Reviewerly/exHarmony huggingface dataset:
OMGH: Online Manifold-Guided Hashing for Flexible Cross-Modal Retrieval

Cross-modal hashing hasrecently gained an increasing attention for its efficiency and fast retrieval speed in indexing the multimedia data across different modalities. Nevertheless, the multimedia data points often emerge in a streaming manner, and existing online methods often lack of learning capacity to handle both labeled and unlabeled data.To alleviate these concerns, this paper proposes an Online Manifold-Guided Hashing (OMGH) framework, which can incrementally learn the compact hash code of streaming data while adaptively optimizing the hash function in a streaming manner. To be specific, OMGH first exploits a matrix tri-factorization framework to learn the discriminative hash codes for streaming multi-modal data. Then, an online anchor-based manifold structure is designed to sparsely represent the old data and adaptively guide the hash code learning process, which can wellreduce the complexity in preserving the semantic correlation between the old data and streaming data. Meanwhile, such anchor-based manifold embedding is adaptive to the unsupervised and supervised learning strategies in a flexible way. Besides, an online discrete optimization method is efficiently addressed to incrementally update the hash functions and optimize the hash codes on streaming data points. As a result, the derived hash codes are more semantically meaningful for various online cross-modal retrieval tasks. Extensive experiments verify the advantages of the proposed OMGH model, by achieving and improving the state-of-the-art cross-modal retrieval performances on three benchmark datasets.
Analysis of Modern Methods Used to Assess the Quality of Video Sequences During Signal Streaming

videos are going to occupy 82% of Internet traffic by 2022, as now we can observe a trend that video streaming is taking up more and more Internet bandwidth. With video traffic skyrocketing, improvements in video encoding technology are critical for video streaming companies. Assessing the quality of video compression in terms of human perception is an important area of research. Subjective evaluations of video quality require a lot of time for experiments and are not feasible in real-time applications. Therefore, interest in the development of models for the objective evaluation of video quality has a high growth trend in recent years. The main problem of modern objective quality assessments is unreliable information provided by developers, poor correlation with the visual perception of artefacts by the end user-human. In this paper, we present an analysis of modern video quality assessments used to assess the quality of video sequences during signal streaming. This work will be useful to video compression researchers, and will also be useful in creating additional test materials, planning future experiments, and improving adaptive codecs.
Quantum Computing Technology

Quantum Computing has inspired scientists as well as hardware and software developers and is fundamentally changing the landscape of information technology. Quantum computing systems are built from the bottom up reaching the limits of what can be classically simulated. The IBM Quantum Development Roadmap describes our vision of creating a quantum computing ecosystem delivering quantum applications through the cloud. This requires developing the entire quantum computing stack starting from the qubit and quantum processor technology, control electronics to software, algorithms and applications for quantum computing, implemented in the cloud and integrated with high performance computing. Though today's noisy near-term quantum computing systems are still limited, the roadmap targets to achieve an inflection point benefiting of continuous improvements across the stack driving quantum performance described by more qubits, higher quantum volume and speed.
Soft and hard skills identification: insights from IT job advertisements in the CIS region

Labor market transformations significantly affect the sphere of information technologies (IT) introducing new instruments, architectures, and frameworks. Employers operate with new knowledge domains which demand specific competencies from workers including combinations of both technical ("hard") and non-technical ("soft") skills. The educational system is now required to provide the alumni with up-to-date skill sets covering the latest labor market trends. However, there is a big concern about the self-adaptation of educational programs for meeting the companies' needs. Accordingly, frequent changes in job position requirements call for the tool for in-time categorization of vacancies and skills extraction. This study aims to show the demand for skills in the IT sphere in the Commonwealth of Independent States (CIS) region and discover the mapping between required skill sets and job occupations. The proposed methodology for skills identification uses natural language processing, hierarchical clustering, and association mining techniques. The results reveal explicit information about the combinations of "soft" and "hard" skills required for different professional groups. These findings provide valuable insights for supporting educational organizations, human resource (HR) specialists, and state labor authorities in the renewal of existing knowledge about skill sets for IT professionals. In addition, the provided methodology for labor market monitoring has a high potential to ensure effective matching of employees.
Anomaly Detection of High-Speed Railway Catenary Damage

The Railway Catenary system is an integral and essential component of railway electrification. The working state of the catenary system can directly affect the normal operation of the locomotive. The section insulator (SI) in the catenary system is required for the electrical segregation of two feeds such as in a crossover while deciding the destined track from two adjacent tracks. We propose a deep learning-based approach for abnormal detection of insulator breakage in high-speed railway catenary. Semantic segmentation is an important theory in deep learning-based image segmentation. Aiming at the abnormal detection of insulator breakage in high-speed railway catenary, semantic segmentation technology is used to detect the abnormalities, which can quantitatively describe the area of insulator breakage or damage. Based on the DeepLab V3+ semantic segmentation network, the characteristics of the insulator's data are retrieved. The Multi Feature DeepLab V3+ network and the Attention DeepLab V3+ networks are proposed to solve the anomaly detection problem of the damaged area from the insulator's images. The results show that the improved two-folded semantic segmentation networks can better mark the contours of the damaged area of the insulator to safeguard the Catenary system. The outcomes are compared with the original DeepLab V3+ and the MIoU values of the two improved semantic segmentation networks have been improved to a remarkable extent that improves the overall performance of the deep learning-based segmentation of insulator's images.
AMOD-Net: Attention-based Multi-Scale Object Detection Network for X- Ray Baggage Security Inspection

X-ray baggage security checking is an extremely important task, which can detect various dangerous objects in airports, stations and other public places to prevent crimes and protect personal safety. However, at present, most of the recognition is done manually, which is inefficient and error-prone. As a complementary, object detection algorithm is beneficial to avoiding errors caused by manual detection. Although the universal object detection is well developed and the performance of the universal detectors is very advanced, the performance of these detectors in X-ray image detection is mediocre. In this paper, we propose an Attention-based Multi-Scale Object Detection Network (called AMOD-Net) for X-ray baggage security inspection. To solve the problems of stacking and occlusion existed in the X- ray baggage image, we design a channel selection attention module for AMOD-Net. To make better use of the feature information, we construct a deep feature fusion structure for AMOD-Net. Experiments on the X-ray baggage dataset demonstrate that our approach achieves very competitive results.
Energy-Aware Edge Association for Cluster-Based Personalized Federated Learning

Federated Learning (FL) over wireless network enables data-conscious services by leveraging the ubiquitous intelligence at network edge for privacy-preserving model training. As the proliferation of context-aware services, the diversified personal preferences cause disagreeing conditional distributions among user data, which leads to poor inference performance. In this sense, clustered federated learning is proposed to group user devices with similar preference and provide each cluster with a personalized model. This calls for innovative design in edge association that involves user clustering and also resource management optimization. We formulate an accuracy-cost trade-off optimization problem by jointly considering model accuracy, communication resource allocation and energy consumption. To comply with parameter encryption techniques in FL, we propose an iterative solution procedure which employs deep reinforcement learning based approach at cloud server for edge association. The reward function consists of minimized energy consumption at each base station and the averaged model accuracy of all users. Under our proposed solution, multiple edge base station are fully exploited to realize cost efficient personalized federated learning without any prior knowledge on model parameters. Simulation results show that our proposed strategy outperforms existing strategies in achieving accurate learning at low energy cost.
A Model and Method of Information System Security Risk Assessment based on MITRE ATT&amp;CK

In order to improve the efficiency of information system risk early warning, an information system risk assessment method (Advantageous Tactics, Technologies, and Common Knowledge - Optimized Analytic Hierarchy Process - Bayesian Network, ATT&CK-OAHP-BN) based on MITRE ATT&CK model is proposed. Firstly, a hierarchical risk assessment indicators system is constructed based on the ATT&CK knowledge graph. Then, the weight of each indicator is calculated by OAHP algorithm, and the initial probability of each risk indicator is determined. Finally, according to the Bayesian network reasoning model, the posterior probability that each risk indicator may lead to security threat is calculated. The example analysis results show that this method can calculate the risk value of security threat caused by various attack tactics and techniques, so as to effectively determine the risk indicator that should be paid attention to when the information system is under security threat.
Semantic-aligned Fusion Transformer for One-shot Object Detection

One-shot object detection aims at detecting novel objects according to merely one given instance. With extreme data scarcity, current approaches explore various feature fusions to obtain directly transferable meta-knowledge. Yet, their performances are often unsatisfactory. In this paper, we attribute this to inappropriate correlation methods that misalign query-support semantics by overlooking spatial structures and scale variances. Upon analysis, we leverage the attention mechanism and propose a simple but effective architecture named Semantic-aligned Fusion Transformer (SaFT) to resolve these issues. Specifically, we equip SaFT with a vertical fusion module (VFM) for cross-scale semantic enhancement and a horizontal fusion module (HFM) for cross-sample feature fusion. Together, they broaden the vision for each feature point from the support to a whole augmented feature pyramid from the query, facilitating semantic-aligned associations. Extensive experiments on multiple benchmarks demonstrate the superiority of our framework. Without fine-tuning on novel classes, it brings significant performance gains to one-stage baselines, lifting state-of-the-art results to a higher level.
Virtual-Reality Training of Elite Boxers Preparing for the Tokyo 2020 Olympics During the COVID-19 Pandemic: A Case Study

The COVID-19 pandemic struck right during the Olympic preparation, leading to significant training restrictions such as noncontact practices for combat sports. This case study research describes the application of a complementary virtual-reality (VR) intervention to train elite boxers preparing for Tokyo 2020 during the pandemic. It also addresses the evaluation of broader visuocognitive functions in elite boxers. Six boxers were allocated to two groups: one experimental group trained on a 360° VR (360VR) temporal video-occlusion program, and one active control group trained on a VR game simulation during 11 sessions. Pre- and postevaluations of specific decision-making performance were performed on a 360VR evaluation test. Fundamental visual and visuocognitive functions were assessed at baseline. Greater on-test decision-making improvements were observed in the 360VR-trained group compared with VR game, and 360VR offered self-reported satisfactory, representative, and safe individual training opportunities for the boxers. More research is warranted to explore the applications of 360VR and VR simulation for psycho-perceptual-motor-skill evaluation and training. Superior visuocognitive performance was observed in elite boxers and should also be a topic of further investigation. The methodological approach, implementation, and reflections are provided in detail to guide practitioners toward the applied use of VR in the sporting environment.
EditSpeech: A Text Based Speech Editing System Using Partial Inference and Bidirectional Fusion

This paper presents the design, implementation and evaluation of a speech editing system, named EditSpeech, which allows a user to perform deletion, insertion and replacement of words in a given speech utterance, without causing audible degradation in speech quality and naturalness. The EditSpeech system is developed upon a neural text-to-speech (NTTS) synthesis framework. Partial inference and bidirectional fusion are proposed to effectively incorporate the contextual information related to the edited region and achieve smooth transition at both left and right boundaries. Distortion introduced to the unmodified parts of the utterance is alleviated. The EditSpeech system is developed and evaluated on English and Chinese in multi-speaker scenarios. Objective and subjective evaluation demonstrate that EditSpeech outperforms a few baseline systems in terms of low spectral distortion and preferred speech quality. Audio samples are available online for demonstration <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://daxintan-cuhk.github.io/EditSpeech/.
Towards Explainable Formal Methods: From LTL to Natural Language with Neural Machine Translation

[Context and motivation] Requirements formalisation facilitates reasoning about inconsistencies, detection of ambiguities, and identification critical issues in system models. Temporal logic formulae are the natural choice when it comes to formalise requirements associated to desired system behaviours. [Question/problem] Understanding and mastering temporal logic requires a formal background. Means are therefore needed to make temporal logic formulae interpretable by engineers, domain experts and other stakeholders involved in the development process. [Principal ideas/results] In this paper, we propose to use a neural machine translation tool, named OpenNMT, to translate Linear Temporal Logic (LTL) formulae into corresponding natural language descriptions. Our results show that the translation system achieves an average BLEU (BiLingual Evaluation Understudy) score of 93.53%, which corresponds to high-quality translations. [Contribution] Our neural model can be applied to assess if requirements have been correctly formalised. This can be useful to requirements analysts, who may have limited confidence with LTL, and to other stakeholders involved in the requirements verification process. Overall, our research preview contributes to bridging the gap between formal methods and requirements engineering, and opens to further research in explainable formal methods.
Generative Design Inspiration for Glyphs with Diatoms

We introduce Diatoms, a technique that generates design inspiration for glyphs by sampling from palettes of mark shapes, encoding channels, and glyph scaffold shapes. Diatoms allows for a degree of randomness while respecting constraints imposed by columns in a data table: their data types and domains as well as semantic associations between columns as specified by the designer. We pair this generative design process with two forms of interactive design externalization that enable comparison and critique of the design alternatives. First, we incorporate a familiar small multiples configuration in which every data point is drawn according to a single glyph design, coupled with the ability to page between alternative glyph designs. Second, we propose a small permutables design gallery, in which a single data point is drawn according to each alternative glyph design, coupled with the ability to page between data points. We demonstrate an implementation of our technique as an extension to Tableau featuring three example palettes, and to better understand how Diatoms could fit into existing design workflows, we conducted interviews and chauffeured demos with 12 designers. Finally, we reflect on our process and the designers' reactions, discussing the potential of our technique in the context of visualization authoring systems. Ultimately, our approach to glyph design and comparison can kickstart and inspire visualization design, allowing for the serendipitous discovery of shape and channel combinations that would have otherwise been overlooked.
Audio to Sign Language Translator Web Application

Sign language is an effective mode of conversation for persons who have difficulty speaking or hearing. There are numerous media accessible for translation or for identifying sign languages and converting those to text format, However, methods for converting text to sign language have been few and even not web-based software, owing to the scarcity of resources. The proposed web application seeks to develop a translating mechanism or automation that includes a parser element that converts the incoming speech data or English text to a phrase structure grammar representation, which is then used by another module that contains Indi Sign language grammatical format. This is accomplished through the means of removing stop-words from the reordered input format. Because Indian sign language does not provide word inflections, stemming and lemmatization are used to turn words into their root form. Following sentence filtration, all words are tested against the words in the database, which is represented as a dictionary comprising video representations of each word. If the words are missing from the database, the algorithm will then look for its related synonym and replace it with that term.In many ways, the proposed system is more innovative and efficient than existing systems, because Existing methods can only convert words directly into Indi sign language, and they were not as efficient as this system, whereas this in the actual world, the system tries to translate these phrases into Indian sign language grammatical order. Because this is a web-based programmed, it is straightforward to access and use. This technology is platform agnostic and more versatile to use, and it transforms phrases to sign language in real time.
Multi-Constraint Adversarial Networks for Unsupervised Image-to-Image Translation

Unsupervised image-to-image translation aims to learn the mapping from an input image in a source domain to an output image in a target domain without paired training dataset. Recently, remarkable progress has been made in translation due to the development of generative adversarial networks (GANs). However, existing methods suffer from the training instability as gradients passing from discriminator to generator become less informative when the source and target domains exhibit sufficiently large discrepancies in appearance or shape. To handle this challenging problem, in this paper, we propose a novel multi-constraint adversarial model (MCGAN) for image translation in which multiple adversarial constraints are applied at generator's multi-scale outputs by a single discriminator to pass gradients to all the scales simultaneously and assist generator training for capturing large discrepancies in appearance between two domains. We further notice that the solution to regularize generator is helpful in stabilizing adversarial training, but results may have unreasonable structure or blurriness due to less context information flow from discriminator to generator. Therefore, we adopt dense combinations of the dilated convolutions at discriminator for supporting more information flow to generator. With extensive experiments on three public datasets, cat-to-dog, horse-to-zebra, and apple-to-orange, our method significantly improves state-of-the-arts on all datasets.
OCR based medical prescription and report analyzer

More often than not, we find prescriptions are not properly written which are being handed over to patients and the diagnostic reports handed can't be monitored without the doctor's involvement due to lack of medical knowledge. This has resulted in putting the patient's safety at risk, and also sometimes there is an unavailability of doctors for the required time for monitoring of reports because of which there is also a danger to patients life. The objective of this MPRA model is to help patients get an easy understanding of their report and prescribed medicine by addressing the important challenge of extraction of handwritten text data from images, analyzing it using test data then giving a much- refined output. HCR (Handwriting Character Recognition) technique, as well as PCR (Printer Character Recognition) technique, have been used to identify both handwritten as well as printed text in prescription and report, and techniques such as image processing to refine image to give an accurate output.
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings

Recent efforts towards video anomaly detection (VAD) try to learn a deep autoencoder to describe normal event patterns with small reconstruction errors. The video inputs with large reconstruction errors are regarded as anomalies at the test time. However, these methods sometimes reconstruct abnormal inputs well because of the powerful generalization ability of deep autoencoder. To address this problem, we present a novel approach for anomaly detection, which utilizes discriminative prototypes of normal data to reconstruct video frames. In this way, the model will favor the reconstruction of normal events and distort the reconstruction of abnormal events. Specifically, we use a prototype-guided memory module to perform discriminative latent embedding. We introduce a new discriminative criterion for the memory module, as well as a loss function correspondingly, which can encourage memory items to record the representative embeddings of normal data, i.e. prototypes. Besides, we design a novel two-branch autoencoder, which is composed of a future frame prediction network and an RGB difference generation network that share the same encoder. The stacked RGB difference contains motion information just like optical flow, so our model can learn temporal regularity. We evaluate the effectiveness of our method on three benchmark datasets and experimental results demonstrate the proposed method outperforms the state-of-the-art.