Reviewerly/exHarmony huggingface dataset:
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense

We proposes a novel algorithm, ANTHRO, that inductively extracts over 600K human-written text perturbations in the wild and leverages them for realistic adversarial attack.Unlike existing character-based attacks which often deductively hypothesize a set of manipulation strategies, our work is grounded on actual observations from real-world texts.We find that adversarial texts generated by AN-THRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness-i.e.indistinguishable from human writings hence harder to be flagged as suspicious.Specifically, our attacks accomplished around 83% and 91% attack success rates on BERT and RoBERTa, respectively.Moreover, it outperformed the TextBugger baseline with an increase of 50% and 40% in terms of semantic preservation and stealthiness when evaluated by both layperson and professional human workers.ANTHRO can further enhance a BERT classifier's performance in understanding different variations of human-written toxic texts via adversarial training when compared to the Perspective API.
Federated Learning Role in Big Data, Jot Services and Applications Security, Privacy and Trust in Jot A Survey

The Internet of things or JOT is a large number of inter connected or interrelated devices that share data through the internet without manual intervention. The data collected in JOT is very large and Big Data plays an important role in analyzing this data through various AI mechanisms or algorithms like Machine learning, deep learning and federated learning. Big data analytics play a vital role in JOT which helps in decision making With Internet of Things (JOT) emerging as the next phase of the internet evolution, it is important for us to discuss various applications, and research challenges that are associated with these applications. While using the benefits of JOT, new problems with JOT have also been emerging. To understand these application and their challenges this survey will explain the applications of JOT, various challenges faced in the application of JOT. We also address the issue of security, privacy and trust of JOT. In order to address this issue, Federated Learning (FL) technology is combined with JOT by the researchers’. In this paper we give a complete overview of combining FL with JOT. The survey starts with explaining the applications of JOT, fundamentals of Machine Learning and Distributed Learning concepts in JOT. The motivation behind integrating Fl and JOT is explained. We also give the overview of the FL in JOT applications Finally we concentrate on the challenges, some possible solutions and directions for future research.
Learning Spatio-Temporal Attention Based Siamese Network for Tracking UAVs in the Wild

The popularity of unmanned aerial vehicles (UAVs) has made anti-UAV technology increasingly urgent. Object tracking, especially in thermal infrared videos, offers a promising solution to counter UAV intrusion. However, troublesome issues such as fast motion and tiny size make tracking infrared drone targets difficult and challenging. This work proposes a simple and effective spatio-temporal attention based Siamese method called SiamSTA, which performs reliable local searching and wide-range re-detection alternatively for robustly tracking drones in the wild. Concretely, SiamSTA builds a two-stage re-detection network to predict the target state using the template of first frame and the prediction results of previous frames. To tackle the challenge of small-scale UAV targets for long-range acquisition, SiamSTA imposes spatial and temporal constraints on generating candidate proposals within local neighborhoods to eliminate interference from background distractors. Complementarily, in case of target lost from local regions due to fast movement, a third stage re-detection module is introduced, which exploits valuable motion cues through a correlation filter based on change detection to re-capture targets from a global view. Finally, a state-aware switching mechanism is adopted to adaptively integrate local searching and global re-detection and take their complementary strengths for robust tracking. Extensive experiments on three anti-UAV datasets nicely demonstrate SiamSTA’s advantage over other competitors. Notably, SiamSTA is the foundation of the 1st-place winning entry in the 2nd Anti-UAV Challenge.
CMC-Net: 3D calf muscle compartment segmentation with sparse annotation

Accurate 3D segmentation of calf muscle compartments in volumetric MR images is essential to diagnose as well as assess progression of muscular diseases. Recently, good segmentation performance was achieved using state-of-the-art deep learning approaches, which, however, require large amounts of annotated data for training. Considering that obtaining sufficiently large medical image annotation datasets is often difficult, time-consuming, and requires expert knowledge, minimizing the necessary sizes of expert-annotated training datasets is of great importance. This paper reports CMC-Net, a new deep learning framework for calf muscle compartment segmentation in 3D MR images that selects an effective small subset of 2D slices from the 3D images to be labelled, while also utilizing unannotated slices to facilitate proper generalization of the subsequent training steps. Our model consists of three parts: (1) an unsupervised method to select the most representative 2D slices on which expert annotation is performed; (2) ensemble model training employing these annotated as well as additional unannotated 2D slices; (3) a model-tuning method using pseudo-labels generated by the ensemble model that results in a trained deep network capable of accurate 3D segmentations. Experiments on segmentation of calf muscle compartments in 3D MR images show that our new approach achieves good performance with very small annotation ratios, and when utilizing full annotation, it outperforms state-of-the-art full annotation segmentation methods. Additional experiments on a 3D MR thigh dataset further verify the ability of our method in segmenting leg muscle groups with sparse annotation.
Shoulder Surfing Attack Trusted Verification – A Survey

Individual performance such as select a mistaken code word or inflowing a code word in an unsure of yourself manner result in the easiest linkage in authentication. An invader is able to harm the hardware, software, or information by exploiting the weakest link. As a result, our goal is to give a elegant method for users to validate their bank accounts. In computer or IT security, authentication-based passwords are used. As an alternative of using alphanumeric as a code word, the user is able to choose to use an image as a password. The customer is capable of depicting the shoulder surfing attack using the mobile application. Passwords can be observed by attackers using spyware or shoulder surfing. To solve the problem, the Pass Matrix authentication system is proposed, which is based on graphical passwords and can withstand a shoulder surfing attack. Pass Matrix does not provide any suggestion or stature, still when conducting camera-based attacks, through a just the once suitable login display and navigation button cover the whole range of pass-image. As a result, the future system outperforms the competition in provisos of shoulder surfing resistance.
High extinction ratio thermo-optic based reconfigurable optical logic gates for programmable PICs

In this paper, a new scheme is proposed to realize reconfigurable and multifunction optical logic gates (XOR, XNOR, NAND, and OR) using a Mach–Zehnder interferometer with a tunable thermo-optic phase shifter (TOPS). The reconfigurable optical logic gates are realized by tuning the phase of an optical signal using TOPS without changing the physical device structure. The logical input “0” or “1” is considered corresponding to the phase of the optical signal at TOPS. The logical output of the proposed device depends on the light intensity at output ports. The device is designed on silicon on insulator (SOI) platform and the simulation result shows that the on–off extinction ratio is greater than 37 dB at 1550 nm and &amp;gt;25 dB for the C-band. Moreover, it has a low insertion loss of 0.09 dB at a wavelength of 1550 nm and &amp;lt;0.8 dB for the C-band window. The proposed optical logic gates can be a promising logical device for programmable photonic integrated circuits.
The Concordance Index Decomposition - A Measure for a Deeper Understanding of Survival Prediction Models

The Concordance Index (C-index) is a commonly used metric in Survival Analysis to evaluate how good a prediction model is. This paper proposes a decomposition of the C-Index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition allows a more fine-grained analysis of the pros and cons of survival prediction methods. The utility of the decomposition is demonstrated using three benchmark survival analysis models (Cox Proportional Hazard, Random Survival Forest, and Deep Adversarial Time-to-Event Network) together with a new variational generative neural-network-based method (SurVED), which is also proposed in this paper. The demonstration is done on four publicly available datasets with varying censoring levels. The analysis with the C-index decomposition shows that all methods essentially perform equally well when the censoring level is high because of the dominance of the term measuring the ranking of events versus censored cases. In contrast, some methods deteriorate when the censoring level decreases because they do not rank the events versus other events well.
Neural Network-Based Decoding Input Stimulus Data Based on Recurrent Neural Network Neural Activity Pattern

Abstract The paper reports the assessment of the possibility to recover information obtained using an artificial neural network via inspecting neural activity patterns. A simple recurrent neural network forms dynamic excitation patterns for storing data on input stimulus in the course of the advanced delayed match to sample test with varying duration of pause between the received stimuli. Information stored in these patterns can be used by the neural network at any moment within the specified interval (three to six clock cycles), whereby it appears possible to detect invariant representation of received stimulus. To identify these representations, the neural network-based decoding method that shows 100% efficiency of received stimuli recognition has been suggested. This method allows for identification the minimum subset of neurons, the excitation pattern of which contains comprehensive information about the stimulus received by the neural network.
Changes to team autonomy in large-scale software development: a multiple case study of Scaled Agile Framework (SAFe) implementations

Large-scale transformations of agile ways of working have received more attention in the industry in recent years. Some organizations have developed their own solutions for scaling, whereas many have chosen trademarked frameworks. In large-scale agile software development, many developers and development teams carry out work simultaneously. When autonomous teams need to coordinate toward a common goal, they must sacrifice some level of autonomy. Development, testing, and integrations need to be coordinated with other teams and aligned with an organization´s programs or portfolio. Through the conducting of 28 interviews and 17 on-site visits, this multiple case study explored how team autonomy changed in three agile software development organizations that implemented the Scaled Agile Framework (SAFe). The positive changes to team autonomy that they experienced as a result included getting a better overview, making better long-term decisions, giving and receiving help, and signaling limitations. We found two negative impacts on team autonomy: limited feature choice and enforced refinement. The study extends previous research on large-scale agile software development and improves our understanding of impacts on team autonomy.
Face Mask Detection Using Lightweight Deep Learning Architecture and Raspberry Pi Hardware: An Approach to Reduce Risk of Coronavirus Spread While Entrance to Indoor Spaces

The COVID-19 pandemic continues to spread around the world at full speed, threatening public health. In response, the World Health Organization recommends various preventive measures to reduce the spread of the COVID-19 virus. Wearing a mask is one of the preventive measures to reduce the contagion of the disease, and many governments around the world advise people to wear masks. One of the prominent symptoms of coronavirus is high fever. A person with a fever above normal is likely to have contracted the corona virus. This requires the identification of people with a high fever in order to prevent the epidemic in the public arena. This situation has caused people who want to enter public places to need masks and officers who control their body temperature. The aim of this study is to detect people who do not wear masks or do not wear them properly, and also to detect people with high fever through a system. The proposed system is designed as a system that can be integrated into automatic door systems. The system was basically implemented by running Mobile Net, one of the deep learning models, on the Raspberry Pi card. In the proposed method, 97.0% accuracy was obtained. Experimental results show that the proposed method can effectively recognize face masks and whether people have a high fever. This work is necessary for many closed areas that will make masks and fever control in public areas.
A survey on hand gesture recognition for mobile devices

As smartphones and mobile devices become ubiquitous in the daily life of human beings, facilitating interaction with them will have a positive impact on their use. This paper presents an emerging and important area for research on human-machine interaction, involving interaction with mobile devices. Hand gesture recognition is already a quite mature field and its integration on advanced technologies like smartphones makes a survey an interesting and needed contribution. The design of interactive mobile applications should differ from traditional desktop applications. For this purpose, the paper discusses some concepts that help to understand new challenges and introduces techniques that may be useful to properly exploit these devices. This paper gives also an overview of recent works done on vision-based and contact-based hand gesture recognition systems for mobile devices. Used techniques and approaches are also discussed. At the end, we conclude with some reflections.
Nature inspired computational intelligence implementation for privacy preservation in MapReduce framework

The next generation technologies made huge impact on the extent of data usage and motivated researches in data-management field along-with the advances in the automation in machine-human interactions. Cloud data storage plays significant role in handling big data. However, data security and data privacy-preservation are still very challenging issues. Several techniques are developed to do privacy preservation keeping in mind the data utility and data obfuscation; however, the trade-off among the privacy of data and its utility is not properly tackled. To solve many optimisation problems, the current trend is use of nature-inspired optimisation algorithms. This paper proposes implementation of two nature inspired optimisation algorithms; cat swarm optimisation and grey wolf optimiser; along with adaptation of k-anonymisation criteria in the MapReduce framework for achieving privacy preservation goal. A fitness function is defined that maintains trade-off between privacy and utility of information given to end-user. Comparative analysis of proposed technique with established techniques is done on parameters: classification accuracy and information loss.
In‐Memory Binary Vector–Matrix Multiplication Based on Complementary Resistive Switches

Binary Neural Networks In article number 2000134, Stephan Menzel and co-workers explore a computation in-memory concept for binary vector-matrix multiplications based on complementary resistive switches. Experimental results on a small-scale demonstrator are shown and the influence of device variability is investigated. The simulated inference of a 1-layer fully connected binary neural network trained on the MNIST data set resulted in an accuracy of nearly 86%.
Stages of Information Systems Development in the Context of Educational Mobility Formation

This article discusses historical and logical analysis of the stages of e-technologies development for educational mobility. The paper presents tendencies of educational mobility development and current problems that motivate the development of SMART-technologies and augmented reality systems.
TASP: A Time-Phased Model for Sepsis Prediction

Background: As a lift-threatening condition, sepsis is one of the major public health issues around the world.Early prediction can improve the sepsis outcomes by prompt treatment.Method: As part of the Physionet/Computing in Cardiology Challenge 2019, our team (FlyingBubble) proposed a Time-phAsed model for Sepsis Prediction (TASP).Realizing the fact that the incidence of sepsis is time-dependent, our model is a fusion of different frameworks along the time dimension.In the beginning stage of ICU stay, a gradient boosting tree model is used to figure out the patients with relatively high risk of sepsis.Following that, another tree model with more features is adopted to identify the risk in middle stage.If a patient stayed in ICU more than 50 hours, a deep learning framework will be used to capture the long-term relations for sepsis risk prediction in late stage.We construct proper features for each sub-models with different missing value imputation strategies.Result: The proposed model obtains a score of 0.415 by means of 10-fold cross-validation on the training set.Two simplified versions of the model respectively get scores 0.420 and 0.419 on official online test set A. And the higher one is ranked in 4 th with score 0.337 on full test set.1.
A new weighted two-dimensional vector quantisation encoding method in bag-of-features for histopathological image classification

Automated histopathological image analysis is a challenging problem due to the complex morphological structure of histopathology images. Bag-of-features is one of the prominent image representation methods which has been successfully applied in histopathological image analysis. There are four phases in the bag-of-features method, namely feature extraction, codebook construction, feature encoding, and classification. Out of which feature encoding is one of the prime phases. In feature encoding phase, images are represented in terms of visual words before feeding into support vector machine classifier. However, the feature encoding phase of the bag-of-features framework considers the one feature to encode each image in terms of visual words due to which the system can not use the merits of other features. Therefore, to improve the efficacy of the bag-of-features framework, a new weighted two-dimensional vector quantisation encoding method is proposed in this work. The proposed method is tested on two histopathological image datasets for classification. The experimental results show that the combination of SIFT and ORB features with two dimensional vector quantisation encoding method returns 80.13% and 77.13% accuracy on ADL and Blue Histology datasets respectively which is better than other considered encoding methods.
A new neighbourhood formation approach for solving cold-start user problem in collaborative filtering

Collaborative filtering (CF) is the most widely accepted recommendation technique. Despite its popularity, this approach faces some major challenges like that of a cold-start user problem where a user has rated a handful of items. Due to very few ratings available for the cold-start users, their similarities with rest of the users has been questioned in the past, none have focused on their approach for neighbour identification. Whilst the traditional CF approaches select only those similar users as neighbours who have rated the item under consideration, the neighbourhood comprises of weak neighbours of the cold-start users. To address this shortcoming, our proposed approach selects neighbours with highest similarity irrespective of their availability of ratings for that item. Moreover, for the selected similar neighbours with missing ratings, an item based regression is performed to partially populate the matrix. The efficacy of the proposed neighbourhood formation approach addressing cold-start user problem is validated on two publicly available MovieLens datasets. Our approach provides superior quality of recommendations evaluated on a range of prediction and classification accuracy metrics. The results are encouraging particularly for systems having higher percentage of cold-start users which indicates the effectiveness of our approach in practical settings of new internet portals.
Features of Development of Control Programs by Means of the Modular Description of Elementary Forms

The features of the design of control programs for CNC machines using parametric programming are presented. The proposed unified method of obtaining the model of the processing elements described by the macro. The principles of building fragments of control programs for structural elements of the part with the introduction of processing conditions in the working code are formed.
A survey of term weighting schemes for text classification

Text document classification approaches are designed to categorise documents into predefined classes. These approaches have two main components: document representation models and term-weighting methods. The high dimensionality of feature space has always been a major problem in text classification methods. To resolve high dimensionality issues and to improve the accuracy of text classification, various feature selection approaches were presented in the literature. Besides which, several term-weighting schemes were introduced that can be utilised for feature selection methods. This work surveys and investigates various term (feature) weighting approaches that have been presented in the text classification context.
Automatic Speech Recognition Systems for Regional Languages in India

Speech recognition systems has made remarkable progress in last ¬few decades such as Siri, Google assistant, Cortana. For improving the automation in services of all sectors including medical, agriculture, voice dialling, directory services, education, automobile etc., ASR systems must be built for regional languages as most of the Indian population in not familiar with English. Lots of work is done for English language but not for regional languages in India. Developing ASR and ASU systems will change the scenario of current service sector. There are many challenges in building ASR system, Noise reduction is a one of the challenging and still unsolved parameters which affects a lot on performance of any ASR system. Basically, three models required for building any ASR systems- Language model, acoustic model and pronunciation model. In this paper, discussed various parameters affecting on building ASR systems, development of ASR systems, Tools and Techniques used for building an ASR system and research on regional languages ASR system. Deep Neural network (DNN) provides a better way of recognising a speech and accuracy is high.