Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTION AND CLASSIFICATION OF COVID-19 FROM X-RAY USING DEEP LEARNING

: After its first reported occurrence on 31st December 2019 and getting its name on Feb 11, 2020, Covid-19 was declared a Pandemic on March 11, 2020, by the World Health Organization (WHO). The highly contagious nature of the disease along with its incompletely understood transmission process and the absence of a definitive cure made it extremely difficult for World Governments and their Health care providers to effectively contain the infection. In extreme cases, Covid-19 has been observed to result in severe respiratory discomfort, multiple organ failure, and eventually death. Covid-19 based Pneumonia was observed in a small percentage (2% to 8%) of the infected patients which was found to be more fatal compared to regular pneumonia. RT-PCR is the current recommended standard for the detection of Covid-19, but RT-PCR has been proven to be oblivious to early stages of Covid-19 Pneumonia and is associated with low sensitivity, reportedly as low as 30% to 70%. Chest CT and X-rays exhibit superior sensitivity over RT-PCR but cannot differentiate between Covid-19 and Non Covid-19 pneumonia. Therefore, this paper proposes the use of Artificial Intelligence (AI) to assist in the detection and classification of Covid-19 pneumonia from Non Covid-19 pneumonia and No Covid using X-Ray images. A 2D Deep Learning CNN architecture is proposed for solving Binary classification (Covid-19 vs Normal) and Multiclass Classification (Covid-19 pneumonia vs Non Covid-19 pneumonia vs Normal) problem of a custom created diverse X-ray dataset.
Information Extraction from Patient Care Reports for Intelligent Emergency Medical Services

Every year, more than 30 million emergency medical incidents are responded to in the U.S. Upon arrival at an incident scene, responders assess the situation and provide emergency medical care to the patients before transporting them to hospitals. In this process, the responders collect substantial amounts of data with different levels of importance and confidence, including the patient's present medical conditions, past medical history, and interventions performed. Although there are several standards and tools for collecting, storing, and sharing EMS data, less attention has been given to reliably translating this wealth of information into actionable knowledge for assessing the performance of emergency operations and evaluating response protocols. This paper presents the analysis of over 35,900 EMS pre-hospital electronic Patient Care Reports (ePCR) from an urban ambulance agency. We used both the structured and unstructured information in the dataset to develop a domain-specific EMS ontology with a standardized lexicon for medications, procedures, responders’ impressions, call types, chief complaints, and signs and symptoms. The EMS ontology was used to develop methods for automated segmentation of narratives, detection and correction of incorrect/incomplete information in the reports, and generation of time-series data to represent the progression of incidents and the most common sequences of response actions (models of EMS protocols). Finally, we performed an analysis on the relationships among different aspects of incidents to provide insights for the design of future EMS assistive technologies.
Unsupervised Testing of NLU models with Multiple Views

In Natural Language Understanding (NLU) systems in voice assistants, new domains are added on a regular basis. This poses the practical problem of evaluating the performance of NLU models on domains where no manually annotated data is available. In this paper, we present an unsupervised testing method that we call Cross-View Testing (CVT) for ranking multiple intent classiﬁcation models using only unlabeled test data. The approach relies on a number of labeling functions to automatically anno-tate test data in the target domain. The labeling functions include intent classiﬁcation models trained on different domains, as well as heuristic rules. Speciﬁcally, we combine the annotations of multiple models with different output spaces by training a combiner model on synthetic data. In our experiments, the proposed model outperforms the target models by very large margins, and its predictions can be used as a proxy of ground truth for unsupervised model evaluation.
Market research on the project management needs of small to medium sized technology companies in Finland

Tampereen ammattikorkeakoulu Tampere University of Applied Sciences International Business TUCKETT, ALEKSANDER: Market research on the project management needs of small to medium sized technology companies in Finland. Bachelor's thesis 42 pages, of which appendices 1 page. October 2021 Start-up companies have a hard time deciding what type of market segment they should concentrate on, to achieve their first sales. Most entrepreneurs believe that they miss out if they do not take on every potential market opportunity they can. It is well documented however, that by concentrating on the right market niche a company has a greater chance at success than by trying to sell their products or services to everyone who resides in a certain market. This study aims to identify the needs of a specific market segment and see if the needs are matched with the solution that is provided by the commissioner of this study. In this context the market segment is small to medium sized information technology companies that reside in Finland. To test the hypothesis of are the needs of this market segment matched with the commissioner’s services, interviews were conducted with 7 different companies of various sizes who belonged to this market segment. The results were then analysed by using thematic analysis. The findings showed companies within this market segment did not have needs that could be resolved with the current solution that the commissioner has. The results do suggest however, that there is a need for communication and transparency training within the market segment. This has potential benefit for the commissioner, as clear project management also allows for transparency within an organization. These findings have a potential of showing up in further research with larger companies. Therefore, by having a good communication and transparency package included in the commissioners’ services, the results for growth and sales are potentially very high.
Position Paper: Reducing Amazon’s packaging waste using multimodal deep learning

Since 2015, Amazon has reduced the weight of its outbound packaging by 36%, eliminating over 1,000,000 tons of packaging material worldwide, or the equivalent of over 2 billion shipping boxes, thereby reducing carbon footprint throughout its fulfillment supply chain. In this position paper, we share insights on using deep learning to identify the optimal packaging type best suited to ship each item in a diverse product catalog at scale so that it arrives undamaged, delights customers, and reduces packaging waste. Incorporating multimodal data on products including product images and class imbalance handling technique are important to improving model performance.
TWO-DIMENSIONAL COMPUTATIONAL MODEL FOR WAVE ROTOR FLOW DYNAMICS

A two-dimensional (0,z) Navier-Stokes solver for multi-port wave rotor flow simulation is described. The finite-volume form of the unsteady thin-layer Navier-Stokes equations are integrated in time on multi-block grids that represent the stationary inlet and outlet ports and the moving rotor passages of the wave rotor. Computed results are compared with three-port wave rotor experimental data. The model is applied to predict the performance of a planned four-port wave rotor experiment. Two-dimensional flow features that reduce machine performance and influence rotor blade and duct wall thermal loads are identified. The performance impact of rounding the inlet port wall, to inhibit separation during passage gradual opening, is assessed.
Speeding Up Post-Quantum TLS handshakes by Suppressing Intermediate CA Certificates

Traditionally, the most data-heavy part of a (D)TLS handshake has been authentication which includes a handshake signature and digital certificates. Although most common (D)TLS use cases are not significantly affected by big certificates or certificate chains, some constrained usecases such as low bandwidth environments or delay sensitive applications can see drastic performance degradation. That has led the security community to seek options to alleviate the issue. Post-quantum signatures and keys, on the other hand, have been proven to noticeably slow down handshakes even for common Internet (D)TLS or QUIC applications due to the significantly more amounts of post-quantum authentication data they include. One alleviation mechanism proposed in the literature is caching some of the authentication information. In this work, we make the case for speeding up (D)TLS and QUIC handshakes by omitting the intermediate certificate authority certificates in the handshake. We present how that can be achieved along with the usecases that will mostly benefit from such a mechanism. We offer quantitative analyses to show that this approach is relatively straightforward, backwards compatible and with little overhead introduced for caching the certificates. We also discuss caching mechanisms based on different optimization goals.
Effect of freeway corridor attributes upon motorist diversion responses to real-time travel time information

This report describes the results of laboratory experiments performed to assess whether certain freeway corridor attributes specified in a real-time motorist information display affect motorists' expected responses to travel time information. Specifically, the study was designed to determine whether (1) the recommended alternative route, (2) the location where motorists were told to divert from the freeway, or (3) the location of the reported onset of congestion relative to where motorists were told to divert, influenced motorist diversion threshold values to time saved travel time information. The procedures and results of the studies are described, and recommendations are presented for application of the results to the design and operation of freeway corridor motorist information displays.
A P ] 2 2 O ct 2 02 1 ALMOST SURE WELL-POSEDNESS AND SCATTERING OF THE 3 D CUBIC NONLINEAR SCHRÖDINGER EQUATION

We study the random data problem for 3D, defocusing, cubic nonlinear Schrödinger equation in H x(R ) with s < 1 2 . First, we prove that the almost sure local well-posedness holds when 1 6 6 s < 1 2 in the sense that the Duhamel term belongs to H 1/2 x (R). Furthermore, we prove that the global well-posedness and scattering hold for randomized, radial, large data f ∈ H x(R3) when 37 < s < 1 2 . The key ingredient is to control the energy increment including the terms where the first order derivative acts on the linear flow, and our argument can lower down the order of derivative more than 1 2 . To our best knowledge, this is the first almost sure large data global result for this model.
Stopband‐improved dual‐mode bandpass filter using side‐slit patch resonator

A rectangular patch resonator with four equal slits etched on sides is presented to make up a stopband‐improved dual‐mode filter. By stretching these four side slits towards the patch center, the first resonant frequency gets a gradual decrease while its second one keeps almost unchanged. This feature is attractive in widening upper stopband while miniaturizing overall size in filter design. To realize a relatively wide passband with DC‐blocking, the narrowed feed lines are deeply inserted into their respective slits, resulting in the enhanced capacitive coupling degree. Our studies imply that the spacing between the first two resonant frequencies of this resonator itself is increased by about 50% while the first spurious resonant frequency is fully rejected by introducing a pair of open‐circuited stubs in the feed lines. In final, a dual‐mode filter is optimally designed with a fractional bandwidth of 4.5% at 1.60 GHz and predicted results are confirmed by experiment. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 717–720, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22221
Predicting the Dynamic Fracture of Steel via a Non-Local Strain-Energy Density Failure Criterion

Predicting the onset of fracture in a material subjected to dynamic loading conditions has typically been heavily mesh-dependent, and often must be specifically calibrated for each geometric design. This can lead to costly models and even costlier physical testing. In response to this, a failure criterion was created based on the strain energy density (SED) of the material. Calculations to obtain the SED were developed to take advantage of a non-local length scale, wherein the sensitivity to mesh density was partially reduced. This method was applied to a steel coupon subjected to dynamic uniaxial tension. A one-time calibration was used to determine the material’s critical SED in the non-local length scale. This length scale was dependent on the mesh density of the model and a prescribed magnifier, such that the failure criterion was a function of the length scale. Steel coupons were modeled and tested dynamically. Thicknesses of those coupons were varied and stress concentrations were included. Differing grades of steel were also employed. The non-local SED failure criterion provided consistent and accurate predictions, regardless of the changes in dimensions of the coupons.
Control of Parallel-connected Inverters to Achieve Proportional Load Sharing

Abstract In this paper, a completely new concept has been brought to the controller design for inverters. The capacitor C of the inverter LC filter is regarded as a part of the load and, hence, the control plant for the inverter controller is just the inductor L. This reduces the order of the control plant to be 1, reduces the variables to be measured for feedback to two, and considerably simplifies the design and analysis of the controller. The inherent limitations of the conventional droop control scheme are then revealed and it is proved that parallel-connected inverters should have the same per-unit impedance in order for them to share the load accurately in proportional to their power ratings if the conventional droop control scheme is adopted. The droop controllers should also generate the same voltage set-point for the inverters. Both of these two are impractical and difficult to meet, which results in errors in proportional load sharing. An improved droop controller is then proposed to achieve accurate proportional load sharing. It is robust against parameter drifts and component mismatches. The strategy also reduces the output voltage drop due to the effect of loading and droop control so that the output voltage can be maintained within the desired range around the rated value. Experimental results are provided to verify the analysis and design. Copyright © 2011 IFAC.
First steps towards conceptualization and measurement of consumers' perceptions of the nostalgia aroused by brands

Nostalgia evoked by brands is at the forefront of contemporary marketing theory and practice. To date, scales have been developed to measure the propensity to be nostalgic as an individual difference but little attention has been paid to understanding consumers’ perceptions of the nostalgia aroused by brands. This article outlines research designed to conceptualize, develop, and validate a new scale to measure brand nostalgia. Results indicate that our three-dimensional scale is valid and shows a high degree of reliability.
Deep Learning for Flight Demand Forecasting

Inspired by the success of deep learning (DL) in natural language processing (NLP), we applied cutting-edge DL techniques to predict flight departure demand in a strategic time horizon (4 hours or longer). This work was conducted in support of a MITRE-developed mobile application, Pacer, which displays predicted departure demand to general aviation (GA) flight operators so they can have better situation awareness of the potential for departure delays during busy periods. Field demonstrations involving Pacer’s previously designed rulebased prediction method showed that the prediction accuracy of departure demand still has room for improvement. This research strives to improve prediction accuracy from two key aspects: better data sources and robust forecasting algorithms. We leveraged two data sources, Aviation System Performance Metrics (ASPM) and System Wide Information Management (SWIM), as our input. We then trained forecasting models with DL techniques of sequence to sequence (seq2seq) and seq2seq with attention. The case study has shown that our seq2seq with attention performs best among four forecasting algorithms tested. In addition, with better data sources, seq2seq with attention can reduce mean squared error (mse) over 60%, compared to the classical autoregressive (AR) forecasting method.
Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions

Large-scale ride-hailing systems often combine real-time routing at the individual request level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing and vehicle relocation. The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization. However, the longer horizon increases computational complexity and forces the MPC to operate at coarser spatial-temporal granularity, degrading the quality of its decisions. This paper addresses these computational challenges by learning the MPC optimization. The resulting machine-learning model then serves as the optimization proxy and predicts its optimal solutions. This makes it possible to use the MPC at higher spatial-temporal fidelity, since the optimizations can be solved and learned offline. Experimental results show that the proposed approach improves quality of service on challenging instances from the New York City dataset.
Effect of resveratrol administration on muscle glycogen levels in rats subjected to acute swimming exercise.

The present study aims to examine how resveratrol administration affects muscle glycogen levels in rats subjected to an acute swimming exercise bout. The study was conducted on adult male rats of Wistar-Albino. The 28 rats used in the study were equally divided among four groups: Group 1, Control Group: The group fed on a standard diet and not subjected to any procedure. Group 2, Control Swimming Group: The group fed on a standard diet and subjected to an acute swimming exercise bout for 30 minutes at the end of the study. Group 3, Resveratrol Group: The group fed on a standard diet and given (10 mg/kg) resveratrol in drinking water for four weeks. Group 4, Resveratrol + Swimming Group: The group fed on a standard diet, given (10 mg/kg) resveratrol in drinking water for four weeks and subjected to a 30-minute acute swimming exercise at the end of the study. At the end of the four weeks, the animals were decapitated, muscle glycogen levels using immunohistochemical method. The highest muscle glycogen levels were obtained in the resveratrol-administered Group 3 and the lowest levels in group 2 (swimming group) (p<0.05). The results of the study demonstrate that resveratrol support had a protective and/or regulatory effect on mucle glycogen in both exercised and not-exercised rats.
Misbehavior Detection Using Collective Perception under Privacy Considerations

In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).
New streaming algorithms for high dimensional EMD and MST

We study streaming algorithms for two fundamental geometric problems: computing the cost of a Minimum Spanning Tree (MST) of an n-point set X ⊂ {1,2,…,Δ}d, and computing the Earth Mover Distance (EMD) between two multi-sets A,B ⊂ {1,2,…,Δ}d of size n. We consider the turnstile model, where points can be added and removed. We give a one-pass streaming algorithm for MST and a two-pass streaming algorithm for EMD, both achieving an approximation factor of Õ(logn) and using (n,d,Δ)-space only. Furthermore, our algorithm for EMD can be compressed to a single pass with a small additive error. Previously, the best known sublinear-space streaming algorithms for either problem achieved an approximation of O(min{ logn , log(Δ d)} logn). For MST, we also prove that any constant space streaming algorithm can only achieve an approximation of Ω(logn), analogous to the Ω(logn) lower bound for EMD. Our algorithms are based on an improved analysis of a recursive space partitioning method known generically as the Quadtree. Specifically, we show that the Quadtree achieves an Õ(logn) approximation for both EMD and MST, improving on the O(min{ logn , log(Δ d)} logn) approximation.
The decompositions and positive semidefiniteness of fourth-order conjugate partial-symmetric tensors with applications

Conjugate partial-symmetric (CPS) tensor is a generalization of Hermitian matrices. For the CPS tensor decomposition some properties are presented. For real CPS tensors in particular, we note the subtle difference from the complex case of the decomposition. In addition to traditional decompositions in the form of the sum of rank-one tensors, we focus on the orthogonal matrix decomposition of CPS tensors, which inherits nice properties from decomposition of matrices. It then induces a procedure that reobtain the CPS decomposable property of CPS tensors. We also discuss the nonnegativity of the quartic real-valued symmetric conjugate form corresponding to fourth-order CPS tensors in real and complex cases, and establish its relationship to different positive semidefiniteness based on different decompositions. Finally, we give some examples to illustrate the applications of presented propositions.
Higher-Order MDS Codes

An improved Singleton-type upper bound is presented for the list decoding radius of linear codes, in terms of the code parameters <inline-formula> <tex-math notation="LaTeX">$[n,k,d]$ </tex-math></inline-formula> and the list size <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>. <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-MDS codes are then defined as codes that attain this bound (under a slightly stronger notion of list decodability), with 1-MDS codes corresponding to ordinary linear MDS codes. Several properties of such codes are presented; in particular, it is shown that the 2-MDS property is preserved under duality. Finally, explicit constructions for 2-MDS codes are presented through generalized Reed–Solomon (GRS) codes.