Reviewerly/exHarmony huggingface dataset:
SPK-CG: Siamese Network based Posterior Knowledge Selection Model for Knowledge Driven Conversation Generation

Building a human-computer conversational system that can communicate with humans is a research hotspot in the field of artificial intelligence. Traditional dialogue systems tend to produce irrelevant and non-information responses, which reduce people’s interest in engaging in a conversation. This often leads to boring conversations. To alleviate this problem, many researchers use external knowledge to assist conversation generation. The accuracy of knowledge selection is the prerequisite to ensure the quality of knowledge conversation. This approach has worked positively to a certain extent, but generally only searches knowledge information based on entity words themselves, without considering the specific conversation context. Therefore, if irrelevant knowledge is retrieved, the quality of conversation generation will be reduced. Motivated by this, we propose a novel neural knowledge-based conversation generation model, named Siamese Network based Posterior Knowledge Selection Model for Knowledge Driven Conversation Generation (SPK-CG) . We have designed a novel knowledge selection mechanism to obtain knowledge information that is highly relevant to the context of the conversation. Specifically, the posterior knowledge distribution is used as a soft label to make the prior distribution consistent with the posterior distribution in the training process. At the same time, in order to narrow the gap between prior and posterior distributions and improve the accuracy of knowledge selection, we leverage siamese network and design multi-granularity matching module for knowledge selection. Compared with previous knowledge-based models, our method can select more appropriate knowledge and use the selected knowledge to generate responses that are more relevant to the conversation context. Extensive automatic and human evaluations demonstrate that our model has advantages over previous baselines.
MobileDenseNet: A new approach to object detection on mobile devices

Object detection problem-solving has witnessed vast developments in the past few years. There is a need for lighter models in cases where hardware limitations exist, together with a demand for models to be designed to run on mobile devices. This paper assesses methods used for algorithms which are created to address these issues. The main goal of this article is to improve accuracy in state-of-the-art algorithms while maintaining speed and real-time efficiency. Small objects and inaccurate localization are the main issues in one-stage object detection. To resolve these issues, in this paper, a new network entitled "MobileDenseNet" was created, which is suitable for embedded systems. We also developed a light neck, called "FCPNLite" for mobile devices, which facilitates the detection of small objects. Our research suggests that necks in embedded systems have been sporadically mentioned by the existing studies. The novelty of our network lies in the use of concatenation features. A small yet significant change to the head of the network improved accuracy without increasing speed or limiting parameters. Overall, the accuracy of our method on the challenging CoCo and Pascal VOC datasets were 24.8 and 76.8 percentage, respectively. This has been by far the highest rate recorded by other state-of-the-art systems. Our network has the capability to increase accuracy while maintaining real-time efficiency in mobile devices. We calculated the operating speed on Pixel 3 (Snapdragon 845) at 22.8 fps. The source code of this research is available on https://github.com/hajizadeh/MobileDenseNet.
Chaotic Image Encryption Based on Boson Sampling

Abstract As a fundamental form of information representation, image encryption is a significant research issue in the multimedia era. An image encryption scheme is proposed benefiting from chaotic random behavior characteristics of Boson sampling (BS) probability distribution, where the chaotic random number sequence is used to implement the encryption process including scrambling image pixels and altering the values of pixels. Further, a BS‐based image encryption prototype system is constructed on the programmable silicon photonic processor chip for BS with 5‐mode 2‐photon, which can encrypt 4 × 4 grayscale images. Besides, experiments for encrypting 256 × 256 gray‐scale images with 20‐mode 8‐photon BS are simulated on the StrawberryFields photonic quantum software platform. Numerical analyses demonstrate the proposed scheme has good effectiveness in terms of randomness test, information entropy, histogram, correlation, key space, key sensitivity, and anti‐attack, offering gains in many cryptographic applications. The BS‐based image encryption prototype system extends the application of BS to solve practical problems in addition to proving quantum advantages.
Grid-Connected Solar Photovoltaic (PV) System for Covered Linkways

Solar photovoltaic (PV) technology is a current trend worldwide, offering many environmental benefits. With the flagship SolarNova Programme in Singapore, solar PV has gained its momentum. However, it remains important to explore new avenues to introduce solar PV in the urban settings. The housing development board (HDB) green towns promote sustainable living. Therefore, introducing solar PV is essential. This research study aims to propose and conduct an economic evaluation on solar PV for the HDB’s covered linkways. HDB covered linkways connect buildings within the green towns. Hence, installing solar PV systems in covered linkways facilitates to self-produce required energy and export extra electricity to the grid. This research study used PVWatts calculator to calculate the power generation. A thin film solar PV is used for the study with fixed array type and azimuth is 180°. Four solar PV systems, namely (1) 4 kW, (2) 5 kW, (3)10 kW, and (4) 20 kW, were evaluated in this research study. The initial cost ranges from S$7000 to S$38,000 for the four types. For 4 kW and 5 kW systems, the payback period is 6.22 years. The highest payback period is for a 20-kW system, which is 7.4 years. The 10-kW system generates a significant portion of the electricity requirements, and the payback period is 6.04 years. This research contributes to the solar PV domain by proposing a novel grid-connected solar PV system for covered linkways while identifying the most cost-effective solution.
UAV4PE: An Open-Source Framework to Plan UAV Autonomous Missions for Planetary Exploration

Autonomous Unmanned Aerial Vehicles (UAV) for planetary exploration missions require increased onboard mission-planning and decision-making capabilities to access full operational potential in remote environments (e.g., Antarctica, Mars or Titan). However, the uncertainty introduced by the environment and the limitation of available sensors has presented challenges for planning such missions. Partially Observable Markov Decision Processes (POMDPs) are commonly used to enable decision-making and mission-planning processes that account for environmental, perceptional (extrinsic) and actuation (intrinsics) uncertainty. Here, we propose the UAV4PE framework, a testing framework for autonomous UAV missions using POMDP formulations. This framework integrates modular components for simulation, emulation, UAV guidance, navigation and mission planning. State-of-the-art tools such as python, C++, ROS, PX4 and JuliaPOMDP are employed by the framework, and we used python data-science libraries for the analysis of the experimental results. The source code and the experiment data are included in the UAV4PE framework. The POMDP formulation proposed here was able to plan and command a UAV-based planetary exploration mission in simulation, emulation and real-world experiments. The experiments evaluated key indicators such as the mission success rate, the surface area explored and the number of commands (actions) executed. We also discuss future work aimed at improving the UAV4PE framework, and the autonomous UAV mission planning formulation for planetary exploration.
QuickFaaS: Providing Portability and Interoperability between FaaS Platforms

Serverless computing hides infrastructure management from developers and runs code on-demand automatically scaled and billed during the code’s execution time. One of the most popular serverless backend services is called Function-as-a-Service (FaaS), in which developers are often confronted with cloud-specific requirements. Function signature requirements, and the usage of custom libraries that are unique to cloud providers, were identified as the two main reasons for portability issues in FaaS applications, leading to various vendor lock-in problems. In this work, we define three cloud-agnostic models that compose FaaS platforms. Based on these models, we developed QuickFaaS, a multi-cloud interoperability desktop tool targeting cloud-agnostic functions and FaaS deployments. The proposed cloud-agnostic approach enables developers to reuse their serverless functions in different cloud providers with no need to change code or install extra software. We also provide an evaluation that validates the proposed solution by measuring the impact of a cloud-agnostic approach on the function’s performance, when compared to a cloud-non-agnostic one. The study shows that a cloud-agnostic approach does not significantly impact the function’s performance.
Security and Privacy Challenges in Blockchain Application

The blockchain technology, proposed by Satoshi Nakamoto, is a digital distribution system that is mainly anticipated for financial transactions such as bitcoin and later used the delivery of a distributed database that depends on a peer-to-peer (P2P) grid system with a high level of confidence, availability, and integrity (CIA) deprived of requiring the help of a trusted third party (TTP). The blockchain system encompasses numerous distributed linked blocks controlled by the use of DLT technology (distributed ledger technology). Every block in the chain retains a number of transactions, and when a new transaction takes place, a new block is attached to the end of the chain. This decentralized environment and structural design of blockchain systems make blockchain’s robustness, immutability, and anonymity. Thus it is a secure technology that is difficult to attack or cheat. Consequently, blockchain has gained admissibility to use diverse fields such as governments, industry, cryptocurrency, healthcare, and Internet of Things (IoT) technology. A short review of blockchain technology will invite this chapter to familiarize the reader with the main concepts of blockchain systems such as blocks, chains, the decentralized architecture of blockchain technology, and the different features that correlate the blockchain system with the diverse security applications such as smart contract platform, consensus mechanism and mining, and the distributed ledger. After that, we will consider reviewing some of the research studies that use blockchain to provide security solutions in different areas, including governments, industry, cryptocurrency, healthcare, and IoT. After that, we will traverse through the security of blockchain policies, features, and algorithms. Moreover, we will develop a systematic brief for security issues and exploitations of blockchain technology such as 51% vulnerability risk, double spending, exploring Sybil, and others.
Mortality Prediction and Safe Drug Recommendation for Critically-ill Patients

Drug recommendation is denoted as the task of predicting drug combinations for patients' therapies with complex diseases (i.e., thrombosis, diabetes, etc.). These patients usually suffer from polypharmacy, and consequently various drug drug interactions. In this paper, we integrate the patients' Electronic Health Records (EHRs) with an adversarial Drug-Drug Interaction (DDI) knowledge graph to predict the next drug combination for a patient's therapy and minimize the drug side effects. In particular, we integrate an EHR graph, which incorporates the patient, the disease, the therapy, and the drug information, with an Adversarial DDI knowledge graph to recommend both accurate and safe medication. We also predict mortality and the time to death of critically-ill patients, to identify clinically meaningful predictors (e.g., harmful drug combinations). By identifying those drugs which can act adversarially, we are able to improve either the efficacy of the patient's therapy or minimize the toxicity and drug side effects. We have run experiments with a real-life medical data set. Our results show that we can assist doctors to prescribe effective and safe medication for the patients' treatment.
Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism

With the rapid development of Internet technology, numerous malicious URLs have appeared, which bring a large number of security risks. Efficient detection of malicious URLs has become one of the keys for defense against cyber attacks. Deep learning methods bring new developments to the identification of malicious web pages. This paper proposes a malicious URL detection method based on a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The method is based on the BiGRU model. A regularization operation called a dropout mechanism is added to the input layer to prevent the model from overfitting, and an attention mechanism is added to the middle layer to strengthen the feature learning of URLs. Finally, the deep learning network DA-BiGRU model is formed. The experimental results demonstrate that the proposed method can achieve better classification results in malicious URL detection, which has high significance for practical applications.
Accurate Ship Detection Using Electro-Optical Image-Based Satellite on Enhanced Feature and Land Awareness

This paper proposes an algorithm that improves ship detection accuracy using preprocessing and post-processing. To achieve this, high-resolution electro-optical satellite images with a wide range of shape and texture information were considered. The developed algorithms display the problem of unreliable detection of ships owing to clouds, large waves, weather influences, and shadows from large terrains. False detections in land areas with image information similar to that of ships are observed frequently. Therefore, this study involves three algorithms: global feature enhancement pre-processing (GFEP), multiclass ship detector (MSD), and false detected ship exclusion by sea land segmentation image (FDSESI). First, GFEP enhances the image contrast of high-resolution electro-optical satellite images. Second, the MSD extracts many primary ship candidates. Third, falsely detected ships in the land region are excluded using the mask image that divides the sea and land. A series of experiments was performed using the proposed method on a database of 1984 images. The database includes five ship classes. Therefore, a method focused on improving the accuracy of various ships is proposed. The results show a mean average precision (mAP) improvement from 50.55% to 63.39% compared with other deep learning-based detection algorithms.
Virtual Reality in Space Technology Education

The simplification of space science and technology for students K–12 is a challenging task for educators. Virtual reality and augmented reality are educational techniques that introduce the concept of educational games. Moreover, those techniques have a stunning effect on students. This work presents the utilization of virtual reality models to teach students about the satellite types, satellite subsystems, the satellite assembly and integration process, watching the rocket launch carrying the satellite and observing the satellite in its orbit in virtual space laboratories. A 10-min mission in virtual laboratories will effectively improve the learning outcomes. In addition to the VR feature, a set of activities and short movies are considered to be beneficial for use by students to enrich the teaching results. Finally, the VR model results confirmed that the students’ knowledge about the space technology cycle is boosted.
An Approach to Accurate Ship Image Recognition in a Complex Maritime Transportation Environment

In order to monitor traffic in congested waters, permanent video stations are now commonly used on interior riverbank bases. It is frequently challenging to identify ships properly and effectively in such images because of the intricate backdrop scenery and overlap between ships brought on by the fixed camera location. This work proposes Ship R-CNN(SR-CNN), a Faster R-CNN-based ship target identification algorithm with improved feature fusion and non-maximum suppression (NMS). The SR-CNN approach can produce more accurate target prediction frames for prediction frames with distance intersection over union (DIOU) larger than a specific threshold in the same class weighted by confidence scores, which can enhance the model’s detection ability in ship-dense conditions. The SR-CNN approach in NMS replaces the intersection over union (IOU) filtering criterion, which solely takes into account the overlap of prediction frames, while DIOU, also takes into account the centroid distance. The screening procedure in NMS, which is based on a greedy method, is then improved by the SR-CNN technique by including a confidence decay function. In order to generate more precise target prediction frames and enhance the model’s detection performance in ship-dense scenarios, the proposed SR-CNN technique weights prediction frames in the same class with DIOU greater than a predetermined threshold by the confidence score. Additionally, the SR-CNN methodology uses two feature weighting methods based on the channel domain attention mechanism and regularized weights to provide a more appropriate feature fusion for the issue of a difficult ship from background differentiation in busy waters. By gathering images of ship monitoring, a ship dataset is created to conduct comparative testing. The experimental results demonstrate that, when compared to the three traditional two-stage target detection algorithms Faster R-CNN, Cascade R-CNN, and Libra R-CNN, this paper’s algorithm Ship R-CNN can effectively identify ship targets in the complex background of far-shore scenes where the distinction between the complex background and the ship targets is low. The suggested approach can enhance detection and decrease misses for small ship targets where it is challenging to distinguish between ship targets and complex background objects in a far-shore setting.
A multi‐parametric machine learning approach using authentication trees for the healthcare industry

Abstract The Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi‐parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in‐depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis.
Tutorial: Toward Robust Deep Learning against Poisoning Attacks

Deep Learning (DL) has been increasingly deployed in various real-world applications due to its unprecedented performance and automated capability of learning hidden representations. While DL can achieve high task performance, the training process of a DL model is both time- and resource-consuming. Therefore, current supply chains of the DL models assume the customers obtain pre-trained Deep Neural Networks (DNNs) from the third-party providers that have sufficient computing power. In the centralized setting, the model designer trains the DL model using the local dataset. However, the collected training data may contain erroneous or poisoned data points. The model designer might craft malicious training samples and inject a backdoor in the DL model distributed to the users. As a result, the user’s model will malfunction. In the federated learning setting, the cloud server aggregates local models trained on individual local datasets and updates the global model. In this scenario, the local client could poison the local training set and/or arbitrarily manipulate the local update. If the cloud server incorporates the malicious local gradients in model aggregation, the resulting global model will have degraded performance or backdoor behaviors. In this article, we present a comprehensive overview of contemporary data poisoning and model poisoning attacks against DL models in both centralized and federated learning scenarios. In addition, we review existing detection and defense techniques against various poisoning attacks.
Vision-based Pakistani sign language recognition using bag-of-words and support vector machines

In order to perform their daily activities, a person is required to communicating with others. This can be a major obstacle for the deaf population of the world, who communicate using sign languages (SL). Pakistani Sign Language (PSL) is used by more than 250,000 deaf Pakistanis. Developing a SL recognition system would greatly facilitate these people. This study aimed to collect data of static and dynamic PSL alphabets and to develop a vision-based system for their recognition using Bag-of-Words (BoW) and Support Vector Machine (SVM) techniques. A total of 5120 images for 36 static PSL alphabet signs and 353 videos with 45,224 frames for 3 dynamic PSL alphabet signs were collected from 10 native signers of PSL. The developed system used the collected data as input, resized the data to various scales and converted the RGB images into grayscale. The resized grayscale images were segmented using Thresholding technique and features were extracted using Speeded Up Robust Feature (SURF). The obtained SURF descriptors were clustered using K-means clustering. A BoW was obtained by computing the Euclidean distance between the SURF descriptors and the clustered data. The codebooks were divided into training and testing using fivefold cross validation. The highest overall classification accuracy for static PSL signs was 97.80% at 750 × 750 image dimensions and 500 Bags. For dynamic PSL signs a 96.53% accuracy was obtained at 480 × 270 video resolution and 200 Bags.