Reviewerly/exHarmony huggingface dataset:
The Use of Monologue Speaking Tasks to Improve First-Year Students’ English-Speaking Skills

A significant number of studies are devoted to the psychological and social adaptation of students to the educational process at university. This research contributes to the solution of the problem of first-year students’ academic performance in the framework of studying a foreign language by working with monologue speaking tasks. The study offers an analysis of the improvement of academic performance in this particular type of language activity. The study took place at Peter the Great Saint Petersburg Polytechnic University, Russia, and involved 274 first-year students enrolled in undergraduate programs. Mixed qualitative and quantitative methods were applied to collect and analyse data for the study. The research included the qualitative content analysis of monologue speaking tasks. Results of the study make it possible to conclude that the use of monologue speaking tasks paired with peer interaction and peer assessment can improve first-year students’ English-speaking skills.
Secure-reliable smart contract applications based blockchain technology in smart cities environment

Over time, our lives turn to digitization and technology and its multiple applications and uses. The smart city, its technologies and the services provided during it have become a way of life. The idea of smart cities relied on different mechanisms to provide reliable services. One of the important technologies is the blockchain that has proven to be extremely reliable and has a high security level technology. This paper presents a mechanism to explain the application of blockchain technology in smart contracts, how to increase reliability, data security, and many positive benefits as part of the multiple services provided by the smart city environment. This paper also provides important details about this technology and its impact on the overall administrative system of any service provided by smart governments. The paper dealt with an example of how to manage the real estate rental file electronically to explain the advantages of blockchain technology through which we overcome existing problems in this type of contract and service.
A Novel Artificial Neural Network (ANN) Using The Mayfly Algorithm for Classification

Training of Artificial Neural Networks (ANNs) have been improved over the years using meta heuristic algorithms that introduce randomness into the training method but they might be prone to falling into a local minima in a high-dimensional space and have low convergence rate with the iterative process. To cater for the inefficiencies of training such an ANN, a novel neural network is presented in this paper using the bio-inspired algorithm of the movement and mating of the mayflies. The proposed Mayfly algorithm is explored as a means to update weights and biases of the neural network. As compared to previous meta heuristic algorithms, the proposed approach finds the global minima cost at far less number of iterations and with higher accuracy. The network proposed, which is named Mayfly Algorithm based Neural Network (MFANN) consists of an input layer, a single hidden layer of 10 neurons and an output layer. Two University of California Irvine (UCI) database sample datasets have been used as benchmark for this study, namely the Banknote Authentication (BA) and the Cryotherapy, for which the training accuracy achieved is 99.2350% and 96.6102%, whereas the Testing accuracy is 99.1247% and 90% respectively. Comparative study with grey wolf optimization neural network (GWONN) and particle swarm optimization neural network (PSONN) reveal that the proposed MFANN achieves 1-2% better accuracy with training dataset and 2% better accuracy with testing dataset.
Deep metric learning for image retrieval in smart city development

Deep metric learning (DML) aims to learn a consistent distance embedding where an anchor is closer within the same category than others. It underpins a variety of essential and significant tasks in the development of smart city including face recognition, landmark retrieval, pedestrian detection, person/vehicle re-identification, and so on. Traditional pair-based DML methods try to make full use of the data-to-data relations within a (mini-)batch, but they cannot grasp the data distribution information due to the batch size limitation. On the other hand, proxy-based DML schemes use different proxies to approximate the data distribution. However, the proxies are too sample to represent the intra-category variance. In this paper, we propose a simple but effective method, named soft-instance-label proxy, for embedding learning. It can capture the globe data distribution information while depicting the detailed intra-class data structure. The state-of-the-art empirical results on three public image retrieval benchmarks and two backbone networks demonstrate the superiority of our proposed method. Our Soft-instance-label proxy method can have a Recall@1 improvement of 2.4% with Googlenet, largely surpassing the current state-of-art-methods while demonstrating great potential in the development of smart city.
Domain-robust VQA with diverse datasets and methods but no target labels

The observation that computer vision methods overfit to dataset specifics has inspired diverse attempts to make object recognition models robust to domain shifts. However, similar work on domain-robust visual question answering methods is very limited. Domain adaptation for VQA differs from adaptation for object recognition due to additional complexity: VQA models handle multimodal inputs, methods contain multiple steps with diverse modules resulting in complex optimization, and answer spaces in different datasets are vastly different. To tackle these challenges, we first quantify domain shifts between popular VQA datasets, in both visual and textual space. To disentangle shifts between datasets arising from different modalities, we also construct synthetic shifts in the image and question domains separately. Second, we test the robustness of different families of VQA methods (classic two-stream, transformer, and neuro-symbolic methods) to these shifts. Third, we test the applicability of existing domain adaptation methods and devise a new one to bridge VQA domain gaps, adjusted to specific VQA models. To emulate the setting of real-world generalization, we focus on unsupervised domain adaptation and the open-ended classification task formulation.
SPGNet: Serial and Parallel Group Network

Neural-network Processing Units (NPU), which specializes in the acceleration of deep neural networks (DNN), is of great significance to latency-sensitive areas like robotics or edge computing. However, there are few works focusing on the network design for NPU in recent studies. Most of the popular lightweight structures (e.g. MobileNet) are designed with depthwise convolution, which has less computation in theory but is not friendly to existing hardwares, and the speed tested on NPU is not always satisfactory. Even under similar FLOPs (the number of multiply-accumulates), vanilla convolution operation is always faster than depthwise one. In this paper, we will propose a novel architecture named Serial and Parallel Group Network (SPGNet), which can capture discriminative multi-scale information and at the same time keep the structure compact. Extensive evaluations have been conducted on different computer vision tasks, e.g. image classification (CIFAR and ImageNet), object detection (PASCAL VOC and MS COCO) and person re-identification (Market-1501 and DukeMTMC-ReID). The experimental results show that our proposed SPGNet can achieve comparable performance with the state-of-the-art networks while the speed is 120% faster than MobileNetV2 under similar FLOPS and over 300% faster than GhostNet with similar accuracy on NPU.
Removing Adversarial Noise via Low-Rank Completion of High-Sensitivity Points

Deep neural networks are fragile under adversarial attacks. In this work, we propose to develop a new defense method based on image restoration to remove adversarial attack noise. Using the gradient information back-propagated over the network to the input image, we identify high-sensitivity keypoints which have significant contributions to the image classification performance. We then partition the image pixels into the two groups: high-sensitivity and low-sensitivity points. For low-sensitivity pixels, we use a total variation (TV) norm-based image smoothing method to remove adversarial attack noise. For those high-sensitivity keypoints, we develop a structure-preserving low-rank image completion method. Based on matrix analysis and optimization, we derive an iterative solution for this optimization problem. Our extensive experimental results on the CIFAR-10, SVHN, and Tiny-ImageNet datasets have demonstrated that our method significantly outperforms other defense methods which are based on image de-noising or restoration, especially under powerful adversarial attacks.
Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise

Mobile robots are often subject to multiplicative noise in the target tracking tasks, where the multiplicative measurement noise is correlated with additive measurement noise. In this paper, first, a correlation multiplicative measurement noise model is established. It is able to more accurately represent the measurement error caused by the distance sensor dependence state. Then, the estimated performance mismatch problem of Cubature Kalman Filter (CKF) under multiplicative noise is analyzed. An improved Gaussian filter algorithm is introduced to help obtain the CKF algorithm with correlated multiplicative noise. In practice, the model parameters are unknown or inaccurate, especially the correlation of noise is difficult to obtain, which can lead to a decrease in filtering accuracy or even divergence. To address this, an adaptive CKF algorithm is further provided to achieve reliable state estimation for the unknown noise correlation coefficient and thus the application of the CKF algorithm is extended. Finally, the estimated performance is analyzed theoretically, and the simulation study is conducted to validate the effectiveness of the proposed algorithm.
Weak segmentation supervised deep neural networks for pedestrian detection

Semantic segmentation has been used successfully as a complementary information source in pedestrian detection. However, it requires accurate pixel-level semantic segmentation annotations for training, but it is extremely time-consuming to obtain these. In this work, we solve this problem by using weak segmentation masks automatically generated by depth images. This enables joint semantic segmentation and pedestrian detection with only ground truth bounding boxes for training. We show that this joint training boosts the performance of the pedestrian detector. Moreover, we show that fusing the outputs of the classification network and the generated segmentation masks leads to a further detection performance improvement. Extensive experiments have been conducted on three RGBD pedestrian datasets to demonstrate the effectiveness of our proposed method. As a byproduct, we also obtain pedestrian segmentation results of good quality, without using pixel-level segmentation annotations during training.
GAN-GLS: Generative Lyric Steganography Based on Generative Adversarial Networks

Steganography based on generative adversarial networks (GANs) has become a hot topic among researchers.Due to GANs being unsuitable for text fields with discrete characteristics, researchers have proposed GANbased steganography methods that are less dependent on text.In this paper, we propose a new method of generative lyrics steganography based on GANs, called GAN-GLS.The proposed method uses the GAN model and the largescale lyrics corpus to construct and train a lyrics generator.In this method, the GAN uses a previously generated line of a lyric as the input sentence in order to generate the next line of the lyric.Using a strategy based on the penalty mechanism in training, the GAN model generates non-repetitive and diverse lyrics.The secret information is then processed according to the data characteristics of the generated lyrics in order to hide information.Unlike other text generation-based linguistic steganographic methods, our method changes the way that multiple generated candidate items are selected as the candidate groups in order to encode the conditional probability distribution.The experimental results demonstrate that our method can generate highquality lyrics as stego-texts.Moreover, compared with other similar methods, the proposed method achieves good performance in terms of imperceptibility, embedding rate, effectiveness, extraction success rate and security.
Robot-to-robot relative pose estimation using humans as markers

In this paper, we propose a method to determine the 3D relative pose of pairs of communicating robots by using human pose-based key-points as correspondences. We adopt a ‘leader-follower’ framework, where at first, the leader robot visually detects and triangulates the key-points using the state-of-the-art pose detector named OpenPose. Afterward, the follower robots match the corresponding 2D projections on their respective calibrated cameras and find their relative poses by solving the perspective-n-point (PnP) problem. In the proposed method, we design an efficient person re-identification technique for associating the mutually visible humans in the scene. Additionally, we present an iterative optimization algorithm to refine the associated key-points based on their local structural properties in the image space. We demonstrate that these refinement processes are essential to establish accurate key-point correspondences across viewpoints. Furthermore, we evaluate the performance of the proposed relative pose estimation system through several experiments conducted in terrestrial and underwater environments. Finally, we discuss the relevant operational challenges of this approach and analyze its feasibility for multi-robot cooperative systems in human-dominated social settings and feature-deprived environments such as underwater.
Imaging Time Series of Eye Tracking Data to Classify Attentional States

It has been shown that conclusions about the human mental state can be drawn from eye gaze behavior by several previous studies. For this reason, eye tracking recordings are suitable as input data for attentional state classifiers. In current state-of-the-art studies, the extracted eye tracking feature set usually consists of descriptive statistics about specific eye movement characteristics (i.e., fixations, saccades, blinks, vergence, and pupil dilation). We suggest an Imaging Time Series approach for eye tracking data followed by classification using a convolutional neural net to improve the classification accuracy. We compared multiple algorithms that used the one-dimensional statistical summary feature set as input with two different implementations of the newly suggested method for three different data sets that target different aspects of attention. The results show that our two-dimensional image features with the convolutional neural net outperform the classical classifiers for most analyses, especially regarding generalization over participants and tasks. We conclude that current attentional state classifiers that are based on eye tracking can be optimized by adjusting the feature set while requiring less feature engineering and our future work will focus on a more detailed and suited investigation of this approach for other scenarios and data sets.
NTRU Algorithm: Nth Degree Truncated Polynomial Ring Units

NTRU is a public key cryptosystem designed over a polynomial ring. It is based on the polynomial algebra. NTRU operations are based on addition, modular inverse, convolutional product, etc. The modular inverse plays an important role in generating the public/private keys. It provides low memory use and high speed compared to other cryptosystems. It is a lattice-based shortest vector problem. Its security is based on the product of polynomials and reducing the coefficients using two co-prime numbers p and q. Its smallest key size grants it better performance over other numerical based cryptosystems. It is the first asymmetric cryptosystem that is independent of the discrete algorithmic problem (ECC and Elgamal cryptosystem) or factorization (RSA cryptosystem).
A Lightweight Anonymous Authentication Protocol for Resource-Constrained Devices in Internet of Things

With the rapid development of Internet of Things (IoT) in recent years, the security of IoT becomes more and more prominent. To protect data privacy and device availability, identity authentication technology has been applied to the IoT environment. However, although scholars have designed a variety of authentication protocols for the IoT environment, the resource costs of these protocols are still expensive for resource-constrained devices. In particular, when the frequent upgrade of computer hardware has led to a gradual increase in the key length of the cryptosystem, IoT devices are hard to keep up with the upgrade speed because of the balance between cost and tasks. Although the storage space has increased in recent years, the computational and communication capabilities are still limited by some physical factors, such as energy, power, and communication link bandwidth. To improve the situation, this article proposes an anonymous authentication protocol suitable for IoT devices with current hardware performance, which is based on elliptic curve and signcryption techniques. Compared to other protocols, our protocol further reduces the communication and computing cost of the client devices while ensuring security requirements. Thus, we formally prove its theoretical security under the random oracle model and use simulation experiments to verify that the protocol can effectively reduce the resource requirements.
JTAG-based PLC memory acquisition framework for industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLC) are the embedded devices that directly control and monitor critical industrial infrastructure processes such as nuclear plants and power grid stations. Cyberattacks often target PLCs to sabotage a physical process. A memory forensic analysis of a suspect PLC can answer questions about an attack, including compromised firmware and manipulation of PLC control logic code and I/O devices. Given physical access to a PLC, collecting forensic information from the PLC memory at the hardware-level is risky and challenging. It may cause the PLC to crash or hang since PLCs have proprietary, legacy hardware with heterogeneous architecture. This paper addresses this research problem and proposes a novel JTAG (Joint Test Action Group)-based framework, Kyros, for reliable PLC memory acquisition. Kyros systematically creates a JTAG profile of a PLC through hardware assessment, JTAG pins identification, memory map creation, and optimizing acquisition parameters. It also facilitates the community of interest (such as ICS owners, operators, and vendors) to develop the JTAG profiles of PLCs. Further, we present a case study of Kyros implementation over Allen-Bradley 1756-A10/B to help understand the framework's application on a real-world PLC used in industry settings. The sample PLC memory dumps are shared with the research community to facilitate further research.
The complexity of internet of things forensics: A state-of-the-art review

The rapid growth and usefulness of Internet of Things (IoT) has seen it being deployed in critical and strategic infrastructure sectors like healthcare, transport, agriculture, home automation, and smart industries among many others. The benefits of comfort and reliability of IoT technologies to human beings have brought with them security concerns. This is due to its large-scale connectivity and over reliance on the internet for communication making it susceptible to cyberattacks. Digital forensics experts face a daunting task of handling these cyberattacks because of the unique and complex challenges posed by IoT. Recently, researchers have been drawn to finding solutions to these challenges, however, this is still in its infancy. This paper carries out a Systematic Literature Review (SLR) of the current research advancements in IoT forensics. We define key IoT fundamentals, IoT applications, the need for IoT forensics, identify the key factors affecting IoT forensics, and review the practicality of the available IoT forensics frameworks, models, and methodologies. The SLR reveals research gaps indicating that most of the current research is more theoretical than practical. There is a need for more practical approaches to tackle the unique IoT forensics challenges. Finally, for future research directions from the SLR, we have highlighted and discussed the open challenges and requirements for IoT forensics. • Systematic Literature Review (SLR) of the current research advancements in IoT forensics. • Definitions of key IoT fundamentals and IoT applications. • Affirmation for the need for IoT Forensics by exposing key challenges and research gaps. • Future research directions in IoT Forensics.
Hostility Detection in Hindi Leveraging Pre-trained Language Models

Hostile content on social platforms is ever increasing. This has led to the need for proper detection of hostile posts so that appropriate action can be taken to tackle them. Though a lot of work has been done recently in the English Language to solve the problem of hostile content online, similar works in Indian Languages are quite hard to find. This paper presents a transfer learning based approach to classify social media (i.e. Twitter, Facebook, etc.) posts in Hindi Devanagari script as Hostile or Non-Hostile. Hostile posts are further analyzed to determine if they are Hateful, Fake, Defamation, and Offensive. This paper harnesses attention based pre-trained models fine-tuned on Hindi data with Hostile-Non hostile task as Auxiliary and fusing its features for further sub-tasks classification. Through this approach, we establish a robust and consistent model without any ensembling or complex pre-processing. We have presented the results from our approach in CONSTRAINT-2021 Shared Task [] on hostile post detection where our model performs extremely well with 3rd runner up in terms of Weighted Fine-Grained F1 Score (Refer Sect. 4.3 for description of Weighted Fine-grained f1-score).
CSRNet: Cascaded Selective Resolution Network for real-time semantic segmentation

Real-time semantic segmentation has received considerable attention due to growing demands in many practical applications, such as autonomous vehicles, robotics, etc. Existing real-time segmentation approaches often utilize feature fusion to improve segmentation accuracy. However, they fail to fully consider the feature information at different resolutions and the receptive fields of the networks are relatively limited, thereby compromising the performance. To tackle this problem, we propose a light Cascaded Selective Resolution Network (CSRNet) to improve the performance of real-time segmentation through multiple context information embedding and enhanced feature aggregation. The proposed network builds a three-stage segmentation system, which integrates feature information from low resolution to high resolution and achieves feature refinement progressively. CSRNet contains two critical modules: the Shorted Pyramid Fusion Module (SPFM) and the Selective Resolution Module (SRM). The SPFM is a computationally efficient module to incorporate the global context information and significantly enlarge the receptive field at each stage. The SRM is designed to fuse multi-resolution feature maps with various receptive fields, which assigns soft channel attentions across the feature maps and helps to remedy the problem caused by multi-scale objects. Comprehensive experiments on well-known road scene datasets demonstrate that the proposed CSRNet outperforms the main-stream efficient semantic segmentation approaches by accuracy and can be performed at a fast inference speed.