Reviewerly/exHarmony huggingface dataset:
Emotional Paraphrasing Using Pre-trained Language Models

Emotion style transfer is a recent and challenging problem in Natural Language Processing (NLP). Transformer-based language models are becoming extremely powerful, so one wonders if it would be possible to leverage them to perform emotion style transfer. So far, previous work has not used transformer-based models for this task. To address this task, we fine-tune a GPT-2 model with corrupted emotional data. This will train the model to increase the emotional intensity of the input sentence. Coupled with a paraphrasing model, we develop a system capable of transferring an emotion into a paraphrase. We conducted a qualitative study with human judges, as well as a quantitative evaluation. Although the paraphrase metrics show poor performance compared to the state of the art, the transfer of emotion proved to be effective, especially for the emotions fear, sadness, and disgust. The perception of these emotions were improved both in the automatic and human evaluations. Such technology can significantly facilitate the automatic creation of training sentences for natural language understanding (NLU) systems, but it can also be integrated into an emotional or empathic dialogue architecture.
Deep Learning for Satellite Image Reconstruction

In this work, deep learning neural network is proposed as handcrafted prior onto hazy satellite image for its reconstruction. The proposed model is applied on occurrence of cloud while capturing satellite data of earth. Experiments on vast satellite datasets were conducted, and efficiency of the proposed approach, both quantitative and qualitative, is demonstrated in this paper at the same time removing the dependency on pretraining datasets. We considered the obscured area of varying shapes, lying in the range of 15–35%, on RGB monochromatic satellite images and reconstructed images with MSE 0.125 and PSNR of 80 are obtained. We also observed that the structural symmetry index (SSIM) values are better for red and green bands when compared to blue band. At the same time, image hash value is also calculated and found satisfactory.
An Approach Toward Measurement of Reusability of Component-Based Software (CBS)

Software reusability development methodology like Component-Based Software (CBS) has a progressive impact on various software parameters such as reliability, quality as compared to the traditional way of software development. Previous studies show that for an integrated system, there is a requirement of a scientific model for evaluation and up-gradation of various parameters of the component. In the integrated environment, the adaptability of every component is not assessed by component metrics lonely. Hence, there is a need for new software reusability metric. In this paper, a reusable metric designed for CBS has been evaluated to validate the scope of reusability. Using Chidamber and Kemerer (CK) metrics, new Commercial-off-the-shelf (COTS) metrics have been introduced. This is an investigation report to measure the COTS reusability. The reusability of Object-oriented components and inter components have been calculated using multilevel inheritance. The parameters used to calculate reusability of COTS based system are Coupling Between Objects (CBO), Coupling Within Component and Coupling Between Component. The calculated result of three objects are X = 2, Y = 4, Z = 4 and three components outputs are Response Of Component (RFC) of Component 1 = 10, RFC of Component 2 = 14, RFC of Component 3 = 12. Finally RFC of CBS is 36. The purpose of the study is to give direction to software developers about component reusability by measuring cost and effort factors.
Recent advances in particle swarm optimization analysis and understanding 2021

tutorial Open AccessRecent advances in particle swarm optimization analysis and understanding 2021 Authors: A P Engelbrecht Stellenbosch University, South Africa Stellenbosch University, South AfricaView Profile , C W Cleghorn University of the Witwatersrand, South Africa University of the Witwatersrand, South AfricaView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 341–368https://doi.org/10.1145/3449726.3461431Published:08 July 2021Publication History 0citation223DownloadsMetricsTotal Citations0Total Downloads223Last 12 Months84Last 6 weeks15 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Automated enumeration of block cipher differentials: An optimized branch-and-bound GPU framework

Block ciphers are prevalent in various security protocols used daily such as TLS, OpenPGP, and SSH. Their primary purpose is the protection of user data, both in transit and at rest. One of the de facto methods to evaluate block cipher security is differential cryptanalysis. Differential cryptanalysis observes the propagation of input patterns (input differences) through the cipher to produce output patterns (output differences). This probabilistic propagation is known as a differential; the identification of which is a measure of a block cipher’s security margins. This paper introduces an optimized GPU-based branch-and-bound framework for differential search. We optimize search efficiency by parallelizing all branch-and-bound operations, completing the entire search on the GPU without communicating with the CPU. The meet-in-the-middle (MITM) approach is also adopted for further performance gains. We analyze the financial and computational costs of the proposed framework using Google Cloud VM to showcase its practicality. When optimized for performance, we can attain up to 90x speedup while saving up to 47% of the running cost as compared to a single CPU core. When optimized for cost, the proposed framework can save up to 83% of financial costs while retaining a speedup of up to 40x. As a proof of concept, the proposed framework was then applied on 128-bit TRIFLE-BC, 64-bit PRESENT, and 64-bit GIFT. Notably, we identified the best differentials for PRESENT (16 rounds) and 64-bit GIFT (13 rounds) to date, with estimated probabilities of 2−61.7964 and 2−60.66 respectively. Although the differential results for TRIFLE-BC were incremental, the proposed framework was able to construct differentials for 43 rounds that consisted of approximately 5.8x more individual trails than previous work, making it one of the most efficient approaches for larger block ciphers.
Superpixel Based Face Sketch Recognition Scheme

Face sketch recognition is one of the main spheres of criminalistics science. Face sketch is created by artists and sends to recognize by current methods. Face sketch recognition process looks like to face recognition. However, in this process we need all landmarks of face. In this paper is proposed superpixel based face sketch recognition scheme. In addition, face landmarks are detected by Viola-Jones method and eigenfeatures are extracted from segmented face sketch by PCA.
Detection of Touchscreen-Based Urdu Braille Characters Using Machine Learning Techniques

Revolution in technology is changing the way visually impaired people read and write Braille easily. Learning Braille in its native language can be more convenient for its users. This study proposes an improved backend processing algorithm for an earlier developed touchscreen-based Braille text entry application. This application is used to collect Urdu Braille data, which is then converted to Urdu text. Braille to text conversion has been done on Hindi, Arabic, Bangla, Chinese, English, and other languages. For this study, Urdu Braille Grade 1 data were collected with multiclass (39 characters of Urdu represented by class 1, Alif (ﺍ), to class 39, Bri Yay (ے). Total (N = 144) cases for each class were collected. The dataset was collected from visually impaired students from The National Special Education School. Visually impaired users entered the Urdu Braille alphabets using touchscreen devices. The final dataset contained (N = 5638) cases. Reconstruction Independent Component Analysis (RICA)-based feature extraction model is created for Braille to Urdu text classification. The multiclass was categorized into three groups (13 each), i.e., category-1 (1–13), Alif-Zaal (ﺫ - ﺍ), category-2 (14–26), Ray-Fay (ﻒ - ﺮ), and category-3 (27–39), Kaaf-Bri Yay (ے - ﻕ), to give better vision and understanding. The performance was evaluated in terms of true positive rate, true negative rate, positive predictive value, negative predictive value, false positive rate, total accuracy, and area under the receiver operating curve. Among all the classifiers, support vector machine has achieved the highest performance with a 99.73% accuracy. For comparisons, robust machine learning techniques, such as support vector machine, decision tree, and K-nearest neighbors were used. Currently, this work has been done on only Grade 1 Urdu Braille. In the future, we plan to enhance this work using Grade 2 Urdu Braille with text and speech feedback on touchscreen-based android phones.
Prediction of Students’ Perceptions towards Technology’ Benefits, Use and Development

The utilization of technology in education has been increasing during the unprecedented time of the Covid-19 pandemics. It has not only affected the opinions of teachers, but also students’ perceptions have been impacted. This paper identified and compared students’ perceptions of Indian and Hungarian universities towards technology use, benefits, and development. A stepwise regression filtered out six significant variables that explained the perception of Indian students ($R^{2}=.91$), and three variables identified the perception of Hungarian students towards technology ($R^{2}=.61$). Indian students’ perception has been recognized with six technology variables: “Sharing of resources expertise and advice”, “ICT tools/techniques promoting workshops policy”, “Up to date learning materials”, “Desktop Computers equipped with internet access”, “E-library”, and “E-Reader”. Three variables such as “Improving analytical skills”, “Download/Browse material”, “High-quality lessons” also impacted the viewpoints of Hungarian students.
Q-Learning With Uniformly Bounded Variance

Sample complexity bounds are a common performance metric in the reinforcement learning literature. In the discounted cost, infinite horizon setting, all of the known bounds can be arbitrarily large, as the discount factor approaches unity. These results seem to imply that a very large number of samples is required to achieve an epsilon-optimal policy. The objective of the present work is to introduce a new class of algorithms that have sample complexity <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">uniformly bounded over all discount factors</i> . One may argue that this is impossible, due to a recent min–max lower bound. The explanation is that these prior bounds concern value function approximation and not policy approximation. We show that the asymptotic covariance of the tabular Q-learning algorithm with an optimized step-size sequence is a quadratic function of a factor that goes to infinity, as discount factor approaches 1; an essentially known result. The new <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">relative Q-learning</i> algorithm proposed here is shown to have asymptotic covariance that is uniformly bounded over all discount factors.
SMRD: A Local Feature Descriptor for Multi-modal Image Registration

Image registration among multimodality has received increasing attention in the scope of computer vision and computational photography nowadays. However, the non-linear intensity variations prohibit the accurate feature points matching between modal-different image pairs. Thus, a robust image descriptor for multi-modal image registration is proposed, named shearlet-based modality robust descriptor(SMRD). The anisotropic feature of edge and texture information in multi-scale is encoded to describe the region around a point of interest based on discrete shearlet transform. We conducted the experiments to verify the proposed SMRD compared with several state-of-the-art multi-modal/multispectral descriptors on four different multi-modal datasets. The experimental results showed that our SMRD achieves superior performance than other methods in terms of precision, recall and F1-score.
How Scrum Inhibits Agility

People are celebrating and enjoying the 25th anniversary of Scrum, mostly with many successes and evidence about how it supports agility, leaving a limited space for how it inhibits and even harms it. There are limited studies in the literature covering this topic, posing no complete and comprehensive study focusing on other side of the coin. To fill this gap, this study aims to uncover a comprehensive list of how Scrum inhibits and harms agility and to open a gate to underpinning Scrum promises not sufficiently questioned so far. The following inhibitors were identified and discussed along with their reasoning: Productizing for Marketing, Determinism, the Method Prison, Running and Running without Enough Thinking, Using Proxies of Reality, Fragmentation, Falling in Love with Physical Things, and Blessing the Customer. Regarding the issues, some prominent recommendations were also made. This study shows that Scrum can be better than some others or “the best way to do software”, yet it has still some way to go ahead.
Phenomenology within the Framework of Naturalistic Turn’s Paradigm

The subject matter of the paper proposed can be defined as one of the most sig­nificant contemporary trends in interrelation between philosophy and cognitive sciences (as far as cognition and consciousness studies are concerned), that is natural extensions of phenomenology in the framework of neurophenomenol­ogy’s paradigm (F. Varela, E. Thompson, D. Zahavi, S. Gallagher). Proposed integrative models of phenomenology and neurobiology: phenomenological analysis of introspective presumptions of psychological and neurosciences’ ex­periments, phenomenological participation in experimental design’s formation, at last, phenomenological analysis of psychological and neurosciences’ concep­tual resources’ investigations have clearly been examined in the light of classical Husserl’s phenomenology. As this paper intends to show, in cognition and con­sciousness studies it seems methodologically more correct to speak not about integrative models (as proposed), but rather about research strategies’ comple­mentarity, mutual enhancement of cognitive sciences, neuroscience and phe­nomenology. Nevertheless both ontological and epistemological presumptions in their philosophical foundations (V.S. Stepin) are irreducible to phenomeno­logical ones. The final conclusion is that the term “neurophenomenology” seems not to be rigorous philosophical concept, but rather interdisciplinary metaphor, which presents contemporary aspiration to integrate the results achieved in phi­losophy and different fields of disciplinary researches. It interlocks naturalistic attitude to verified truth’s destination with constructive-realistic perspective of the human reason.
Blockchain Based Administration Model: A Small Scale Governance Demo-System

Today, Blockchain technology for the information administration systems is being studied extensively due to its transparency, reliability and security. For the governance administration system, the privacy and confidentiality of personal information are the matters of primary concern. In addition, the complexity of complying with the government administrative procedures, such as granting, updating or authenticating information costs the time-consuming and financial resources. In this paper, we propose a Blockchain-based smart contract with two-layer security techniques to build a small scale model of governance administration system. The proposed system model provides a high level of security against the theft of personal information and simplifies the administrative procedures of granting, updating and authenticating information. A small-scale governance demonstration system is implemented to show that the proposed model workes properly.
Distance-Based Mapping for General Game Playing

In the field of General Game Playing (GGP), artificial agents (bots) may be required to play never-before-seen games with less than one minute to initialize and train. Although tabula rasa approaches, like Monte-Carlo Tree Search, are popular in this domain, they do not leverage information from the many different games that a bot has previously encountered. A major barrier to transfer learning has been the difficulty in identifying similar features in the rule descriptions of two different games. We present two methods, called MMap and LMap, for heuristically approximating a distance between two games' graphs, and producing a mapping for the symbols of one to the other, thereby enabling transfer. We evaluate the effectiveness of these methods across a variety of transfer scenarios, and find that both methods are far more accurate than a simpler baseline mapper. MMap is found to be more robust than LMap, but LMap is much faster, and so more suitable for general use in GGP.
Design and Implementation of Smart Wheelchair for Disabled/Elderly People

People with cognitive/motor/sensory impairment due to disability can use a wheelchair for their locomotive needs. The wheelchair can be manually or mechanically operated. Driving a manual wheelchair is always a challenge since the user always needs the help of another person to move from place to place. This work aims to provide ease in the movement of physically disabled by legs or elderly. For the movement of the wheelchair, 5 different keys are used by which the wheelchair can move forward, reverse, right, left, and stop. The wheelchair is also provided with a physical parameter measuring device with sensors that monitor the primary health conditions like temperature, pulse, and heartbeat as a salient feature for alarming about the emergency conditions of the disabled/elderly. With the help of a Wi-Fi module, the data from the sensors are sent to an IoT device wherein the data is stored and can be monitored periodically. The movement of the wheelchair can also be controlled via the IoT device and is incorporated in such a way that it is user-friendly. This wheelchair is cost-effective and can be widely used for disabled/elderly persons.
Construction Method of Knowledge Graph in Textile Field Facing Industrial Internet

In the industrial Internet environment in the textile field, the structure and standardization of data is the basis for the configuration optimization of network manufacturing resources. In combination with the characteristics of the textile industry data, we propose a method for knowledge map construction method for the textile field facing industrial Internet. This method is guided by Ontology theory, and the natural language processing technology is integrated with artificial, which is fully integrated with a wide range of knowledge, complex structure and poor demultiplexing. Compared with the construction method of traditional knowledge map, the accuracy and coverage of knowledge are improved. Finally, its future application scenarios and support for industrial Internet manufacturing are analyzed.
Forecasting Pseudo Random Numbers Using Deep Learning

Software algorithms are incapable of generating truly random numbers. Algorithmic pseudo random number generators (PRNG&#x2019;s) are used instead. Any pure randomness, like initiating a random seed, requires a query external to the PRNG algorithm. Is it possible for deep learning to mimic the performance of a PRNG algorithm using a generated string of numbers for training? There are at least two approaches to doing this. In the first, a class of parameterized PRNG&#x2019;s is assumed a priori and learning consists of identification the PRNG parameters. The second approach is nonparametric and makes no model assumption. It seeks to forecast subsequent pseudo random numbers learned solely from a string of numbers generated by the PRNG. Our approach closely resembles the latter. We show that learning the sequence of a variation of the linear congruential generator random number generator to a good approximation is possible using neural network deep learning.
Dynamic Length Factorization Machines for CTR Prediction

Ad click-though rate prediction (pCTR) is one of the core tasks of online advertising. Driving the pCTR models of Yahoo Gemini native advertising is OFFSET - a feature enhanced collaborative-filtering based event prediction algorithm. Due to data sparsity issues OFFSET models both users and items by mapping their features into a latent space, where the resulting user vector is a non-linear function of the user feature vectors (e.g., age, gender, hour, etc.) which allows pairwise dependencies. This pairwise dependencies concept is also used by other algorithms such as the Field-aware Factorization Machines (FFM). However, both in OFFSET and in FFM, the different pairwise interactions are modeled by latent vectors of constant and equal lengths. When prediction models are used online for serving real traffic, where the total serving model size is often limited, a non uniform representation of the pairwise interactions should be considered in order to maximize the accuracy of the model while consuming the same or even less space. In this work we present a Dynamic Length Factorization Machines (DLFM) algorithm that dynamically optimizes the length of the vectors for each feature interaction during training, while not exceeding a maximal overall latent vector size. After showing good online performance of 1.46% revenue lift and a 2.15% CTR lift, serving Gemini native traffic, the DLFM was pushed into production. Since integrated into production, the DLFM has not only improved the accuracy of the model by optimizing the length of each latent space, but has also reduced the total size of the model by 25%. Although the algorithm was applied to OFFSET, we show that DLFM can be applied to any FFM-like algorithm to optimize its pairwise feature vector lengths. We also present an Educated Model Initialization - a novel mechanism for initializing a new model based on an existing model that has some mutual user features. Using this mechanism, we managed to reduce the training time of our models by more than 90% when compared to an equivalent model that is trained from "scratch".
Translation of CCS into CSP, Correct up to Strong Bisimulation

We present a translation of CCS into CSP which is correct with respect to strong bisimulation. To our knowledge this is the first such translation to enjoy a correctness property. This contributes to the unification of the CCS and CSP families of concurrent calculi, in the spirit of Hoare and He’s unification programme through Unifying Theories of Programming. To facilitate this translation, we define CCSTau, the extension of CCS with visible synchronisation actions and the hiding operator. This separation of concerns between synchronisation and hiding turns out be sufficient to obtain our correct translation. Our translation, implemented in a Haskell prototype, makes it possible to use CSP-based verifiers such as FDR to reason about trace and failure (hence may- and must-testing) preorders for CCS processes.