Reviewerly/exHarmony huggingface dataset:
LZ4r - A New Fast Compression Algorithm for High-Speed Data Storage Systems

LZ4 data compression algorithm is the current state-of-art compression algorithm in the high speed compression algorithm class, and has been adopted and integrated by lots modern high-speed data storage systems. We propose a fast lossless compression algorithm, named LZ4r. A new format of the data sequence is designed, and by integrating it into the proposed algorithm, a better compression ratio than LZ4 is achieved. Numerous evaluation tests are conducted with different sets of data corpus. The results consistently show that LZ4r gains a significant improvement in compression ratio than LZ4, with a similar high compression speed. Although LZ4r is slower than LZ4 in decompression speed, the decompression speed of LZ4r is still fast enough not to reduce the overall performance of the system. Thus, LZ4r can become a practical and competitive alternative or replacement of LZ4 in many high-speed data storage systems to improve the overall performance and lower the overall cost. More details about LZ4r algorithm design and performance evaluation can be found at [1].
Constructing the CDAWG CFG using LCP-Intervals

It is known that a context-free grammar (CFG) that produces a single string can be derived from the compact directed acyclic word graph (CDAWG) for the same string. In this work, we show that the CFG derived from a CDAWG is deeply connected to the maximal repeat content of the string it produces and thus has O(m) rules, where m is the number of maximal repeats in the string. We then provide a generic algorithm based on this insight for constructing the CFG from the LCP-intervals of a string in O(n) time, where n is the length of the string. This includes a novel data-structure to support stabbing queries on LCPintervals in O(1+k) time after O(n) preprocessing time, where k is the number of intervals stabbed. These results connect the CFG to properties of the string it produces and relates it to other string data-structures, allowing it to be studied independently of the CDAWG and providing opportunity for innovation of grammar-based compression algorithms.
Invert-and-project (IVP): A Lossless Compression Method of Multi-scale JPEG Images via DCT Coefficients Prediction

JPEG is a widely used format for images. Most JPEG variants are based upon a block-based DCT transformation followed by quantization and entropy coding. Redundancy at row/column level is explored in [1]. Brunsli [2] and Lepton [3], lossless JPEG repacking libraries, explore redundancy at block level.
Fully Integrated Silicon Photonic Tensor Core for Next-Generation Applications

Here we present our architecture for Silicon Photonic Tensor Core, capable of responding to the needs of Neural Networks, Augmented and Virtual Reality applications. We present a novel version fully integrated, from lasers to photodetectors.
Demonstration of Voice User Interface for Intelligent Network Orchestration

We demonstrate a voice user interface for ONOS, where natural language processing (NLP) is applied to translate human-spoken language into ONOS northbound API requests and to answer network-related questions with numerical reasoning.
Real-time 50-Gbit/s Spatially Multiplexed Quantum Random Number Generator Based on Vacuum Fluctuation

We demonstrate four-channel parallel high-speed random number generation using a quantum entropy source based on vacuum fluctuations and a real-time FPGA random number extractor. A record-high aggregated generation rate of 50 Gb/s is achieved.
Experimental Demonstration of Optical Modulation Format Identification Using SOI-based Photonic Reservoir

We experimentally show modulation format identification of optical signals using silicon-on-insulator (SOI) photonic-integrated-circuit-based reservoir. After 100-km SSMF transmission, we achieve identification of 32 GBd 4QAM, 16QAM, 32QAM and 64QAM signals with up to ~97% accuracy.
Spiking Neural Network Linear Equalization: Experimental Demonstration of 2km 100Gb/s IM/DD PAM4 Optical Transmission

A linear feed-forward equalizer is implemented by a potentially low-power spiking neural network. For a 100Gb/s PAM-4 IM/DD optical 2km transmission, no performance penalty compared to a digital implementation is observed.
Constrained Autoencoders: Incorporating equality constraints in learned scientific data compression

In scientific data compression, it is crucial to preserve Quantities of Interest (QoI) derived from the data for accurate post-analysis of scientific applications. In this work, we present Constrained Autoencoders (CAEs) where we impose linear QoI as constraints on neural network activations. We circumvent the difficulty of using standard convex optimization methods on the output predictor in the context of autoencoder-driven compression.
On Stochastic Codebook Generation for Markov Sources

This paper proposes an effective universal (on-the-Hy’’ mechanism for stochastic codebook generation in lossy coding of Markov sources. Earlier work has shown that the ratedistortion bound can be asymptotically achieved by a “natural type selection” (NTS) mechanism that iteratively considers asymptotically long source strings (from an unknown distribution P) and regenerates the codebook from a distribution obtained within a maximum likelihood distribution estimation framework, based on observation of a set of K codewords that “d-match’’ (i.e., satisfy the distortion constraint for) a respective set of K independently generated source words. This result was later generalized, in a straightforward manner, to account for source memory, by considering the source as a vector source, i.e., a sequence of super-symbols from a corresponding super-alphabet. While ensuring asymptotic optimality, this extension suffered from a significant practical flaw: it requires asymptotically long vectors or super-symbols, hence exponentially large super-alphabet, in order to approach the rate-distortion bound, even for finite memory sources, e.g., Markov sources. Such exponentially large super-alphabet implies that even a single NTS iteration is intractable, thus compromising the promise of NTS to approach the rate-distortion function, in practice, for sources with memory. This work describes a considerably more efficient and tractable mechanism to achieve asymptotically optimal performance given a prescribed memory constraint, within a practical framework tailored to Markov sources. Specifically, the algorithm finds, asymptotically, the optimal codebook reproduction distribution, within a constrained set of distributions satisfying a prescribed Markovian property, e.g., of the same order as the source, which achieves the minimum per letter coding rate while maintaining a specified distortion level.
A debanding algorithm for AV2

Banding is a visually unpleasing artifact appearing in flat areas of encoded content that no video standard has fully addressed. We propose a normative debanding filter to tackle banding artifacts and have tested it as an inloop and post-loop filter in AVM. Debanding is achieved by introducing dithering on a frame level to the luma component. The proposed filter shows CAMBI gains for content with banding while not affecting other content. Although the added dithering has a minor negative impact on some objective metrics, subjective improvements in banding-prone content are (informally) observed. On the test set, encoding time increases on average by ~0.5%, while decoding time increases by around 0.5% for in-loop and 1.5% for post-loop.
Deep Reservoir Computing for 100 Gbaud PAM6 IM/DD Transmission Impairment Mitigation

We experimentally evaluate a deep Reservoir Computing (RC)-based post-equalization for 100 Gbaud PAM6 IM/DD transmissions. It achieves ~1 dB higher sensitivity than DFE, and ~50% implementation complexity reduction compared with the conventional RC configuration. © 2022 The Author(s)
Bit-Parallel (Compressed) Wavelet Tree Construction

The wavelet tree is a data structure that indexes a text over an integer alphabet for efficient rank and select queries. Using the Huffman encoding, it can be stored in zero-order entropycompressed space. We present a highly engineered open source implementation of an efficient sequential construction algorithm that makes use of bit parallelism via vector instructions. On hardware featuring ultrawide registers of up to 512 bits, it outperforms the currently fastest known practical sequential construction algorithms by a factor of up to 2.5.
Contextual Pattern Matching in Less Space

We revisit the Contextual Pattern Matching Problem, defined as follows: preprocess a text T[1, n], so that given a query consisting of a string P and a length P, the occurrences of all distinct strings XPY where |X|=|Y|=P can be reported. This problem was introduced by Navarro, who presented an O($\overline{r}\log(n/\overline{r}))$ space data structure, where $\overline{r}$ is the maximum of the number of runs in the BWT of the text $\mathrm{T}[1,n]$ and its reverse. His solution reports all c contextual occurrences in $O(|P|+c\log n)$ time. However, the only known bounds on $\overline{r}$ are $\overline{r}=O(r\log^{2}n)$ where r is the number of runs in the BWT of T, making it desirable to avoid using structures with space dependent on $\overline{r}$. We demonstrate that this is possible without a significant sacrifice in query time by providing an $O(r\log(n/r))$ space solution that answers queries in $O(|P|+c\log P\cdot\log(n/r))$ time.
Model Compression for Data Compression: Neural Network Based Lossless Compressor Made Practical

In the recent few years, lossless compressors based on deep learning emerged as a new research direction. However, existing DL compressors focus mostly on compression ratio at the expense of compression speed (typically 10-1000 times slower than popular compressors, operating at the KB/s level), rendering them impractical to be deployed in industrial settings. To address the throughput issue of network inference, we propose to utilize model compression in DL-based lossless data compression. Our approach originates from the sparse learning framework to flexibly balance the model complexity and the compression ratio. Through systematic numerical experiments, we found that compressed models with only tens of thousands of parameters can still retain competitive compression ratios comparing with the original large models which are typically 100 times larger. The proposed scheme reveals novel properties concerning deep compression models and data–logarithmic relations between the model size and compressed data size for general mixed data, and a turning point indicating minimum representation size for relatively formatted data.
Demonstration of Quantum Channel Monitoring via Quantum Wrappers

We experimentally demonstrate quantum channel monitoring by wavelength-time multiplexing of classical wrapper bits with quantum payloads. Bit-error-rate measurements of 5 Gb/s classical bits infer the coincidence-to-accidental ratio of the quantum channel up to 13.3 dB.
Artificial Intelligence Using Complex Photonics: Decision Making and Reservoir Computing

We overview recent development on photonic decision making and reservoir computing for artificial intelligence using complex photonics. Parallel implementations of photonic devices can accelerate information processing in decision making and reservoir computing.
Fully Integerized End-to-End Learned Image Compression

End-to-end learned image compression (LIC) has become promising alternatives for lossy image compression. However, deployments of LIC models are limited in real-world applications due to excessive network parameters and high computational complexity. Existing LIC models realized with integer networks are significantly de-graded in rate-distortion (R-D) performance. In this paper, we propose a novel fully integerized LIC model that simultaneously achieves channel-wise weight and 8-bit activation quantization for alleviating the loss of R-D performance. For weight quantization, we develop an internal bit width increment (IBWI) via nonlinear logarithmic mapping to convert the stored low-precision integer weights to high bit-width weights for inference. Moreover, outlier channel splitting (OCS) [1] is employed to address large outliers of weight distribution by duplicating channels and constrain the integer weights within the scope of INT8. For activation quantization, we leverage activation equalization to balance the channel-wise distribution of activations. Experimental results demonstrate that the proposed method achieves a reduction of 75% storage cost with subtle performance loss compared to full-precision pre-trained models, and outperforms existing integer-only networks, as shown in Table 1.
High performance predictor blending lossless image coder

In the paper a highly efficient algorithm for lossless image coding is described. The algorithm is a predictor blending one, a sample estimate is computed as a weighted sum of estimates given by subpredictors, here 27 ones, hence the name Blend-27. The subpredictors varies from 15 based on 1-3 neighbouring pixels, to much more powerful even if working alone. The predictor blending formula is adaptive, and favors locally the best sub-predictors in a neighborhood. The algorithm is a cascade one: some sub-predictors are followed by two consecutive NLMS filters. Additionaly, in the fourth cascade stage sub-predictor bias cancellation can be done. Update formulas on NLMS coefficients and description of twelve bias canceling methods can be found in [1]. Entropy coder is a highly sophisticated arithmetic one, it is described in [1].
AsyncFedKD: Asynchronous Federated Learning with Knowledge Distillation

Federated learning (FL) allows for the decentralized training of a global model on edge devices without transferring data samples, thus preserving privacy. Due to the ubiquitous wearable devices and mobile devices with health applications, FL has shown promise in the medical field for applications such as medical imaging, disease diagnosis, and electronic health record (EHR) analysis. However, slower edge devices with limited resources can slow down the training process. To address this issue and increase efficiency, we propose the use of Asynchronous Federated Learning with Knowledge Distillation (AsyncFedKD). AsyncFedKD asynchronously trains a lightweight global student model using a pre-trained teacher model, preventing a decrease in training efficiency due to slow edge devices. The knowledge distillation aspect of AsyncFedKD effectively compresses the size of model parameters for efficient communication during training. AsyncFedKD has been tested on a sensitive mammography cancer dataset and achieved an accuracy of 88% on the global model.
Short: Precision Polysubstance Use Episode Detection in Wearable Biosensor Data Streams

Wearable biosensors create the opportunity for continuous health monitoring by generating streams of measurements that reflect users' physiological conditions in natural environments. Continuous health monitoring is a key enabler of precision health, a means to detect individual-level changes early and initiate personalized preventive measures or other clinical interventions. Although the amount of data generated is theoretically unbounded, precise labeling is rare outside of controlled clinical environments. Using data streams from a study of patients recovering from cocaine use disorder, we demonstrate early results of a novel method to detect polysubstance use without precisely labeled training data using an anomaly detection paradigm. RP-STREAM performed better than an alternative in detecting polysubstance use in wearable biosensor data streams. The proposed semi-supervised learning model makes efficient use of training data and computational resources while also automating parameter selection. We also identify the effects of THC and cocaine polysubstance use in wearable biosensor data.
AI-Based Sign Language Interpreter (GestPret)

Hearing, speech, and sight are the three key factors that aid in communication. Hearing and speech impaired people often find it hard to communicate their emotions because they lack two of the three essential communication requirements. The communication gap persists despite the availability of sign language (hand gestures used for communication), as sign language is incomprehensible to those who are not impaired. GestPret (Gesture Interpreter) is a portable stand-alone device, suitable for all ages, designed to bridge the communication gap between signers and non- signers. GestPret is an AI-based translator that transforms Arabic sign language gestures into text in real time. Initially, the user creates a hand gesture in front of the camera which will be captured and processed using image processing. The Graphical user interface allows the users to easily navigate through the software to utilize its features. GestPret operates at 9.3 frames per second and generates results in real time. The trained model also assigns the sign to one of 41 distinct classes with an average classification accuracy of 94.6%.
A Novel Mobile Vision Based Technique for 3D Human Pose Estimation

In this work, we propose a novel technique for accurately constructing 3D human poses based on mobile phone camera recordings. From the originally recorded video frames by a mobile phone camera, firstly a mask R-CNN network is applied to detect the human body and extract 2D body skeletons. Based on the 2D skeletons, a temporal convolutional network (TCN) is then applied to lift 2D skeletons to 3D ones for the 3D human pose estimation. From the experimental evaluations, it is shown that 3D human poses can be accurately reconstructed by the proposed technique in this work based on mobile phone camera recordings while the reconstruction result is very close to the one by a specialized motion capture system.
Supporting Practical URI Mappings in Virtual Knowledge Graph-based Relational Data Integration

In this paper, we address the problem of mapping identifiers in non-RDF data to URIs. Virtual knowledge graphs (VKGs), where non-RDF data, such as relational databases, CSV files, etc., are published as RDF data, allowing users to access them using a standard query language (SPARQL), has been gaining much attention to integrating heterogeneous data. There have been several VKG systems, but there has been a problem of assigning an appropriate URI to an entity included in a record, and existing systems only support simple methods to generate a URI by adding a URI prefix to the ID value in a record. However, in practice, more complex mappings are needed to meet the demands of real applications. To address this problem, we proposed to extend the relation-to-RDF mapping rules where users are allowed to specify how entities in relations are mapped to URIs in terms of a user-defined URI function. More precisely, we integrate this method into our relation-to-RDF mapping framework. We conduct a set of experiments to assess the feasibility of the proposed method.
Multi-scale Heterogeneous Graph Contrastive Learning<sup>*</sup>

In recent years, heterogeneous graph neural networks have become the mainstream approach for handling heterogeneous graph data. However, due to the sparsity of labels, most existing methods for heterogeneous graph neural networks typically employ a semi-supervised learning approach, which has certain limitations in practical applications. To address this issue, we propose a self-supervised heterogeneous graph representation learning method, namely Multi-scale Heterogeneous Graph Contrastive Learning (MHGCL). This approach decodes encoded information from two perspectives: meta-paths and network patterns, in a multi-scale fashion. It uses a loss function that maximizes the similarity between positive pairs at different scales and minimizes the similarity between negative pairs. This encourages related nodes and edges to be close to each other in the embedding space, while unrelated nodes and edges are pushed farther apart. Experimental results demonstrate that MHGCL comprehensively captures semantic information at different scales between nodes. It exhibits excellent performance in node classification tasks, validating its effectiveness in heterogeneous graph node embedding learning.
Study and Survey on Gesture Recognition Systems

In recent years, there has been a considerable amount of research in the Gesture Recognition domain, mainly owing to the technological advancements in Computer Vision. Various new applications have been conceptualised and developed in this field. This paper discusses the implementation of gesture recognition systems in multiple sectors such as gaming, healthcare, home appliances, industrial robots, and virtual reality. Different methodologies for capturing gestures are compared and contrasted throughout this survey. Various data sources and data acquisition techniques have been discussed. The role of gestures in sign language has been studied and existing approaches have been reviewed. Common challenges faced while building gesture recognition systems have also been explored.
Age and Gender Detection using Deep Learning

Automatic age and gender prediction from face photos have received a lot of attention due to its use in a variety of facial analysis applications, including access control, marketing intelligence, enforcement, and visual surveillance. These are also the two facial attributes that play a pivotal part in social relations. Different facial features are uprooted using the Deep Convolution neural network. It works efficiently indeed with limited data. In a way, CNNs are regularized multilayer feedforward. In recent years, CNNs have been successfully applied to the task of age and gender detection. The ability of CNNs to automatically learn features relevant to the job at hand eliminates the need for manual feature engineering, which is one of their key advantages. Additionally, CNNs can be trained on large datasets, which allows them to generalize well to new, unseen data. Purpose of this research paper is to develop an algorithm that estimates the age and gender of a person correctly. In this research, we propose the use of a CNN for the task of age and gender detection. To train the network, we used a dataset consisting of images of faces along with their corresponding age and gender labels. This paper is divided into 5 sections, Section-1 contains introduction, Section-2 contains Literature Review/ Previous work, Section-3 contains Methodology, Section-4 Result and Discussion and finally section-5 contains Conclusion and Future Scope
Unsupervised Style Transfer of Modern Hebrew using Generative Language Modeling and Zero-Shot Prompting

Style transfer is one of the most intriguing hallmarks of natural language processing. It involves the semantic preserving conversion of artistic “style”. Style transfer of the Hebrew language is an exceptionally challenging task due to the language’s intricate morphology, inflectional structure, and orthography, which have undergone significant transformations throughout its history. In this work, we present the first generative language model for unsupervised textual style transfer for modern Hebrew, which rewrites sentences in a target style in the absence of parallel style corpora. We create a pseudo-parallel corpus through back translation, fine-tunes a pre-trained Hebrew language model, and leverages zero-shot learning. Our results demonstrate the first significant results in Hebrew style transfer in terms of transfer accuracy, semantic similarity, and fluency.