Miracle-dz/UniFAR-FTUs huggingface dataset:
Oxidation-reduction properties of rat liver cytochromes P-450 and NADPH-cytochrome P-450 reductase related to catalysis in reconstituted systems

: A series of equilibrium and kinetic measurements involving the oxidation-reduction properties of purified rat liver NADPH-cytochrome P-450 reductase and eight different purified rat liver cytochromes P-450 (P-450s) were carried out. Apparent spin states of P-450 iron were determined in the absence and presence of a number of known substrates by using second-derivative and conventional near-UV absorbance spectroscopy. Many of the substrates examined did not produce significant changes in the apparent iron spin state, even when binding could be demonstrated with equilibrium dialysis. Further, the spin state was not correlated to catalytic activity of the P-450s in reconstituted enzyme systems. The oxidation-reduction potentials were determined for the ferric/ferrous couples of each of the eight P-450s in the presence and absence of known substrates, as well as other proteins suspected of altering the potentials. The midpoint potential (Em,7) ranged from -350 to -289 mV for the P-450s under these conditions. In some cases Em,7 was raised with the addition of substrates, but the extent of the increase was no greater than +33 mV. The Em,7 of one P-450 (P-450 beta NF/ISF-G) was not changed significantly when the fraction of high-spin iron varied between 11 and 67%. Steady-state spectral studies provided evidence for the accumulation of an oxygenated ferrous intermediate (or a derived product) of one P-450 (P-450PB-B) in the presence of a substrate, cyclohexane. Studies on the donation of electrons from cytochrome b5 and a series of dyes to this complex suggest that it has an effective Em,7 (for reduction) of approximately +50 mV. In studies with one of the P-450s, steady-state spectral studies indicated that the three-electron-reduced form of NADPH-P-450 reductase accumulates, consistent with the view that this form of the reductase is involved in the reduction of P-450 from the ferric to the ferrous state.
Towards a Better Understanding of Agile Mindset by Using Principles of Agile Methods

The right way to agility should start with a proper agile mindset instead of applying Agile methods directly. However, apart from the manifesto, it is unlikely to find a comprehensive set of Agile principles that can serve for an improved agile mindset. Our study intends to fulfill this gap in a systematic way: providing a list of the Agile methods along with their principles within a single source, in the way of providing a better understanding of the concept of agility from a wide and exhaustive perspective. To do so, the collected 105 principles were content-analyzed in order to group them into 32 categories for a higher-level abstraction. These categories then were subsumed into two main categories. The whole grouping process was reviewed by one expert and the list was adjusted accordingly. Then, based on the consolidated list of the categorized principles, analysis and evaluations were made by the authors. As a part of the evaluations, semi-structured interviews with two experts were conducted to evaluate the categorized principles in general, especially in terms of their contribution to agility.
Testbed for thermal and performance analysis in MPSoC systems

Many modern computing platforms in the safetycritical domains are based on heterogeneous Multiprocessor System-on-Chip (MPSoC). Such computing platforms are expected to guarantee high-performance within a strict thermal envelope. This paper introduces a testbed for thermal and performance analysis. The testbed allows the users to develop advanced scheduling and resource allocation techniques aiming at finding an optimal trade-off between the peak temperature and the achieved performance. This paper presents a new, opensource Thermobench tool for data collection and analysis of userdefined workloads. Furthermore, a methodology for shortening the time needed for the data collection is proposed. Experiments show that a significant amount of time can be saved. Specifically, time reduction from 60 minutes to 15 minutes is achieved with the i.MX8 MPSoC from NXP while running a set of user-defined benchmarks that stress CPU, GPU, and different levels of the memory hierarchy.
Voice authentication based on the Russian-language dataset, MFCC method and the anomaly detection algorithm

Almost all people's data is stored on their personal devices. There is a need to protect information from unauthorized access. PIN codes, passwords, tokens can be forgotten, lost, transferred, brute-force attacked. For this reason, biometric authentication is gaining in popularity. Biometric data are unchanged for a long time, different for users, and can be measured. This paper explores voice authentication due to the ease of use of this technology, since obtaining voice characteristics of users doesn't require an equipment in addition to the microphone. The method of voice authentication based on an anomaly detection algorithm has been proposed. The software module for text-independent authentication has been implemented on the Python language. It's based on a new Mozilla's open source voice dataset "Common voice". Experimental results confirmed the high accuracy of authentication by the proposed method.
Enterprise Modeling: From Digital Transformation to Digital Ubiquity

While digital transformation is still a challenge for many companies when introducting digital technologies in existing processes and business models, digital ubiquity stands for the next step in digitalization. It characterizes the omnipresence of a large range of digital technologies, connectivity, and data as well as entirely digital organizations. This includes for example upcoming technologies such as distributed ledgers, artificial intelligence or augmented reality and according interfaces and data sources as well as decentralized apps and autonomous organizations. The challenge thus becomes to optimally deal with these opportunities and deploy them efficiently in business scenarios. In this paper we will investigate the role of enterprise modeling under this paradigm and how it can contribute to a well-structured, systematic understanding of complex digital phenomena for supporting business and technological decisions.
Proposed Method for Partial Node Replacement by Software Defined Network

Since it is impractical to replace the entire traditional network by the SDN network due to some constraints i.e. financial budget, limited skills to SDN, in addition to the need to have the benefits and flexibility of the traditional network, the partial replacement implemented by deploying or replacing some legacy nodes by the SDN switches have emerged. Such replacement requires routing and security addressing coordination issues. In this research, we present our proposed solution for automatic replacement of a segment of the legacy network by SDN nodes, and generation of a set of OpenFlow rules and switches configuration that meets the traditional network behavior requirements. The rules are identified based on the analysis of the network traffic acquired from the legacy segment.
Harmonizing IT Frameworks and Agile Methods: Challenges and Solutions for the case of COBIT and Scrum

Information Technology (IT) is a complex domain. In order to properly manage IT related processes, several frameworks including ITIL (Information Technologies Infrastructure Library), COBIT (Control OBjectives for Information and related Technologies), IT Service CMMI (IT Service Capability Maturity Model) and many others have emerged in recent decades. Meanwhile, the prevalence of Agile methods has increased, posing the coexistence of Agile approach with different IT frameworks already adopted in organizations. More specifically, the pursuit of being agile in the area of digitalization pushes organizations to go for agile transformation while preserving full compliance to IT frameworks for the sake of their survival. The necessity for this coexistence, however, brings its own challenges and solutions for harmonizing the requirements of both parties. In this paper, we focus on harmonizing the requirements of COBIT and Scrum in a same organization, which is especially challenging when a full compliance to COBIT is expected. Therefore, this study aims to identifying the challenges of and possible solutions for the coexistence of Scrum and COBIT (version 4.1 in this case) in an organization, by considering two case studies: one from the literature and the case of Akbank delivered in this study. Thus, it extends the corresponding previous case study from two points: adds one more case study to enrich the results from the previous case study and provides more opportunity to make generalization by considering two independent cases.
InferLine: latency-aware provisioning and scaling for prediction serving pipelines

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction between model batch size, the choice of hardware accelerator, and variation in the query arrival process. In this paper we introduce InferLine, a system which provisions and manages the individual stages of prediction pipelines to meet end-to-end tail latency constraints while minimizing cost. InferLine consists of a low-frequency combinatorial planner and a high-frequency auto-scaling tuner. The low-frequency planner leverages stage-wise profiling, discrete event simulation, and constrained combinatorial search to automatically select hardware type, replication, and batching parameters for each stage in the pipeline. The high-frequency tuner uses network calculus to auto-scale each stage to meet tail latency goals in response to changes in the query arrival process. We demonstrate that InferLine outperforms existing approaches by up to 7.6x in cost while achieving up to 34.5x lower latency SLO miss rate on realistic workloads and generalizes across state-of-the-art model serving frameworks.
A New Optimized Adaptive Approach for Estimation of the Wigner Kernel

In this paper we study numerically an optimized Adaptive Monte Carlo algorithm for the Wigner kernel - an important problem in quantum mechanics represented by difficult multidimensional integrals. We will show the advantages of the optimized Adaptive MC algorithm and compare the results with the Adaptive approach from our previous work [4] and other stochastic approaches for computing the Wigner kernel in 3,6,9dimensional case. The 12-dimensional case will be considered for the first time. A comprehensive study and an analysis of the computational complexity of the optimized Adaptive MC algorithm under consideration has also been presented.
Knowledge Detection and Discovery using Semantic Graph Embeddings on Large Knowledge Graphs generated on Text Mining Results

Knowledge graphs play a central role in big data integration, especially for connecting data from different domains. Bringing unstructured texts, e.g. from scientific literature, into a structured, comparable format is one of the key assets. Here, we use knowledge graphs in the biomedical domain working together with text mining based document data for knowledge extraction and retrieval from text and natural language structures. For example cause and effect models, can potentially facilitate clinical decision making or help to drive research towards precision medicine. However, the power of knowledge graphs critically depends on context information. Here we provide a novel semantic approach towards a context enriched biomedical knowledge graph utilizing data integration with linked data applied to language technologies and text mining. This graph concept can be used for graph embedding applied in different approaches, e.g with focus on topic detection, document clustering and knowledge discovery. We discuss algorithmic approaches to tackle these challenges and show results for several applications like search query finding and knowledge discovery. The presented remarkable approaches lead to valuable results on large knowledge graphs.
BiLSTM with Data Augmentation using Interpolation Methods to Improve Early Detection of Parkinson Disease

The lack of dopamine in the human brain is the cause of Parkinson disease (PD) which is a degenerative disorder common globally to older citizens. However, late detection of this disease before the first clinical diagnosis has led to increased mortality rate. Research effort towards the early detection of PD has encountered challenges such as: small dataset size, class imbalance, overfitting, high false detection rate, model complexity, etc. This paper aims to improve early detection of PD using machine learning through data augmentation for very small datasets. We propose using Spline interpolation and Piecewise Cubic Hermite Interpolating Polynomial (Pchip) interpolation methods to generate synthetic data instances. We further investigate on reducing dimensionality of features for effective and real-time classification while considering computational complexity of implementation on real-life mobile phones. For classification we use Bidirectional LSTM (BiLSTM) deep learning network and compare the results with traditional machine learning algorithms like Support Vector Machine (SVM), Decision Tree, Logistic regression, KNN and Ensemble bagged tree. For experimental validation we use the Oxford Parkinson disease dataset with 195 data samples, which we have augmented with 571 synthetic data samples. The results for BiLSTM shows that even with a holdout of 90%, the model was still able to effectively recognize PD with an average accuracy for ten rounds experiment using 22 features as 82.86%, 97.1%, and 96.37% for original, augmented (Spline) and augmented (Pchip) datasets, respectively. Our results show that proposed data augmentation schemes have significantly (p < 0.001) improved the accuracy of PD recognition on a small dataset using both classical machine learning models and BiLSTM
Conceptualization of an AI Based Assistant to Support the Compatibility of Family and Paid Work

—The goal of the presented intelligent AI-based assistant is to simplify the reconciliation of paid work and family. In times of the corona pandemic and the challenges it poses, solutions are needed to mitigate the impact on workers and families and to strengthen the compatibility of work and family life. We are developing a conceptual reference model of proven methods and technologies for the generation of information that can be queried by interested parties via an bot service, thus simplifying professional and family challenges. Essentially, we provide a framework and starting point for research, development, and evaluation of an AI-based chatbot for the reconciliation of paid work and family.
HBV X gene transfection upregulates IL-1β and IL-6 gene expression and induces rat glomerular mesangial cell proliferation

The HBV X gene encodes a 17-kd protein,termed HBx,that has been shown to function as a transcriptional trans-activator of a variety of viral and cellular promoter/enhancer elements.This study was aimed to investigate the effect of HBx on gene expression of interluekin(IL)-1β and IL-6,and proliferation of rat mesangial cell in vitro. The HBV X gene was amplified with PCR assay,and inserted into the eukaryotic expression vector PCI-neo. The structure of recombinant PCI-neo-X plasmid was confirmed by restrict endonuclease digestion and sequencial analysis. PCI-neo-X was transfected into cultured rat mesangial cell line in vitro via liposome. HBx expression in transfected mesangial cells was assessed by Western blot. The IL-1β and IL-6 mRNA expression in those cells was assessed by semi-quantitative RT-PCR. Mesangial cell proliferation was assessed by MTT. The results showed that HBx was significantly expressed in cultured mesangial cell line at 36 and 48 hours after transfection. The IL-1β and IL-6 mRNA levels were simutanously increased. The cell proliferation was also elevated at the same time compared to the control groups. Therefore,it can be concluded that HBx gene tranfection can induce IL-1β and IL-6 expression and mesangial cell proliferation.HBx may play a critical role in mesangial cell proliferation through upregulation of IL-1β and IL-6 expression.
Measuring the Polarity of Conversations between Chatbots and Humans: A Use Case in the Banking Sector

This paper describes a study on opinion analysis applied to both human to chatbot conversations, but also to human to human conversations using data coming from the banking sector. A polarity classifier SVM model applied to conversations provides insights and visualisations of the satisfaction of users at a given time and its evolution. We conducted a study on the evolution of the opinion on the conversations started with the chatbot and then transferred to a human agent. This work illustrates how opinion analysis techniques can be applied to improve the user experience of the customers but also detect topics that generate frustrations with a chatbot or with human experts.
A Globally-optimized Co-design Approach for Heterogeneous Systems Using Convex Optimization

Manufacturing methods for heterogeneous systems allow for high-density integration of multiple semiconductor devices in one system or package. Current design tools and approaches do not incorporate the intensified heterogeneity of 3D systems sufficiently. They provide suboptimal design solutions because the overall global solutions cannot be optimized. In this paper, we present a co-design methodology to globally solve and optimize the design problem and so to fully exploit the potential of emerging technologies. We use a design example to show the advantage and potential of a co-design methodology compared to conventional top-down approaches. Our approach should guide further research and tool development in this emerging field of heterogeneous system design.
PCA, Random-Forest and Pearson Correlation for Dimensionality Reduction in IoT IDS

The exposure of Internet of Things (IoT) networks led to an exponential increase in traffic size. Thus, the traffic data meet the 5V big data model, which results in the rise of many cyber threats and the appearance of new security challenges. Intrusion Detection Systems (IDS) need to apply dimensionality reduction to handle the enormous data size and to speed-up cyber threats detection. This research analyzes the impact of Principal Component Analysis (PCA), Random-Forest (RF), and filter-based dimensionality reduction methods on several detection models in four different datasets. Additionally, it studies the impact of some parameters on dimensionality reduction methods’ performance. Results showed that dimensionality reduction methods were able to reduce datasets’ size without compromising the detection models’ efficiency. Moreover, results showed that PCA was the best method in reducing datasets’ size over all the datasets. At the same time, RF showed a better distinguishing ability with the minimum amount of information.
Development of a Fault-Tolerant Control System for a Swarm of Drones

One of the important challenges in an autonomous swarm of drones is the dependability of the swarm to continue its mission. Engine failure or propeller disintegration poses a significant risk to the operation of each node of the swarm and if it happens the system should be able to tolerate such malfunction by reconfiguring the swarm and reforming if it is necessary. In this paper, a fault-tolerant control system for an autonomous leader–follower based swarm of drones is presented. For defining the fault model, the full failure of an engine is considered as an emergency situation, and the controller of each node is facilitated to reconfigure the swarm from imposing a bottom-up reformation to bypass the faulty node, which keeps the formation intact as much as possible. The simulation results show the effectiveness of the proposed technique with respect to reliability and robust stability.
Deep-Dual-Learning-Based Cotask Processing in Multiaccess Edge Computing Systems

Multiaccess edge computing (MEC) systems provide low-latency computing services for Internet of Things (IoT) applications by processing IoT data on edge servers. In the era of heterogeneous IoT environments, the success of IoT applications hinges on the processing of diversified IoT data. To leverage MEC systems to enable timely IoT services, we characterize IoT applications as cotasks, where each cotask is completed only if all its constituent subtasks (e.g., IoT data processing) are finished. Existing works have been devoted to the design of task offloading and scheduling decisions for MEC-enabled IoT applications, but they mostly neglect the cotask feature. In this article, we investigate the problem of cotask processing in MEC systems, and we formulate it as a nonlinear program (NLP) to minimize total cotask completion time (TCCT). In the light of uncertain communication latency, we transform the NLP to a parameterized and unconstrained version, based on which we propose the deep dual learning (DDL) method, where the learner keeps updating primal and dual variables based on randomly perturbed samples. Furthermore, we provide the duality gap and time complexity analyses for the DDL method. Our simulation results demonstrate that the proposed solution can gradually converge over iterations, and its TCCT performance outperforms other comparison schemes under various system settings.
Power Distribution Network Modeling and Design of Re-Distribution Layer in DDR Application

In this paper, the design tips considering power integrity (PI) for the power distribution network (PDN) in an re-distribution layer (RDL) are presented. First, the methodology of chip-RDL co-simulation is introduced. It indicates that decreasing the PDN loop inductance is critical for a robust PDN design in a high-speed transmission channel. Then, the loop inductance based on a simplified PDN model is derived, which reveals several possible ways to reduce the voltage ripple between power/ground terminals. Finally, the PDN design tips are proposed and verified based on the derived results. These design tips demonstrate that the PI issues of an RDL can be avoided at an early stage of the chip-package co-design.
FPGA-based SPHINCS+ Implementations: Mind the Glitch

The digital signature scheme SPHINCS+ is a candidate in the NIST post-quantum project, whose aim is to standardize cryptographic systems that are secure against attacks originating from both quantum and classical computers. We present an efficient and, to our knowledge, first hardware implementation for SPHINCS+. Our systematic approach of a performance-optimized FPGA architecture results in a 100x speed-up compared to the reference software-only implementation. Our investigation on a real-world implementation revealed a weakness regarding fault injection. The attack breaks the scheme completely. Collecting enough private information to forge a signature is a matter of seconds on our setup. We discuss possible countermeasures. A “sign-then-verify” operation unfortunately does not detect a faulty signature, but a full replication of the hardware might make a detection possible.
A Machine Learning-Based Epistemic Modeling Framework for EMC and SI Assessment

A novel machine learning-based framework is presented to evaluate the effect of design parameters, affected by epistemic uncertainty, on the Signal Integrity (SI) and Electromagnetic Compatibility (EMC) performance of electronic products. In particular, possibility theory is leveraged to characterize the epistemic variations, and is combined with Bayesian optimization to accurately and efficiently perform uncertainty quantification (UQ). A suitable application example validates the proposed method.