Reviewerly/exHarmony huggingface dataset:
Ψ-NET: A Novel Encoder-Decoder Architecture for Animal Segmentation

This paper proposes a novel Ψ-Net architecture that consists of three encoders and a decoder for animal image segmentation. The main characteristic of our proposed architecture is that the outputs at each depth level of the three encoders are summed up and then concatenated in the corresponding depth levels of the decoder for the upsampling process. We col-lected a new dataset consisting of 200 images for training the model, and we manually labelled the ground truth segmentation masks for these images. We trained our proposed model Ψ-Net on this dataset and compared the segmentation accu-racy with the classical U-Net and Y-Net architectures. Our proposed model achieved the highest accuracy on the dataset with 93% pixel accuracy, and 81.6% mean intersection-over-union (IoU).
An approach to forecasting and filtering noise in dynamic systems using LSTM architectures

Some of the limitations of state-space models are given by the difficulty of modeling certain systems, the filters convergence time, or the impossibility of modeling dependencies in the long term. Having agile and alternative methodologies that allow the modeling of complex problems but still provide solutions to the classic challenges of estimation or filtering, such as the position estimation of a mobile with noisy measurements and unknown motion models, are of high interest. In this work, we address the problem of position estimation of 1-D dynamic systems from a deep learning paradigm, using Long-Short Term Memory (LSTM) architectures designed to solve problems with long term temporal dependencies, in combination with other recurrent networks. A deep neuronal architecture inspired by the Encoder-Decoder language systems is implemented, remarking its limits and finding a solution capable of making predictions of high accuracy with models learnt from training data of a moving object. We use a panel data model for training and validation. In the experimentation, we use sliding overlapping time windows in a recursive and standardized way to avoid the saturation problem of the networks in increasing trend estimates. The results are finally compared with the optimal values from the Kalman filter, obtaining comparable results in error terms. These results show the proposed system has great potential for target tracking.
Certificateless multivariate ring signcryption scheme

Certificateless ring signcryption can guarantee the unconditional anonymity of signcrypter, and can realize the confidentiality and unforgeability at the same time. However, the certificateless ring signcryption based on traditional cryptosystem cannot resist the quantum computing attacks. In view of this, we propose certificateless multivariable ring signcryption scheme (CL-MRSCS) with anti-quantum, whose security relies on the hardness of multivariate quadratic (MQ) problem and isomorphism of polynomials (IP) problem. CL-MRSCS satisfies the unconditional anonymity, confidentiality and unforgeability; in addition, it has high computation efficiency and is suitable for applications in electronic election system and electronic cash system.
Moral Guidelines in the System of Engineering Education in the Society of Industry 4.0 according to the Results of Remote Sociological Monitoring

Industry 4.0 requires new qualities of engineering work. The modern specialist, receiving engineering education, acquires system-related competences - professional abilities and skills, attitudinal and socio-cultural knowledge, personal qualities necessary for successful work activities. Soft Skills - "soft competences" are based on moral culture. The report presents some results of sociological surveys conducted by the Centre for Applied Sociological Research of NRU MPEI, which provide an insight into moral orientations of engineering students.
Ensemble Compressed Language Model Based on Knowledge Distillation and Multi-Task Learning

The success of pre-trained language representation models such as BERT benefits from their “overparameterized” nature, resulting in training time consuming, high computational complexity and superior requirement of devices. Among the variety of model compression and acceleration techniques, Knowledge Distillation(KD) has attracted extensive attention for compressing pre-trained language models. However, the major two challenges for KD are: (i)Transfer more knowledge from the teacher model to student model without scarifying accuracy while accelerating. (ii)Higher training speed of the lightweight model is accompanied by the risk of overfitting due to the noise influence. To address these problems, we propose a novel model based on knowledge distillation, called Theseus-BERT Guided Distill CNN(TBG-disCNN). BERT-of-Theseus [1] is employed as the teacher model and CNN as the student model. Aiming at the inherent noise problem, we propose coordinated CNN-BiLSTM as a parameter-sharing layer for Multi-Task Learning (MTL), in order to capture both regional and long-term dependence information. Our approach has approximately good performance as BERT-base and teacher model with $12 \times$ and $281 \times$ speedup of inference and $19.58 \times$ and $8.94 \times$ fewer parameters usage, respectively.
Modeling the effect of steering mirrors on polarization for free-space quantum key distribution

In polarization-encoding free-space Quantum Key Distribution (QKD), unwanted variations in the states of polarization may cause errors, leading to an increase in the quantum bit error rate (QBER), and a reduction of the Secure Key Rate (SKR). Optical elements that reflect or transmit the quantum signal at the transmitter and/or receiver are the main cause of polarization variations in the quantum states. Although most of these optical elements affect polarization in a constant manner, requiring relatively simple compensation techniques, mobile elements such as steering mirrors, cause time-varying polarization variations that are more complex to characterize and correct. In this work, we simulate the effect of steering mirrors' typical values of angular rotation on the QBER. In particular, we show that, for most cases, an active correction of the polarization reference frame and a passive correction of the phase shift between the electric field components is sufficient to obtain QBER values due to the steering mirrors below 0.2%. This avoids the need of a fully active correction strategy, which increases the complexity, weight, and cost of the implementation. Furthermore, we present different design considerations to further reduce the steering mirrors' contribution to the QBER (less than 0.05%).
Modified SHARK Cipher and Duffing Map-Based Cryptosystem

Recent years have seen a lot of interest in the study of chaotic structures and their accompanying cryptography frameworks. In this research, we came up with a new way to encrypt images that used the chaos and a modified block cipher named the SHARK cipher. The new algorithm looks at the creation of random sequences as a problem that needs to be solved in the best way possible, and then it uses the Duffing chaotic map to get even better random sequences. Chaos has been combined with a revised edition of the SHARK structure to make the algorithm design more robust with increased confusion and diffusion. The offered algorithm includes a complex encryption and decryption structure with minimal time consumption for secure data transmission. The proposed algorithm is verified with the encryption of some standard images of different sizes. Numerous analyses have been performed to see how well the algorithm works against a variety of assaults, and the outcomes show that the cryptosystem has a good level of robustness. The comparative results are also performed in this work, which guarantees the excellent performance of our cryptosystem. The system is also subjected to chosen-plaintext and chosen-ciphertext attacks which implies that it can resist many classical cryptographic attacks. Therefore, our cryptosystem is robust enough to use for image encryption.
Surmounting the Five-Headed Dragon

In India when all educational institutes were closed in March, the delivery of knowledge shifted completely to online mode overnight. In the current age of technology and to combat the corona crisis, this online learning approach was highly commendable. There were phenomenal challenges, like a significant proportion of the rural population in Maharashtra continues to fall short of the required internet bandwidth and knowledge to recognize devices and digital terminologies. The absence of supporting infrastructural facilities such as a stable flow of electricity and unavailability of high-speed internet is the second major issue involved. Despite these challenges, a few technical institutes in Maharashtra had worked hard on surmounting the five-headed dragon, namely rural settings, first-generation learners, digital illiteracy, lack of supporting infrastructure, and inadequate online teaching skills. The chapter will present the educational transformations brought in by these technical institutes during and post-pandemic and the way forward to rethink learning in the new normal.
Club- A Web based Operating System

A web-based Operating System is a type of operating system that uses user interface especially, the internet-based user interface. It allows people to access the applications which are not stored on their computers fully or partially. This paper describes about Club- a web based operating system, which is our proposed work. The proposed web based operating systems is created as there are some lacking in cloud based operating systems. This paper will review the objective of club. Finally the proposed model is compared with the existing WebOS in terms of OS & utilities and also the Linux distribution with existing WebOS to make sure that the proposed work is better than the existing WebOS models.
Eye Fixation Duration for Detecting Gazing Area Using Text Reading Speed

This study aims to develop a gazing detection method for detecting stumbling during PC work and deteriorating health. Gazing detection is required to set a threshold for the eye fixation time; however, no previous studies have thoroughly examined the method of setting the threshold in detail. Furthermore, the reading speed of each person is different. Different reading speeds result in different eye fixation times. We propose a threshold estimation method for estimating the appropriate eye fixation time (threshold). Depending on the subject’s reading speed, the proposed method detects the gazing area from general eye fixation. However, the usefulness of applying the threshold to other texts has not yet been evaluated. Therefore, a threshold estimated from a sentence was used to evaluate the accuracy of detecting gazing in another sentence. The experimental results showed that even when the threshold values were obtained from different texts, the error of the proposed method was smaller than that of the method used for comparison. Even if the threshold was obtained from text that was not the detection target, the reading speed of the subject was reflected in it.
Coal Mine Personnel Safety Monitoring Technology Based on Uncooled Infrared Focal Plane Technology

In an effort to overcome the difficulty of real-time early warning via traditional infrared imaging technology caused by the complex working environment in coal mines, this paper proposes a mine early warning method based on uncooled infrared focal plane technology. The infrared thermal spectrogram of the detected object was visually displayed in a pseudo-color image with high resolution and high sensitivity, which can realize the real-time detection and early warning of personnel safety in modern mines. The multipoint compression correction algorithm based on human visual characteristics divided the response units of all acquisition units into gray intervals according to a threshold value, then the corresponding parameters were set in different intervals, and finally, each interval was compressed using a two-point correction algorithm. The volume of stored data was the sum of the calibration curve and the data from an encode table corrected by a MATLAB simulation, and the number of CPU cycles was run by a CCS 3.3 clock calculation algorithm. The results showed that when the temperature of the blackbody reached 115 °C, the nonuniformity before correction was 6.32%, and the nonuniformity after the multipoint correction of human eyes was 2.99%, which implied that the algorithm proposed in this paper had good denoising ability. The number of CPU cycles occupied by this algorithm was 18,257,363 cycles/frame with a frequency of 29.97 Hz. The sharpness of the compressed infrared images was obviously improved, and the uniformity was better. The method proposed in this paper can meet the need for modern mine personnel search and rescue, equipment supervision and dangerous area detection and other early warning requirements so as to achieve the goal of developing smart mines and ensuring safety in coal mine production.
“I was Holding a Magic Box”: Investigating the Effects of Private and Projected Displays in Outdoor Heritage Walks

Location-aware mobile guides are a popular technology for enhancing the experience of heritage walks in historical outdoor sites. Smartphones, as typical mediums for such systems, have been criticised for limiting users’ opportunities for embodied engagement with the environment. In this work, we investigate how display technologies beyond the traditional personal mobile screen can facilitate embodied experiences during outdoor heritage walks. To do this, we revisit the use of portable projected displays as a medium that allows us to explore the effects of overt and blended displays in this context. We conducted a study with 42 participants on an outdoor heritage walk, using two display modalities: smartphone and projected display. We discovered that besides the display modality, users’ attitude toward technology, their embodied relationship with the device, and incorporation of spatial aspects in interaction play a key role in generating engagement and shaping the experience of heritage walks.
DDRF: The Drone Data Regulatory Framework Based on Blockchain

Civilian UAVs have developed rapidly in recent years. However, due to the lack of supervision, cumbersome flight plan application process, and other problems, many people use drones at will, and many accidents occur as a result. We proposed a framework for using blockchain to regulate drone behavior and implemented a demo. With this framework, users don’t have to go through a complicated approval process to apply for a flight plan. Besides, because of the nature of blockchain, the system implemented based on our framework is transparent and decentralized, which meets the requirement of supervision. Information is authentic and all on-chain information is available for viewing. If there is a drone accident, the person responsible can also be identified based on immutable information records. Moreover, we also process the data to make the overhead of appending repeating data smaller and make automated data checks. The framework includes three modules, a data processing module, a data registration module, and a data audit module. Our demo implements these modules based on Hyperledger Fabric. We carry out experiments on the model, and the results showed our framework is efficient.
Designing for Culturally Sensitive Cultural Change

Online self-presentation is recognized as a global phenomenon largely influenced by and sensitive to users’ cultural norms. However, incorporating cultural understanding into the design process can be challenging. Designing for culture creates dilemmas between designing for a pre-existing cultural ‘status quo’ or for cultural change. We argue that culturally sensitive design should not be a tool for (i) perpetuating existing cultural inequalities or (ii) empowering the individual isolated from their wider cultural milieu. We propose “designing for culturally sensitive cultural change”—a process in which we support creating a trajectory departing from the status quo, to bridge the gap between people's aspirations and practices related to cultural change. We demonstrate this in a case study on designing for Saudi women's self-presentation in digital media. We conclude with reflections on cultural sensitivity in designing for cultural change and broader implications for HCI.
An Analysis of Sequential Pattern Mining Approach for Progressive Database by Deep Learning Technique

In the disciplines of enterprise, electronic commerce, bioinformatics, and other related subjects, sequential form mining is one of the most broadly utilized approaches. Traditional techniques are unable to accurately mine massive amounts of data due to their inherent limitations. Thus, the suggested study makes use of Deep Structured Learning-based Sequent form mining to reduce the complexity associated with dealing with large amounts of data. Data in application sectors like banking and e-commerce are prone to dynamic changes, increasing non-stationarity. To convert the data to a stochastic/time series format, employing wavelet analysis has been suggested. A modified Long Short-Term Memory (LSTM) was also built to increase mining precision. By removing old data from a progressive database, the recommended technique extracts the sequential pattern. To demonstrate the robustness of the suggested work, the execution time of the proposed work is compared to that of traditional algorithms.
A Performance Analysis of Deep-Learning-Based Thai News Abstractive Summarization: Word Positions and Document Length

This paper presents a performance analysis of deep-learning-based Thai news abstractive summarization. The analysis focuses on the position of the words in the original document that are generated into the summary. Also, the analysis includes the behavior of word generation of the system. Moreover, we analyse how the document length affects the performance of the models regarding word positions of the original document. The result of the experiment shows that the models generated the output summary by generating most words from the beginning part more than those from the reference summary about 1.79 times on the TR testing dataset and about 2.03 times on the TPBS testing dataset. Additionally, the models occasionally generated words that do not exist in the original document about 1.68% of word number of the summary on the TR testing dataset and about 0.88% of word number of the summary on the TBPS testing dataset. According to the result, it is found that the models generated words in the system summary is not consistent with words in the reference summary. In the document length, it is found that the models can summarize a short document better than a long document.
Software Bug Prediction Model Based on Mathematical Graph Features Metrics

Quality assurance is one of the most important activities in software development and maintenance. Software source codes are modified via change requests, functional improvement, and refactoring. When software changes, it is difficult to define the scope of test cases, and software testing costs tend to increase to maintain software quality. Therefore, change analysis is a challenge, and static testing is a key solution to this challenge. In this study, we propose new static testing metrics using mathematical graph analysis techniques for the control flow graph generated from the three-address code of the implementation codes based on our hypothesis of the existing correlation between the graph features and any software bugs. Five graph features are strongly correlated with the software bugs. Hence, our bug prediction model exhibits a better performance of 0.25 FN, 0.04 TN ratio, and 0.08 precision than a model based on the traditional bug prediction metrics, which are complexity, line of code (steps), and CRUD.
Smart decision for device selection in D2D‐assisted multi‐path video transmission network

Abstract Watching the live video on a bus/train/tram has become an important pattern for people to enjoy their travel time. Due to the complex environment changes and obstacles caused by the rapid movement, the network state of the user device is often unstable. A large number of interruptions and resolution reductions seriously affect the user's watching experience. Through direct transmission between devices, Device‐to‐Device (D2D) can effectively improve video quality. Accurate decision for helper device selection is the key to optimize video streaming services based on D2D technology. However, the existing methods through D2D rarely consider the states and the intention of the helper devices when selecting cooperative devices, such as the watching status, the state of charge, the state of cache, and the connection quality. This is highly probable to reduce the efficiency of D2D transmission and the cooperation enthusiasm of helper devices. In addition, the existing methods seldom consider the multiple network interfaces in the device. In view of these challenges, we propose a dynamic smart decision method for d evice s election in D 2D‐ a ssisted m ulti‐ p ath video transmission network (named DS‐DAMP ). Specifically, this method takes the current states and the resource constraints into consideration to select the appropriate helper devices for video cooperative transmission, so as to improve the cooperation satisfaction of helper devices and optimize the video quality of the requesting device. Besides, we make full use of multiple network interfaces to realize multi‐path parallel transmission between devices. A large number of experiments have proved the good performance of our method in terms of video quality and network utility.
Spine: Scaling up Programming-by-Negative-Example for String Filtering and Transformation

Program synthesis (a.k.a. programming-by-example, PBE) has been deployed in several widely-used commercial products, such as Microsoft Excel, Power BI, and Google Spreadsheet, due to its effectiveness and user-friendliness. It takes a few user-provided positive and negative examples as input and produces a program that is consistent with all the examples, which helps end-users wrangle messy texts without writing any code. In this paper, we focus on two text wrangling tasks, string filtering and transformation. Existing PBE systems for string filtering do not scale well with negative examples. This is because they first explicitly synthesize all the consistent programs and then greedily search a good one in them. However, when there are negative examples, it could take an exponential time and space to synthesize all the exponential number of consistent programs. In contrast, we propose to synthesize all the programs consistent with the positive examples first and then lazily determine whether a program is also consistent with all the negative examples on demand in the search step. For this purpose, we develop a dynamic programming algorithm to search the optimal consistent program. Many programs are never explored during dynamic programming as they are dominated by other better consistent programs. As for string transformation, existing PBE systems do not even support negative examples. Our approach naturally extends to string transformation. Experimental results show that our methods significantly outperformed the state-of-the-art string filtering and transformation approaches and achieved better scalability.
SIEVE: A Space-Efficient Algorithm for Viterbi Decoding

Can we get speech recognition tools to work on limited-memory devices? The Viterbi algorithm is a classic dynamic programming (DP) solution used to find the most likely sequence of hidden states in a Hidden Markov Model (HMM). While the algorithm finds universal application ranging from communication systems to speech recognition to bioinformatics, its scalability has been scarcely addressed, stranding it to a space complexity that grows with the number of observations.