Reviewerly/exHarmony huggingface dataset:
Human Action Recognition based on Depth maps, Skeleton and Sensor Images using Deep Learning

Human action recognition has become one of the areas of active research in computer vision for various applications, such as security surveillance, health and human computer interaction. Several approaches for human actions detection are being investigated and images are in RGB (red, green, and blue), depth, and skeleton datasets, as well as inertial sensor images. The majority of the algorithms for action categorization employing skeleton datasets are limited in various ways, After data acquisition for simplicity very basic feature extraction techniques are applied to each data type. The first input is depth images For accuracy of action classification, Neural networks channels are trained with a range of inputs, the second input which is a proposed skeleton images that represents the motion of joints in time, and the third input as inertial images. Neural Networks are taken for evaluation model purpose, then to find Score fusion we are planning to use Avg and Max products. Our proposed method was implementation on public datasets like MAD and UTD-MHAD datasets.
Cross-Pollination of Personas, User Stories, Use Cases and Business-Process Models

Agile process management also requires an agile development of business-process specifications. The paper discusses at the example of user stories, use-case slices, personas and subject-oriented business models how concepts of an integrated use of models in user-centered and agile software development can be transferred to business-process modeling. In particular, it is suggested to map subjects of subject-oriented business models to small sets of personas with related user stories to facilitate more expressive behavioral specifications. The user stories can be derived from current and envisaged scenarios that support the communication in enterprises. The proposed splitting of subjects into sub-roles with their specific behaviors makes possible to define slices and to increase the flexibility of process specifications. The ideas are illustrated through an example.
A Systematic Literature Review of Machine Learning Techniques for Software Effort Estimation Models

Estimating software projects is a challenging but necessary process in software development. Predicting the effort needed to build software is an essential part of the project life cycle. This paper examines a variety of machine learning algorithms for estimating effort. There has been a significant increase in research on effort estimation with machine learning approaches during the last two decades, with the objective of improving estimation accuracy. To forecast effort, the estimation techniques such as expert judgment, COCOMO, analogy based, putnam model, and machine learning are used. The algorithmic models’ low accuracy and unreliable architecture resulted in substantial software project risks. As a result, it is essential to predict the cost of project on an annual basis and compare it to alternative methods. However, the effort prediction using machine learning is still limited because a single technique cannot be treated as best. This paper’s main goal is to present a review of several machine learning approaches for predicting effort.
Improving Neural Architecture Search by Mixing a FireFly algorithm with a Training Free Evaluation

Neural Architecture Search (NAS) algorithms are used to automate the design of deep neural networks. Finding the best architecture for a given dataset can be time consuming since these algorithms have to explore a large number of networks, and score them according to their performances to choose the most appropriate one. In this work, we propose a novel metric that uses the Intra-Cluster Distance (ICD) score to evaluate the ability of an untrained model to distinguish between data in order to approximate its quality. We also use an improved version of the FireFly algorithm, more robust to the local optimums problem than the baseline FireFly algorithm, as a search technique to find the best neural network model adapted to a specific dataset. Experimental results on the different NAS Benchmarks show that our metric is valid for either scoring CNNs and RNNs, and that our proposed FireFly algorithm can improve the result obtained by the state-of-art training-free methods.
Free$\rm ^{3}$Net: Gliding Free, Orientation Free, and Anchor Free Network for Oriented Object Detection

Object detection for aerial images has achieved remarkable progress in recent years. Nevertheless, most exiting studies do not differentiate oriented object detection from horizontal detection. Certain schemes ignore the ambiguity of oriented object representation and leverage label assignment designed for horizontal object detection directly. Consequently, it leads to unstable training and causes performance degradation, because high-quality samples surrounding the oriented bounding boxes can not be leveraged effectively. To address this problem, we propose a gliding Free, orientation Free, and anchor Free Network (Free <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm ^{3}$</tex-math></inline-formula> Net) with high-efficiency for oriented object detection. Specifically, we propose an unambiguous oriented object representation scheme, named FreeGliding, by gliding the projection points of samples on each edge of horizontal bounding boxes. It makes the detection largely free from representation ambiguity and multi-task dependency. To overcome the restrictions of label assignment, we put forward a novel Loss-aware Outer Sample Selection (LOSS) scheme, which takes into consideration spatial information and localization capability to retain high-quality samples surrounding the objects. Moreover, we introduce an Oriented Feature Fusion (OFF) scheme to tackle feature alignment by adjusting the receptive field and fusing oriented features dynamically. Experimental results on two large-scale remote sensing datasets HRSC2016 and DOTA demonstrate that Free <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rm ^{3}$</tex-math></inline-formula> Net outperforms the state-of-the-art schemes with a large margin. We hope our work can inspire rethinking the design of anchor-free detectors, and serve as a strong baseline for oriented object detection.
Scalable and Secure Virtualization of HSM With ScaleTrust

Hardware security modules (HSMs) have been utilized as a trustworthy foundation for cloud services. Unfortunately, existing systems using HSMs fail to meet multi-tenant scalability arising from the emerging trends such as microservices, which utilize frequent cryptographic operations. As an alternative, cloud vendors provide HSMs as a service. However, such cloud-managed HSM usage models raise security concerns due to their untrusted and shared operating environment. We propose ScaleTrust, a scalable and secure system for key management. ScaleTrust allows us to scale the number of virtual HSM partitions, each of which is isolated with respect to each other and is robust against cloud insider attacks, while preserving physical isolation of the root of trust. To enable this, ScaleTrust uses Intel SGX and multiple HSM features, such as restricting key usage by controlling key attributes of in-HSM keys and establishing a secure channel using only HSM commands. Finally, we apply ScaleTrust to four real-world systems: Keyless SSL for TLS private key offloading, JSON Web Token authentication for microservices, key provisioning, and encryption in database systems. Our evaluation shows that ScaleTrust achieves multi-tenancy in a scalable way by providing multiple virtual HSMs with legacy HSM devices that are designed to support a single tenant. ScaleTrust provides security against insider threats while incurring 11.9% and 39.0% of end-to-end throughput and latency overhead for Keyless SSL compared to stand-alone HSMs.
Investigating the role of Named Entity Recognition in Question Answering Models

Machine Reading Comprehension (MRC) is a challenging Question - Answering (QA) task that helps the user in providing the answer to the given question. There is a lot of progress in this area due to the availability of large datasets and large pre-trained language models based on transformer architecture (BERT). Named Entity Recognition (NER) was used for neural QA systems to improve performance. However, whether NER plays a vital role in a QA system built using contextual embeddings obtained through BERT variants is not explored. To fill this gap, we investigate whether NER is helpful in improving the performance of QA systems built using BERT variants. We experimented with Squad 2.0 using SpanBERT. The Squad 2.0 dataset has both answerable and unanswerable questions. The proposed model finds the answer span if the question is answerable and, provides justification for the unanswerable questions. We perform question analysis to find the expected answer tag and then use that information to find the relevant parts of the passage in order to retrieve the answer span.
Splitting Quantum-Classical Scripts for the Generation of Quantum Workflows

Quantum applications play an essential role in exploiting the full potential of quantum computers. However, quantum applications are typically hybrid, i.e., they consist of classical and quantum processing steps that must be orchestrated. Workflow technologies enable such orchestration and offer several advantages such as robustness, reusability, and scalability. Nowadays, many quantum applications are only available as script-based implementations. Thus, to benefit from the advantages of workflow technologies, a transformation is necessary; one driving question thereby is how to split hybrid quantum applications into their quantum and classical parts. To address this, we propose an approach that automatically generates equivalent workflow models from existing script-based quantum implementations. Based on syntactical analysis, it first splits the script-based implementations into their quantum and classical parts. These parts are then orchestrated by a generated workflow model that resembles the original execution order of the script and ensures the correct data flow between them. Furthermore, we generate deployment models for each part to enable automated deployment. To validate the practical feasibility of our approach, we present a prototype and a case study examining a concrete quantum algorithm implementation.
Real-Time Video Anonymization in Smart City Intersections

Video cameras in smart cities can be used to provide data to improve pedestrian safety and traffic management. Video recordings inherently violate privacy, and technological solutions need to be found to preserve it. Smart city applications deployed on top of the COSMOS research testbed in New York City are envisioned to be privacy friendly. This contribution presents one approach to privacy preservation - a video anonymization pipeline implemented in the form of blurring of pedestrian faces and vehicle license plates. The pipeline utilizes customized deep-learning models based on YOLOv4 for detection of privacy-sensitive objects in street-level video recordings. To achieve real time inference, the pipeline includes speed improvements via NVIDIA TensorRT optimization. When applied to the video dataset acquired at an intersection within the COSMOS testbed in New York City, the proposed method anonymizes visible faces and license plates with recall of up to 99% and inference speed faster than 100 frames per second. The results of a comprehensive evaluation study are presented. A selection of anonymized videos can be accessed via the COSMOS testbed portal.
Camera Blockage Detection in Autonomous Driving using Deep Neural Networks

The autonomous driving industry is evolving at a pace unimaginable a decade ago. Machine Learning (ML) algorithms powered by availability of large quantity of data and specialized processors in form of graphical processing units (GPUs) have accelerated that growth. However, if the data itself is of poor quality, then the machine learning models used to perceive and react to the environment will lack the robustness which is crucial for safety of the Advanced Driver Assistance Systems/ Autonomous Driving (ADAS/AD) applications in next generation vehicles. ADAS/AD applications are fueled by data from sensors like camera, Light Detection and Ranging (LiDAR), Radio Detection and Ranging (RADAR), Inertial Measurement Unit (IMU) and Global Positioning System (GPS). Camera constitutes one of the crucial parts of an AD system which is susceptible to blockage due to environmental factors like rain, dirt, mud etc. Automotive manufacturers conduct data collection campaigns to acquire data to train the ML algorithms. The motivation for this paper comes from data quality assessment which can be performed on data collected during campaigns to identify video segments containing occluded images due to camera blockage. We propose a DNN based on ResNet architecture which utilizes multiple open-source blockage datasets. The model created iteratively tested using augmented data generate using GAN and its learning validated using AI explainability techniques. The result is a robust and generalized model.
Filling Missing Labels in Label Distribution Learning by Exploiting Label-Specific Feature Selection

Label distribution learning (LDL) is a novel learning paradigm for solving label ambiguity problems. The existing LDL algorithms are mainly constructed by considering complete supervised information. However, in practical applications, there are often partial missing labels in the label space, which destroys the structure and relevance between labels and makes it difficult to design accurately learning algorithms. To solve this problem, we propose an incomplete label distribution learning method to fill missing labels by exploiting label-specific feature selection. Firstly, we use the sparse learning to obtain the specific features of each class label, and the common features related to all class labels, respectively. Secondly, we apply low rank constraint to obtain the potential label correlations, and the prediction results are corrected by reconstructing the label distribution consisting of non-missing labels. Finally, a linear mapping relationship between features and labels is constructed to recover missing labels. Experimental results on several data sets demonstrate the effectiveness of the proposed method.
Viewport-Sphere-Branch Network for Blind Quality Assessment of Stitched 360° Omnidirectional Images

Compared with conventional images/videos, omnidirectional data records rich information with higher resolution and wider Field-of-View. Moreover, the stitching distortions introduced in the panoramic content generation process make the quality assessment task more challenging. Targeting at designing an accurate and fast stitched 360° omnidirectional image quality evaluator, we propose a Viewport-Sphere-Branch Network (VSBNet) via dual-branch quality estimation. Specifically, for the viewport quality estimation, we extract distorted viewports around the stitching seams and conduct distortion rectification through a progressively complementary network to obtain pseudo-reference viewports. The qualitative and quantitative experiments validate that pseudo-reference viewports are reliable. Then, the differences between distorted and pseudo-reference viewports are quantified through transformer architecture to obtain quality scores of viewports. The introduction of pseudo-reference viewports can effectively improve the performance of the viewport quality prediction branch. To establish general scenario awareness and accurately evaluate the immersive experience, we extract feature representation through deformable convolutions to eliminate 2D-to-Sphere intrinsic sampling distortions and use multilayer perceptron to predict score of the whole sphere. The final prediction score is obtained by aggregating the quality scores from viewport and sphere branches. We evaluate the proposed VSBNet on two benchmark databases and results demonstrate that the combination of two branches can obtain more accurate results. Overall, our method is superior to existing full reference and no reference models designed for conventional images and 360° omnidirectional images.
Click Fraud Detection: HK-Index for Feature Extraction from Variable-Length Time Series of User Behavior

Most conventional time series outlier detection techniques exhibit limitations for variable-length time series. To tackle this problem, we propose HK-index to extract features from variable-length time series for any outlier detectors. HK-index identifies the longest subseries which satisfies the conditions by a pre-defined parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> . The length of the longest subseries found is then used as the feature. The novelty of core idea of HK-index is to use the information of subseries to present the whole time series, ignoring the irrelevant information. HK-index has linear time complexity and ease of application. It has been utilized in Alibaba to filter the daily fraud traffics.
Impact of Writing Order Recovery in Automatic Signature Verification

In signature verification, spatio-temporal features offer better performance than the ones extracted from static images. However, estimating spatio-temporal or spatial sequences in static images would be advantageous for recognizers. This paper studies recovered trajectories from skeleton-based images and their impact in automatic signature verification. To this aim, we propose to use a publicly available system for writing order recovery trajectory in offline signatures. Firstly, 8-connected recovered trajectories are generated from our system. Then, we evaluate their impact on the performance of baseline signature verification systems to the original trajectories. Our observations on three databases suggest that verifiers based on distributions are more suitable than those that requiring the exact order of the signatures for the off-2-on challenge.
Dense 3D Model Reconstruction for Digital City Using Computationally Efficient Multi-View Stereo Networks

Deep learning has shown promising results on dense three-dimensional (3D) model reconstruction from RGB images in recent years. However, the reconstruction of large-scale 3D models required for digital city remains very challenging even for such deep learning based methods. In this paper, we propose a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a double U-Net approach searching for image features. The proposed network first utilizes a double U-Net to extract the image features of a coarser resolution for the sake of reduced memory requirements. After that, the cost volume is built via the differentiable homography warping. The cascade structure is designed to extract the information in a small-scale cost volume before a large-scale cost volume carries out fusion and finer depth map estimation. As a result, the proposed network can efficiently produce highly accurate 3D point clouds using a fraction of the GPU memory and runtime required by conventional methods. Extensive experiments on the DTU benchmarks as well as the Tanks and Temples benchmarks confirm that the proposed network can achieve outstanding reconstruction accuracy and model completeness.
Exploring the Use of Machine Learning for Resume Recommendations

Typically, career recommendation systems use content-based and collaborative filtering techniques to create a personalized list of vacancies that a candidate might be interested in. These techniques individually have both advantages and disadvantages. Content-based filtering has a high classification accuracy since it is based on data from the user’s resume. Collaborative filtering gives the best predictable result, but it is necessary to collect data on the user’s interests for such a model to work correctly. This study explores the applicability of hybrid filtering to improve the quality of recommendations and the possible solution to the cold start problem. The cold start problem occurs when the system is unable to form any relation between users and items for which it has insufficient data [1]. While hybrid filtering may help solve the problem of information overload, we also considered the use of skillset vectors to tackle the issue of data sparseness that has also plagued recommender systems. We analyze the applicability and quality of the DistilBERT and BERT models for use in a career recommender system. A comparative analysis of the results obtained from the online evaluation (user testing) and offline evaluation of the ML model quality is given.
KATKA: A KRAKEN-Like Tool with k Given at Query Time

We describe a new tool, KATKA, that stores a phylogenetic tree T such that later, given a pattern P[1..m] and an integer k, it can quickly return the root of the smallest subtree of T containing all the genomes in which the k-mer $$P [i..i + k - 1]$$ occurs, for $$1 \le i \le m - k + 1$$ . This is similar to KRAKEN’s functionality but with k given at query time instead of at construction time.
Machine Reading Comprehension Based on Hybrid Attention and Controlled Generation

With the development of natural language processing technology, machine reading comprehension has been widely used in various fields such as QA systems and Intelligence Engineering. However, despite numerous models are proposed in the general domain, there is still a lack of appropriate dataset and models for specific domains like anti-terrorism or homeland security to address this problem. Therefore, a Chinese reading comprehension data set in the field of anti-terrorism (ATCMRC) is constructed, and a generative machine reading comprehension model (AT-MT5) is proposed. ATCMRC was constructed in a semi-automated manner, and a domain-specific vocabulary is created based on the dataset to assist the AT-MT5. The model uses a hybrid attention layer is and a controlled answer generation layer to enhance text perception. Finally, the ATCMRC dataset and the AT-MT5 model is evaluated against existing approaches. The experimental results show that ATCMRC covers key issues in the domain and presents challenging MRC tasks for existent models, while AT-MT5 achieves better results in the domain specific dataset than the existing methods.
Book Recommender System Using CNN Capturing Feature of Synopses and Reviews

This paper proposes a text-based recommendation system based on Probabilistic Matrix Factorization. ConvMF is one of the successful text-based PMF models, which uses CNN to capture features of items from the item description text and integrates them into PMF. However, ConvMF does not take user characteristics into account, and the effectiveness of item description text and user description text has not been evaluated. Therefore, this paper proposes a method that uses a CNN for ConvMF, extracts features from user description text created from user reviews, and integrates them into PMF as user features. The proposed method improves the recommendation accuracy by 2.8% to 4.4% over existing methods on a dataset of five genres of books. We also evaluated the effectiveness of each book genre and examined the effectiveness of the pre-training model. We also evaluated the robustness of the proposed method by parameter analysis. The proposed method was shown to be less affected by parameters than existing methods and to be more robust.