Reviewerly/exHarmony huggingface dataset:
Are Smart Homes Adequate for Older Adults with Dementia?

Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users' daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients' habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients' habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users' behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.
Absolute size IoU loss for the bounding box regression of the object detection

With the wide application of deep learning in the field of computer vision, the technology of object detection continues to make breakthroughs, and the bounding box regression technology is closely related to the accuracy of object detection results. This study proposes an Absolute size IoU (AIoU) loss function for bounding box regression, which further improves the object detection accuracy. Firstly, this study introduces the common location loss functions in bounding box regression, and then describes the limitations of common loss functions based on Intersection over Union (IoU). To overcome these limitations, this study puts forward an AIoU loss function, which can facilitate bounding box regression. Specifically, when the loss penalty term becomes invalid, it can replace the existing penalty term for model optimization. In addition, it can focus the models further on the difficult objects during training. Moreover, as a comprehensive regression factor, this penalty term contains various optimization features. The effectiveness and wide range of application of the AIoU proposed are demonstrated in experiments with three different detectors. It improves the performance of YOLOv4 by 0.61% mAP on the VOC dataset and by 1.98% mAP on the COCO dataset. Finally, we have obtained α-AIoU which uses a power function for AIoU improvement, and it achieves the best performance in the experiments. The evaluation results on several different detectors show that the method proposed in this study has important application significance for object detection technology. The code related to the AIoU loss experiment is at https://github.com/tiandii/AIoU-loss-experimental-code-.git.
A Design Space for Single-User Cross-Reality Applications

Cross-reality (CR) applications allow users to utilize tools for problem solving that reside on different points of the Milgram reality-virtuality spectrum. Design guidelines for such applications are missing. In this paper, we are proposing a design space focusing on single-user Cross Reality applications as a tool for guiding research and development projects.
Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach

Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country’s low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.
Implementation of Super Resolution in Images Based on Generative Adversarial Network

A 3D visualization of a microscopic object is provided by the integral imaging microscopy system. A generative-adversarial-network (GAN) relied on super resolution (SR) algorithm is suggested in this research to improve resolution. The generator in GAN network regresses the highresolution (HR) outcome out of the low-resolution (LR) input image, where the discriminator differentiates among the original as well as generated images. It could perhaps recover the edges and boost the resolution besides 2, 4, or indeed 8 times without compromising image quality for different sector in different field. The framework is validated using a variation of decreased microscopic specimen images as well as appropriately develops images with considerable directional view and compared with each other to get the best model among them in different sector. The quantifiable investigation reveals that the suggested framework outperforms the existing algorithms for microscopic images.
Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Protecting the privacy of data in the multi-cloud is a crucial task.Data mining is a technique that protects the privacy of individual data while mining those data.The most significant task entails obtaining data from numerous remote databases.Mining algorithms can obtain sensitive information once the data is in the data warehouse.Many traditional algorithms/techniques promise to provide safe data transfer, storing, and retrieving over the cloud platform.These strategies are primarily concerned with protecting the privacy of user data.This study aims to present data mining with privacy protection (DMPP) using precise elliptic curve cryptography (PECC), which builds upon that algebraic elliptic curve in finite fields.This approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing techniques.Also, it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the data.Average approximation error, computational cost, anonymizing time, and data loss are considered performance measures.The suggested approach is practical and applicable in real-world situations according to the experimental findings.
Look &amp; Turn: One-handed and Expressive Menu Interaction by Gaze and Arm Turns in VR

A user's free hands provide an intuitive platform to position and design virtual menu interfaces. We explore how the hands and eyes can be integrated in the design of hand-attached menus. We synthesise past work from the literature and derive a design space that crosses properties of menu systems with an hand and eye input vocabulary. From this, we devise three menu systems that are based on the novel concept of Look & Turn: gaze indicates menu selection, and rotational turn of the wrist navigates menu and manipulates continuous parameters. Each technique allows users to interact with the hand-attached menu using the same hand, while keeping the other hand free for drawing. Based on a VR prototype that combines eye-tracking and glove-based finger tracking, we discuss first insights on technical and human factors of the promising interaction concept.
Graphite

Graph Neural Networks (GNNs) are becoming popular because they are effective at extracting information from graphs. To execute GNNs, CPUs are good platforms because of their high availability and terabyte-level memory capacity, which enables full-batch computation on large graphs. However, GNNs on CPUs are heavily memory bound, which limits their performance.
Vehicle Detection and Tracking in Real-time using YOLOv4-tiny

Vehicle detection and tracking is a popular research topic in Intelligent Transportation Systems. The goal of this paper is to detect, identify, and track vehicles in surveillance camera footage in order for them to be extracted efficiently and accurately. You Only Look Once version 4 (YOLOv4) algorithm is used in this paper to propose a real-time vehicle detection and tracking system. The suggested system has been evaluated using a variety of measures, including accuracy, precision, and recognition recall. For the experimental data, the system attained an accuracy of 96.30 percent and an overall accuracy of 94.17 percent. The results reveal that the suggested system successfully tracks vehicles in the scene.
Node co-activations as a means of error detection—Towards fault-tolerant neural networks

Machine learning has proved an efficient tool, but the systems need tools to mitigate risks during runtime. One approach is fault tolerance: detecting and handling errors before they cause harm. This paper investigates whether rare co-activations – pairs of usually segregated nodes activating together – are indicative of problems in neural networks (NN). These could be used to detect concept drift and flagging untrustworthy predictions. We trained four NNs. For each, we studied how often each pair of nodes activates together. In a separate test set, we counted how many rare co-activations occurred with each input, and grouped the inputs based on whether its classification was correct, incorrect, or whether its class was absent during training. Rare co-activations are much more common in inputs from a class that was absent during training. Incorrectly classified inputs averaged a larger number of rare co-activations than correctly classified inputs, but the difference was smaller. As rare co-activations are more common in unprecedented inputs, they show potential for detecting concept drift. There is also some potential in detecting single inputs from untrained classes. The small difference between correctly and incorrectly predicted inputs is less promising and needs further research.
Long- and Short-Term Preference Modeling Based on Multi-Level Attention for Next POI Recommendation

The next point-of-interest (POI) recommendation is one of the most essential applications in location-based social networks (LBSNs). Its main goal is to research the sequential patterns of user check-in activities and then predict a user’s next destination. However, most previous studies have failed to make full use of spatio-temporal information to analyze user check-in periodic regularity, and some studies omit the user’s transition preference for the category at the POI semantic level. These are important for analyzing the user’s preference for check-in behavior. Long- and short-term preference modeling based on multi-level attention (LSMA) is put forward to solve the above problem and enhance the accuracy of the next POI recommendation. This can capture the user’s long-term and short-term preferences separately, and consider the multi-faceted utilization of spatio-temporal information. In particular, it can analyze the periodic hobbies contained in the user’s check-in. Moreover, a multi-level attention mechanism is designed to study the multi-factor dynamic representation of user check-in behavior and non-linear dependence between user check-ins, which can multi-angle and comprehensively explore a user’s check-in interest. We also study the user’s category transition preference at a coarse-grained semantic level to help construct the user’s long-term and short-term preferences. Finally, experiments were carried out on two real-world datasets; the findings showed that LSMA modeling outperformed state-of-the-art recommendation systems.
Greening the Cloud: A Load Balancing Mechanism to Optimize Cloud Computing Networks

Approximately 55 percent of the world's 7.3 billion people access the Internet, creating a significant demand in information technology (IT) services for both organizations and consumers. The number of data centers continue to increase to meet this demand, consuming 2 percent of the energy produced worldwide. Many organizations moving towards cloud computing due to its ability to meet users' needs on demand. Greening cloud computing technology has become an important aspect of an organization's design and use of cloud computing. One aspect of greening the cloud is through efficiently using cloud-based resources. In this study, we focus on the resource allocation applications of cloud computing technologies to green an organizations cloud. We design a pricing and allocation mechanism for a private cloud computing service that allows the firm to effectively load balance their cloud computing resources. Our optimal pricing mechanism is a dynamic pricing model, which maximizes the net value of users in a private cloud computing service. Additionally, we create a job allocation algorithm based on our dynamic pricing model to load balance the cloud. We show that balancing the job allocation such that the number of jobs at individual resource servers as close to equal as possible is optimal. Furthermore, simulations were run on the allocation mechanism to examine the effects on the cloud resources while gaining insight to effectively distribution resources to public clouds. Our model can help organizations to efficiently distribute their cloud-based resources, which allows for a greener cloud computing system.
Cyclical Adversarial Attack Pierces Black-box Deep Neural Networks

Deep neural networks (DNNs) have shown vulnerability to adversarial attacks. By exploiting the transferability of adversarial examples, attackers can fool models under black-box settings without accessing the underlying information. However, they often exhibit weak performance when transferring to defenses, which may give a false sense of security. In this paper, we propose Cyclical Adversarial Attack (CA2), a general and straightforward method to boost the transferability to break defenders. We first revisit the momentum-based methods from the perspective of optimization and find that they usually suffer from the transferability saturation dilemma. To address this, CA2 performs cyclical optimization algorithm to produce adversarial examples. Unlike the standard momentum policy that accumulates the velocity to continuously update the solution, we divide the generation process into multiple phases and treat the velocity vectors from the previous phase as proper knowledge to guide a new adversarial attack with larger steps. Moreover, CA2 applies a novel and compatible augmentation algorithm at every optimization in a loop manner for enhancing the black-box transferability further, referred to as cyclical augmentation. Extensive experiments conducted on a variety of models not only validate the efficacy of each designed algorithm in CA2, but also illustrate the superiority of our method compared with the state-of-the-art transferable attacks. Our implemental code is publicly available at https://github.com/mesunhlf/CA2.
SMSPROTECT: An automatic smishing detection mobile application

Short Messaging Service (SMS) has grown to become the most widely used feature in mobile devices. The technological advancements that birthed other alternative messaging applications have not been able to phase out the use of the SMS. However, hackers have been exploiting this SMS feature to perpetrate smishing acts. Existing research has focused on how spam SMS could be detected and separated from ham messages but have not really done much at preventing the act of smishing. Therefore, this research presents a mobile application that used a rule-based SMS service to detect and prevent smishing attacks. Specifically, the developed SMS service allows the developed SMS mobile application to intercept incoming SMS to a smartphone. The intercepted messages were then forwarded through an Application Programming Interface (API) to the rule-based machine learning model. The model uses the carefully selected rules to analyze the retrieved message and asserts if it is a spam or ham. The result of the analysis is then forwarded to the mobile application through the API. However, the final decision to retain or discard the spam or ham depends on the user after receiving notification from the user.
A user-centred design framework for disaster risk visualisation

Visualisations are powerful communication tools that have the potential to help societies assess and manage natural hazard and disaster risks. However, the diversity of risk management contexts and user characteristics is a challenge to develop understandable and useable visualisations. We conducted a systematic literature review to understand the current state developing disaster risk visualisations following design best practices and accounting for the heterogeneity between end-users and disaster risk contexts. We find that, despite being widely recommended, tailoring visualisations to users through the process of user-centred design remains a relatively unexplored topic within disaster risk. To address this, we present a unifying user-centred design framework for disaster risk visualisation, based on existing visualisation frameworks. The framework contains three phases: the Define phase, which aims to define and characterise the disaster risk management context and end-user group who will benefit from a visualisation; the Design phase, which is highly iterative and presents an opportunity to test how users interpret different design elements; and the Refine phase, which focuses on evaluating how users understand, respond to, and make decisions based on the visualisation. The framework is sufficiently flexible to be applied to any disaster risk management and natural hazard context to identify challenges and design effective disaster risk visualisations that are understandable and useable.
Weighted Incremental–Decremental Support Vector Machines for concept drift with shifting window

We study the problem of learning the data samples' distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Machines (SVMs) can learn weighted input instances and that they can also be trained online (incremental-decremental learning). Combining these two SVM properties, the open problem is to define an online SVM concept drift model with shifting weighted window. The classic SVM model should be retrained from scratch after each window shift. We introduce the Weighted Incremental-Decremental SVM (WIDSVM), a generalization of the incremental-decremental SVM for shifting windows. WIDSVM is capable of learning from data streams with concept drift, using the weighted shifting window technique. The soft margin constrained optimization problem imposed on the shifting window is reduced to an incremental-decremental SVM. At each window shift, we determine the exact conditions for vector migration during the incremental-decremental process. We perform experiments on artificial and real-world concept drift datasets; they show that the classification accuracy of WIDSVM significantly improves compared to a SVM with no shifting window. The WIDSVM training phase is fast, since it does not retrain from scratch after each window shift.
FPGA Accelerator for Homomorphic Encrypted Sparse Convolutional Neural Network Inference

Homomorphic Encryption (HE) is a promising solution to the increasing concerns of privacy in machine learning. But HE-based CNN inference remains impractically slow. Pruning can significantly reduce the compute and memory footprint of CNNs. However, homomorphic encrypted Sparse Convolutional Neural Networks (SCNN) have vastly different compute and memory characteristics compared with unencrypted SCNN. Simply extending the design principles of existing SCNN accelerators may offset the potential acceleration offered by sparsity. To realize fast execution, we propose an FPGA accelerator to speedup the computation of linear layers, the main computational bottleneck in HE SCNN batch inference. First, we analyze the memory requirements of various linear layers in HE SCNN and discuss the unique challenges. Motivated by the analysis, we present a novel dataflow specially designed to optimize HE SCNN data reuse coupled with an efficient scheduling policy that minimizes on-chip SRAM access conflicts. Leveraging the proposed dataflow and scheduling algorithm, we demonstrate the first end-to-end acceleration of HE SCNN batch inference targeting CPU-FPGA heterogeneous platforms. For a batch of 8K images, our design achieves up to 5.6× speedup in inference latency compared with the CPU-only solution for widely studied 6-layer and 11-layer HE CNNs.
A 3D-CAE-CNN model for Deep Representation Learning of 3D images

Deep Representation Learning technologies based on supervised Convolutional Neural Networks (CNNs) have attained significant interest mainly due to their superior performance for learning abstract and robust features used in object detection and image classification tasks. However, to efficiently train such models requires a large number of labeled instances especially when these instances are high dimensional such as for 3-Dimensional (3D) Image inputs. Due to this extra dimension the dimensionality of such instances increases drastically. Therefore, the utilization of Unsupervised CNNs topologies such 3D Convolutional AutoEncoders (3D-CAE) have also been proposed. CAEs can learn features (and later used for classification tasks using common machine learning classifiers), without relying on instance labels and thus they are not prone to label limitation. Nevertheless, it is not clear if the features that CAEs learn, are relevant regarding the classification or object detection task since these features are learned via no target output class. For these reasons, in this work we combine 3D-CAE and 3D-CNN to work synergistically together in order to build a hybrid deep representation learning framework model which exploits the advantages of both unsupervised and supervised representation/feature learning approaches, applied on 3D Image inputs. In order to evaluate our strategy, we performed extensive experimental simulations for the DeepFake and Pneumonia detection problems utilizing Video and 3D Scans datasets respectively. Our proposed framework outperformed all the other utilized frameworks, revealing the efficiency of our applied methodology.