Reviewerly/exHarmony huggingface dataset:
An attention-based multi-task model for named entity recognition and intent analysis of Chinese online medical questions

The architecture of the attention-based multi-task model for named entity recognition and intent analysis and its application on a simple question–answering system that supports users verify the logic of answer generation. • An attention-based multi-task model for text classification and sequence tagging. • A multi-task learning framework for named entity recognition and intent analysis. • Our neural network model could be used to build a simple question-answering system. • Users and service providers can monitor the logic of the answers by this system. In this paper, we propose an attention-based multi-task neural network model for text classification and sequence tagging and then apply it to the named entity recognition and the intent analysis of Chinese online medical questions. We found that the use of both attention and multi-task learning improved the performance of these tasks. Our method achieved superior performance in named entity recognition and intent analysis compared with other baseline methods; the method is a light-weight solution that is suitable for deployment on small servers. Furthermore, we took advantage of the model’s capabilities for these two tasks and built a simple question-answering system for cardiovascular issues. Users and service providers can monitor the logic of the answers generated by this system.
Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis

This paper focuses on the problem of student knowledge diagnosis that is a basic task of realizing personalized education. Most traditional methods rely on the question-concept matrix empirically designed by experts. However, the expert concepts are expensive and inter-overlapping in their constructions, leading to ambiguous explanations. With the intuition that each student can master a part of the knowledge involved in all questions, in this paper, we propose a novel learning-based model for student knowledge diagnosis, dubbed Meta-knowledge Dictionary Learning (metaDL). MetaDL aims to learn a meta-knowledge dictionary from student responses, where any knowledge entity (e.g., student, question or expert concept) is a linear combination of a few atoms in the meta-knowledge dictionary. The resultant problem could be effectively solved by developing the alternating direction method of multipliers. This study has three innovations: learning independent meta-knowledges instead of traditional complex concepts, sparely representing knowledge entity instead of densely weighted representation, and interpreting expert concepts with the resulting meta-knowledges. For evaluation, the diagnosis results from metaDL are used to group students and predict responses on two public datasets and a private dataset from our institution. The experiment results show that metaDL delivers an effective student knowledge diagnosis and then results in good performances on the two applications in comparison with other methods. This technique could provide significant insights into student’s knowledge state and facilitate the progress on personalized education.
BlendSPS: A BLockchain-ENabled Decentralized Smart Public Safety System

Due to the recent advancements in the Internet of Things (IoT) and Edge-Fog-Cloud Computing technologies, the Smart Public Safety (SPS) system has become a more realistic solution for seamless public safety services that are enabled by integrating machine learning (ML) into heterogeneous edge computing networks. While SPS facilitates convenient exchanges of surveillance data streams among device owners and third-party applications, the existing monolithic service-oriented architecture (SOA) is unable to provide scalable and extensible services in a large-scale heterogeneous network environment. Moreover, traditional security solutions rely on a centralized trusted third-party authority, which not only can be a performance bottleneck or the single point of failure, but it also incurs privacy concerns on improperly use of private information. Inspired by blockchain and microservices technologies, this paper proposed a BLockchain-ENabled Decentralized Smart Public Safety (BlendSPS) system. Leveraging the hybrid blockchain fabric, a microservices based security mechanism is implemented to enable decentralized security architecture, and it supports immutability, auditability, and traceability for secure data sharing and operations among participants of the SPS system. An extensive experimental study verified the feasibility of the proposed BlendSPS that possesses security and privacy proprieties with limited overhead on IoT based edge networks.
Convex optimization of programmable quantum computers

A programmable quantum processor is a fundamental model of quantum computation. In this model, any quantum channel can be approximated by applying a fixed universal quantum operation onto an input state and a quantum `program' state, whose role is to condition the operation performed by the processor. It is known that perfect channel simulation is only possible in the limit of infinitely large program states, so that finding the best program state represents an open problem in the presence of realistic finite-dimensional resources. Here we prove that the search for the optimal quantum program is a convex optimization problem. This can be solved either exactly, by minimizing a diamond distance cost function via semi-definite programming, or approximately, by minimizing other cost functions via gradient-based machine learning methods. We apply this general result to a number of different designs for the programmable quantum processor, from the shallow protocol of quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits. We benchmark the various designs by investigating their optimal performance in simulating arbitrary unitaries, Pauli and amplitude damping channels.
Reliability for Smart Healthcare: A Network Slicing Perspective

Pursuing improvements in the healthcare system is mandatory for its efficiency and cost reduction. The fast popularization of implantable and wearable sensors promotes the diversity of healthcare applications and services, ranging from real-time and critical care monitoring to telemedicine. For smart healthcare (s-health), reliability plays an essential role, given the sensitivity of its data and services. In this article, we envision an architecture based on network slicing that can provide reliability for s-health applications and services. The architecture relies on fingerprinting healthcare applications to quickly customize resources and meet the level of reliability required for each s-health application. A fingerprinting study case is presented for s-health, based on a dataset containing real traffic. Results show that application fingerprinting reaches 90 percent accuracy, assisting in network customization. Finally, we discuss the main open issues and opportunities that network slicing technology provides for s-health.
An evaluation of an adaptive learning system based on multimodal affect recognition for learners with intellectual disabilities

Abstract Artificial intelligence tools for education (AIEd) have been used to automate the provision of learning support to mainstream learners. One of the most innovative approaches in this field is the use of data and machine learning for the detection of a student’s affective state, to move them out of negative states that inhibit learning, into positive states such as engagement. In spite of their obvious potential to provide the personalisation that would give extra support for learners with intellectual disabilities, little work on AIEd systems that utilise affect recognition currently addresses this group. Our system used multimodal sensor data and machine learning to first identify three affective states linked to learning (engagement, frustration, boredom) and second determine the presentation of learning content so that the learner is maintained in an optimal affective state and rate of learning is maximised. To evaluate this adaptive learning system, 67 participants aged between 6 and 18 years acting as their own control took part in a series of sessions using the system. Sessions alternated between using the system with both affect detection and learning achievement to drive the selection of learning content (intervention) and using learning achievement alone (control) to drive the selection of learning content. Lack of boredom was the state with the strongest link to achievement, with both frustration and engagement positively related to achievement. There was significantly more engagement and less boredom in intervention than control sessions, but no significant difference in achievement. These results suggest that engagement does increase when activities are tailored to the personal needs and emotional state of the learner and that the system was promoting affective states that in turn promote learning. However, longer exposure is necessary to determine the effect on learning.
Artificial intelligence based data processing algorithm for video surveillance to empower industry 3.5

Nowadays, the demand of camera surveillance systems (CSS) has been increasingly adopted in various industries for smart manufacturing. However, the increase of utilizing CSS will pose many drawbacks in capacity storage and overload the transmission bandwidth. Focusing on the needs in real settings, this study aims to develop a novel algorithm based on artificial intelligence (AI) for data processing. Artificial intelligence (AI) is very helpful to process a large number of videos recorded by the CSS, while computer vision algorithms can also be employed to detect abnormal behaviors or noticeable objects, thus reducing the manpower. Since applications of AI for handling the above problems consume a lot of computational resources. This paper proposes a method to solve the above CSS issues. The idea is that focus on processing the valid background and moving object in the scene. After that, it will be transmitted to the server sides for further purposes. Indeed, the proposed method significantly reduces data transmission and storage and also improves the performance. The experimental results show that suggested method reduces storage capacity up to 80% and shows promising performance in which the number of calculations is reduced several times at the sever side compared to existing methods. Towards this end, the study proposes a method to solve the above drawbacks. It would be considered to apply for Industry 3.5 which is a mixture strategy in between the best practices of Industry 3.0 and to-be Industry 4.0.
IoT Traffic Flow Identification using Locality Sensitive Hashes

Systems get smarter with computing capabilities, especially in the form of Internet of Things (IoT) devices. IoT devices are often resource-limited as they are optimized for a certain task. Hence, they are prone to be compromised and have become a target of malicious activities. Since IoT devices lack computing power for security software, network administrators need to isolate such devices and limit traffic to the device based on their communication needs. To this end, network administrators need to identify IoT devices when they join a network and detect anomalous traffic when they are compromised. In this paper, we introduce a novel approach to identify the IoT device based on the Nilsimsa hash of its traffic flow. Different from previous studies, the proposed approach does not require feature extraction from the data. In our evaluations, our approach has an average precision and recall of 93% and 90%, respectively.
A Comparison of Security and its Performance for Key Agreements in Post-Quantum Cryptography

Nowadays, we are surrounded by devices collecting and transmitting private information. Currently, the two main mathematical problems that guarantee security on the Internet are the Integer Factorization Problem and the Discrete Logarithm Problem. However, Shor's quantum algorithm can easily solve both problems. Therefore, research into cryptographic algorithms that run in classical computers and are resistant to quantum computers is extremely necessary. This area is known as post-quantum cryptography and usually studies asymmetric cryptography. By means of asymptotic analysis, the purpose of this paper is to provide an evaluation of security and its performance for the types of cryptographic systems considered safe against quantum attacks in the second-round NIST Post-Quantum Standardization Process, namely isogeny cryptosystems based on supersingular elliptic curves, error correction code-based encryption system, and lattice-based ring learning with errors. We performed a security comparison of Key Agreements protocols based on these three post-quantum cryptographic primitives and compared them with Discrete Logarithm Problem and Integer Factorization Problem. The comparison of security and its performance is presented by security level, the former by complexity analyses to achieve theoretical minimum key sizes, and the latter by simulation to assess a practical performance comparison. In the complexity analysis, as we increase the security level and then the size of the cryptographic keys increases, techniques based on isogeny outperform all other post-quantum algorithms in relation to key sizes at practical security level. In the performance comparison, the results show that the code-based protocol presents the best results among the others.
EC-ElGamal and Genetic Algorithm-Based Enhancement for Lightweight Scalable Blockchain in IoT Domain

Security, data privacy and decentralization are significant challenges in the Internet of Things (IoT) domain. These challenges are inherited attribute of another emerging technology, Blockchain. This enforced convergence of IoT and Blockchain, attracting researchers to study on the effective use of Blockchain's strength to solve the challenges of IoT. Rapid IoT adoption requires standardization and mature solution on security, data protection for compliance and performance for commercialization. These demands made a surge in variant blockchain flavours and combinations catering to different problems, and one such is Lightweight Scalable Blockchain (LSB). LSB had considerable caveats that require improvement for better adoption in the IoT domain. This paper focuses on encrypting transaction transmission, improving transaction flow, block validation, hash quality, hash rate and storage cost to improvise security and performance. The experimental evaluation is demonstrated on data from the temperature sensor to showcase superior applicability of the proposed work in the IoT domain. Implementation and result comparison with conventional LSB proves, the following achievements 1) An additional layer of transaction encryption using hybrid Elliptic Curve ElGamal (EC-ElGamal) method increases the security of the transmitted transaction for security enhancement. 2) Obtained 20% reduction in transaction processing time, 22% reduction on block validation processing time, 53% improvement on the hash operation and quality with an overall 7% saving on the storage cost thereby increased the overall performance.
Learning label correlations for multi-label image recognition with graph networks

Multi-label image recognition is a task that predicts a set of object labels in an image. As the objects co-occur in the physical world, it is desirable to model label dependencies. Previous existing methods resort to either recurrent networks or pre-defined label correlation graphs for this purpose. In this paper, instead of using a pre-defined graph which is inflexible and may be sub-optimal for multi-label classification, we propose the A-GCN, which leverages the popular Graph Convolutional Networks with an Adaptive label correlation graph to model label dependencies. Specifically, we introduce a plug-and-play Label Graph (LG) module to learn label correlations with word embeddings, and then utilize traditional GCN to map this graph into label-dependent object classifiers which are further applied to image features. The basic LG module incorporates two 1 × 1 convolutional layers and uses the dot product to generate label graphs. In addition, we propose a sparse correlation constraint to enhance the LG module, and also explore different LG architectures. We validate our method on two diverse multi-label datasets: MS-COCO and Fashion550K. Experimental results show that our A-GCN significantly improves baseline methods and achieves performance superior or comparable to the state of the art.
Stick: A Harmonious Fusion of Buffer-based and Learning-based Approach for Adaptive Streaming

Off-the-shelf buffer-based approaches leverage a simple yet effective buffer-bound to control the adaptive bitrate (ABR) streaming system. Nevertheless, such approaches in standard parameters fail to always provide high quality of experience (QoE) video streaming services under all considered network conditions. Meanwhile, state-of-the-art learning-based ABR approach Pensieve outperforms existing schemes but is impractical to deploy. Therefore, how to harmoniously fuse the buffer-based and learning-based approach has become a key challenge for further enhancing ABR methods. In this paper, we propose Stick, an ABR algorithm that fuses the deep learning method and traditional buffer-based method. Stick utilizes the deep reinforcement learning (DRL) method to train the neural network, which outputs the buffer-bound to control the buffer-based approach for maximizing the QoE metric with different parameters. Trace-driven emulation illustrates that Stick betters Pensieve by 3.5% - 9.41% with an overhead reduction of 88%. Moreover, aiming to further reduce the computational costs while preserving the performances, we propose Trigger, a light-weighted neural network that determines whether the buffer-bound should be adjusted. Experimental results show that Stick+Trigger rivals or outperforms existing schemes in average QoE by 1.7%-28%, and significantly reduces the Stick's computational overhead by 24%-61%. Meanwhile, we show that Trigger also helps other ABR schemes mitigate the overhead. Extensive results on real-world evaluation demonstrate the superiority of Stick over existing state-of-the-art approaches.
Improved Quantum Particle Swarm Optimization Algorithm for Offline Path Planning in AUVs

This paper proposes an offline path planning method based on the Improved Quantum Particle Swarm Optimization (IQPSO) algorithm for Autonomous Underwater Vehicles (AUVs) in the underwater environment. The spherical modelling method is adopted to represent irregular underwater obstacles as spheres with a specified radius. Then, the IQPSO algorithm is developed to solve the problem of the limitations of the convergence and optimization ability of the traditional Quantum Particle Swarm Optimization (QPSO) algorithm and to identify the best path for AUVs. In this algorithm, to satisfy the three factors of path safety, path length and angle change of the path point, the fitness function is constructed to achieve multi-objective optimization. A smooth path is designed using the cubic spline interpolation algorithm. Different scenes or the same scene with different obstacles are designed to verify the effectiveness of the algorithm. The simulation results show that compared with PSO algorithm, QPSO algorithm, EGA algorithm and DENPSO algorithm, the path generated by IQPSO algorithm in various scenes is shorter, smoother and more stable.
Fraud Detection in Credit Card Data using Unsupervised Machine Learning Based Scheme

Development of communication technologies and e-commerce has made the credit card as the most common technique of payment for both online and regular purchases. So, security in this system is highly expected to prevent fraud transactions. Fraud transactions in credit card data transaction are increasing each year. In this direction, researchers are also trying the novel techniques to detect and prevent such frauds. However, there is always a need of some techniques that should precisely and efficiently detect these frauds. This paper proposes a scheme for detecting frauds in credit card data which uses a Neural Network (NN) based unsupervised learning technique. Proposed method outperforms the existing approaches of Auto Encoder (AE), Local Outlier Factor (LOF), Isolation Forest (IF) and K-Means clustering. Proposed NN based fraud detection method performs with 99.87% accuracy whereas existing methods AE, IF, LOF and K Means gives 97%, 98%, 98% and 99.75% accuracy respectively.
Solar power forecast for a residential smart microgrid based on numerical weather predictions using artificial intelligence methods

Solar power forecast is a much needed means for grid operators, particularly in residential microgrids, to manage the produced energy in a dispatchable fashion. Deterministic methods are unable to accurately forecast the intermittent solar power generation since they depend on unique sets of inputs and outputs. Therefore, stochastic methods and artificially intelligent (AI) strategies are utilized for solar power forecast. In this work, a neural network (NN)-based numerical weather prediction (NWP) model is developed for a residential microgrid in San Diego, California considering all key weather parameters such as cloud coverage, dew point, solar zenith angle, precipitation, humidity, temperature, and pressure in the year 2016. The developed weather model is then used to predict the generated power in the residential smart microgrid. To validate the accuracy of the model, the solar irradiance and generated solar power in the residential microgrid are predicted for the year 2017 using the obtained NN-based model. The results are compared with the actual solar irradiance and power in 2017 to evaluate and validate the accuracy of the developed model. Furthermore, to showcase the effectiveness of neural networks in forecasting solar power and the accuracy of the NN-based model, the results are compared with those of two other methods including multi-variable regression (MVR) and support vector machine (SVM) approaches using mean absolute percentage error (MAPE) and mean squared error (MSE) criteria.
GTFS-Madrid-Bench: A benchmark for virtual knowledge graph access in the transport domain

A large number of datasets are being made available on the Web using a variety of formats and according to diverse data models. Ontology Based Data Integration (OBDI) has been traditionally proposed as a mechanism to facilitate access to such heterogeneous datasets, providing a unified view over their data by means of ontologies. Recently, the term "Virtual Knowledge Graph Access" has begun to be used to refer to the mechanisms that provide query-based access to knowledge graphs virtually generated from heterogeneous data sources. Several OBDI engines exist in the state of the art, with overlapping capabilities but also clear differences among them (in terms of the data formats that they can deal with, mapping languages that they support, query expressivity that they allow, etc.). These engines have been evaluated with different testbeds and benchmarks. However, their heterogeneity has made it difficult to come up with a common comprehensive benchmark that allows for comparisons among them to facilitate their selection by practitioners, and more importantly, for their continuous improvement by the teams that maintain them. In this paper we present GTFS-Madrid-Bench, a benchmark to evaluate OBDI engines that can be used for the provision of access mechanisms to virtual knowledge graphs. Our proposal introduces several scenarios that aim at measuring the query capabilities, performance and scalability of all these engines, considering their heterogeneity. The data sources used in our benchmark are derived from the GTFS data files of the subway network of Madrid. They have been transformed into several formats (CSV, JSON, SQL and XML) and scaled up. The query set aims at addressing a representative number of SPARQL 1.1 features while covering usual queries that data consumers may be interested in.