Reviewerly/exHarmony huggingface dataset:
A framework for differentially-private knowledge graph embeddings

Knowledge graph (KG) embedding methods are at the basis of many KG-based data mining tasks, such as link prediction and node clustering. However, graphs may contain confidential information about people or organizations, which may be leaked via embeddings. Research recently studied how to apply differential privacy to a number of graphs (and KG) analyses, but embedding methods have not been considered so far. This study moves a step toward filling such a gap, by proposing the Differential Private Knowledge Graph Embedding (DPKGE) framework. DPKGE extends existing KG embedding methods (e.g., TransE, TransM, RESCAL, and DistMult) and processes KGs containing both confidential and unrestricted statements. The resulting embeddings protect the presence of any of the former statements in the embedding space using differential privacy. Our experiments identify the cases where DPKGE produces useful embeddings, by analyzing the training process and tasks executed on top of the resulting embeddings.
SSL++: Improving Self-Supervised Learning by Mitigating the Proxy Task-Specificity Problem

The success of deep convolutional networks (ConvNets) generally relies on a massive amount of well-labeled data, which is labor-intensive and time-consuming to collect and annotate in many scenarios. To eliminate such limitation, self-supervised learning (SSL) is recently proposed. Specifically, by solving a pre-designed proxy task, SSL is capable of capturing general-purpose features without requiring human supervision. Existing efforts focus obsessively on designing a particular proxy task but ignore the semanticity of samples that are advantageous to downstream tasks, resulting in the inherent limitation that the learned features are specific to the proxy task, namely the proxy task-specificity of features. In this work, to improve the generalizability of features learned by existing SSL methods, we present a novel self-supervised framework SSL++ to incorporate the proxy task-independent semanticity of samples into the representation learning process. Technically, SSL++ aims to leverage the complementarity, between the low-level generic features learned by a proxy task and the high-level semantic features newly learned by the generated semantic pseudo-labels, to mitigate the task-specificity and improve the generalizability of features. Extensive experiments show that SSL++ performs favorably against the state-of-the-art approaches on the established and latest SSL benchmarks.
A lightweight framework for abnormal driving behavior detection

As one of the most serious hazards in the world, traffic accidents have caused huge casualties and property losses. The detection of abnormal behaviors of drivers is of great importance for intelligence transportation systems. However, the training of most abnormal behavior detection artificial intelligence algorithms demands huge computation and memory resources. Therefore, the training process is preferred to be done in cloud. In this paper, we propose a lightweight abnormal driving behavior detection framework, which applies intelligence to the edge and gives the IoT (Internet of Things) devices the ability to process and understand the data. The framework consists of four modules: bus driver wearing mask detection, bus driver abnormal motion detection, bus driver fatigue driving detection, and video recovery. Comprehensive experiments on real-world datasets have demonstrated the effectiveness and applicability of the proposed framework.
Design and Implementation of an Optimized PID Controller for the Adaptive Cruise Control System

We have designed and implemented an optimized PID controller for an adaptive cruise control system in this paper. The mathematical model for a cruise control system has been developed, and it is observed that it is a nonlinear first-order model with dead time. The objective functions chosen for optimizing the PID controller are ITE, ITAE and ITSE. The design of the optimized PID controller is based on the particle swarm optimization technique and teacher learning-based optimization technique. The results are scientifically compared with the conventionally tuned PID and fuzzy-based controllers. The optimized Proportional Integral Derivative controller shows better performance than a conventional PID and fuzzy-based controller. The overshoot of the system has been reduced to 0% from 46%, and the rise time has been reduced to 0.6150 s. This is the new work in the literature that will be quite useful for the performance enhancement of the cruise control system.
Object Detection Improves Tumour Segmentation in MR Images of Rare Brain Tumours

Tumour lesion segmentation is a key step to study and characterise cancer from MR neuroradiological images. Presently, numerous deep learning segmentation architectures have been shown to perform well on the specific tumour type they are trained on (e.g., glioblastoma in brain hemispheres). However, a high performing network heavily trained on a given tumour type may perform poorly on a rare tumour type for which no labelled cases allows training or transfer learning. Yet, because some visual similarities exist nevertheless between common and rare tumours, in the lesion and around it, one may split the problem into two steps: object detection and segmentation. For each step, trained networks on common lesions could be used on rare ones following a domain adaptation scheme without extra fine-tuning. This work proposes a resilient tumour lesion delineation strategy, based on the combination of established elementary networks that achieve detection and segmentation. Our strategy allowed us to achieve robust segmentation inference on a rare tumour located in an unseen tumour context region during training. As an example of a rare tumour, Diffuse Intrinsic Pontine Glioma (DIPG), we achieve an average dice score of 0.62 without further training or network architecture adaptation.
Salient object detection by aggregating contextual information

Salient object detection is a fundamental computer vision task. Deep learning techniques have been successfully applied for salient object detection, however, the saliency maps still suffer from incomplete predictions owing to the difficulty and complexity of objects. We propose a novel convolutional neural network based on aggregating contextual information that can improve the accuracy of salient object detection. In order to obtain high-level global information for fine-level feature maps, we add a pyramid pooling module to the proposed network. Moreover, building a guiding module that consists of four context-information feature modules, the algorithm can gain more effective information and enrich the details of saliency maps. Extensive experiments conducted using six benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art algorithms.
Unsupervised cross-domain person re-identification by instance and distribution alignment

Most existing person re-identification (re-id) methods assume supervised model training on a separate large set of training samples from the target domain. While performing well in the training domain, such trained models are seldom generalisable to a new independent unsupervised target domain without further labelled training data from the target domain. To solve this scalability limitation, we develop a novel Hierarchical Unsupervised Domain Adaptation (HUDA) method. It can transfer labelled information of an existing dataset (a source domain) to an unlabelled target domain for unsupervised person re-id. Specifically, HUDA is designed to model jointly global distribution alignment and local instance alignment in a two-level hierarchy for discovering transferable source knowledge in unsupervised domain adaptation. Crucially, this approach aims to overcome the under-constrained learning problem of existing unsupervised domain adaptation methods. Extensive evaluations show the superiority of HUDA for unsupervised cross-domain person re-id over a wide variety of state-of-the-art methods on four re-id benchmarks: Market-1501, DukeMTMC, MSMT17 and CUHK03.
The state of research on software engineering competencies: A systematic mapping study

Considering the critical role of software in modern societies, we face an urgent need to educate more competent software professionals. Software engineering competencies (SEC) are considered the backbone of successfully developing software products. Consequently, SEC has become a hotspot for software engineering research and practice. Although scientific literature on SEC is not lacking, to our knowledge, a comprehensive overview of the current state of SEC research is missing. To that end, we conducted an extensive and systematic review of the SEC literature. We provide an overview of the current state of research on SEC, with a particular focus on common SEC research areas. In addition to reporting the available SEC models and frameworks, we compile a list of 49 unique essential competencies of software professionals. Finally, we highlight several gaps in the literature that deserve further research. In particular, we call for a better understanding of how the essential competencies of software professionals change over time, as well as fresh accounts of the essential competencies of software professionals. Additionally, considering recent shifts toward Agile and DevOps methods, future research must explore the competencies required for developing software products in modern development environments.
Information and communication technologies (ICTs): The potential for enhancing the dissemination of agricultural information and services to smallholder farmers in sub-Saharan Africa

The transformation of smallholder farming is poised to be one of the key drivers of achieving the dual objectives of food security and poverty reduction in Sub-Saharan Africa (SSA). Smallholder farmers account for between 60–80% of the food produced in the region but face many challenges that impede their productivity. Such challenges include a lack of timely access to appropriate agricultural information and services, which results in poor decision-making, particularly in addressing challenges and responding effectively to opportunities. In that context, the effective use of Information and Communication Technologies (ICTs) in improving accessibility to appropriate agricultural information and services presents substantial prospects for transforming the productivity and livelihoods of the farmers. Currently, the region experiences massive penetration and propagation of mobile and web-based applications. However, there is a dearth of compelling, comprehensive reviews evaluating their importance in enhancing agricultural information and services dissemination to smallholder farmers. Therefore, the current review explores the potential of enhancing agricultural information and services dissemination to smallholder farmers through ICTs and highlights gaps in their development and deployment in SSA. Five existing mobile applications used to disseminate agricultural information and services to smallholder farmers were identified, and their advantages, limitations, and opportunities were discussed. These were Esoko, iCow, Community Knowledge Workers, WeFarm and DigiFarm. The development and deployment of user-driven mobile applications that provide curated skill-sharing platforms, encourage farmers to give feedback to extension systems in real-time and promote the participation of women and youth in agriculture are recommended.
Identifying cybersickness causes in virtual reality games using symbolic machine learning algorithms

Virtual reality (VR) and head-mounted displays are constantly gaining popularity in various fields such as education, military, entertainment, and health. Although such technologies provide a high sense of immersion, they can also trigger symptoms of discomfort. This condition is called cybersickness (CS) and is quite popular in recent virtual reality publications. This work proposes a novel experimental analysis using symbolic machine learning to rank potential causes of CS in VR games. We estimate CS causes and rank them according to their impact using classical machine learning. Experiments are performed using two virtual reality games and 6 experimental protocols along with 37 valid samples from a total of 88 volunteers. Our results show that rotation and acceleration triggered cybersickness more frequently in a flight game in contrast to a race game. We could also observe that subjects that are less experienced with VR are more prone to feel discomfort. Former experience plays a more important role on the race game, as this game provides more liberty to the user in terms of controllers, more displacement alternatives and a more user-controlled acceleration. Furthermore, different causes that trigger discomfort arise based on short or long term VR exposures. We suggest strategies for mitigating CS for these two scenarios: short and long term exposure experiences and compare the two highlighted scenarios (race and flight).
Online Classroom Teaching Quality Evaluation System Based on Facial Feature Recognition

With the rapid development of computer big data technology, online education in the form of online courses is increasingly becoming an important means of education. In order to objectively evaluate the teaching quality of online classroom, a teaching quality evaluation system based on facial feature recognition is proposed. The improved (MTCNN) multitask convolutional neural network is used to determine the face region, and then the eye and mouth regions are located according to the facial proportion relationship of the face. The light AlexNet classification based on Ghost module was used to detect the open and close state of eyes and mouth and combined with PERCLOS (percentage of eye closure) index values to achieve fatigue detection. Large range pose estimation from pitch, yaw, and roll angles can be achieved by easily locating facial feature angles. Finally, the fuzzy comprehensive evaluation method is used to evaluate students’ learning concentration. The simulation experiments are conducted, and the results show that the proposed system can objectively evaluate the teaching quality of online courses according to students' facial feature recognition.
Pancreas Co-segmentation based on dynamic ROI extraction and VGGU-Net

Pancreas segmentation is one of the most challenging tasks in medical image segmentation for its anatomical variability, large individual differences, irregular shape, small volume and complex surroundings. Many methods can achieve more accurate segmentation results on abdominal organs other than the pancreas. To overcome this problem, this paper proposes a novel segmentation method ROI-VGGU-Net (Region of Image-Vision Geometry Group U-shaped Net) that can segment pancreases more accurately from a dynamical extracted region-of-interest (ROI) by our proposed deep learning model VGGU-Net, where the ROI is obtained by combining various location information of other surrounding organs (such as liver, spleen and kidney). Specifically, we first obtain the location of other organs around a pancreas through our proposed VGGU-Net. And then, we compute the center points of these located surrounding organs in every CT slice sequentially to form different ROIs of the pancreas through these center points. Finally, an accurate pancreas segmentation can be achieved through VGGU-Net according to the acquired ROIs. Our various experiments demonstrate the effectiveness of ROI-VGGU-Net.
Improving research quality: the view from the UK Reproducibility Network institutional leads for research improvement

The adoption and incentivisation of open and transparent research practices is critical in addressing issues around research reproducibility and research integrity. These practices will require training and funding. Individuals need to be incentivised to adopt open and transparent research practices (e.g., added as desirable criteria in hiring, probation, and promotion decisions, recognition that funded research should be conducted openly and transparently, the importance of publishers mandating the publication of research workflows and appropriately curated data associated with each research output). Similarly, institutions need to be incentivised to encourage the adoption of open and transparent practices by researchers. Research quality should be prioritised over research quantity. As research transparency will look different for different disciplines, there can be no one-size-fits-all approach. An outward looking and joined up UK research strategy is needed that places openness and transparency at the heart of research activity. This should involve key stakeholders (institutions, research organisations, funders, publishers, and Government) and crucially should be focused on action. Failure to do this will have negative consequences not just for UK research, but also for our ability to innovate and subsequently commercialise UK-led discovery.
Human-related anomalous event detection via spatial-temporal graph convolutional autoencoder with embedded long short-term memory network

Automatic detection of human-related anomalous events in surveillance videos is challenging, owing to unclear definition of anomalies and insufficiency of training data. Generally, the irregular human motion patterns can be regarded as human-related abnormal events. Therefore, we propose a novel method to operate directly on sequences of human skeleton graphs for discovering the normal patterns of human motion. The sequence of skeleton graphs is decomposed into two sub-components: global movement and local posture sequences. The global component is utilized to compute local component. The local component sequences are then input to our network for capturing normal spatial-temporal motion patterns of human skeleton. Our network is established on a Spatial-temporal Graph Convolutional Autoencoder (ST-GCAE) and embedded with Long Short-Term Memory (LSTM) network in hidden layers for exploring the temporal cues, which is thus called Spatial-temporal Graph Convolutional Autoencoder with Embedded Long Short-Term Memory Network (STGCAE-LSTM). Different from traditional autoencoder, STGCAE-LSTM owns a single-encoder-dual-decoder architecture, which is capable of reconstructing the input and predicting the unseen future simultaneously. Then, samples that deviate from normal patterns are detected as anomalies with fusion of reconstruction and prediction errors. Experimental results on four challenging datasets demonstrate advantages of our method over other state-of-the-art algorithms.
A Novel Video Copyright Protection Scheme Based on Blockchain and Double Watermarking

With the continuous development of multimedia, more and more digital works such as videos are spread, stored, and used in the network. In recent years, digital copyright infringement disputes have occurred frequently. The traditional copyright protection system has some problems, such as difficulty confirming copyright, monitoring infringement, and obtaining evidence for rights protection. To this end, we have designed and implemented a novel video copyright protection scheme based on the blockchain and double watermarking technology. We use the image correlation coefficient method to extract video keyframes. And we combine with Contourlet Transform domain, QR decomposition, and SIFT algorithm to improve the robustness of watermark against geometric attacks on the premise of invisibility. After that, we use Arnold Transformation (Cat Map) based on the Maximum Entropy Threshold Segmentation to encrypt the robust watermark and strengthen the security. Moreover, based on the characteristics of the fragile watermarking, we accurately locate the attacked video’s tamper position and complete the integrity authentication of the watermarked video. In addition, the hash digest of the video watermark and the user ID of the copyright owner is signed by SM2 and uploaded to the blockchain. The user can register the copyright after passing the identity authentication. We conduct tests and security analysis on the blockchain performance of the system, the performance of the commercial cryptography algorithm, and the security of the watermarking system. The experimental results show that the blockchain used in this system conforms to the industry standard, the performance of SM2 and SM3 is better than ECC-256 and SHA-256, and the system security is well guaranteed.
Object Detection and Pose Estimation Approaches for Soldering Danger Detection

In Japan, the soldering technique is taught in classes at junior high schools, high schools and technical colleges. The beginner of soldering faces the problems such as experience burns, soldering failures and so on. However, it is hard for educators to monitor all learners and educators are required to support them. In this paper, we propose an object detection and pose estimation based motion analysis system for improving danger usage and techniques in soldering.
An unsupervised approach of colonic polyp segmentation using adaptive markov random fields

In this article, an unsupervised image segmentation method is proposed to segment out polyps in endoscopic images. Based on the skeleton of an over-segmented image, an adaptive Markov Random Field (MRF)-based framework is employed. The polyps or abnormal regions show markedly different texture and color characteristics in contrast to normal tissues. In our method, the endoscope images are first over-segmented into superpixels. For final refinement, Local binary pattern (LBP) and color features are used in the adaptive MRF. The experimental results show that the proposed method can perfectly localize polyp regions, and it can give a mean dice value of 60.77% in a test set of 612 images taken from 29 different video sequences. Deep learning models require a huge number of training images for tweaking of network parameters. In this context, it is sometimes challenging to deploy a deep model for unseen medical datasets having many segmentation challenges, like complex background, specularity, non-uniform illuminations, etc. Also, manual labeling of a huge number of images is another challenge. That is why we adopted an unsupervised approach in this paper.