Reviewerly/exHarmony huggingface dataset:
A study on data de-duplication schemes in cloud storage

Digital data is growing at immense rates day by day and finding efficient storage and security mechanisms is a challenge. Cloud storage has already gained popularity because of the huge data storage capacity in storage servers made available to users by the cloud service providers. When lots of users upload data in cloud there can be much redundant data as well and this can waste storage space as well as affect transmission bandwidth. To promise efficient storage handling of this redundant data is very important which is done by the concept of deduplication. The major challenge for deduplication is that most users upload data in encrypted form for privacy and security of data. There are many prevailing mechanisms for deduplication some of which handle encrypted data as well. The purpose of this paper is to conduct a survey of the existing deduplication mechanisms in cloud storage and to analyse the methodologies used by each of them.
DeCoach: Deep Learning-based Coaching for Badminton Player Assessment

Wearable devices have gained immense popularity among various pervasive computing and Internet-of-Things (IoT) applications in the past decade. Sports analytics researchers recently focused on improving a player’s performance to help devise a winning strategy based on the player’s gameplay. Especially in a racquet-based badminton sport, it is assumed that handling the racquet during the gameplay is one of the primary reasons to influence the players’ performance. On the contrary, we posit that the players’ stance, body movements, and posture are equally significant in evaluating a player’s performance during the game. A shot characterized by a recommended posture, stance, and body movements allows a player to play a stroke efficiently, thus aiding the player in guiding the shuttle to strategic spots and making it difficult for the opponent to return the shot and score a point. Relying on this hypothesis, we propose DeCoach, a data-driven framework that leverages the stance and posture of the players and ranks them based on their performances. In this effort, we first employ a deep learning-based algorithm to classify the strokes and stances of the players. Secondly, we propose a distance-based methodology to compare the obtained stance of a player with that of a professional player. Finally, we devise a deep learning-based regressor to predict the player’s performance which commences with ranking based on their performance. We evaluate DeCoach using our in-house dataset, Badminton Activity Recognition (BAR) Dataset that is collected using inertial measurement unit (IMU) sensors by placing them on the upper and lower limbs of the players. The BAR dataset is collected from 11 players in the controlled and uncontrolled environment settings for 12 frequently played shots in the game. Empirical results indicate that DeCoach achieves 89.09% accuracy for strokes detection and R2 score of 88.84% in estimating the players’ performance.
DNN-Based Arabic Speech Synthesis

This article discusses a Deep Neural Network-based Text-to-Speech synthesis for the Arabic language. Subjective and objective tests were used to evaluate the system. We used the Mean Opinion Score (MOS) for subjective evaluation, and the Diagnostic Rhyme Test (DRT) to test the intelligibility of some consonants and vowels. We use the Perceptual Evaluation of Speech Quality (PESQ) for objective evaluation. The results have a mean of 3.92/5, 3.88/5 for the MOS and DRT tests, respectively, and 3.17/5 for the PESQ test; the majority of words and sentences were recognized, and the system's overall evaluation quality was satisfactory. Furthermore, the results show a significant improvement in the quality of synthesized speech for DNN-based TTS when compared to its HMM-based counterpart.
SentiCode: A new paradigm for one-time training and global prediction in multilingual sentiment analysis

The main objective of multilingual sentiment analysis is to analyze reviews regardless of the original language in which they are written. Switching from one language to another is very common on social media platforms. Analyzing these multilingual reviews is a challenge since each language is different in terms of syntax, grammar, etc. This paper presents a new language-independent representation approach for sentiment analysis, SentiCode. Unlike previous work in multilingual sentiment analysis, the proposed approach does not rely on machine translation to bridge the gap between different languages. Instead, it exploits common features of languages, such as part-of-speech tags used in Universal Dependencies. Equally important, SentiCode enables sentiment analysis in multi-language and multi-domain environments simultaneously. Several experiments were conducted using machine/deep learning techniques to evaluate the performance of SentiCode in multilingual (English, French, German, Arabic, and Russian) and multi-domain environments. In addition, the vocabulary proposed by SentiCode and the effect of each token were evaluated by the ablation method. The results highlight the 70% accuracy of SentiCode, with the best trade-off between efficiency and computing time (training and testing) in a total of about 0.67 seconds, which is very convenient for real-time applications.
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding

Natural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query.In spite of the great advances, most existing methods rely on dense video frame annotations, which require a tremendous amount of human effort.To achieve effective grounding under a limited annotation budget, we investigate one-shot video grounding, and learn to ground natural language in all video frames with solely one frame labeled, in an end-toend manner.One major challenge of end-toend one-shot video grounding is the existence of videos frames that are either irrelevant to the language query or the labeled frames.Another challenge relates to the limited supervision, which might result in ineffective representation learning.To address these challenges, we designed an end-to-end model via Information Tree for One-Shot video grounding (IT-OS).Its key module, the information tree, can eliminate the interference of irrelevant frames based on branch search and branch cropping techniques.In addition, several selfsupervised tasks are proposed based on the information tree to improve the representation learning under insufficient labeling.Experiments on the benchmark dataset demonstrate the effectiveness of our model.
Casual 6-DoF: Free-Viewpoint Panorama Using a Handheld 360° Camera

Six degrees-of-freedom (6-DoF) video provides telepresence by enabling users to move around in the captured scene with a wide field of regard. Compared to methods requiring sophisticated camera setups, the image-based rendering method based on photogrammetry can work with images captured with any poses, which is more suitable for casual users. However, existing image-based rendering methods are based on perspective images. When used to reconstruct 6-DoF views, it often requires capturing hundreds of images, making data capture a tedious and time-consuming process. In contrast to traditional perspective images, 360° images capture the entire surrounding view in a single shot, thus, providing a faster capturing process for 6-DoF view reconstruction. This article presents a novel method to provide 6-DoF experiences over a wide area using an unstructured collection of 360° panoramas captured by a conventional 360° camera. Our method consists of 360° data capturing, novel depth estimation to produce a high-quality spherical depth panorama, and high-fidelity free-viewpoint generation. We compared our method against state-of-the-art methods, using data captured in various environments. Our method shows better visual quality and robustness in the tested scenes.
Self-Paced Broad Learning System

Broad learning system (BLS), an efficient neural network with a flat structure, has received a lot of attention due to its advantages in training speed and network extensibility. However, the conventional BLS adopts the least square loss, which treats each sample equally and thus is sensitivity to noise and outliers. To address this concern, in this article we propose a self-paced BLS (SPBLS) model by incorporating the novel self-paced learning (SPL) strategy into the network for noisy data regression. With the assistance of the SPL criterion, the model output is used as feedback to learn appropriate priority weight to readjust the importance of each sample. Such a reweighting strategy can help SPBLS to distinguish samples from "easy" to "difficult" in model training, equipping the model robust to noise and outliers while maintaining the characteristics of the original system. Moreover, two incremental learning algorithms associated to SPBLS have also been developed, with which the system can be updated quickly and flexibly without retraining the entire model when new training samples are added or the network needs to be expanded. Experiments conducted on various datasets demonstrate that the proposed SPBLS can achieve satisfying performance for noisy data regression.
Distance Learning Support Measures for Teachers in Poland during the COVID-19 Pandemic

The COVID-19 pandemic made higher education institutions switch to distance learning in a very short period of time. The situation was challenging not only for universities themselves but also for the students and teachers. Some universities did not have the means, in terms of infrastructure, for a smooth transition to distance learning. Some teachers were not prepared for the extensive usage of ICT in their work. The pandemic developed dynamically, and it made it extremely difficult for both governments and universities to plan and implement firm solutions on how to conduct the teaching process. The presented paper focuses on the situation of Polish higher education institutions between March 2020 and March 2022. It reviews legal acts and ordinances introduced in the stated period, which focused on the sustainability of the teaching process, countermeasures for the spread of COVID-19 and the implementation of distance learning. The case of the University of Economics in Katowice, Poland, is used to show the correlation between governmental legal acts and those introduced by the university as part of the COVID -19 spread prevention and teaching process support.
(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering

Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequences of 2D ``views'' of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. Specifically, we first create a 2.5D (pseudo-3D) scene graph by transforming every 2D frame to have an inferred 3D structure using an off-the-shelf 2D-to-3D transformation module, following which we register the video frames into a shared (2.5+1)D spatio-temporal space and ground each 2D scene graph within it. Such a (2.5+1)D graph is then segregated into a static sub-graph and a dynamic sub-graph, corresponding to whether the objects within them usually move in the world. The nodes in the dynamic graph are enriched with motion features capturing their interactions with other graph nodes. Next, for the video QA task, we present a novel transformer-based reasoning pipeline that embeds the (2.5+1)D graph into a spatio-temporal hierarchical latent space, where the sub-graphs and their interactions are captured at varied granularity. To demonstrate the effectiveness of our approach, we present experiments on the NExT-QA and AVSD-QA datasets. Our results show that our proposed (2.5+1)D representation leads to faster training and inference, while our hierarchical model showcases superior performance on the video QA task versus the state of the art.
Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings

Recent studies have determined that the learned token embeddings of large-scale neural language models are degenerated to be anisotropic with a narrow-cone shape.This phenomenon, called the representation degeneration problem, facilitates an increase in the overall similarity between token embeddings that negatively affect the performance of the models.Although the existing methods that address the degeneration problem based on observations of the phenomenon triggered by the problem improves the performance of the text generation, the training dynamics of token embeddings behind the degeneration problem are still not explored.In this study, we analyze the training dynamics of the token embeddings focusing on rare token embedding.We demonstrate that the specific part of the gradient for rare token embeddings is the key cause of the degeneration problem for all tokens during training stage.Based on the analysis, we propose a novel method called, adaptive gradient gating (AGG).AGG addresses the degeneration problem by gating the specific part of the gradient for rare token embeddings.Experimental results from language modeling, word similarity, and machine translation tasks quantitatively and qualitatively verify the effectiveness of AGG.
Interaction-Matrix Based Personalized Image Aesthetics Assessment

Personalized image aesthetics assessment (IAA) aims to estimate aesthetic experiences subject to the preferences of individual users, contrary to generic IAA that estimates aesthetic experiences subject to average preferences. Most existing personalized IAA methods treat personalized aesthetic experiences as deviations from a generic aesthetic experience, and therefore, personalized IAA models are designed to build upon the prior knowledge on generic IAA. However, we propose that acquiring knowledge on generic IAA is not necessary for building a personalized IAA model. Instead of modeling personalized IAA on the basis of generic IAA, this work proposes to directly estimate personalized aesthetic experiences from the interactions between image contents and user preferences (i.e., preference-content interaction), where interaction-matrices representing preference-content interactions are constructed without needs for prior generic IAA knowledge. To this end, we construct interaction-matrices from content features constructed from pre-trained image classification features and latent preference features. To realize a robust interaction-matrix based personalized IAA model, we discuss in detail on different strategies for constructing interaction-matrices and estimating personalized aesthetic scores from the interaction-matrices. Besides the personalized IAA scenario, we further propose strategies to adapt the proposed personalized IAA model to different scenarios of generic IAA. Extensive experiments show that: 1) our method significantly outperforms 5 previous relevant personalized IAA methods on FLICKR-AES dataset, especially the methods that require generic IAA knowledge as the basis; 2) in terms of generic IAA, the proposed approach also outperforms 13 generic IAA methods on AVA dataset.
A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation

We propose Composition Sampling, a simple but effective method to generate diverse outputs for conditional generation of higher quality compared to previous stochastic decoding strategies.It builds on recently proposed plan-based neural generation models (Narayan et al., 2021) that are trained to first create a composition of the output and then generate by conditioning on it and the input.Our approach avoids text degeneration by first sampling a composition in the form of an entity chain and then using beam search to generate the best possible text grounded to this entity chain.Experiments on summarization (CNN/DailyMail and XSum) and question generation (SQuAD), using existing and newly proposed automatic metrics together with human-based evaluation, demonstrate that Composition Sampling is currently the best available decoding strategy for generating diverse meaningful outputs.Haman Written Summary: Chelsea star Eden Hazard is set to make his 100th top-flight appearance.Santi Cazorla should hit the same milestone when Arsenal meet Burnley.Both players have impressed since moving to the Premier League in 2012.Hazard has more goals this season but Cazorla has one more assist.Sportsmail's reporters choose the player who has excited them the most.Beam Search: Eden Hazard and Santi Cazorla are both set to make their 100th Premier League appearances this weekend.Both players have been hugely influential since they moved to London.Here, Sportsmail's reporters choose the player they most enjoy seeing in action.Nucleus Sampling: Eden Hazard and Santi Cazorlag will each make their 100th Premier League appearance this weekend.nightstandapplication.com.Sportsmail's hovercraft reporters choose their man of the match countermeasures.
A Fast and Accurate Method for Parameter Estimation of Multi-Component LFM Signals

This letter presents a fast and accurate method for parameter estimation of multi-component linear frequency modulated (MC-LFM) signals. We first show that the auto-terms of signal in fractional auto-correlation (FrAc) domain are concentrated along lines passing through the origin with slopes equal to chirp rates. The connection between the FrAc and fractional Fourier transform (FrFT) is exploited to find the chirp rates. We then easily obtain central frequencies from the same FrFT coefficients which are used for the estimation of chirp rates. We finally propose our efficient algorithm to accelerate the precise estimate of the MC-LFM signal parameters. Simulation results validate performance of the approach.
High-throughput Pairwise Alignment with the Wavefront Algorithm using Processing-in-Memory

We show that the wavefront algorithm can achieve higher pairwise read alignment throughput on a UPMEM PIM system than on a server-grade multi-threaded CPU system.
Aging shifts the relative contributions of episodic and semantic memory to decision-making.

Healthy aging is accompanied by well-characterized shifts in memory systems: episodic memory tends to decline with age while semantic memory remains relatively intact, with some knowledge domains strengthening. Beyond reflecting on the past, these distinct memory systems often guide decisions about the future. Yet how such age-related memory shifts influence simple value-based choices remain understudied. Here, younger (18-24 years) and older (61-75 years) adults completed a card game in which they could use task-relevant episodic memories to maximize the number of points they earned. Critically, they could also use task-irrelevant semantic memories to guide their choices. Both younger and older adults successfully used episodic memory to make decisions, but older adults did so less reliably than younger adults. Further, while younger adults strategically suppressed task-irrelevant semantic memories when a relevant episodic memory could be used, older adults used semantic memory to guide their decisions regardless of the relevance of episodic memory. We provide evidence that declining inhibitory control may play a role in how older adults arbitrate between competing memory sources when making decisions. These effects are consistent with the literature on age-related shifts in memory and cognitive control systems and add to a growing body of work on how episodic memories inform reinforcement learning and value-based decision-making. Our findings highlight how patterns of age-related memory differences can have consequences for value-based choices, which has implications for other types of decision-making, from the economic to the mundane. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Sentiment Analysis of Twitter Data Using TF-IDF and Machine Learning Techniques

Sentiment analysis technique plays an important role in natural language processing to analyze complex human statements. In the last few years, this technique has become a powerful tool for several social media communication mediums such as WhatsApp, Twitter, Facebook, Instagram, YouTube, LinkedIn, Blog, etc. This paper proposes a machine learning (ML) based method to analyze social media data for sentiment analysis on text data. The presented method is divided into three distinct stages. In the first stage, pre-processing is performed to filter and refine the text data. In the second stage, the feature extraction is performed using the Term Frequency and Inverse Document Frequency (TF-IDF) technique. Moreover, during the third stage, the extracted features are supplied to make predictions for the classifier. The experiments are carried out on a publicly available Twitter dataset for US Airlines. Several ML techniques are utilized for analysis and classification. The results are reported for different evaluation metrics like accuracy, precision, recall, and F1 score. Finally, the support vector machine yielded the most relevant results.
Focus Your Distribution: Coarse-to-Fine Non-Contrastive Learning for Anomaly Detection and Localization

The essence of unsupervised anomaly detection is to learn the compact distribution of normal samples and detect outliers as anomalies in testing. Meanwhile, the anomalies in real-world are usually subtle and fine-grained in a high-resolution image especially for industrial applications. Towards this end, we propose a novel framework for unsupervised anomaly detection and localization. Our method aims at learning dense and compact distribution from normal images with a coarse-to-fine alignment process. The coarse alignment stage standardizes the pixel-wise position of objects in both image and feature levels. The fine alignment stage then densely maximizes the similarity of features among all corresponding locations in a batch. To facilitate the learning with only normal images, we propose a new pretext task called non-contrastive learning for the fine alignment stage. Non-contrastive learning extracts robust and discriminating normal image representations without making assumptions on abnormal samples, and it thus empowers our model to generalize to various anomalous scenarios. Extensive experiments on two typical industrial datasets of MVTec AD and BenTech AD demonstrate that our framework is effective in detecting various real-world defects and achieves a new state-of-the-art in industrial unsupervised anomaly detection.