Miracle-dz/UniFAR-FTUs huggingface dataset:
Phenotypic and Genomic Properties of a Novel Deep-Lineage Haloalkaliphilic Member of the Phylum Balneolaeota From Soda Lakes Possessing Na+-Translocating Proteorhodopsin

Stable development of a heterotrophic bacterial satellite with a peculiar cell morphology has been observed in several enrichment cultures of haloalkaliphilic benthic filamentous cyanobacteria from a hypersaline soda lake in Kulunda Steppe (Altai, Russia). The organism was isolated in pure culture (strain Omega) using sonicated cyanobacterial cells as substrate and it was identified as a deep phylogenetic lineage within the recently proposed phylum Balneolaeota. It is an obligately aerobic heterotroph utilizing proteins and peptides for growth. The cell morphology significantly varied from semicircles to long filaments depending on the growth conditions. The cultures are red-orange colored due to a presence of carotenoids. The isolate is an obligate alkaliphile with a pH range for growth from 8.5 to 10.5 (optimum at 9.5–10) and moderately salt-tolerant with a range from 0.3 to 3 M total Na+ (optimum at 1 M). The genome analysis of strain Omega demonstrated a presence of gene, encoding a proteorhodopsin forming a separate branch in the sodium-translocating proteorhodopsin family. Experiments with washed cells of Omega confirmed light-dependent sodium export. A possible physiological role of the sodium proteorhodopsin in strain Omega is discussed. Phylogenomic analysis demostrated that strain Omega forms an deep, independent branch of a new genus and family level within a recently established phylum Balneolaeota.
Resource Monitoring and Scheduling for Post Disaster Management Using Internet of Things

Internet based information and communication technology can serve as a catalyst in saving many people lives during disaster situations. Disasters affect not only the wellbeing of family groups and individuals, but also the welfare of communities. In this regard, effective manner of monitoring and scheduling the resources in real time is very essential in post disaster management. Various tasks in a network typically assigned to perform operations such as rescuing and recovering the injured people and gather the information in and around the disaster places which is critical. The data and information, thus acquired, needs to be disseminated for effective scheduling of different resources that are available in such networks. Therefore, Internet based resource monitoring in real time has become an aid for various applications. In this context, for efficient visualization of resource monitoring and scheduling is addressed in this paper using modified banker's algorithm. Effective utilization of the available resources and scheduling them in the most required places of disaster is implemented in android based application and integrated with Google map based web service.
Towards Viewpoint-driven Visual Analysis for Effective Architecture Recovery

In the context of software architecture recovery, visual representations can allow architects or developers to perceive and comprehend the recovered information faster. Many visualizations address various facets of the complex information space, but there are no systematic approaches for the architect to efficiently evaluate candidate visualizations for suitability, or to explore ways to extend them. The resultant ad-hoc selection from available generic visualizations leads to ineffective comprehension and analysis. We propose a viewpoint-driven approach towards tackling this problem. From a comparison of the conceptual models of architectural viewpoints and information visualization, we observe that focusing on the concerns framed by a viewpoint can guide the suitable selection of visualization tasks. The viewpoint description also serves a source for the definition of the visualization dataset. Starting from a seed visualization, a simple measure called the task/concern coverage allows the architecture visualization designer to decide if the resulting visualization can express all the desired concerns. This approach is illustrated with an example of the Polyptychon hierarchical dependencies visualization.
Fault-Tolerance Evaluation of a New SQL Database

The need of systems able to deal with large amounts of data is increasing in the big data era. These systems run on top of a distributed system where failures will happen. In the last decade a large number of data stores have been developed for providing scalability. These systems avoid some of the properties traditional relational databases provided in order to achieve scalability producing a variety of data stores known as NoSQL data stores. In the last years, NewSQL systems have been developed in order to meet the best of both worlds: transactional processing and SQL language and scalability. In this paper we present a practical experience on how failures affect the regular operation of a scalable fault-tolerant relational database (NewSQL), CumuloNimbo. The paper evaluates the performance of CumuloNimbo using the TPC-C benchmark varying the number of nodes where the database runs under different failure scenarios.
Vehicles Detection On Expressway Via Deep Learning: Single Shot Multibox Object Detector

Nowadays, the growing number of vehicles in Taiwan, traffic jams on the expressway, especially on holidays, and during rush hours are increasing. To solve this problem, the first requirement is to detect the vehicles on the expressway. However, vehicle detection is a challenging problem due to among other things, small size of the vehicles, low resolution videos, high speeds of vehicles, and the long distance between the camera and the vehicles. Recently, deep learning-based approaches have shown extraordinary performance in many computer vision tasks. That is, the deep learning model: SSD (Single Shot MultiBox Detector) is applied and fine-turnedto detect vehicles in expressway. From the experimental results described herein, it is demonstrated that the method presented can detect more vehicles than the pre-trained SSD model.
Applied Implications of Understanding the Natural Development of Effortful Control

Effortful control (EC) is an important target for many interventions intended to facilitate positive psychosocial outcomes. The first wave of these efforts has emphasized adult-to-child instruction and individualized practice of target skills. Future tests of these ideas will be facilitated by efforts to critically evaluate and improve the construct validity of EC measures. New avenues for these applied approaches will also grow out of a more complete understanding of the processes that govern EC development. Specifically, I argue that the natural development of EC includes important roles for contextual and peer relationship factors that have yet to be capitalized on in efforts to increase EC in children.
A 1mW 20MHz Bandwidth 9.51-ENOB Dynamic-Amplifier-Based Noise-Shaping SAR ADC

A 10-bit 160MS/s 20MHz bandwidth low-power noise-shaping successive approximation register (SAR) analog-to-digital converter (ADC) using a novel dynamic amplifier-based filter is presented. Thanks to noise-shaping architecture, the proposed scheme can achieve 10-bit resolution while employing only 8-bit capacitor-DAC array. The prototype ADC is designed in 40nm CMOS technology, with a peak signal-to-noise-distortion ratio (SNDR) 59 dB and 68 dB spurious-free-dynamic-range (SFDR) at 160 MS/s sampling frequency, while consuming 1 mW power from 1.1 V supply voltage. The figure-of-merit (FoM) is 34.29 fJ/conv. -step.
Techno Economic Analysis of Spectrum License Price in Indonesia for NB-IoT Deployment

Trend of internet of things (IoT) creates new innovation in wireless technology. Low power wide area (LPWA) technology is one of the emerging technology that support IoT paradigm. Low power and long range characteristic of LPWA enables connectivity for low power device in large geographical area. LPWA as wireless communication technology require radio spectrum in its operation. LPWA can run both in licensed and unlicensed spectrum, but LPWA in licensed spectrum can give service assurance. As a limited resource, the use of the frequency spectrum is regulated by the government by applying a licensing system. This research will calculate the price of spectrum license in LPWA according to Indonesian regulation. NB-IoT system will be used as input parameter in calculating the spectrum price in Indonesia. The spectrum price then inserted into economical analysis as a component in operational cost and initial investment. As a result of this research, NPV analysis will be conducted to see profitability of investment in LPWA NB-IoT technology.
[Engineering Paper] RECKA and RPromF: Two Frama-C Plug-ins for Optimizing Registers Usage in CUDA, OpenACC and OpenMP Programs

Pointer aliasing still hinders compiler optimizations. The ISO C standard 99 has added the restrict keyword that allows programmer to specify non-aliasing as an aid to the compiler's optimizer. The task of annotating pointers with the restrict keyword is still left to the programmer and this task is, in general, tedious and prone to errors. Scalar replacement is an optimization widely used by compilers. In this paper, we present two new Frama-C plug-ins, RECKA for automatic annotation of CUDA kernels arguments with the restrict keyword, and RPromF for scalar replacement in OpenACC and OpenMP 4.0/4.5 codes for GPU. More specifically, RECKA works as follows: (i) an alias analysis is performed on CUDA kernels and their callers; (ii) if not found any alias then CUDA kernels are cloned, the clones are renamed and their arguments are annotated with the restrict qualifier; and (iii) instructions are added to kernels call sites to perform at runtime a less-than check analysis on kernel actuals parameters and determine if the clone must be called or the original one. RPromF includes five main steps: (i) OpenACC/OpenMP offloading regions are identified; (ii) functions containing these offloading codes and their callers are analyzed to check that there is no alias; (iii) if there is no alias then the offloading codes are cloned; (iv) clone's instructions are analyzed to retrieve data reuse information and perform scalar replacement; and instructions are added to be able to use the optimized clone whenever possible. We have evaluated the two plug-ins on PolyBench benchmark suite. The results show that both scalar replacement and the usage of restrict keyword are effective for improving the overall performance of OpenACC, OpenMP 4.0/4.5 and CUDA codes.
Predicting Computer Network Traffic: A Time Series Forecasting Approach Using DWT, ARIMA and RNN

This paper proposes the Discrete Wavelet Transform (DWT), Auto Regressive Integrated Moving Averages (ARIMA) model and Recurrent Neural Network (RNN) based technique for forecasting the computer network traffic. Computer network traffic is sampled on computer networking device connected to the internet. At first, discrete wavelet transform is used to decompose the traffic data into non-linear (approximate) and linear (detailed) components. After that, detailed and approximate components are reconstructed using inverse DWT and predictions are made using Auto Regressive Moving Average (ARIMA) and Recurrent Neural Networks (RNN), respectively. Internet traffic is a time series which can be used to predict the future traffic trends in a computer network. Numerous computer network management tasks depend heavily on the information about the network traffic. This forecasting is very useful for numerous applications, such as congestion control, anomaly detection, and bandwidth allocation. Our method is easy to implement and computationally less expensive which can be easily applied at the data centers, improving the quality of service (QoS) and reducing the cost.
Dynamic Spectrum Sharing for Hybrid Access in OFDMA-Based Cognitive Femtocell Networks

Cognitive femtocell has emerged as one of the promising ways to solve the indoor coverage and spectrum shortage problems in cellular networks. However, it is a challenge to motivate both macro base station (<inline-formula> <tex-math notation="LaTeX">$\text{MBS}$</tex-math></inline-formula>) and femtocell access points (<inline-formula> <tex-math notation="LaTeX">$\text{FAPs}$</tex-math></inline-formula>) to adopt this hybrid access policy. In this paper, we propose a systematic dynamic spectrum sharing framework for hybrid access in orthogonal frequency-division multiple-access-based cognitive macro-femtocell networks. In this framework, the <inline-formula> <tex-math notation="LaTeX">$\text{MBS}$</tex-math></inline-formula> offloads some macro user equipments ( <inline-formula><tex-math notation="LaTeX">$\text{MUEs}$</tex-math></inline-formula>) to <inline-formula> <tex-math notation="LaTeX">$\text{FAP}$</tex-math></inline-formula> to improve the transmission performance and save energy. In return, the MBS leases a portion of its spectrum to <inline-formula><tex-math notation="LaTeX">$\text{FAP}$ </tex-math></inline-formula>. Then, the <inline-formula><tex-math notation="LaTeX">$\text{FAP}$</tex-math> </inline-formula> can use a part of spectrum for its own transmission and use another part to serve <inline-formula> <tex-math notation="LaTeX">$\text{MUEs}$</tex-math></inline-formula>. We design an auction-based approach where each <inline-formula><tex-math notation="LaTeX">$\text{FAP}$</tex-math></inline-formula> receives additional <inline-formula> <tex-math notation="LaTeX">$\text{MUE}$</tex-math></inline-formula> as bidders, while MBS acts as auctioneer. We formulate the dynamic resource sharing as a sum utility maximization problem. Numerical results demonstrate that both the macro and femtocell could get benefit from our framework.
Formalization of Attack Prediction Problem

The use of information is inextricably linked with its security. The presence of vulnerabilities enables a third party to breach the security of information. Threat modeling helps to identify those infrastructure areas, which would be most likely exposed to attacks. In some cases, threat modeling cannot be classified as sufficient protection method. This paper entitled "Formalization of attack prediction problem" presents an analysis of different techniques with an attempt to identify the most informative parameters and attack prediction markers, which would lay the foundation for the development of attack probability functions. The obtained functional dependencies should be formally verified for further testing by a real system. The findings of this research could be applied during the future assessment of information system risk levels to ensure more effective information security management.
An Integrated Approach for Detecting Ransomware Using Static and Dynamic Analysis

Ransomware has been a rising threat in the past few years. By analyzing the number of systems that are infected by ransomware and the amount of ransom paid by the user, the damage done to an IT infrastructure is measured. There are many deployment methods in which the ransomware get into the system. These are similar to malware and ransomware is a form of malware whose central behavior is to prevent legitimate users from accessing the resources when needed. This paper discusses the static and dynamic techniques that can be used to detect and prevent a ransomware attack.
Combination of Agility and Plyometric Training Provides Similar Training Benefits as Combined Balance and Plyometric Training in Young Soccer Players

Introduction: Studies that combined balance and resistance training induced larger performance improvements compared with single mode training. Agility exercises contain more dynamic and sport-specific movements compared with balance training. Thus, the purpose of this study was to contrast the effects of combined balance and plyometric training with combined agility and plyometric training and an active control on physical fitness in youth. Methods: Fifty-seven male soccer players aged 10–12 years participated in an 8-week training program (2 × week). They were randomly assigned to a balance-plyometric (BPT: n = 21), agility-plyometric (APT: n = 20) or control group (n = 16). Measures included proxies of muscle power [countermovement jump (CMJ), triple-hop-test (THT)], muscle strength [reactive strength index (RSI), maximum voluntary isometric contraction (MVIC) of handgrip, back extensors, knee extensors], agility [4-m × 9-m shuttle run, Illinois change of direction test (ICODT) with and without the ball], balance (Standing Stork, Y-Balance), and speed (10–30 m sprints). Results: Significant time × group interactions were found for CMJ, hand grip MVIC force, ICODT without a ball, agility (4 m × 9 m), standing stork balance, Y-balance, 10 and 30-m sprint. The APT pre- to post-test measures displayed large ES improvements for hand grip MVIC force, ICODT without a ball, agility test, CMJ, standing stork balance test, Y-balance test but only moderate ES improvements with the 10 and 30 m sprints. The BPT group showed small (30 m sprint), moderate (hand grip MVIC, ICODTwithout a ball) and large ES [agility (4 m × 9 m) test, CMJ, standing stork balance test, Y-balance] improvements, respectively. Conclusion: In conclusion, both training groups provided significant improvements in all measures. It is recommended that youth incorporate balance exercises into their training and progress to agility with their strength and power training.
Image Analysis to Study Cytoskeleton Alterations in Cancer Cells Exposed to Nanosecond Pulsed Electric Field

The cytoskeleton is a dynamical protein structure that plays a key role in cellular physiological processes. The assessment of the cytoskeleton dynamics is of prime importance to reveal mechanisms involved in cell adaptation to any type of stress. Fluorescence imaging of cytoskeleton structures enables analysis of the impact of mechanical stimulation on the cytoskeleton, e.g. the exposure to nanosecond pulsed electric field (nsPEF). NsPEF utilizes pulses in nanosecond range (from few up to 300 ns) and high voltage (up to 100 kVs), which can significantly affect all cell structures. This method can be applied in medicine, industry and molecular biology for the efficient molecule transport across the cell membranes. The detailed analysis of the separate cell structures and protein organization seems to be important in the overall evaluation of the nsPEF impact on treated cells. The automatic image analysis seems crucial for proper evaluation of the therapeutic efficiency. The aim of the study is a computational approach for efficient F-actin filament analysis in cells exposed to electroporation process in the nanosecond range. The research was performed on cancer cells derived from gastrointestinal tract exposed to the nanosecond pulsed electric fields in relation to untreated controls. The presented tool might be useful in precise evaluation of the electroporation protocols effectiveness in cancer and normal cells. The proposed methodology showed high sensitivity values and similar accuracy compared to state-of-the-art methods.
CED: Credible Early Detection of Social Media Rumors

Rumors spread dramatically fast through online social media services, and people are exploring methods to detect rumors automatically. Existing methods typically learn semantic representations of all reposts to a rumor candidate for prediction. However, it is crucial to efficiently detect rumors as early as possible before they cause severe social disruption, which has not been well addressed by previous works. In this paper, we present a novel early rumor detection model, Credible Early Detection (CED). By regarding all reposts to a rumor candidate as a sequence, the proposed model will seek an early point-in-time for making a credible prediction. We conduct experiments on three real-world datasets, and the results demonstrate that our proposed model can remarkably reduce the time span for prediction by more than 85 percent, with better accuracy performance than all state-of-the-art baselines.
Single Image Dehazing Using Color Correction and Fusion Technology

Image dehazing is an important preprocessing in the visibility of outdoor scene analysis. Image fusion based strategy computes the features of the image by computing three measures as weight map as luminance, chrominance and salience factors. Using fusion strategy the information is derived from the single image input. The proposed method derived the features from the color corrected input and histogram equalized input. The color correction has carried out using Adaptive Color Correction Matrix (ACCM) with defined gamma value. This is a direct method of spatial domain. The resultant image reconstructed using Laplacian and Gaussian pyramid with reduced artifacts. The accuracy and time complexity compared with the existing algorithm and shows better results than the state-of-art technologies.
Bandwidth Enhancement of Square Microstrip Patch Antenna Using Defected Ground Structure

This work presents a probe fed square microstrip patch antenna with broad bandwidth. By cutting the slots in radiating elements and in ground plane broadband performance can be obtained. The proposed antenna is designed and fabricated using economical FR-4 substrate with size of 50×50 mm2, The simulated and measured bandwidth is 138 MHz and 100 MHz respectively. The average gain of the antenna is observed to be less than unity.
The Attribution of Emotional State - How Embodiment Features and Social Traits Affect the Perception of an Artificial Agent

Understanding emotional states is a challenging task which frequently leads to misinterpretation even in human observers. While the perception of emotions has been studied extensively in human psychology, little is known about what factors influence the human perception of emotions in robots and virtual characters. In this paper, we build on the Brunswik lens model to investigate the influence of (a) the agent's embodiment using a 2D virtual character, a 3D blended embodiment, a recording of the 3D platform and a recording of a human, as well as (b) the level of human-likeness on people's ability to interpret emotional facial expressions in an agent. In addition, we measure social traits of the human observers and analyze how they correlate to the success in recognizing emotional expressions. We find that interpersonal differences play a minor role in the perception of emotional states. However, both embodiment and human-likeness as well as related perceptual dimensions such as perceived social presence and uncanniness have an effect on the attribution of emotional states.
An Iterative Matching-Stackelberg Game Model for Channel-Power Allocation in D2D Underlaid Cellular Networks

In device-to-device (D2D) underlaid cellular networks, several D2D pairs can share one channel to improve the system throughput. Most existing works allow D2D pairs to reuse all the channels, which may incur complicated interference management. Therefore, each D2D pair is limited to reuse at most one channel to reduce overhead. We aim to maximize the throughput of D2D pairs while suppressing the interference to cellular links. However, the optimization problem is an intractable mixed integer non-linear programming (MINLP) problem. Meanwhile, as network size increases, acquiring the global channel state information (CSI) is expensive even impossible in practice. Therefore, we propose a novel local CSI-based distributed channel-power allocation scheme. The base station (BS) broadcasts a control signal to indicate its received interference from D2D pairs. Upon this signal, each D2D pair executes channel selection and power update independently and iteratively. Specifically, the channel allocation problem is formulated as a many-to-one matching game with externalities. The power control problem is modeled as a Stackelberg game. We prove the existence of two-sided swap-stable matching and show that the outcomes of the Stackelberg game are locally optimal under some mild conditions. Simulation results show that our scheme is efficient with low overhead.