Reviewerly/exHarmony huggingface dataset:
Drone-based Autonomous Human Identification for Search and Rescue Missions in Real-time

Drones can be very useful in search and rescue missions primarily due to the aerial imaging capability. Drones can assist ground teams searching for a missing person, and law enforcement officers in crowd control. But there are instances where the observer on the ground control station misses the subtle information on the video feed coming from the drone. In fact, it is a very difficult job for the observer to be so vigilant in viewing a lot of images looking for a sign of a human in those images. This paper presents a drone-based human identification system to make search and rescue missions more effective. The drone, once detects signs of human presence in real-time aerial videos, autonomously navigates towards the suspicious location to get a better view to verify human presence. The drone has been made fully autonomous with operator override as an option. It processes images and sends selected frames for the operator for verification and issuance of instructions for follow-up actions. A custom built Tensorflow neural network is used as the object detector, which processes images in real-time and report human objects if detected on the ground. A single board computer, while running the real-time image processing, reads GPS location also and generates flight commands to the flight controller for autonomous navigation.
SIGNify – A mobile solution for Indian sign language using MobileNet architecture

Sign language assists in visual communication for the vocal and hearing-impaired population. It benefits people with other disabilities such as autism, down syndrome etc. The work recommends an intelligent system specifically to recognize the Indian Sign Language (ISL). It is an interesting and challenging problem, as the solution brings a leap in both social and technological aspects. A signer independent methodology based on different techniques of deep learning, is a real-time recognition system for Indian sign language developed to avoid the isolation of disabling groups from the rest of the society. In our work, we build and compare two pre-trained CNN models, MobileNet and InceptionV3 architectures. The suggested approach using the MobileNet model showed an accuracy of 99% on a custom-built dataset. The working CNN model can perform real-time recognition of ISL alphabets, numbers and a few selected gestures integrated into an Android mobile platform called SIGNify using React-Native, for better accessibility and user-friendly access. The study highlights a small step involved in human-computer interaction.
Agriculture as a service

In this emerging digital world, mobile devices are the leading technologies available and used by almost everyone everywhere in every field. These ubiquitous devices have become part of our modern lifestyle and have proven to be affordable tools for farmers. If these devices could assist in farming, it would contribute a great deal toward developing the socioeconomy of any nation.
A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers

Creating effective mechanisms to detect misogyny online automatically represents significant scientific and technological challenges. The complexity of recognizing misogyny through computer models lies in the fact that it is a subtle type of violence, it is not always explicitly aggressive, and it can even hide behind seemingly flattering words, jokes, parodies, and other expressions. Currently, it is even difficult to have an exact figure for the rate of misogynistic comments online because, unlike other types of violence, such as physical violence, these events are not registered by any statistical systems. This research contributes to the development of models for the automatic detection of misogynistic texts in Latin American Spanish and contributes to the design of data augmentation methodologies since the amount of data required for deep learning models is considerable.
Smart Home Agents and Devices of Today and Tomorrow: Surveying Use and Desires

How are people using current smart home technologies, and how do they conceptualize future ones that are more interconnected and more capable than those available today? We deployed an online survey study to 150 participants to investigate use of and opinions about smart speakers, home robots, virtual assistants, and other smart home devices. We also gauged how impressions of connected smart home devices are shaped by the way the devices interact with one another. Through a mixed-methods qualitative and quantitative approach, we found that people mostly use single devices for single functions, and have simple and brief interactions with virtual assistants. However, they imagine their future devices to have more control over the physical environment (i.e., interact with each other) and envision them interacting with people in more socially complex ways. These findings motivate design considerations and research directions for connected smart home technologies.
A Median Filtering Forensics CNN Approach Based on Local Binary Pattern

Median filtering forensics has gradually become a research hotspot because of the wide application of the traditional median filtering (MF) in image tampering and anti-forensics. The difficulty of traditional median filtering based on machine learning forensics is feature extraction which is a manual selection process, and the Convolutional Neural Network (CNN) cannot also well perceive the traces left by median filtering straightly. By taking the Local Binary Pattern (LBP) data of an image as a presentation of the streaking artifact that is a very strong indication for median filtering, a median filtering forensics CNN approach with LBP is proposed, which can automatically learn and get median filtering features from image. Different from traditional CNN, an LBP perception layer is added before the following deep learning layers. Then, five convolution layers constitute the feature extraction group. Finally, the classifier group is composed of three full connected layers to decide whether the image is median filtered or not. The proposed approach is tested on several experiments and the experimental results demonstrate its effectiveness.
Explainable AI in Aerospace for Enhanced System Performance

Explainable AI (XAI) is maturing as a pivotal subfield of AI, focused on uncovering complex AI models to human users in a highly systematic, interpretable, and understandable manner. This paper is a snapshot of XAI implementation in aerospace, reflecting the existing discourse in this field with respect to decision-making in critical situations. Supported by a comprehensive XAI taxonomy, the explanation framework is put forth with human-centric and scientific explanations as its integral components to help describe and interpret the model outcome. A use-case formed around the aircraft operation and its essential constituents - the pilots, air-traffic controllers, maintainers, and aircraft manufacturers, is presented as a representative implementation of XAI. It is shown how the decisions that come along with the meaningful information can help operators such as pilots and air traffic controllers’ decisions, so they can consider information consciously to make decisions within a certain time.
Methods of Improving and Optimizing React Web-applications

As web browsers become more advanced every day and support many new technologies, web applications become more complex. Web application performance is vital for its users to have a good experience. It refers to how quickly an application loads and renders in a web browser, and how well it responds to user requests. Good performance may not be obvious to most web application users, but they will immediately recognize a sluggish web application. Paying attention to web application performance is important for both Single Page Applications and traditional server-side rendered web applications. In this paper, we describe and discuss a couple of common approaches to speed up the performances and the initial loading of a React web application. First, we discuss techniques for minimizing program code, then using optimized image formats and the importance of server-side rendering, and finally, we provide a few examples of other performance-optimization techniques that can further improve the user experience.
Don't Forget the Stuffing! Revisiting the Security Impact of Typo-Tolerant Password Authentication

To enhance the usability of password authentication, typo-tolerant password authentication schemes permit certain deviations in the user-supplied password, to account for common typographical errors yet still allow the user to successfully log in. In prior work, analysis by Chatterjee et al. demonstrated that typo-tolerance indeed notably improves password usability, yet (surprisingly) does not appear to significantly degrade authentication security. In practice, major web services such as Facebook have employed typo-tolerant password authentication systems. In this paper, we revisit the security impact of typo-tolerant password authentication. We observe that the existing security analysis of such systems considers only password spraying attacks. However, this threat model is incomplete, as password authentication systems must also contend with credential stuffing and tweaking attacks. Factoring in these missing attack vectors, we empirically re-evaluate the security impact of password typo-tolerance using password leak datasets, discovering a significantly larger degradation in security. To mitigate this issue, we explore machine learning classifiers that predict when a password's security is likely affected by typo-tolerance. Our resulting models offer various suitable operating points on the functionality-security tradeoff spectrum, ultimately allowing for partial deployment of typo-tolerant password authentication, preserving its functionality for many users while reducing the security risks.
Histogram Encoding of SIFT Based Visual Words for Target Recognition in Infrared Images

Several algorithms for target recognition in infrared images were proposed by reasearchers to develop an efficient advanced driver assistance systems. In this paper, an approach based on bag of features framework, SIFT and SVM, is evaluated for target recognition problem. First, SIFT extractor is applied to all the training set. Then, features were clustered by K-means; the cluster centers are regarded as visual words to form a visual vocabulary. For each image, a histogram of quantized local descriptors is computed according to the frequency of visual words in each sub-region, which are obtained by the spatial pyramid matching technique. The generated feature vector will be mapped for later use as an input to SVM. Extensive experiments are carried out in FLIR dataset. Our experimental results show that the proposed method exceeds the-state-of-art in target recognition on two class FLIR dataset with 3% improvement in accuracy classification.
An Automatic Real-Time Face Mask Detection using CNN

Coronavirus (Covid-19) is continuously spreading all over the world now with different variances like alpha, beta, gamma, Epsilon, and Zeta. There are numerous careful steps are taken to minimize the spread of corona virus. Wearing face mask is one among the important measures that people should follow but unfortunately the majority of individuals are not following this safety measure in public spaces. In this paper, we have proposed a novel face mask detection model by cascading three CNN models such as YOLO V3, Facenet and Mobilenet. Here, YOLO V3 model is used for detecting people in a surveillance video data which helps to reduce the search space for face detection techniques. Second CNN model namely Facenet is incorporated to detect the face which is then fed to Mobilenet for mask detection. In the proposed work, mask detection is considered as a binary class problem where a people without mask are discriminated from the people with face mask. A transfer learning is adopted for YOLO V3 and Facenet models. The output layer of Mobilenet is modified for the binary classification. Mobilenet is trained with 3833 instances belonging to both the classes were collected from a realtime data acquired in the laboratory environment during offline classes and from sources like Kaggle and Google images. The trained model has achieved 99.2% accuracy for unseen data.
Attention based Object Classification for Drone Imagery

This paper shows how to make the drone imagery for surveillance or tracking the object in the ground. To detect or classify objects on the ground, convolutional neural networks was adopted and compared with some existed methods and the proposed attention blocks in it. The objects on the ground from the drone images are relatively very small and diversity of the appearance from its perspective projections. This is mainly due to the arbitrary viewpoints from the bird eye views. Furthermore, the distance from its viewpoint in the sky is quite much changeable so that the image of the object is too diverse in appearance and its size. However, the drone is so useful to see widely while navigating in the sky. It is much more attentive to use for real application. Here, some proposed target objects are mainly located in the ground, like static and dynamic objects such as street lamps or trees, vehicles, trucks and pedestrians. These works were done for the national projects to establish the general AI services in Korea recently. For the experiments such as buildup the ground truth of target objects after taken in regulated distance and viewing angles and performed to detect exactly objects in an arbitrary image. For experiments of detection and classification of five categories of objects, attention based CNN architecture was adopted and compared comprehensively with the existed networks like MobileNet, VGG16, SqueezeNet, and ResNet. The experimental results outperformed for the archived drone image dataset with 87.12% in precision. The architecture shows almost 3 times faster with respect to VGG16 or 2 times faster than MobileNet in the speed but a half slimer and twice thicker respectively in the number of parameters. Thus, the Attention Block is useful while a drone navigates through a certain route according to the ground location regardless of the appearance and size of the target region in image.
Semantic Data Integration for Investigations: Lessons Learned and Open Challenges

In today’s world we are confronted with increasing amounts of information every day coming from a large variety of sources. People and corporations are producing data on a large scale, and since the rise of the internet, e-mail and social media the amount of produced data has grown exponentially. From a law enforcement perspective we have to deal with these huge amounts of data when a criminal investigation is launched against an individual or company. Relevant questions need to be answered like who committed the crime, who were involved, what happened and on what time, who were communicating and about what? Not only the amount of available data to investigate has increased enormously, but also the complexity of this data has increased. These communication patterns need to be combined with the objective to extract entities, relations and events. Furthermore, the information management processes within crime investigations are very complex and often delegated to the investigator’s computer skills. Despite that, the application of natural language processing techniques based on crime data can prove to be beneficial in several processes of the criminal justice industry. Up to date, it is not feasible for the law enforcement agencies to get into the detail of these available massive crime reports and get the answers and furthermore is not available a system that permits to integrate information coming from text document with structured data. Starting from these considerations in this work, we propose a system to support the prosecutors to identify suspicious activities managing information of different kinds of format and sources. To tackle these problems associated with criminal justice industry, the proposed work describes a complete data flow that permits to manage the entity lifecycle, and to extract the relations from these entities.
Blockchain Assembled Supply Chains to Foster Secure Trading Using Distributed Ledger

In this paper, we study how to assemble a supply chain with the avail of Blockchain to establish a secure trading environment. Blockchain is distributed, decentralized and a transparent mechanism which makes it suitable to share data on publicly accessible networks such that it can further be verified and audited independently thus maintaining data integrity. This paper suggests that the Blockchain technology offers great potential to foster secure trading using distributed ledger that propounds ingenious platforms as it makes the history of any digital asset unalterable, traceable and transparent. We further study how Blockchain mitigates the subsisting quandary in supply chains, how does it provide an edge over other technologies. The model is proposed to implement the Blockchain manufactured supply chain and it is further explained how it eliminates the challenges faced by the current supply chain management system. Also, challenges to adopting this technology have been discussed in the latter section of the paper.
Comparable prediction of breast cancer risk from a glimpse or a first impression of a mammogram

Abstract Expert radiologists can discern normal from abnormal mammograms with above-chance accuracy after brief (e.g. 500 ms) exposure. They can even predict cancer risk viewing currently normal images (priors) from women who will later develop cancer. This involves a rapid, global, non-selective process called “gist extraction”. It is not yet known whether prolonged exposure can strengthen the gist signal, or if it is available solely in the early exposure. This is of particular interest for the priors that do not contain any localizable signal of abnormality. The current study compared performance with brief (500 ms) or unlimited exposure for four types of mammograms (normal, abnormal, contralateral, priors). Groups of expert radiologists and untrained observers were tested. As expected, radiologists outperformed naïve participants. Replicating prior work, they exceeded chance performance though the gist signal was weak. However, we found no consistent performance differences in radiologists or naïves between timing conditions. Exposure time neither increased nor decreased ability to identify the gist of abnormality or predict cancer risk. If gist signals are to have a place in cancer risk assessments, more efforts should be made to strengthen the signal.
Unifying Presampling via Concentration Bounds

Auxiliary-input (AI) idealized models, such as auxiliary-input random oracle model (AI-ROM) and auxiliary-input random permutation model (AI-PRM), play a critical role in assessing non-uniform security of symmetric key and hash function constructions. However, obtaining security bounds in these models is often much more challenging.
Gaze estimation via bilinear pooling-based attention networks

Attention mechanism has been found effective for human gaze estimation, and the attention and diversity of learned features are two important aspects of attention mechanism. However, the traditional attention mechanism used in existing gaze model is more prone to utilize first-order information that is attentive but not diverse. Though the existing bilinear pooling-based attention could overcome the shortcoming of traditional attention, it is limited to extract high-order contextual information. Thus we introduce a novel bilinear pooling-based attention mechanism, which could extract the second-order contextual information by the interaction between local deep learned features. To make the gaze-related features robust for spatial misalignment, we further propose an attention-in-attention method, which consists of a global average pooling and an inner attention on the second-order features. For the purpose of gaze estimation, a new bilinear pooling-based attention networks with attention-in-attention is further proposed. Extensive evaluation shows that our method surpasses the state-of-the-art by a big margin.
UKF Parameter Tuning for Local Variation Smoothing

The unscented Kalman filter (UKF) is a method to solve nonlinear dynamic filtering problems, which internally uses the unscented transform (UT). The behavior of the UT is controlled by design parameters, seldom changed from the values suggested in early UT/UKF publications. Despite the knowledge that the UKF can perform poorly when the parameters are improperly chosen, there exist no wide spread intuitive guidelines for how to tune them. With an application relevant example, this paper shows that standard parameter values can be far from optimal. By analyzing how each parameter affects the resulting UT estimate, guidelines for how the parameter values should be chosen are developed. The guidelines are verified both in simulations and on real data collected in an underground mine. A strategy to automatically tune the parameters in a state estimation setting is presented, resulting in parameter values in line with developed guidelines.
The modified layer-by-layer weakening solar radiation models based on relative humidity and air quality index

Air pollution has a significant weakening effect on solar radiation. Therefore, it is important for the development and utilization of solar energy to establish a solar radiation model under the influence of fog and haze. Based on the analysis of existing layer-by-layer weakening solar radiation models, the discontinuous meteorological data provided by MODIS satellite are replaced by continuous measured data from meteorological stations. The beam solar radiation model and diffuse solar radiation model are modified by using relative humidity (φ) and air quality index (AQI). The results show that the correction effects of these modified models are better. The R value of beam solar radiation models have increased by 5.74%, the R value of diffuse solar radiation models have increased by 41.27%, and other statistical parameters (RSE, RMSE and NSE) have also been improved to a certain extent. The calculated value of the modified models is closer to the measured data. In addition, the order of correction parameters also has an effect on the results. The research shows that the best correction effect is to use relative humidity first and then AQI. According to this method, the layer-by-layer weakening solar radiation models can be more accurate and reliable.
Distributed Knowledge Inference Framework for Intelligent Fault Diagnosis in IIoT Systems

Knowledge inference using knowledge graphs is one of the recent research trends for fault diagnosis. As complex Industrial Internet of Things (IIoT) systems evolve to incorporate distributed subsystems, each subsystem may have local measurements interpreted by a local Knowledge Base (KB). In this work, we propose a distributed fault diagnosis framework with distributed path-based reasoning. In contrast to centralized reasoning that requires merging of the distributed KBs, the proposed framework employs a centralized coordinator to transfer the model among decentralized agents and allows an agent to handover training paths for others to continue training by filling in missing links using their local KBs. Our work is the very first distributed knowledge inference framework to reason among distributed fault diagnosis systems to tackle the KB isolation, scalability and flexibility issues of centralized fault diagnosis systems. To validate our framework with IIoT datasets, we build a fault knowledge graph from the Tennessee Eastman (TE) process simulations. Evaluations show that our framework outperforms the non-cooperative distributed reasoning method in terms of prediction accuracy. With the TE process dataset, the mean reciprocal rank is improved from 0.1612 to 0.19 with transferred models, and further to 0.206 with hand-over queries between agents.