Reviewerly/exHarmony huggingface dataset:
A Deep Diacritics-Based Recognition Model for Arabic Speech: Quranic Verses as Case Study

Arabic is the language of more than 422 million of the world’s population. Although classic Arabic is the Quran language that 1.9 billion Muslims are required to recite, limited Arabic speech recognition exists. In classic Arabic, diacritics affect the pronunciation of a word, a change in a diacritic can change the meaning of a word. However, most of the Arabic-based speech recognition models discarded the diacritics. This work aims to recognize the classic Arabic speech while considering diacritics by converting audio signals to diacritized text using Deep Neural Network (DNN)-based models. The DNN-based model recognizes speech using DNN which outperformed the traditional speech recognition systems’ phonetics dependency. Three models were developed to recognize Arabic speech: (i) Time Delay Neural Network-Connectionist Temporal Classification (CTC), (ii) Recurrent Neural Network (RNN)-CTC, and (iii) transformer. A 100hours dataset of the Quran recordings has been used. Based on the results, the RNN-CTC model obtained state-of-the-art results with the lowest word error rate of 19.43% and a 3.51% character error rate. RNN-CTC model recognized character-by-character which is more reliable compared to transformers’ whole-sentence recognition behaviour. The model performed well with clear unstressed recordings of short sentences. Moreover, the RNN-CTC model effectively recognized out-of-the-dataset sounds. The findings recommend continuing the efforts in enhancing the diacritics-based Arabic speech recognition models using clear and unstressed recordings to obtain better performance. Moreover, pretraining large speech models could obtain accurate recognition. The outcomes can be used to enhance the existing classic Arabic speech recognition solutions by supporting diacritics recognition.
Imaging Simulation and Learning-Based Image Restoration for Remote Sensing Time Delay and Integration Cameras

Time delay and integration cameras (TDI cameras) are widely used in remote sensing areas because they capture high-resolution, high signal-to-noise ratio (SNR) images and to image in low-light environments. However, the image quality captured by TDI cameras may be affected by many degradation factors, including jitter, charge transfer time mismatch and drift angle. Moreover, compared with the single-line push-broom cameras and area gaze cameras used in remote sensing, the degraded effect of the TDI camera may accumulate during the charge accumulation process. In this paper, we present a fast imaging simulation method for remote sensing TDI cameras based on image resampling that can accurately simulate the degraded image quality affected by different degradation factors. The simulated image pairs can provide a sufficient dataset for modern supervised-learning image restoration methods. In addition, we present a novel network, containing a row-attention block and row-encoder block to help resolve the row-variant blur to resolve the degraded images. We test our image restoration method on the simulated degraded image datasets and real images; the results show that the proposed method can effectively restore degraded images. Our restoration method does not rely on auxiliary information detected by high-frequency sensors or multispectral bands, and it achieves better results than other blind restoration methods.
Adaptive Powerball Stochastic Conjugate Gradient for Large-Scale Learning

The extreme success of stochastic optimization (SO) in large-scale machine learning problems, information retrieval, bioinformatics, etc., has been widely reported, especially in recent years. As an effective tactic, conjugate gradient (CG) has been gaining its popularity in accelerating SO algorithms. This paper develops a novel type of stochastic conjugate gradient descent (SCG) algorithms from the perspective of the Powerball strategy and the hypergradient descent (HD) technique. The crucial idea behind the resulting methods is inspired by pursuing the equilibrium of ordinary differential equations (ODEs). We elucidate the effect of the Powerball strategy in SCG algorithms. The introduction of HD, on the other side, makes the resulting methods work with an online learning rate. Meanwhile, we provide a comprehension of the theoretical results for the resulting algorithms under non-convex assumptions. As a byproduct, we bridge the gap between the learning rate and powered stochastic optimization (PSO) algorithms, which is still an open problem. Resorting to numerical experiments on numerous benchmark datasets, we test the parameter sensitivity of the proposed methods and demonstrate the superior performance of our new algorithms over state-of-the-art algorithms.
Graph Attention Neural Network Model With Behavior Features for Knowledge Tracking

The existing deep knowledge tracking models ignore the high-order relationship between students’ behavior features and questions with overlapping skills in the learning process. As a result, the models cannot learn the complete learning track of students and the dependence between students’ historical answer records and questions, which affect the predictive performance of the model. In order to solve the above problems, a graph attention neural network model with behavior features for knowledge tracking (GAKT-BF) is proposed in this paper. Firstly, GAKT-BF designs a student learning behavior feature information extraction module, constructs a learning behavior feature matrix, incorporates students’ behavior features into the questions representation, and designs a new questions representation method. Then, GAKT-BF designs a question relationship extraction module and constructs a graph of correlations between questions and questions, uses graph attention neural network (GAT) to extract question vector representations, and finally predicts students’ next answers through long short-term memory (LSTM). Experiments on Assistments 2009, KDD CUP and Assistment-17 datasets show that GAKT-BF has significantly improved in both evaluation metrics AUC and ACC compared with existing advanced knowledge tracking models, and has better prediction results.
NELLIE: Never-Ending Linking for Linked Open Data

Knowledge graphs (KGs) that follow the Linked Data principles are created daily. However, there are no holistic models for the Linked Open Data (LOD). Building these models( i.e., engineering a pipeline system) is still a big challenge in order to make the LOD vision comes true. In this paper, we address this challenge by presenting NELLIE, a pipeline architecture to build a chain of modules, in which each of our modules addresses one data augmentation challenge. The ultimate goal of the proposed architecture is to build a single fused knowledge graph out of the LOD. NELLIE starts by crawling the available knowledge graphs in the LOD cloud. It then finds a set of matching KG pairs. NELLIE uses a two-phase linking approach for each pair (first an ontology matching phase, then an instance matching phase). Based on the ontology and instance matching, NELLIE fuses each pair of knowledge graphs into a single knowledge graph. The resulting fused KG is then an ideal data source for knowledge-driven applications such as search engines, question answering, digital assistants and drug discovery. Our evaluation shows an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Hit</i> @1 score of the link prediction task on the resulting fused knowledge graph by NELLIE in up to 94.44% of the cases. Our evaluation also shows a runtime improvement by several orders of magnitude when comparing our two-phases linking approach with the estimated runtime of linking using a naïve approach.
Genomic Compression With Read Alignment at the Decoder

We propose a new compression scheme for genomic data given as sequence fragments called reads. The scheme uses a reference genome at the decoder side only, freeing the encoder from the burdens of storing references and performing computationally costly alignment operations. The main ingredient of the scheme is a multi-layer code construction, delivering to the decoder sufficient information to align the reads, correct their differences from the reference, validate their reconstruction, and correct reconstruction errors. The core of the method is the well-known concept of distributed source coding with decoder side information, fortified by a generalized-concatenation code construction enabling efficient embedding of all the information needed for reliable reconstruction. We first present the scheme for the case of substitution errors only between the reads and the reference, and then extend it to support reads with a single deletion and multiple substitutions. A central tool in this extension is a new distance metric that is shown analytically to improve alignment performance over existing distance metrics.
Leopard: Sublinear Verifier Inner Product Argument Under Discrete Logarithm Assumption

An inner product (IP) argument is a proof system that convinces the verifier of an IP relation between committed integer vectors. IP arguments are crucial building blocks for range proof and zero knowledge arguments, which can be applied to verifiable computation, confidential transactions, decentralized identification, and so on. This paper proposes a novel efficient IP argument with a trustless setup. For integer vectors of size N, the proposed IP argument provides a proof size of O(log2 N), a verification cost of O(√N), and a size of public parameter size of O(√N). The construction uses bilinear pairings and its security relies solely on the discrete logarithm (DL) assumption, a well-established standard cryptographic assumption. Consequently, we obtain the first DL-based IP argument with a trustless setup that achieves a sublinear verifier and logarithmic proof size, which we call Leopard. Furthermore, We empirically evaluate the performance of Leopard. The experimental results demonstrate that Leopard is highly efficient and scalable compared to previous works.
Digital Image Steganography With Error Correction on Extracted Data

The hiding capacity (HC), imperceptibility, and security are the 3 important quality measures for a steganography technique.While the stego-image is on transit on the internet, the hidden data may be changed because of various reasons.The existing techniques does neither focus on detecting the errors in the data nor to correct the errors in data.Therefore, this article brings forward a steganography technique, wherein error detection and correction can be performed at recipient side.The original image is logically sliced into 2×2 disjoint blocks.From these 4 pixels, 4 quotients and 4 least significant bits (LSBs) are generated.Each quotient is the decimal value of 7 most significant bits (MSBs) of a pixel.In every block 8 data bits can be camouflaged.From the 8 data bits, 4 redundant bits are computed using modified Hamming code.The 8 data bits and one redundant bit are camouflaged in the quotients by either quotient value differencing (QVD) or bit substitution.If camouflaging is performed in quotients using QVD, then indicator bit is set to1.Otherwise, if camouflaging in quotients is performed using bit substitution, then indicator bit is set to 0. The 3 remaining redundant bits along with the indicator bit are stored in the LSBs of the 4 pixels.At the receiver side, data could be extracted, and error correction procedure could be applied to correct 1-bit error over the 8 bits of data extracted from a block.From the experimental reports it could be concluded that the errors in the retrieved data at the recipient can be detected and corrected without reducing the HC and without increasing the distortion.
Interview with Alessio Antonini

Dr Alessio Antonini is a research fellow at the Knowledge Media Institute of the Open University (UK). He leads the "Knowledge Architectures" group that focuses on conceptual modeling for research and inter-organization infrastructures applied to, e.g., digital humanities and applied Artificial Intelligence to community and city digital innovations. His research focuses on the phenomenology and aesthetics of interactive and intelligent media, positive computing for digital healthcare and the design of ***Al-enabled health and well-being interventions. His methods focus on the scoping and design of technologies, connecting philosophy of mind, communication pragmatics, cognition and social ontology theories with information and data models, and software engineering requirements for such infrastructures. He is actively advocating and supporting the adoption of digital-informed methodologies and agenda in the Humanities, and Humanities interpretative methods in computer science.
Meta-Learning for Indian Languages: Performance Analysis and Improvements With Linguistic Similarity Measures

Indian languages share a lot of overlap in acoustic and linguistic content. Though different languages use different writing systems, the phoneme sets logically overlap. Most of these languages are low-resourced, lacking enough annotated speech data to build good automatic speech recognition (ASR) systems. Recently proposed model-agnostic meta-learning (MAML) algorithm has shown great success in the fast adaptation of multilingual models to unseen datasets. In this work, we establish the usefulness of MAML pretraining in quickly building reasonably good ASRs for low-resource Indian languages. MAML significantly outperforms joint multilingual training in its capability for few-shot learning and faster adaptation. On average, MAML yields absolute improvements of 5.4% in CER and 20.3% in WER over joint multilingual pretraining in the fast-adaptation setting with five epoch fine-tuning. Further, we exploit the similarities of the source transcriptions with target data through a loss-weighing scheme during the training to improve the performance of MAML models. Similarity-based loss-weighings yield absolute improvements of 0.2% in CER and 1% in WER on average.
MuTrans: Multiple Transformers for Fusing Feature Pyramid on 2D and 3D Object Detection

One of the major components of the neural network, the feature pyramid plays a vital part in perception tasks, like object detection in autonomous driving. But it is a challenge to fuse multi-level and multi-sensor feature pyramids for object detection. This paper proposes a simple yet effective framework named MuTrans (Mu ltiple Trans formers) to fuse feature pyramid in single-stream 2D detector or two-stream 3D detector. The MuTrans based on encoder-decoder focuses on the significant features via multiple Transformers. MuTrans encoder uses three innovative self-attention mechanisms: S patial-wise B oxAlign attention (SB) for low-level spatial locations, C ontext-wise A ffinity attention (CA) for high-level context information, and high-level attention for multi-level features. Then MuTrans decoder processes these significant proposals including the RoI and context affinity. Besides, the L ow and H igh-level F usion (LHF) in the encoder reduces the number of computational parameters. And the Pre-LN is utilized to accelerate the training convergence. LHF and Pre-LN are proven to reduce self-attention's computational complexity and slow training convergence. Our result demonstrates the higher detection accuracy of MuTrans than that of the baseline method, particularly in small object detection. MuTrans demonstrates a 2.1 higher detection accuracy on APS index in small object detection on MS-COCO 2017 with ResNeXt-101 backbone, a 2.18 higher 3D detection accuracy (moderate difficulty) for small object-pedestrian on KITTI, and 6.85 higher RC index (Town05 Long) on CARLA urban driving simulator platform.
Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach

In this work, we focus on solving a decentralized consensus problem in a private manner. Specifically, we consider a setting in which a group of nodes, connected through a network, aim at computing the mean of their local values without revealing those values to each other. The distributed consensus problem is a classic problem that has been extensively studied and its convergence characteristics are well-known. However, state-of-the-art consensus methods build on the idea of exchanging local information with neighboring nodes which leaks information about the users’ local values. We propose an algorithmic framework that is capable of achieving the convergence limit and rate of classic consensus algorithms while keeping the users’ local values private. The key idea of our proposed method is to carefully design noisy messages that are passed from each node to its neighbors such that the consensus algorithm still converges precisely to the average of local values, while a minimum amount of information about local values is leaked. We formalize this by precisely characterizing the mutual information between the private message of a node and all the messages that another adversary collects over time. We prove that our method is capable of preserving users’ privacy for any network without a so-called generalized leaf, and formalize the trade-off between privacy and convergence time. Unlike many private algorithms, any desired accuracy is achievable by our method, and the required level of privacy only affects the convergence time.
A Quality-of-Experience Database for Adaptive Omnidirectional Video Streaming

Recent advances in virtual reality (VR) technologies and devices have enabled new forms of media content, such as omnidirectional video (ODV) that attracts increasing attention of both academic and industrial communities. Omnidirectional video, which is also called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^\circ$</tex-math></inline-formula> video, represents panoramic spherical video that can give users an immersive viewing experience. Compared with traditional 2D video, the complex characteristic (high resolution, bandwidth intensive, etc.) of ODVs brings up new challenges to stream them under volatile network conditions and model the quality-of-experience (QoE) of end-users. To meet the requirements of practical VR applications, it is desired to design effective methods for adaptive bitrate streaming (ABR) and QoE evaluation of ODVs. In this article, we establish a large omnidirectional video streaming QoE database (VRQoE-JUFE), containing 1,440 adaptive streaming ODVs generated with diverse content. A comprehensive subjective experiment is conducted, where viewing behaviors and human opinions of total 180 subjects are collected. We provide a thorough statistical data analysis and carry out performance evaluation of the-state-of-art objective QoE models on the proposed database. The results suggest that QoE modeling for ODV streaming is an extremely challenging problem and there is a large space for improvement. Many interesting observations are made that may shed light on the improvement in both omnidirectional video QoE modeling and ABR strategies in the future. The annotated dataset from the tests is made publicly available for the research community.
The Impact of Mitigation Strategies for Socio-Cultural Distance Issues in GSD: An Empirical Study

Global Software Development (GSD) offers several benefits to software development organizations, including reduced development costs, the availability of low-wage and highly skilled employees, and an improved marketplace. Meanwhile, it faces severe communication, coordination, and control issues. The most important of these is the communication issue which is further categorized into socio-cultural, temporal, and geographical issues. Among these issues, researchers believe the socio-cultural issue is the most critical factor and, if not mitigated properly, may lead to software project failure. Although, in the past, many studies have identified socio-cultural distance-related issues, and a few studies proposed mitigation strategies. However, studies have yet to be carried out to prioritize and empirically evaluate all mitigation strategies. Thus the main objectives of this study are: a) to identify socio-cultural distance issues and mitigation strategies through a Systematic Literature Review (SLR), b) to empirically evaluate the impact of identified mitigation strategies on identified socio-cultural distance issues through a survey, and c) to prioritize effective mitigation strategies through a recommended Analytical Hierarchy Process (AHP). A total of six socio-cultural issues and twenty-eight mitigation strategies are identified from the SLR and survey. Out of which, seven mitigation strategies are most effective. This study’s findings will help software organizations to overcome socio-cultural distance issues by using the highest priority mitigation strategies to reduce losses.
An Insight to Estimated Item Response Matrix in Item Response Theory

This paper investigates the performance of item response theory based on distance criteria rather than likelihood criteria. For this purpose, the estimated item response matrix is introduced. This matrix is a reconstruction of the item response matrix using maximum likelihood estimates of the parameters in item response theory. Then the distance between the observed and estimated matrices can be determined using the Frobenius matrix norm. An approximated low-rank matrix can be generated from the observed item response matrix by singular value decomposition, and the distance between the observed and low-rank matrices can be obtained in the same way. By comparing these two distances, we can evaluate the performance of the estimated item response matrix comparable to the performance of an approximated low-rank matrix. Applying this comparison to actual examination data, it is found that the rank of the approximated low-rank matrix that is equivalent to the estimated item response matrix is very low when using matrices as training data. However, using test data, the predictive ability of item response theory seems high enough since the minimum distance between the approximated low-rank matrix and the observed item response matrix is approximately equal to or slightly less than the distance between the estimated item response matrix and the observed item response matrix. This fact has been first discovered by utilizing the estimated item response matrix defined here.