Reviewerly/exHarmony huggingface dataset:
Interactive storytelling for children: A case-study of design and development considerations for ethical conversational AI

Conversational Artificial Intelligence (CAI) systems and Intelligent Personal Assistants (IPA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, the implications of which is receiving increased attention, specifically with respect to the effects of these systems on children's cognitive, social and linguistic development. Recent advances address the implications of CAI with respect to privacy, safety, security, and access. However, there is a need to connect and embed the ethical and technical aspects in the design. Using a case-study of a research and development project focused on the use of CAI in storytelling for children, this paper reflects on the social context within a specific case of technology development, as substantiated and supported by argumentation from within the literature. It describes the decision making process behind the recommendations made on this case for their adoption in the creative industries. Further research that engages with developers and stakeholders in the ethics of storytelling through CAI is highlighted as a matter of urgency.
Page-level Optimization of e-Commerce Item Recommendations

The item details page (IDP) is a web page on an e-commerce website that provides information on a specific product or item listing. Just below the details of the item on this page, the buyer can usually find recommendations for other relevant items. These are typically in the form of a series of modules or carousels, with each module containing a set of recommended items. The selection and ordering of these item recommendation modules are intended to increase discover-ability of relevant items and encourage greater user engagement, while simultaneously showcasing diversity of inventory and satisfying other business objectives. Item recommendation modules on the IDP are often curated and statically configured for all customers, ignoring opportunities for personalization. In this paper, we present a scalable end-to-end production system to optimize the personalized selection and ordering of item recommendation modules on the IDP in real-time by utilizing deep neural networks. Through extensive offline experimentation and online A/B testing, we show that our proposed system achieves significantly higher click-through and conversion rates compared to other existing methods. In our online A/B test, our framework improved click-through rate by 2.48% and purchase-through rate by 7.34% over a static configuration.
Fast-Slow Transformer for Visually Grounding Speech

We present Fast-Slow Transformer for Visually Grounding Speech, or FaST-VGS. FaST-VGS is a Transformer-based model for learning the associations between raw speech waveforms and visual images. The model unifies dual-encoder and cross-attention architectures into a single model, reaping the superior retrieval speed of the former along with the accuracy of the latter. FaST-VGS achieves state-of-the-art speech-image retrieval accuracy on benchmark datasets, and its learned representations exhibit strong performance on the ZeroSpeech 2021 phonetic and semantic tasks.
Identity-Based Linkable Ring Signature on NTRU Lattice

Although most existing linkable ring signature schemes on lattice can effectively resist quantum attacks, they still have the disadvantages of excessive time and storage overhead. This paper constructs an identity-based linkable ring signature (LRS) scheme over NTRU lattice by employing the technologies of trapdoor generation and rejection sampling. The security of this scheme relies on the small integer solution (SIS) problem on NTRU lattice. We prove that this scheme has unconditional anonymity, unforgeability, and linkability under the random oracle model (ROM). Through the performance analysis, this scheme has a shorter size of public/private keys, and when the number of ring members is small (such as <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> <mo>≤</mo> <mn>8</mn> </math> ), this scheme has a shorter signature size compared with other existing latest lattice-based LRS schemes. The computational efficiency of signature has also been further improved since it only involves multiplication in the polynomial ring and modular operations of small integers. Finally, we implemented our scheme and other similar schemes, and it is shown that the time for the signature generation and verification of this scheme decreases roughly by 44.951% and 33.503%, respectively.
Dew Intelligence: Federated learning perspective

Newly emerging and evolving technologies such as Cloud, Fog and Edge Computing, as well as Internet of Things, Cyber-Physical Systems and Distributed Ledger Technology (such as blockchain) together with advances in Artificial Intelligence (AI) research are increasingly becoming a common and pervasive phenomenon in our everyday lives. Their co-evolution with society is driving the emergence of future socio-technical systems, which further promote ubiquitous entanglement between humans and machines. Fog, Edge and Dew computing as post-Cloud computing paradigms aim to relocate computing resources closer to end users in order to mitigate cloud-specific issues of highly centralized computation. Dew computing as the youngest of the post-cloud paradigms promotes human centered independence and collaboration between devices within scalable distributed computing infrastructures. Meanwhile, the field of artificial intelligence is adapting to recent challenges posed by user data privacy regulations as well as opportunities for applications on mobile devices based on their growing computational abilities. The usage of artificial intelligence in pervasive and scalable distributed computing systems is a natural step towards ubiquitous intelligent infrastructures and collaborative human and machine environments. Federated learning is an artificial intelligence technique enabling collaborative learning in distributed devices environment without sharing the training data sets, which are often private. This paper provides the overview of the federated learning paradigm showing that it inherently leverages both independence and collaboration, thus exemplifying implementation of dew intelligence within scalable distributed computing hierarchy.
Multimodal-adaptive hierarchical network for multimedia sequential recommendation

Recommender system has a pivotal role in electronic economy especially for the online shopping platforms. Studies over the past two decades have proved that exploiting the inherent properties of items contributes a lot to the accuracy of multimedia sequential recommendation. There is no doubt that multimedia information including images and texts of a product have an impact on user's purchase decision. However, modeling user's dynamic preferences for multimodal (visual and textual in this paper) information over time is still a challenging problem. To solve this problem, we propose a Multimodal-Adaptive Hierarchical Network (MAHN for short) for multimedia sequential recommendation, which includes a hierarchical recurrent neural network and an information modulation module between the hierarchical structure. Specifically, the hierarchical recurrent neural network achieves the re-selection of multimodal information from the first layer to the second layer, the information modulation module realizes the selection of each modal information at time step t based on the previous time steps. Finally, to improve the generalization ability of our model, we adopt the multi-task training style to jointly optimize BPR loss and reconstruction loss of multimodal information. Experiments are conducted on two real world public datasets, and the results demonstrate that our model outperforms the other methods.
Relational Reasoning for Group Activity Recognition via Self-Attention Augmented Conditional Random Field

This paper presents a new relational network for group activity recognition. The essence of the network is to integrate conditional random fields (CRFs) with self-attention to infer the temporal dependencies and spatial relationships of the actors. This combination can take advantage of the capability of CRFs in modelling the actors’ features that depend on each other and the capability of self-attention in learning the temporal evolution and spatial relational contexts of every actor in videos. Additionally, there are two distinct facets of our CRF and self-attention. First, the pairwise energy of the new CRF relies on both of the temporal self-attention and spatial self-attention, which apply the self-attention mechanism to the features in time and space, respectively. Second, to address both local and non-local relationships in group activities, the spatial self-attention takes account of a collection of cliques with different scales of spatial locality. The associated mean-field inference thereafter can thus be reformulated as a self-attention network to generate the relational contexts of the actors and their individual action labels. Lastly, a bidirectional universal transformer encoder (UTE) is utilized to aggregate the forward and backward temporal context information, scene information and relational contexts for group activity recognition. A new loss function is also employed, consisting of not only the cost for the classification of individual actions and group activities, but also a contrastive loss to address the miscellaneous relational contexts between actors. Simulations show that the new approach can surpass previous works on four commonly used datasets.
Sidechains With Fast Cross-Chain Transfers

With the rapid evolution of the blockchain technologies, the interoperability of different blockchain systems is emerging as one of the essential features of blockchains. Sidechains, a mechanism providing communications between different blockchains, have been heralded as the crucial factor of blockchain interoperability. However, there are still issues that need to be addressed in terms of security and feasibility. In this article, for proof-of-stake (PoS) and proof-of-work (PoW) blockchains, we propose efficient sidechain constructions with fast cross-chain transfers and small proof size by novel cross-chain certificate generation process and committee selection methods. Moreover, we also provide an extra functionality of supporting instant cross-chain transfers, such that emergent cross-chain transactions can be processed immediately. Compared to prior sidechains, our PoS sidechain construction can achieve faster cross-chain transfers, which improves the promptness of cross-chain transfers. While our PoW sidechain construction is more efficient with smaller proof size, reducing the storage and bandwidth overhead. Furthermore, we formally prove our sidechain constructions satisfying the properties of atomicity and timeliness. Finally, we develop a proof-of-concept implementation of our sidechains, and the experimental results show our constructions is not only faster, but also efficient with low storage and bandwidth overhead.
Research on Educational Information Platform Based on Cloud Computing

The traditional method only pays attention to hardware construction and ignores the data processing steps, which leads to high redundant resource occupancy rate, untimely resource sharing, and low platform data safety factor. In order to solve this problem, this paper establishes an educational information platform based on cloud computing. The platform gives the overall structure of the education information platform, including the business layer and the support layer. Then the external interface is designed. Based on the MySQL database interface, users are allowed to use custom data formats and storage management modes to ensure flexibility in using data resources and improve the compatibility between smart terminals and virtual machines through the remote desktop terminal architecture. Through the educational data compression and educational resource sharing model, the generation of redundant messages is reduced, thereby realizing the design of the educational information platform. Experimental results show that this method can effectively reduce the occupancy rate of redundant resources, save network bandwidth, and improve the data safety factor of the platform. The resource sharing time is always less than 2.0 s, which verifies the effectiveness of the method
Two-Phase Segmentation for Intensity Inhomogeneous Images by the Allen--Cahn Local Binary Fitting Model

This paper proposes a new variational model by integrating the Allen-Cahn term with a local binary fitting energy term for segmenting images with intensity inhomogeneity and noise.An inhomogeneous graph Laplacian initialization method (IGLIM) is developed to give the initial contour for two-phase image segmentation problems.To solve the Allen-Cahn equation derived from the variational model, we adopt the exponential time differencing (ETD) method for temporal discretization, and the central finite difference method for spatial discretization.The energy stability of proposed numerical schemes can be proved.Experiments on various images demonstrate the necessity and superiority of proper initialization and verify the capability of our model for two-phase segmentation of images with intensity inhomogeneity and noise.
Radio frequency fingerprint identification based on stream differential constellation trace figures

Recently, radio frequency fingerprint (RFF) technique, characterized by unique hardware impairments, has seen vast attention for specific transmitter identification and authorized access. Differential constellation trace figure (DCTF) emerges as an effective image-based RFF method, which forms a stable constellation figure by performing a key differential process. Previous studies that are based on a single differential interval (DI) are susceptible to noise, which leads to under-performance in cases with low signal-to-noise ratios (SNRs). To address this problem, this paper proposes a stream DCTFs scheme that mainly consists of (i) RFF extraction deriving from numerous DIs, (ii) devising a feature reduction method containing feature importance ranking (FR) and feature set selection (FS), and (iii) extensive experimental evaluations. When the SNR ranges from 5 dB up to 10 dB, the classification results reach 96.3% and 98.4% respectively among 54 ZigBee devices, which are about 40% and 20% higher than existing DCTF-based methods. Moreover, the authentication accuracy reaches 100% in verification.
A survey of Convolutional Neural Networks —From software to hardware and the applications in measurement

The convolutional neural network is a subfield of artificial neural networks and has made great achievements in various domains over the past decade. The technique has been widely applied including computer vision, natural language processing, image analysis, etc. The field of measurement also achieves exciting progress with convolutional neural networks. This paper gives a survey of convolutional neural networks from software algorithms to hardware architectures perspective and the applications in measurement. The survey hopes to give contributions to the cross-research of artificial intelligent and measurement.
A Frame-Based Feature Model for Violence Detection from Surveillance Cameras Using ConvLSTM Network

Vision-based action detection is a puzzling research topic of computer vision and pattern recognition. Violence detection in video has found a particularly fundamental application in real-life scenes, aiming at monitoring and protecting the safety of people and pedestrians. In previous years, violence detection in surveillance cameras gained more and more attention from researchers, due to a huge amount of video data collected by Closed-circuit television (CCTV) cameras that covers different places such as schools, hospitals, shopping malls, streets, etc… However, the manual control and detection of violent behaviors and report of those incidents on time based on videos is very tiresome and laborious for operators which may results in the loss of lives. With the rise of deep learning and its applications in different domains of human daily lives, automatic detection models for abnormal behaviors or violence incidents are highly needed for this concern. In this paper, we address this research problem and explore ConvLSTM network as a solution. We conducted extensive experiments on six benchmark datasets, where our method shows superior performance on all these datasets by improving the state-of-the-art performance accuracy for violence detection.
Summary2vec: Learning Semantic Representation of Summaries for Healthcare Analytics

The ever-increasing amount of text data makes it challenging for humans to extract the required information. This problem is even more vital for the medical domain, where accessing up-to-date information is essential. Physicians and researchers face diverse and extensive medical sources such as medical journal articles, websites, or patient records. Therefore, they require to analyze them based on their interests and needs quickly. Intelligent content summarization approaches are assisting tools in such situations to provides an overview of a set of documents. However, the summary is required to be tailored to two different users type preferences: the physician and the patient. This paper proposes a novel embedding method, called Summary2vec, where each summary is presented by a fixed -length vector covering various aspects of information space. Summary2vec is remedial to design automatic services for various analytic purposes that require information-seeking activity. We leverage Summary2vec to produce a hierarchical summarization structure to enable users navigating through the hierarchy to gain more elaborated information upon request by engaging them.
Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data's accuracy by performing an outlier data detection process before data aggregation is implemented. This paper concerns evaluating multivariate outlier detection (MOD) analysis on aggregated accuracy of data generated by a forest fire environment using OMNeT++ and performing the analysis in MATLAB R2018b. The findings of the study showed that the MOD algorithm conserved approximately 59.5% of aggregated data accuracy, compared with an equivalent algorithm, such as the FTDA algorithm, which conserved 54.25% of aggregated data accuracy for the same event.
Robust Precision Landing for Autonomous Drones Combining Vision-based and Infrared Sensors

One of the challenges in drone-based systems is to support automated landing with a high precision that goes beyond the accuracy of standard off-the-shelf GPS. Various efforts have been made to support this, mainly using vision-based and infrared sensors. However, using a single sensor inevitably introduces a single point of failure. To address this problem, we combine a vision-based sensor that detects special visual markers with a sensor that tracks an infrared beacon. We also support a more cautious landing approach for the case where these sensors temporarily fail to detect their targets. We implement our solution in the context of a mature autopilot framework, through modular extensions that are transparent to the rest of the software stack. We evaluate these mechanisms by conducting field experiments using a custom drone, activating faults in the individual precision landing sensor subsystems in a controlled way through interactive commands that are sent to the drone at runtime. The results show that our solution achieves robust precision landing under different failure scenarios while maintaining the accuracy of fault-free sensor operation.
Two-Stage Resampling for Convolutional Neural Network Training in the Imbalanced Colorectal Cancer Image Classification

Data imbalance remains one of the open challenges in the contemporary machine learning. It is especially prevalent in case of medical data, such as histopathological images. Traditional data-level approaches for dealing with data imbalance are ill-suited for image data: oversampling methods such as SMOTE and its derivatives lead to creation of unrealistic synthetic observations, whereas undersampling reduces the amount of available data, critical for successful training of convolutional neural networks. To alleviate the problems associated with over- and undersampling we propose a novel two-stage resampling methodology, in which we initially use the oversampling techniques in the image space to leverage a large amount of data for training of a convolutional neural network, and afterwards apply undersampling in the feature space to fine-tune the last layers of the network. Experiments conducted on a colorectal cancer image dataset indicate the usefulness of the proposed approach.
Prediction of Metacarpophalangeal Joint Angles and Classification of Hand Configurations Based on Ultrasound Imaging of the Forearm

With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for inter-acting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand movement recognition is widely used to enable such interaction. Hand configuration classification and metacarpophalangeal (MCP) joint angle detection are important for a comprehensive reconstruction of hand motion. Surface electromyography (sEMG) and other technologies have been used for the detection of hand motions. Ultrasound images of the forearm offer a way to visualize the internal physiology of the hand from a musculoskeletal perspective. Recent works have shown that these images can be classified using machine learning to predict various hand configurations. In this paper, we propose a Convolutional Neu-ral Network (CNN) based deep learning pipeline for predicting the MCP joint angles. We supplement our results by using a Support Vector Classifier (SVC) to classify the ultrasound information into several predefined hand configurations based on activities of daily living (ADL). Ultrasound data from the forearm were obtained from six subjects who were instructed to move their hands according to predefined hand configurations relevant to ADLs. Motion capture data was acquired as the ground truth for hand movements at three speeds (0.5 Hz, 1 Hz, and 2 Hz) for the index, middle, ring, and pinky fingers. We demonstrated the perfect prediction of hand configurations through SVC classification and a correspondence between the predicted MCP joint angles and the actual MCP joint angles for the fingers, with an average root mean square error of 7.35 degrees. A low latency (6.25 – 9.10 Hz) pipeline was implemented for the prediction of both MCP joint angles and hand configuration estimation aimed for real-time implementation.