Reviewerly/exHarmony huggingface dataset:
Training of Photonic Neural Networks through In Situ Backpropagation

We provide a protocol for training photonic neural networks based on adjoint methods. The gradient of the network with respect to its tunable degrees of freedom is computed by physically backpropagating an optical error signal.
PENGARUH WAKTU PELEPASAN TOURNIQUET TERHADAP KADAR KALIUM PADA PENGAMBILAN DARAH VENA

ABSTRACTTourniquet can be a problem if it is tied too long and too tightly when taking venous blood can affect potassium levels and result in high false potassium levels (pseudohiperkalemia). This study aims to determine the effect of Tourniquet release time on potassium levels in venous blood collection. This study uses the Quasy Experiment method using a Non-Equivalent Control group design involving two groups with two different treatments. The variables of this study were the time of tourniquet release and potassium levels. Data processing in this study was conducted in univariate and bivariate. Analysis of the data in this study also used the Independent T Test to determine the effect between variables.The results of the study through statistical analysis showed that the average blood potassium level in the first group was 3.784 mEq / L and the second group was 4.896 mEq / L. From the results of statistical tests obtained a value of P value (0,000) &lt;α (0.05) which means Ha is accepted so that the results of this study are there are differences in the average potassium levels at the moment of release of tourniquet blood entering and releasing the tourniquet after blood. The conclusion of this study is that the release time of the tourniquet can affect potassium levels and it is recommended for medical personnel to release the tourniquet while the blood enters the syringe needle when taking venous blood to avoid high levels of potassium in the serum.Keywords: Tourniquet Release, Potassium Levels, and Pseudohiperkalemia
Blockchain-based Price Priority Matching Two-stage Energy Trading Algorithm

With the rapid development of energy Internet, the implementation of smart meters and distributed energy resources (DER) provide new opportunities for consumers. Distributed energy needs a new settlement mechanism. Blockchain technology can provide a means for the electricity trading market. This paper proposes a decentralized, two-stage double auction energy trading algorithm. The proposed market is a distributed and decentralized application and its rules may be specified through smart contracts. Market participants conduct energy transactions by interacting with smart contracts. The smart contract first collects the quotations and performs the same price priority matching, and then performs a unified price clearing on the remaining quotations to obtain the market clearing price. Compared with the traditional double auction, our method improves the consumption capacity and security, improves the economic income of the overall market, and makes the profits of producers and consumers higher.
Super Acceleration of Dilithium in MPSoCs Critical Environments

Digital signature is a key security technology for authenticating systems and devices, thus enabling the existence of wide collaborative environments. This is also true for safety-critical systems that are constrained by strict performance requirements. Such applications are usually implemented through Multi-processors System-on-Chip (MPSoC). The dawn of quantum computing represents a threat for current cryptography, including the digital signatures. In order to prepare for such an event, electronic systems must integrate quantum-secure (post-quantum) cryptography. Dilithium is one of the main alternatives for practical implementation of post-quantum signatures. While most of the attention has been given to the security analysis and single-core software implementation, the Dilithium MPSoC exploration for high performance has been neglected. To this end, this work presents two contributions. First, the design and exploration of optimized Dilithium multi-core implementations. Second, the deployment of Dilithium on real life MPSoCs used in automotive applications and operated with a commercial RTOS. Results show that Dilithium can be efficiently implemented and optimized on a multicore architecture, improving the performance up to 48% for key generation, 34% for signature and 42% for verification when compared to single core solutions.
Efficient Oblivious Evaluation Protocol and Conditional Disclosure of Secrets for DFA

In oblivious finite automata evaluation, one party holds a private automaton, and the other party holds a private string of characters. The objective is to let the parties know whether the string is accepted by the automaton or not, while keeping their inputs secret. The applications include DNA searching, pattern matching, and more. Most of the previous works are based on asymmetric cryptographic primitives, such as homomorphic encryption and oblivious transfer. These primitives are significantly slower than symmetric ones. Moreover, some protocols also require several rounds of interaction. As our main contribution, we propose an oblivious finite automata evaluation protocol via conditional disclosure of secrets (CDS), using one (potentially malicious) outsourcing server. This results in a constant-round protocol, and no heavy asymmetric-key primitives are needed. Our protocol is based on a building block called “an oblivious CDS scheme for deterministic finite automata” which we also propose in this paper. In addition, we propose a standard CDS scheme for deterministic finite automata as an independent interest.
Analysis of Approaches for Irony Detection in Tweets for Online Products

In sentiment analysis, the most common attention and broad current research topic is irony/sarcastic/satire detection. The most complex problem is irony detection which is identifying the meaning or emotion of satire reviews. Sarcastic reviews have positive words but emotion is negative and vice versa. This research work has carried out for irony detection on Twitter Tweets of Amazon product’s reviews. Lexicon-based features with N-gram and Skip-gram-based methods are explored for irony detection. To recognize the best approach for irony detection and prediction, a total of 22,000 variegated irony and non-irony Tweets of Amazon products are collected and used with new deep learning (DL) approach. The results are compared with various machine learning approaches namely, decision tree (DT), support vector machine, logistic regression (LR), and random forest (RF). The proposed work is implemented to find irony detection. It is seen the proposed DL model has produced average results when compared to the classical machine learning DT and RF model.
‘Love’ or ‘Hate’? TAM-guided Frameworks of Lecturers’ and Students’ Assessment of Online Teaching/Learning

The Covid-19 pandemic brought radical pedagogical shifts to global higher education. Online teaching/learning has become the mainstream under the context of the pandemic. Although vaccines have been increasingly provided and face-to-face teaching has resumed in some institutions, people are advised to adapt themselves to the changes under the new normal. Lecturers and students still need to be prepared for online teaching/learning in case of another round of outbreak. This study was conducted to generate and compare lecturers’ and students’ assessment of online teaching/learning in hospitality and tourism undergraduate programmes in Mainland China. With the theoretical guidance of the technology acceptance model (TAM), from semi-structured interviews with seventeen lecturers and ten students, a ‘Love’ framework and a ‘Hate’ framework were proposed explaining why lecturers and students love or hate online teaching/learning. Consistency and variances were identified comparing lecturers’ and students’ concerns. Both frameworks extend TAM in the online education setting and can be used as theoretical guidance for future online teaching/learning studies. Findings provide stakeholders clear directions and practical solutions to better prepare for the new normal.
(Semi-) Automatic Construction of Knowledge Graph Metadata

Recently a huge number of knowledge graphs (KGs) has been generated, but there has not been enough attention to generate high-quality metadata to enable users to reuse the KGs for their own purposes. The main challenge is to generate standardized and high quality descriptive metadata which helps users understand the content of the large KGs. Some existing solutions make use of a combination of schema-level patterns derived from graph summarization with instance-level snippets. I will follow this trend and develop a method based on a combination of content-based patterns with user activity data such as SPARQL query logs to make generated metadata more informative and useful than other developed approaches. The problem of current models is generating complex, long or insufficient metadata which I plan to tackle by proposing a guideline to generate standard metadata during my Ph.D.
An Approach to Software Defect Prediction Combining Semantic Features and Code Changes

Software defect prediction (SDP), which predicts defective code regions, can help developers reasonably allocate limited resources for locating bugs and prioritizing their testing efforts. Previous work on defect prediction has used machine learning and artificial software metrics. However, traditional defect prediction features extracted from artificial software metrics often fail to capture the syntactic and semantic information of defective modules. This work on defect prediction mostly focuses on abstract syntax tree (AST). Moreover, because current research on AST technology is relatively mature, it is difficult to further improve the accuracy of defect prediction when only using AST to characterize codes. In this paper, in order to capture more semantic features, we extract semantic information both from the sequences of AST tokens and code change tokens. In addition, to leverage the traditional features extracted from statistical metrics, we also combine the semantic features with traditional defect prediction features to perform SDP, and use the gated fusion mechanism to determine the combination ratio of the two kinds of features. In our empirical studies, 10 open-source Java projects from the PROMISE repository are chosen as our empirical subjects. Experimental results show that our proposed approach can perform better than several state-of-the-art baseline SDP methods.
Contexts Embedding for Sequential Service Recommendation

There exist two pervasive context conceptualizations (i.e. interactional and representational contexts) in sequential service recommendations. These approaches are typically limited in their ability to capture diverse transition patterns because they assume a single global context space for all sequences. In this work, we propose a context embedding model that augments an attention RNN encoder-decoder with a deep clus-tering component, to address the above limitation. The deep clustering component predicts a soft cluster assignment for the final encoder hidden state of each input sequence. The clustering assignment is subsequently used to condition both interactional and representational context and ultimately improve sequential prediction. We perform experiments to evaluate and validate our proposed model on four benchmark online services datasets. Our experiments show that our proposed model mostly outperforms state-of-the-art approaches on the sequential recommendation task.
Watch and Act: Dual Interacting Agents for Automatic Generation of Possession Statistics in Soccer

Pass localization and team identification are two primary tasks for pass-count based possession statistics generation of a soccer match. While the existing works perform these two tasks separately, we propose dual interacting reinforcement learning agents to jointly perform these tasks. The proposed model has a localization agent, that decides which direction to move a temporal window to localize a pass. On the other hand, there is an identification agent that decides if the temporal window contains a pass for team-A (or team-B), or the localization agent needs to readjust the temporal window further. In this multi-agent setup, an agent may communicate by sharing some message to guide the other agent to achieve its task. To achieve this inter-agent communication, we extend the Dueling DQN architecture and share the value of a state as a message to the other agent. Two agents watch, act independently and cooperate with each other in order to detect a valid pass in a soccer video. A novel reward function is proposed that helps the agents to learn the optimal policy. Experiments performed on online videos show that our method is 3% better at localization of pass than the competitive methods.
Implementation of a real-time fall detection system based on hybrid threshold analysis algorithm and machine learning algorithm

With the increasing global aging population, the health of the elderly has become a global concern. Accidental falls, as one of the major causes of health and safety issues affecting the elderly, can cause serious hazards. In this paper, a fall detection system is proposed to be able to deliver timely information after a fall. The acceleration and angular velocity time series extracted from motion were used to describe human motion features. Hybrid threshold analysis algorithm and machine learning algorithm are used for classification between falls and activities of daily living (ADLs). The fall detection results showed 98.55% accuracy, 98.16% sensitivity, and 98.73% specificity. The result is higher than the single-threshold algorithm and slightly lower than the machine learning algorithm. In addition, the hybrid algorithm of fall detection in this paper is to put the threshold analysis algorithm in the edge device for calculation and put the machine learning algorithm in the cloud server for calculation. Since the single machine learning algorithm needs to transmit data to the cloud server all the time, the hybrid algorithm has lower power consumption than machine learning algorithms, and the average alarm time is shorter, making it more suitable for actual systems.
Efficient twin-field quantum key distribution with heralded single-photon source

Twin-field quantum key distribution (TF-QKD) can overcome the fundamental rate-loss limit in the point-to-point QKD without a quantum repeater, so as to realize key distribution over long distance. In general, weak coherent pulse (WCP) emitted from the attenuated laser is often used as the photon source for TF-QKD and its variants, however, the key generation rate and transmission distance may be restricted for TF-QKD protocols using this type of photon source as it contains a large proportion of vacuum part. Another candidate of photon source within reach of current technology is the heralded single-photon source (HSPS). In this paper, we propose two TF-QKD variants with HSPS and provide the analytical derivation of upper bounds of yields separately with active and passive decoy-state methods. Numerical simulations show that the active decoy-state TF-QKD with HSPS (referred to as Protocol (1) achieves higher secret key rate and tolerates more large transmission loss than the original TF-QKD with WCP source. However, since the active modulation of decoy intensities may leak additional information to the eavesdropper Eve, thus we extend the result of Protocol 1 and further propose a passive decoy-state TF-QKD with HSPS (referred to as Protocol (2). The simulation results demonstrate that the performance of Protocol 2 is close to the original active TF-QKD protocol, and its security and flexibility are further improved. • An efficient passive twin-field quantum key distribution with heralded single-photon source is proposed for the first time. • Deriving the tighter upper bounds on yields separately with active and passive decoy-state methods. • Performance advantage of heralded single-photon source in the practical realization of quantum key distribution is demonstrated. • Passive twin-field quantum key distribution provides a comparable performance to current protocols with improved security.
SUMLP: A Siamese U-shaped MLP-based network for change detection

Remote sensing image change detection (CD) technology is an important means to understand the changes of the earth surface. In recent years, many excellent CD methods based on convolutional neural networks (CNN) have been proposed. Although these methods have achieved good results, CD is still a very challenging task. Recently, many visual algorithms based on multi-layer perceptron (MLP) have been proposed and achieved similar or even better results than CNN-based algorithms in many visual fields. Inspired by the success of these MLP-based algorithms, in this paper we propose a siamese U-shaped MLP-based CD network named SUMLP. First, SUMLP uses CycleMLP block as the basic unit of the network and combines the characteristics of the CD task to construct a MLP-based CD network with siamese U-shaped structure. Secondly, to better optimize the CD network, we design a new loss function, weighted cosine cross entropy (WCCE) loss. WCCE loss has two advantages: 1. WCCE loss gives different weights to the changed samples and the unchanged samples, which can cope with the data imbalance problem in the CD task; 2. WCCE loss uses cosine margin to increase the inter class distance and the intra class compactness, so as to better distinguish the changed samples from the unchanged samples. For comprehensively verifying the validity of our proposed method, we carried out many experiments on three public CD datasets: LEVIR-CD dataset, CDD dataset and WHU-CD dataset. On these datasets, SUMLP’s F1−score are at least 0.33%, 1.41% and 3.56% higher than the other relevant CD methods respectively.
Higher Education Teachers’ Perceptions About Teaching and Learning Related Qualities of Telepresence Robots

Recent literature points to emerging interest in using telepresence robots in higher education. This use is often challenged by insufficient knowledge and immature expectations that teachers have about the technical abilities of telepresence robots. In this study, we investigated how the technology of telepresence robotics was perceived by higher education teachers with no previous experience with these robots. We also examined how this perception changed after a workshop where the teachers experimented with the robots. Fifteen staff members from Tallinn Technical University and Tallinn University took part in the study. The results reveal that the first hands-on experience with telepresence robots changes remarkably the teachers’ expectations and understanding about the telepresence robots. In particular, the participants gained a better understanding of the technology, of the opportunities it provides and about its limitations.
Communicating accessible messages for food insecure communities in Northern Mozambique: Supporting Sustainable Development Goal 2

This paper describes a collaborative project between Trinity College Dublin in Ireland and the United Nations' World Food Programme (WFP) Mozambique country office. The Sphere standards require that information on humanitarian assistance should be in languages and formats accessible to people who cannot read or who have communication difficulties. Nevertheless, there remains a gap in both implementing this guidance consistently and in understanding the impact of doing so when engaging with affected populations.This commentary describes the process of developing key messages regarding targeting of humanitarian food assistance in communication-accessible formats, and field testing of these materials with community committees and partners.The communication-accessible materials were well received by communities, and humanitarian staff and partners found them to be useful in community engagement.Materials designed to be maximally accessible to people with communication differences and disabilities may also address inclusion for affected populations with different education, literacy, and language backgrounds. This commentary focuses on Sustainable Development Goal (SDG) 2 as an exemplar of the use of communication accessible messaging in humanitarian response.
DRP:Discrete Rank Pruning for Neural Network

Although deep neural networks (DNNs) have achieved excellent performance in computer vision applications in recent years, it is still challenging to deploy them on resource-limited devices such as mobile phones. To solve this problem, we propose a novel filter pruning method for neural network named Discrete Rank Pruning (DRP). Moreover, many methods apply sparse regularization on the filter weights of the convolution layers to reduce the degradation of performance after pruning. We analyze these methods and find that it is necessary to consider the influence of the bias term. Based on these, we propose a novel sparse method named Consideration Bias Sparsity (CBS). Extensive experiments on MNIST, CIFAR-10 and CIFAR-100 datasets with LeNet-5, VGGNet-16, ResNet-56, GoogLeNet and DenseNet-40 demonstrate the effectiveness of CBS and DRP. For LeNet-5, CBS achieves 1.87% increase in accuracy than sparse regularization on MNIST. For VGGNet-16, DRP achieves 66.6% reduction in FLOPs by removing 83.3% parameters with only 0.36% decrease in accuracy on CIFAR-10. For ResNet-56, DRP achieves 47.45% reduction in FLOPs by removing 42.35% parameters with only 0.82% decrease in accuracy on CIFAR-100.
Factoring 3D Convolutions for Medical Images by Depth-wise Dependencies-induced Adaptive Attention

It turns out that convolutional neural networks (CNNs) have excellent medical image processing capabilities. Hence, effectively and efficiently deploying CNNs on devices with varying computing power to make computer-aided diagnosis puts on the agenda. However, it is a dilemma to balance the limited computing resources and model complexity. Previously, we proposed factorized convolution with spectral normalization (FConvSN) to mitigate the bottleneck of deploying CNNs for 2D medical images. But due to the cube structure of 3D convolutional kernels, it does not work well for 3D medical images. Directly flattening 3D kernels to 2D weights for matrix factorization may undermine the learning ability along depth-wise, resulting in the loss of depth information and the decline of model performance. To this end, we factorize a 3D convolutional kernel to 2D weight matrices with depth-wise dimensions, then assign an attentive score for each 2D weight matrix by a depth-wise dependencies-induced adaptive attention block (AA). AA with a temperature hyper-parameter helps convolution kernel to better capture depth-wise dependencies in 3D medical images, improving its learning ability along the depth direction. We term this novel factorized convolution as FConvAA used for compressing model complexity without impairing the depth-wise expressivity. We also impose spectral normalization (SN) for FConvAA to constrain spectral norm-wise weights. We conduct extensive experiments on the public lung CT dataset LUNA16 and the private retina OCT dataset to demonstrate the effectiveness and feasibility of our FConvAA.