Reviewerly/exHarmony huggingface dataset:
An unsupervised semantic text similarity measurement model in resource-limited scenes

As the basis of many artificial intelligence tasks, text similarity measurement has received extensive attention in current studies. However, few of them focus on the resource-limited scenes (i.e., limited computational resources and few training datasets), which are becoming increasingly popular and challenging with the development of the Internet of Things. Worse still, popular methods such as the deep-neural-network-based methods may lose their power in such scenes, since they typically require considerable computational resources. As for most current traditional methods, they also have issues of not effectively exploiting the semantic information in the sentences. As an alternative, this paper proposes a lightweight and semantically rich text similarity measurement model named the TES-TK model. In this model, a sentence is first transformed into a tree structure called TES-Tree with the integration of syntactic information, semantic knowledge, and topic distribution, aiming to comprehensively represent the multidimensional semantics of sentences. Afterward, a modified tree kernel model is designed to calculate the similarity between each pair of TES-Trees. In this way, the similarity score between the two related sentences can be retrieved. Experiments on 19 public benchmark datasets (STS2012–2015) demonstrate that the proposed approach exhibits significantly better performance than the compared eight peer methods on most datasets. Especially in resource-limited scenes, our approach achieved highly competitive results compared with the latest methods, such as BERT.
A Framework to Co-Optimize Robot Exploration and Task Planning in Unknown Environments

Robots often need to accomplish complex tasks in unknown environments, which is a challenging problem, involving autonomous exploration for acquiring necessary scene knowledge and task planning. In traditional approaches, the agent first explores the environment to instantiate a complete planning domain and then invokes a symbolic planner to plan and perform high-level actions. However, task execution is inefficient since the two processes involve many repetitive states and actions. Hence, this letter proposes a framework to co-optimize robot exploration and task planning in unknown environments. To afford robot exploration and symbolic planning not being independent and separated, we design a unified structure named subtask, which is exploited to decompose the robot exploration and planning phases. To select the appropriate subtask each time, we develop a value function and a value-based scheduler to co-optimize exploration and task processing. Our framework is evaluated in a photo-realistic simulator with three complex household tasks, increasing task efficiency by 25%–29%.
A Ranking Learning Model by K-Means Clustering Technique for Web Scraped Movie Data

Business organizations experience cut-throat competition in the e-commerce era, where a smart organization needs to come up with faster innovative ideas to enjoy competitive advantages. A smart user decides from the review information of an online product. Data-driven smart machine learning applications use real data to support immediate decision making. Web scraping technologies support supplying sufficient relevant and up-to-date well-structured data from unstructured data sources like websites. Machine learning applications generate models for in-depth data analysis and decision making. The Internet Movie Database (IMDB) is one of the largest movie databases on the internet. IMDB movie information is applied for statistical analysis, sentiment classification, genre-based clustering, and rating-based clustering with respect to movie release year, budget, etc., for repository dataset. This paper presents a novel clustering model with respect to two different rating systems of IMDB movie data. This work contributes to the three areas: (i) the “grey area” of web scraping to extract data for research purposes; (ii) statistical analysis to correlate required data fields and understanding purposes of implementation machine learning, (iii) k-means clustering is applied for movie critics rank (Metascore) and users’ star rank (Rating). Different python libraries are used for web data scraping, data analysis, data visualization, and k-means clustering application. Only 42.4% of records were accepted from the extracted dataset for research purposes after cleaning. Statistical analysis showed that votes, ratings, Metascore have a linear relationship, while random characteristics are observed for income of the movie. On the other hand, experts’ feedback (Metascore) and customers’ feedback (Rating) are negatively correlated (−0.0384) due to the biasness of additional features like genre, actors, budget, etc. Both rankings have a nonlinear relationship with the income of the movies. Six optimal clusters were selected by elbow technique and the calculated silhouette score is 0.4926 for the proposed k-means clustering model and we found that only one cluster is in the logical relationship of two rankings systems.
Enhancing relevant concepts extraction for ontology learning using domain time relevance

• Concept extraction is the baseline task for an ontology learning system. • Ontology is a vital way of representing knowledge in more meaningful and machine-readable forms. • Sustainability distribution is important to extract modern terms and concepts. • Concepts extraction stopwords list for scientific publications is used to avoid noise data. Concepts extraction is the backbone task for an ontology construction system. Identifying the relevant concepts is considered one of the main challenges of automatic ontology construction (called Ontology Learning (OL)). This research introduces a novel relevance metric that estimates the sustainability distribution of concepts to identify the modern and relevant concepts, called Domain Time Relevance (DTR). Also, this research proposes a Developed Concepts extraction Model based on the proposed DTR, namely DTR-DCEM. This proposed model uses the proposed Concepts extraction stopwords (CE-stopwords) list to avoid noise data. This proposed model, DTR-DCEM, aims to extract the relevant concepts from scientific publications. The experiments are conducted in two different datasets: DL2019 and ACLRelAcS. The experimental results show that the proposed model DTR-DCEM outperforms the state-of-the-art models. It got between (3.32∼51.07)% better performance than comparative models for the DL2019 dataset and between (3.29∼24.51)% better performance for the ACLRelAcS dataset. Moreover, the statistical significance of the proposed model DTR-DCEM has been proved using paired t-test.
One4All: Manipulate one agent to poison the cooperative multi-agent reinforcement learning

Reinforcement Learning (RL) has achieved a plenty of breakthroughs in the past decade. Notably, existing studies have shown that RL is suffered from poisoning attack, which results in failure or even catastrophe in decision processes. However, these studies almost focus on single-agent RL setting, the cooperative Multi-Agent Reinforcement Learning (c-MARL) setting is less explored, which is a generalization of the single-agent RL setting and has achieved great success in many areas. As a sub-field of RL setting, c-MARL also faces some security issues, e.g., poisoning attack. In this paper, we introduce two novel poisoning attack techniques, i.e., State Noise Poisoning Attack (SNPA) and Target Action Poisoning Attack (TAPA), to attack the c-MARL setting stealthily and efficiently, which achieves the goal that poisoning the c-MARL setting while only manipulate one agent. The first attack technique, termed SNPA, a black-box attack method, modifies the state observation data of one agent, which results in the c-MARL setting performance degradation. The second attack technique, termed TAPA, a white-box attack method, injects a backdoor and triggers the target action by manipulating the action and reward function of one agent. The extensive experiments are conducted in two popular c-MARL games, i.e., MCSPT and MCHT. The experiment results show that the presented novel poisoning attacks, SNPA and TAPA, are effective on c-MARL scenarios. Specifically, the total reward is reduced by 1/3 and the winning rate of team drops down to 0 under the two proposed attacks. Furthermore, the TAPA experiments verify that the victim agent executes the target action once the backdoor is triggered. It is worth noting that the trigger rate of target action rises up to 99.31% and 99.01% in two c-MARL games.
Vizaj—A free online interactive software for visualizing spatial networks

In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been developed based on the rearrangement of the nodes so as to avoid link criss-cross. However, real networks are often embedded in a geometrical space and the nodes code for an intrinsic physical feature of the system that one might want to preserve. For these spatial networks, it is therefore crucial to find alternative strategies operating on the links and not on the nodes. Here, we introduce Vizaj a javascript web application to render spatial networks based on optimized geometrical criteria that reshape the link profiles. While optimized for 3D networks, Vizaj can also be used for 2D networks and offers the possibility to interactively customize the visualization via several controlling parameters, including network filtering and the effect of internode distance on the link trajectories. Vizaj is further equipped with additional options allowing to improve the final aesthetics, such as the color/size of both nodes and links, zooming/rotating/translating, and superimposing external objects. Vizaj is an open-source software which can be freely downloaded and updated via a github repository. Here, we provide a detailed description of its main features and algorithms together with a guide on how to use it. Finally, we validate its potential on several synthetic and real spatial networks from infrastructural to biological systems. We hope that Vizaj will help scientists and practitioners to make sense of complex networks and provide aesthetic while informative visualizations.
An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Convolutional Neural Networks

Deep neural networks (DNNs) are sensitive to adversarial data in a variety of scenarios, including the black-box scenario, where the attacker is only allowed to query the trained model and receive an output. Existing black-box methods for creating adversarial instances are costly, often using gradient estimation or training a replacement network. This paper introduces Query-Efficient Evolutionary Attack—QuEry Attack—an untargeted, score-based, black-box attack. QuEry Attack is based on a novel objective function that can be used in gradient-free optimization problems. The attack only requires access to the output logits of the classifier and is thus not affected by gradient masking. No additional information is needed, rendering our method more suitable to real-life situations. We test its performance with three different, commonly used, pretrained image-classifications models—Inception-v3, ResNet-50, and VGG-16-BN—against three benchmark datasets: MNIST, CIFAR10 and ImageNet. Furthermore, we evaluate QuEry Attack’s performance on non-differential transformation defenses and robust models. Our results demonstrate the superior performance of QuEry Attack, both in terms of accuracy score and query efficiency.
Geochemical Assessment of Mineral Resource Potential in a Hg-Sb-Pb-Zn Mining Area: The Almadén and Guadalmez Synclines (South-Central Spain)

The geochemical data from surface soils are often neglected or questioned when prospecting for ore deposits within active mining districts due to the “background noise” produced by anthropogenic pollution derived from mining activity. Large datasets on a national and international scale offer interesting possibilities to discover prospective zones. In the present work, data from the Geochemical Atlas of Castilla–La Mancha were treated in an area with an intense history of mining Hg, Pb, Zn, and Sb: the Almadén and Guadalmez synclines and the Alcudia Valley. The sampling grid was densified to adapt it to the scale of the main geological formations, and a cluster analysis was carried out to establish the relationships between the variables and a factor analysis to distinguish between geogenic and metallogenic factors. The results showed very high concentrations of some elements of prospective interest in local background areas: Hg (51 mg kg−1), Pb (1190 mg kg−1), and Sb (45 mg kg−1), with high variation coefficients. Cluster analysis unveiled a relationship between most of the local ore-forming metals (As, Sb, Hg, Pb, and Ag) with a clear correlation between Hg and organic matter, suggesting a notable contribution of Hg in soil. The factor analysis highlighted five factors, three geogenic and two ore-forming elements. Despite Hg being the main candidate to form a separate factor, its aerial deposition and a large number of outliers in the Almadén syncline contributed to integrating the element into a geogenic factor. Instead, factors F4 (Pb and Zn) and F5 (As and Sb) delimited the prospective areas in both synclines far from the known and the exploited mines. Some of these areas coincided with discovered mineralized zones, specifically in the area SE of a derelict Sb mine, confirming the usefulness of these datasets and statistical tools in areas with recent mining activity.
Teaching science language grammar to would be translators in vocationally oriented language learning via m-learning

Today, as pandemic has shown, m-learning has turned into the only possible form of education in numerous cases. To take full advantage of m-learning, one should create a suitable environment and choose appropriate techniques to provide the desired results. This study aims to prove the efficiency and effectiveness of teaching science language grammar to would-be translators in vocationally oriented language learning (VOLL) via m-learning within a student-centered approach. The research covered the spring semesters of 2019–2020, 2020–2021, sampling 120 second-year students from Peoples’ Friendship University of Russia (RUDN University), Academy of Engineering (seven departments). The researchers used theoretical, empirical, and experimental methods and prepared questionnaires and tests as data collection tools. The outcomes revealed all tricky issues for the students among the selected science language grammar points. The students of the experimental group used both MOODLE and the technology developed by the researchers. The experimental groups scored better than the control groups (90.1% vs. 65.4% and 89.5% vs. 67.2% correct answers in the final test, which meets the research criteria) and proved the efficiency and effectiveness of the proposed teaching technology.
Multiparameter transmission estimation at the quantum Cramér–Rao limit on a cloud quantum computer

Abstract Estimating transmission or loss is at the heart of spectroscopy. To achieve the ultimate quantum resolution limit, one must use probe states with definite photon number and detectors capable of distinguishing the number of photons impinging thereon. In practice, one can outperform classical limits using two-mode squeezed light, which can be used to herald definite-photon-number probes, but the heralding is not guaranteed to produce the desired probes when there is loss in the heralding arm or its detector is imperfect. We show that this paradigm can be used to simultaneously measure distinct loss parameters in both modes of the squeezed light, with attainable quantum advantages. We demonstrate this protocol on Xanadu’s X8 chip, accessed via the cloud, building photon-number probability distributions from 10 6 shots and performing maximum likelihood estimation (MLE) on these distributions 10 3 independent times. Because pump light may be lost before the squeezing occurs, we also simultaneously estimate the actual input power, using the theory of nuisance parameters. MLE converges to estimate the transmission amplitudes in X8’s eight modes to be 0.392 02(6), 0.307 06(8), 0.369 37(6), 0.287 30(9), 0.382 06(6), 0.304 41(8), 0.372 29(6), and 0.286 21(8) and the squeezing parameters, which are proxies for effective input coherent-state amplitudes, their losses, and their nonlinear interaction times, to be 1.3000(2), 1.3238(3), 1.2666(2), and 1.3425(3); all of these uncertainties are within a factor of two of the quantum Cramér–Rao bound. This study provides crucial insight into the intersection of quantum multiparameter estimation theory, MLE convergence, and the characterization and performance of real quantum devices.
A Causal Study On Hybrid Model And Its Impact On Employee Job Performance

Organizations are changing to meet the changing needs of their customers and employees. The functional way of working from conservative to technological mode has gained the attention of employers to be more focused on employees' work life balance and performance. In the technology era, the concept of hybrid models has become most commonly adopted in the third decade of the 21st century, especially in the IT sector. They were conducting research with millennial generations when the COVID-19 pandemic occurred, causing an abrupt global change. This has completely changed the way the organisation was functioning and affected each and every aspect of its functions. This includes working style, communication patterns, working hours, job performance, and interpersonal relationships.&#x0D; Organizations are forced to implement the hybrid model to meet the changing needs of the business to ensure the business runs on its own and employees are performing at their best. The digital transformation has become the medium of choice for organisations to integrate the office workplace and flexible working hours. Employees nowadays prefer to work from home because technology allows them to work from anywhere while still having time to care for their families. This was the least preferred for employers before the pandemic, and after the pandemic, the employee’s choice has become the liberal working style. To meet the changing needs of the employees, the hybrid work model is implemented across the globe at all levels. Though it gives the advantage of working from anywhere, at the same time, it has impacted job performance. The individual contributor’s performance was doing well, but team performance has suffered remarkably. The teleworking model has affected interpersonal relationships among employees and job performance has become a questionable element for organizations. The front line managers are focused on tasks, extended working hours, and the employees' personal concerns are ignored due to lack of personal touch and exchange of encouraging words. This impacted the job performance and led to high employee turnover. Organizations are trying to address employees' concerns through various programmes to build relationships and improve job performance. The objective of this study is to analyse the impact of the hybrid model and to provide insights on how to manage employees to perform better at work.
The (In)effectiveness of Attention Guidance Methods for Enhancing Brand Memory in 360° Video

Sensing and remembering features in visual scenes are conditioned by visual attention and methods to guide it. This should be relevant in terms of product placement, which has become an important part of incorporating brands into different mass media formats with a commercial purpose. The approach can be challenging in 360° video, where an omnidirectional view enables consumers to choose different viewing perspectives, which may result in overlooking the brands. Accordingly, attention guidance methods should be applied. This study is the first to explore diegetic guidance methods as the only appropriate guiding method for an unobtrusive and unconscious nature of product placement. To test the effectiveness of three different diegetic guiding methods, a between-subject design was employed, where the participants were assigned randomly to one of four videos with the same scene but different guiding methods. The findings show and explain the discrepancy with studies on guiding attention in other contexts, as there were no significant differences between the guiding cues according to brand recall and brand recognition. The results also indicate a significant influence of brand familiarity on brand recall in 360° video. The article concludes by providing limitations, future research directions, and recommendations for audiovisual policy.
Impacts of the Aerosol Representation in WRF-Solar Clear-Sky Irradiance Forecasts over CONUS

Abstract Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) the default Tegen climatology, 2) imposing AOD forecasts from the GEOS-5 model, 3) imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model, and 4) the Thompson–Eidhammer aerosol-aware water/ice-friendly aerosol climatology. More than 8 months of these 15-min output forecasts are compared with high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD 550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected because of the lesser sensitivity of GHI to aerosol loading. Hourly average clear-sky DNI and GHI were also analyzed, and they were additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD 550 than WRF-Solar is. Significance Statement Particles in the atmosphere called aerosols, which can include dust, smoke, sea salt, sulfates, black carbon, and organic carbon, absorb and scatter incoming sunlight. Improving the representation of aerosols in numerical weather prediction models reduces forecast errors in solar irradiance at ground level, particularly direct normal irradiance, during cloud-free conditions. This in turn should result in improved accuracy of solar power forecasts, especially for concentrated solar power (CSP) plants. CSP plants tend to be built in more arid, less cloudy regions that are also prone to dust loading, so accurate aerosol forecasts are particularly relevant. Comparing four representations of aerosols in the WRF-Solar model over eight months of forecasts across the United States reveals substantial differences in clear-sky irradiance forecast skill.