Reviewerly/exHarmony huggingface dataset:
Phase Measurement Beyond the Standard Quantum Limit Using a Quantum Neuromorphic Platform

Phase measurement constitutes a key task in many fields of science, both in the classical and quantum regime. The higher precision of such measurement offers significant advances, and can also be utilised to achieve finer estimates for quantities such as distance, the gravitational constant, electromagnetic field amplitude, etc. Here we theoretically model the use of a quantum network, composed of a randomly coupled set of two-level systems, as a processing device for phase measurement. An incoming resource state carrying the phase information interacts with the quantum network, whose emission is trained to produce a desired output signal. We demonstrate phase precision scaling following the standard quantum limit, the Heisenberg limit, and beyond. This can be achieved using quantum resource states such as NOON states or other entangled states, however, we also find that classically correlated mixtures of states are alone sufficient, provided that they exhibit quantum coherence. Our proposed setup does not require conditional measurements, and is compatible with many different types of coupling between the quantum network and the phase encoding state, hence making it attractive to a wide range of possible physical implementations.
Focused surface acoustic wave induced nano-oscillator based reservoir computing

We demonstrate using micromagnetic simulations that a nanomagnet array excited by surface acoustic waves (SAWs) can work as a reservoir. An input nanomagnet is excited with focused SAW and coupled to several nanomagnets, seven of which serve as output nanomagnets. To evaluate memory effect and computing capability, we study the short-term memory (STM) and parity check (PC) capacities, respectively. The SAW (4 GHz carrier frequency) amplitude is modulated to provide a sequence of sine and square waves of 100 MHz frequency. The responses of the selected output nanomagnets are processed by reading the envelope of their magnetization states, which is used to train the output weights using the regression method. For classification, a random sequence of 100 square and sine wave samples is used, of which 80% are used for training, and the rest are used for testing. We achieve 100% training and 100% testing accuracy. The average STM and PC are calculated to be ∼4.69 and ∼5.39 bits, respectively, which is indicative of the proposed acoustically driven nanomagnet oscillator array being well suited for physical reservoir computing applications. The energy dissipation is ∼2.5 times lower than a CMOS-based echo-state network. Furthermore, the reservoir is able to accurately predict Mackey-Glass time series up to several time steps ahead. Finally, the ability to use high frequency SAW makes the nanomagnet reservoir scalable to small dimensions, and the ability to modulate the envelope at a lower frequency (100 MHz) adds flexibility to encode different signals beyond the sine/square waves classification and Mackey-Glass predication tasks demonstrated here.
Video violence recognition and localization using a semi-supervised hard attention model

The significant growth of surveillance camera networks necessitates scalable AI solutions to efficiently analyze the large amount of video data produced by these networks. As a typical analysis performed on surveillance footage, video violence detection has recently received considerable attention. The majority of research has focused on improving existing methods using supervised methods, with little, if any, attention to the semi-supervised learning approaches. In this study, a reinforcement learning model is introduced that can outperform existing models through a semi-supervised approach. The main novelty of the proposed method lies in the introduction of a semi-supervised hard attention mechanism. Using hard attention, the essential regions of videos are identified and separated from the non-informative parts of the data. A model’s accuracy is improved by removing redundant data and focusing on useful visual information in a higher resolution. Implementing hard attention mechanisms using semi-supervised reinforcement learning algorithms eliminates the need for attention annotations in video violence datasets, thus making them readily applicable. The proposed model utilizes a pre-trained I3D backbone to accelerate and stabilize the training process. The proposed model achieved state-of-the-art accuracy of 90.4% and 98.7% on RWF and Hockey datasets, respectively. • Videos contain irrelevant information like background and surrounding objects. • Isolating the valuable information in a video increases the classification accuracy. • Reinforcement learning-based hard attention can identify critical visual information. • Hard attention learns violence localization without explicit spatial annotation.
`Less Than One'-Shot Learning: Learning N Classes From M &lt; N Samples

Deep neural networks require large training sets but suffer from high computational cost and long training times. Training on much smaller training sets while maintaining nearly the same accuracy would be very beneficial. In the few-shot learning setting, a model must learn a new class given only a small number of samples from that class. One-shot learning is an extreme form of few-shot learning where the model must learn a new class from a single example. We propose the 'less than one'-shot learning task where models must learn N new classes given only M
Explainable Models with Consistent Interpretations

Given the widespread deployment of black box deep neural networks in computer vision applications, the interpretability aspect of these black box systems has recently gained traction. Various methods have been proposed to explain the results of such deep neural networks. However, some recent works have shown that such explanation methods are biased and do not produce consistent interpretations. Hence, rather than introducing a novel explanation method, we learn models that are encouraged to be interpretable given an explanation method. We use Grad-CAM as the explanation algorithm and encourage the network to learn consistent interpretations along with maximizing the log-likelihood of the correct class. We show that our method outperforms the baseline on the pointing game evaluation on ImageNet and MS-COCO datasets respectively. We also introduce new evaluation metrics that penalize the saliency map if it lies outside the ground truth bounding box or segmentation mask, and show that our method outperforms the baseline on these metrics as well. Moreover, our model trained with interpretation consistency generalizes to other explanation algorithms on all the evaluation metrics. The code and models are publicly available.
Assessing global interest in decentralized finance, embedded finance, open finance, ocean finance and sustainable finance

Purpose This paper analyzes global interest in Internet information about decentralized finance (DeFi), embedded finance (EmFi), open finance (OpFi), ocean finance (OcFi) and sustainable finance (SuFi) and the relationship among them. Design/methodology/approach The paper used a comparative methodology based on regression and correlation analyses to assess global interest in Internet information about DeFi, EmFi, OpFi, OcFi and SuFi. Findings The findings reveal that global interest in Internet information about EmFi was more popular in Asian and European countries. Global web search for Internet information about OcFi decreased during the financial crisis while global web search for Internet information about OpFi and EmFi increased during financial crisis years. Global web search for Internet information about DeFi, SuFi and EmFi increased during the pandemic years. There is a significant and positive correlation between interest in DeFi, EmFi, OcFi and SuFi. Also, there is a significant and negative correlation between interest in EmFi and interest in OpFi. The regression coefficient matrix shows that OpFi, EmFi, OcFi, DeFi and SuFi are significantly related. Originality/value To the best of the author’s knowledge, this is the first paper that analyses the association between interest in DeFi, EmFi, OpFi, OcFi and SuFi. Thus, this study addressed an important knowledge gap in the literature by exploring people’s interest in Internet information about DeFi, EmFi, OpFi, OcFi and SuFi.
Never Mind the Malware, Here’s the Stegomalware

In recent years, the use of steganographic techniques has been progressively observed to endow threats with cloaking capabilities and launch sophisticated attack campaigns. This requires partially rethinking the perception of malware.
SpaDE

Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching. Owing to the pre-computed inverted index, it supports fast ad-hoc search but incurs the vocabulary mismatch problem. Although recent neural ranking models using pre-trained language models can address this problem, they usually require expensive query inference costs, implying the trade-off between effectiveness and efficiency. Tackling the trade-off, we propose a novel uni-encoder ranking model, Sparse retriever using a Dual document Encoder (SpaDE), learning document representation via the dual encoder. Each encoder plays a central role in (i) adjusting the importance of terms to improve lexical matching and (ii) expanding additional terms to support semantic matching. Furthermore, our co-training strategy trains the dual encoder effectively and avoids unnecessary intervention in training each other. Experimental results on several benchmarks show that SpaDE outperforms existing uni-encoder ranking models.
QuickVR: A standard library for virtual embodiment in unity

In the last few years the field of Virtual Reality (VR) has experienced significant growth through the introduction of low-cost VR devices to the mass market. However, VR has been used for many years by researchers since it has proven to be a powerful tool across a vast array of research fields and applications. The key aspect of any VR experience is that it is completely immersive, which means that the virtual world totally surrounds the participant. Some game engines such as Unity already support VR out of the box and an application can be configured for VR in a matter of minutes. However, there is still the lack of a standard and easy to use tool in order to embody participants into a virtual human character that responds synchronously to their movements with corresponding virtual body movements. In this paper we introduce QuickVR, a library based on Unity which not only offers embodiment in a virtual character, but also provides a series of high level features that are necessary in any VR application, helping to dramatically reduce the production time. Our tool is easy to use by coding novices, but also easy extensible and customizable by more experienced programmers.
Blockchain-Based Traceability System From the Users’ Perspective: A Case Study of Thai Coffee Supply Chain

We explored the use of blockchain technology for traceability to improve the safety and value of food, focusing on the coffee supply chain as a case study. The main goal was to evaluate the feasibility in terms of design, perceived benefits, and challenges of applying blockchain and traceability from the users’ perspective. We implemented a prototype using a user-centered iterative interface design. Then we used the prototype to answer our research questions in mixed-method research, including in-depth interviews (10 participants) and a survey (350 participants) with stakeholders in the coffee supply chain in Thailand. The results showed that timeline-based design was preferred over map-based or text-based design for the visualization of traceability information and that blockchain was a promising technology, as 67% of the survey participants saw a positive influence of blockchain on the adoption of applications. The most notable benefits were origin checking and increasing product trustworthiness. The most notable challenges were inaccurate or incomplete information and the disclosure of trade secrets. More work is required to address the challenges for everyone in the supply chain ecosystem to adopt the proposed traceability system, including (1) providing trustworthiness and completeness of information by cross-checking with third parties or other users, (2) protecting sensitive information by aligning users’ interests or allowing control of information disclosure, and (3) educating and giving producers the motivation for the difficulty and the extra work.
Decentralization Using Quantum Blockchain: A Theoretical Analysis

Blockchain technology has been prominent recently due to its applications in cryptocurrency. Numerous decentralized blockchain applications have been possible due to blockchains' nature of distributed, secured, and peer-to-peer storage. One of its technical pillars is using public-key cryptography and hash functions, which promise a secure, pseudo-anonymous, distributed storage with non-repudiation. This security is believed to be difficult to break with classical computational powers. However, recent advances in quantum computing have raised the possibility of breaking these algorithms with quantum computers, thus, threatening the blockchains' security. Quantum-resistant blockchains are being proposed as alternatives to resolve this issue. Some propose to replace traditional cryptography with post-quantum cryptography—others base their approaches on quantum computer networks or quantum internets. Nonetheless, a new security infrastructure (e.g., access control/authentication) must be established before any of these could happen. This article provides a theoretical analysis of the quantum blockchain technologies that could be used for decentralized identity authentication. We put together a conceptual design for a quantum blockchain identity framework (QBIF) and give a review of the technical evidence. We investigate its essential components and feasibility, effectiveness, and limitations. Even though it currently has various limitations and challenges, we believe a decentralized perspective of quantum applications is noteworthy and likely.
Machine-Learning-Based Human Fall Detection Using Contact- and Noncontact-Based Sensors

Automated human fall detection is an essential area of research due to its health implications in day-to-day life. Detecting and timely reporting of human falls may lead to saving human life. In this paper, fall detection has been targeted using machine-learning-based approaches from two perspectives regarding data sources, that is, contact-based and noncontact-based sensors. In both of these cases, various methods based on deep learning and machine learning techniques have been attempted, and their performances were compared. The approaches analyze data in fixed time windows and extract features in the time domain or spatial domain which obtain relative information between consecutive data samples. After experimentation, it was found that the proposed noncontact-based sensor techniques outperformed the contact-based sensor techniques by a margin of 1.82%. After this, it was also found that the noncontact-based sensor techniques outperformed the state of the art of noncontact-based sensor results by a margin of 3.15%. To better suit these techniques for real-world applications, embedded board implementation and privacy preservation of subject by using advanced methods such as compressive sensing and feature encoding need to be attempted.
HMFVC: A Human-Machine Friendly Video Compression Scheme

Recent studies on Video Coding for Machine (VCM) have achieved remarkable results. However, in practical visual-analytic applications, except for transmitting the visual feature for machine analysis, video textures are also mandatory for human monitoring and decision-making. To this end, this paper proposes a human-machine friendly video compression scheme (HMFVC) which can satisfy both human viewing and machine analysis well. First, we propose a learned semantic representation (LSR) method to extract semantic information between temporal neighboring frames. LSR could be utilized in signal reconstruction for human viewing and visual analysis for machine understanding. Second, given the proposed LSR, we design an end-to-end optimized video compression framework to jointly optimize the visual quality for human perception, analysis accuracy for machines, and compression efficiency as well. Finally, an HMFVC codec is developed, which can achieve higher action recognition accuracy and better reconstruction quality than the traditional codecs and learned video compression approaches. Specifically, HMFVC saves 77% bitrate to achieve the same analysis performance with the original videos compared to x265. To our knowledge, HMFVC is the first end-to-end optimized video compression scheme to serve both humans and machines. It is a promising framework for human-machine friendly video compression approaches.
Transformer Lesion Tracker

Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6 mm vs. 7 mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT .
Partial-Expansion A* with Selective Node Generation

A* is often described as being 'optimal,' in that it expands the minimum number of unique nodes. But, A* may generate many extra nodes which are never expanded. This is a performance loss, especially when the branching factor is large. Partial Expansion A* (PEA*) addresses this problem when expanding a node, n, by generating all the children of n but only storing children with the same f-cost as n. We introduce an enhanced version of PEA* (EPEA*). Given a priori domain knowledge, EPEA* only generates the children with the same f-cost as the parent. State-of-the-art results were obtained for a number of domains. Drawbacks of EPEA* are also discussed. A full version of this paper appears in the proceedings of AAAI-2012
APPLICATION OF PARTICLE SWARM OPTIMIZATION TO MICROWAVE TAPERED MICROSTRIP LINES

Application of metaheuristic algorithms has been of continued interest in the field of electrical engineering because of their powerful features. In this work special design is done for a tapered transmission line used for matching an arbitrary real load to a 50Ω line. The problem at hand is to match this arbitray load to 50 Ω line using three section tapered transmission line with impedances in decreasing order from the load. So the problem becomes optimizing an equation with three unknowns with various conditions. The optimized values are obtained using Particle Swarm Optimi zation. It can easily be shown that PSO is very strong in solving this kind of multiobjective optimization problems .
Understanding the factors influencing user intention to continue contributing knowledge in social Q&A communities

Purpose: A social question & answer (SQA) community’s long-term sustainability depends on its members’ willingness to stay and contribute their knowledge continuously in the community. This research aims to investigate the critical factors which influence users’ intention to continue contributing knowledge in the SQA community. Design/methodology/approach: Grounded on information systems (IS) continuance theory, this study put forward a model of the factors that influence SQA community members’ intention to continue contributing knowledge. Survey was conducted to gather data from knowledge contributors of four major Chinese SQA communities (Baidu Knows, Sina iAsk, Soso Ask and Yahoo! Knowledge). By using the partial least squares (PLS) technique, research hypotheses derived from the proposed model were empirically validated. Findings: Except enjoyment in helping others and knowledge self-efficacy, all other factors including extrinsic reward, reputation enhancement, realization of self-worth, perceived usefulness, attitude towards knowledge contribution, and satisfaction exert significant impacts on users’ continuance intentions in an SQA community. Research limitations: First, important factors such as the ease of use of information systems which may influence users’ continuance intentions were not investigated in the study. Second, the study sample needs to be enlarged, and users of smaller SQA communities should also be included, to make the results more representative. Practical implications: This study will help SQA community designers and managers develop or improve incentive mechanisms to attract more people to contribute their knowledge and promote the development of the SQA community. Originality/value: This study improves the previous research models and puts forward a model of user continuance intention to contribute knowledge in an SQA community. It will extend the understanding of SQA community users’ intention to continue contributing knowledge by distinguishing these users’ different roles and focusing only on knowledge contributors.
Newspaper and Magazine Production: Mass Media Аctivities and a Training Course

The article summarizes the experience of teaching of the course “Newspaper and Magazine Production” at the Institute of Journalism of Taras Shevchenko National University of Kyiv and gives an assessment of effectiveness of various educational tasks in order to acquire competencies by the students that are required for successful work in print media. The today’s higher education institutions around the world, and in Ukraine in particular, are shifting the emphasis from providing purely theoretical knowledge to practical competencies gained by the students for their successful practical implementation in various fields of activities. It entirely concerns to future journalists’ training. The practice of training of future journalists of newspapers and magazines at the Institute of Journalism convincingly shows that the most effective form of training of such media professionals is a long-term business game, when educational editorial offices are set up, the result of which is to produce the pilot issues of newspapers and magazines. In this case, the scope of theoretical education is minimized, and the students learn in practice to choose the segment of information market, to develop the concept of the edition, to establish an editorial office with all structural units necessary for its effective functioning, to write and to prepare for publication the journalistic materials and eventually to reach the final result – the pilot issues of newspapers and magazines.