Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT and WSN based Effluent Treatment Plant Monitoring System

Contaminated water became a major issue for our country from the last few decades. One of the main reasons behind this scenario is urbanization and industrialization. Every industry should have an Effluent Treatment Plant (ETP) for treating industrial wastewater and safe disposal to the environment. We implement a system that monitors whether an industry uses ETP or not. To monitor ETP, we need to monitor the untreated waste water quality. The traditional way offers us a method that is time-consuming and inefficient. To solve this problem, we adopt a model based on Wireless Sensor Networking (WSN), which allows us to keep track of the water quality parameters in real-time. This paper proposes a water quality monitoring system that uses WSN and Internet of Things (IoT) based devices to monitor different parameters of water: temperature by a temperature sensor, turbidity by a turbidity sensor, and pH by a pH sensor. Moreover, the microcontroller of Arduino Uno R3 collects the parameter values from these sensors and transmits the values to the IoT based cloud server using the GSM module. The GSM module also used to alert the supervisors by sending SMS in case of an emergency. Integrating modules such as sensors, Arduino Uno R3,GSM module, enhances the purpose of the desired system. Finally, we calculate the Water Quality Index (WQI) for the pH and turbidity data to report the water quality status. Also, we compare the WQI status with our cloud status, and it shows an excellent performance.
A STDP Rule that Favours Chaotic Spiking over Regular Spiking of Neurons

We compare the number of states of a Spiking Neural Network (SNN) composed from chaotic spiking neurons versus the number of states of a SNN composed from regular spiking neurons while both SNNs implementing a Spike Timing Dependent Plasticity (STDP) rule that we created. We find out that this STDP rule favors chaotic spiking since the number of states is larger in the chaotic SNN than the regular SNN. This chaotic favorability is not general; it is exclusive to this STDP rule only. This research falls under our long-term investigation of STDP and chaos theory.
FQDN-Based Whitelist Filter on a DNS Cache Server Against the DNS Water Torture Attack

A distributed denial-of-service (DDoS) attack is a major social issue, such as the Domain Name System (DNS) DDoS attack against Dyn Inc., a DNS provider, which caused serious outages to several web services in 2016. This paper tackles the DNS water torture attack that was observed in the Dyn cyberattack. In the DNS water torture attack, attackers create a large number of unique and unresolvable fully qualified domain names (FQDNs) with random labels and send them to DNS cache servers and authoritative DNS servers, causing these servers to fail. Although countermeasures on DNS cache servers have been proposed to prevent such attack, one drawback of these countermeasures is that they cannot detect malicious DNS queries generated by an advanced DNS water torture attack. To address this shortcoming, we propose an FQDN-based whitelist filter that registers actually existing FQDNs and drops the non-existent ones created by the attackers. This whitelist filter eliminates malicious DNS queries while mitigating the negative impact of falsely dropping legitimate ones.
Prediction of HFMD Cases by Leveraging Time Series Decomposition and Local Fusion

Hand, foot, and mouth disease (HFMD) is an infection that is common in children under 5 years old. This disease is not a serious disease commonly, but it is one of the most widespread infectious diseases which can still be fatal. HFMD still poses a threat to the lives and health of children and adolescents. An effective prediction model would be very helpful to HFMD control and prevention. Several methods have been proposed to predict HFMD outpatient cases. These methods tend to utilize the connection between cases and exogenous data, but exogenous data is not always available. In this paper, a novel method combined time series composition and local fusion has been proposed. The Empirical Mode Decomposition (EMD) method is used to decompose HFMD outpatient time series. Linear local predictors are applied to processing input data. The predicted value is generated via fusing the output of local predictors. The evaluation of the proposed model is carried on a real dataset comparing with the stateof-the-art methods. The results show that our model is more accurately compared with other baseline models. Thus, the model we proposed can be an effective method in the HFMD outpatient prediction mission.
CacheInspector

Infrastructure-as-a-Service cloud providers sell virtual machines that are only specified in terms of number of CPU cores, amount of memory, and I/O throughput. Performance-critical aspects such as cache sizes and memory latency are missing or reported in ways that make them hard to compare across cloud providers. It is difficult for users to adapt their application’s behavior to the available resources. In this work, we aim to increase the visibility that cloud users have into shared resources on public clouds. Specifically, we present CacheInspector, a lightweight runtime that determines the performance and allocated capacity of shared caches on multi-tenant public clouds. We validate CacheInspector’s accuracy in a controlled environment, and use it to study the characteristics and variability of cache resources in the cloud, across time, instances, availability regions, and cloud providers. We show that CacheInspector’s output allows cloud users to tailor their application’s behavior, including their output quality, to avoid suboptimal performance when resources are scarce.
Nationwide Deployment and Operation of a Virtual Arrival Detection System in the Wild

We report a 30-month nationwide deployment and operation study of an indoor arrival detection system based on Bluetooth Low Energy called VALID in 364 Chinese cities. VALID is pilot-studied, deployed, and operated in the wild to infer real-time indoor arrival status of couriers, and improve their status reporting behavior based on the detection. During its full nationwide operation (2018/12-2021/01), VALID consists of virtual devices at 3 million shops and restaurants, where 530,859 of them are in multi-story malls and markets to infer and influence 1 million couriers’ behavior, and assist the scheduling of 3.9 billion orders for 186 million customers. Although indoor arrival detection is straightforward in controlled environments, the scale of our platform makes the cost prohibitively high. In this work, we explore to use merchants’ smartphones under their consent as a virtual infrastructure to design, build, deploy, and operate VALID from in-lab conception to nationwide operation in three phases for 30 months. We consider metrics including system evolution, reliability, utility, participation, energy, privacy, monetary benefits, along with couriers’ behavior changes. We share three lessons and their implications for similar wireless sensing or communication systems with large geospatial operations.
A review on Euryale Ferox: Daily dietary regime for Covid -19 patients

In recent pandemic of Covid-19 i.e. Corona Virus disease, patients who are affected by this disease shows symptoms related to different system of body in which most commonly affected parts are lungs which usually results in mortality of patients. It has also seen that older adults and those having COPD, Heart Disease, Diabetes, Chronic Kidney Disease, and obesity are at increased risk for severe COVID -19 requiring hospitalization 1. This Covid 19 can also produce abnormally high blood sugar levels, even in people without diagnosed diabetes. It also causes fluctuation in Blood Pressure by affecting ACE and RAAS system. Fox Nut or Euryale Ferox is rich in protein, fibres and carbohydrates. The Euryale ferox seeds is a grain dish good for baby and also for an old man to prolong life. It has characteristics of strengthening the spleen, nourishing the stomach, supplementing the kidney 2. Thus, makhana can be used as nutraceutical supplement for Covid -19 Patients. In a daily diet, it is also believed to increase humoral immunity and cell mediated immunity to some extent. The Euryale ferox seeds and glutinous rice are used in 1:3 ratio to prepare rice dumplings in China. These act as super natural food in diet therapy 11. Euryale ferox has many bioactive constituents which performs anti-oxidant activity, anti-diabetic activity, Anti-hyper lipidemic activity, Hepatoprotective activity, anti-cytotoxicity, cardioprotective, anti-depressant activity and much more. Thus, Euryale ferox could be an ideal supplement for Covid -19 patients.
Seasonal variability of trends in regional hot and warm temperature extremes in Europe

<p>Extremely hot or warm weather over the course of the year may have significant impacts on many aspects of human life, economy, and the natural environment. Until now a comprehensive assessment of changes of extreme heat or warm events in Europe was hindered by the number of metrics employed, time periods examined, and most studies being conducted in the summer season only. This study was designed to systematically investigate long-term trends in the number of extremely hot or warm days in Europe over the course of the year, with a special focus on their spatial extent and intensity. Here, an extreme temperature event (ETE) is defined as a day with an unusually high temperature for a given location and season, even if such a temperature would not be considered extremely high in an absolute sense. </p><p>The research was conducted in five spatial domains representing different regions of Europe that together cover a large portion of the continent. The period from spring 1950 to winter 2019/2020 was considered using E-OBS gridded dataset v.21. 0e. ETEs were analysed from a spatial perspective and were defined as days when the maximum air temperature exceeds the local percentile-based threshold across at least 10% of the area of the given domain. The severity of each ETE was assessed using the Extremity Index (EI) that combines information on the intensity and spatial range of an event. Given that EI is based on relative measures, it enables the comparison of results obtained in different geographic areas and seasons of the year. The main focus of the analysis was on the evaluation of seasonal EI trends and variability, as well as trends of frequency, spatial range, and intensity of ETEs in five spatial domains. </p><p>Climate warming in Europe during the studied 70-year period was accompanied by an increase in the frequency and severity of ETEs expressed in terms of their increasing intensity and spatial range. Yet, as our study demonstrates, every spatial domain is characterized by its own event pattern, and trends across Europe strongly vary geographically and seasonally. Our study highlights that examined trends of temperature extremes are accelerating and in the last 40 years the rate of change has been even more than three times greater than in the entire study period. The greatest changes were noted for summer season in Central Europe and Eastern Europe for the most recent 40-year period. </p>
Hospital-Wide Inpatient Flow Optimization

An ideal that supports quality and delivery of care is to have hospital operations that are coordinated and optimized across all services in real time. As a step toward this goal, we propose a multistage adaptive robust optimization approach combined with machine learning techniques. Informed by data and predictions, our framework unifies the bed assignment process across the entire hospital and accounts for present and future inpatient flows, discharges, and bed requests from the emergency department, scheduled surgeries and admissions, and outside transfers. We evaluate our approach through simulations calibrated on historical data from a large academic medical center. For the 600-bed institution, our optimization model was solved in seconds and reduced off-service placement by 24% on average and boarding delays in the emergency department and postanesthesia units by 35% and 18%, respectively. We also illustrate the benefit of using adaptive linear decision rules instead of static assignment decisions. This paper was accepted by Carri Chan, healthcare management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023. 4933 .
Adaptive Instance Normalization Style Transfer

Do you want Van Gogh to paint your selfie? Using a technique called style transfer, you may be able to. Style transfer is a group of software algorithms that change the aesthetic and style of an image to match that of a target image. We decided to tackle the problem of style transfer since we are very interested in topics which synthesize the arts with computer technology. We were also inspired by the neural-style repository on GitHub created by Dr. Justin Johnson [1] and thought that changing the styles of images to an aesthetic which resembled some of the most famous paintings to be a very intriguing concept. This problem is fascinating since it opens up an opportunity to create digital art through the use of machine learning. This problem can be extended to programs that make their own art and visualize their own worlds. We are building off of many already-existing methods for style transfer. For instance, the paper “A Neural Algorithm of Artistic Style” [2] is one of the most well known style transfers that uses an optimization based style transfer. Other examples include the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” [3] which uses a CycleGAN to transfer the style between unpaired sets of images. For this project, we decided to implement the adaptive instance normalization (AdaIN) style transfer method proposed by Huang et al. [4]. We also implemented the well-known optimization technique proposed by Gatys et al. [2] to compare and contrast the results. The AdaIN style transfer method is an arbitrary style transfer method, meaning it trains on a set of images to “remember” styles so that it can apply an arbitrary style to an image after it is trained. The method involves using a pre-trained encoder to extract style and content features, and then the AdaIN formula to shift the statistics of the content image to match the statistics of the style image. It is then passed through a decoder, which is trained to produce the content image with the style image’s style transferred to it.
Hand-Drawn Ray Tracing

We present a strongly parallelized implementation of a two stage non-photorealistic ray-tracer designed to output rendered scenes in a hand-drawn aesthetic. Our program is designed to run on IBM’s Blue Gene supercomputer and takes advantage of a novel, chunk-based bargaining system to maximize efficiency through strategic load balancing. We present a metric for estimating the cost of rendering each pixel, and we compare the performance of various parallel optimizations to analyze the efficiency gained by our final algorithm.
PhysIK: Physics based Inverse Kinematics for Character Posing and Animation

Posing and animating rigs can frustrating for animators. Among the tools they use to make things easier, inverse kinematics solvers calculate the relative rotations of rigid bodies connected by di erent types of joints which is useful for both robotics and animation. However, when posing a rig for 3D modeling and animation, some inverse kinematics solvers can produce unpredictable and unintuitive results, especially for large displacements. Along with the issues above, IK solvers are also limited in their ability to create dynamic poses. It can take a large number of target point speci cations to create a pose or animation that has feasible secondary e ects. For example, when a person walks, the legs are not the only parts of the body that moves, but the arms, torso, and head all move in reaction. PhysIK is a physics-based character posing and inverse kinematics algorithm inspired by how artists manually pose wooden mannequins. To pose a wooden mannequin, the artist just has to move it into place. Part of what makes it so easy to use is that we have an intuitive sense of how physical objects work. Objects in the real world do not jump around or pass through themselves. In order to capture this intuition, PhysIK simulates physics on a rig to move the rig into the desired con guration. By taking into account the form and mass of the rig, PhysIK can accurately simulate plausible secondary motions. For this reason, PhysIK is useful for animation as well as static posing. In addition, with a physics-based simulation, there is no need to worry about whether a solution exists. Even if the target pose is impossible, the simulation will not break down. Instead, it will nd a good compromise between what the user speci ed and what is physically possible.
Measurement of carbon and nitrogen biomass, and biovolume from naturally derived marine bacterioplankton

Bacterial production often needs to be presented in carbon or nitrogen biomass. Conversions to such universal units are particularly necessary to interpret the data in terms of energy or material flow in relation to other trophic groups of organisms. For field applications, bacterial biomass is routinely estimated from cell concentrations or cell volumes (biovolume) with appropriate conversion factors. For the direct determination of a conversion factor, in principle, three independent parameters are measured; number of cells, biovolume of the cells, and biomass. Current methods for all of those three are subject to potential errors. Described here is a method that combines the acridine orange (AO) direct count method for cell enumeration, AO epifluorescence photomicrography for the biovolume measurement, and CHN analysis for C and N biomass. This method offers several improvements including bacterial cells are derived from natural seawater and grown in unsupplemented natural seawater; corrections are made for the bacterial cells passing through glass fiber GF/F filters; and biovolume measurement is calibrated with fluorescent microspheres of known diameters.
[Re] Synbols : Probing Learning Algorithms with Synthetic Datasets

This report focuses on the reproduction of some results presented of the above-mentioned paper [3]. Authors introduced 3 a new data generator called Synbols allowing fast generation of low-resolution images rich in latent features. Researchers 4 explored the capabilities of the tool by training popular machine learning algorithms in various M.L paradigms with 5 their synthetically generated datasets. The tool is also trying to address some broader issues relevant to the whole field 6 (i.e. faster iteration cycles for the training, less reliance on expensive hardware, etc.). 7
Causal Structure Learning from Experiments and Observations

approved: Prasad Tadepalli In this research, we address the problem of learning a single causal network structure from multiple dataset generated from different experiments. The experiments can be observational or interventional. We assume that each dataset is generated by an unknown causal network altered under different experimental conditions (interventions, manipulation or perturbation). As a result, we get a collection of heterogeneous datasets having different distributions. Manipulated distribution implies manipulated graphs over the variables. Combining all the data to learn a network might increase statistical power but only if it assumes a single encapsulating network that is true for all the datasets, which is not always the case under uncertain interventions. Pooling under uncertainty leads to spurious changes in correlations among variables. While learning causal network by pooling data from different experiments is common, we found by experimenting that this paves the way for false causal discoveries, if the effects of interventions are uncertain. We address these issues and present a Bayesian approach of combining data from multiple experiments with observations to learn a single and accurate causal network. Our approach, called ‘Learn and Vote’ learns causal links using data from each experiment and combines them by weighted averaging. We show through studies on synthetic and natural datasets that our method outperforms many state of the art approaches and is more robust with respect to modelling assumptions about the nature of the interventions. c ©Copyright by Meghamala Sinha March 22, 2019 All Rights Reserved Causal Structure Learning from Experiments and Observations
Extraordinary Doping Effects on Quasiparticle Scattering and Bandwidth in Iron-Based Superconductors

The diversities in crystal structures and ways of doping result in extremely diversified phase diagrams for iron-based superconductors. With angle-resolved photoemission spectroscopy, we have systematically studied the effects of chemical substitution on the electronic structure of various series of iron-based superconductors. Beyond the Fermi-surface alteration that has been reported most often in the past, we found two more extraordinary effects of doping: (1) the site and band dependencies of quasiparticle scattering and, more importantly, (2) the ubiquitous and significant change of electronic correlation by both isovalent and heterovalent dopants in the iron-anion layer. Moreover, we found that the electronic correlation could be suppressed by applying either the chemical pressure or doping electrons but not by doping holes. Together with other findings provided here, these results complete the microscopic picture of the electronic effects of dopants, which facilitates a unified understanding of the diversified phase diagrams and resolutions to many open issues of various iron-based superconductors.
Impact of COVID-19 pandemic on sickness absence for mental ill health in National Health Service staff

Objective To explore the patterns of sickness absence in National Health Service (NHS) staff attributable to mental ill health during the first wave of the COVID-19 epidemic in March–July 2020. Design Case-referent analysis of a secondary dataset. Setting NHS Trusts in England. Participants Pseudonymised data on 959 356 employees who were continuously employed by NHS trusts during 1 January 2019 to 31 July 2020. Main outcome measures Trends in the burden of sickness absence due to mental ill health from 2019 to 2020 according to demographic, regional and occupational characteristics. Results Over the study period, 164 202 new sickness absence episodes for mental ill health were recorded in 12.5% (119 525) of the study sample. There was a spike of sickness absence for mental ill health in March–April 2020 (899 730 days lost) compared with 519 807 days in March–April 2019; the surge was driven by an increase in new episodes of long-term absence and had diminished by May/June 2020. The increase was greatest in those aged >60 years (227%) and among employees of Asian and Black ethnic origin (109%–136%). Among doctors and dentists, the number of days absent declined by 12.7%. The biggest increase was in London (122%) and the smallest in the East Midlands (43.7%); the variation between regions reflected the rates of COVID-19 sickness absence during the same period. Conclusion Although the COVID-19 epidemic led to an increase in sickness absence attributed to mental ill health in NHS staff, this had substantially declined by May/June 2020, corresponding with the decrease in pressures at work as the first wave of the epidemic subsided.