Reviewerly/exHarmony huggingface dataset:
Feature Fused Detector for Small Objects

Detecting small objects remains an unsolved challenge for object detection task due to their poor resolution and limited contexts. In this study, we put forward a novel method named Feature Fused Detector (FFD), which fuses multi-scale features from shallow to deep layers for object detection. FFD enhances the semantic information of small objects by combining two feature fusion modules named Multi-scale Feature Module (MFM) and TransHead. MFM uses the residual connection and a spatial pyramid pooling layer to connect features from different scales. TransHead is the detection head that contains convolution layers and Transformer Blocks, which forms three prediction branches by fusing top-down features. Comparison experiments are conducted on the Priority Research Application dataset, and the results demonstrate that our FFD outperforms YOLOv3 by 2.8% and achieves 0.8% improvement over YOLOv5l in AP metric, showing that our proposed method can be used in small objects scenarios.
ATVNet: Adaptive Thin Cost Volume for Stereo Disparity Estimation

Recently, stereo models based on coarse-to-fine approaches have drastically alleviated the memory footprint and speed limitations of complex network models. However, previous designs used a uniformly defined range for disparity estimation, which ignored the difficulty of pixel matching in different regions and introduced many unnecessary matching candidates. In this paper, we construct Adaptive Thin Volume Network (ATVNet) to improve accuracy and reduce computation time. Firstly, multi-scale feature maps are extracted to construct a combined cost volume, then the rough disparity map is derived from the low-resolution volume regression. Finally, the Disparity Correction Range (DCR) module adaptively adjusts the matching space through uncertainty estimation, which constructs thin volumes for gradually retrieving a more accurate high-resolution disparity map. With the gradual improvement of volume resolution and the adaptive reduction of correction range, the disparity map is recovered in a coarser-to-fine strategy. When training and testing on the Scene Flow and KITTI 2015 datasets, our ATVNet model exceeds the mainstream end-to-end model in accuracy and efficiency.
Multi-Scale Depthwise Convolution Network for Action Recognition

Convolutional networks have been widely used in action recognition, however, due to the fact that action recognition uses videos containing a large number of images as input data, the networks often have high complexity and produce a lot of computation which put forward high requirements for equipments. And because of the diversity of action classes and the multi-scale of action in temporal and spatial domain in the videos, there are still challenges to accurately recognize actions. In this paper, we use depthwise convolution and kernel factorizations to design a lightweight spatiotemporal feature extraction structure to reduce network computational complexity, and considering the diversity of human actions in temporal and spatial scales, we use a convolutional pyramid structure with multiple convolution kernels to extract multi-scale features. We name the proposed structure multi-scale depthwise module (MSD). We embed the MSD module in the two-stream convolutional network, and called multi-scale depthwise convolutional network (MSDCN). Experiments are carried out on human action datasets UCF101, HMDB51 and Kinetics, and the accuracy is 92.13%, 65.90% and 72.73%. The results show that the proposed MSD module is effective and MSDCN gets comparable results. In addition, in terms of network parameters and computational complexity, the proposed MSDCN network has a very low amount of parameters and computation, which is more than 60% lower than the baseline network.
Unsupervised Real-World Super-Resolution via Degradation Representations

Most current deep convolutional neural networks achieve great performance on single image super-resolution (SISR), trained with corresponding of high-resolution(HR) and low-resolution(LR) images. Conversely, the super-resolution performance of them is reduced testing in real-word super-resolution, since these methods create paired LR images by simple interpolation downsampling from HR images (e.g. bicubic) which is far more different from the natural degradation. In this paper, we design a novel unsupervised framework conditioned on degradation representations for real-world super-resolution. Our proposed method is composed of three stages: we first learn implicit degradation representations from real-world LR images and then acquire the LR images by downscaling network which would sharing a similar degradation with real-world images. Finally, we make paired data of the generated real LR images and HR images for training the SR network. Our model achieves better performance than recent SR methods on the NTIRE2020 real-world SR challenge Track1 dataset.
Strategic Improvement in Malls for Customers by using Machine Learning

The application for malls that divide the customers into profitable and non-profitable customers plays a vital role in the marketing sector. The administrator within the shopping centres chooses to promote methods and client division points to form a relationship with the first profitable clients by arranging the foremost appropriate marketing procedure. Numerous methods are applied to separate the advertising, but outstandingly tremendous data is uncommonly effective in decreasing their adequacy. In this paper, association rule learning is used to establish a relationship between variables. With the use of an appropriate algorithm in this, we find what items customers frequently buy together by generating the set of rules and can be used those rules for various market strategies. By using that rules, we also develop a recommender framework that will offer assistance the mall managers to empowering the market strategies.
Proposed Model for Document/Answer extraction using Machine Learning Techniques

Billions of search queries are put out on the search engines every single day. The need for the accurate and quality responses is at the peak of this century. There exists an extensive number of ranges which can be taken as categories for the searches, from the data related to Agriculture, share markets, News, Images, Research documents, Sports, Politics, Learning materials, Universities, Fees and countless others. This paper intends in covering best algorithms out there which are responsible in providing suitable answer. User entered query is comprehended with the help of the classification algorithms. The methods to give Responses for such queries are being discussed in this paper. The query might be in the form of a questionnaire, keywords, or Google searches. Extraction the answer can be made in three different types: Static document, Website link or a PSB file which can be downloaded using some web API's. Response can be extracted through these resources. Methods have been discussed here in the paper which finds an efficient answer based on the keywords received by the query from user.
Reconnaissance of Credentials through Phishing Attacks &amp; it’s Detection using Machine Learning

A cybercrime is what that best describes a Phishing Attack through which even a normal citizen of a country copies a true person/institution by encouraging them as an official person via e-mail or other means of communication. A person who is prone to do malicious things is known as an attacker who sends malicious Links or Payloads which may evolve into cyber attack via phishing e-mails that can execute multiple tasks, including capturing the victim’s login credentials or account data.. Due to cash loss and identity theft, these e-mails damage victims in numerous ways as in economically, mentally and much more. In this study, we have done phishing attacks which is a malicious act, usually made through email, to steal personal and private data of the Users without their knowledge. We have performed this using LinkedIn and Facebook Login Pages so that the attack could appear to the victim as a real scenario. Somehow, the attacker manipulates the users such a way that the user visits a faked web site which is send by the attackers through faked e-mails or instant messages, and without a sound gives his personal information such as user name, password, i.e. Confidential data and other private data which is of utmost importance to the victim to the user unknowingly. The method to perform this Attack is described in this study. Several techniques are mentioned for the purpose of accomplishing phishing attack and eventually providing prevention method to avoid phishing attack. We have also covered the ways in which Machine Learning Algorithms could somehow detect the Phishing Links & could play a essential method in detecting and preventing from serious loss of data i.e. Loss of Credentials. We will also learn ways in which we can classify b/w Malicious Links and other normal Links.
Deploying Mobile Application on Cloud

Today is the era of cloud-based applications. Over a span of time cloud has emerged as nascent term to new era of computing that gives access to anything, anywhere. This paper involves the study on the topic ‘Mobile Application Deployment on Cloud’. The paper has been split into 3 major domains that are: Creating the front-end of the application, Creating and maintaining the back end of the application and finally deploying the application on cloud. The application to be deployed on cloud was built on ‘Flutter’. The main reason to choose flutter as a platform is that, using a single codebase one can make and maintain an application on both the major mobile operating systems namely, Android and iOS. After the dummy application was built, we started the process of hosting the application on AWS cloud. At first, a reference architecture taken into contemplation for beginning the process. And at various stages the architecture was altered according to the use case and services were modified. After we were ready with the cloud architecture, we finally deployed our application with dummy data on the AWS cloud. Once the application got hosted on the cloud, we started to change the test data with actual data and as an outcome of which we successfully accomplished our goal.
Multiclass Classification Using Arctangent Activation Function and Its Variations

Deep learning have been applied in life changing areas. Wide range of areas shows how successful deep learning is. There are several reasons why deep neural networks works well. The most importantly, activation functions since they are very powerful for solving non-linear problems. For that reason, it became a focus point for artificial intelligence researchers who want to improve the performance of neural networks. Special irrational numbers like pi and the golden ratio are shown up themselves in many areas such as art, geometry, architecture, etc. The wide range of occurrences of the pi and golden ratio inspire us to apply them to activation functions. This document is written for comprehensive explanation and comparison of activation functions which mainly focuses on arc tangent and its' variations defined in the paper. Experimental results are showed that variations which are obtained using irrational numbers pi and golden ratio, and also self-arctan, give promising results. Especially arctan with golden ratio have given better results. Multi-class classification problem was taken consider in the paper.
Generalizing Direct Sequence Spread-Spectrum to Mitigate Autocorrelation-Based Attacks

In this paper, we propose to generalize the Direct Sequence Spread Spectrum (DSSS) communication scheme with a Dual Hidden Markov Model-based Spread Spectrum (DHSS) communication scheme. The generalization aims to defeat existing autocorrelation-based attacks, which are proven effective on DSSS. DHSS is highly resistant to the autocorrelation-based attacks because chip sequences in DHSS are generated randomly for each information bit with Hidden Markov Models. As a spread spectrum communication technique, DHSS still takes advantage of benefits brought by processing gain. The cost of resistance to the autocorrelation-based attacks for DHSS is slight degradation in BER performance, about 2dB inferior to DSSS.
Zineography

Zines (as in magazines) are self-published publications that are often created, produced, and distributed by their author(s). When members of a community of interest co-create zines together, they construct narratives through their editorial juxtaposition and styling of individual contributions on a given topic. While zines are increasingly being used in the HCI field as a medium or format for knowledge-sharing and/or community-building, collaborative zine making is less explored as a participatory design process that may illuminate both individual and shared narratives. Zineography is an unfolding exhibit of the process of collaborative zine making, through which PDC 2022 attendees are invited to creatively contribute and collaborate on zines exploring personal experiences related to conference themes. Zineography highlights how collaborative zine making can operate as a participatory design process that retains the plurality of individual expressions while facilitating the co-construction of shared narratives through editorial design practices. Let's make zines!
Making Together Across Space &amp; Time

Designers construct a material sensibility through ‘making’ within a design process; a sensibility which is co-constructed by giving shape to artefacts and systems together with other stakeholders in a participatory design process. Complex research contexts have required reconsideration of the ways in which this ‘making’ takes place. Particularly during recent pandemics, requirements of having to collaborate across different places and/or time periods prompted designers to adapt their praxes, generating reflective insight into how the materiality of participatory making (which includes sketches, toolkits, probes, samples, role play performances, prototypes, and others) may support or challenge the co-construction of shared material sensibilities. In this workshop, we invite PD designers and researchers to reflect on and play with the materiality of participatory making while apart in order to co-create a constellation or network of design practices, materials, and audiences which can provide important insights for designers who face restrictions on meeting stakeholders face-to-face.
Participatory Design: An East African Take

The East Africa Participatory Design Conference (PDC) place seeks to catalyse engagement and learning between global PDC communities and East Africa. While there has been significant work in participatory design in Africa, much of it remains invisible to international fora, and vice versa. Moreover, much of the ways by which research and innovation in Design is articulated in Africa, differs from how it is at top tier academic venues, further marginalising participation and contribution from African geographies. The East Africa PDC place attempts to tackle these by 1) engaging academics and industry practitioners in Africa in defining and showcasing participatory design work in the region (both East Africa in particular and Africa in general), and 2) making visible to the East Africa community research from PDC that is relevant or interesting to the continent.
It Starts with Healing: Acknowledging Collective Trauma in Participative Futuring

This workshop explores the links between healing ourselves and our aspirations for transformative social change. We call for the development of methodologies for participatory and experiential futures based on PDC’s nascent research directions such as autoethnography, somaesthetics, and embodied design. Over the last two years, we have lived through the ongoing Covid-19 pandemic and a continued climate crisis. For many, these events have led to/contribute to symptoms like anxiety, depression, body pain, and scattered focus. Without acknowledging our feelings about these events, our symptoms could become worse and it will become more difficult to effectively work towards more hopeful and healthy futures. This workshop thus insists on taking our recent collective traumas seriously in building futures that we want to live in. Our aim is to work towards a better understanding of how PD might support healing ourselves in service of collective healing and social change.
Design decision competence

The paper suggests the notion of design decision competence to describe the competence that users need to develop in order to participate in design decisions in Participatory Design (PD). We present two PD projects with older adults where they participated actively in making design decisions and we describe the competence they developed as a basis for these design decisions. We argue that experiences with materials and prototypes that demonstrate technical possibilities are important for developing such competence. We conclude by proposing a refined notion of design decision competence which enables real participation in design decisions.
Exploring Boundary Objects in Artefact Based Probes in Capturing Tacit Knowledge

Visualization and prototyping tools have been extensively used in facilitation of communication with self and peers in participatory research. The process of ‘making’ aids participants in expressing tacit knowledge and surface assumptions that are often difficult to identify by other means. The collaborative process of making and creating together gives agency to excluded voices and promotes socially inclusive practices. Artefacts and prototypes are thus valuable in eliciting deep user insights through conceptual, procedural and emotional probes, bringing out preconceptions and assumptions. Artefacts act like ‘boundary objects’ that bridge gaps in knowledge, perception and experience with a tangible form of expression, creating a shared knowledge and understanding of participants from different backgrounds and experiences. This workshop seeks to explore the use of boundary objects in prototyping in identifying issues of limited usage of public transport by females in India.
Reworlding: Participatory Design Capabilities to Tackle Socio-Environmental Challenges

Rising societal polarisations around health and climate crises have brought more attention to the close relations between social and environmental challenges. These polarisations triggered an interest in the participatory design (PD) field in developing approaches that enhance connections between diverse actors operating across societal and environmental sectors. However, the capabilities needed for these approaches have not been sufficiently articulated in PD research and education. To fill in this gap, we define ‘reworlding’ as an operation of self-critique within PD that engages with capabilities needed to reveal and articulate radical interdependencies between humans and more-than-humans, across social and environmental worlds, and within situated contexts. We propose both the redefinition of the design capabilities needed for (re)connecting these worlds (retracing, reconnecting, reimagining and reinstitutioning), as well as a reconsideration of learning environments where these capabilities can be tested and enhanced.
QNSGA-II: A Quantum Computing-Inspired Approach to Multi-Objective Optimization

This paper proposes a novel quantum computing-inspired approach to multi-objective optimization, called Quantum Computing Inspired Non-dominated Sorting Genetic Algorithm II (QNSGA-II). Although Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been effectively used in the literature to solve a variety of optimization issues, it may encounter some difficulties especially in handling heavily constrained problems due to its premature convergence. The proposed approach mitigates this difficulty by combining conventional NSGA-II with the concept and principles of quantum computing. QNSGA-II exploits quantum bits and superposition of states to reduce the convergence time and improve search space capability by evolving the probabilistic model. This paper aims to provide more detailed information about our proposed algorithm and its advantages.
PDC Place Brisbane: A localised focus on the connections between Participatory Design and the More-than-Human

PDC Place will be hosted at Queensland University of Technology, Brisbane Australia, located on the lands of the Turrbal and Yugara people. QUT’s Kelvin Grove campus is located next to the Victoria Parklands/Barrambin – an urban park in the beginning stages of transformation by Brisbane City Council. PDC Place Brisbane provides an opportunity for the participatory design community within Australia to reconnect after some time separated by hard border closures and travel restrictions. Motivation for a Brisbane-based PDC Place event stems from an enthusiasm from the local PD community within South-East Queensland to come together with each other, with Country, and with the broader PD community within our region. We particularly encourage Higher Degree Research students and early to mid-career researchers and academics interested in reflection and discussion around the intersection of participatory design with a more-than-human focus.
3D Environment-based Multiobjective Path Planning for Unmanned Ground Vehicles

With the rapid development of driverless technology, supporting the safe and rapid path planning of Unmanned Ground Vehicles in complex environments turns to be a significant challenge. In this paper, we propose a 3D environment-based multiobjective path planning strategy with consideration of the environmental characteristics and time cost By using the deep reinforcement learning algorithm, we further transform the initial problem into Markov decision process which can be solved. We consider grid quantization approach where the 3D terrain of the environment surface can be converted into a height-based hierarchical matrix and feed the matrix into the reinforcement learning architecture as the information. Meanwhile, we propose an action judgement mechanism to judge the legitimacy of actions in advance before execution to solve the collision problem in the real training process of the agent. Simulation results show the effectiveness and robustness of the proposed strategy with applications to two different types of 3D terrains and varying degrees of terrain disturbances.
A Simple High-performance Generation Method for Spoofing Jamming Signals

Spoofing jamming with satisfactory concealment and strong aggression can effectively mislead the operation of the non-cooperative system. This is a kind of effective jamming signal. However, it has costly construction and limited application since a lot of prior information is required. In this paper, a new high-performance generation method for spoofing jamming signals is proposed. We used a one-dimensional generative adversarial network (GAN) to learn the latent distribution of the DS/SS signals and construct a generated signal set. Then, we choose the signals in this set with highly aggressive for the DSSS system as spoofing jamming signals. Simulation results show that this method can more easily generate spoofing signals with strong aggressiveness to the DS/SS system, and effectively solve the problem that it is difficult to construct spoofing jamming under the condition of limited prior information. Our proposed method provides a new idea for the construction of spoofing jamming signals, which is helpful for its application in the field of communication countermeasures.