Reviewerly/exHarmony huggingface dataset:
CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator

Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and performance compared to CPUs and GPUs. In this paper, we propose a novel cross-layer optimized neural network accelerator called CrossLight that leverages silicon photonics. CrossLight includes device-level engineering for resilience to process variations and thermal crosstalk, circuit-level tuning enhancements for inference latency reduction, and architecture-level optimizations to enable better resolution, energy-efficiency, and throughput. On average, CrossLight offers 9.5x lower energy-per-bit and 15.9x higher performance-per-watt than state-of-the-art photonic deep learning accelerators.
Big tech, big data and the new world of digital health

This commentary shows the exponential growth of digital health and the accompanying explosion of health data. It discusses three major shifts in the global health landscape. The first will be the move of the big tech companies into healthcare, the second will be the monetization of consumer data and the creation of health data marketplaces; and the third will be the growth of Asia as a leader in digital health. Big tech already has the advantage of a massive consumer base, data and analytics which enable them to understand consumers; and complementary technologies, like wearables, that will drive the consumerization of healthcare. This expansion can happen quickly and already is creating challenges for regulators as they try to catch up. The vast volumes of data and the ability of technology such as blockchain to enable data owners to monetize their data, will lead to the development of health data marketplaces, which can connect and monetize data for data owners and make it available for scientific discovery. The developments in self-sovereign identity, will make it possible for individuals to monetize their health data in the future. Finally, we see the emergence of Asia as a powerhouse for the digital health of the future, with vast populations, mobile technology and increasing adoption of wearable devices. Consumer focused health care driven by data will change the institutional models of the past.
Komodo Mlipir Algorithm

This paper proposes Komodo Mlipir Algorithm (KMA) as a new metaheuristic optimizer. It is inspired by two phenomena: the behavior of Komodo dragons living in the East Nusa Tenggara, Indonesia, and the Javanese gait named mlipir. Adopted the foraging and reproduction of Komodo dragons, the population of a few Komodo individuals (candidate solutions) in KMA are split into three groups based on their qualities: big males, female, and small males. First, the high-quality big males do a novel movement called high-exploitation low-exploration to produce better solutions. Next, the middle-quality female generates a better solution by either mating the highest-quality big male (exploitation) or doing parthenogenesis (exploration). Finally, the low-quality small males diversify candidate solutions using a novel movement called mlipir (a Javanese term defined as a walk on the side of the road to reach a particular destination safely), which is implemented by following the big males in a part of their dimensions. A self-adaptation of the population is also proposed to control the exploitation–exploration balance. An examination using the well-documented twenty-three benchmark functions shows that KMA outperforms the recent metaheuristic algorithms. Besides, it provides high scalability to optimize thousand-dimensional functions. The source code of KMA is publicly available at: https://suyanto.staff.telkomuniversity.ac.id/komodo-mlipir-algorithm and https://www.mathworks.com/matlabcentral/fileexchange/102514-komodo-mlipir-algorithm.
Food 4.0: Implementation of the Augmented Reality Systems in the Food Industry

The food industry is very resource-intensive and continuously under stakeholder's scanner to address the impact of climate change, resource scarcity, ever-changing consumer demands and stringent legislations. It has resulted in the food industry adopting Industry 4.0 initiatives for changes. In this context, one of the key focus is on enhancing transparency through Augmented Reality (AR) experiences. Although the food industry has seen a rise in the adoption of Industry 4.0 technologies, the implementation of AR remains significantly low. This paper defines AR, its benefits, and challenges, and proposes a framework for AR implementation in the food industry.
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making

Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values. While feature selection approaches and imputation techniques address the former problems, the class imbalance is typically addressed using augmentation techniques. However, these techniques have been developed for big data analytics, and their suitability for clinical data sets is unclear.This study analyzed different augmentation techniques for use in clinical data sets and subsequent employment of machine learning-based classification. It turns out that Gaussian Noise Up-Sampling (GNUS) is not always but generally, is as good as SMOTE and ADASYN and even outperform those on some datasets. However, it has also been shown that augmentation does not improve classification at all in some cases.
Robust feature matching via neighborhood manifold representation consensus

Feature matching, which aims at seeking dependable correspondences between two sets of features, is of considerable significance to various vision-based tasks. This paper attempts to eliminate false correspondences from given tentative correspondences created on the basis of descriptor similarity. A simple yet efficient approach named neighborhood manifold representation consensus (NMRC) for robust feature matching is presented considering the stable neighborhood topologies of the potential true matches. The core principle of the proposed method is to preserve the local neighborhood structures between two feature points in a potential true match along a low-dimension manifold. Meanwhile, a neighborhood similarity-based iterative filtering strategy for neighborhood construction is designed to improve the matching performance under the circumstance of seriously deteriorated data. The matching problem is further formulated into a mathematical optimization model based on the neighborhood manifold representation and iterative filtering strategy, and a closed-form solution with linearithmic time complexity (i.e., O(NlogN)) is derived, which requires only tens of milliseconds to handle over 1000 putative correspondences. Extensive experiments on general feature matching (F-score > 94% for most cases), remote sensing image registration (RMSE < 3 for most cases), and loop closure detection demonstrate the significant superiority of the proposed method over several state-of-the-art competitors, such as RANSAC, MAGSAC++, and LPM.
LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model

Customer reviews on the Internet reflect users’ sentiments about the product, service, and social events. As sentiments can be divided into positive, negative, and neutral forms, sentiment analysis processes identify the polarity of information in the source materials toward an entity. Most studies have focused on document-level sentiment classification. In this study, we apply an unsupervised machine learning approach to discover sentiment polarity not only at the document level but also at the word level. The proposed topic document sentence (TDS) model is based on joint sentiment topic (JST) and latent Dirichlet allocation (LDA) topic modeling techniques. The IMDB dataset, comprising user reviews, was used for data analysis. First, we applied the LDA model to discover topics from the reviews; then, the TDS model was implemented to identify the polarity of the sentiment from topic to document, and from document to word levels. The LDAvis tool was used for data visualization. The experimental results show that the analysis not only obtained good topic partitioning results, but also achieved high sentiment analysis accuracy in document- and word-level sentiment classifications.
An improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism

Metaheuristic optimization algorithms are one of the most effective methods for solving complex engineering problems. However, the performance of a metaheuristic algorithm is related to its exploration ability and exploitation ability. Therefore, to further improve the African vultures optimization algorithm (AVOA), a new metaheuristic algorithm, an improved African vultures optimization algorithm based on tent chaotic mapping and time-varying mechanism (TAVOA), is proposed. First, a tent chaotic map is introduced for population initialization. Second, the individual's historical optimal position is recorded and applied to individual location updating. Third, a time-varying mechanism is designed to balance the exploration ability and exploitation ability. To verify the effectiveness and efficiency of TAVOA, TAVOA is tested on 23 basic benchmark functions, 28 CEC 2013 benchmark functions and 3 common real-world engineering design problems, and compared with AVOA and 5 other state-of-the-art metaheuristic optimization algorithms. According to the results of the Wilcoxon rank-sum test with 5%, among the 23 basic benchmark functions, the performance of TAVOA has significantly better than that of AVOA on 13 functions. Among the 28 CEC 2013 benchmark functions, the performance of TAVOA on 9 functions is significantly better than AVOA, and on 17 functions is similar to AVOA. Besides, compared with the six metaheuristic optimization algorithms, TAVOA also shows good performance in real-world engineering design problems.
Large-scale and energy-efficient tensorized optical neural networks on III–V-on-silicon MOSCAP platform

This paper proposes a large-scale, energy-efficient, high-throughput, and compact tensorized optical neural network (TONN) exploiting the tensor-train decomposition architecture on an integrated III–V-on-silicon metal–oxide–semiconductor capacitor (MOSCAP) platform. The proposed TONN architecture is scalable to 1024 × 1024 synapses and beyond, which is extremely difficult for conventional integrated ONN architectures by using cascaded multi-wavelength small-radix (e.g., 8 × 8) tensor cores. Simulation experiments show that the proposed TONN uses 79× fewer Mach–Zehnder interferometers (MZIs) and 5.2× fewer cascaded stages of MZIs compared with the conventional ONN while maintaining a &amp;gt;95% training accuracy for Modified National Institute of Standards and Technology handwritten digit classification tasks. Furthermore, with the proven heterogeneous III–V-on-silicon MOSCAP platform, our proposed TONN can improve the footprint-energy efficiency by a factor of 1.4 × 104 compared with digital electronics artificial neural network (ANN) hardware and a factor of 2.9 × 102 compared with silicon photonic and phase-change material technologies. Thus, this paper points out the road map of implementing large-scale ONNs with a similar number of synapses and superior energy efficiency compared to electronic ANNs.
A blockchain-based Fog-oriented lightweight framework for smart public vehicular transportation systems

Rapid urbanization is putting a strain on the transport systems of cities worldwide. The effects of this trend include prolonged traffic jams and increasing environmental pollution from rising CO2 emissions. As city planning requires innovative ways of dealing with the rapid urbanization trend, technological solutions were proposed such as cloud computing, smart vehicles, and Vehicular Ad hoc NETwork (VANET). In this paper, we take advantage of next-generation network technologies to propose a responsive and lightweight framework for smart transportation system which employs blockchain for authentication using fog computing’s improvement over cloud computing for distributed applications to provide an efficient and secure transportation system. We take into account the future technologies of 5G and Beyond 5G (B5G) and argue that the integration of B5G technologies, federated learning, blockchain, and edge computing provides the perfect platform necessary for a smart transportation system The evaluation of the proposed framework is done by comparing it to the current cloud-based approach in iFogSim, a popular simulation tool for fog computing research. The evaluation of blockchain-based authentication was done using a customized implementation of blockchain executed in an experimental setup. The simulation results showed that the proposed framework provides superior performance in terms of security, latency, and energy consumption of the system.
DSVP: Dual-Stage Viewpoint Planner for Rapid Exploration by Dynamic Expansion

We present a method for efficiently exploring highly convoluted environments. The method incorporates two planning stages - an exploration stage for extending the boundary of the map, and a relocation stage for explicitly transiting the robot to different sub-areas in the environment. The exploration stage develops a local Rapidly-exploring Random Tree (RRT) in the free space of the environment, and the relocation stage maintains a global graph through the mapped environment, both are dynamically expanded over replanning steps. The method is compared to existing state-of-the-art methods in various challenging simulation and real environments. Experiment comparisons show that our method is twice as efficient in exploring spaces using less processing than the existing methods. Further, we release a benchmark environment to evaluate exploration algorithms as well as facilitate development of autonomous navigation systems. The benchmark environment and our method are open-sourced.
Effects of lighting CCT and illuminance on visual perception and task performance in immersive virtual environments

In recent decades, immersive virtual environments (IVEs) have enabled users to experience various lighting scenarios with a high sense of presence and immersion, thereby having the potential to simulate office lighting design. To realistically experience lighting design cases in IVE lighting simulations, the CCT and illuminance levels should be accurately reproduced. However, there is still a lack of empirical evidence considering these factors. Thus, this study examined whether CCT and illuminance levels could be accurately reflected in IVE lighting simulations by investigating users’ responses to variations in CCT and illuminance levels in IVEs. To this end, we created nine IVE lighting cases and investigated the responses of participants regarding visual perception (comfort, naturalness, dimness, and warmness) and task performance measured through a questionnaire and the Landolt C test. The results indicated that, although using head-mounted displays affected the perception of visual comfort in some respects, the participants showed responses corresponding to the general findings of field lighting simulations of previous studies. In terms of task performance, participants showed improvement as the illuminance increased in IVEs, while there was no specific relationship between CCT and task performance due to the different effects of CCT on visual acuity and contrast sensitivity. The present study empirically identified the potential of IVE lighting simulations considering CCT and illuminance. With further investigations on more diverse office lighting conditions, IVE lighting simulations will help architects design optimal office lighting.
Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers

In this article, a new idea of chaos synchronization and chaos-based secure communication is developed. First, the chaotic master system is used as a transmitter in chaos-based secure communication, then a drive signal is constructed, and the information message is encrypted into the drive signal to form a transmitted signal for secure communication. Second, in the receiver, a recurrent Takagi-Sugeno-Kang (TSK) fuzzy brain emotional learning cerebellar model articulation controller (RTFBECAC) is developed to control the slave system to follow the master system in the transmitter. Third, after descripting the chaotic signal, the embedded information message can be recovered. Besides, the stability problem is analyzed in detail based on the stability theory. Finally, two simulation examples, including audio signal and image, are introduced to illustrate the effectiveness and the advantages of the proposed method.
Artificial Bee Colony Algorithm with Distant Savants for constrained optimization

Most of the scientific and engineering problems are defined as constrained optimization functions. It can be very difficult due to their complex structures. Artificial Bee Colony Algorithm (ABC) is a remarkable metaheuristic developed for global optimization problems. However, due to the inadequacy of ABC's search capability, it cannot handle constraint optimization problems very well. In this study, an ABC variant adapted for solving constrained optimization problems called Artificial Bee Colony Algorithm with Distant Savants (ABCDS) is proposed to overcome this deficiency. ABCDS is based on a new and adaptable search equation that enables learning with savants that are at a certain distance from each other. Also, the algorithm is hybridized with competitive local search mechanism. To test the performance of ABCDS, benchmark set for Constrained Real-Parameter Optimization defined in CEC 2017 conference (CEC2017COP) and some of the problems in the benchmark set on real-world problems defined in CEC 2020 conference (CEC2020) are used. The results obtained by the algorithm are compared with recent ABC algorithms and some state-of-the-art algorithms. According to the experimental results, ABCDS is better and competitive than the compared algorithms.
Techno-economic perspective of a floating solar PV deployment over urban lakes: A case study of NUST lake Islamabad

Water bodies like small lakes, canals, and rivers in urban areas serve to be a way forward to deploy photovoltaic technology with no constraints to involve land procurement. This article aims to estimate the potential deployment of a floating photovoltaic system on an urban lake site to assess its scope and compare it with a similar specification on-ground photovoltaic system. System Advisor Model (SAM) has been used for techno-economic analysis of a site in Pakistan. The technical analysis involves observing the effect of real time temperature drop and calculation of water reduction efficiency for FPV systems. The economic parameters like net present value (NPV) and payback period are used to judge the economic feasibility of the floating photovoltaic deployment project. The floating photovoltaic deployment in an urban area is subject to soiling as the water reservoir being used exists in an area close to or within the city boundaries. The required cleaning water costs a one-time extraction rate of $1435, while for a floating photovoltaic system, the extraction cost is estimated to be $1.35. Under standard temperature conditions (STC) one-year capacity factor turns out to be 0.70% more, producing an additional energy yield of 64 kWh/kW for lake scenarios when a 10 °C temperature drop is considered. The total power potential for the entire NUST Lake turns out to be 4.47 MW. A 1 MW FPV system in NUST lake would result in a water reduction efficiency of 11.6%/year. Under standard temperature conditions, the net present value for the on-ground system becomes negative while it remains optimistic for the floating photovoltaic system as no land costs are required. Similarly, once the land cost is included in the feasibility analysis, the payback period for the on-ground system goes beyond 15 years which is only 5.37 years for a floating photovoltaic system signifying the economic feasibility of the floating photovoltaic project.
The effect of spectral albedo in bifacial photovoltaic performance

This paper analyzes 15-months of spectral albedo measurements collected at the Technical University of Denmark (55.6°N, 12.1°E). High-resolution spectroradiometers are used to monitor four albedo scenarios, which include green vegetation, dry vegetation, gravel, and snow. Spectral mismatch and spectral impact are calculated for the front and backside of three different bifacial cell concepts mounted on horizontal single axis trackers and fixed-tilt substructures. The spectral nature of albedo is shown to have significant influence on bifacial photovoltaic performance wherein backside spectral impact as high as 1.20 is observed for fixed-tilt systems above green vegetation and as low as 0.98 for systems above snow. The results reveal that spectral impact is always lower on tracked than fixed-tilt systems because a greater fraction of sky diffuse light reaches the backside of tracked systems. Given the variety of albedos tested here, we find that the normalized difference vegetation index is a good predictor of backside spectral effects. When the high-resolution measurements are truncated to 4 to 8 carefully selected wavelengths, we find that this limited measurement resolution sufficiently captures the seasonal spectral albedo fluctuations that influence bifacial photovoltaic energy production. Finally, to alleviate the dearth of spectral datasets presently available to the PV community, the spectral irradiance and albedo measurements are made freely available in open access format (https://doi.org/10.11583/DTU.14695437.v1).
An informal institution comparative study of occupational safety knowledge sharing via French and English Tweets: languaculture, weak-strong ties and AI sentiment perspectives

To study the social structure of English and French Tweets of occupational safety and their sentiment distributions, this study applied NodeXL and MeaningCloud to analyse 17,147 English Tweets and 16,618 French Tweets about “occupational safety” in Twitter. We found that French and English Twitter users who are interested in this topic did not usually interact. While top English Twitter influencers were professors, top French influencers were government officers and individuals. Clusters of Twitter members interested in occupational safety had a low tendency to reach people in other groups. Most failed to make good use of weak ties to increase their impact and shared information about occupational safety outside their circle of friends. This overthrows previous research that Twitter’s social network was built based on the weak tie: Twitter users follow commentators, celebrities, and opinion leaders who do not know personally. Besides, we also conducted sentiment analysis via machine learning algorithms. We found that the more positive sentiment of an English Tweets, the more likely it will be retweeted. Yet, the more negative sentiment of a French Tweets, the more likely the Tweets will be retweeted. Thus, negative occupational safety Tweets have stronger impacts than positive ones among French but not English Tweets. While sentiment analysis results of French Tweets indicated that most Twitter users discussed occupational safety issues with a neutral tone, the number of extreme negative in French Tweets was a lot more than that of English. That reflects languaculture differences, and informal institutions impact users’ behaviours.