Miracle-dz/UniFAR-FTUs huggingface dataset:
IslandViz: A Tool for Visualizing Modular Software Systems in Virtual Reality

We propose the tool IslandViz for exploring modular software systems in virtual reality. We use an island metaphor, which represents every module as a distinct island. The resulting island system is displayed in the confines of a virtual table, where users can explore the software visualization on multiple levels of granularity by performing navigational tasks. Our approach allows users to get a first overview about the complexity of an OSGi-based software system by interactively exploring its modules as well as the dependencies between them.
Selective Blockchain Transaction Pruning and State Derivability

Distributed ledger technologies, such as blockchain systems, have in recent years emerged as promising platforms for machine-to-machine commerce and other forms of multi-stakeholder applications. However, despite the potential demonstrated by projects such as Bitcoin, Ethereum, and Hyperledger Fabric, the disk space typically required to host a copy of a ledger may be prohibitively large for many categories of devices. In this paper, we introduce an approach for reducing ledger size in blockchain systems, based on arbitrary pruning predicate functions, allowing each network participant to independently select and remove any already applied transactions. We also show that if only pruning certain ledger transactions, the ability to derive an unmodified state data structure from the remaining transactions is maintained. The approach is validated through a supply chain use case utilizing a modified version of Hyperledger Fabric, in which ledger size is reduced by about 84.49% via selective transaction pruning.
User Behavior Adopt Utilizing Fin Tech Services on Online Transportation in Indonesia (Scale Validation and Developed Instrument)

The Aim this study is to explore the views of consumer intentions in using FinTech services in online transport. this study focuses only on the process testing phase of the research instrument to be used at the next stage of the survey phase. Technology Acceptance Model (TAM) is used as the foundation of conceptual model theory and research instrument developed. Models and instruments were then validated by a pilot study method involving 133 communities as respondents who had been declared valid. analysis of survey data obtained through Smart PLS version 3 applications. This analysis is used to ensure the reliability and validity of the instruments that have been developed. The results of this study indicate that the variable Attitude toward use with 3 instruments, Perceived Useful variable with 5 instruments, Perceived Ease of Use variables with 5 instruments, Intention to use variables with 6 instruments that stated reliability with values above 0.5 and validity with values above 0.7. the instrument has been eligible to collect data from the actual survey in this study.
Sentiment Analysis of Tweets Using Machine Learning Approach

Microblogging websites like Twitter and Facebook, in this new era, is loaded with opinions and data. One of the most widely used micro-blogging site, Twitter, is where people share their ideas in the form of tweets and therefore it becomes one of the best sources for sentimental analysis. Opinions can be widely grouped into three categories good for positive, bad for negative and neutral and the process of analyzing differences of opinions and grouping them in all these categories is known as Sentiment Analysis. Data mining is basically used to uncover relevant information from web pages especially from the social networking sites. Merging data mining with other fields like text mining, NLP and computational intelligence we are able to classify tweets as good, bad or neutral. The main emphasis of this research is on the classification of emotions of tweets' data gathered from Twitter. In the past, researchers were using existing machine learning techniques for sentiment analysis but the results showed that existing machine learning techniques were not providing better results of sentiment classification. In order to improve classification results in the domain of sentiment analysis, we are using ensemble machine learning techniques for increasing the efficiency and reliability of proposed approach. For the same, we are merging Support Vector Machine with Decision Tree and experimental results prove that our proposed approach is providing better classification results in terms of f-measure and accuracy in contrast to individual classifiers.
Evolution of Endolymph Secretion and Endolymphatic Potential Generation in the Vertebrate Inner Ear

The ear of extant vertebrates reflects multiple independent evolutionary trajectories. Examples include the middle ear or the unique specializations of the mammalian cochlea. Another striking difference between vertebrate inner ears concerns the differences in the magnitude of the endolymphatic potential. This differs both between the vestibular and auditory part of the inner ear as well as between the auditory periphery in different vertebrates. Here we provide a comparison of the cellular and molecular mechanisms in different endorgans across vertebrates. We begin with the lateral line and vestibular systems, as they likely represent plesiomorphic conditions, then review the situation in different vertebrate auditory endorgans. All three systems harbor hair cells bathed in a high (K+) environment. Superficial lateral line neuromasts are bathed in an electrogenically maintained high (K+) microenvironment provided by the complex gelatinous cupula. This is associated with a positive endocupular potential. Whether this is a special or a universal feature of lateral line and possibly vestibular cupulae remains to be discovered. The vestibular system represents a closed system with an endolymph that is characterized by an enhanced (K+) relative to the perilymph. Yet only in land vertebrates does (K+) exceed (Na+). The endolymphatic potential ranges from +1 to +11 mV, albeit we note intriguing reports of substantially higher potentials of up to +70 mV in the cupula of ampullae of the semicircular canals. Similarly, in the auditory system, a high (K+) is observed. However, in contrast to the vestibular system, the positive endolymphatic potential varies more substantially between vertebrates, ranging from near zero mV to approximately +100 mV. The tissues generating endolymph in the inner ear show considerable differences in cell types and location. So-called dark cells and the possibly homologous ionocytes in fish appear to be the common elements, but there is always at least one additional cell type present. To inspire research in this field, we propose a classification for these cell types and discuss potential evolutionary relationships. Their molecular repertoire is largely unknown and provides further fertile ground for future investigation. Finally, we propose that the ultimate selective pressure for an increased endolymphatic potential, as observed in mammals and to a lesser extent in birds, is specifically to maintain the AC component of the hair-cell receptor potential at high frequencies. In summary, we identify intriguing questions for future directions of research into the molecular and cellular basis of the endolymph in the different compartments of the inner ear. The answers will provide important insights into evolutionary and developmental processes in a sensory organ essential to many species, including humans.
Information Technology Service Performance Management Using COBIT and ITIL Frameworks : A Case Study

In managing, regenerating, and fixing the operational performance of university required an information systems. IT division responsible to provide the proper information systems based on business and user perspective with decent quality IT service. Therefore, it is pertinent to measure the IT service performance must matching with the user needs and make users satisfied for using information systems. This research purpose is to identify and analyze the service maturity level that provided by IT division of BINUS University to information systems users, so that an evaluation of IT service performance is obtained. This research methodology is a questionnaire, literature study, and a data analysis of BINUS University, designing and measuring the maturity level by using the COBIT framework and the ITIL framework, as well as testing the relationship between the COBIT and ITIL maturity level results.
Energy-Efficient Predictive HTTP Adaptive Streaming in Mobile Cellular Networks

The rapid growth of mobile video traffic puts significant pressure on energy drain at the network as well as on the end users. Exploiting predicted channel information and designing energy-efficient content delivery protocols have recently drawn attention, which is referred to as predictive, anticipatory, or context-aware resource allocation. In this paper, we investigate how predicted user rates can be exploited for mobile video streaming with the popular HyperText Transfer Protocol (HTTP) based Adaptive Streaming (HAS) (e.g., dynamic adaptive streaming over HTTP). To this end, we develop a robust mobile edge-cloud assisted stochastic Predictive HTTP Adaptive Streaming (PHAS) optimization framework that utilizes unreliable predictions of wireless data rates in a finite look-ahead window to achieve the following objectives: first, to obtain an edge-cloud assisted framework for prediction-based HAS and identify its key functional entities and their interactions; second, to model uncertainty in predicted user rates and propose a robust two-stage QoE optimization approach that dynamically allocates the risks and optimizes system efficiency over a time horizon; and third, to propose a heuristic algorithm allocating time slot ratio for multi-users, which improves network efficiency, fairness, and overall QoE under different prediction error variances, wireless link conditions, and buffer length constraints. Simulation studies and analytical results show that the proposed solution outperforms traditional methods in terms of average QoE, fairness, and energy efficiency.
Reliable and Modeling Attack Resistant Authentication of Arbiter PUF in FPGA Implementation With Trinary Quadruple Response

Field programmable gate array (FPGA) is a potential hotbed for malicious and counterfeit hardware infiltration. Arbiter-based physical unclonable function (A-PUF) has been widely regarded as a suitable lightweight security primitive for FPGA bitstream encryption and device authentication. Unfortunately, the metastability of flip-flop gives rise to poor A-PUF reliability in FPGA implementation. Its linear additive path delays are also vulnerable to modeling attacks. Most reliability enhancement techniques tend to increase the response predictability and ease machine learning attacks. This paper presents a robust device authentication method based on the FPGA implementation of a reliability enhanced A-PUF with trinary digit (trit) quadruple responses. A two flip-flop arbiter is used to produce a trit for metastability detection. By considering the ordered responses to all four combinations of first and last challenge bits, each quadruple response can be compressed into a quadbit that represents one of the five classes of trit quadruple response with greater reproducibility. This challenge-response quadruple classification not only greatly reduces the burden of error correction at the device but also enables a precise A-PUF model to be built at the server without having to store the complete challenge-response pair (CRP) set for authentication. Besides, the real challenge to the A-PUF is generated internally by a lossy, nonlinear, and irreversible maximum length signature generator at both the server and device sides to prevent the naked CRP from being machine learned by the attacker. The A-PUF with short repetition code of length five has been tested to achieve a reliability of 1.0 over the full operating temperature range of the target FPGA board with lower hardware resource utilization than other modeling attack resilient strong PUFs. The proposed authentication protocol has also been experimentally evaluated to be practically secure against various machine learning attacks including evolutionary strategy covariance matrix adaptation.
Multibeam Transmitarrays for 5G Antenna Systems

In this paper, preliminary results on the feasibility of a multibeam antenna based on the use of a transmitarray are presented. A reduced size configuration has been properly designed and optimized, with the aim to minimize the degradation of the radiation pattern introduced by the variation in the direction of maximum radiation. Thereafter, the numerical analysis of the obtained antenna has been performed.
Estimation of Far End Crosstalk and Near End Crosstalk Noise with Mutually Coupled RLC Interconnect Models

The rapid advances in Very Large Scale Integration (VLSI) technology has resulted in the reduction of minimum feature size to sub-quarter microns and switching time in terms of Pico seconds or even less. As a result, the degradation of highspeed digital circuits due to signal integrity issues such as coupling effects, clock feed through, crosstalk noise and delay uncertainty noise. Crosstalk noise in VLSI interconnects is the most effectively considered in Deep Sub Micron (DSM) and Ultra Deep Sub Micron (UDSM) technology and Reduction of crosstalk noise in VLSI interconnect has become more important for highspeed digital circuits. In this paper estimated the Far End Crosstalk(FEXT) and Near End Crosstalk (NEXT) noise of mutually connected RLC interconnect models with simulations results in Advanced Design System(ADS). And also investigated the crosstalk reduction of mutually coupled RLC interconnects through shield insertion technique.
Pseudo-Exhaustive Random Access Memory Testing Based on March Tests with Random Background Variation

Studying the efficiency of memory system tests, we have to take into consideration the complexity of generating all 2k combinations for k memory cells, which is an essential and in many cases sufficient condition, which allows detecting different complex faults given by a parameter k. This paper introduces a new concept for pseudo exhaustive computer memory testing based on multi-run march tests with random backgrounds. The analytical estimation as the approximation of the pseudo-exhaustive test complexity based on the Coupon Collector's Problem was obtained. The set of experimental validation results have been presented and analyzed.
Secure Routing for Multihop Ad Hoc Networks With Inhomogeneous Eavesdropper Clusters

This paper studies finding the secure path according to the secrecy connectivity probability (SCP) in the multihop ad hoc networks in the presence of inhomogeneous eavesdropper clusters. We consider both random and fixed eavesdropper clusters, where the former case assumes that there is no knowledge of the locations of the clusters and the latter case assumes that the locations of the clusters can be estimated accurately. First, we derive the end-to-end SCP to characterize the secrecy performance of a given path in a general multihop wireless network with half-duplex randomize-and-forward relaying. Then, we consider a full-duplex scheme at the legitimate receiver, which receives the useful information while broadcasting a jamming signal to the potential eavesdroppers to further enhance the secrecy connectivity. Then, a novel secure routing algorithm that can provide the maximum SCP for any legitimate transmitter/receiver pair in a distributed manner is proposed. The theoretical analysis is verified by Monte Carlo simulation results. The results show that our secure routing algorithm provides similar results compared to an exhaustive search. For the random eavesdropper cluster case, the optimal route is independent of the knowledge of the cluster, which is the same as the homogeneous eavesdropper case. However, for the case where eavesdropper clusters are fixed and their locations are known a priori, the optimal path selection depends on the radii and locations of the eavesdropper clusters and the average number of eavesdroppers in each cluster.
Screen Window Propagating for Image Inpainting

Image repair often has errors in filling. An exemplar-based technique has the advantage of filling texture and structure simultaneously, but there exist some problems with this, such as not considering the overall situation of inpainting, the unreasonable calculation of priority, and random selection when there are multiple candidate patches. In view of these situations, this paper proposes a method of screen window propagating where the upper layer guides the lower layer inpainting from the top layer by means of multiple resolution decomposition. Screen window propagating aims to constrain the inpainting of the lower layer and maintain the overall profile. Improving the priority calculation using the upper repair result can lead to getting a better repair order. When there is more than one candidate patch, the structural similarity index measure (SSIM) is used to obtain the best candidate patch. We consider four cases of rotation and inversion when looking for the best exemplar. The experiments show that our method can obtain more satisfactory results than other methods based on subjective and objective indicators, such as peak signal-to-noise ratio (PSNR), SSIM, and feature similarity index measurement. Screen window propagating for image inpainting aims toward image restoration. Whole contours are preserved and the lower layer can utilize more upper layer information. Our method considers information from the whole and its parts.
An Empirical Analysis of Japanese Teachers' Educational Ability Evaluation under the MOOC Environment

As a novel production of the deep integration of information technology and education, MOOC plays an important role in foreign language teaching, especially Japanese teaching. Firstly, we propose a Japanese teaching quality evaluation index system, which contains four main parts, such as 1) Teaching attitude, 2) Teaching content, 3) Teaching method and teaching ability, and 4) Teaching effectiveness. Secondly, to determine the weight for each factor in the proposed system, we introduce the Analytic Hierarchy Process technology, which can achieve a priority of the importance of each alternative. In particular, in AHP, an overall object is located at the top level, and the criteria in the middle level refer to the overall object. Finally, the proposed method can evaluate Japanese teachers' educational ability under the MOOC environment with high accuracy.
Survey on Threats in Online Social Media

In recent years Online Social Networks are raising day by day. People are attracted towards the use of Social Networks due to its virtual method of Socializing people. Young Generation are in large numbers who are currently involved a lot with the Social Networks. Along with the increasing usage of Online Social Networks, Security threats have been increased simultaneously. There are different types of threats currently prevails which make the users as victim for Security attacks called as Cyber Security Risks. In this study we will discuss about similar threats to networks and the proposed solutions to protect the networks from such threats.
TIP60: an actor in acetylation of H3K4 and tumor development in breast cancer.

AIM The acetyltransferase TIP60 is reported to be downregulated in several cancers, in particular breast cancer, but the molecular mechanisms resulting from its alteration are still unclear. MATERIALS & METHODS In breast tumors, H3K4ac enrichment and its link with TIP60 were evaluated by chromatin immunoprecipitation-qPCR and re-chromatin immunoprecipitation techniques. To assess the biological roles of TIP60 in breast cancer, two cell lines of breast cancer, MDA-MB-231 (ER-) and MCF-7 (ER+) were transfected with shRNA specifically targeting TIP60 and injected to athymic Balb-c mice. RESULTS We identified a potential target of TIP60, H3K4. We show that an underexpression of TIP60 could contribute to a reduction of H3K4 acetylation in breast cancer. An increase in tumor development was noted in sh-TIP60 MDA-MB-231 xenografts and a slowdown of tumor growth in sh-TIP60 MCF-7 xenografts. CONCLUSION This is evidence that the underexpression of TIP60 observed in breast cancer can promote the tumorigenesis of ER-negative tumors.
Closed-loop parametric identification of DC-DC converter

This study provides a step-by-step analysis of closed-loop parametric system identification for DC-DC buck converter. In closed-loop parametric identification, input–output experimental data are used to estimate the transfer function coefficients of DC-DC buck converter. For system identification purpose, a high-frequency perturbation signal is injected in to the closed-loop system which acts as an input signal for identification experiment. Different input–output models such as Auto-Regressive eXogenous, Auto-Regressive Moving Average with eXogenous, output error, and Box–Jenkins are used to model the converter structure and prediction error method is used to estimate the parameters. Model validation schemes are used to validate the estimated model. Simulation and experimental analysis have been provided to validate the results obtained.
A Comparative Study of Machine Learning Methods for Traffic Sign Recognition

Recent years have brought a large change in the way that computer vision is done, with the direction shifting from specialized feature description and rather simple algorithms to deep learning and its flavors like deep residual networks and convolutional neural nets. We present the implementation details of several methods in the framework of classifying traffic signs, comparing the results and formulating some insights as to what a good approach for traffic sign recognition might be, based on the state of the art. The first method consists of extracting HOG descriptors and using an SVM classifier in order to learn the boundary between the different classes of traffic signs, showing better results for larger resolutions. The second one uses a deep convolutional neural network [1] which learns features from the images not much unlike the human brain and eye [2] and the third method uses the more recently developed deep residual network [3], which is currently the best computer vision architecture available. In training the models, the BelgiumTS data set is used, consisting of around 60 classes of traffic signs. We shall observe that newer methods have a much better accuracy, being capable of learning models of great complexity. A comparison between the methods is presented and conclusions are drawn towards the end.