Miracle-dz/UniFAR-FTUs huggingface dataset:
MemNet: A Knowledge Graph for Common-Sense Reasoning and Generalization about Real-World Actions and Objects

: In this paper, we explore how artificial agents (AAs) can understand and reason about so called ”action pat-terns” within real-world settings. Essentially, we want AAs to determine which tools fit specific actions, and which actions can be executed with certain tools, objects or agents, based on real-world situations. To achieve this, we utilize a comprehensive Knowledge Graph, called ”Memory Net” filled with interconnected everyday concepts, common actions, and environmental data. Our approach involves an inference technique that harnesses semantic proximity through subgraph matching. Comparing our approach against human responses and a state-of-the-art natural language model based machine learning approach in a home scenario, our Knowledge Graph method demonstrated strong generalization capabilities, suggesting its promise in dynamic, incremental and interactive real world settings.
Perspective and Prospects on Applying Artificial Intelligence to Perspective and Prospects on Applying Artificial Intelligence to Address Water and Environmental Challenges of 21st Century Address Water and Environmental Challenges of 21st Century

Abstract One of the most pervasive challenges affecting human and planetary well-being is inadequate access to clean water and sanitation. Problems with water are expected to become worse in the coming decades, with water scarcity occurring globally, in the face of ever-growing populations, intensive human activities, and climatic variation. Addressing the aforementioned water security has been achieved consensus and has been included into the sustainable development goals (SDGs) set by the United Nations' Agenda 2030. Despite these ample opportunities, it remains challenging to create reliable, sustainable, and affordable solutions to providing universal access to clean water and sanitation. In this context, the emerging artificial intelligence (AI) technology can be an attractive solution to help with this challenge. We summarized the core of the SDGs-Goal 6 (Clean Water and Sanitation) and the problems encountered during the progress to date. Building upon which, we conducted a literature review and provided a state-of-the-art analysis of leveraging AI to help achieving SDGs-Goal 6 alongside the resultant impacts. Afterwards, we highlighted the key issues necessary to be tackled in the coming years if AI is expected to be well applied with its maximum benefits. Plus, we put forward the prospects of future efforts on this revolution.
Code of Corporate Governance and Tax Compliance: Evidence from Malaysia

This study examined the extent of intellectual capital (IC) disclosure practices between Malaysian and Indonesian agricultural companies. This study relied on content analysis on the annual reports of public listed agricultural companies in Malaysia and Indonesia. The results show that the extent of IC disclosure practices of the companies between the two countries are similar. However, the Malaysian companies disclose more information on human capital and are more quantitative in nature whilst the Indonesian companies disclose more information on relational capital and more qualitative. The results also show a significant positive relationship between the extent of IC capital disclosure and companies’ performance. The findings in this study contribute further understanding on the extent of IC capital disclosure in the agriculture industry. This study extends the understanding of the role of IC and its interaction in generating competitive edge from the perspective of a developing nation such as Malaysia and Indonesia.
Annual Reviews in Control

This article provides an overview of certain direct data-driven control results, where control sequences are computed from (noisy) data collected during offline control experiments without an explicit identification of the system dynamics. For the case of noiseless datasets, we derive several closed-form data-driven expressions that solve a variety of optimal control problems for linear systems with quadratic cost functions of the state and input (including the linear quadratic regulator problem, the minimum energy control problem, and the linear quadratic control problem with terminal constraints), discuss their advantages and drawbacks with respect to alternative data-driven and model-based approaches, and showcase their effectiveness through a number of numerical studies. Interestingly, these results provide an alternative and explicit way of solving classic control problems that, for instance, does not require the solution of an implicit and recursive Riccati equation as in the model-based setting. For the case of noisy datasets, we show how the closed-form expressions derived in the noiseless setting can be modified to compensate for the bias induced by noise, and perform a sensitivity analysis to reveal favorable asymptotic robustness properties of the derived data-driven controls. We conclude the paper with some considerations and a discussion of outstanding questions and directions of future investigation.
MIT Open Access Articles Mucin Biopolymers As Broad-Spectrum Antiviral Agents

Mucus is a porous biopolymer matrix that coats all wet epithelia in the human body and serves as the first line of defense against many pathogenic bacteria and viruses. However, under certain conditions viruses are able to penetrate this infection barrier, which compromises the protective function of native mucus. Here, we find that isolated porcine gastric mucin polymers, key structural components of native mucus, can protect an underlying cell layer from infection by small viruses such as human papillomavirus (HPV), Merkel cell polyomavirus (MCV), or a strain of influenza A virus. Single particle analysis of virus mobility inside the mucin barrier reveals that this shielding effect is in part based on a retardation of virus diffusion inside the biopolymer matrix. Our findings suggest that purified mucins may be used as a broad-range antiviral supplement to personal hygiene products, baby formula or lubricants to support our immune system.
Can Hypoxia-Inducible Factor 1α be used as a Biomarker to Evaluate Disease Severity and Prognosis in COVID-19 Patients?

Background: This study was aimed to answer the questions of whether the serum levels of Hypoxia-Inducible Factor 1α (HIF-1α), which is increased by up to 100 times in many tissues including pulmonary tissue in cases of acute lung injury, could be used as a parameter for monitoring the severity and prognosis in COVID-19 patients. Material and Method: 40 patients, who were admitted to the hospital with COVID-19 clinical symptoms, and 20 healthy control subjects were included in the study. The diagnosis of 20 patients within the patient group were confirmed by the PCR test. The remaining 20 patients were regarded as COVID-19 suspect group. Clinical and laboratory data of patients on admission were recorded. Clinical laboratory tests and serum HIF-1α levels were measured from the blood samples of COVID-19 group on the day of admission and one week after hospitalization. COVID-19 group was divided into four subgroups according to disease severity and HIF-1α values of each group were compared. Results: In this study, serum HIF-1α values of confirmed COVID-19 patient group were measured higher than healthy control group’s serum HIF-1α values, however no significant difference was found for the COVID-19 suspect group. Within confirmed COVID-19 group, serum HIF-1α values on admission were higher than values after hospitalization, whereas Monocyte count, Platelet count and Ferritin values were lower. Among the confirmed COVID-19 cases, critically ill subgroup’s serum HIF-1α levels of the first week were significantly lower than mild subgroup’s serum HIF-1α levels of both the first week and the day of admission. HIF-1α values of COVID-19 group were strongly negative correlated with age, whereas weakly positive correlated with platelet counts. Conclusions: HIF-1α, which are thought to prevent alveolar damage, increased in COVID-19 patients. Additionally, low levels of HIF-1α in COVID-19 patients might be considered as a factor responsible for the aggravation of the clinical severity.
FPDM: Domain-Specific Fast Pre-training Technique using Document-Level Metadata

Pre-training Transformers has shown promising results on open-domain and domain-specific down-stream tasks. However, state-of-the-art Transformers require an unreasonably large amount of pre-training data and compute. In this paper, we propose FPDM (Fast Pre-training Technique using Document Level Metadata), a novel, compute-efficient framework that utilizes Document metadata and Domain-Specific Taxonomy as supervision signals to pre-train transformer encoder on a domain-specific corpus. The main innovation is that during domain-specific pretraining, an open-domain encoder is continually pre-trained using sentence-level embeddings as inputs (to accommodate long documents), however, fine-tuning is done with token-level embeddings as inputs to this encoder. We show that FPDM outperforms several transformer-based baselines in terms of character-level F1 scores and other automated metrics in the Customer Support, Scientific, and Legal Domains, and shows a negligible drop in performance on open-domain benchmarks. Importantly, the novel use of document-level supervision along with sentence-level embedding input for pre-training reduces pre-training compute by around 1 , 000 , 4 , 500 , and 500 times compared to MLM and/or NSP in Customer Support, Scientific and Legal Domains, respectively 1 .
Knowledge Mapping for Open Sensemaking Communities

By analogy to cartographic representations of spatial worlds, Knowledge Maps provide an ‘aerial view’ of a topic by highlighting key elements and connections. Moreover, just as spatial maps simplify the world and can fuel controversy, maps of conceptual worlds provide vehicles for summarising and negotiating meaning. In conjunction with the UK Open University’s Open Educational Resources OpenLearn project, we are investigating the role of such maps for both learners and educators to share – and debate – interpretations of OERs. In this brief update, we describe how a mapping tool (Compendium) has been integrated with OpenLearn’s elearning platform (Moodle) in order to support tasks such as concept analysis, problem-solving, literature review, learning path planning, argument analysis and OER design.
Gay Men’s Understanding and Education of New HIV Prevention Technologies in Vancouver, Canada

Effective rollout of HIV treatment-based prevention such as pre-exposure prophylaxis and treatment as prevention has been hampered by poor education, limited acceptability, and stigma among gay men. We undertook a thematic analysis regarding the education sources and acceptability of these New Prevention Technologies (NPTs) using 15 semistructured interviews with gay men in Vancouver, Canada, who were early adopters of NPTs. NPT education was derived from a variety of sources, including the Internet, health care providers, community organizations, sexual partners, and peers; participants also emphasized their own capacities as learners and educators. Acceptable forms of NPT education featured high-quality factual information, personal testimony, and easy access. Stigma was highlighted as a major barrier. For public health, policy makers, and gay communities to optimize the personal and population benefits of NPTs, there is a need for increased community support and dialogue, antistigma efforts, early NPT adopter testimony, and personalized implementation strategies.
Hybrid Machine Learning-Based Intelligent Distance Protection and Control Schemes With Fault and Zonal Classification Capabilities for Grid- Connected Wind Farms

The poor performance of distance relays may stem from the inherently intermittent nature of wind power generation, presence of power electronic converters, the impact of power system harmonics, existence of series-compensated transmission lines, uncertainty of fault resistance, etc., which may adversely impact the performance of the distance protection relays. Thus, the distance relays in wind farms became a significant concern for utilities committed to ensuring compliance to the grid codes such as voltage-ride through capability and provide a stable and reliable power to the customers. Specialized algorithms, intelligent controllers, and advance monitoring systems must be developed to address these issues. Therefore, this article presents the analysis of several hybrid intelligent protection and control algorithms to improve the reliability of doubly fed induction generator (DFIG)-based wind farms during faults, and other dynamic operating conditions. First, a decision tree (DT) classification algorithm is developed as a fault classifier for the purpose of distinguishing between different types of faults, as well as normal operation and grid disturbances. Next, a support vector machine (SVM) as a fault location estimator and zonal protection scheme is proposed to assist with the decision-making process of distance relay by detecting the location of any type of fault on the transmission line, and precise line zoning protection with a high reliability. Lastly, a combined direct PI control-based scheme is developed for both rotor and grid side converters of the DFIG based wind energy conversion system (WECS). This scheme avoids extra PI based current loop to achieve robust performance at the time of grid side voltage dip as well as normal operating condition. The analysis of the proposed intelligent protection and control schemes exhibits satisfactory results in improving the reliability and stability of grid-connected wind farms.
Post-Layout Optimization for Field-coupled Nanotechnologies

While conventional computing technologies reach their limits, the demand for computation power keeps growing, fueling the interest in post-CMOS technologies. One promising contestant in this domain is Field-coupled Nanocomputing (FCN), which conducts computations based on the repulsion of physical fields at the nanoscale. However, to realize a dedicated functionality in this technology design methods are needed that create corresponding FCN lay-outs. While several methods for FCN layout generation have been proposed in the past, the underlying complexity requires them to resort to heuristic approaches—leading to results of sub-par quality and offering room for improvement. In conventional CMOS design, post-layout optimization methods are available to exploit this potential for further improvement. Unfortunately, no such methods exists yet for FCN. In this work, we are addressing this gap and introduce the first post-layout optimization approach for FCN. Experimental evaluations show the benefits of the approach: Applied to layouts generated by two complementary state-of-the-art methods, the proposed post-layout optimization allows for a further area reduction of 50 . 79% and 20 . 00% on average, respectively—confirming the potential of post-layout optimization for FCN.
α2-Adrenoceptors Potentiate Angiotensin II- and Vasopressin-Induced Renal Vasoconstriction in Spontaneously Hypertensive Rats

Hypertension in spontaneously hypertensive rats (SHRs) is due in part to enhanced effects of vasoactive peptides on the renal vasculature. We hypothesize that the Gi signal transduction pathway enhances renovascular responses to vasoactive peptides in SHRs more so than in normotensive Wistar-Kyoto (WKY) rats. To test this hypothesis, we examined in isolated perfused kidneys from SHRs and WKY rats the renovascular responses (assessed as changes in renal perfusion pressure in mm Hg) to angiotensin II (10 nM) and vasopressin (3 nM) in the presence and absence of UK-14,304 [5-bromo-N-(4,5-dihydro-1H-imidazol-2-yl)-6-quinoxalinamine; an agonist that selectively activates the Gi pathway by stimulating α2-adrenoceptors]. In SHR, but not WKY, kidneys, UK-14,304 (10 nM) enhanced (P < 0.05) renovascular responses to angiotensin II (control WKY, 43 ± 6; UK-14,304-treated WKY, 52 ± 19; control SHR, 66 ± 17; UK-14,304-treated SHR, 125 ± 16) and vasopressin (control WKY, 42 ± 17; UK-14,304-treated WKY, 36 ± 11; control SHR, 16 ± 8; UK-14,304-treated SHR, 83 ± 17). Pretreatment of SHRs with pertussis toxin (30 μg/kg, intravenously, 3–4 days before study) to inactivate Gi blocked the effects of UK-14,304. Western blot analysis of receptor expression in whole kidney and preglomerular microvessels revealed similar levels of expression of AT1, V1a, and α2A receptors in SHRs compared with WKY rats. We conclude that activation of α2-adrenoceptors selectively enhances renovascular responses to angiotensin II and vasopressin in SHRs via an enhanced cross talk between the Gi signal transduction pathway and signal transduction pathways activated by angiotensin II and vasopressin.
Detecting Toxic Content Online and the Effect of Training Data on Classification Performance

The spread of toxic content online has attracted a wealth of research into methods of automatic detection and classification in recent years. However, two limitations still exist: 1) the lack of support for multi-label classification; and 2) the lack of understanding of the impact of the typical unbalanced datasets on such tasks. In this work, we build three state of the art methods for the task of multi-label classification of toxic content online, and compare the effect of training data size on their performance. The three methods of choice are based on Support Vector Machine (SVM), Convolutional Neural Networks (CNN) and Long-Short-Term Memory Networks (LSTM), respectively. We conduct learning curve analysis and show that CNN is the most robust method as it outperforms the other two regardless of the sizes of the dataset, even on very small amounts of data. This challenges the conventional belief that Neural Networks require significant amounts of data to train accurate models. We also empirically derive indicative thresholds of training data size to help determine a reliable estimate of classifier performance, or maximise potential classifier performance in such tasks.
Characterisation of particle single scattering albedo with a modified airborne dual-wavelengths CAPS monitor

Atmospheric aerosols impact the Earth’s climate system directly by scattering and absorbing solar radiation, and it is important to characterise the aerosol optical properties in detail. This study reports the development and validation of an airborne dual-wavelength cavity-attenuated phase shift-single (CAPS) monitor, named A2S2 (Aerosol Absorption Spectral Sizer) based on the commercial CAPS single scattering albedo monitor (CAPS-PM SSA , Aerodyne), to simultaneously measure the aerosol 15 optical scattering and extinction at both 450 nm and 630 nm wavelengths. New pressure and temperature sensors and an additional flow control system were incorporated into the A2S2 for its utilization onboard research aircraft measuring within the troposphere. The evaluation of A2S2 characteristics was performed in the laboratory and included the investigation of the signal-to-noise ratio, validation of performance at various pressure levels, optical-closure studies and intercomparing with the currently validated techniques. These laboratory characterisation experiments show that the A2S2 can perform measurements 20 at sample pressures as low as 550 hPa and at sample temperatures as high as 315K, with an uncertainty of 1 Mm -1 at 450 nm and 0.3 Mm -1 at 630 nm for 1 Hz measurements of both scattering coefficients ( 𝜎 ! "# ) and extinction coefficients ( 𝜎 $%& ). The optical-closure study with size-selected polystyrene latex (PSL) particles show that the truncation error of the A2S2 is negligible for particles with particle volume diameter ( D p ) < 200 nm, while for the larger sub-micrometre particles, the measurement uncertainty of A2S2 increases but remains less than 20%. The A2S2 shows good agreement with the validated 25 instruments for the 𝜎 ! "# and 𝜎 $%& at 450 nm and 630 nm. The A2S2 was successfully deployed during an aircraft measurement campaign (ACROSS) conducted in the vicinity of Paris and the surrounding regions. The average SSA measured during the entire ACROSS flight campaign is 0.86 and 0.88 at 450 nm and 630 nm, respectively, while the Scattering Ångström Exponent (SAE) varies due to measurements in various pollution conditions. The A2S2 measured 𝜎 ! "# results exhibit overall good agreement with the nephelometer results, and it successfully produced altitude profile results over the varied background 30 conditions. The results presented in this study indicate that the A2S2 instrument is reliable for measuring aerosol 𝜎 ! "# and 𝜎 $%& at both blue and red wavelengths, and it is suitable to replace the nephelometer onboard for future aircraft campaigns.
MEASURING INSTANTANEOUS VELOCITY IN FOUR SWIM STROKES USING AN AUTOMATIC VIDEO-BASED SYSTEM: A COMPARISON STUDY

This study compared instantaneous swimming velocity from an automated video-based system to a tethered speedometer. Twenty-two state-and national-level swimmers (7 M, 15 F; 14.5 ± 2.5 yrs) swam 25 m of each stroke at maximal intensity. Bland-Altman plots showed good agreement between systems for backstroke and freestyle but poorer agreement for butterfly and breaststroke. The RMS error was also lower in backstroke and freestyle compared to butterfly and breaststroke. The differences in systems may be explained by the different body segments tracked by each system (head vs hips) and with differences being more apparent during butterfly and breaststroke due to the wave-like motion of these strokes. While the automated video-based system is suitable for measuring instantaneous swimming velocity, coaches, sports scientists, and swimmers should be aware of larger discrepancies between systems when assessing butterfly and breaststroke.
Helmet Detection and Number Plate Recognition using Machine Learning

5Professor-K.G. Shinde, Dept. of Computer Engineering, Sinhgad College of Engineering, Maharashtra, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract There are very few automobiles in developing countries because motorcycles have always been the predominant mode of transport. Motorcycle crashes have been on the rise in the last few years. A number of people who are involved in traffic collisions include motorcyclists who do not wear reflective helmets, since they do not believe they provide sufficient protection. Once the traffic police spot those driving motorcycls on a whole-or mMotorcycles in junctions-without helmets, they also use video from CCTV to take control of the drivers of those vehicles and penalise those who are riding without one. However, it can only be achieved through human action and commitment. Secondly, the classifies moving vehicles as motorcycle or nonmotorcycle. for example, when referring to the head component, in the case of a motorcyclists, it is graded as either full face or non-full face. An excellent image analysis of the motorcycle number is then used to extract the characters that were missed by the identification software and/ Finally, the character count of the motorcycle is found, and from the motorcycle is examined using OCR software. It is an Object Detection Algorithm used to identify faces in an image or a real time video. The algorithm uses edge or line detection features proposed by Viola and Jones in their research paper “Rapid Object Detection using a Boosted Cascade of Simple Features. A Convolutional Neural Network is a Deep Learning algorithm which can take in an input image, assign importance to various aspects/objects in the image and be able to differentiate one from the other. CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed.