Reviewerly/exHarmony huggingface dataset:
Real-Time Fuzzy-PID for Mobile Robot Control and Vision-Based Obstacle Avoidance

In this work, the differential mobile robot is controlled utilizing fuzzy PID speed control, which combines fuzzy control with conventional PID control in real time. The path may be convoluted, and the surrounding environment may contain a range of arbitrary shape and size obstacles. A monocular camera is used to detect obstacles during the navigation process. To enable a robot to travel within an indoor space while avoiding obstacles, a basic image processing approach based on area of interest was used. The goal of this research is to develop a fuzzy PID speed controller on a real robot, as well as a simple and efficient visual obstacle avoidance system. MATLAB is used to implement the control system. GUIDE (graphical user interface development environment) has enabled the creation of graphical user interfaces. These interfaces make it easy to manipulate the system in real time and capture live video. The proposed methodologies are tested on a non-holonomic dr robot i90 mobile robot, and the results are satisfactory.
Guidelines for the development of a critical software under emergency

During the first wave of the COVID-19 pandemic, an international and heterogeneous team of scientists collaborated on a social project to produce a mechanical ventilator for intensive care units (MVM). MVM has been conceived to be produced and used also in poor countries: it is open-source, no patents, cheap, and can be produced with materials that are easy to retrieve.The objective of this work is to extract from the experience of the MVM development and software certification a set of lessons learned and then guidelines that can help developers to produce safety-critical devices in similar emergency situations.We conducted a case study. We had full access to source code, comments on code, change requests, test reports, every deliverable (60 in total) produced for the software certification (safety concepts, requirements specifications, architecture and design, testing activities, etc.), notes, whiteboard sketches, emails, etc. We validated both lessons learned and guidelines with experts.We contribute a set of validated lessons learned and a set of validated guidelines, together with a discussion of benefits and risks of each guideline.In this work we share our experience in certifying software for healthcare devices produced under emergency, i.e. with strict and pressing time constraints and with the difficulty of establishing a heterogeneous development team made of volunteers. We believe that the guidelines will help engineers during the development of critical software under emergency.
A secure gray-scale image watermarking technique in fractional DCT domain using zig-zag scrambling

Security of watermark is a huge concern when dealing with imperceptible watermarking for copyright protection and owner identification. A novel watermarking technique that is intrinsically secure against unauthorized extraction of watermark and robust against a variety of attacks has been proposed in this paper. Semi-blind in nature, the technique employs fractional discrete cosine transform (FDCT) and zig-zag scrambling to add a layer of security while embedding watermark bits in the cover image. Transformation through multiple fractional value pairs yields robust results against attacks for every cover image. A pair out of these is chosen while embedding the watermark. The same pair is needed at the time of extraction of original watermark from a signed image. This pair of unique values adds a layer of security in the proposed technique. After dividing the cover image into sub-blocks that are non-overlapping, fuzzy entropy is used to make the selection of appropriate sub-blocks in which watermark bits are to be embedded. These blocks are first mapped in a zig-zag fashion and then, after selection of half sub-blocks, FDCT is applied on each of them. FDCT coefficients serve as training data for Kernel extreme learning machine (KELM). Used as a non-linear regression model, KELM predicts the value of a particular coefficient with the help of neighboring transform domain coefficients. Multiple scaling factors are used to control the extent to which the watermark bit modifies the predicted coefficient. Strength of these scaling factors is determined and controlled with the help of standard deviation of every selected sub-block. The technique shows perfect extraction of watermarks (Normalized correlation=1.0, Bit error ratio=0.0) against attacks like median filtering (3 × 3), Gaussian filtering, Weiner filtering, JPEG compression (Quality factor 40) and gamma correction. Even for other attacks, the extracted watermarks are not too bad in quality that they are beyond recognition.
Improved affine encryption algorithm for color images using LFSR and XOR encryption

In this study, a novel Improved Affine Algorithm (IAA) for color image encryption is proposed. Affine Algorithm (AA) is generally known as an algorithm used for plain text encryption. In the proposed IAA algorithm, Linear Feedback Shift Register (LFSR), XOR encryption, and the AA are combined for color images encryption. The plane image is firstly split into three channels: R, G, and B. The RGB channel image is encrypted using AA encryption with ten keys based on pixel locations and pixel values. The rows and columns of the image are encrypted with LFSR keys and XOR encryption procedures. Finally, the proposed algorithm is tested in Matlab environment to obtain the Histogram, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Unified Average Changing Intensity (UACI), Number of Pixel Change Rate (NPCR), and Entropy analyses. The values are compared with other algorithms. The results show that the proposed image encryption algorithm is secure and powerful, outperforming other algorithms.
Decision support system for blockchain (DLT) platform selection based on ITU recommendations: A systematic literature review approach

Blockchain technologies, also known as Distributed Ledger Technologies (DLT), are increasingly being explored in many applications, especially in the presence of (potential) dis-/mis-/un-trust among organizations and individuals. Today, there exists a plethora of DLT platforms on the market, which makes it challenging for system designers to decide what platform they should adopt and implement. Although a few DLT comparison frameworks have been proposed in the literature, they often fail in covering all performance and functional aspects, adding that they too rarely build upon standardized criteria and recommendations. Given this state of affairs, the present paper considers a recent and exhaustive set of assessment criteria recommended by the ITU (International Telecommunication Union). Those criteria (about fifty) are nonetheless mostly defined in a textual form, which may pose interpretation problems during the implementation process. To avoid this, a systematic literature review regarding each ITU criterion is conducted with a twofold objective: (i) to understand to what extent a given criterion is considered/evaluated by the literature; (ii) to come up with 'formal' metric definition (i.e., on a mathematical or experimental ground) based, whenever possible, on the current literature. Following this formalization stage, a decision support tool called CREDO-DLT, which stands for "multiCRiteria-basEd ranking Of Distributed Ledger Technology platforms", is developed using AHP and TOPSIS, which is publicly made available to help decision-maker to select the most suitable DLT platform alternative (i.e., that best suits their needs and requirements). A use case scenario in the context of energy communities is proposed to show the practicality of CREDO-DLT.
Multi-Scale MLP-Mixer for image classification

MLP-Mixer is a vision architecture that solely relies on multilayer perceptrons (MLPs), which despite their simple architecture, they achieve a slightly inferior accuracy to the state-of-the-art models on ImageNet. Given that the MLP-Mixer segments each input image into a fixed number of patches, small-scale MLP-Mixers are preferred due to attaining better accuracy because the image is segmented into more patches. However, this strategy significantly increases the computational burden. Nevertheless, this paper argues that even in the same dataset, each image has a different recognition difficulty due to its characteristics. Therefore, in the ideal case, choosing an independently scaled MLP-Mixer for each image is the most economical computational approach. Hence, this paper experimentally verifies the objective existence of this phenomenon, which inspires us to propose the Multi-Scale MLP-Mixer (MSMLP) that utilizes a suitably scaled MLP-Mixer for each input image. MSMLP comprises several MLP-Mixers of different scales. During testing, these MLP-Mixers are activated in order of scale from large to small (increasing number of patches and decreasing patch size). In addition, to reduce redundant computations, a feature reuse mechanism is designed between neighboring MLP-Mixers so that the small-scale MLP-Mixer downstream can reuse the features learned by the larger-scale MLP-Mixer upstream. Finally, extensive experiments on the public dataset CIFAR10/100 reveal that our method’s theoretically estimated computational cost and actual inference speed are significantly higher than those of MLP-Mixer.
Dual Prototype Contrastive learning with Fourier Generalization for Domain Adaptive Person Re-identification

Unsupervised domain adaptive (UDA) person re-identification (ReID) focuses on improving the model’s generalization capability from one labeled source domain to the unlabeled target domain. Recently, contrastive learning based on pseudo label assignment has attracted much attention and dominated the field. However, existing methods usually consider bridging the domain gap at the feature level, and they only pull each query instance to get close to its cluster centroid which is stored or computed through a memory bank. The relationship of inter-instance within cluster, especially with the hard instances, is ignored. To this end, we propose a Dual Prototype Contrastive learning with Fourier Generalization (DPCFG) framework for domain adaptive Person Re-identification. First, we introduce the Fourier Generalization (FG) strategy at image level to bridge the domain gap. Concretely, the FG strategy is implemented by replacing the amplitude component of each source domain image with a randomly selected target domain image. Then, the Dual Prototype Contrastive learning (DPC) strategy is further developed to fully exploit the hard positive instances within each cluster. DPC optimizes two contrastive losses by forcing each query to be close to two prototypes: the cluster centroid prototype and the hard positive prototype. The cluster centroid prototype ensures the basic classification accuracy, and the hard positive prototype further improves the classification accuracy by dynamically depicting a certain class boundary for each cluster as the model iterates. Experimental results on the real-world datasets, Market, DukeMTMC-reID, and MSMT17, and synthetic dataset PersonX, demonstrate that DPCFG is effective and achieves state-of-the-art UDA person ReID performance.
Real-time Threshold-Based Fall Detection System Using Wearable IoT

This paper presents a Real-Time Fall Detection System (FDS) in the form of a wearable device integrating an ADXL335 accelerometer as a fall detection sensor, and classify the falling condition based on the threshold method. This system detects the wearer's movements and analyses the result in binary output conditions of ‘Fall’ for any fall occurrence or ‘Normal’ for other activities. The transmitter or FDS-Tx which is attached to the user’s garment will constantly transmit data reading to the receiver or FDS-Rx via XBee module for data analysis. Raspberry Pi as the processor in FDS-Rx provides computational resources for immediate output analysis, by using threshold method, the computed results are sent to the cloud utilizing the Wi-Fi to display the user’s condition on the authority’s dashboard for further action. The working conditions of the systems are validated through an experiment of 10 volunteers whose perform several activities including fall events. Based on the threshold proposed, the results showed 97% sensitivity, 69% specificity and 83% accuracy from the experiment. Thus, this system fulfilled the real-time working condition integrating (IoT) as accordingly.
Analysis of Breast Cancer Classification with Machine Learning based Algorithms

Nowadays experienced radiologists can perform successful detection of malignant tumors by examining the histological images or patients’ data. However, experts may have different diagnosis or decisions about the type of cancer. Recently, breast cancer has become a trend topic because of its mortality rate affected by this disease. With the improvement of computer aided systems, specialist can benefit from more accurate results and therefore detect the cancer and apply the required treatment in early stages. Knowing the success achieved in Artificial Intelligence field, the biomedical sector has been attracted to this technology as well as its new techniques. Recent studies have proven the ability of artificial intelligence to give accurate results that help specialists make better decisions due to its ability to capture details better than Humans. In this paper, four of Machine Learning algorithms, which are Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Convolutional Neural Networks (CNN), with five different breast cancer datasets are tested and analyzed to verify their performance in a binary classification of breast cancer. The results show that CNN obtained higher accuracy than the other tested algorithms in this type of data. This study will help future researchers in breast cancer field to continue their research and focus on improving the performance of specific algorithms.
Tiny-PULP-Dronets: Squeezing Neural Networks for Faster and Lighter Inference on Multi-Tasking Autonomous Nano-Drones

Pocket-sized autonomous nano-drones can revolutionize many robotic use cases, such as visual inspection in narrow, constrained spaces and ensure safer human-robot interaction due to their tiny form factor and weight - i.e., tens of grams. This compelling vision is challenged by the high level of intelligence needed aboard, which clashes against the limited computational and storage resources available on PULP (parallel-ultra-low-power) MCU class navigation and mission controllers that can be hosted aboard. This work moves from PULP-Dronet, a State-of-the-Art convolutional neural network for autonomous navigation on nano-drones. We introduce Tiny-PULP-Dronet: a novel methodology to squeeze by more than one order of magnitude model size (50× fewer parameters), and number of operations (27× less multiply-and-accumulate) required to run inference with similar flight performance as PULP-Dronet. This massive reduction paves the way towards affordable multi-tasking on nano-drones, a fundamental requirement for achieving high-level intelligence.
Semi-Supervised Trajectory-Feedback Controller Synthesis for Signal Temporal Logic Specifications

There are spatio-temporal rules that dictate how robots should operate in complex environments, e.g., road rules govern how (self-driving) vehicles should behave on the road. However, seamlessly incorporating such rules into a robot control policy remains challenging especially for real-time applications. In this work, given a desired spatio-temporal specification expressed in the Signal Temporal Logic (STL) language, we propose a semi-supervised controller synthesis technique that is attuned to human-like behaviors while satisfying desired STL specifications. Offline, we synthesize a trajectory-feedback neural network controller via an adversarial training scheme that summarizes past spatio-temporal behaviors when computing current controls, and then online, we perform gradient steps to improve specification satisfaction. Central to the offline phase is an imitation-based regularization component that fosters better policy exploration and induces naturalistic human behaviors. Our experiments demonstrate that having imitationbased regularization leads to better qualitative and quantitative performance compared to optimizing for STL satisfaction only as done in prior work. We demonstrate the efficacy of our approach with an illustrative case study and show that our proposed controller outperforms a state-of-the-art shooting method in both performance and online computation time.
Improving small objects detection using transformer

General artificial intelligence counteracts the inductive bias of an algorithm and tunes the algorithm for out-of-distribution generalization. A conspicuous impact of the inductive bias is an unceasing trend in improving deep learning performance. Although a quintessential attention-based object detection technique, DETR, shows better accuracy than its predecessors, its accuracy deteriorates for detecting small-sized (in-perspective) objects. This study examines the inductive bias of DETR and proposes a normalized inductive bias for object detection using data fusion, SOF-DETR. A technique of lazy-fusion of features is introduced in SOF-DETR, which sustains deep contextual information of objects present in an image. The features from multiple subsequent deep layers are fused for object queries that learn long and short-distance spatial association in an image using the attention mechanism. Experimental results on the MS COCO and Udacity Self Driving Car datasets assert the effectiveness of the added normalized inductive bias and feature fusion techniques, showing increased COCO mAP scores on small-sized objects.
Differential Evolution with an Unbounded Population

The notion of a population with individuals which are replaced by newly generated individuals is a pervasive idea in differential evolution (DE). Techniques such as archives have been previously proposed to supplement the central idea of a population, motivated by improved performance compared to a simple population structure. We consider Unbounded DE (UDE), which uses a population where individuals are never replaced, and the population monotonically grows as new individuals are generated. Behaviors similar to standard populations, as well as previous population modifications such as archives can be implemented within the UDE framework by implementing specific selection policies. We show experimentally that UDE can be configured to be competitive with standard DE as well as adaptive DE algorithms, showing that the traditional notion of replacement is not necessary for good performance.
Building a secure mHealth data sharing infrastructure over NDN

Exploratory efforts in mobile health (mHealth) data collection and sharing have achieved promising results. However, fine-grained contextual access control and real-time data sharing are two of the remaining challenges in enabling temporally-precise mHealth intervention. We have developed an NDN-based system called mGuard to address these challenges. mGuard provides a pub-sub API to let users subscribe to real-time mHealth data streams, and uses name-based access control policies and key-policy attribute-based encryption to grant fine-grained data access to authorized users based on contextual information. We evaluate mGuard's performance using sample data from the MD2K project.
In Situ Measurement of Cyclic Plastic Zone and Internal Strain Response of Q&amp;P Steel near Fatigue Crack Tip Region Based on Micro-DIC

The shape and internal dynamic response characteristics of the plastic zone near the fatigue crack tip region, especially the cyclic plastic zone (CPZ), are the main factors affecting the fatigue crack initiation and propagation behaviors of ductile metal materials. The existing methods for characterizing the CPZ have some problems, which include the complexity of the process, the difficulty of achieving in situ measurement, and the inability to characterize the dynamic response in the CPZ during the crack propagation process. Therefore, a novel method is proposed for the in situ measurement of the CPZ near the crack tip region based on image stitching and matching algorithms, a load-strain loop curve characteristic judgement algorithm, and the microscopic digital image correlation (DIC) method. A microscopic camera and a macroscopic camera are used to simultaneously capture the micro crack tip speckle images and the global crack image of the two sides of the Compact Tension (CT) specimen for calculating in situ crack length and crack tip strain fields. The proposed method was performed and verified by a fatigue crack growth (FCG) test and micro-hardness experiments with Quenching and Partitioning 980 (Q&P980) steel, and the results show that the method is feasible because the maximum error is less than 5%. A "butterfly wings" shape of the CPZ and a strain concentration phenomenon in the CPZ of the Q&P980 were observed. Moreover, as the fatigue crack propagates, the area of the CPZ and the degree of the strain concentration increase gradually. This method, which can obtain the in situ and tracking measurements of the crack tip CPZ, will help to increase our understanding of CPZ characteristics, the FCG mechanism, and the behavior of Q&P steel and the plastic metal materials similar to Q&P steel.
Efficient reconstruction and validation of heterogeneous microstructures for energy applications

The digital reconstruction of microstructures is necessary for simulations in fields ranging from geology to electrochemistry, but the state-of-the-art digital reconstruction techniques often compromise between resolution and field of view. It is challenging to retain detailed microstructure information in large-scale reconstructions. This study investigates different aspects of the Yeong-Torquato algorithm based on correlation functions to make it more efficient. We achieve this goal by reducing the computational complexity of the chord-length distribution function and the two-point correlation function, applying the random sphere-packing method as the initial condition, and restricting potential voxel swaps to interfaces. In addition, a novel superposition parallel scheme is introduced to aid in searching for potential voxel swaps. The algorithm proposed is validated by comparing the pore-size distributions of reconstructed 3D custom battery electrodes from a sample dataset obtained from transmission X-ray microscopy. From a sample image with 200 × 200 pixels, the code can reconstruct a 300 × 300 × 300 structure in under 22 h and reconstruct a 400 × 400 × 400 structure in 43 h with eight cores.