithm. Extensive experimental results demonstrate the effectiveness of our proposed approach.
Recognition of Human Activities via Wearable Sensors

Worldwide, the composition of the population registers an increase in life expectancy and a reduction in the birth rate. The creative use of technology and the omnipresence of sensor-based devices allow the development of software solutions and platforms that offer services to older people towards increasing quality of life, reducing expenses, and promoting aging in place while preserving autonomy and security: a case in point is wearable sensor-based Human Activity Recognition (HAR). Given that previous work observed the lack of attention given to older people by the researchers investigating HAR systems, we conducted a systematic mapping aiming at identifying resources, as variables and associated values, used in the development of HAR systems for older people. The systematic mapping selected 21 papers published between 2010 and 2019, which exposed the use of 16 variables and associated values.
Hull Number Detection for Ship Images Based on Image Super-Resolution

At present, natural scene text detection methods based on deep learning have achieved outstanding results in many applications. Hull number belongs to the text object, detecting the hull number successfully plays an important role in maritime military and shipping. However, the hull number occupies a relatively small area in the ship image as well as it could be blurred or deformed due to the reason of the photo environment, which made the accuracy of detecting the hull number directly on ship images has great room for improvement. Therefore, this article proposes a hull number detection method based on image super-resolution(SR), which first performs SR on a single ship image, then implements hull number detection on the SR ship image. To reduce the time consumption of the SR process, the original image is divided into multiple grids that perform SR in parallel. Finally, these multiple SR grids are synthesized into a new SR image. Experiments proved that the detection accuracy of the hull number is significantly improved on SR ship images.
Japanese Pronunciation Evaluation Based on DDNN

In recent years, speech recognition technology based on deep learning model has made great progress, and the accuracy of speech recognition has reached more than 90%. In foreign language learning, speech evaluation is an important application. Billions of foreign language learners need to practice effective pronunciation. However, due to the different goals between speech recognition and speech evaluation, a single speech recognition model cannot be directly applied to pronunciation evaluation. This paper proposes a DDNN (double-layer deep neural network) model, which includes the speech text alignment model and speech recognition model. In the first layer of the speech alignment model, a new Viterbi algorithm method is proposed to find the best path for the alignment of speech and text. In the second layer of speech evaluation and scoring, we are the first to use the CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) on the encoding part of Attention. The accuracy of CTC model reaches 76.7%, and that of attention model is 81.2%. The experimental results show that the speech and text alignment method is effective, and the speech evaluation results based on the Attention model are better. The FRR (false rejection rate), FAR (false acceptance rate), and DER (diagnostic rate) in the Attention model were 4.5%, 5.1%, and 17.9%, respectively. At the same time, the evaluation of each sentence of the DDNN model in the online experiment is within 1 second, so the model can also be applied to the online real-time evaluation of speech pronunciation.
Intelligent Commodity Settlement System based on Embedded Equipment and Convolutional Neural Network

The rapid development of deep neural networks makes unmanned supermarket solutions based on computer vision possible. However, the computational complexity of convolutional neural networks is much higher than traditional algorithms, and the limitations of limited resources on embedded devices cannot meet real-time requirements. This article proposes an intelligent commodity settlement system based on embedded devices and deep learning. It uses CenterNet network and heterogeneous convolution filters to fuse. The initial layer convolution kernel is designed as a heterogeneous kernel to solve the detection of large differences in commodity scales. Experimental results show that the improved network structure has an average detection accuracy improvement of 3.2% compared to the original network structure, and the IoU index is increased by 3.1%, which can meet the real-time commodity recognition requirements of embedded devices.
V2A - Vision to Action: Learning Robotic Arm Actions Based on Vision and Language

In this work, we present a new AI task - Vision to Action (V2A) - where an agent (robotic arm) is asked to perform a high-level task with objects (e.g. stacking) present in a scene. The agent has to suggest a plan consisting of primitive actions (e.g. simple movement, grasping) in order to successfully complete the given task. Queries are formulated in a way that forces the agent to perform visual reasoning over the presented scene before inferring the actions. We propose a novel approach based on multimodal attention for this task and demonstrate its performance on our new V2A dataset. We propose a method for building the V2A dataset by generating task instructions for each scene and designing an engine capable of assessing whether the sequence of primitives leads to a successful task completion.
Multi-turn Dialogue System Based on Improved Seq2Seq Model

The automatic dialogue system is an intelligent system built by combining various artificial intelligence technologies. In recent years, with the introduction of multi-turn dialogue generation systems, semantic relevance and topic consistency between different dialogue turns have become important evaluation criteria for the success of the model. However, these problems have not been resolved and still face many challenges. In this paper, we propose a sequence to sequence (seq2seq) model based on multi-encoder structure and theme-oriented decoder, and these innovations enable the proposed model to obtain semantic correlation between different turns of conversation and maintain the consistency of topics. Meanwhile, aiming at the disadvantages of the basic seq2seq model, BiLSTM cells, attention mechanism and beam search algorithm are adopted to solve the problem of long-distance dependence and obtain richer semantic information. Experiments show that the proposed model can generate coherent, appropriate and diverse replies on multi-turn dialogue datasets.
Extending XACC for Quantum Optimal Control

Quantum computing vendors are beginning to open up application programming interfaces for direct pulse-level quantum control. With this, programmers can begin to describe quantum kernels of execution via sequences of arbitrary pulse shapes. This opens new avenues of research and development with regards to smart quantum compilation routines that enable direct translation of higher-level digital assembly representations to these native pulse instructions. In this work, we present an extension to the XACC system-level quantum-classical software framework that directly enables this compilation lowering phase via user-specified quantum optimal control techniques. This extension enables the translation of digital quantum circuit representations to equivalent pulse sequences that are optimal with respect to the backend system dynamics. Our work is modular and extensible, enabling third party optimal control techniques and strategies in both C++ and Python. We demonstrate this extension with familiar gradient-based methods like gradient ascent pulse engineering (GRAPE), gradient optimization of analytic controls (GOAT), and Krotov's method. Our work serves as a foundational component of future quantum-classical compiler designs that lower high-level programmatic representations to low-level machine instructions.
Decoherence-Resilient Linear Optical Two-Qubit Quantum Gate

Quantum information can be protected from noise and decoherence by encoding it into a decoherence-free subspace of the full Hilbert space. In this approach, it is important to devise implementations of quantum logic gates acting on the encoded logical qubits. Here, we experimentally investigate the implementation of an entangling linear optical controlled-$Z$ gate on two logical qubits encoded into pairs of physical qubits, which protects the quantum information from collective dephasing. This decoherence-resilient two-qubit gate on logical qubits is realized using a suitable four-qubit quantum gate on the physical qubits, which may help to protect the encoded quantum information from systematic errors arising due to imperfect operations on single logical qubits. We characterize the gate operation on input superposition states and demonstrate its ability to protect quantum coherence even under complete collective dephasing.
Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing

As cybercriminals scale up their operations to increase their profits or inflict greater harm, we argue that there is an equal need to respond to their threats by scaling up cyber-security. To achieve this goal, we have to develop a co-productive approach towards data collection and sharing by overcoming the cybersecurity data sharing paradox. This is where we all agree on the definition of the problem and end goal (improving cybersecurity and getting rid of cybercrime), but we disagree about how to achieve it and fail to work together efficiently. At the core of this paradox is the observation that public interests differ from private interests. As a result, industry and law enforcement take different approaches to the cybersecurity problem as they seek to resolve incidents in their own interests, which manifests in different data sharing practices between both and also other interested parties, such as cybersecurity researchers. The big question we ask is can these interests be reconciled to develop an interdisciplinary approach towards co-operation and sharing data. In essence, all three will have to co-own the problem in order to co-produce a solution. We argue that a few operational models with good practices exist that provide guides to a possible solution, especially multiple third-party ownership organisations which consolidate, anonymise and analyse data. To take this forward, we suggest the practical solution of organising co-productive data collection on a sectoral basis, but acknowledge that common standards for data collection will also have to be developed and agreed upon. We propose an initial set of best practices for building collaborations and sharing data and argue that these best practices need to be developed and standardised in order to mitigate the paradox.
Multi-sensor scheduling method based on Cuckoo and Particle Swarm optimization algorithm

Abstract In order to solve the problem that particle swarm optimization (PSO) tends to fall into the local optimal solution and the convergence speed is slow when solving multi-sensor resource scheduling model, a cuckoo particle swarm optimization (CPSO) algorithm is proposed on the basis of PSO. On the basis of target tracking model, the multi-sensor scheduling model is established. Then the LEVY flight was introduced from cuckoo algorithm into particle swarm algorithm, the algorithm can jump out of the local optimal solution as soon as possible and improve the convergence speed and accuracy of the algorithm. The simulation results show that the improved algorithm is effectively improved in terms of convergence speed and accuracy, and is applied to the solution of sensor scheduling model to further enhance the optimization ability and achieve good results.
A formal adversarial perspective: Secure and efficient electronic health records collection scheme for multi‐records datasets

Abstract The collection of private health data without compromising privacy is an imperative aspect of privacy‐aware data collection mechanisms. Privacy‐preserved data collection is achieved by anonymizing private data before its transmission from data holders to data collectors. Though there exist ample literature on private data collection for 1:1 (single record of a data holder) datasets, collecting multi‐records (multiple records of a data holder) datasets (referred to as 1:M datasets) has not received due attention from the research community. Therefore, the current studies experience serious privacy breaches in 1:M dataset thereby limiting their application in secure healthcare applications and systems. In this work, we have formally classified main privacy disclosures on these data collection mechanisms and proposed an improved privacy scheme, namely, horizontal sliced permuted permutation (H‐SPP) for 1:M datasets. It uses the composite slicing and anatomy‐based approach to protect against the privacy violations like identity, attribute, and membership disclosures. Moreover, we perform formal modeling of the proposed scheme using high‐level Petri nets (HLPN) and show that it effectively prevents the identified external and internal privacy attacks. Experimental results show that H‐SPP provides robust privacy for health data with high performance.
On truthful auction mechanism for cloud resources allocation and consumption shifting with different time slots

Abstract The flexibility and high reliability of cloud computing have generated a tremendous amount of economic benefits. With the growing demand for cloud resources, the task about how to making users' demand adaptive to the supply of cloud resources during peak and nonpeak hours becomes extremely challenging. However, most of the existing mechanisms mainly focus on the allocation of cloud resources, and ignore the balance between supply and demand of cloud resources. To improve the situation, this paper presents a combinatorial auction model for the cloud resource allocation and consumption shifting problem, and then puts forward a truthful auction mechanism with two different price curve functions, that is, staircase and continuous price curve. The proposed mechanism can effectively allocate and shift cloud resources to alleviate the pressure of cloud infrastructure during peak hours. Both the theoretical analysis and simulation results show that the proposed mechanism guarantees desired properties including individual rationality, budget balance, truthfulness and computational efficiency. In addition, extensive simulation results also show that continuous price curve outperforms stair case price curve in terms of system efficiency and balance efficiency.
Exploiting native language interference for native language identification

Abstract Native language identification (NLI)—the task of automatically identifying the native language (L1) of persons based on their writings in the second language (L2)—is based on the hypothesis that characteristics of L1 will surface and interfere in the production of texts in L2 to the extent that L1 is identifiable. We present an in-depth investigation of features that model a variety of linguistic phenomena potentially involved in native language interference in the context of the NLI task: the languages’ structuring of information through punctuation usage, emotion expression in language, and similarities of form with the L1 vocabulary through the use of anglicized words, cognates, and other misspellings. The results of experiments with different combinations of features in a variety of settings allow us to quantify the native language interference value of these linguistic phenomena and show how robust they are in cross-corpus experiments and with respect to proficiency in L2. These experiments provide a deeper insight into the NLI task, showing how native language interference explains the gap between baseline, corpus-independent features, and the state of the art that relies on features/representations that cover (indiscriminately) a variety of linguistic phenomena.
A Knowledge Graph Based Framework in Relationship Modelling and Real-time Monitoring of Market Participants

Abstract Various types of relationships among participants exist in the foreign exchange market, such as transaction, credit relations inside the market and subcompany, investing and shareholding relations outside the market. In the market with complex relationships, an incident happened in one institution may have “butterfly effect”. To monitor systemic financial risks, we integrate various types of data and establish a Knowledge Graph describing the relationships between market participants. Based on the Knowledge Graph, we design a framework to measure the impact of various events on the entire market by connecting the internal market relationships to external market related incidents. By using the BiLSTM-CRF model, we extract the relevant entities in the market news with high accuracy and measure the impact on the market with graph related indicators such as closeness and betweenness centralities. In addition, to use external unstructured data, we use a document-level event extraction method to analyse these unstructured datasets and extract new events and relationships from them to dynamically update the Knowledge Graph.