Reviewerly/exHarmony huggingface dataset:
A pixel-wise annotated dataset of small overlooked indoor objects for semantic segmentation applications.

The purpose of the dataset is to provide annotated images for pixel classification tasks with application to powered wheelchair users. As some of the widely available datasets contain only general objects, we introduced this dataset to cover the missing pieces, which can be considered as application-specific objects. However, these objects of interest are not only important for powered wheelchair users but also for indoor navigation and environmental understanding in general. For example, indoor assistive and service robots need to comprehend their surroundings to ease navigation and interaction with different size objects. The proposed dataset is recorded using a camera installed on a powered wheelchair. The camera is installed beneath the joystick so that it can have a clear vision with no obstructions from the user's body or legs. The powered wheelchair is then driven through the corridors of the indoor environment, and a one-minute video is recorded. The collected video is annotated on the pixel level for semantic segmentation (pixel classification) tasks. Pixels of different objects are annotated using MATLAB software. The dataset has various object sizes (small, medium, and large), which can explain the variation of the pixel's distribution in the dataset. Usually, Deep Convolutional Neural Networks (DCNNs) that perform well on large-size objects fail to produce accurate results on small-size objects. Whereas training a DCNN on a multi-size objects dataset can build more robust systems. Although the recorded objects are vital for many applications, we have included more images of different kinds of door handles with different angles, orientations, and illuminations as they are rare in the publicly available datasets. The proposed dataset has 1549 images and covers nine different classes. We used the dataset to train and test a semantic segmentation system that can aid and guide visually impaired users by providing visual cues. The dataset is made publicly available at this link.
Accelerating Object Detection Models Inference within Deep Learning Workbench

Recent breakthroughs in the Deep Learning field have resulted in neural models surpassing human intelligence in a variety of specialized tasks. Modern neural networks have proven their effectiveness in object detection and segmentation applications. However, real-time performance is becoming more challenging for them, especially in cases when a vehicle&#x0027;s autonomy depends on its ability to perceive the surrounding environment within moments. As a result, model acceleration has gained extreme significance in the Deep Learning community. In this paper, we propose a simple yet effective lossy optimization that constitutes a part of the model optimization pipeline. This optimization allows to increase the model throughput by reducing the model input at the expense of an acceptable accuracy drop. For example, we demonstrate that by applying optimization pipeline including input reduction by 40&#x0025;, 8-bit integer quantization and optimal inference configuration, RetinaNet throughput rocketing in 45 times with an accuracy drop of only 0.01&#x0025;. We evaluated our acceleration technique on a range of object detection models and used the Intel&#x00AE; Distribution of OpenVINO&#x2122; toolkit Deep Learning Workbench as the optimization platform since it provides application engineers with all the required functionality to optimize and deploy the models.
Evaluating True Cryptographic Key Space Size

Cybersecurity professionals have relied on the key space of a cipher to compare encryption algorithms and select the best encryptions for transmitted data. Peer reviewed strong ciphers have been assumed to maintain strength for all messages. It is thought that only brute force attacks can break these ciphers, so the key space calculation for these algorithms uses the maximum key space to determine the unicity distance. Unfortunately, the key space is heavily dependent on the user and habits of the user, as well as the content of the message. In this paper, we present factors that affect the key space size and show that the effects of these factors can seriously decrease the security of a cipher for a particular message. By considering these factors, a cybersecurity practitioner can properly assess vulnerability and choose the best security for that message.
Research on Face Detection and Face Attribute Recognition based on Deep Learning

In recent years, facing recognition technology has been widely used in video surveillance, image retrieval, advertising, human-computer interaction and other fields. In terms of face detection, the traditional image classification model has the problems of long training time, huge amount of parameters and huge amount of calculation. To resolve these problems, this paper proposes a gender recognition and age estimation algorithm based on multitask learning,with improving the classification model that achieves excellent performance in the image classification field. Using Convolutional Neural Networks can extract preliminary feature, and then join the multi-task learning layer, performs gender recognition and age estimation tasks by guiding the weight update of the network through the backpropagation algorithm. In this paper, by designing an efficient neural network structure for multi-attribute learning of faces, it realizes that multi-attributes share features in the shallow layer, and reduces network overfitting. It also learns features independently at high levels, and improves model accuracy. The algorithm can predict static face images and the dynamic video, and can also perform multi-target detection and multi-attribute recognition.
Recovering Real-World Reflectance Properties and Shading From HDR Imagery

We propose a method to estimate the bidirectional reflectance distribution function (BRDF) and shading of complete scenes under static illumination given the 3D scene geometry and a corresponding high dynamic range (HDR) video. By splitting the BRDF into its diffuse and non-diffuse parts we solve the estimation of each component separately. For the diffuse component, we sample the incident illumination at each point in the scene using Monte Carlo ray tracing, allowing us to factor the captured surface color into albedo and shading. We then use a novel ray tracing-based optimization strategy to estimate the non-diffuse parameters of the BRDF. In a variety of experiments, we demonstrate that our method efficiently generates realistic copies of the observed scenes.
Towards a New Link Recommendation Indicator

This paper introduces a new link recommendation indicator which intends to suggest subsequent websites for a given user and starting website. The proposed algorithm takes existing links and user navigation sequences, obtained from a P2P network of users, into account. The links are weighted based on user similarity, contextual relevance, and user affection. An initial experiment delivered some promising results: The algorithm was capable of suggesting and ranking links, and also indicates a slight correlation with the satisfaction of users' navigational needs.
Comparison of algorithms for error prediction in manufacturing with automl and a cost-based metric

Abstract In order to manufacture products at low cost, machine learning (ML) is increasingly used in production, especially in high wage countries. Therefore, we introduce our PREFERML AutoML system, which is adapted to the production environment. The system is designed to predict production errors and to help identifying the root cause. It is particularly important to produce results for further investigations that can also be used by quality engineers. Quality engineers are not data science experts and are usually overwhelmed with the settings of an algorithm. Because of this, our system takes over this task and delivers a fully optimized ML model as a result. In this paper, we give a brief overview of what results can be achieved with a state-of-the-art classifier. Moreover, we present the results with optimized tree-based algorithms based on RandomSearchCV and HyperOpt hyperparameter tuning. The algorithms are optimized based on multiple metrics, which we will introduce in the following sections. Based on a cost-oriented metric we can show an improvement for companies to predict the outcome of later product tests. Further, we compare the results from the mentioned optimization approaches and evaluate the needed time for them.
Light-Weight Present Block Cipher Model for IoT Security on FPGA

The Internet of Things (IoT) plays an essential role in connecting a small number of billion devices with people for diverse applications.The security and privacy with authentication are challenging work for IoT devices.A lightweight block cipher is designed and modeled with IoT security for real-time scenarios to overcome the above challenges.The light-weight PRESENT module with the integration of encryption (E)-decryption (D) is modeled and implemented on FPGA.The PRESENT module has 64-bit data input with 80/128/256-bit symmetric keys for IoT security.The PRESENT module performs16/32/64 round operations for state register and key updation.The design mainly uses Substitution-permutation (SP) network for state updation.The permutation layer is used to create more diffusion and confusion in the state for unauthorized access.The results and analysis of the PRESENT-80/128/256 are designed using Verilog-HDL with Xilinx Environment and implemented on Artix-7 FPGA.The PRESENT-80/128/256 module is compared with similar recent works with performance improvements.Similarly, the proposed work is compared with different light-weight algorithms with improvements for better security in IoT devices.The proposed PRESENT-256 module with 64-rounds on Artix-7 FPGA utilizes less than 2% Chip area (Slices and LUTs), works at 412.4 MHz frequency, and consumes 192 mW total power and 0.58 Mbps/slice of hardware efficiency.
Development of a metrological reference system for the form measurement of aspheres and freeform surfaces based on a tilted-wave interferometer

Abstract Aspheres and freeform surfaces play an important role in today’s optics industry. However, the measurement of such complex surfaces is still challenging even with state-of-the-art manufacturing technology, and there is an urgent need in industry for a non-contact, highly accurate reference measurement technique. To meet this demand, at Physikalisch-Technische Bundesanstalt (PTB), a metrological reference system for the contact-free form measurement of aspheres and freeform surfaces is under development. The measurement system is based on a tilted-wave interferometer. Advances in computational capabilities have made it possible to solve the complex inverse problems associated with this measurement system and to develop sophisticated analysis procedures for reconstructing the surface under test from the measured interferogram data. In this paper, we will present the status of the tilted-wave interferometer-based measurement system at PTB, describe the analysis procedures we have designed and show initial measurement results. The benefit of the implementation presented here is that it allows insight to be gained into the performance of the measurement system and enables traceable measurements to be established with low uncertainty.
RCGA-Net: An Improved Multi-hybrid Attention Mechanism Network in Biomedical Image Segmentation

Drawing support from an effective Medical Image Segmentation (MIS) is conducive to a substantial diagnostic basis for the physicians to identify the focus lesion in the patient body and give the subsequent clinical assessment of the patient status. Although various works have tried the challenging quantitative analysis problem, it is still difficult to conduct precise automatic segmentation, especially the soft tissue organs. In this decade, with the increased amount of available datasets, deep learning-based networks have achieved remarkable performance in image processing. Inspired by the state-of-the-art deep learning works, in this paper, we propose an end-to-end multi-layer network named RCGA-Net. It consists of an encoder-decoder backbone that integrates a coordinate attention mechanism based on space and channel and a global context extraction module to highlight more valuable information. To evaluate the performance of RCGA-Net, we apply it to different kinds of clinical and experimental MIS tasks to testify its generalization ability. Extensive experiments represent that our schema has taken the outperform or compatible results among the comparison methods group. Specifically, the numeric result of RCGA-Net on the pulmonary dataset has achieved a 99.12% optimum F1-score.
A novel Orca Cultural Algorithm and applications

Abstract In this article, the paradigm of machine culture as an extension to machine intelligence is introduced. This new concept is modelled based on animal intelligence and culture. The example of orca intelligence and culture is considered as orcas possess in addition to skills allowing them to reach preys, the ability to transmit their culture from generation to generation. The orca intelligence is studied and then simulated to design an algorithm called Orca Algorithm (OA). OA consists in modelling the orca lifestyle and in particular the orcas social organization, echolocation behaviour and hunting techniques. In order to integrate the cultural dimension, OA was hybridized with the Cultural Algorithm (CA) to get an algorithm called Orca Cultural Algorithm (OCA). OCA was tested on 22 benchmark problems of the literature to evaluate its performance. Extensive experiments were first performed to set the algorithm parameters prior to measure its effectiveness and efficiency. In a second stage, OCA was adapted to discrete problems and applied to the maze game with four level of complexity. Additional experiments were held to compare the designed algorithm with recent state‐of‐the‐art evolutionary algorithms. The overall obtained results are very promising.
An Empirical Study on Spectral Clustering-based Software Defect Detection

Software defect detection is essential in software development. Most existing approaches often apply Supervised Machine Learning (SML) techniques for software defect detection. However, SML techniques need to a large number of manual labelling for model training, which is time-consuming and laborious. An alternative solution is to apply UnSupervised Machine Learning (USML) in software defect detection. USML techniques, as an approach without requiring labeled datasets, have been applied for software defect detection. Spectral clustering, as one of approaches in USML, shows the potential performance in software defect detection. The core of spectral clustering is the similarity algorithms, which calculate the similarity between metric values of software entities to detect software defects. Yet, the current studies on spectral clustering-based software defect detection models rarely consider the impact of different similarity algorithms on defect detection results.To address this problem, we construct an empirical study to investigate the impact of similarity algorithms in the spectral clustering-based software defect detection models. We compare the differences of three similarity algorithms, which contains k-nearest neighbours, fully connected, and vector dot product. We conduct experiments on the two real-world data sets of AEEEM and PROMISE, and the experimental results show the fully connected algorithm has better performance than other algorithms in the spectral clustering-based software defect detection.
Decoherence-Optimized Circuits for Multidimensional and Multilevel-Decomposable Quantum Wavelet Transform

Algorithms such as quantum wavelet transform (QWT) can be utilized on quantum processors to gain significant speedup compared to their classical counterparts. Domains such as high-energy-physics and remote-sensing hyperspectral imagery require high compute capabilities and can benefit from applying QWT for dimension reduction of multidimensional data. However, quantum circuits for QWT include permutations that contribute to large overall circuit depth. Deep circuits pose a critical challenge for state-of-the-art quantum computers because of quantum decoherence. In this work, we propose QWT circuits, which are optimized in terms of circuit depth, to account for the effects of decoherence, and resulting in high fidelity and efficient implementation on quantum processors. We present the circuits in generalized forms and show that they can be used for multilevel decomposable, multidimensional wavelet operations. Experimental evaluation of the proposed circuits is performed through simulation using MATLAB and IBM-Q qasm, and implemented on a real 15-qubit quantum processor from IBM.
Action Recognition Improved by Correlations and Attention of Subjects and Scene

Comprehensive activity understanding of multiple subjects in a video requires subject detection, action identification, and behavior interpretation as well as the interactions among subjects and background. This work develops the action recognition of subject(s) based on the correlations and interactions of the whole scene and subject(s) by using the Deep Neural Network (DNN). The proposed DNN consists of 3D Convolutional Neural Network (CNN), Spatial Attention (SA) generation layer, mapping convolutional fused-depth layer, Transformer Encoder (TE), and two fully connected layers with late fusion for final classification. Especially, the attention mechanisms in SA and TE are implemented to find out meaningful action information on spatial and temporal domains for enhancing recognition performance, respectively. The experimental results reveal that the proposed DNN shows the superior accuracies of 97.8%, 98.4% and 85.6% in the datasets of traffic police, UCF101-24 and JHMDB-21, respectively. Therefore, our DNN is an outstanding classifier for various action recognitions involving one or multiple subjects.
Word-Level Embeddings for Cross-Task Transfer Learning in Speech Processing

Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to date, most of these approaches are task-specific and designed for within-task transfer learning between different datasets or setups of a particular task. In turn, learning task-independent representation of speech and cross-task applications of transfer learning remain less common. Here, we introduce an encoder capturing word-level representations of speech for cross-task transfer learning. We demonstrate the application of the pre-trained encoder in four distinct speech and audio processing tasks: (i) speech enhancement, (ii) language identification, (iii) speech, noise, and music classification, and (iv) speaker identification. In each task, we compare the performance of our cross-task transfer learning approach to task-specific baselines. Our results show that the speech representation captured by the encoder through the pre-training is transferable across distinct speech processing tasks and datasets. Notably, even simple applications of our pre-trained encoder outperformed task-specific methods, or were comparable, depending on the task.
Similarity Analytics for Semantic Text Using Natural Language Processing

Determining the similarity among the sentences is a predominant task in natural language processing. The semantic determining task is one of the important research area in today’s applications related to text analytics. The semantic of the sentences get varied according to the textual context it is used. In natural language processing, determining the semantic likeness between sentences is an important research area. As a result, a lot of research is done in determining the semantic likeness in the text. For example, there exists many possible semantics for a word (polysemy) and the synonym of the word; and also these techniques avoid considering the stop words in English which are critical for English phrase/word division, speech investigation, and meaningful comprehension. Our proposed work utilizes Term Frequency-based Inverse Document Frequency model and Glove algorithm-based word embeddings vector for determining the semantic similarity among the terms in the textual contents. Lemmatizer is utilized to reduce the terms to the most possible smallest lemmas. The outcomes demonstrate that the proposed methodology is more prominent than the TF-idf score in ranking the terms with respect to the search query terms. The Pearson correlation coefficient achieved for the semantic similarity model is 0.875.
A survey on Attribute-Based Signatures

Attribute-Based Signature (ABS), where messages are signed concerning a signing strategy conveyed as predicates, provides a necessary platform for implementing fine-grained access control over signed messages in an attribute-based cryptosystem. Research into ABS has seen rapid progress in recent years, and we consider it a fitting proposition to achieve anonymous authentication. Due to its unique characteristic, ABS is widely adopted in various application scenarios. Considering the fruitful and compelling prospect of ABS, it is desirable to survey the ABS systematically and thoroughly. This paper offers an exhaustive survey and examines the state-of-the-art of ABS designs and schemes. Furthermore, the survey proffers precise insights into some classic concretely constructed algorithms as well as the design philosophy behind these algorithms. Additionally, we examine the extensions (the several directions) of ABS and its advancement after its inception. We compare related work from the perspectives of performance, functionality, and security level and discuss open research issues. • Publications on Attribute-based signature since inception in 2008 to the year 2020. • A comprehensive taxonomy and state-of-the-art of Attribute-based signature schemes. • Detailed insights on some essential techniques on some classic constructed algorithms. • Extensions of Attribute-based signature and its progress since its inception. • Design principles of Attribute-based signature and comparisons between existing works.
Trust-Based Context-Aware Collaborative Filtering Using Denoising Autoencoder

In recent times, extensive studies have been initiated to leverage deep learning strategies to enhance context-aware recommendation. Classical collaborative filtering approaches have shown potency in a wide variety of recommendation activities; however, they are inadequate to grasp dynamic interactions between people and products in addition to data sparsity and cold start problem. There is indeed a burst of attention in using deep learning to recommendation systems owing to its nonlinear modeling potential. In this article, we implement the idea of denoising autoencoders for personalized context-aware recommendation. In specific, the proposed method comprises of split item rating according to all contextual conditions resulting in fictive items that is being fed into the denoising autoencoder augmented with trust information to overcome sparsity, referred to as Item Splitting_Trust-based collaborative filtering using denoising autoencoder (IS_TDAE). Thereby, IS_TDAE is able to predict context-based item preference under all possibility of context situation and able to suggest recommendation according current context situation of the target user. Experiments conducted on two public datasets demonstrate the effectiveness of the proposed model significantly outperforms in top-N recommendation task over the state-of-the-art recommenders.