Reviewerly/exHarmony huggingface dataset:
Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification

Contrastive Vision-Language Pre-training, known as CLIP, has provided a new paradigm for learning visual representations using large-scale image-text pairs. It shows impressive performance on downstream tasks by zero-shot knowledge transfer. To further enhance CLIP's adaption capability, existing methods proposed to fine-tune additional learnable modules, which significantly improves the few-shot performance but introduces extra training time and computational resources. In this paper, we propose a Training-free adaption method for CLIP to conduct few-shot classification, termed as Tip-Adapter, which not only inherits the training-free advantage of zero-shot CLIP but also performs comparably to those training-required approaches. Tip-Adapter constructs the adapter via a key-value cache model from the few-shot training set, and updates the prior knowledge encoded in CLIP by feature retrieval. On top of that, the performance of Tip-Adapter can be further boosted to be state-of-the-art on ImageNet by fine-tuning the cache model for 10 $$\times $$ fewer epochs than existing methods, which is both effective and efficient. We conduct extensive experiments of few-shot classification on 11 datasets to demonstrate the superiority of our proposed methods. Code is released at https://github.com/gaopengcuhk/Tip-Adapter .
Non-Fungible Tokens (NFT): A Systematic Review

Non-fungible tokens (NFTs) are gaining in popularity and are already extensively implemented. New use cases for NFTs are constantly developing. NFTs may prevent counterfeiting since each token carries the owner’s digital signature and is thus unique. For the usage of NFTs to progress in an institutional environment, the potential for using NFTs must be investigated in detail. This discovery prompted a comprehensive examination of NFTs developed between 2012 and 2022. The scope is confined to the journal and the keywords “Blockchain”, “Block-chain”, “Non-fungible Token”, and “NFT” are used. Also excluded are studies based on interviews, articles in the press, non-English articles, reviews, conferences, book chapters, dissertations, and monographs. This evaluation includes 34 papers from the last decade. This research examines the current state and development trends of NFT. In addition, the gaps and difficulties in the related literature have been explored, with an emphasis on the limits. These results highlight many unsolved research questions and potential future research avenues that would likely be beneficial to academics and professionals.
Binary Starling Murmuration Optimizer Algorithm to Select Effective Features from Medical Data

Feature selection is an NP-hard problem to remove irrelevant and redundant features with no predictive information to increase the performance of machine learning algorithms. Many wrapper-based methods using metaheuristic algorithms have been proposed to select effective features. However, they achieve differently on medical data, and most of them cannot find those effective features that may fulfill the required accuracy in diagnosing important diseases such as Diabetes, Heart problems, Hepatitis, and Coronavirus, which are targeted datasets in this study. To tackle this drawback, an algorithm is needed that can strike a balance between local and global search strategies in selecting effective features from medical datasets. In this paper, a new binary optimizer algorithm named BSMO is proposed. It is based on the newly proposed starling murmuration optimizer (SMO) that has a high ability to solve different complex and engineering problems, and it is expected that BSMO can also effectively find an optimal subset of features. Two distinct approaches are utilized by the BSMO algorithm when searching medical datasets to find effective features. Each dimension in a continuous solution generated by SMO is simply mapped to 0 or 1 using a variable threshold in the second approach, whereas in the first, binary versions of BSMO are developed using several S-shaped and V-shaped transfer functions. The performance of the proposed BSMO was evaluated using four targeted medical datasets, and results were compared with well-known binary metaheuristic algorithms in terms of different metrics, including fitness, accuracy, sensitivity, specificity, precision, and error. Finally, the superiority of the proposed BSMO algorithm was statistically analyzed using Friedman non-parametric test. The statistical and experimental tests proved that the proposed BSMO attains better performance in comparison to the competitive algorithms such as ACO, BBA, bGWO, and BWOA for selecting effective features from the medical datasets targeted in this study.
An intelligence-based hybrid PSO-SA for mobile robot path planning in warehouse

Mobile robots play crucial roles in industry and commerce, and automatic guided vehicles (AGV) are one of the primary parts of smart manufactory and intelligent logistics. Path planning is the core task for the AGV system, and it generates the path from origin to destination. The motivation of the study is to improve the scalability, flexibility, adaptability, and performance of the robot path planning systems. We propose the hybrid PSO-SA algorithm for the optimization of AGV path planning. Compared with other heuristic algorithms by benchmark functions, including HS, FA, ABC and GA, the proposed algorithm shows excellent performance in dealing with optimization problems. It reduces the possibility of getting trapped in one local optimum and enhances the efficiency to get the best global solution with faster convergence and less time consumption. It is evaluated with multiple cost functions and path planning with simulations and experiments. The objective of the proposed algorithm is to minimize the path length and produce a smooth path without collision. The proposed PSO-SA algorithm is compared with PSO in the path planning application, and the mean runtime and iteration times are usually significantly lower than PSO.
ReLU-type Hopfield neural network with analog hardware implementation

Rectified Linear Unit (ReLU) is an extremely simple activation function with two-segment linearity and is usually used in convolutional neural network. To simplify the analog hardware implementation, this article presents a bi-neuron Hopfield neural network (HNN) using ReLU function instead of hyperbolic tangent (Tanh) function as the activation function. With the HNN model, the boundedness properties can be proved by virtue of the Lyapunov method and the stability distributions of time-varying equilibrium point are clarified according to four independent affine regions. Using numerical measures, complex dynamical behaviors are uncovered therein, including period-adding bifurcation, spiking/bursting pattern, fast-slow effect, and coexisting multistable patterns. In addition, a multiplierless electronic circuit is developed for implementing the HNN model and its experimental measurements well validate the numerical ones. The results manifest that the ReLU-type HNN model can display intricate and rich dynamics, and its analog hardware implementation is simpler than other HNN models.
Navigating through an energy crisis: Challenges and progress towards electricity decarbonisation, reliability, and affordability in Italy

Following Russia's invasion of Ukraine and amidst COVID-19 recovery efforts, the energy crisis has put enormous pressure to policymakers to balance climate action, sustainable development, and management of the impacts of fuel supply disruptions and price shocks. Policy and market responses, such as liquefied natural gas infrastructure investments and use of every available fossil-fuel lever to make up for Russian gas supply cuts, are feared to trigger new lock-ins, jeopardising decarbonisation. This is also the case in Italy, which is highly dependent on Russia-imported gas. Energy models typically used to support such decisions take time to produce meaningful scenarios and, in times of crisis, are largely driven by highly uncertain parameters. This study uses fuzzy cognitive maps to engage with experts in a workshop and elicit their knowledge and perceptions with the aid of a questionnaire, towards simulating the impact of selected strategies and important uncertainties on the three pillars of Italy's progress to electricity-sector sustainability: decarbonisation, affordability, and reliability. In a framework of deliberation and simulation, experts displayed strong preference for renewable energy, compared to new gas infrastructure. Renewables were notably deemed to have positive impacts across all three sustainabiltiy dimensions and were found more robust against uncertainties, such as regulatory and political instability, which was highlighted as the biggest risk. Critically, despite their expectedly positive impact, demand-side transformations including demand reductions and broader behavioural shifts—a core component of the EU's current approach—may prove inadequate, should large system pressures from negative socio- and techno-economic developments persist.
How solar radiation forecasting impacts the utilization of solar energy: A critical review

The demand for energy generation from solar energy resource has been exponentially increasing in recent years. It is integral for a grid operator to maintain the balance between the demand and supply of the grid. Solar radiation forecasting paves the way for proper planning, reserve management, and elude penalty since solar energy is sporadic in nature. Several methods can forecast solar radiation; the prior classifications are machine learning models, numerical weather prediction models, satellite imaging, sky imager and hybrid model. This article presents a comprehensive review of all those models with the working principle, challenges and future research direction. Sky imagers provide the Normalized Root Mean Square Error (nRMSE) value of 6%–9% for a time horizon of 30 min, and the satellite imagery technique provides the Root Mean Square Error (RMSE) value of 61.28 W/m2 – 346.05 W/m2 for a time horizon of 4 h ahead. Similarly, NWP mesoscale models provide the RMSE value of 411.6 W/m2 - for three days ahead of forecasting with a spatial resolution of 50 km. Machine learning models are good at delivering accurate results with the time horizon up to 1 day ahead by yielding the results of RMSE in the range of 0.1170 W/m2 – 93.04 W/m2. Deep learning and hybrid models are being developed to overcome the issues faced by the standalone techniques. In many research works, artificial intelligence techniques are integrated with NWP models, sky imagers and satellite imagers to improve the data handling algorithm, which implicitly results in forecasting accuracy.
What do responses of financial markets to the collapse of FTX say about investor interest in cryptocurrencies? Event-study evidence

Studying the impact of the collapse of FTX on financial markets, we identify that, while this event was important to cryptocurrency markets, it was mostly irrelevant to traditional assets. Bitcoin, ethereum, and binance responded significantly negatively, while equity, energy, and currency markets were largely unmoved. Gold and silver having significant positive returns suggests cryptocurrency investors fled to purported safe havens. Results are consistent with traditional investors already having evolved to indifference to cryptocurrencies during the bear market prior to the FTX collapse. Results also suggest that cryptocurrencies are likely not as important a concern to financial stability as previously regarded.
Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning

In-sensor multi-task learning is not only the key merit of biological visions but also a primary goal of artificial-general-intelligence. However, traditional silicon-vision-chips suffer from large time/energy overheads. Further, training conventional deep-learning models is neither scalable nor affordable on edge-devices. Here, a material-algorithm co-design is proposed to emulate human retina and the affordable learning paradigm. Relying on a bottle-brush-shaped semiconducting p-NDI with efficient exciton-dissociations and through-space charge-transport characteristics, a wearable transistor-based dynamic in-sensor Reservoir-Computing system manifesting excellent separability, fading memory, and echo state property on different tasks is developed. Paired with a 'readout function' on memristive organic diodes, the RC recognizes handwritten letters and numbers, and classifies diverse costumes with accuracies of 98.04%, 88.18%, and 91.76%, respectively (higher than all reported organic semiconductors). In addition to 2D images, the spatiotemporal dynamics of RC naturally extract features of event-based videos, classifying 3 types of hand gestures at an accuracy of 98.62%. Further, the computing cost is significantly lower than that of the conventional artificial-neural-networks. This work provides a promising material-algorithm co-design for affordable and highly efficient photonic neuromorphic systems.
Split Learning Over Wireless Networks: Parallel Design and Resource Management

Split learning (SL) is a collaborative learning framework, which can train an artificial intelligence (AI) model between a device and an edge server by splitting the AI model into a device-side model and a server-side model at a cut layer. The existing SL approach conducts the training process sequentially across devices, which incurs significant training latency especially when the number of devices is large. In this paper, we design a novel SL scheme to reduce the training latency, named <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> luster-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> arallel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SL</u> (CPSL) which conducts model training in a “first-parallel-then-sequential” manner. Specifically, the CPSL is to partition devices into several clusters, parallelly train device-side models in each cluster and aggregate them, and then sequentially train the whole AI model across clusters, thereby parallelizing the training process and reducing training latency. Furthermore, we propose a resource management algorithm to minimize the training latency of CPSL considering device heterogeneity and network dynamics in wireless networks. This is achieved by stochastically optimizing the cut layer selection, device clustering, and radio spectrum allocation. The proposed two-timescale algorithm can jointly make the cut layer selection decision in a large timescale and device clustering and radio spectrum allocation decisions in a small timescale. Extensive simulation results on non-independent and identically distributed data demonstrate that the proposed solution can greatly reduce the training latency as compared with the existing SL benchmarks, while adapting to network dynamics.
Autonomous Vehicles Enabled by the Integration of IoT, Edge Intelligence, 5G, and Blockchain

The wave of modernization around us has put the automotive industry on the brink of a paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily transitioning towards automated driving to constitute an integral part of the intelligent transportation system (ITS). The term autonomous vehicle has become ubiquitous in our lives, owing to the extensive research and development that frequently make headlines. Nonetheless, the flourishing of AVs hinges on many factors due to the extremely stringent demands for safety, security, and reliability. Cutting-edge technologies play critical roles in tackling complicated issues. Assimilating trailblazing technologies such as the Internet of Things (IoT), edge intelligence (EI), 5G, and Blockchain into the AV architecture will unlock the potential of an efficient and sustainable transportation system. This paper provides a comprehensive review of the state-of-the-art in the literature on the impact and implementation of the aforementioned technologies into AV architectures, along with the challenges faced by each of them. We also provide insights into the technological offshoots concerning their seamless integration to fulfill the requirements of AVs. Finally, the paper sheds light on future research directions and opportunities that will spur further developments. Exploring the integration of key enabling technologies in a single work will serve as a valuable reference for the community interested in the relevant issues surrounding AV research.
Natural Language Processing Implementation for Sentiment Analysis on Tweets

In this article, we describe our early efforts with sentiment analysis on tweets. This project is meant to extract sentiment from tweets depending on their topic matter. It utilises natural language processing methods to determine the emotion associated with a certain issue. We used three different approaches to identify emotions in our study: classification based on subjectivity, semantic association and classification based on polarity. The experiment makes advantage of emotion lexicons by establishing the grammatical relationship between them and the subject. Due to the unique structure of tweets, the proposed method outperforms current text sentiment analysis methods.
Neural Feature Fusion Fields: 3D Distillation of Self-Supervised 2D Image Representations

We present Neural Feature Fusion Fields <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(N3F),\ a$</tex> method that improves dense <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2D$</tex> image feature extractors when the latter are applied to the analysis of multiple images reconstructible as a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3D$</tex> scene. Given an image feature extractor, for example pre-trained using self-supervision, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N3F$</tex> uses it as a teacher to learn a student network defined in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3D$</tex> space. The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3D$</tex> student network is similar to a neural radiance field that distills said features and can be trained with the usual differentiable rendering machinery. As a consequence, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N3F$</tex> is readily applicable to most neural rendering formulations, including vanilla <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$NeRF$</tex> and its extensions to complex dynamic scenes. We show that our method not only enables semantic understanding in the context of scene-specific neural fields without the use of manual labels, but also consistently improves over the self-supervised <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2D$</tex> baselines. This is demonstrated by considering various tasks, such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2D$</tex> object retrieval, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3D$</tex> segmentation, and scene editing, in diverse sequences, including long egocentric videos in the EPIC-KITCHENS benchmark. Project page: https://www.robots.ox.ac.uk/-vadim/n3f/
Medical supply chain integrated with blockchain and IoT to track the logistics of medical products

Nowadays blockchain technology plays a vital role in creative developments and important discoveries in the world. Blockchain develops secure and trustworthy platforms for data sharing in various application areas such as secure sharing of medical data, Anti-money laundering, tracking systems, Supply chain, and logistics monitoring, Crypto-currency exchange, etc. Today's Supply chain in the healthcare sector faces many problems like security, transparency, tampering with medical products, counterfeit drugs, more paperwork, high cost, and more time-consuming process while transporting medical equipment from manufacture to end-users. To overcome these problems, we introduce Novel Approach for Integrated IoT (Internet of Things) With Blockchain in Health Supply Chain (NAIBHSC) approach. By using this approach, we can eliminate all supply chain-related issues between suppliers and end-users. The goal of this research is by combining Blockchain technology with IoT to develop a smart health supply chain management system. This approach provides security, privacy, trust, visibility, decentralized tracking and tracing of the medical product, avoids counterfeit drugs, avoids the damage to medical components, authentication, reduces the cost, and provides the status of the products during the shipment process between manufacturers to end-user. In this approach, we conduct a series of experiments on a different group of users. The experimental results show that compare to existing approaches our proposed NAIBHSC approach gives better response time that is the average Transaction Per Second (TPS) for a group of 500 users is 100 milliseconds, reduces the latency time that is average latency time for 500 users group has 403 milliseconds, and improves the overall performance of the smart health supply chain management system.
Particle Swarm Optimization or Differential Evolution—A comparison

In the mid 1990s two landmark metaheuristics have been proposed: Particle Swarm Optimization and Differential Evolution. Their initial versions were very simple, but rapidly attracted wide attention. During the last quarter century hundreds of variants of both optimization algorithms have been proposed and applied in almost any field of science or engineering. However, no broader comparison of performance between both families of methods has been presented so far. In the present paper ten Particle Swarm Optimization and ten Differential Evolution variants, from historical ones from the 1990s up to the most recent ones from 2022, are compared on numerous single-objective numerical benchmarks and 22 real-world problems. On average Differential Evolution algorithms clearly outperform Particle Swarm Optimization ones. Such advantage of Differential Evolution over Particle Swarm Optimization is in contradiction with popularity: In the literature Particle Swarm Optimization algorithms are two–three times more frequently used than Differential Evolution ones. Problems for which Particle Swarm Optimization performs better than Differential Evolution do exist but are relatively few. Although this result may be an effect of the choice of specific variants, experimental settings or problems used for comparison, some re-consideration of algorithmic philosophy may be needed for Particle Swarm Optimization variants to make them more competitive.