Reviewerly/exHarmony huggingface dataset:
Dehazing Cost Volume for Deep Multi-view Stereo in Scattering Media

We propose a learning-based multi-view stereo (MVS) method in scattering media such as fog or smoke with a novel cost volume, called the dehazing cost volume. An image captured in scattering media degrades due to light scattering and attenuation caused by suspended particles. This degradation depends on scene depth; thus it is difficult for MVS to evaluate photometric consistency because the depth is unknown before three-dimensional reconstruction. Our dehazing cost volume can solve this chicken-and-egg problem of depth and scattering estimation by computing the scattering effect using swept planes in the cost volume. Experimental results on synthesized hazy images indicate the effectiveness of our dehazing cost volume against the ordinary cost volume regarding scattering media. We also demonstrated the applicability of our dehazing cost volume to real foggy scenes.
Thermodynamic identities with sunray diagrams

Abstract One of the hurdles in teaching undergraduate thermodynamics is a plethora of complicated partial derivative identities. Students suffer from difficulties in deriving, justifying, or interpreting the identities, misconceptions about partial derivatives, and a lack of in-depth understanding of the meanings of identities. We propose a novel diagrammatic method for the calculus of differentials and partial derivatives called the ‘sunray diagram’ that resolves the difficulties above. The sunray diagram technique relates a partial derivative with ‘arrow sliding’, which enables an aesthetic and intuitive manipulation of partial derivative expressions in the form of successive arrow slidings. Furthermore, the sunray diagram is more than an ad hoc or abstract machinery but is based on the symplectic structure of thermodynamics; the sunray diagram admits a direct physical interpretation on the P – V (or T – S ) plane. We elaborate on such physical semantics of the sunray diagram by taking Maxwell’s approach to the geometry of thermodynamic structures—reinterpreted in terms of differential geometry—as a reference point. We anticipate that our discussion introduces the geometry of thermodynamics to learners and enriches the graphical pedagogy in physics education.
An online tool for analyzing written student feedback

Collecting student feedback is commonplace in universities. Feedback surveys usually have both open-ended questions and Likert-type questions, but the answers to open questions tend not to be analysed further than simply reading them. This paper presents a tool for analyzing written student feedback using topic modeling and emotion analysis. We demonstrate the utility of this tool using course survey responses from a software engineering (SE) programme.
Vec2UAge: Enhancing underage age estimation performance through facial embeddings

Automated facial age estimation has drawn increasing attention in recent years. Several applications relevant to digital forensic investigations include the identification of victims, suspects and missing children, and the decrease of investigators’ exposure to psychologically impacting material. Nevertheless, due to the lack of accurately labelled age datasets, particularly for the underage age range, sufficient performance accuracy remains a major challenge in the field of age estimation. To address the problem, a novel regression-based model was created, Vec2UAge. FaceNet embeddings were extracted and used as feature vectors to train the model from the VisAGe and Selfie-FV datasets. A balanced, unbiased dataset was created for testing and validation. Data augmentation techniques were evaluated to further be used to expand the training dataset. The learning rate (lr) is one of the most important hyper-parameters for deep neural networks; a cyclic learning rate approach was used to find the optimal initial value for lr and the performance was evaluated. The distribution of model performance was presented per optimiser and one of the winning models with a Stochastic Weight Averaging (SWA) optimised training run reached a mean absolute error rate as low as 2.36 years. Additionally, the time of convergence using SWA was significantly faster than other optimisers evaluated, i.e., ADAGRAD, ADAM and Stochastic Gradient Descent. The evaluation model metric is presented in a form of a distribution rather than a single value, giving more insights into the effects of the random initialisations, optimisers and the learning rate on the outcome.
The development and comparison of face recognition algorithms based on different technical characteristics

Face recognition technology is the classic issue in the computer vision field. As the most significant biological feature of human beings, human face has attracted the wide attention of large amount of researchers. Although, the continuously emerging new face recognition algorithms have their unique benefits, the reference significance of classic old algorithms cannot be ignored. Therefore, the regular conclusion of face recognition algorithms is very necessary. In the essay, the classification is conducted based on the development history and major technology characteristics of face recognition technology. The essay separately introduces and comparatively analyzes the three representative traditional face recognition algorithms which are template matching technology, AdaBoost framework and DPM model. In addition, it also introduces the deep-learning face recognition algorithms, including R-CNN, Cascade CNN, DenseBox, MTCNN and YOLO. And then, the advantages and disadvantages of each algorithm are separately described.
Research on Ordering Recommendation System of Traditional Chinese Medical Health Preserving Ontology Model based on Context-aware Environment

Integrating the health preserving concept of Traditional Chinese medicine (TCM) into the recommendation system can provide users with personalized and healthy information push. By constructing an ontology-based TCM health preserving information model in the ordering system, we designed context rules that conform to the health preserving concept, and by iteratively improving user information, based on demographic recommendations, we improved the status of the sparseness of the ordering system and the tendency of singular recommendations recommend based on the similarity of the User Profiling and the context.
Some important results on 𝒯-Direct codes

[Formula: see text]-Direct codes are an extension to the class of linear codes having complementary duals (LCD codes). Defined over a finite field [Formula: see text], it is comprised of [Formula: see text] linear codes [Formula: see text], [Formula: see text] with [Formula: see text], where [Formula: see text] is the dual with respect to [Formula: see text]. A 2-Direct code [Formula: see text] with respect to [Formula: see text] is comprised of only LCD codes: [Formula: see text]. On the contrary, two LCD codes do not set up a [Formula: see text]-Direct code in general. This paper presents some important and generalized results on [Formula: see text]-Direct codes, in that it attempts to construct [Formula: see text]-Direct codes from LCD codes. The class of [Formula: see text]-cyclic maximum rank distance (MRD) codes as having complementary duals over [Formula: see text] are generalized. Dual bases such as self-dual basis and self-dual normal basis play a crucial role in constructions. Further, construction implausibility of [Formula: see text]-Direct codes from almost self-dual bases is also dealt. Results obtained are demonstrated through examples.
Privacy-Protection Path Finding Supporting the Ranked Order on Encrypted Graph in Big Data Environment

Since data outsourcing in big data environment become popular and become a trend, large quantities of graph data are outsourced to the big data server for saving cost. As the big data server can not be fully reliable, people usually encrypt the graph data before they are outsourced to the big data platform for the privacy protection. The path finding is a frequently-used action and can be useful for production and living. The path finding supporting the ranked order is a more useful operation, and a user can obtain a ranked search result set. Because of the outsourced graph data being encrypted on the big data server, the path finding supporting the ranked order becomes a task with enough challenge. In this paper, we propose a solution to perform privacy-protection path finding supporting the ranked order on encrypted graph in big data environment (PPFR). Our research uses an encryption mechanism and a ranking strategy to achieve path finding supporting the ranked order. We formally analyze the security of our scheme. We demonstrate the efficiency of our proposed scheme on a real graph data set by experiment.
Creating Memories and Engagement in College Student Through Virtual Reality

This study intends to extend the understanding of drivers of student engagement in the educational context and analyse mindfulness as a moderator of the different associations in the proposed model. The proposed model regards VR experiences as stimuli, telepresence, pleasantness of the experience and memory as an organism and student engagement as the response. A sample of 136 participants allowed us to test the model. Findings revealed that all hypotheses were supported except H6 linking pleasantness of the experience with student engagement. Only the relationship between pleasantness and memory is higher for mindful students than non-mindful ones. These findings mean that students do not need to feel pleasure about what they are learning to be engaged
Do You Live a Healthy Life? Analyzing Lifestyle by Visual Life Logging

In this work, we investigate the problem of lifestyle analysis and build a visual lifelogging dataset for lifestyle analysis (VLDLA). The VLDLA contains images captured by a wearable camera every 3 seconds from 8:00 am to 6:00 pm for seven days. In contrast to current lifelogging/egocentric datasets, our dataset is suitable for lifestyle analysis as images are taken with short intervals to capture activities of short duration; moreover, images are taken continuously from morning to evening to record all the activities performed by a user. Based on our dataset, we classify the user activities in each frame and use three latent fluents of the user, which change over time and are associated with activities, to measure the healthy degree of the user’s lifestyle. Experimental results show that our method can be used to analyze the healthiness of users’ lifestyles.
Quantum Circuits for Functionally Controlled NOT Gates

We generalize quantum circuits for the Toffoli gate presented by Selinger [1] and Jones [2] for functionally controlled NOT gates, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> gates controlled by arbitrary n- variable Boolean functions. Our constructions target the gate set consisting of Clifford gates and single qubit rotations by arbitrary angles. Our constructions use the Walsh-Hadamard spectrum of Boolean functions and build on the work by Schuch and Siewert [3] and Welch et al [4]. We present quantum circuits for the case where the target qubit is in an arbitrary state as well as the special case where the target is in a known state. Additionally, we present constructions that require no auxiliary qubits and constructions that have a rotation depth of 1.
A Novel Sampling Technique for Multistart-Based Methods

The problem of locating the global minimum of a function is a challenging one with application in many problems. A common method to tackle this problem is the so-called multistart method, which is the base method for many modern optimization methods. This article proposes a new sampling technique for multistart-based methods, that utilizes an artificial neural network as an approximator of the original objective function. The proposed sampling technique is tested against uniform sampling on a wide set of well-known benchmark optimization problems from the relevant literature and the results are reported.
Using CNN to Predict the Transformed Spatial Distance for a Pair of Image Patches

Matching image feature points is a very fundamental task in computer vision. Unlike the previous approaches that were designed from the viewpoint of classification, our study instead employs convolutional neural network (CNN) to estimate the transformed spatial distance of two features in the image plane. This spatial distance can not only be used to classify a match into correct or incorrect, but it also provides additional information about the equality of the match. To evaluate the performance of the proposed CNN, we have generated a large dataset for network training and testing. The experimental results show that if the true transformed spatial distance of a pair of corresponding features is between 0 and 5 pixels, the root mean square error produced by our network is 0.97 pixels.
Blockchain Technology Application In A Refuse Management System

The blockchain mechanism in managing refuse management for a household, is much needed to facilitate peer to peer exchange of data and consent within the community. This can be publicly accessible yet very secure using unanimous user authentication protection system through the blockchain mechanism. Therefore, the main aim of the study is to identify why blockchain should be used as mechanism in the refuse management system in an urban area and to propose a new model of the pollution-free environment through blockchain technology. The research embarks into a qualitative method where simulation and observation were carried out to examine the current situation. The ultimate outcome is to produce a pollution-free environment by integrating the blockchain mechanism in the refuse management system for the utilization of the surrounding community.
The Role of the Internet of Things (IoT) in Energy Management of a Smart City

Abstract The 2030 climate &amp; energy framework contains three key targets for Europe: to cut greenhouse gas (GHG) emissions and increase the share of renewable energy (RE) sources and promote energy efficiency. These tasks are relevant for Russia as well. As one of the frontrunners, Germany has raised its RE share target to 65% by 2030. At the same time, global energy needs are expected to increase by 30% by 2040. In this context, energy systems should not only be planned for the current supply but also be designed for a substantial increase in energy demand. To achieve these goals and avoid an energy crisis, the general idea is to shift from centralized to decentralized energy systems. Furthermore, energy sectors are currently integrated with Information and Communication Technology (ICT) solutions, especially the Internet of Things (IoT), for efficient and sustainable energy management. The article analyses the role of IoT technology in existing and future energy systems according to three scales: flat, building and city.
A revisited visual-based geolocalization framework for forensic investigation support tools

Visual-based geolocalization (VBL) is a well-studied computer vision topic, and has undergone a dramatic improvement in the last decade by the emergence of deep-learning techniques. However, previous studies about VBL have not mainly focused on forensic purposes. In this study, we revisit the traditional VBL framework by replacing its elements with state-of-the-art machine learning techniques, and discuss what points of a VBL framework are important for investigation support tools. Based on this revisit, we claim that there are two essential points for investigation support tools. One is maintainability that results from the ease of replacing the implemented algorithms with more appropriate algorithms. The other is the interpretability of bottlenecks in the current system. These two advantages are specific and essential to forensic science where practicality is important.
Tutor-Guided Interior Navigation With Deep Reinforcement Learning

Traditional reinforcement learning makes policy based on the current system state. However, insufficient system information and few rewards lead to its limited applicability, especially in a partially observed environment with sparse rewards. In this work, we propose a tutor–student network (TSN) for improving an agent’s performance with additional auxiliary information. In the tutor–student framework, a tutor module generates auxiliary information, while a student module refers to the tutor’s suggestion during training. The key of our proposed approach is that tutor provides prior knowledge that does not correspond to a specified environment to help the student module accelerate the learning procedure. We build 12 indoor mazes in ViZDoom, including empty mazes and mazes with obstacles, evaluate the performance of TSN compared with advantage actor–critic (A2C) and show that the proposed network learned navigation faster and obtained higher accumulated rewards. More importantly, our approach could generalize well to new and unseen domains.
Liver Segmentation in CT Images Using Deep Neural Networks

Automatically extracting the liver from CT or MR images due to its heterogeneous shape and proximity to other organs is a challenging task. In recent times, Deep Learning have shown good results in medical image segmentation. Among the developed networks, U-Net has recorded many successes in medical image segmentation. This research presents an algorithm to perform a detailed liver segmentation. In this algorithm, images are first classified with a classification network to be separated into the liver included and non-liver included classes, then the class containing the liver are analyzed with the segmentation network. The segmentation network is an extended version of the U-Net, which takes full advantage of ConvLSTM, densely convolutional layers, recurrent and residual blocks. In the construction and extraction path, common convolutional blocks have been replaced by R2Conv blocks, to train the network more abstractions from input features and prevent gradient vanishing. Also, the mechanism of densely convolutional layers has been used in the last convolutional layer of the construction path. This idea improves the power of network representation by allowing information propagation through the network and reusing features. To concatenate the feature maps in the corresponding contracting path and the up-sampled output, instead of a simple concatenation in skip connections, ConvLSTM was used. Finally, applying this algorithm to the data used in the trial CHAOS challenge for CT, has resulted in a Dice value of %97.5.
Asset Inspection Management System as a Reliable Inspection/ Monitoring Tool

Enterprise asset management (EAM) systems are used by asset owners and/or operators to manage the maintenance of their physical assets. These assets, including equipment, facilities, vehicles, and infrastructure, need maintenance to sustain their operations. An EAM system provides the means to have less unplanned downtime and extended asset longevity, which offers clear business benefits that improve the profit and loss statement and balance sheet. Particularly for capital-intensive industries, like drilling and exploration, the failure of on-time delivery of critical equipment or processes is disruptive and costs nonproductive time and customer satisfaction. Organizations understand these issues and employ an appropriate asset management system to engineer their asset maintenance and management. An EAM system is needed to manage the people, assets/equipment, and processes. EAMs are used to plan, optimize, execute, and track the needed maintenance activities with associated priorities, skills, materials, tools, and information. Similarly, nondestructive testing (NDT) is used as a tool for integrity assessment of assets in drilling and exploration. The main advantage of using NDT is that the item’s intended use or serviceability is not affected. The selection of a specific technique should be based on knowledge and skills that include design, material processing, and material evaluation. Validating the purpose of this paper, we emphasize the importance of optimizing the asset utilization and serviceability to enhance overall efficiency by integrating EAM software that manages assets, the operation management system (OMS) controlling the processes, and asset inspection management systems (AIMSs).
Residual Fusion Network Based Attention for Aspect Level Sentiment Analysis

Abstract Aspect level sentiment analysis is a more fine-grain task compared to document level and sentence level. The aim of the task is to identify the sentiment polarity expressed to the given aspect in the text. In this paper, we propose a residual aspect fusion network with attention for the aspect level sentiment classification. In this network, bidirectional GRU is used to collect semantic information of both sentence words and aspect words, and then attention mechanism is applied to construct aspect vectors for each word in the sentence. In the residual fusion module, the model fuses the aspect information and sentence information together by residual connection structure, in which position weight is applied to utilize the position information of aspect words. At the end of the model, Transformer encode layer is used to extract global feature and fully connected layers are used as the classifier. We train and test the network on SemEval2014 task 4 (Restaurant and Laptop) and Twitter datasets, and the experiments demonstrate that our model performs better than previous methods.
Point Proposal Based Instance Segmentation with Rectangular Masks for Robot Picking Task

In this paper, we focus on instance segmentation of a top-view image for robot picking task. One difficulty in this setting is that objects are located in various orientations and highly overlapped, where a traditional box proposal approach such as Mask R-CNN does not work well because more than one objects often have very similar bounding-boxes. To address this issue, we adopt a recently developed point proposal approach. This approach firstly generates point proposals instead of box proposals, then an instance mask is predicted over an image for each proposal point. This procedure enables us to obtain pixel-precise masks even for objects sharing the same bounding-box. However, mask prediction over an image may produce a few false positive pixels apart from objects and these false positives are problematic for robot picking task. To suppress them, we introduce rectangular masks. A rectangular mask for each proposal point restricts the existence area of the corresponding object within the rectangle. The experimental result on WISDOM dataset shows that our method achieves superior performance to Mask R-CNN with the same backbone model and introduction of rectangular masks gives small improvement of mask AP and large improvement of box AP.
Poster: Maintaining Training Efficiency and Accuracy for Edge-assisted Online Federated Learning with ABS

This paper proposes Adaptive Batch Sizing (ABS) for online federated learning. ABS is an iteration process-efficient solution that adaptively adjusts batch size of the training process at edge nodes. Preliminary results show that ABS maintains training efficiency and accuracy, compared with existing iteration round-efficient solutions.