Miracle-dz/UniFAR-FTUs huggingface dataset:
Hiding in Plain Sight? On the Efficacy of Power Side Channel-Based Control Flow Monitoring

Physical side-channel monitoring leverages the physical phenomena produced by a microcontroller (e.g. power consumption or electromagnetic radiation) to monitor program execution for malicious behavior. As such, it offers a promising intrusion detection solution for resource-constrained embedded systems, which are incompatible with conventional security measures. This method is especially relevant in safety and security-critical embedded systems such as in industrial control systems. Side-channel monitoring poses unique challenges for would-be attackers, such as: (1) limiting attack vectors by being physically isolated from the monitored system, (2) monitoring immutable physical side channels with uninterpretable data-driven models, and (3) being speciﬁcally trained for the architectures and programs on which they are applied to. As a result, physical side-channel monitors are conventionally believed to provide a high level of security. In this paper, we propose a novel attack to illustrate that, despite the many barriers to attack that side-channel monitoring systems create, they are still vulnerable to adversarial attacks. We present a method for crafting functional malware such that, when injected into a side-channel-monitored system, the detector is not triggered. Our experiments reveal that this attack is robust across detector models and hardware im-plementations. We evaluate our attack on the popular ARM microcontroller platform on several representative programs, demonstrating the feasibility of such an attack and highlighting the need for further research into side-channel monitors.
Where’s the Learning in Representation Learning for Compositional Semantics and the Case of Thematic Fit

Observing that for certain NLP tasks, such as semantic role prediction or thematic fit estimation, random embeddings perform as well as pre-trained embeddings, we explore what settings allow for this, and examine where most of the learning is encoded: the word embeddings, the semantic role embeddings, or “the network”. We find nuanced answers, depending on the task and its relation to the training objective. We examine these representation learning aspects in multi-task learning, where role prediction and role-filling are supervised tasks, while several thematic fit tasks are outside the models’ direct supervision. We observe a non-monotonous relation between some tasks’ quality scores and the training data size. In order to better understand this observation, we analyze these results using easier, per-verb versions of these tasks.
Frame Detection in German Political Discourses: How Far Can We Go Without Large-Scale Manual Corpus Annotation?

Automated detection of frames in political discourses has gained increasing attention in natural language processing (NLP). Earlier studies in this area however focus heavily on frame detection in English using supervised machine learning approaches. Addressing the difﬁculty of the lack of annotated data for training and/or evaluating supervised models for low-resource languages, we investigate the potential of two NLP approaches that do not require large-scale manual corpus annotation from scratch: 1) LDA-based topic modelling, and 2) a combination of word2vec embeddings and handcrafted framing keywords based on a novel, expert-curated framing schema. We test these approaches using a novel corpus consisting of German-language news articles on the “Eu-ropean Refugee Crisis” between 2014-2018. We show that while topic modelling is insufﬁcient in detecting frames in a dataset with highly homogeneous vocabulary, our second approach yields intriguing and more humanly interpretable results. This approach offers a promising opportunity to incorporate domain knowledge from political science and NLP techniques for bottom-up, explorative political text analyses.
Realistic Face Reconstruction from Deep Embeddings

Modern face recognition systems use deep convolution neural networks to extract 1 latent embeddings from face images. Since basic arithmetic operations on embed-2 dings are needed to make comparisons, generic encryption schemes cannot be used. 3 This leaves facial embedding unprotected and susceptible to privacy attacks that 4 reconstruction facial identity. We propose a search algorithm on the latent vector 5 space of StyleGAN [7] to ﬁnd a matching face. Our process yields latent vectors 6 that generate face images that are high-resolution, realistic, and reconstruct relevant 7 attributes of the original face. Further, we demonstrate that our process is capable 8 of fooling FaceNet [11], a state-of-the-art face recognition system. 9
Design of Resource-Aware Load Allocation for Heterogeneous Fog Computing Environments

The architecture employed by most of the researchers for the deployment of latency-sensitive Internet of Things (IoT) applications is fog computing. Fog computing architecture offers less delay as compared to the cloud computing paradigm by providing resource constraint fog devices close to the edge of the network. Fog nodes process the incoming data by utilizing available resources which reduces the volume of data to be sent to the cloud server. Fog devices having dissimilar processing capabilities are present in a system. The connection of suitable sensor nodes to the parent fog node plays an essential role in achieving the optimum performance of the system. In this paper, we have designed an algorithm that dynamically assigns appropriate sensor devices to fog nodes to achieve a reduction in network utilization and latency. The proposed algorithm estimates the volume of information detected by an edge device from the rate of sensing frequency of the sensor attached to the edge device. The proposed policy while connecting the network nodes takes into account the heterogeneity and processing capability of the devices. Several evaluations are performed on multiple scales for the evaluation of the proposed algorithm. The outcomes of the evaluations confirm the effectiveness of the proposed algorithm in achieving a reduction in network consumption and end-to-end delay.
Theoretical modelling of epigenetically modified DNA sequences [version 1; peer review: 1 approved, 1 approved with reservations]

We report herein a set of calculations designed to examine the effects of epigenetic modifications on the structure of DNA. The incorporation of methyl, hydroxymethyl, formyl and carboxy substituents at the 5-position of cytosine is shown to hardly affect the geometry of CG base pairs, but to result in rather larger changes to hydrogen-bond and stacking binding energies, as predicted by dispersion-corrected density functional theory (DFT) methods. The same modifications within double-stranded GCG and ACA trimers exhibit rather larger structural effects, when including the sugar-phosphate backbone as well as sodium counterions and implicit aqueous solvation. In particular, changes are observed in the buckle and propeller angles within base pairs and the slide and roll values of base pair steps, but these leave the overall helical shape of DNA essentially intact. The structures so obtained are useful as a benchmark of faster methods, including molecular mechanics (MM) and hybrid quantum mechanics/molecular mechanics (QM/MM) methods. We show that previously developed MM parameters satisfactorily reproduce the trimer structures, as do QM/MM epigenetic This paper presents a study of different simulation methods to investigate different modification of dCMp in base pairs or dsDNA and their influence of the surrounding structure. Disparate statement in the abstract – please modify/clarify: “The same modifications within double-stranded GCG and ACA trimers exhibit rather larger structural effects” versus “but these leave the overall helical shape of DNA essentially intact. ” larger changes but DNA shape of helix intact?
α decay in the complex-energy shell model

Background: Alpha emission from a nucleus is a fundamental decay process in which the alpha particle formed inside the nucleus tunnels out through the potential barrier. Purpose: We describe alpha decay of 212Po and 104Te by means of the configuration interaction approach. Method: To compute the preformation factor and penetrability, we use the complex-energy shell model with a separable T = 1 interaction. The single-particle space is expanded in a Woods-Saxon basis that consists of bound and unbound resonant states. Special attention is paid to the treatment of the norm kernel appearing in the definition of the formation amplitude that guarantees the normalization of the channel function. Results: Without explicitly considering the alpha-cluster component in the wave function of the parent nucleus, we reproduce the experimental alpha-decay width of 212Po and predict an upper limit of T1/2 = 5.5 10 7 sec for the half-life of 104Te. Conclusions: The complex-energy shell model in a large valence configuration space is capable of providing a microscopic description of the alpha decay of heavy nuclei having two valence protons and two valence neutrons outside the doubly magic core. The inclusion of proton-neutron interaction between the valence nucleons is likely to shorten the predictedmore » half-live of 104Te. « less
A Machine Learning Approach for Predicting the Maximum Spreading Factor of Droplets upon Impact on Surfaces with Various Wettabilities

Drop impact on a dry substrate is ubiquitous in nature and industrial processes, including aircraft de-icing, ink-jet printing, microfluidics, and additive manufacturing. While the maximum spreading factor is crucial for controlling the efficiency of the majority of these processes, there is currently no comprehensive approach for predicting its value. In contrast to the traditional approach based on scaling laws and/or analytical models, this paper proposes a data-driven approach for estimating the maximum spreading factor using supervised machine learning (ML) algorithms such as linear regression, decision tree, random forest, and gradient boosting. For this purpose, a dataset of hundreds of experimental results from the literature and our own—spanning the last thirty years—is collected and analyzed. The dataset was divided into training and testing sets, each representing 70% and 30% of the input data, respectively. Subsequently, machine learning techniques were applied to relate the maximum spreading factor to relevant features such as flow controlling dimensionless numbers and substrate wettability. In the current study, the gradient boosting regression model, capable of handling structured high-dimensional data, is found to be the best-performing model, with an R2-score of more than 95%. Finally, the ML predictions agree well with the experimental data and are valid across a wide range of impact conditions. This work could pave the way for the development of a universal model for controlling droplet impact, enabling the optimization of a wide variety of industrial applications.
A second derivative SQP method with imposed descent

Sequential quadratic programming (SQP) methods form a class of highly efficient algorithms for solving nonlinearly constrained optimization problems. Although second derivative information may often be calculated, there is little practical theory that justifies exact-Hessian SQP methods. In particular, the resulting quadratic programming (QP) subproblems are often nonconvex, and thus finding their global solutions may be computationally nonviable. This paper presents a second-derivative Sl1QP method based on quadratic subproblems that are either convex, and thus may be solved efficiently, or need not be solved globally. Additionally, an explicit descent constraint is imposed on certain QP subproblems, which "guides" the iterates through areas in which nonconvexity is a concern. Global convergence of the resulting algorithm is established.
Level of Gaming, Social Support and Emotional Intelligence among Online Gamers

: Since 2020, the pandemic has given rise to solitary activities and many of us got dependent on technology. Likewise, the influence online games have was also seen to be elevating. Putting a light on this issue, this study deals with two variables, social support and emotional intelligence, which might have an effect on level of gaming. India being a collectivistic society, social support plays a vital role in behaviour pattern of an individual. This research aimed to investigate the effect of social support on level of gaming and emotional intelligence. A sample of 161 young adults (16-26 years old) from India, were chosen. It was also found that social support can be a protective factor while concerning with level of gaming and emotional intelligence. This paper also discusses the limitations of the research and recommendation for further study in the area.
Long and short memory in dynamic term structure models

I provide a unified theoretical framework for long memory term structure models and show that the recent state-space approach suffers from a parameter identification problem. I propose a different framework to estimate long memory models in a state-space setup, which addresses the shortcomings of the existing approach. The proposed framework allows asymmetrically treating the physical and risk-neutral dynamics, which simplifies estimation considerably and helps to conduct an extensive comparison with standard term structure models. Relying on a battery of tests, I find that standard term structure models perform just as well as the more complicated long memory models and produce plausible term premium estimates.
IoT-Based Remote Health Monitoring System Employing Smart Sensors for Asthma Patients during COVID-19 Pandemic

COVID-19 and asthma are respiratory diseases that can be life-threatening in uncontrolled circumstances and require continuous monitoring. A poverty-stricken South Asian country like Bangladesh has been bearing the brunt of the COVID-19 pandemic since its beginning. The majority of the country’s population resides in rural areas, where proper healthcare is difficult to access. This emphasizes the necessity of telemedicine, implementing the concept of the Internet of Things (IoT), which is still under development in Bangladesh. This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system, specifically for asthma patients who are at an increased risk of COVID-19. Since on-time treatment is essential, this system will allow doctors and medical staff to receive patient information in real time and deliver their services immediately to the patient regardless of their location. The proposed system consists of various sensors collecting heart rate, body temperature, ambient temperature, humidity, and air quality data and processing them through the Arduino Microcontroller. It is integrated with a mobile application. All this data is sent to the mobile application via a Bluetooth module and updated every few seconds so that the medical staff can instantly track patients’ conditions and emergencies. The developed prototype is portable and easily usable by anyone. The system has been applied to five people of different ages and medical histories over a particular period. Upon analyzing all their data, it became clear which participants were particularly vulnerable to health deterioration and needed constant observation. Through this research, awareness about asthmatic symptoms will improve and help prevent their severity through effective treatment anytime, anywhere.
A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task.
Changes in Pulmonary Function after Three-Dimensional Conformal Radiation Therapy, Intensity-Modulated Radiation Therapy, or Proton Beam Therapy for Non-Small Cell Lung Cancer

Summary We investigated the effect of modern radiation techniques on pulmonary function in non-small cell lung cancer patients. We found that lung diffusing capacity for carbon monoxide (DLCO) is reduced in the majority of patients after radiation. Moreover, we found that multiple factors, including pretreatment DLCO ≤50% and lung and heart dosimetric data >median were associated with larger posttreatment declines in DLCO. Purpose— Definitive radiotherapy for non-small cell lung cancer (NSCLC) adversely affects pulmonary function. However, the extent of these effects after radiation delivered with modern techniques is not well known. Methods Materials— 3-dimensional intensity-modulated radiotherapy (IMRT), amongst individual compared to the same patient's pre-radiation value at the following intervals: to 4 (T1), 5 8 (T2), to 12 (T3) months. Results— Lung diffusing capacity for carbon monoxide (DLCO) is reduced in the majority of patients along the 3 time periods after radiation, whereas the forced expiratory volume in 1 second per unit of vital capacity (FEV1/VC) showed an increase and decrease after radiation in a similar percentage of patients. There were baseline differences (stage, RT dose, concurrent chemotherapy) among the radiation technology groups. On multivariate analysis, the following features were associated with larger posttreatment declines in DLCO: pretreatment DLCO, gross tumor volume (GTV), lung and heart dosimetric data, and total radiation dose. Only pretreatment DLCO was associated with larger posttreatment declines in FEV1/VC. Conclusions— DLCO is reduced in the majority of the patients after radiotherapy with modern techniques. Multiple factors, including GTV, pre-radiation lung function and dosimetric parameters, are associated with the DLCO decline. Prospective studies are needed to better understand whether new radiation technology such as PBT or IMRT may decrease the pulmonary impairment through greater lung sparing.
Bayesian "Confidence Intervals" for the Cross-validated Smoothing Spline

SUMMARY We consider the model Y(t) =g(ti) + ei, i = 17 2, . , n, where g(t), t [0, 1] is a smooth function and the {ei) are independent N(0, a2 ) errors with G2 unknown. The cross-validated smoothing spline can be used to estimate g non-parametrically from
Trajectories of Quality of Life after Hematopoietic Cell Transplantation: Secondary Analysis of BMT CTN 0902 Data Blood Marrow

Summary Quality of life is increasingly recognized as an important secondary endpoint of hematopoietic cell transplantation (HCT). The current study examined the extent to which attrition results in biased estimates of patient quality of life. The study also examined whether patients differ in terms of trajectories of quality of life in the first six months post-transplant. A secondary data analysis was conducted of 701 participants who enrolled in the Blood and Marrow Transplantation Clinical Trials Network (BMT CTN) 0902 trial. Participants completed the SF-36, a measure of quality of life, prior to transplant and 100 and 180 days post-transplant. Results indicated that attrition resulted in slightly biased overestimates of quality of life but the amount of overestimation remained stable over time. Patients could be grouped into three distinct classes based on physical quality of life: 1) low and stable; 2) average and declining, then stable; and 3) average and stable. Four classes of patients emerged for mental quality of life: 1) low and stable; 2) average, improving, then stable; 3) higher than average (by almost 1 SD) and stable; and 4) average and stable. Taken together, these data provide a more comprehensive understanding of quality of life that can be used to educate HCT recipients and their caregivers.
Mitochondrial Ca 2 1 Dynamics Reveals Limited Intramitochondrial Ca 2 1 Diffusion

To reveal heterogeneity of mitochondrial function on the single-mitochondrion level we have studied the spatiotemporal dynamics of the mitochondrial Ca 2 1 signaling and the mitochondrial membrane potential using wide-ﬁeld ﬂuorescence imaging and digital image processing techniques. Here we demonstrate ﬁrst-time discrete sites—intramitochon-drial hotspots—of Ca 2 1 uptake after Ca 2 1 release from intracellular stores, and spreading of Ca 2 1 rise within the mitochondria. The phenomenon was characterized by comparison of observations in intact cells stimulated by ATP and in plasma membrane permeabilized or in ionophore-treated cells exposed to elevated buffer [Ca 2 1 ]. The ﬁndings indicate that Ca 2 1 diffuses laterally within the mitochondria, and that the diffusion is limited for shorter segments of the mitochondrial network. These observations were supported by mathematical simulation of buffered diffusion. The mitochondrial membrane potential was investigated using the potentiometric dye TMRM. Irradiation-induced ﬂuctuations (ﬂickering) of TMRM ﬂuorescence showed synchronicity over large regions of the mitochondrial network, indicating that certain parts of this network form electrical syncytia. The spatial extension of these syncytia was decreased by 2-aminoethoxydiphenyl borate (2-APB) or by propranolol (blockers of nonclassical mitochondrial permeabilities). Our data suggest that mitochondria form syncytia of electrical conductance whereas the passage of Ca 2 1 is restricted to the individual organelle.