Miracle-dz/UniFAR-FTUs huggingface dataset:
CECILIA: Comprehensive Secure Machine Learning Framework

Since ML algorithms have proven their success in many different applications, there is also a big interest in privacy preserving (PP) ML methods for building models on sensitive data. Moreover, the increase in the number of data sources and the high computational power required by those algorithms force individuals to outsource the training and/or the inference of a ML model to the clouds providing such services. To address this, we propose a secure 3-party computation framework, CECILIA, offering PP building blocks to enable complex operations privately. In addition to the adapted and common operations like addition and multiplication, it offers multiplexer, most significant bit and modulus conversion. The first two are novel in terms of methodology and the last one is novel in terms of both functionality and methodology. CECILIA also has two complex novel methods, which are the exact exponential of a public base raised to the power of a secret value and the inverse square root of a secret Gram matrix. We use CECILIA to realize the private inference on pre-trained RKNs, which require more complex operations than most other DNNs, on the structural classification of proteins as the first study ever accomplishing the PP inference on RKNs. In addition to the successful private computation of basic building blocks, the results demonstrate that we perform the exact and fully private exponential computation, which is done by approximation in the literature so far. Moreover, they also show that we compute the exact inverse square root of a secret Gram matrix up to a certain privacy level, which has not been addressed in the literature at all. We also analyze the scalability of CECILIA to various settings on a synthetic dataset. The framework shows a great promise to make other ML algorithms as well as further computations privately computable by the building blocks of the framework.
Degradation of the Escherichia coli Essential Proteins DapB and Dxr Results in Oxidative Stress, which Contributes to Lethality through Incomplete Base Excision Repair

Bacterial cell death is a poorly understood process. The generation of reactive oxygen species (ROS) is an apparently common response to challenges by a wide variety of lethal stresses, including bactericidal antibiotics. ABSTRACT Various lethal stresses, including bactericidal antibiotics, can trigger the production of reactive oxygen species (ROS) that contribute to killing. Incomplete base excision repair (BER) of oxidized nucleotides, especially 8-oxo-dG, has been identified as a major component of ROS-induced lethality. However, the relative contributions of this pathway to death vary widely between stresses, due in part to poorly understood complex differences in the physiological changes caused by these stresses. To identify new lethal stresses that kill cells through this pathway, we screened an essential protein degradation library and found that depletion of either DapB or Dxr leads to cell death through incomplete BER; the contribution of this pathway to overall cell death is greater for DapB than for Dxr. Depletion of either protein generates oxidative stress, which increases incorporation of 8-oxo-dG into the genome. This oxidative stress is causally related to cell death, as plating on an antioxidant provided a protective effect. Moreover, incomplete BER was central to this cell death, as mutants lacking the key BER DNA glycosylases MutM and MutY were less susceptible, while overexpression of the nucleotide sanitizer MutT, which degrades 8-oxo-dGTP to prevent its incorporation, was protective. RNA sequencing of cells depleted of these proteins revealed widely different transcriptional responses to these stresses. Our discovery that oxidative stress-induced incomplete BER is highly dependent on the exact physiological changes that the cell experiences helps explain the past confusion that arose concerning the role of ROS in antibiotic lethality. IMPORTANCE Bacterial cell death is a poorly understood process. The generation of reactive oxygen species (ROS) is an apparently common response to challenges by a wide variety of lethal stresses, including bactericidal antibiotics. Incomplete BER of nucleotides damaged by these ROS, especially 8-oxo-dG, is a significant contributing factor to this lethality, but the levels of its contribution vary widely between different lethal stresses. A better understanding of the conditions that cause cells to die because of incomplete BER may lead to improved strategies for targeting this mode of death as an adjunct to antimicrobial therapy.
Performance-Based Comparison of Different Design Alternatives and Retrofit Techniques for Residential Buildings Subjected to Hurricane Hazards

In recent years, severe hurricanes have caused enormous economic losses for the society and placed a tremendous burden on the insurance industry. Although different retrofit measures can be used to mitigate hurricane damage and reduce the associated social and economic losses, it is a challenge to choose the most cost effective one. In the case of residential buildings, the resources available for hurricane risk mitigation are limited and the upfront costs of improved design alternatives and retrofit measures are usually high. In this study, a probabilistic Performance-Based Hurricane Engineering (PBHE) framework is used to compare the effectiveness of different hurricane retrofit techniques for residential building from a cost-benefit point of view. A realistic case study is presented to illustrate the methodology and to evaluate the costs and benefits of different hurricane hazard mitigation techniques and design alternatives for residential buildings over the entire design life of the benchmark building.
SARS-CoV-2 infects, replicates, elevates angiotensin II and activates immune cells in human testes

Although much has been published since the first cases of COVID-19, there remain unanswered questions regarding SARS-CoV-2 impact on testes and the potential consequences for reproductive health. We investigated testicular alterations in deceased COVID-19-patients, the precise location of the virus, its replicative activity, and the molecules involved in the pathogenesis. We found that SARS-CoV-2 testicular tropism is higher than previously thought and that reliable viral detection in the testis requires sensitive nanosensoring or RT-qPCR using a specific methodology. Macrophages and spermatogonial cells are the main SARS-CoV-2 lodging sites and where new virions form inside the Endoplasmic Reticulum Golgi Intermediate Complex. Moreover, we showed infiltrative infected monocytes migrating into the testicular parenchyma. SARS-CoV-2 maintains its replicative and infective abilities long after the patient infection, suggesting that the testes may serve as a viral sanctuary. Further, infected testes show thickening of the tunica propria, germ cell apoptosis, Sertoli cell barrier loss, evident hemorrhage, angiogenesis, Leydig cell inhibition, inflammation, and fibrosis. Finally, our findings indicate that high angiotensin II levels and activation of mast cells and macrophages may be critical for testicular pathogenesis. Importantly, our data suggest that patients who become critically ill exhibit severe damages and may harbor the active virus in testes.
A method for separating adhesive vehicles based on corner detection

In order to solve the problem of adhesive vehicles in video surveillance, this paper presents a simple segmentation method based on corner detection. Firstly, Shi-Tomasi corner detection is used to detect the corners of the vehicle area. Secondly, the corners are separated by K-means clustering method, and then the nearest corner from the line connecting the cluster centers will be found. And at last, the split line is determined with the nearest corner. With strong adaptability, the proposed method does not need any prior knowledge except for the vehicle shape. Experimental results show that this method is simple and effective, and it is potential to be developed for real-time.
The mF mode of authenticated encryption with associated data

Abstract In recent years, the demand for lightweight cryptographic protocols has grown immensely. To fulfill this necessity, the National Institute of Standards and Technology (NIST) has initiated a standardization process for lightweight cryptographic encryption. NIST’s call for proposal demands that the scheme should have one primary member that has a key length of 128 bits, and it should be secure up to 2 50 − 1 {2}^{50}-1 byte queries and 2 112 {2}^{112} computations. In this article, we propose a tweakable block cipher (TBC)-based authenticated encryption with associated data (AEAD) scheme, which we call mF {\mathsf{mF}} . We provide authenticated encryption security analysis for mF {\mathsf{mF}} under some weaker security assumptions (stated in the article) on the underlying TBC. We instantiate a TBC using block cipher and show that the TBC achieves these weaker securities, provided the key update function has high periodicity. mixFeed {\mathsf{mixFeed}} is a round 2 candidate in the aforementioned lightweight cryptographic standardization competition. When we replace the key update function with the key scheduling function of Advanced Encryption Standard (AES), the mF {\mathsf{mF}} mode reduces to mixFeed {\mathsf{mixFeed}} . Recently, the low periodicity of AES key schedule is shown. Exploiting this feature, a practical attack on mixFeed {\mathsf{mixFeed}} is reported. We have shown that multiplication by primitive element satisfies the high periodicity property, and we have a secure instantiation of mF {\mathsf{mF}} , a secure variant of mixFeed {\mathsf{mixFeed}} .
Numerical Analysis of NACA64-418 Airfoil with Blunt Trailing Edge

The aerodynamic performance of blunt trailing edge airfoils was investigated. The flow fields around the modified NACA64-418, which consists of the tip blade of the wind turbine and Mexico model of IEA wind, were analyzed. To imitate the repaired airfoil, the original NACA64-418 airfoil, a cambered airfoil, is modified by the adding thickness method, which is accomplished by adding the thickness symmetrically to both sides of the camber line. The thickness ratio of the blunt trailing edge of the modified airfoil, t TE /t max , is newly defined to analyze the effects of the blunt trailing edge. The shape functions describing the upper and lower surfaces of the modified NACA64-418 with blunt trailing edge are obtained from the curve fitting of the least square method. To verify the accuracy of the present numerical analysis, the results are first compared with the experimental data of NACA64-418 with high Reynolds number, Re=6×10 6 , measured in the Langley low-turbulence pressure tunnel. Then, the aerodynamic performance of the modified NACA64-418 is analyzed. The numerical results show that the drag increases, but the lift increases insignificantly, as the trailing edge of the airfoil is thickened. Re-circulation bubbles also develop and increase gradually in size as the thickness ratio of the trailing edge is increased. These re-circulations result in an increase in the drag of the airfoil. The pressure distributions around the modified NACA64-418 are similar, regardless of the thickness ratio of the blunt trailing edge.
Degenerate binomial and Poisson random variables associated with degenerate Lah-Bell polynomials

Abstract The aim of this paper is to study the Poisson random variables in relation to the Lah-Bell polynomials and the degenerate binomial and degenerate Poisson random variables in connection with the degenerate Lah-Bell polynomials. Among other things, we show that the rising factorial moments of the degenerate Poisson random variable with parameter α \alpha are given by the degenerate Lah-Bell polynomials evaluated at α \alpha . We also show that the probability-generating function of the degenerate Poisson random variable is equal to the generating function of the degenerate Lah-Bell polynomials. Also, we show similar results for the Poisson random variables. Here the n n th Lah-Bell number counts the number of ways a set of n n elements can be partitioned into non-empty linearly ordered subsets, the Lah-Bell polynomials are natural extensions of the Lah-Bell numbers and the degenerate Lah-Bell polynomials are degenerate versions of the Lah-Bell polynomials.
Emotions as Studied in Psychology and Cognitive Science

evolutionary approach, appraisal approach, and constructionism, which empha­ sizes the biological givens of emotions, the cause of emotions, and the components of emotions, respectively [Niedenthal and Ric 2017]. In particular, the evolution­ ary approach and the appraisal approach have been commonly used in current affective computing research [Poria et al. 2017]. Emotions as Studied in Psychology and Cognitive Science
Frequency Space Measurement of the Spectrum of a Mode-Locked Diode Laser

This thesis presents the development of a method for the direct measurement of the spectrum of a mode-locked diode laser using the beat note between the mode-locked laser and a continuous wave laser. This beat note frequency allows for the measurement of the optical frequency comb created by the mode-locked diode laser. The spectrum of the mode-locked diode laser was optimized using a spectrometer and an electronic amplification and filtering circuit was created to try and see the beat note frequency as it is expected to be small compared to the higher frequency terms created by the mode-locked diode laser. A beat note was observed with the diode laser in continuous wave mode and in a partially mode-locked state. The beat note with the diode laser in full mode-locking was not seen and various methods are suggested to explain and resolve this difficulty.
A transient analysis to the M(τ)/M(τ)/k queue with time-dependent parameters

Abstract This work considers the infinite multi-server Markovian queueing model with balking and catastrophes where the rates of arrivals, service, balking, and catastrophes are time dependent. The catastrophes arrive as negative customers to the system. The arrival of negative customers to a queueing system removes the positive customers. The catastrophes may come either from another service station or from outside the system. In this paper, we obtained the transient solution of this model using the approach of probability-generating function. Also, we derived an expression of transient probabilities in terms of Volterra equation of the second kind. Furthermore, we obtained a measure for time-dependent expected number of customers in the system.
CASExplorer: A Conversational Academic and Career Advisor for College Students

Choosing the right major is one of the most important steps for academic success for college students. However, making the right decision can be overwhelming caused by the lack of or too much information, especially for science, technology, engineering and mathematics (STEM) majors. The advancement of artificial intelligence (AI) and conversational agents, such as chatbots, has provided opportunities for assisting student learning. Originally used in messaging systems and customer service, chatbots have currently also been used in various fields such as health, human resources, and education. In this work in progress, we investigated the use of chatbots in higher education, especially in academic and career counseling services. We designed and implemented a chatbot called CASExplorer (career, academic, and self-explorer) to assist college students in STEM fields in learning about their majors and discovering related careers that match the students’ personality strengths.
An urban sensing architecture as essential infrastructure for future cities

Climate change and migration have become one of the most challenging problems for our civilization. In this context, city councils work hard to manage essential services for citizens such as waste collection, street lamp lighting, and water supply. Increasingly, digitalization and the Internet of Things (IoT) help cities improve services, increase productivity and reduce costs. However, to understand how this may happen, we explore the urban sensing capabilities from citizen- to city-scale, how sensing at different levels is interlinked, and the challenges of managing innovations based on IoT data and devices. Local authorities collaborate with researchers and deploy testbeds as a part of demonstration and research projects to perform the above data collection, improve city services, and support innovation. The data gathered is about indoor and outdoor environmental conditions, energy usage, built environment, structural health monitoring. Such monitoring requires IT infrastructure at three different tiers: at the endpoint, edge, and cloud. Managing infrastructure at all tiers with provisioning, connectivity, security updates of devices, user data privacy controls, visualization of data, multi-tenancy of applications, and network resilience, is challenging. So, in turn, we focus on performing a systematic study of the technical and non-technical challenges faced during the implementation, management, and deployment of devices into citizens' homes and public spaces. Our third piece of work explores IoT edge applications' resiliency and reliability requirements that vary from non-critical (best delivery efforts) to safety-critical with time-bounded guarantees. We investigate how to meet IoT application mixed-criticality QoS requirements in multi-communication networks. Finally, to demonstrate the principles of our framework in the real world, we implement an open-source air quality platform Open City Air Quality Platform (OpenCAQP), that merges a wide range of data sources and air pollution parameters into a single platform. The OpenCAQP allows citizens, environmentalists, data analysts, and developers to access and visualize that data.
General data protection regulation: an individual's perspective

Rapid digitalisation has resulted in a massive exchange of digital data between individuals and organisations, accelerating the importance of privacy-preserving legal frameworks, such as the General Data Protection Regulation (GDPR). Despite the importance of the implementation of such a framework, current research lacks evidence about how individuals perceive GDPR compliance. Given that, the objective of this study was to explore individuals' attitudes towards GDPR compliance in line with Protection Motivation Theory. This study employed a cross-sectional research design and collected 540 valid responses to test a model using structural equational modelling. The result of the analysis showed that perceived threat severity, response efficacy and self-efficacy have positive relationships with attitude towards GDPR compliance. In addition, it was found that attitude correlates with emotional empowerment. The findings of this paper contribute to the literature on privacy-preserving mechanisms by shedding light on individuals' perceptions of the GDPR. The evidence also adds to the current body of literature on information systems management by giving insights into the factors that determine the utilisation of privacy-preserving technologies. These pieces of evidence offer implications for policymakers by providing guidelines on how to communicate the benefits of the GDPR to the public.
Affective Computing for Enhancing Well-Being

research looks to identify the overall factors that influence long-term well-being. As a result, much of the research done in this area has comprised longitudinal studies using smartphones to effectively study people throughout their day. In this chap­ ter, we present a review of several key studies of well-being research using smartphones, discuss opportunities on other compute platforms, and provide examples of interventions that have used affective computing to enhance well-being. Affective Computing for Enhancing Well-Being
Towards failure correlation for improved cloud application service resilience

Autonomously dealing with disruptions is necessary for maintaining the quality of a cloud application service. A fault, error, or failure in any component across the application service stack can potentially disrupt the service delivery. Fault localization and failure prediction are essential techniques in managing service failures. Emerging cloud computing paradigms are pushing application services to be built as loosely coupled distributed components for independent scaling. However, such architectures render existing approaches for fault localization and failure prediction to be limiting. Prevalent works on fault localization and failure prediction focus on a specific cloud service architecture layer or a subset of service components or specific fault types. These approaches restrict the view on the impact of the fault on the application service and obviate more intelligent methods for localizing faults or predicting failures, and thus efficiently dealing with service disruptions in an autonomous way. This paper contemplates the propagation of faults in multi-tiered architectures like clouds and uses a real-world disruption scenario to emphasize the need for correlating the faults across the service layers to acquire insights for end-to-end fault analysis for cloud application services.
Low-memory and high-performance CNN inference on distributed systems at the edge

Nowadays, some applications need CNN inference on resource-constrained edge devices that may have very limited memory and computation capacity to fit a large CNN model. In such application scenarios, to deploy a large CNN model and perform inference on a single edge device is not feasible. A possible solution approach is to deploy a large CNN model on a (fully) distributed system at the edge and take advantage of all available edge devices to cooperatively perform the CNN inference. We have observed that existing methodologies, utilizing different partitioning strategies to deploy a CNN model and perform inference at the edge on a distributed system, have several disadvantages. Therefore, in this paper, we propose a novel partitioning strategy, called Vertical Partitioning Strategy, together with a novel methodology needed to utilize our partitioning strategy efficiently, for CNN model inference on a distributed system at the edge. We compare our experimental results on the YOLOv2 CNN model with results obtained by the existing three methodologies and show the advantages of our methodologies in terms of memory requirement per edge device and overall system performance. Moreover, our experimental results on other representative CNN models show that our novel methodology utilizing our novel partitioning strategy is able to deliver CNN inference with very reduced memory requirement per edge device and improved overall system performance at the same time.
Multimodal Data Collection and Processing for Applied Affective Computing

expression [Pantic and Rothkrantz 2000], speech [Cowie and Douglas-Cowie 1996], movement [Bianchi-Berthouze and Kleinsmith 2003, Camurri et al. 2003], text [Wu et al. 2006], and physiological signals [Picard et al. 2001]. In the early days of affective computing research, unimodal data were used for recognizing affect; however, recently, multimodal data have been used in affective computing [Pantic et al. 2005a, Sebe et al. 2005, Caridakis et al. 2007, D’Mello and Kory 2015, Poria et al. 2017, Siddharth et al. 2018]. In this chapter, we will introduce methods and techniques to collect and process multimodal data for affective recognition by reviewing previous research. Multimodal Data Collection and Processing for Applied Affective Computing