Reviewerly/exHarmony huggingface dataset:
Two Elements of Pair Programming Skill

Background: Pair programming (PP) can have many benefits in industry. Researchers and practitioners recognize that successful and productive PP involves some skill that might take time to learn and improve. Question: What are the elements of pair programming skill? Method: We perform qualitative analyses of industrial pair programming sessions following the Grounded Theory Methodology. We look for patterns of problematic behavior to conceptualize key elements of what 'good' and 'bad' pairs do differently. Results: Here, we report two elements of pair programming skill: Good pairs (1) manage to maintain their Togetherness and (2) keep an eye on their session's Expediency. We identify three problematic behavioral patterns that affect one or both of these elements: Getting Lost in the Weeds, Losing the Partner, and Drowning the Partner. Conclusion: Pair programming skill is separate from general software development skill. Years of PP experience are neither a prerequisite nor sufficient for successful pair programming.
Recognizing Bengali Word Images - A Zero-Shot Learning Perspective

Zero-Shot Learning(ZSL) techniques could classify a completely unseen class, which it has never seen before during training.Thus, making it more apt for any real-life classification problem, where it is not possible to train a system with annotated data for all possible class types.This work investigates recognition of word images written in Bengali Script in a ZSL framework.The proposed approach performs Zero-Shot word recognition by coupling deep learned features procured from various CNN architectures along with 13 basic shapes/stroke primitives commonly observed in Bengali script characters.As per the notion of ZSL framework those 13 basic shapes are termed as "Signature/Semantic Attributes".The obtained results are promising while evaluation was carried out in a Five-Fold cross-validation setup dealing with samples from 250 word classes.
Egocentric Distance Judgments in Full-Cue Video-See-Through VR Conditions are No Better than Distance Judgments to Targets in a Void

Understanding the extent to which, and conditions under which, scene detail affects spatial perception accuracy can inform the responsible use of sketch-like rendering styles in applications such as immersive architectural design walkthroughs using 3D concept drawings. This paper reports the results of an experiment that provides important new insight into this question using a custom-built, portable video-see-through (VST) conversion of an optical-see-through head-mounted display (HMD). Participants made egocentric distance judgments by blind walking to the perceived location of a real physical target in a real-world outdoor environment under three different conditions of HMD-mediated scene detail reduction: full detail (raw camera view), partial detail (Sobel-filtered camera view), and no detail (complete background subtraction), and in a control condition of unmediated real world viewing through the same HMD. Despite the significant differences in participants' ratings of visual and experiential realism between the three different video-see-through rendering conditions, we found no significant difference in the distances walked between these conditions. Consistent with prior findings, participants underestimated distances to a significantly greater extent in each of the three VST conditions than in the real world condition. The lack of any clear penalty to task performance accuracy not only from the removal of scene detail, but also from the removal of all contextual cues to the target location, suggests that participants may be relying nearly exclusively on context - independent information such as angular declination when performing the blind-walking task. This observation highlights the limitations in using blind walking to the perceived location of a target on the ground to make inferences about people's understanding of the 3D space of the virtual environment surrounding the target. For applications like immersive architectural design, where we seek to verify the equivalence of the 3D spatial understanding derived from virtual immersion and real world experience, additional measures of spatial understanding should be considered.
Timing-Optimized Hardware Implementation to Accelerate Polynomial Multiplication in the NTRU Algorithm

Post-quantum cryptographic algorithms have emerged to secure communication channels between electronic devices faced with the advent of quantum computers. The performance of post-quantum cryptographic algorithms on embedded systems has to be evaluated to achieve a good trade-off between required resources (area) and timing. This work presents two optimized implementations to speed up the NTRUEncrypt algorithm on a system-on-chip. The strategy is based on accelerating the most time-consuming operation that is the truncated polynomial multiplication. Hardware dedicated modules for multiplication are designed by exploiting the presence of consecutive zeros in the coefficients of the blinding polynomial. The results are validated on a PYNQ-Z2 platform that includes a Zynq-7000 SoC from Xilinx and supports a Python-based programming environment. The optimized version that exploits the presence of double, triple, and quadruple consecutive zeros offers the best performance in timing, in addition to considerably reducing the possibility of an information leakage against an eventual attack on the device, making it practically negligible.
In Codice Ratio: A crowd-enabled solution for low resource machine transcription of the Vatican Registers

In Codice Ratio is a research project to study techniques for analyzing the contents of historical documents conserved in the Vatican Apostolic Archives. In this paper, we present our efforts to develop a system to support the automatic transcription of medieval manuscripts, while maintaining the training data collection effort minimal. We focus on crowdsourcing as a means for scalable, expertless training data collection: using crowdsourced character symbols, we train a custom convolutional neural network able to jointly learn correct character shape identification and character recognition. Our approach generates candidate transcriptions by submitting over-segmented character strokes and their combinations to this classifier, while ranking and choosing the most promising outputs by combining the recognition confidence with character and word level statistical language models. We conducted experiments on an unreleased corpus, the Vatican Registers: training our system on 20 pages annotated by the crowd, we were able to obtain good results (19% CER); comparisons to an off-the-shelf system trained with 20 pages annotated with the same process, and to a professional system trained with more than 300 pages transcribed by skilled paleographers demonstrate the opportunities of the proposed approach.
SVSV: Online handwritten signature verification based on sound and vibration

Handwritten signature is one of the most important behavioral biometrics and plays an important role in the field of identity verification. It is regarded as a legal means to verify personal identity by administrative and financial institutions. Traditional manual signature verification requires large labor costs and the probability of verification error is relatively high. Nowadays, tablets are often used for signature capturing, which motivates us to explore the feasibility of using tablets for signature verification. In this paper, we propose an online handwriting signature verification system based on sound and vibration (SVSV) generated during the signing process. We develop an application to collect signature-related vibration and sound data. We first extract the time domain features of the sound signal and use Fast Fourier Transform to extract the frequency domain features of the sound data. For vibration data, we use Discrete Cosine Transform for dimensionality reduction and feature extraction. Then we fuse the sound and vibration features. Finally, we design an efficient one-class classifier based on the Convolutional Neural Network to perform signature verification. Through extensive experiments with 12 volunteers, the results show that SVSV is a robust and efficient system with an AUC of 0.984 and an EER of 0.05.
Enhancement of Fall Detection Algorithm Using Convolutional Autoencoder and Personalized Threshold

Falling is a hazardous situation for elderly people living alone and labor workers as they are easy to happen and can lead to serious injuries. Hence, a fall detection mechanism is an indispensable way to rescue victims without a delay. Various fall detection systems detect falling by using supervised deep-learning algorithms. However, labeling training data and collecting various falling motions data large enough for deep-learning is time-consuming and tiresome. Therefore, this study aims to develop a fall detection system utilizing the data not from falling but from usual motions of daily life. In this paper, an unsupervised learning method, convolutional autoencoder, and a wearable sensor, inertial measurement unit (IMU), were employed. The motion data from the IMU is converted to monochrome images for training and evaluating the developed fall detection algorithm. Falling is determined by comparing the input and output images of the model and a method for setting a threshold was investigated. After confirming the accuracy of the proposed method using a publicly available dataset and our dataset, the proposed method to train the model and to determine the threshold were addressed. Finally, the fall detection result with a sensitivity and a specificity of 100% and 99% was obtained.
Secure and covert communication using steganography by Wavelet Transform

This paper presents a hybrid image steganography along with cryptography algorithm based on LS (Logical & Statistical) encryption scheme and lift wavelet domain of digital images. Currently, cryptography data can be easily intercepted by an intruder during transmission. So it may be treated as ineffectual in terms of security. In such a scenario, the proposed algorithm provides secured transmission by using cryptography and steganography techniques. In this algorithm a new LS encryption scheme is implemented for converting secret message into ciphertext. It is being done by using several parameters like 'data indicator', 'count indicator', 'gray code' and novel 'data conversion' techniques. The 'gray code' and novel 'data conversion' techniques are used to recover the inherent information at the receiver and to improve the security of the secret data. Moreover, it reduces the ciphertext length compared to the length of original secret data. Hence this scheme improves the payload capacity and helps to improve the quality metrics like Number of Pixels Change in Rate (NPCR), Peak Signal to Noise Ratio (PSNR), and Correlation (CORR) in the stego image. Furthermore, steganography scheme provides an ability to tackle the various attacks like noise, rotational, histogram and visual attacks. It is also shown that how the original message is recovered back from stego image successfully.
Graph Sanitation with Application to Node Classification

The past decades have witnessed the prosperity of graph mining, with a multitude of sophisticated models and algorithms designed for various mining tasks, such as ranking, classification, clustering and anomaly detection. Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is the best way to mine it?
Replacing Human Audio with Synthetic Audio for on-Device Unspoken Punctuation Prediction

We present a novel multi-modal unspoken punctuation prediction system for the English language which combines acoustic and text features. We demonstrate for the first time, that by relying exclusively on synthetic data generated using a prosody-aware text-to-speech system, we can outperform a model trained with expensive human audio recordings on the unspoken punctuation prediction problem. Our model architecture is well suited for on-device use. This is achieved by leveraging hash-based embeddings of automatic speech recognition text output in conjunction with acoustic features as input to a quasi-recurrent neural network, keeping the model size small and latency low.
Evaluating Unit Testing Practices in R Packages

Testing Technical Debt (TTD) occurs due to shortcuts (non-optimal decisions) taken about testing; it is the test dimension of technical debt. R is a package-based programming ecosystem that provides an easy way to install third-party code, datasets, tests, documentation and examples. This structure makes it especially vulnerable to TTD because errors present in a package can transitively affect all packages and scripts that depend on it. Thus, TTD can effectively become a threat to the validity of all analysis written in R that rely on potentially faulty code. This two-part study provides the first analysis in this area. First, 177 systematically-selected, open-source R packages were mined and analysed to address quality of testing, testing goals, and identify potential TTD sources. Second, a survey addressed how R package developers perceive testing and face its challenges (response rate of 19.4%). Results show that testing in R packages is of low quality; the most common smells are inadequate and obscure unit testing, improper asserts, inexperienced testers and improper test design. Furthermore, skilled R developers still face challenges such as time constraints, emphasis on development rather than testing, poor tool documentation and a steep learning curve.
A Reconfigurable Mobile Head-Mounted Display Supporting Real World Interactions

We propose a new mobile head-mounted display, ModularHMD, that uses a modular mechanism with a manually reconfigurable structure to enable ad-hoc peripheral interaction with real-world objects and people while maintaining an immersive VR experience. We designed and built a proof-of-concept prototype of ModularHMD using a base commercial HMD and three removable display modules installed in the periphery of the HMD cowl. The user can rapidly switch between different HMD configurations according to their needs. The modules can be removed to ensure peripheral awareness of the real world, used as instant interaction devices (e.g, keyboards), and then returned to their original positions to terminate the peripheral interaction.
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Most existing deep learning based single-image super-resolution (SISR) methods mainly improve the reconstruction performances from the perspective of data-driven, i.e., widening or deepening the networks according to the huge scale of the training data. However, it will bring a huge amount of weights and biases, and cost the expensive computations. Recently, some people have proposed a new frame for designing the deep networks according to the algorithms deduced from the ℓ2-optimization problem. But they did not consider the case with outliers. Since ℓ1-norm can describe the sparsity of the outliers better than ℓ2-norm, we propose an effective deep network designed according to the new algorithm deduced from the ℓ1-optimization problem. In our proposed method, an effective iterative algorithm for the ℓ1 reconstructed optimization problem is deduced based on the split Bregman algorithm, majorization–minimization algorithm, and soft thresholding operator. Then according to the deduced iterative algorithm, an effective deep network, named ℓ1 Model-Driven Recursive Multi-Scale Denoising Network (ℓ1-MRMDN), is designed. Due to the iteration form of the deduced algorithm, the proposed ℓ1-MRMDN contains an inner recursion and an outer recursion. Therefore, our proposed method can not only relieve its sensitiveness to the outliers because of the ℓ1 data fidelity term, but also avoid designing the deep network blindly via the guidance of prior knowledge. Extensive experimental results illustrate that our proposed method is superior to some related popular SISR methods.
Survey Paper on Web Content Extraction &amp; Classification

Over the last few years, web data extraction has gained popularity. Product information on the Ecommerce website floods the internet with big data. Web-based business sites these days have gotten one of the most significant hotspots for getting a large amount of relevant data. Wide range of software application designs to extract relevant data from web pages in order to draw in more business. The extracted data can be used for retailer business and data analysis purposes. The web pages on such sites are based on different technologies, and the generated web documents are in structured or unstructured formats. Manually extract such relevant product data and multimedia type Information from the websites is complex and time-consuming. After extraction of data needs to be classified because web content contains unwanted data e.g. design information, advertising content. This paper describes different Procedures for web document classification and extraction.
Digital Distance And Blended Learning Quality Assessment In Oriental And European Languages University Programs: Regions Of Ukraine Survey Study

The global pandemic and subsequent quarantine measures and restrictions have posed an array of challenges to the structure and procedure of higher education workflow, which influenced significantly the scope of individual experiences, projected outcomes and estimated quality of higher education in countries across the world. This study focus is the in-depth assessment of individual digital distance and blended learning experiences by relevant groups of stakeholders in Oriental (Mandarin Chinese, Japanese) and European (French, Italian, Spanish, English,) Languages university level programs in regions of Ukraine through the span of educational activities in the timeframe of COVID-19 quarantine measures of March 2020 to January 2021. The survey and analysis of different ICT tools is used to assess the parameters and challenges of individual quality and efficiency of translation of the real life Foreign Languages Acquisition practices into digital blended format, involving activation of interdisciplinary skills and cross-sectorial activities, assisted by ICT tools. The online survey of 14 universities in regional centers of Ukraine provides for disclosure of individual experiences and quality estimation of e-learning and hybrid learning in the framework of COVID-19 lockdown; assessment of individual experiences and quality of e-learning and hybrid learning in the framework of COVID-19 lockdown as compared to traditional, face-to-face learning formats for foreign languages; individual quality assessment of learning process design, programmed leaning outcomes and projected competences for university programs of Oriental and European languages in the framework of COVID-19 lockdown and quarantine measures.
HSAN: A Hierarchical Self-Attention Network for Multi-Turn Dialogue Generation

In the multi-turn dialogue system, response generation is not only related to the sentences in context but also relies on the words in each utterance. Although there are lots of methods that pay attention to model words and utterances, there still exist problems such as tending to generate common responses. In this paper, we propose a hierarchical self-attention network, named HSAN, which attends to the important words and utterances in context simultaneously. Firstly, we use the hierarchical encoder to update the word and utterance representations with their position information respectively. Secondly, the response representations are updated by the mask self-attention module in the decoder. Finally, the relevance between utterances and response is computed by another self-attention module and used for the next response decoding process. In terms of automatic metrics and human judgements, experimental results show that HSAN significantly outperforms all baselines on two common public datasets.
An Elastic System Architecture for Edge Based Low Latency Interactive Video Applications

5G and edge computing have brought great changes to video industry. Interactive video is becoming an emerging application form of multimedia service, which provides attraction beyond typical scenarios like cloud gaming and remote virtual reality (VR), and puts forward great challenges in resource capacity, response latency, and function flexibility to its service system. In this paper, we propose an elastic system architecture with low latency features to accommodate generic interactive video applications on near user edges. To increase system flexibility, we firstly design a dynamic Directed Acyclic Graph (dDAG) model for efficient task representation. Secondly, based on the model, we present the elastic architecture together with its scalable workflow pipeline. Thirdly, we propose a set of novel latency measurement metrics to analyze and optimize the performance of an interactive video system. Based on the proposed approaches, we disassemble a real world free-viewpoint synthesis application and benchmark its performance with the metrics. Extensive experimental results show the flexibility of our system to handle the stochastic human interactions during a video service session, with less than 5 ms additional scheduling latency introduced. End to end latency is kept within 43 ms for complex functions, and 28 ms for simpler scenarios, which satisfies the restrictions of most interactive video applications provided by an edge. Client of the architecture serves as a pure video player, which is also friendly to power limited terminals such as 5G phones. Efficiency and stability analyses of the system show superiorities over existing work, and also reveal potential optimization directions for future research.
An Efficient QP Variable Convolutional Neural Network Based In-loop Filter for Intra Coding

In this paper, a novel QP variable convolutional neural network based in-loop filter is proposed for VVC intra coding. To avoid training and deploying multiple networks, we develop an efficient QP attention module (QPAM) which can capture compression noise levels for different QPs and emphasize meaningful features along channel dimension. Then we embed QPAM into the residual block, and based on it, we design a network architecture that is equipped with controllability for different QPs. To make the proposed model focus more on examples that have more compression artifacts or is hard to restore, a focal mean square error (MSE) loss function is employed to fine tune the network. Experimental results show that our approach achieves 4.03% BD-Rate saving on average for all intra configuration, which is even better than QP-separate CNN models while having less model parameters.