Reviewerly/exHarmony huggingface dataset:
A Multi-Mechanism Particle Swarm Optimization Algorithm Combining Hunger Games Search and Simulated Annealing

Particle Swarm Optimization (PSO) algorithm is a meta-heuristic algorithm inspired by the foraging behavior of birds, which has received a lot of attention from many scholars because of its simple principle and fast convergence rate. However, the traditional particle update mechanism limits the performance of the algorithm and makes it easy to fall into local extremums, leading to a reduced convergence rate at a later stage. In this paper, we propose a Multi-Mechanism Particle Swarm Optimization (HGSPSO) algorithm. The algorithm optimizes the position update formula of the particles by the Hunger Game Search (HGS) algorithm to accelerate the convergence speed at the later stage of the algorithm, and then the Simulated Annealing (SA) algorithm is introduced to dynamically update the inertia weights to balance the exploration and utilization of the algorithm to help the particles jump out of the local extrema. In addition, the double variational restrictions strategy is used to simultaneously restrict the velocity and position of the particles to avoid particle transgressions. We tested the proposed algorithm with five compare algorithms on 20 benchmark functions in 30, 50, 100, and 1000 dimensions using Eclipse Kepler Release software. The experimental results show that HGSPSO shows significant superiority in all four evaluation metrics and five assessment schemes.
Re-Thinking the Social to Survive the Ruins Human Has Made - About Designing Encounters and Web Making Moves as Matters of Care

Confronted with the challenges of our time, i.e. globalization and its consequences, ecological crises, social inequality, etc., the need arises for designers to recalibrate the modes of design as we know them. In this paper, the concept of the social entails the interconnectedness of social, environmental and self. From there, I elaborate on two micro-design modes that sprouted from my bodymind work, Designing Encounters and Web Making Moves, linking them to Maria Puig de la Bellacasa’s book ‘Matters of Care’. By doing so I elaborate on the potential of carefully designing Encounters and Web makings as a design proposal to debunk the value of (human) independency, and the corresponding tendency of objectifying all other than human.
Research and Construction of Image Classification Model Based on Deep Adaptive Network Method

Due to the limitations of convolutional neural network in the process of image classification, this paper proposes an image classification model fused with deep multi-feature transfer network based on the existing image classification models, and improves the accuracy of image classification by optimizing the convolutional neural network model. The convolutional model optimized in this paper adopts an eight-layer network structure, and the eighth layer is regarded as an adaptive layer. The loss value of the network is about 81% accurate. Finally, the deep network adaptation algorithm is used to compare the accuracy of the two pre-training models, AlexNet and ResNet, and it is obtained that the migration rate of ResNet after adaptive layer processing is significantly higher than that of AlexNet, and the accuracy rate is increased from 75% to 80%, the classification effect is significantly improved.
The Change of Code Metrics for Predicting the Label Change on Evolutionary Projects: An Empirical Study

Traditional software defect prediction approaches often focus on static code metrics. Software evolution could cause changes to the source code of the software, as well as changes to the code metrics and label. In recent years, researchers have proposed many process metrics for evolutionary projects, but they are mainly used to predict the defect proneness of software modules. Whether the change of code metrics (CCMs) could be used to predict the label change on evolutionary projects, and which CCMs are more correlated to the label change? To answer these questions, this paper proposes a framework for building the new datasets with CCMs and label change, and explores the correlations between CCMs and label change with feature ranking approaches. An empirical study is conducted on 40 versions of 11 open-source projects. The experimental results indicate that CCMs can predict the label change.
Targeted Training Improves Security Culture

A growing reliance on information technology has resulted in an increasingly higher proportion of cyber attacks aimed at human targets.
Real-Time Image Inpainting using PatchMatch Based Two-Generator Adversarial Networks with Optimized Edge Loss Function

Image inpainting algorithms based on the deep learning are rapidly developed in the past few years, as they can synthesize textures and structures while maintaining semantic continuity. However, due to the limitations of existing loss functions and convolution, the generated images are usually blurry and have artificial textures. In this paper, a real-time image inpainting system using PatchMatch based two-generator adversarial network (PatchMatch-GAN), is proposed to improve the clarity of generated images. The first generator is committed to continuous semantic textures, and the second one focuses on the image sharpness. Parallel-dilated convolution is used to enlarge the receptive field of filters. For the first generator, a pre-trained VGG16 is used as an encoder to extract features. For the second generator, a DCGAN-like network is designed to generate images. And a feature replacement method is utilized to improve the resolution. The edge loss has also been proposed as a part of the loss function to emphasize the role of the shape of objects. Experiments are carried out on place2 and irregular mask datasets. Compared with GLGAN and CAGAN, in PSNR, the improvements of our system are 3.33% and 2.7%, respectively; in FID, the improvements of our system are 59.54% and 35.59%, respectively. Moreover, the average processing time of the whole system is 29.96 ms, which is 6.56 times faster than GLGAN, and 21.33 times faster than CAGAN. These show that our system can achieve better image inpainting results while meeting the requirements of real-time processing speed.
Tibetan Automatic Word Segmentation Method Based on Deep Learning

The automatic word segmentation of Tibetan is a fundamental task in Tibetan natural language processing. The result of automatic word segmentation will directly affect the other task effect, such as Information Retrieval, Intelligent Question Answering systems (IQAS), and Machine Translation. Current automatic word segmentation uses Tibetan syllables as input, but it is impossible to mine syllable component information. This paper proposes a deep learning model that fuses syllable components, called TBiLSTM. After comparing with the existing Tibetan automatic word segmentation methods, this method has a better effect.
Implication-Realization Model

In this chapter, we will start by explaining about gestaltGestalt (Geschtalt) which appears in music, upon which the ImplicationImplication-Realization model stands. Multiple basic patterns are defined in the ImplicationImplication-Realization model, and at a glance these can seem to have been constructed ad hoc, but we shall demonstrate that actually they are constructed systematically.
Curvature-Based Real-time Brightness Adjustment for Ultra HD Video

In conditions such as inclement weather or in-sufficient lighting at night, video captured by camera equipment may be insufficiently bright and have low contrast. With the popularity of ultra-high-definition video images, its ultra-high resolution makes the performance requirements of video processing algorithms more and more stringent. Aiming at these problems, this paper proposes a curvature-based real-time brightness adjustment algorithm for ultra-high-definition video. The algorithm is implemented in two steps: global brightness adaptive enhancement and local contrast adaptive enhancement. The experimental results show that the algorithm proposed in this paper not only highlights the details of low-brightness areas of video images, but also avoids excessive enhancement of high-brightness areas. It is superior or comparable to the comparison algorithm in both subjective visual effects and objective evaluation indicators. In addition, because the algorithm in this paper is simple and easy to implement, it can be easily implemented in parallel, and can be applied to real-time processing of ultra-high-definition video, which has high practical value.
Path Planning Algorithms for Mobile Robots in Hospital Environment during Covid-19

Since 2019, the COVID-19 virus has spread worldwide, posing a significant health and safety concern. The application of mobile robots in the medical field has gradually demonstrated their unique advantages. Therefore, we focus on the application of mobile robots inwards. By collating and summarizing some of the most popular existing path planning algorithms, this paper illustrates that different algorithms can produce varying outcomes depending on different environments and hardware used. MATLAB is used in this study to simulate four algorithms: To determine the most efficient path, A*, RRT, RRT*, and PRM in a specific hospital map are compared, as well as parameters including path length, average execution time, and resource consumption. Modelling a single-layer hospital map makes it possible for mobile robots in the medical field to execute tasks more efficiently between entry and ward in the COVID-19 hospital environment. Based on a comparison and comprehensive consideration of the data derived from the simulations, it is found that the A* algorithm is superior in terms of optimality, completeness, time complexity, and spatial complexity. Therefore, the A* algorithm is more valuable in finding the best path for a mobile robot in a hospital environment.
Computational Support for Trope Analysis of Textual Narratives

Narrative tropes are repeated patterns of recognizable communicative elements across stories. Tropes are a set of patterns that aid readers in story comprehension. They are also a reflection of socio-cultural norms that are formally or informally present in the particular context for authors and readers. Trope-based analyses are common in media studies but are limited to close readings and individual analyst perspectives. Distant reading of tropes is challenging due to the lack of precise definitions and the variety of forms in which tropes manifest in language, and over space and time within story worlds. This paper presents a trope labeled dataset of scripts, an initial analysis framework, and a system for computational support of trope analysis. For highlighting the challenges of developing computational models, we present a trope prediction algorithm on a movie script dataset based on a model trained with human-annotated tropes from TVTropes.
The Algorithmic Thing, The Real, and Contestation: Tracing the Fringes of Critical Constructivism

A unique and powerful approach in the philosophy of technology, Critical Constructivism endeavors to show how technologies instantiate and consolidate power relations. This chapter elaborates on an enduring vulnerable spot in the framework, namely, that Critical Constructivism only starts to do its work as soon as some problem, struggle, or controversy is recognized as such. Only then, the work of “contestation” can begin. For problems that have not been unearthed yet, but that may be brooding in the background or behind the “screen of attention,” the approach is, as it stands, less equipped. This issue is exacerbated nowadays with recent developments around “algorithmic technologies” such as artificial intelligence or the internet of things, whose inner workings are extremely difficult to access from a user’s point of view, and even increasingly from an expert one. To start orienting Critical Constructivism toward these issues, this chapter pairs it with an unlikely partner, object-oriented ontology (OOO), in order to help it make sense of the problem of the “discovery of problems.” The Heideggerian notion of “breakdown” is crucial in this respect. In fact, it will turn out there is already much “object-oriented potential” in Critical Constructivism, but to start bringing that out fully, we need to take a fresh look at several components of Critical Constructivism from the perspective of OOO, specifically the latter’s provocative take on breakdown.
Empirical Studies Aimed at Understanding Conversational Recommender Systems and Accessibility Aspects

Conversational systems allow communication by voice, touch, and text between system and user through interactive interfaces. They also allow users to carry out tasks such as shopping, answering emails, and with few interactions, the user reaches the goal. The industry usually disregards the limitations of the elderly public when designing conversational systems. Such users may have problems with dexterity, vision, and hearing, impacting their interactions with the system and, consequently, requiring applications accessible to their cognitive and physical characteristics. The goal of this work was to propose a set of functional and non-functional accessibility requirements for the development of conversational systems for elderly. A first set of requirements were gathered through the Wizard of Oz technique, applied with eleven participants. For this, a conversational system, named Bob, was designed to recommend YouTube videos to the user. Recommendations were made according to the user’s needs and preferences, adapting visual and auditory resources through the interface. After the trials, the results showed that, especially for the elderly population, conversational systems can also play the role of a friend or virtual company, with adaptable and adaptive accessibility and interaction settings.
Breast Cancer Prediction Using Deep Learning Technique RNN and GRU

One of the most investigated issues in health informatics is breast cancer diagnostics. Cancer treatment has also been widely researched, showing the importance of early cancer detection. Health records are integrated into a Deep Learning Model to provide an enhanced prognosis. The goal of the work is to create a system that automated those leverages with deep learning and recurrent neural network (RNN) models. The proposed work used the technique for cancer prediction as RNN and a Gated Recurrent Unit (GRU). In the simulation, Python and the cancer dataset have been used. The performance of the system represents that cancer predictions using GRU and RNN models gives a significant role.
Multi-Period-Clustering Algorithm for Carrier Doppler-Shift Acquisition in Space Communications

Introducing the clustering idea in machine learning, a multi-period-clustering (MPC) algorithm is proposed to address the energy dispersion problem for the carrier Doppler-shift acquisition in space communications. The MPC algorithm first selects some large elements, then conducts the density-clustering on them, finally searches the largest cluster as the acquisition result. By clustering multiple signal elements into one larger signal cluster, the MPC algorithm re-accumulates the dispersed energy. Simulation results demonstrate that the proposed algorithm achieves a higher acquisition probability than the FFT, STFT and SRC algorithms and consumes a lower computational complexity than the ES and FRFT algorithms.
Making Digital Money “Work” for Low-Income Users

This paper adds to the research on digitization and money in HCI. By presenting a case of rickshaw drivers in India and their use of Ola, an app-based taxi service like Uber, and Ola Money, an embedded m-wallet, this paper makes a threefold contribution. First, it shows how cash and digital money are not simply different manifestations of the ‘same' money for users. They provide distinct affordances and have different meanings and values, yielding rich insights for design. Second, it seeks to highlight the hidden work done by users around making digital money ‘work' for them. In doing so, it calls for a broader understanding of ‘moneywork' that goes beyond a temporal analysis, through the concept of ‘mobility work'. Finally, it highlights the role of ‘friction' in design. Friction is crucial to users' negotiation of the trade-off between consumption and saving, and can be leveraged to provoke reflection and user-awareness.
AI and the big unknown

The creative and cognitive input of the translator is being changed by their interactions with the Artificial Intelligence (AI) subset of machine translation. Mechanised approaches, the mining of the internet commons and big data contribute in ways that have machines learning from sources with hidden biases and blind spots, especially in many of the freely available tools. Computer Assisted Tools (CAT), Machine Translation (MT) and other technologies used by the language industry/profession have spurred reflection on what academia can offer that industry cannot and vice versa, and how students should be prepared for work in the industry of the future (Rodríguez de Céspedes 2019, Rodríguez de Céspedes 2020).
Motion-Aligned and Hardness-Aware Dynamic Update Network for Weakly-Supervised Vehicle Detection in Satellite Videos

Though the deep learning methods have achieved effective moving vehicle detection in satellite videos, there is a non-negligible premise that these methods require lots of object-level annotations for hundreds of small and blurry vehicles in the vast observation scene. These annotations can be quite labor-intensive and time-consuming. To address this problem, this paper is committed to realizing the vehicle detection based on point-level annotations, and a motion-aligned and hardness-aware dynamic update network is proposed, which consists of the basic detector, motion-aligned initialization method and online pseudo label update scheme. Specifically, the high-quality pseudo bounding boxes are initialized by revising the Gaussian mixture model to fully exploit the motion information in the video sequence and the location information from the point annotations. Then, the pseudo bounding boxes are utilized as the supervision for the basic detector. During the training phase, an online label refinement scheme is designed to refine the pseudo bounding box continuously, and the confidence-aware loss function is defined to adjust the example weight dynamically according to its learning hardness. Extensive experiments on the Jilin-1 and SkySat satellite video datasets show that our method achieves the comparative performance compared with fully-supervised learning methods.
FAN-VE: A Tool for Facial Animation Generation in Virtual Reality Education

Teachers play a critical role in the education process. The importance of this role is not diminished when transitioning into Education inside Virtual Reality. With the rise of popularity in VR and remote education, the optimization of virtual teachers becomes needed. In this paper, we present a tool that provides facial animations to virtual teachers. The tool is designed to be user friendly, generic, and work across different models and facial expressions in order to serve a wide range of use cases. The tool was tested for usability and feedback was collected regarding its functionality. Our results indicate that our tool is user friendly and needed and has potential for further development and adoption.
Designing HCI Strategies to Communicate and Suggest Action for Climate Change Mitigation

Climate change is arguably the most urgent issue of our time, demanding the participation of all types of stakeholders, from individuals to governments. However, climate change communication has been focused on negative framings based on the mere presentation of data, in many cases demotivating or simply not helping in action. This Ph.D. proposes using a Research through Design (RtD) approach to create and evaluate communication design and HCI strategies to meaningfully encode and present outside the obvious, actionable climate-related messages to diverse audiences of non-experts. The studies will focus on the development of HCI projects focused on less explored climate change topics, addressing the gaps found and implementing the implications for design proposed in the first stage of the research. The purpose is to design, test, and evaluate different interactive communication strategies through iterative studies to contribute to new design solutions and guidelines for future work.
CEMENT: On the Use of Evolutionary Coupling Between Tests and Code Units. A Case Study on Fault Localization

Many software engineering techniques, such as fault localization, operate based on relevance relationships between tests and code. These relationships are often inferred using dynamic test execution information that approximates the link between relevant code units and asserted, by the tests, program behaviour. Unfortunately, in practice, dynamic information is not always available due to the instrumentation overheads or the nature of the production environments. This paper proposes CEMENT, a static technique that automatically infers such test and code relationships given the projects' evolution. The key idea is that developers make relevant changes to tests and code units around the same period, and thus their co-evolution reflects probable links between them. We evaluate CEMENT on 15 open-source projects, showing that it captures relevant links. We further conduct a fault localization case study and demonstrate that CEMENT can perform comparably to a current Information Retrieval-based Fault Localization (IRFL) approach. Additional analysis reveals that CEMENT and the IRFL localize different faults, suggesting complementarity. We leverage this complementarity by combining these two through voting, identifying 82% (32) of the faults located by at least one of them. These results suggest that test and code evolutionary coupling can effectively support test and debugging activities.
Dynamic Refining Knowledge Distillation Based on Attention Mechanism

Knowledge distillation is an effective strategy to compress large pre-trained Convolutional Neural Networks (CNNs) into models suitable for mobile and embedded devices. In order to transfer better quality knowledge to students, several recent approaches have demonstrated the benefits of introducing attention mechanisms. However, the existing methods suffer from the problems that the teachers are very rigid in their teaching and the application scenarios are limited. In face of such problems, a dynamic refining knowledge distillation is proposed in this paper based on attention mechanism guided by the knowledge extraction (KE) block whose parameters can be updated. With the help of the KE block, the teacher can gradually guide students to achieve the optimal performance through a question-and-answer format, which is a dynamic selection process. Furthermore, we are able to select teacher networks and student networks more flexibly with the help of channel aggregation and refining factor r. Experimental results on the CIFAR dataset show the advantages of our method for training small models and having richer application scenarios compared to other knowledge distillation methods.
Online Back-Lit Image Enhancement Based on Learning Restoration

This paper presents a method for online image restoration and enhancement via learning SVM model. The proposed method first obtains the images with the camera's laptop, then, process this image with learning approach via machine learning model (i.e. SVM model), where each image is divided into front-lit and back-lit segments. After that, the front-lit segment is expanded with the exposure of each pixel. Finally, both segments are merged together to produce the final restored image. Experiments and comparisons are performed on the proposed method, and shows that it outperformed standard restoration method.