Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Contributions of Information Technology on Bank’s Service Offering: The Case of National Microfinance Bank (NMB) Ltd Tanzania

Increasing uses of information technology (IT) helps to solve problems of efficiency, speed and prompt services. This study aimed at measuring contributions of IT on bank services delivery and customer satisfaction (CS), specifically to assess whether customers are satisfied with operations, to determine major service quality (SQ) determinants and identify its relationship between these determinants and SQ as well as CS. This study adapted SERVQUAL instrument model developed by Parasuraman and others (1990) to gather information. Descriptive statistics (frequencies, means), multiple regressions and correlation analyses were used. Study found that customers were dissatisfied with service rendered, there are six factors that determine SQ and among those, service delivery system has strong influence on CS, there is strong positive multiple correlations between SQ determinants and CS as well as SQ itself. Likewise there was significant weak negative correlation between SQ and time of being a customer as well as insignificant weak positive correlation between SQ and reasons of shifting bank. Also all SQ determinants showed differences of less than one (P-E), indicating some improvement on service rendered. Full automation of Bank’s services, carrying out a regular and thorough study of customer behavior, increase communication between top manager and customer-contact personnel were recommended to solve existing problems. Further, this study recommends that Bank of Tanzania should develop among others, rules and regulations that would enforce use of modern IT.
Active surveillance for patients with very low‐risk thyroid cancer

Over the past 30 years in the United States, increasing identification of small thyroid nodules has led to a dramatic rise in the detection of small thyroid cancers, many of which are unlikely to progress to overt clinical disease. Because autopsy studies reveal that up to 30% of people harbor clinically occult thyroid cancers, the growing use of diagnostic technologies has identified an increasing number of small, clinically low risk papillary thyroid cancers (PTCs). In recent years, clinical practice has evolved to de‐intensify the treatment for PTCs, with fewer total thyroidectomy and nodal dissection procedures being performed, in favor of more limited operations. In addition, vigilant observation of selected low risk cancers has demonstrated outcomes comparable to those patients who undergo immediate surgical intervention. Active surveillance has emerged as a new option within the treatment algorithm of PTCs. There is now robust data from cancer centers in Japan and Korea which have reported excellent oncologic outcomes among patients undergoing active surveillance for PTC, as well as more recent, similar data from the United States. American Thyroid Association guidelines now include the option of active surveillance for appropriately selected patients with low‐risk PTC. With active surveillance now one option within the standard of care for patients with certain thyroid cancers, surgeons have become critical to facilitating shared decision‐making for patients facing this diagnosis.
BIOLOGICAL POTENTIAL OF VARIOUS MARINE PEPTIDES: A REVIEW

Marine organisms are important sources of bioactive molecules that have been used to treat various diseases. Unusual marine environments are associated with chemical diversity, leading to a resource of novel active substances for the development of bioactive products. Recently, marine-derived bioactive peptides have attracted attention owing to their numerous beneficial effects. Moreover, several studies have reported that marine peptides exhibit various activities, such as antimicrobial, antifungal, antidiabetic, antitumor, antimalarial, antiprotozoal, antituberculosis, and antiviral activities. In the last several decades, studies of marine plants, animals, and microbes have revealed tremendous number of structurally diverse and bioactive secondary metabolites. In this review, we will present the structures and activities of peptides isolated from marine sources (sponges, algae, bacteria, fungi and fish).
Comparison of the diagnostic efficacy between transrectal prostate biopsy and transperineal cognitive fusion biopsy

Background: This study aimed to compare the detection efficacy of transrectal ultrasound -guided systematic prostate biopsy (TR-SB) and transperineal cognitive fusion targeted +systematic biopsy (TP-SB+COG-TB) in patients with suspected prostate cancer (PCa). In addition, the relative clinical characteristics of PCa were evaluated. Methods: 385 patients were enrolled in this study, who underwent transrectal (n=275) or transperineal (n = 110) cognitive fusion biopsy. Relative factors of PCa including age, prostate volume and PSA level were collected for multivariable analysis. The cancer detection rates were compared, and logistic regression was used to assess the impact of patient characteristics on PCa detection Results: For all patients, the overall detection rates of TR-SB and TP-SB+COG-TB were 121/275(40%) and 67/110（60.91%）, respectively. TP-SB+COG-TB detected a higher rate of PCa (P =0.003) and more clinically significant prostate cancers (csPCa) (P=0.001) than TR-SB. Logistic regression analyses revealed that age、TPSA（total prostate specific antigen）and PV were related to the detection rate of PCa (P≤0.05). Conclusion ： TP-SB+COG-TB could find more clinically significant PCa than TR-SB. Due to the high detection rate at certain ages, PSA levels and PV, patents’ clinical characteristics should be considered in biopsy.
Automatic classification of tongue texture color and tongue coating color based on BP neural network

Tongue diagnosis is an important part of the traditional Chinese medicine diagnosis, and it is one of the main criteria for clinical diagnosis of TCM. Tongue texture color and tongue coating color are the main research contents of tongue diagnosis. This paper first uses computer technology to achieve separation of tongue texture and tongue coating on the tongue image. According to the position information, the tongue texture and tongue coating template are discriminated, and the color information of tongue texture area and tongue coating area are obtained respectively. Finally, the automatic classification of tongue texture color and tongue coating color is realized through neural network.
A Temporal CNN-based Approach for Autonomous Drone Racing

Convolutional Neural Networks (CNN) and Deep Learning (DL) have become a popular tool to address all sorts of artificial intelligent challenges. The Autonomous Drone Racing is a challenge consisting of developing an autonomous drone capable of beating a human in a drone race, and DL is a tool that has been included in state of the art solutions to address this problem. Current works have proposed to use CNN and DL to detect the gates, whereas other works have proposed to use a CNN to obtain drone’s control commands and a goal point, with all of these approaches using a single image as input. In this work we propose a CNN based on the well known pose-net network. Originally used for camera relocalisation, we propose to use pose-net to provide control commands to drive the drone towards and to cross the gate autonomously. In contrast to previous works, we also propose to use a temporal set of images as input for the network. In specific, we use 6 images captured every 166 milliseconds in one second to create a mosaic. The latter is used as input of the CNN to predict the control commands. We compare this proposed temporal approach against using a single image as input for the CNN. Our results, although in simulation, demonstrate that the using only our temporal approach is feasible, less noisy and more effective than the single image approach, enabling the drone to autonomously cross a set of gates placed randomly, and even under the scenario where the gate moves dynamically.
IEEE International Symposium on Hardware Oriented Security and Trust (HOST): Past, Present, and Future

Hardware plays an integral role in system security with many emerging vulnerabilities and defense mechanisms targeting hardware. The IEEE International Symposium on Hardware Oriented Security and Trust (HOST) aims to facilitate the rapid growth of hardware-based security research and development. Since 2008, HOST has provided an environment to present cutting-edge developments in hardware security and trust. With the recent expansion of its scope to include all areas of overlap between hardware and security, HOST has become a premier event in the field of cybersecurity, and is one of the few to bridge the gap between computer security, mircoelectronics, and electronic design automation (EDA) communities.
Performance Improvement of On-Chip Solenoid Type Power Inductors

An on-chip solenoid type power inductor utilizing double layer laminated thin film cores with nanogranular magnetic materials of Fe-Co-Ti-O is presented. Increments of 23% in inductance up to 500 MHz and 21% of quality factor at 100 MHz are obtained compared with usual solenoid inductor with single layer laminated thin film core. And the L improvement is proved by simulation to be bigger if the magnetic films have a higher permeability or thicker thickness. The influence of copper winding thickness on DC performance is also analyzed. The results show that increasing the thickness of winding coils within the process technology ability can effectively improve the performance of the proposed inductor at low frequency. The coupling coefficient achieves 0.85 and can be easily adjusted by changing the turn number of parallel windings to easily meet the requirement of circuit design.
Synchrophasor Measurement-based Modal Analysis in Power Grids

Mode, mode shape, and participation factor are important modal parameters in small signal stability analysis. Most of the existing measurement-based methods focus on mode or mode shapes estimation, few of them explore the participation factors estimation. This paper proposes a multichannel modal analysis approach to estimate not only dominant modes and mode shapes, but also participation factors from synchrophasor measurements. The wavelet power spectrum is first applied to primary wavelet coefficient matrices of multichannel measurements to detect the critical scale ranges associated with the dominant modes, and new wavelet coefficient matrix with same critical scale in the detected scale ranges are constructed from the primary wavelet coefficient matrices. Applying singular value decomposition to these constructed matrices whose scales located in a range associated with one dominant mode, a maximum singular value vector is formed from each singular value matrix. Using the right and left singular value vectors of the maximum component in the formed maximum singular value vector, the dominant modes and mode shapes are all estimated. Meanwhile, the corresponding participation factors of the generators associated with the determined dominant mode are solved via the estimated mode shapes and their inverses. The proposed approach is tested and evaluated on a 16-machine 68-bus test system. The results demonstrate high accuracy and efficiency of the proposed approach in the estimation of dominant modes, mode shapes and participation factors utilizing synchrophasor measurements.
Active Learning for Feasible Region Discovery

Often in the design process of an engineer, the design specifications of the system are not completely known initially. However, usually there are some physical constraints which are already known, corresponding to a region of interest in the design space that is called feasible. These constraints often have no analytical form but need to be characterised based on expensive simulations or measurements. Therefore, it is important that the feasible region can be modeled sufficiently accurate using only a limited amount of samples. This can be solved by using active learning techniques that minimize the amount of samples w.r. t. what we try to model. Most active learning strategies focus on classification models or regression models with classification accuracy and regression accuracy in mind respectively. In this work, regression models of the constraints are used, but only the (in) feasibility is of interest. To tackle this problem, an information-theoretic sampling strategy is constructed to discover these regions. The proposed method is then tested on two synthetic examples and one engineering example and proves to outperform the current state-of-the-art.
Shape-Morphing Wheel Design and Analysis for Step Climbing in High Speed Locomotion

The ability of wheeled mobile robots to overcome steps is often limited by wheel size. Enhancing the ability of mobile robots to overcome obstacles is essential for extending their operation area, and many previous attempts have been made by using transformable wheels, a linkage mechanism, and a spoke-type wheel-leg mechanism. In this study, we propose a shape-morphing wheel mechanism for step climbing at high speed. In the general case of low speed locomotion, a robot's wheels can be used normally. However, to overcome relatively large obstacles, the robot's wheels can extend its shape by using the proposed morphing mechanism with centrifugal force at high speed locomotion. Two modes of step climbing are analyzed that use kinetic energy conversion or impact on the steps. Detail design issues with comprehensive analyses results are presented. Results demonstrate that a robot with morphing wheels can climb a 46.67 mm obstacle at 1.82 m/s, which is 1.33 times larger than the wheel radius. We expect that this method can be applied to other locomotion modes of wheeled mobile robots.
On the Convergence of Bregman ADMM With Variational Inequality

The alternating direction method of multipliers (ADMM) is one of most foundational algorithms for linear constrained composite minimization problems. For different specific problems, variations of ADMM (like linearized ADMM, proximal ADMM) are developed. By using the Bregman distances, lots of ADMMs can be formulated into a uniform mathematical scheme. Although variational inequalities have been well used to study ADMMs, the use for BADMM has still been missing. In this paper, we study the convergence of BADMM by variational inequalities. We present a proof framework for BADMMs. And then, we present very concise convergence proof for the basic BADMM. As applications, we consider several variations of BADMM and obtain corresponding convergence results.
Development of the Backside Loading Inductive Tactile Force Sensor Using the Flip-Chip Bonding of CMOS Sensing Chip

This study presents a backside loading design inductive tactile sensor with the monolithic integration of thermometer. Based on the commercially available standard complementary metal-oxide-semiconductor (CMOS) process (the TSMC $0.35\mu \text{m}$ 2P4M CMOS process) and the in-house post CMOS processes, the sensing chip is designed and implemented. The electrical connection is further achieved by using the flip-chip bonding process, and hence the breakage of bonding wires by the tactile load can be avoided. The CMOS sensing chip consists of the magnetic sensing coil and the polysilicon RTD (resistive thermal detector). Moreover, a cavity is defined on the backside (silicon substrate) of CMOS chip to act as a mold for the polymer filler as well as the housing for the chrome steel ball contact interface. The tactile force will cause the polymer deformation and chrome steel ball displacement, and further lead to the magnetic flux change on the sensing coil. Thus, the tactile force is detected by the inductance variation of magnetic sensing coil on the CMOS chip. Moreover, the temperature variation is detected by the resistance change of RTD thermometer, and the in-situ temperature monitoring during tactile loading is achieved. The force response of the proposed tactile sensor and the temperature response of the thermometer have been characterized. The influence of temperature variation on the tactile sensor has also been calibrated. Moreover, the characteristics of the thermometer on sensing chip at different contact conditions are also investigated.
Dronument: System for Reliable Deployment of Micro Aerial Vehicles in Dark Areas of Large Historical Monuments

This letter presents a self-contained system for robust deployment of autonomous aerial vehicles in environments without access to global navigation systems and with limited lighting conditions. The proposed system, application-tailored for documentation in dark areas of large historical monuments, uses a unique and reliable aerial platform with a multi-modal lightweight sensory setup to acquire data in human-restricted areas with adverse lighting conditions, especially in areas that are high above the ground. The introduced localization method relies on an easy-to-obtain 3-D point cloud of a historical building, while it copes with a lack of visible light by fusing active laser-based sensors. The approach does not rely on any external localization, or on a preset motion-capture system. This enables fast deployment in the interiors of investigated structures while being computationally undemanding enough to process data online, onboard an MAV equipped with ordinary processing resources. The reliability of the system is analyzed, is quantitatively evaluated on a set of aerial trajectories performed inside a real-world church, and is deployed onto the aerial platform in the position control feedback loop to demonstrate the reliability of the system in the safety-critical application of historical monuments documentation.
Remote Sensing Scene Classification Based on Multi-Structure Deep Features Fusion

Convolutional neural networks (CNNs) have been widely used in remote sensing scene classification due to their excellent performance in natural image classification. However, the complementarity of features extracted by different CNNs is seldom exploited, which limits the further improvement of classification accuracy. To solve this problem, we propose a classification method based on multi-structure deep features fusion (MSDFF). First, a data augmentation method based on random-scale cropping is adopted to achieve the expansion of limited data. Then, three popular CNNs are respectively used as feature extractors to capture deep features from the image. Finally, a deep feature fusion network is adopted to fuse these features and implement the classification. The effectiveness of the proposed method is verified on UC Merced, AID, and NWPU-RESISC45 datasets. The proposed method can achieve better performance than state-of-the-art scene classification methods.
Transition to SDN is HARMLESS: Hybrid Architecture for Migrating Legacy Ethernet Switches to SDN

Software-Defined Networking (SDN) offers a new way to operate, manage, and deploy communication networks and to overcome many long-standing problems of legacy networking. However, widespread SDN adoption has not occurred yet due to the lack of a viable incremental deployment path and the relatively immature present state of SDN-capable devices on the market. While continuously evolving software switches may alleviate the operational issues of commercial hardware-based SDN offerings, namely lagging standards-compliance, performance regressions, and poor scaling, they fail to match the cost-efficiency and port density. In this paper, we propose HARMLESS, a new SDN switch design that seamlessly adds SDN capability to legacy network gear, by emulating the OpenFlow switch OS in a separate software switch component. This way, HARMLESS enables a quick and easy leap into SDN, combining the rapid innovation and upgrade cycles of software switches with the port density and cost-efficiency of hardware-based appliances into a fully dataplane-transparent and vendor-neutral solution. HARMLESS incurs an order of magnitude smaller initial expenditure for an SDN deployment than existing turnkey vendor SDN solutions while, at the same time, yields matching, or even better, data plane performance for smaller enterprises.
Efficient Algorithm for the Identification of Node Significance in Complex Network

Identifying vital nodes is a significant issue for the study of network robustness, epidemic controlling and targeted destruction of networks. Previous studies on weak ties theory recovered that ties with weak strength usually act as the important bridges that connect different clusters and play important role in maintaining the network connectivity. In this paper, we quantify the strength of links based on local information of the network topology, and design a simple yet effective method to evaluate nodes’ importance in terms of the number of their connections and overlap of their neighbors. Experimental analyses on synthetic and real networks demonstrate that the proposed algorithm identifies vital nodes leading to faster network collapse in target destruction than some well-known methods.
Compact 77 GHz 4×4 Patch Array Design Using Backside Metal Grounded GaAs-based IPD Technology

A compact 77 GHz 4 × 4 patch array using backside metal grounded GaAs-based integrated passive device (IPD) is realized in this paper. The efficiency of patch array is 83 % with size of 6.6 × 6.3 mm2. The proposed array system is well suited for millimeter-wave high-gain beam forming wireless communication systems.
Evaluation of an Educational Video Production Environment

In this paper, a cloud-based solution for the easy production and meaningful distribution of video-based learning material is presented and evaluated. Using this system, lecturers can produce video material with their own computers anywhere, at any time. The system enables the production of longer lecture videos as well as short videos, each containing only one of the course's topics. The system also handles video sharing for students automatically in a meaningful way through the course's table of contents. An evaluation of the first production version of the video production and distribution system was carried out by collecting qualitative material from the lecturers and students.
An Authenticated Key Exchange Protocol for Multi-Server Architecture in 5G Networks

Currently, the popularity of the Internet of Things (IoT) has brought about an increase in the amount of data, so multi-server distributed cloud computing has been widely used in various applications that have brought convenience to our daily lives. At the same time, the development of the fifth generation (5G) of mobile communication technology has gradually become the main driving force for the popularization of the IoT. Because the 5G network is a heterogeneous network with multiple servers and small cells, the mutual authentication protocol under multiple servers is also applicable to the 5G network environment. However, much of the data will have serious storage and security issues during transmission. Aiming at the security issues in a multi-server (M-S) architecture, in 2018, Wu et al. proposed an authentication protocol in a distributed cloud environment. They claimed that their protocol is secure and resistant to various known types of attacks. However, we found that their protocol does not guarantee perfect forward secrecy (PFS) and suffers from privileged insider (PI) attacks. Such attacks will cause data to be out of sync. Therefore, we improved Wu et al.’s protocol and proposed an improvement in the 5G network environment. Finally, we performed a security analysis on the proposed protocol, including the automatic encryption protocol tool ProVerif, BAN logic, and informal security analysis, which proved that our protocol is secure. Compared with similar existing schemes, we have proved the efficiency of the scheme and achieved higher security standards.