Miracle-dz/UniFAR-FTUs huggingface dataset:
PISA: Power-robust Multiprocessor Design for Space Applications

Recently the conservative space industry driven by the requirements of novel applications decided to introduce multiprocessor systems. Following the same line of motivation we introduce the PISA multiprocessor chip with improved power robustness for space applications which is successfully produced and tested in IHP 130 nm technology. The paper brings several novelties in respect to the current state-of-the-art. The chip uses the Waterbear framework in which the multiprocessor cores can be dynamically put in one of three different operating modes according to the current application requirements regarding performance, power consumption and fault tolerance. The chip has special power supply architecture with 13 power domains and Adaptive Voltage Scaling (AVS) mechanism based on voltage regulators which imposes a non-standard IC design flow. The measurement results showed that the power supply of the multiprocessor cores can be reduced from the nominal 1,2 V downto 0,82 V without compromising power integrity.
Development of FPAA based Switching PD Controller

Since the development of digital control, most of industrial controllers, e.g. PID, are made of microcontroller, because the controllers can be effectively developed by software. This paper presents an alternative for controller development. By using FPAA, an analog controller can be much more effectively developed than using microcontroller, since the FPAA framework provides GUI software for designing and the hardware for simulation and testing. Therefore the developed controller can be directly applied to the real system after simulation, and the time for implementation can be reduced. The development of FPAA based switching PD controller is shown in this paper. Due to the derivative part, the PD controller cannot be realized, but based on the combination of linear and nonlinear components, the switching PD controller can be created. The performance of the proposed controller is verified by comparison with the simulation program created by Simulink® and the results are in good agreement.
Effect of Dielectric Process on the Interfacial Adhesion of RDL for FOWLP

The effect of low-temperature curable polybenzoxazole (PBO) process conditions on the interfacial adhesion energies between PBO dielectric and Cu redistribution layer for advanced fan-out packaging (FO package) were systematically investigated. The interfacial adhesion energies were 13.8 ± 2.0, 21.7 ± 2.7, and 6.3 ± 0.4 J/m2 under PBO curing temperature at 175, 200, and 225 °C, respectively. The X-ray photoelectron spectroscopy analysis showed that there exists a good correlation between the interfacial adhesion energy and the O 1s peak area fraction. Also, the nanoindentation analysis showed that there exists a little correlation between the interfacial adhesion energy and the hardness. Therefore, the optimized curing temperature of PBO must be carefully controlled to obtain interfacial adhesion of improvement to achieve interfacial reliability for advanced FO package.
Model-Based Testing of Read Only Graph Queries

Modern software applications often interface with graph-structured data managed by graph databases queried with specific graph query languages. Writing, understanding, and maintaining graph queries can be difficult and error-prone. Important decisions taken by the software application are in many cases based on the outcome of these graph queries. If test design does not account for the complexity incorporated in these queries, considerable parts of the business logic of such applications will not be tested appropriately.We aim at tackling the challenge of developing dedicated testing techniques for graph queries. In particular, we present a first model-based testing approach for read only queries supporting automated test creation, execution, and evaluation. We model queries using a well-established graph logic, being expressively equivalent to first-order logic on graphs. We develop a first coverage criterion requiring the presence of a test case returning an empty query result as well as the presence of a test case returning a non-empty result. We present the architecture of our model-based testing approach, which is supported by a first implementation. We illustrate our approach with complex read only graph queries from an industrial benchmark case study.
Source-Codeless Testing for Android Apps

Most of the automated approaches to support software engineering tasks for mobile applications, use source code as entry, which due to privacy factors imposes hard constraints on the implementation of those approaches by third-party services. However, the continuous pressure from market is leading practitioners to crowdsource/outsource software engineering tasks to third-parties that provide on-the-cloud infrastructures. Therefore, approaches that work at closed-source level (i.e., do not require source code) are desirable to enable automated outsourced software engineering tasks by third-party services. Specifically, in this paper we focus on testing since is a task that has broadly accepted approaches at source-code level, but new efforts are required to enable source-codeless approaches. We analyzed the current challenges and shortfalls of the currently proposed approaches that works at source code and APK level, and present our research plan oriented to reduce the existing gap for enabling testing techniques at APK level.
Selection and Application of Building Material Suppliers Based on Intuitionistic Fuzzy Analytic Hierarchy Process (IFAHP) Model

Scientific selection of building materials supplier is of great importance in the management of construction projects. In this work, the selection problem of building material suppliers is investigated under the background of economic globalization based on the IFAHP approach. Firstly, according to the features and key factors of building materials supplier selection, this work constructs evaluation index system by taking five aspects including cost, quality, service level, supplier qualification and risk into account. Secondly, the intuitionistic fuzzy preference relations (IFPRs) of criteria or sub-criteria and IFPRs between the alternatives with respect to each sub-criterion are determined via paired comparisons. The consistency check is carried out subsequently. Considering that the interaction process between experts and decision makers is time consuming and the experts may be not willing to participate in the adjustment and correction of IFPR continuously, an algorithm is proposed to automatically correct the inconsistency while preserving the original information even without experts involved. Consequently, the time costs of experts can be greatly shortened. Based on the above analysis, the optimal scheme can be obtained via calculating the scores of each material supplier. A case study on the selection of building material suppliers is conducted by employing the proposed framework. Results show that the scores of the three suppliers is positively correlated with the weight of the cost but negatively correlated with that of quality, service level, supplier qualification and risk. Besides, the sensitivity analysis illustrates that product quality and service level affect the ranking greatly.
Impact of Dynamic Trapping on High Frequency Organic Field-Effect Transistors

Many emerging organic semiconductor devices suffer from hysteresis effects caused by traps. The impact of such traps on AC device performance has not been investigated yet. In this paper, high-frequency non-quasi-static effects related to dynamic trapping based on a theoretical framework, TCAD simulations and experimental data have been studied. In contrast to previous studies, the focus of this paper is the OFET's high-frequency performance and not the characterization of the traps.
Syrphophilus bizonarius (Gravenhorst, 1829) (Hymenoptera) - new to Malta, with a revised check-list of the Ichneumonidae of the Maltese Islands

Recently, a sample of aphids on Lonicera implexa which were being preyed upon by four syrphid larvae was collected. The larvae and their ichneumonid parasitoid were reared to maturity. The purpose of this note is to record the presence of the ichneumonid, and its biology, for the first time from Malta. Syrphophilus bizonarius (Gravenhorst, 1829)
Fully Integrated Dual-Channel Gate Driver and Area Efficient PID Compensator for Surge Tolerant Power Sensor Interface

This paper presents a power sensor interface (PSI) driving a wide range of load valves with coil resistance ranging from 5 $\Omega$ to 62.5 $\Omega$. It can sustain voltage surges up to 115 V. An integrated high-voltage (HV) dual-channel gate driver with 8.7 ns deadtime (DT) is implemented to efficiently drive e-GaN FETs in a synchronous DC-DC buck converter. In addition, an area-optimization technique of on-chip passive components is proposed to enable full-integration of voltage-mode controller with 87% reduction in area. The achieved peak efficiency is 98.7% @ 4.67 A load. The feedback bandwidth is ~100 kHz to maintain fast transient response at 1MHz switching frequency. The settling time is < 70 $\mu\mathrm{s}$ and < 100 $\mu\mathrm{s}$ at load changes of −5.2 A and 5.2 A respectively. Overshoot (OS) and undershoot (US) voltages are within 2% of nominal VOUT. The layout of the gate driver and feedback control is implemented in $0.35-\mu\mathrm{m}$ HV CMOS process with active die area of 0.75 mm2.
Machine Learning Approach for Identification of Accident Severity from Accident Images Using Hybrid Features

Rapid growth in automobiles has caused an upsurge of accidents per day, which leads to the loss of lives and incurable disabilities to the victims. Therefore, the severity of the accident must be analyzed in real-time to save the injured and enhance emergency services. Accordingly, the accident image is considered as significant data in this work. From the accident image, essential features such as shape, texture and intensity gradient features are extracted using Hu moments, Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HoG) respectively. The extracted image features are combined to form a hybrid feature vector. With an objective to recognize the severity of the accident, the hybrid feature is employed to train the machine learning classifier models such as Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), AdaBoost (AB) and Gradient Boosting (GB). The performance of the classifiers is evaluated in terms of Area under the curve (AUC), precision, recall and F1-score. The results show the Random Forest performs better with AUC 0.75 compared to other models. Moreover, the result also reveals that hybrid features improve the recognition rate compared to the single feature.
Signaling Pathway Analysis Combined With the Strength Variations of Interactions Between Genes Under Different Conditions

Signaling pathway analysis has become a routine task after differentially expressed gene (DEG) studies across disease conditions, drug treatments, or developmental stages. A signaling pathway can be represented by a graph that consists of Genes and interactions (the genetic regulation) between them. However, existing signaling pathway analysis methods ignore the strength variations of interactions in signaling pathways under different conditions. Here, we developed a novel method named SPACI (Signaling Pathway Analysis Combined with the strength variations of Interactions between genes in signaling pathways under different conditions) to improve signaling pathway analysis after DEG studies. To further evaluate the performance of SPACI, we compared SPACI with nine other methods by using a benchmark of 28 gene expression datasets in two standard measures: sensitivity and prioritization. The False positive rate (FPR) of SPACI was also compared with five methods. The results show that SPACI is the second-ranked method in terms of prioritization and the third-ranked method in terms of sensitivity. SPACI is the top method when compared in terms of the sum value of the two ranks. Also, the FPR of SPACI is modest compared with the classic methods. Furthermore, the strength variation of the interaction is demonstrated as coherent with the biological problem. The interactions with high strength variations under different conditions can help improve the discovery of the underlying biological information. The R package of SPACI can be accessed at https://github.com/ZhenshenBao/SPACI.
An Optimal Recovery Approach for Liberation Codes in Distributed Storage Systems

To reduce the storage cost, distributed storage systems are gradually using erasure codes to ensure data reliability. Liberation codes, which satisfy the maximum distance separable (MDS) property and provide optimal modification overhead, are a class of popular two fault tolerant erasure codes. However, erasure codes need to read from surviving nodes and transfer across the network large amounts of data when recovering from single node failures. Existing single node failure recovery approaches for Liberation codes are either time-consuming or suboptimal. In this article, firstly, we prove the minimum number of symbols required to recover one failed node for a Liberation coded system. Then we derive the conditions that optimal recovery solutions need to satisfy. Finally, we propose an algorithm, called Disk Read Optimal Recovery (DROR), which can determine an optimal recovery solution in linear time and recover the failed node reading the minimum amount of data. We have implemented DROR in a real-world storage system Ceph and evaluated DROR on a cluster of Amazon EC2 instances. We show that DROR reduces the reconstruction time by up to 23.6% compared to that of the recovery approach in Ceph.
Estimating Simultaneous and Proportional Finger Force Intention Based on sEMG Using a Constrained Autoencoder

To boost the usability of a robotic prosthetic hand, providing degrees of freedom to every single finger is inevitable. Under the name of simultaneous proportional control (SPC), many studies have proposed methods to achieve this goal. In this paper, we propose a method to generate a regression model of a neuromuscular system called the Constrained AutoEncoder Network (CAEN) that estimates finger forces using a surface electromyogram (sEMG). Modifying the autoencoder from deep learning, the model is generated in a semi-unsupervised manner where only sEMG data and finger labels are used. In the learning process, the finger labels are used at the central layer of the network, where the three finger forces are estimated, to prevent penetration of other finger signals to each finger node and the network is trained in the constrained manner. This process results in independence among estimated finger forces such that the manipulability of multiple fingers is highly improved. The proposed model was compared with four previously reported SPC models in two tests: offline and online tests. In the offline test, the CAEN showed good results but not the best results. However, in the online test, which involved reaching target positions for three fingers simultaneously and proportionally, the proposed model showed the best results for three of six online performance indices (the completion rate, completion time, and throughput). Emphasizing the independence among estimated finger forces in the training process is the key point of the proposed method distinct from previous studies and the results showed that it was effective in the online control.
Detection of Peach Disease Image Based on Asymptotic Non-Local Means and PCNN-IPELM

Aiming at the problems of noise, background interference and low detection in peach disease image, this paper proposes a detection method of peach disease based on the asymptotic non-local means (ANLM) image algorithm and the fusion of parallel convolution neural network (PCNN) and extreme learning machine(ELM) optimized by linear particle swarm optimization(IPSO). Firstly, the method uses the ANLM image denoising algorithm to reduce the interference of the complex background in the image, then uses the parallel convolution neural network proposed by this paper to identify the characteristics of peach disease, uses the improved elu activation function instead of the conventional ReLu activation function, and uses the linear particle swarm optimized ELM (IPELM) proposed by this paper in the last layer instead of the traditional softmax layer, the update method is improved from two aspects to improve the convergence speed and accuracy of the network effectively. The results of 25513 images showed that the highest detection accuracy of brown rot, black spot, anthracnose, scab and normal peach were 89.02, 90.56, 85.37, 86.70 and 89.91 percent respectively, which indicated that this method was an effective method for peach disease detection.
QoX based ETL Design for Business Intelligence System of Lecturers’ Qualifications Analysis

Business Intelligence (BI) system for lecturer qualifications’ analysis is based on Standard Criteria of the Graduate Studies Program (SCGSP) of the country. According to the SCGSP, the procedures to determine the lecturer’s qualifications consists of several factors to be analyzed. The considered factors are involved with several data that is retrieved from various sources and has different data format types. To perform the analysis for BI systems, the integration of data is required through Extract, Transform, and Load, termed ETL workflow process to manage the relevant data. In fact, the ETL workflow process is very complicated, time consuming, and costly. Most ETL designers report that ETL workflow process accounts for 70% of the work in data warehouse development. Moreover, there is no systematic approach and supported tools to accomplish the quality requirement of ETL workflow design and most of them are based on experiences of developers and designers. This is actually affected the performance analysis of BI systems when implemented the poor ETL workflow. Therefore, this paper aims to design ETL workflow for BI of lecturers’ qualifications analysis based on the standard criteria of SCGS. The study looks into identifying and found the Quality of Objectives (QoX) dimension aspects namely; completeness, efficiency, scalability, and accuracy as a quality metrics to measure the quality of ETL design of this research in the future work.
Assessing the condition of Nomex paper-based Insulation in Open Wound and VPI type Dry-type Transformer using Frequency Domain Spectroscopy Data

Dry-type transformers are becoming increasingly popular in urban areas due to its non-flammable properties. Being air-cooled, such units require less installation space compared to their counterpart Oil Impregnated Paper (OIP) having the same power rating. Open Wound types of dry-type transformer insulation like Dip-Bake type or Vacuum Pressure Impregnation (VPI) are popular and are being used extensively for constructing distribution transformers. Nomex paper is usually considered as the main insulation for such dry-type transformer insulation. The present paper describes the effect of thermal aging of Dip -Bake type and VPI type dry-type transformer insulation samples constructed in the laboratory. At the end of thermal aging, Frequency Domain Spectroscopy (FDS) data for both the dry type construction is recorded and compared.
AiRite: Towards Accurate & Infrastructure-Free 3-D Tracking of Smart Devices

Precise tracking of smart wearables in 3-D space is foundational in domains ranging from Augmented Reality (AR) and gesture recognition to robotics and indoor localization. Usually, a sensor-dependent approximation model, and a complementary infrastructure like acoustic, vision etc. is implemented alongside an onboard IMU (Inertial Measurement Unit) for an effective tracking. In this work, we present a novel 3-D tracking solution, ‘AiRite’ for commercial-grade smart wearables/mobiles using only their onboard IMU. Our tracking method mitigates the manifested inertial errors using a novel progressive zero correction, yielding superior results both for 2-D and 3-D trajectories. AiRite aims for a pervasive usage and is independent of device's form factor, or make of sensor enabling it to be used right out-of-the-box. The solution is shown to be more accurate than prior arts in tracking trajectories of basic shapes by moving the hand both in 2-D and 3-D. We depict trajectories of medium to long duration words written in air in a cursive handwriting using both smartwatch and smartphone. We further demonstrate robustness of AiRite by depicting panoramic 3-D texts written in air in a concave shape and visualizing the same from multiple angles, which are first results of their kind in this domain.
Filtering Magnetic Resonance Images to Detect Brain Tumor

Magnetic resonance imaging is one of the best methods for detecting brain tumors. But the images captured by this method may contain different kinds of noises. So it is very essential to remove the noises for properly identifying the specific brain tumor. A filter is usually used to remove the noises. This paper illustrates different image filtering methods, such as low pass filter, high pass filter, and median filter, to improve the image quality by removing the noises from magnetic resonance images to identify the brain tumor. The MSE, RMSE, and the PSNR is used for understanding the quality of the filtered images. A graphical user interface is developed in MATLAB to implement all the filtering process and performance analysis for magnetic resonance images used to detect brain tumor.
Noise Free Fully Homomorphic Encryption Scheme Over Non-Associative Algebra

Among several approaches to privacy-preserving cryptographic schemes, we have concentrated on noise-free homomorphic encryption. It is a symmetric key encryption that supports homomorphic operations on encrypted data. We present a fully homomorphic encryption (FHE) scheme based on sedenion algebra over finite <inline-formula> <tex-math notation="LaTeX">$Z_{n}$ </tex-math></inline-formula> rings. The innovation of the scheme is the compression of a 16-dimensional vector for the application of Frobenius automorphism. For sedenion, we have <inline-formula> <tex-math notation="LaTeX">$p^{16}$ </tex-math></inline-formula> different possibilities that create a significant bijective mapping over the chosen 16-dimensional vector that adds permutation to our scheme. The security of this scheme is based on the assumption of the hardness of solving a multivariate quadratic equation system over finite <inline-formula> <tex-math notation="LaTeX">$Z_{n}$ </tex-math></inline-formula> rings. The scheme results in <inline-formula> <tex-math notation="LaTeX">$256n$ </tex-math></inline-formula> multivariate polynomial equations with <inline-formula> <tex-math notation="LaTeX">$256+16n$ </tex-math></inline-formula> unknown variables for <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> messages. For this reason, the proposed scheme serves as a security basis for potentially post-quantum cryptosystems. Moreover, after sedenion, no newly constructed algebra loses its properties. This scheme would therefore apply as a whole to the following algebras, such as 32-dimensional trigintadunion.