Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating a Multi Depth Camera System to Consolidate Ergonomic Work in the Education of Caregivers

Through the demographic change in Western European countries the demand for nurses in elderly care rises. Additionally, constant high physical stresses causing nurses to leave the profession before the retirement age and having a high number of sick leave days. To reduce the physical strain, focus must be set to ergonomically correct work in nursing schools. Currently given technical infrastructure in the schools lacks the capability to provide nursing instructors with analyzable data from simulated care acts. In this work, we present and evaluate the Multi-Kinect-System, our custom developed depth sensor system for recording and analyzing care acts. In a study, 13 students of a nursing school performed a simulated transfer tasks under observation of a nursing instructor and our system. The instructor gives a more in-depth evaluation of the transfer when using the intuitively analyzable data of our system, regarding the feedback length and information content.
Effect of Interaction Design of Reinforcement Learning Agents on Human Satisfaction in Partially Observable Domains

Interactive machine learning involves humans teaching with agents during their learning process. As this field grows, it is pertinent that laymen teachers, i.e. those without programming or extensive ML experience, are able to easily and effectively teach the agents. Previous work has investigated which factors contribute to the teacher’s experience when training agents in a fully observable domain. In this paper, we investigate how four different interaction methods affect agent performance and teacher experience in partially observable domains. As the domain in which the agent is learning becomes more complex, it accumulates less reward overall and needs more advice from the teacher. It is found that the most salient features that affect teacher satisfaction are agent compliance to advice, response speed, instruction quantity required, and reliability in agent response. It is suggested that machine learning algorithms incorporate a short time delay in the agent’s response and maximize the agent’s adherence to advice to increase reliability of the agent’s behavior. The need to generalize advice over time to reduce the amount of instruction needed varies depending on the presence of penalties in the environment.
Visual Data Story Protocol: Internal Communications from Domain Expertise to Narrative Visualization Implementation

Data stories play an important role in effectively and intuitively communicating data insights as well as enabling the audience to understand important social issues. Crafting a data story needs several sets of skills, we propose a five-phase data story protocol in order to guide data story design and development, and promote interdisciplinary team collaboration. This protocol was developed from our working team reflection on four data story projects and researching the related work. We hope that this protocol could be one potential way for non-journalism organizations to conduct data stories for their target audience.
Categorical Approach to Swarm Computations

We propose the model approaching the problems of organisation, computing and emergent behavior of certain swarms from category theory point of view. In this model the Yoneda embedding to the category of presheaves spanned over the basic category of partial recursive functions, is activated by external stimuli and the resulting excited domains carry collective self-organising processes. We find that the intuitionistic logic of the presheaf topos becomes a primary logic for a way how swarms act and how they should be described algorithmically.
Enhanced CycleGAN Dehazing Network

Single image dehazing is a challenging problem, and it is far from solved. Most current solutions require paired image datasets that include both hazy images and their corresponding haze-free ground-truth. However, in reality lighting conditions and other factors can produce a range of haze-free images that can serve as ground truth for a hazy image, and a single ground truth image cannot capture that range. This limits the scalability and practicality of paired methods in real-world applications. In this paper, we focus on unpaired single image dehazing and reduce the image dehazing problem to an unpaired image-to-image translation and propose an Enhanced CycleGAN Dehazing Network (ECDN). We enhance CycleGAN from different angles for the dehazing purpose. We employ a global-local discriminator structure to deal with spatially varying haze. We define self-regularized color loss and utilize it along with perceptual loss to generate more realistic and visually pleasing images. We use an encoder-decoder architecture with residual blocks in the generator with skip connections so that the network better preserves the details. Through an ablation study, we demonstrate the effectiveness of different modules in the performance of the proposed network. Our extensive experiments over two benchmark datasets show that our network outperforms previous work in terms of PSNR and SSIM.
With a Little Help from My Conversational Agent: Towards a Voice Assistant for Improved Patient Compliance and Medication Therapy Safety

The chronically ill and the elderly often need to take several drugs, which increases the complexity of medication management. This frequently results in a decrease in patient compliance and raises the risks of their drug therapy. To support patients in medication management, we developed a multimodal assistant that includes a conversational agent supplied with data from a database managed by healthcare professionals via a web service. The developed artifact analyzes medication plans, identifies adverse drug reactions and side effects, and reminds patients to take their medication correctly and on time. Applying the design science research paradigm, we systematically identified 16 issues, derived eight meta-requirements, and elaborated three design principles. Based on this, the artifact was implemented and evaluated by three experienced pharmacists, who highlighted the usefulness of the solution and provided feedback for further improvements. Finally, we present an evaluation concept for potential users and discuss the implications of the medication assistant. Overall, the medical assistant comprises valuable functionalities to support patients, and it increases medication therapy safety and patients’ compliance.
Differential Scanning Calorimetric Study of HTPB based Composite Propellants in Presence of Nano Ferric Oxide

A comparative study of the thermal decomposition of ammonium perchlorate (AP)/hydroxy terminated polybutadiene (HTPB) based composite propellants has been carried out in presence and absence of nano iron oxide at different heating rates in a dynamic nitrogen atmosphere using differential scanning calorimetry. The pronounced effect was a lowering of the high temperature decomposition by 49 °C. A higher heat release up to 40% was observed in presence of nano ferric oxide (3.5 nm). The kinetic parameters were evaluated using the Kissinger method. The increase of the rate constant in the catalyzed propellant confirmed the enhancement of the catalytic activity of ammonium perchlorate. The scanning electron micrographs of nano Fe2O3 incorporated in HTPB revealed a well-separated characteristic necklace-like structure of α-Fe2O3 particles at high magnification.
Knock Brush! Perceived Impact of Push-based Notifications on Software Developers at Home and at the Office

: Responding to a digital interruption requires software developers to transfer their attention from their ongoing task to the contents of the notiﬁcation: a shift which will disrupt their original ﬂow of work. This paper distinguishes between different types of notiﬁcations (intrusions and interruptions) and reﬂects upon the results of a survey with 88 respondents. This study contributes to research by providing insights consistent with literature, particularly related to how users react to push-based notiﬁcations during varying contexts, how the software developers perceive the cost and beneﬁts of a notiﬁcation, what features stand out in a notiﬁcation and the strategies used to continue their work following a notiﬁcation.
Machine Learning Algorithms for Predicting Chronic Obstructive Pulmonary Disease from Gene Expression Data with Class Imbalance

Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory lung disease that causes breathlessness and leads to serious illness including lung cancer. It is estimated that COPD caused 5% of all deaths globally in 2015, putting COPD as the three leading causes of death worldwide. This study proposes methods that utilize gene expression data from microarrays to predict the presence or absence of COPD. The proposed method assists in determining better treatments to lower the fatality rates. In this study, microarray data of the small airway epithelium cells obtained from 135 samples of 23 smokers with COPD (9 GOLD stage I, 12 GOLD stage II, and 2 GOLD stage III), 59 healthy smokers, and 53 healthy nonsmokers were selected from GEO dataset. Machine learning and regression algorithms performed in this study included Random Forest, Support Vector Machine, Naïve Bayes, Gradient Boosting Machines, Elastic Net Regression, and Multiclass Logistic Regression. After diminishing imbalance data effect using SMOTE, classification algorithms were performed using 825 of the selected features. High AUC score was achieved by elastic net regression and multiclass logistic regression with AUC of 89% and 90%, respectively. In the metrics including accuracy, specificity, and sensitivity, both classifiers also outperformed the others.
Segment My Object: A Pipeline to Extract Segmented Objects in Images based on Labels or Bounding Boxes

We propose a pipeline (SegMyO – Segment my object) to automatically extract segmented objects in images based on given labels and / or bounding boxes. When providing the expected label, our system looks for the closest label in the list of outputs, using a measure of semantic similarity. And when providing the bounding box, it looks for the output object with the best coverage, based on several geometric criteria. Associated with a semantic segmentation model trained on a similar dataset, or a good region proposal algorithm, this pipeline provides a simple solution to segment efficiently a dataset without requiring specific training, but also to the problem of weakly-supervised segmentation. This is particularly useful to segment public datasets available with weak object annotations (e.g., bounding boxes and labels from a detection, labels from a caption) coming from an algorithm or from manual annotation. An experimental study conducted on the PASCAL VOC 2012 dataset shows that these simple criteria embedded in SegMyO allow to select the proposal with the best IoU score in most cases, and so to get the best of the pre-segmentation.
AI-assisted Automated Pipeline for Length Estimation, Visual Assessment of the Digestive Tract and Counting of Shrimp in Aquaculture Production

Shrimp farming is a century-old practice in aquaculture production. In the past years, some improvements of the traditional farming methods have been made, however, it still involves mostly intensive manual work, which makes traditional farming a neither time nor cost efficient production process. Therefore, a continuous monitoring approach is required for increasing the efficiency of shrimp farming. This paper proposes a pipeline for automated shrimp monitoring using deep learning and image processing methods. The automated monitoring includes length estimation, assessment of the shrimp’s digestive tract and counting. Furthermore, a mobile system is designed for monitoring shrimp in various breeding tanks. This study shows promising results and unfolds the potential of artificial intelligence in automating shrimp monitoring.
ChartSight: An Automated Scheme for Assisting Visually Impaired in Understanding Scientific Charts

The visual or Non-Textual components like charts, graphs, and plots are frequently used to represent the latent information in digital documents. These components bolster in better comprehension of the underlying complex information. However, these data visualization techniques are of not much use to visually impaired. Visually impaired people, especially in developing countries, rely on braille, tactile, or other conventional tools for reading purposes. Through these approaches, the understanding of Non-Textual components is a burdensome process with serious limitations. In this paper, we present ChartSight, an automated and interactive chart understanding system. ChartSight extracts and classifies the document images into different chart categories, and then uses heuristics-based content extraction methods optimized for line and bar charts. It finally represents the summarized content in audio format to the visually impaired users. We have presented a densely connected convolution network-based data-driven scheme for the chart classification problem, which shows comparatively better performance with the baseline models. Multiple datasets of chart images are used for the performance analysis. A comparative analysis of supporting features has also been performed with the other existing approaches.
As Plain as the Nose on Your Face?

We present an investigation into locating nose tips in 2D images of human faces. Our objective is conferenceroom gaze-tracking, in which a presenter can control a presentation or demonstration by gaze from a distance in the range 2m to 10m. In a first step towards this, we here consider faces in the range 150cm to 300cm. Head pose is the major contributing component of gaze direction, and nose tip position within the image of the face is a strong clue to head pose. To facilitate detection of nose tips, we have implemented a combination of two Haar cascades (one for frontal noses and one for profile noses) with a lower failure rate than existing cascades, and we have examined a number of ”hand-crafted ferns” for their potential to locate the nose tip within the nose-like regions returned by our Haar cascades
Driver's Eye Fixation Prediction by Deep Neural Network

The driving environment is a complex dynamic scene in which a driver’s eye fixation interacts with traffic scene objects to protect the driver from dangerous situations. Prediction of a driver’s eye fixation plays a crucial role in Advanced Driving Assistance Systems (ADAS) and autonomous vehicles. However, currently, no computational framework has been introduced to combine the bottom-up saliency map with the driver’s head pose and gaze direction to estimate a driver’s eye fixation. In this work, we first propose convolution neural networks to predict the potential saliency regions in the driving environment, and then use the probability of the driver gaze direction, given head pose as a top-down factor. We evaluate our model on real data gathered during drives in an urban and suburban environment with an experimental vehicle. Our analyses show promising
Reinforcement Learning based Video Summarization with Combination of ResNet and Gated Recurrent Unit

Video cameras are getting ubiquitous with passage of time. Huge amount of video data is generated daily in this world that needs to be handled efficiently in limited storage and processing power. Video summarization renders the best way to quickly review over lengthy videos along with controlling storage and processing power requirements. Deep reinforcement-deep summarization network (DR-DSN) is a popular method for video summarization but performance of this method is limited and can be enhanced with better representation of video data. Most recently, it has been observed that deep residual networks are quite successful in many computer vision applications including video retrieval and captioning. In this paper, we have investigated deep feature representation for video summarization using deep residual network where ResNet 152 is being used to extract deep videos features. To speed up the model, long short term memory is replaced with gated recurrent unit, thus gave us flexibility to add another RNN layer which resulted in significant improvement in performance. With this combination of ResNet-152 and two layered gated recurrent unit (GRU), we performed experiments on SumMe video dataset and got results not only better than DR-DSN but also better than several state of art video summarization methods.
Deep Convolutional Second Generation Curvelet Transform-based MR Image for Early Detection of Alzheimer's Disease

Merging neuroimaging data with machine learning has an important potential for the early diagnosis of Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI). The applicability of multiclass classification and the prediction to define the progress of different stages of the disease have been relatively understudied. This paper presents a short review of the deep learning history and introduces a new solution for delineating changes in each stage of AD. Our Deep Convolutional Second-Generation Curvelet Transform Network (SGCTN) is divided into both levels: The feature learning level is the first task that can combine a Second-Generation Curvelet (SGC) with autoencoder trained features. Then, for each hidden layer, a pooling is used to obtain our convolutional neural network. This network is used to learn predictive information for binary and multiclass classification. Our experiments test uses a different number of Cognitively Normal (CN), AD, early EMCI, and Later LMCI subjects from the AD Neuroimaging Initiative (ADNI). Magnetic Resonance Imaging (MRI) information modalities are considered as input. The proposed DSGCCN achieves 98.1% accuracy for delineating the early MCI from CN. Furthermore, for detecting the distinctive level of AD, a multiclass classification test realizes the global accuracy of , and it more particularly differentiates MCI and AD groups from the CN group with 96% accuracy. Compared to the state-of-the-art deep approach, our results indicate that our architecture can achieve better performance for the same databases. Model analysis based (SGC) can improve the classification performance via comparison experiments.
Apollo - Optimistically Linear and Responsive SMR

Existing Byzantine fault-tolerant (BFT) state-machine replication (SMR) protocols in the standard (bounded) synchrony and weak synchrony models rely on equivocation detection to ensure safety. To perform a commit (or output a transaction block), this detection inherently requires O ( n 2 ) communication overhead among n nodes and waiting for O (∆) time, where ∆ is the worst-case network delay. The quadratic communication overhead limits scalability. Moreover, as the typical network latency δ tends to be much smaller than ∆, 51% honest-majority (and hence synchronous or weakly synchronous) solutions become slow as compared to 67% honest-majority asynchronous protocols working at network speed. The observation that SMR commits do not have to be treated separately motivates this work. We propose UCC (Unique Chain Commit) rule, a novel yet simple rule for hash chains where extending a block by including its hash is treated as a vote for the block and all its direct and indirect parents. When a block obtains f +1 such votes, where f is the maximum number of faulty nodes in the system, we commit the block and its parents. We use this UCC rule to design Apollo , an SMR protocol with rotating leaders which has a linear communication complexity. Apollo proposes and commits a block every δ time units with every block having a latency of ( f + 1) δ . When compared to existing works which use equivocation detection, we improve the optimistic commit latency when ( f + 1) δ < 2∆. We prove the security of our protocol in both standard and weak synchrony models. We also implement and compare Apollo with the state of the art protocol, and demonstrate Apollo having commit latencies independent of and less than the ∆ parameter, and also show signiﬁcant gains in response rates with increasing n . For instance, when n = 3, Apollo can commit blocks of size 2000 as fast 3 ms. For n = 65, Apollo produces 3x more committed transactions per second than the state of the art Sync HotStuﬀ protocol.
Self-supervised Dimensionality Reduction with Neural Networks and Pseudo-labeling

Dimensionality reduction (DR) is used to explore high-dimensional data in many applications. Deep learning techniques such as autoencoders have been used to provide fast, simple to use, and high-quality DR. However, such methods yield worse visual cluster separation than popular methods such as t-SNE and UMAP. We propose a deep learning DR method called Self-Supervised Network Projection (SSNP) which does DR based on pseudo-labels obtained from clustering. We show that SSNP produces better cluster separation than autoencoders, has out-of-sample, inverse mapping, and clustering capabilities, and is very fast and easy to use.
Contextualise, Attend, Modulate and Tell: Visual Storytelling

Automatic natural language description of visual content is an emerging and fast-growing topic that has attracted extensive research attention recently. However, different from typical ‘image captioning’ or ‘video captioning’, coherent story generation from a sequence of images is a relatively less studied problem. Story generation poses the challenges of diverse language style, context modeling, coherence and latent concepts that are not even visible in the visual content. Contemporary methods fall short of modeling the context and visual variance, and generate stories devoid of language coherence among multiple sentences. To this end, we propose a novel framework Contextualize, Attend, Modulate and Tell (CAMT) that models the temporal relationship among the image sequence in forward as well as backward direction. The contextual information and the regional image features are then projected into a joint space and then subjected to an attention mechanism that captures the spatio-temporal relationships among the images. Before feeding the attentive representations of the input images into a language model, gated modulation between the attentive representation and the input word embeddings is performed to capture the interaction between the inputs and their context. To the best of our knowledge, this is the first method that exploits such a modulation technique for story generation. We evaluate our model on the Visual Storytelling Dataset (VIST) employing both automatic and human evaluation measures and demonstrate that our CAMT model achieves better performance than existing baselines.
Applying Uncommon Visualizations to Government Dashboards

: Many governments provide data dashboards to present the state of the countries or administrative activities. Their main target audience is typically the citizens but the dashboard design process is usually top-down and leads to formulaic results. Developing three data dashboard projects for the government of Thailand, we successfully applied two uncommon data visualizations, grid map and connected scatterplot, despite initial resistance from the government agencies. We documented the design process including feedback on the two visualizations and solutions to alleviate their concerns. Academic studies had little success in convincing stakeholders. In both visualizations, animations helped to frame the concept of the uncommon visualizations.