Reviewerly/exHarmony huggingface dataset:
Sentence Generation using LSTM Based Deep Learning

Sentence generation serves the process of predicting relevant words in a specific sequence. The purpose of this research is to come up with a method for generating sentences while maintaining proper grammatical structure. Here, we have implemented a sentence generation system based on Long Short-Term Memory (LSTM) architecture. Our system generally follows the basics of word embedding where words from the dataset get tokenized and turned into vector forms. These vectors are then processed and passed through a Long Short-Term Memory layer. Successive words get generated from the system after each iteration. This process winds up generating relevant words to form a sentence or a passage. The results of the system are pretty convincing compared to different existing methods.
An Approach for Multi-Objective Obstacle Avoidance Using Dynamic Occupancy Grid Map

Path planning in dynamic environments is an important guarantee for mobile robots to complete navigation tasks safely. However, human motion is unpredictable, changing its speed or direction arbitrarily, and traditional dynamic obstacle avoidance strategies based on object detection require massive computing resources in the prediction step, which reduces the real time characteristics of the system. To address this question, we propose a dynamic obstacle avoidance framework. It defines the environment as a random dynamic system by using dynamic grid maps, and obtains the velocity of multiple objects with low delay under GPU parallel acceleration calculations. Naturally, we introduce the relative velocity into the artificial potential field function. By expanding the data structure of costmaps, the dynamic grid map is effectively connected with the local obstacle avoidance algorithm. Our approach reduces the detour time of the robot and improves the system security.
Life Improvements

This paper aims to explore practices and motivations associated with do-it-yourself (DIY) in people from low socio-economic status (SES) backgrounds. We carried out contextual interviews with nine individuals who were involved in a wide range of making activities. Our findings showed that DIY processes were centered around improving our participants? existing living conditions and were manifested through two main motivations: sustainable and economical living; and social and community wellbeing. We contribute to the CSCW research in two ways. First, we provide a nuanced view on DIY involving a group of economically struggling individuals that is not well aligned with the traditional narratives. Second, we highlight the societal and economic factors that influenced the specific types of DIY activities that helped improve their existing lives.
Deaf and hard of hearing in the United Arab Emirates interacting with Alexa, an intelligent personal assistant

BACKGROUND: Intelligent Personal Assistants have been booming around the world since 2014, allowing millions of users to interact with different cloud-based software via speech. Unfortunately, the Deaf and Hard of Hearing individuals have been left out without recognizable accessibility to such technologies, although it might be used to make their daily life routine easier. OBJECTIVE: In this research, the researcher studies the interaction and perception of Amazon’s Alexa among the Deaf and Hard of Hearing in the United Arab Emirates in its current set up (Tap-to-Alexa accessibility option) in addition to Sign Language as an input method. The researcher expands on the Technology Acceptance Model to study the acceptance of Alexa as an assistive technology for the Deaf and Hard of Hearing. Additionally, the researcher discusses more suitable input methods and solutions to allow Alexa, and other Intelligent Personal Assistants, be more accessible for the Deaf and Hard of Hearing. METHODS: The mixed method is used in this research in terms of collecting primary data through hands-on experiments, surveys, and interviews with the Deaf and Hard of Hearing participants. RESULTS: The researcher found that the Deaf and Hard of Hearing in the United Arab Emirates perceive that Sign Language combined with a Live interpreter is better than the accessibility option “Tap-to-Alexa”, which is a solution provided by Amazon. The researcher also found that Sign Language combined with a Live interpreter is the most suitable input method to make the device accessible for the Deaf and Hard of Hearing, in addition to translating the “Tap-to-Alexa” to different languages. Finally, the researcher proposes a modification to the Technology Acceptance Model to suit the research study of the Deaf and Hard of Hearing perception of Alexa. CONCLUSIONS: The researcher concludes that the ideal scenario for the Deaf and Hard of Hearing to interact and benefit the most from Amazon’s Alexa, and IPAs in general, is to include Sign Language as an embedded input method in the device and provide live interpreters; this sheds light on the importance of the interpreters’ jobs around the world. Additionally, “Tap-to-Alexa” must be translated into different languages for a better perception of the input method.
Deep Residual Networks for Impulsive Noise Suppression

We introduce a new method of noise suppression using fully convolutional neural networks for salt and pepper noise. We adopt a well-known residual learning framework to get convergence faster in the training phase than conventional learning. Based on experimental results, our proposed method can outperform the state-of-the-arts method at most levels of noise contamination. In addition, our method is qualitatively proven to maintain the details of the denoised image even at extreme contamination level.
DeepPeep

The remarkable predictive performance of deep neural networks (DNNs) has led to their adoption in service domains of unprecedented scale and scope. However, the widespread adoption and growing commercialization of DNNs have underscored the importance of intellectual property (IP) protection. Devising techniques to ensure IP protection has become necessary due to the increasing trend of outsourcing the DNN computations on the untrusted accelerators in cloud-based services. The design methodologies and hyper-parameters of DNNs are crucial information, and leaking them may cause massive economic loss to the organization. Furthermore, the knowledge of DNN’s architecture can increase the success probability of an adversarial attack where an adversary perturbs the inputs and alters the prediction. In this work, we devise a two-stage attack methodology “DeepPeep,” which exploits the distinctive characteristics of design methodologies to reverse-engineer the architecture of building blocks in compact DNNs. We show the efficacy of “DeepPeep” on P100 and P4000 GPUs. Additionally, we propose intelligent design maneuvering strategies for thwarting IP theft through the DeepPeep attack and proposed “Secure MobileNet-V1.” Interestingly , compared to vanilla MobileNet-V1, secure MobileNet-V1 provides a significant reduction in inference latency (≈60%) and improvement in predictive performance (≈2%) with very low memory and computation overheads.
Active Learning with Crowdsourcing for the Cold Start of Imbalanced Classifiers

We present a novel cooperative strategy based on active learning and crowdsourcing, dedicated to provide a solution to the cold start stage, i.e. initializing the classification of a large set of data with no attached labels. The strategy is moreover designed to handle an imbalanced context in which random selection is highly inefficient. In this purpose, our method is guided by an unsupervised clustering, and the computation of cluster quality and impurity indexes, updated at each active learning step. The strategy is explained on a case study of annotating Twitter content w.r.t. a real flood event. We also show that our technique can cope with multiple heterogeneous data representations.
The Global Indicator of Classicality of an Arbitrary N-Level Quantum System

It is commonly accepted that the deviation of the Wigner quasiprobability distribution of a quantum state from a proper statistical distribution signifies its nonclassicality. Following this ideology, we introduce a global indicator QN for quantifying the “classicality-quantumness” correspondence in the form of a functional on the orbit space O[𝔓N] of the adjoint action of the group SU(N) on the state space 𝔓N of an N-dimensional quantum system. The indicator QN is defined as the relative volume of the subspace $$ \mathrm{O}\left[{\mathfrak{P}}_N^{\left(+\right)}\right]\subset \mathrm{O}\left[{\mathfrak{P}}_N\right] $$ where the Wigner quasiprobability distribution is positive. The algebraic structure of $$ \mathrm{O}\left[{\mathfrak{P}}_N^{\left(+\right)}\right] $$ is revealed and exemplified by the case of a single qubit (N = 2) and a single qutrit (N = 3). For the Hilbert–Schmidt ensemble of qutrits, the dependence of the global indicator on the moduli parameter of the Wigner quasiprobability distribution is found. Bibliography: 18 titles.
Multi-task Coordinate Algorithm Based on Improved PSO

Abstract This paper intends to use particle swarm algorithm as the research object of multi-tasking collaborative algorithm. Based on the specific task of multi-UAV distribution at the end of logistics as the background, the model in the task distribution process is constructed. The group modeling process was improved to solve the problem that the coefficient matrix in the task iteration process was discrete and the final result was not the optimal solution. And the task chain is introduced in the algorithm execution process to achieve conflict-free tasks.
TxtLineSeg: Text Line Segmentation of Unconstrained Printed Text in Devanagari Script

Most of the reports either printed or handwritten comprised of significant data that can be helpful in the future. The papers generally rot with time that can lose data totally or up to some extent. Optical character recognition is the process which is used in sparing information from paper for further processing. Text line segmentation is a significant phase in character recognition because incorrectly divided text lines can cause errors in the recognition stage. In this paper, single-column and multi-column documents from different books, magazines and papers imprinted in Devanagari script had been considered. As a result of the low quality of papers in few documents and the unpredictability and complexity of these documents (background noise, paper decay due to aging, short lines, justified lines, distorted text lines), programmed text line segmentation remains an open research field. In this article, the authors have presented a new technique for unconstrained text line segmentation of Devanagari text using a combination of headline detection and median calculation of text line heights.
A security-attribute-based access control along with user revocation for shared data in multi-owner cloud system

Cloud storage is a cloud implementation that facilitates agencies to set up in-house information storage installations. Cloud storage actually creates security problems. When the information is communicated by groups; both cloud-specific and standard insider threats are visible. A key research problem is the secure sharing among group members, which confronts the internal threats of legitimate and malevolent users. In this paper, in this paper, an effective structured data sharing mechanism for user revocation in the multi-owner cloud system is proposed. The proposed method is based on the following five entities: data owner, cloud storage, central authority, and cryptographic server, and data users. The data owner will submit to cryptographic servers’ data, user list and parameters capable of generating an Access Control List (ACL). A trusted third party is a cryptographic server that provides the symmetric key that triggers the encrypted data. The cryptographic server then divides the key into three sections for each user within the group. Quantitative findings indicate the sustainability of the mechanism proposed in contrast with conventional schemes.
Explanation via Machine Arguing

As AI becomes ever more ubiquitous in our everyday lives, its ability to explain to and interact with humans is evolving into a critical research area. Explainable AI (XAI) has therefore emerged as a popular topic but its research landscape is currently very fragmented. Explanations in the literature have generally been aimed at addressing individual challenges and are often ad-hoc, tailored to specific AIs and/or narrow settings. Further, the extraction of explanations is no simple task; the design of the explanations must be fit for purpose, with considerations including, but not limited to: Is the model or a result being explained? Is the explanation suited to skilled or unskilled explainees? By which means is the information best exhibited? How may users interact with the explanation? As these considerations rise in number, it quickly becomes clear that a systematic way to obtain a variety of explanations for a variety of users and interactions is much needed. In this tutorial we will overview recent approaches showing how these challenges can be addressed by utilising forms of machine arguing as the scaffolding underpinning explanations that are delivered to users. Machine arguing amounts to the deployment of methods from computational argumentation in AI with suitably mined argumentation frameworks, which provide abstractions of “debates”. Computational argumentation has been widely used to support applications requiring information exchange between AI systems and users , facilitated by the fact that the capability of arguing is pervasive in human affairs and arguing is core to a multitude of human activities: humans argue to explain, interact and exchange information. Our lecture will focus on how machine arguing can serve as the driving force of explanations in AI in different ways, namely: by building explainable systems with argumentative foundations from linguistic data focusing on reviews), or by extracting argumentative reasoning from existing systems (focusing on a recommender system) .
SNIPER Based Multi-Target and Multi-Scale Aerial Image Processing Method

Abstract The maintenance of power transmission system appeals automatic inspection system to replace manual inspection. Inspection based on Unmanned Aerial Vehicle (UAV) generates a huge number of photos to be processed. These aerial photos have several challenging features: high density, small, scale variation. In order to ameliorate the performance on detection of important objects as insulators and pin bolts, an efficient multi-scale training algorithm called SNIPER is introduced. SNIPER enhanced Faster RCNN was trained on a powerline dataset for object detection. SNIPER provides a good average precision and average recall on large objects like insulators and medium objects with adequate annotated instances like pin bolts and dampers. However, SNIPER fails to locate missing pin or displaced pin, possibly due to their similarity to pin bolts. Future development of a SNIPER-based cascaded detection scheme could help detect defected small objects.
Progressive learning with style transfer for distant domain adaptation

This article studies a novel transfer learning problem termed distant domain transfer learning. Different from traditional transfer learning which assumes there is a close relation between source and target data, in this study, the objective is to execute an unseen and unrelated task based on a labelled data set training previously without any samples from intermediate domains. To this end, the authors propose deep unsupervised progressive learning (DUPL) framework and its upgraded version, end-to-end DUPL (eDUPL). eDUPL consists of two components, i.e. (i) translating the style of labelled images from irrelevant source domain to the target domain and (ii) learning a domain adaptation model with progressive learning for testing on the target domain. In comparison, eDUPL can integrate the two components of the framework seamlessly. In general, the proposed method is easy to be implemented and can be viewed as a strong convolutional baseline for distant domain adaptation task. Comprehensive experiments based on VeRi Vehicle, CUB-200-2011 Birds and Oxford5k Buildings data sets are conducted and the results indicate that the proposed method robustly achieves state-of-the-art performances compared with existing approaches, which demonstrates the effectiveness and superiority of the proposed algorithm.
Optimizing GPU Memory Transactions for Convolution Operations

Convolution computation is a common operation in deep neural networks (DNNs) and is often responsible for performance bottlenecks during training and inferencing. Existing approaches for accelerating convolution operations aim to reduce computational complexity. However, these strategies often increase the memory footprint with extra memory accesses, thereby leaving much room for performance improvement. This paper presents a novel approach to optimize memory access for convolution operations, specifically targeting GPU execution. Our approach leverages two optimization techniques to reduce the number of memory operations for convolution operations performed on the width and height dimensions. For convolution computations on the width dimension, we exploit shuffle instructions to exchange the overlapped columns of the input for reducing the number of memory transactions. For convolution operations on the height dimension, we multiply each overlapped row of the input with multiple rows of a filter to compute multiple output elements to improve the data locality of row elements. We apply our approach to 2D and multi-channel 2D convolutions on an NVIDIA 2080Ti GPU. For 2D convolution, our approach delivers over faster performance than the state-of-the-art image processing libraries. For multi-channel 2D convolutions, we obtain up to speedups over the quickest algorithm of cuDNN. We apply our approach to 2D and multi-channel 2D convolutions on an NVIDIA 2080Ti GPU. For 2D convolution, our approach delivers over 2× faster performance than the state-of-the-art image processing libraries. For multi-channel 2D convolutions, we obtain up to 1.3× speedups over the quickest algorithm of cuDNN.
Case-Based Reasoning Modifications for Intelligent Systems in Handling In Vitro Fertilization (IVF) Patients Post Embryo Transfer

The successful rate of In Vitro Fertilization (IVF) in Indonesia is around 29%, which is considered low. One of the factors causing the IVF failure is anxiety. About 77.7% of IVF patients have a high anxiety, and 83.3% of IVF patients experienced an IVF failure. The long duration of waiting the answer from the fertility doctors become one cause of the anxiety in IVF patients. On the other hand, the fertility doctors themselves have other responsibilities that cause IVF patients questions are not able to be given immediately. The study focused on the problems of the IVF patients and the fertility doctors.This research uses triangulation to obtain valid data and include challenges in developing health sector systems. To test the effect of IVF patients on the proposed intelligent system, Anova testing has been carried out resulting in a value of F = 9,902 and a Coefficient test that produces a value of t = 3,147, so the test results provide evidence that IVF patient feedback has an effect on improving the quality of the intelligent system handling IVF patients post embryo transfer. The final results of the research have provided a case-based reasoning (CBR) modification recommendation for an intelligent system for handling IVF patients after embryo transfer.
No-Reference Nonuniform Distorted Video Quality Assessment Based on Deep Multiple Instance Learning

Each part of a nonuniform distorted video (NUDV) has a unique distortion degree. When NUDV blocks are used as inputs, traditional machine-learning-based video quality assessment (VQA) methods frequently do not work effectively. Because these methods directly assign the label of the entire video to blocks, causing the unreliability of labels. We creatively propose video bag, a collection of video blocks, to deal with this unreliability. We develop a novel multiple instance learning (MIL) based model, VQA-MIL, which dynamically adjusts the weights by a block-wise attention module and enriches the features of video bags by a MI Pooling layer. Furthermore, we apply the mixup data-augmentation strategy to address the lack of human labels in common video datasets. We test our method on LIVE and CSIQ, and on a relatively large-scale dataset, named NUDV-KT, that we have collected. Results show that our method outperforms popular state-of-the-art no-reference VQA methods on NUDVs.
Ontology of Humanitarian Knowledge in the Information society

The Humanities, throughout the formation of the cultural experience of humankind, depended on the environment in which they developed, and on the concepts of reality that prevailed in this environment. Modern Humanities are in a state of uncertainty as concerns its roles, including those associated with the rapid evolution of the contemporary information society. The purpose of this article is to overcome this uncertainty. The author presents the main stages of interaction evolution and the ontology of humanitarian knowledge in the digital society in the last two decades. The article is a partial representation of the results of intercollegiate research conducted in 2019 at the Faculty of Free Arts and Sciences of Saint Petersburg State University: “Narratives of public communications in modern Russia”. The process of studying the ontology of humanitarian knowledge in the digital environment is a continuous one because reality is changing dynamically. Researchers are in a dual position: it is possible to study and generalise the observed results during the period of transition from post-industrial society to the modern concept of reality; to model the future conceptual sphere as a set of entities and categories that are in event interaction as a communicative environment with predetermined parameters. At the same time, it is necessary to take into account the fact that the social reality of modern society has already formed its interdisciplinary specificity, which is subject to ontological understanding.
Fast Scalable Approximate Nearest Neighbor Search for High-dimensional Data

K-Nearest Neighbor (k-NN) search is one of the most commonly used approaches for similarity search. It finds extensive applications in machine learning and data mining. This era of big data warrants efficiently scaling k-NN search algorithms for billion-scale datasets with high dimensionality. In this paper, we propose a solution towards this end where we use vantage point trees for partitioning the dataset across multiple processes and exploit an existing graph-based sequential approximate k-NN search algorithm called HNSW (Hierarchical Navigable Small World) for searching locally within a process. Our hybrid MPI-OpenMP solution employs techniques including exploiting MPI one-sided communication for reducing communication times and partition replication for better load balancing across processes. We demonstrate computation of k-NN for 10,000 queries in the order of seconds using our approach on ~8000 cores on a dataset with billion points in an 128-dimensional space. We also show 10X speedup over a completely k-d tree-based solution for the same dataset, thus demonstrating better suitability of our solution for high dimensional datasets. Our solution shows almost linear strong scaling.