Reviewerly/exHarmony huggingface dataset:
DE-DARTS: Neural architecture search with dynamic exploration

Neural architecture search (NAS) methods automatically find optimal neural networks without human assistance. Numerous algorithms for NAS have been studied to find architectures with gradient-based search. Differentiable architecture search (DARTS), one of the key papers of gradient-based search, dramatically reduced search cost, and showed outstanding performance through continuous relaxation and meta-learning based approximation. However, one of the issues with DARTS is that the gradient-based search process is biased due to the nested bi-level optimization structure, and the greedy behavior of the gradient descent. As a result, there is a problem that search spaces are limited to a limited set of architectures. To overcome the bias of the gradient-based search in network architecture search (NAS), we used a dynamic search method. This technique allows gradient-based search to have exploration. In this paper, we present a novel approach, namely, Dynamic-Exploration DARTS (DE-DARTS). For effective exploration, we use dynamic attention networks (DANs) in DE-DARTS, which change model architectures based on input data. As our DANs are activated early in the search, more diverse architectures are considered, depending on input data at the beginning of search. Our algorithm is evaluated in multiple image classification datasets including CIFAR-10, and ImageNet, and shows improved performance.
Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI

Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean.However, such learning, particularly in Markov-based query rewriting systems have far from addressed the impact of these models on future training where successive feedback is inevitably contingent on the rewrite itself, especially in a continually updating environment.In this paper, we explore the consequences of this inherent lack of selfawareness towards impairing the model performance, ultimately resulting in both Type I and II errors over time.To that end, we propose augmenting the Markov Graph construction with a superposition-based adjacency matrix.Here, our method leverages an induced stochasticity to reactively learn a locally-adaptive decision boundary based on the performance of the individual rewrites in a bi-variate beta setting.We also surface a data augmentation strategy that leverages template-based generation in abridging complex conversation hierarchies of dialogs so as to simplify the learning process.All in all, we demonstrate that our selfaware model improves the overall PR-AUC by 27.45%, achieves a relative defect reduction of up to 31.22%, and is able to adapt quicker to changes in global preferences across a large number of customers.
A high-performance compilation strategy for multiplexing quantum control architecture

Quantum computers have already shown significant potential to solve specific problems more efficiently than conventional supercomputers. A major challenge towards noisy intermediate-scale quantum computing is characterizing and reducing the various control costs. Quantum programming describes the process of quantum computation as a sequence, whose elements are selected from a finite set of universal quantum gates. Quantum compilation translates quantum programs to ordered pulses to the quantum control devices subsequently and quantum compilation optimization provides a high-level solution to reduce the control cost efficiently. Here, we propose a high-performance compilation strategy for multiplexing quantum control architecture. For representative benchmarks, the utilization efficiency of control devices increased by 49.44% on average in our work, with an acceptable circuit depth expansion executing on several real superconducting quantum computers of IBM.
Multi-Layer Web Services Discovery Using Word Embedding and Clustering Techniques

We propose a multi-layer data mining architecture for web services discovery using word embedding and clustering techniques to improve the web service discovery process. The proposed architecture consists of five layers: web services description and data preprocessing; word embedding and representation; syntactic similarity; semantic similarity; and clustering. In the first layer, we identify the steps to parse and preprocess the web services documents. In the second layer, Bag of Words with Term Frequency–Inverse Document Frequency and three word-embedding models are employed for web services representation. In the third layer, four distance measures, namely, Cosine, Euclidean, Minkowski, and Word Mover, are considered to find the similarities between Web services documents. In layer four, WordNet and Normalized Google Distance are employed to represent and find the similarity between web services documents. Finally, in the fifth layer, three clustering algorithms, namely, affinity propagation, K-means, and hierarchical agglomerative clustering, are investigated for clustering of web services based on observed similarities in documents. We demonstrate how each component of the five layers is employed in web services clustering using randomly selected web services documents. We conduct experimental analysis to cluster web services using a collected dataset consisting of web services documents and evaluate their clustering performances. Using a ground truth for evaluation purposes, we observe that clusters built based on the word embedding models performed better than those built using the Bag of Words with Term Frequency–Inverse Document Frequency model. Among the three word embedding models, the pre-trained Word2Vec’s skip-gram model reported higher performance in clustering web services. Among the three semantic similarity measures, path-based WordNet similarity reported higher clustering performance. By considering the different word representations models and syntactic and semantic similarity measures, we found that the affinity propagation clustering technique performed better in discovering similarities among Web services.
Picture Quality of 360° Images Compressed by Emerging Compression Algorithms

Recently, interest about images and photos in omni-directional format (360°, field-of-view) are gradually increasing. To ensure high image quality at the transfer, storing and sharing of such images, advanced compression tools are required. This paper provides an objective-based comparison of emerging compression algorithms (JPEG XL, HEIC, AVIF), implemented in a graphic viewer and editor, for 360° images. For this purpose, four images from publicly available databases and two self-made images are used and compressed to different quality levels. Different objective metrics are used to evaluate the visual quality of images. Compared to the popular image-based compression algorithm JPEG, the emerging compression tools show high efficiency and applicability for omnidirectional images.
Revisiting network congestion avoidance through adaptive packet-chaining reservation

Endpoint congestion is a bottleneck in high-performance computing (HPC) networks, which severely impacts system performance, especially for latency-sensitive applications. When the long messages (or flows) has a far larger duration than the round-trip time (RTT), the proactive or reactive countermeasures, an effective solution to endpoint solution, can control the injection rate within a proper range dynamically. However, many HPC applications produce hybrid traffic (a mix of short and long messages) and are dominated by short messages. Existing proactive congestion avoidance methods face the great challenge of scheduling the rapidly changing traffic caused by these short messages. In this paper, we first propose the Packet-Chaining Reservation Protocol (PCRP), that is, a novel congestion management strategy which leverages the advantages of proactive (scheduling the whole flow) and reactive (scheduling the single packet) congestion avoidance techniques. In fact, it is an eclectic method of scheduling. We select the chaining of packets as a flexible reservation granularity between the whole flow and a single packet. The PCRP allows small flows to be speculatively transmitted without being discarded. It also gives the small flows an appropriate priority based on the detected traffic conditions. The PCRP can make a quick respond to network conditions, effectively avoiding endpoint congestion and reducing the average flow delay. However, PCRP is only suitable for short-flow-dominant traffic, as it performs poorly when facing other traffic. This is because PCRP can starve longer flows and thus introduces the unbearable tail delay. Therefore, we further propose the Packet-Chaining Reservation Protocol with Adaptive Framework (PCRP+), which is a reservation protocol that can flexibly adjust the scheduling strategy according to the network load. The PCRP+ can achieve and maintain low tail delay and desirable universality by adopting the adaptive framework. We conduct extensive experiments to evaluate the PCRP+ and compare it with the Speculative Reservation Protocol (SRP) and Bilateral Flow Reservation Protocol (BFRP), the two most typical proactive reservation-based protocols. Evaluation results demonstrate that our design can reduce the flow latency by an average of 25.71%, 21.21%, and 29.01% for hotspot traffic, uniform traffic, and GPCNeT traffic, respectively.
Translating medical image to radiological report: Adaptive multilevel multi-attention approach

Medical imaging techniques are widely employed in disease diagnosis and treatment. A readily available medical report can be a useful tool in assisting an expert for investigating the patient's health. A radiologist can benefit from an automatic medical image to radiological report translation system while preparing a final report. Previous attempts on automatic medical report generation task includes image captioning algorithms without taking domain-specific visual and textual contents into account, thus arises the question about credibility of generated report.In this work, a novel Adaptive Multilevel Multi-Attention (AMLMA) approach is proposed by offering domain-specific visual-textual knowledge to generate a thorough and believable radiological report for any view of a human chest X-ray image. The proposed approach leverages the encoder-decoder framework incorporated with multiple adaptive attention mechanisms. The potential of a convolutional neural network (CNN) with residual attention module (RAM) is demonstrated as a strong visual encoder for multi-label abnormality detection. The multilevel visual features (local and global) are extracted from proposed visual encoder to retrieve regional-level and abstract-level radiology-based semantic information. The Word2Vec and FastText word embeddings are trained on medical reports to acquire radiological knowledge and further used as textual encoders, feeding as input to Bi-directional Long Short Term Memory (Bi-LSTM) network to learn the co-relationship between medical terminologies in radiological reports. The AMLMA employs a weighted multilevel association of adaptive visual-semantic attention and visual-based linguistic attention mechanisms. This association of adaptive attention is exploited as a decoder and produces significant improvements in the report generation task.The proposed approach is evaluated on a publicly available Indiana University chest X-ray (IU-CXR) dataset. The CNN with RAM shows the significant improvement in recall (0.4423), precision (0.1803) and F1-score (0.2551) for prediction of multiple abnormalities in X-ray image. The results of language generation metrics for proposed variants were acquired using the COCO-caption evaluation Application Program Interface (API). The trained embeddings with AMLMA model generates the convincing radiology report and outperform state-of-the-art (SOTA) approaches with high evaluation metrics scores for Bleu-4 (0.172), Meteor (0.247), Rouge_L (0.376) and CIDEr (0.381). In addition, a new "Unique Index" (UI) statistic is introduced to highlight the model's ability for generating unique reports.The overall architecture aids to the understanding of various X-ray image views and generating the relevant normal and abnormal radiography statements. The proposed model is emphasized on multi-level visual-textual knowledge with adaptive attention mechanism to balance visual and linguistic information for the generation of admissible radiology report.
Poster Abstract: Learning from Demonstrations with Temporal Logics

Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, we propose to use Signal Temporal Logic (STL) to express high-level robotic tasks and use its quantitative semantics to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards, and are also capable of defining interesting causal dependencies between tasks such as sequential task specifications. We present our completed work that proposed LfD-STL framework that learns from even suboptimal/imperfect demonstrations and STL specifications to infer rewards for reinforcement learning tasks. We have validated our approach through various experimental setups to show how our method outperforms prior LfD methods. We then discuss future directions for tackling the problem of explainability and interpretability in such learning-based systems.
High dimensional autonomous computing on Arabic language classification

Hyper vectors are holographic and randomly processed with independent and identically distributed tools. A hyper vector includes whole data merged as well as spread completely on its pieces as an encompassing portrayal. So, no spot is more dependable to store any snippet of data compared to others. Hyper vectors are joined with tasks likened to expansion, and changed the structure of numerical processing on vector regions. Hyper vectors are intended to analyze the closeness utilizing a separation metric over the vector region. These activities are nothing but hyper vectors in which it can be joined into intriguing processing conduct with novel highlights which make them vigorous and proficient. This paper focuses on a utilization of hyper dimensional processing for distinguishing the language of text tests for encoding sequential letters into hyper vectors. Perceiving the language of a given book is the initial phase in all sorts of language handling. Examples: text examination, arrangement, and interpretation. High dimension vector models are mainstream in Natural Language Processing and are utilized to catch word significance from word insights. In this research work, the first task is high dimensional computing classification, based on Arabic datasets which contain three datasets such as Arabiya, Khaleej and Akhbarona. High dimensional computing is applied to obtain the results from the previous dataset when it is applied to N-gram encoding. When utilizing SANAD single-label Arabic news articles datasets with 12 N-gram encoding, the accuracy of high computing is 0.9665%. The high dimensional computing with 6 N-gram encoding while utilizing RTA dataset, provides the accuracy of 0.6648%. ANT dataset with 12 N-gram encoding in high dimensional computing gives the accuracy 0.9248%. The second task is applying high dimensional computing on Arabic language recognition for Levantine dialects three dataset is utilized. The first dataset is SDC Shami Dialects Corpus which contains Jordanian, Lebanese, Palestinian and Syrian. The same provides an accuracy of 0.8234% while it is applied to high dimensional computing with 7 N-gram encoding. PADIC (Parallel Arabic dialect corpus) is the second dataset which contains Syria and Palestine Arabic dialects that provide an accuracy of 0.7458% when applied high dimensional computing with 5 N-gram encoding. The high dimensional computing when applied to third dataset MADAR (Multi-Arabic dialect applications and resources) with 6 N-gram encoding provides the accuracy rate of 0.7800%.
Phenonizer: A Fine-Grained Phenotypic Named Entity Recognizer for Chinese Clinical Texts

Biomedical named entity recognition (BioNER) from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities (e.g., symptoms, diseases, and laboratory indexes), which could be used for profiling the clinical characteristics of patients in specific disease conditions (e.g., Coronavirus Disease 2019 (COVID-19)). However, general BioNER approaches mostly rely on coarse-grained annotations of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g., "no fever," "no cough," and "no hypertension") in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. In this paper, we developed Human-machine Cooperative Phenotypic Spectrum Annotation System (http://www.tcmai.org/login, HCPSAS) and constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer: Phenonizer, which utilized BERT to capture character-level global contextual representation, extracted local contextual features combined with bidirectional long short-term memory, and finally obtained the optimal label sequences through conditional random field. The results on COVID-19 dataset show that Phenonizer outperforms those methods based on Word2Vec with an F1-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. In addition, we evaluated Phenonizer on two kinds of granular datasets and proved that fine-grained dataset can boost methods' F1-score slightly by about 0.005. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms. Finally, we tested the generalization performance of Phenonizer, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonizer proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.
Secure Storage System Using Cryptographic Techniques

In the era of Internet usage growth, storage services are widely used where users' can store their data, while hackers techniques pose massive threats to users' data security. The proposed system introduces multiple layers of security where data confidentiality, integrity and availability are achieved using honey encryption, hashed random passwords as well as detecting intruders and preventing them. The used techniques can ensure security against brute force and denial of service attacks. Our proposed methodology proofs the efficiency for storing and retrieving data using honey words and password hashing with less execution time and more security features achieved compared with other systems. Other systems depend on user password leading to easily predict it, we avoid this approach by making the password given to the user is randomly generated which make it unpredictable and hard to break. Moreover, we created a simple user interface to interact with users to take their inputs and store them along with the given password in true database, if an adversary detected, he will be processed as a normal user but with fake information taken from another database called false database, after that, the admin will be notified about this illegitimate access by providing the IP address. This approach will make the admin have continuous detection and ensure availability and confidentiality. Our execution time is efficient as the encryption process takes 244 ms and decryption 229 ms.
MulSetRank: Multiple set ranking for personalized recommendation from implicit feedback

Learning user preferences from implicit feedback through collaborative ranking is an approach that has received increased attention from researchers in recent years. The existing approaches focus on modeling the ranking differences between observed and unobserved items for independent individuals, ignoring their collaborative effects on the social recommendation. However, homophily theory suggests that, for a specific user, potential preference items can be mined from its friends or users with similar interests. Motivated by this observation, this paper presents a novel setwise ranking model that considers users’ preference rankings among multiple sets of items. Unlike existing models, which only consider the preference differences between observed and unobserved items, our model assumes that some potential preference items are in-between. We propose a collaborative method that exploits users’ behavioral similarities to mine users’ potential preference items. Our approach allows us to capture users’ collaborative signals at a finer granularity. We also develop a sampling method to efficiently compute the setwise preference probability. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of the proposed model based on several benchmark datasets. The experimental results demonstrate that our approach outperforms the state-of-the-art methods. • We propose a novel setwise recommendation framework. • We develop a new scheme to mine users’ potential preference items. • We propose a sampling scheme to approximate the top one probability. • We conduct extensive experiments to validate the effectiveness.
A single smartwatch-based segmentation approach in human activity recognition

The development of smart wearable devices has driven the rapid progress of activity recognition. However, existing activity recognition methods are still struggling to recognize single arm swings due to coarse-grained sensor data segmentation. Refined arm-swing-wise data segmentation is vital in some specific cases, such as the rehabilitation of disabled patients. In this paper, we propose a smartwatch-based arm-swing-wise data segmentation approach for human activity recognition, which converts original sensor signals into square-wave signals to detect the cut-off points of each arm swing. Particularly, our method can adaptively adjust the window size and step size of a sliding window without considering the change of swing speed. Empirical evaluation on two datasets, a self-collected dataset and a publicly-available benchmark dataset, shows superior performance of our approach over other methods under different settings, such as classifiers, features, and wearing positions.
Multiwavelet toolbox for MATLAB

The Wavelet transform in the last decades gained a lot of popularity among researchers for its properties and abilities. The main subject of this article is the Multiwavelet transform. The Multiwavelet transform derivates from the Wavelet transform and has the same properties and abilities while providing additional properties and abilities, which the Wavelet transform cannot achieve. There exists a lot of freely available and easy-to-use tools to implement Wavelet transform. This cannot be said about the Multiwavelet transform. To overcome this problem and make the Multiwavelet transform more accessible in the signal processing field, the Multiwavelet toolbox for Matlab is proposed in the article. The article consists of two main parts. The first part is focused on Multiwavelet transform description and particularly on its convolution implementation. The second part describes the proposed toolbox and shows some examples of its use. The proposed toolbox consists of more than twenty-five original functions and scripts, and it is offered as GPL licensed software for scientific and educational purposes.