Miracle-dz/UniFAR-FTUs huggingface dataset:
Requirements Engineering in Software Startups: a Grounded Theory Approach

Software startups face a very demanding market: they must deliver high innovative solutions in the shortest possible time. Resources are limited and time to reach market is short then it is extremely important to provide the right requirements. Nevertheless, software requirements are usually not clear and startups struggle to know what they should develop. This context affects how requirements engineering processes in these organizations are executed. This work focus on how these processes are executed in software startups and which effects context has on them. Nine interviews were conducted with founders or managers of different Brazilian software startups that operate in several market sectors and have different maturity level. Data was analyzed using Grounded Theory techniques such open and axial coding. As a result, a conceptual model of requirements engineering state-of-practice for software startups was developed and influences from the context were identified and described.
P 2 P Internet Traffic Classification and Mitigation Based on Snort and ML

Peer to peer applications have modified the nature of internet traffic. It will consume high internet bandwidth and affect the performance of traditional traffic internet applications. Therefore, the management and monitoring activity of internet traffic is the important activities involved in the optimization. In order to detect and mitigate the P2P traffic, port, payload, and transport layer based methods were developed in the past. Nevertheless, the performances of these methods were not up to the expectation. Machine Learning (ML) is one of the promising methods to identify and mitigate the traffic of the Internet. However, the classification accuracy is inconsistent. The reason for the inconsistency is the relevant training datasets generation and feature selection. In this research, a technique based on signature-based and ML is proposed to develop a model for online P2P traffic detection and mitigation. The proposed work can be employed to evaluate the robustness of the online P2P machine learning classifier based on real network traffic traces containing flows labelled by SNORT tool and from special shared resources. Analysis and validation were carried out on traffic traces of University Technology Malaysia. The period of traffic was 2011 and 2013. The output of research is revealing that the proposed work has spent less computation time for classification. This method gives 99.7% accuracy which equals the classification performance attained for P2P using deep packet inspector. The findings show that classifying network traffic at the flow level can differentiate P2P over non-P2P (nP2P) with high confidence for online P2P mitigation.
J2.13 SYNOPTIC AND MESOSCALE PATTERN ASSOICATED WITH SUPER-COOLED LIQUID WATER CONTENT OVER THE MEDICINE BOW AND SIERRA MADRE MOUNTAINS

Snowpack in the Western high mountains during the winter and the attendant spring runoff provides needed water to lower elevations. Agriculture, recreation, water supply, and hydro-electric power are but a few of the users of this renewable resource. Snowpack becomes even more critical over the Intermountain West during the moderate to extreme drought conditions reported by NOAA’s Climate Prediction Center (CPC). Although Snowpack telemetry (SNOTEL) sites within the Medicine Bow and Sierra Madre Mountain ranges initially recorded snow water equivalents (SWE’s) nearly 130% of average by February 2006, much of the Intermountain West remained below seasonal normals. Cloud seeding technology for enhanced snowpack may be beneficial for long-term water management. The Wyoming Weather Modification Pilot Project is a fiveyear cloud seeding program contracted by the Wyoming Water Development Commission. The program contains a cloud seeding phase with snowpack and runoff evaluations within weather patterns that would produce the snowfall over the mountains. It is more important in cloud seeding operations to find synoptic and mesoscale conditions that produce the greatest amount of super-cooled liquid water in clouds than the conditions that produce the greatest snowfall. This study is the initial step in examining the synoptic and mesoscale patterns over the Medicine Bow and Sierra Madre Mountains with the intent of identifying ideal seeding conditions. A future study will compare snowfall rates with WSR-88D Doppler radar reflectivity over the Medicine Bow and Sierra Madre Mountain ranges
Screening For Vitamin D Deficiency In Females In Madina Region; Saudi Arabia. Vitamin D Deficiency In A Sunny Environment

Background And Objective: Poor sunlight exposure leads to vitamin D deficiency even in the sunniest places. So screening for vitamin D deficiency in outpatient clinics and inhospital setting in King Fahad Hospital , Al Madina Al Monwara. METHODS: Screening for vitamin D deficiency by assessment of serum 25(OH)D with radioimmunoassay in 60 female patients aged 18 – 40 years. Serum calcium, phosphorous and alkaline phosphatase were assessed, too. Results: 6 female patients (10%) showed significant vitamin D deficiency with 25(OH) D < 20 ng/ml. However 54 female patients (90%) showed severe vitamin D deficiency with 25 (OH) D < 10 ng/ml. Conclusion: Vitamin D deficiency is very common in Saudia Arabia especially females due to poor sunlight exposure. So being underrecognized and undertreated, vitamin D deficiency represents an important health problem in MADINA REGION, KSA.
Mobile Peer-to-Peer Assisted Coded Streaming

Current video streaming services use a conventional, client-server network topology that puts a heavy load on content servers. Previous work has shown that Peer-to-Peer (P2P) assisted streaming solutions can potentially reduce this load. However, implementing P2P-assisted streaming poses several challenges in modern networks. Users tend to stream videos on the go, using their mobile devices. This mobility makes the network difficult to orchestrate. Furthermore, peers have to contribute their storage to the network, which is challenging, since mobile devices have limited resources compared to desktop machines. In this paper, we introduce an analytical framework for mobile P2P-assisted streaming to estimate the server load that we define as the minimum required server upload rate. Using our framework, we evaluate four caching strategies: infinite cache as a baseline, first in first out (FIFO), random, and Random Linear Network Coded (RLNC) cache. We verify our analytical results with empirical data that was obtained by carrying out extensive measurements on our working P2P system. Our results show that when employing FIFO, random, and RLNC caching strategies, the server load converges to that of the infinite cache as the cache size increases. With a limit of 5 P2P connections per peer, we show that using the random caching, peers can store 40% fewer packets and still achieve the same benefit as with FIFO caching. When using the RLNC caching, it is enough to store 50% fewer packets to achieve the same benefit.
Wybrane standardy leczenia zaburzeń afektywnych dwubiegunowych: podobieństwa i różnice. Selected standards of treatment of bipolar disorder: similarities and differences

Growing interest in bipolar affective disorder, observed in the past 15 years, contributed to the development of methodology and increasing number of research on the subject. Empirical studies concern the need for more precise diagnostic criteria as well as more effective treatment, due to relatively high prevalence and high mortality rate in the population of people affected by this disorder. This second objective is achieved by developing guidelines by experts who works on this topic under the auspices of scientific societies and government agencies in different countries. The paper presents the main strategies and guidelines for the treatment of bipolar disorder with the British, Canadian and Australian published standards. The attention has been directed to the similarity of the methodology used in developing those standards, and substantially congruous hierarchy of the validity of the selection the treatment method for various types of bipolar disorder. The paper summarizes the guidelines for dealing with acute diseases, bipolar disorder in a manic phase and depressive illness and long-term therapy, demonstrating the similarities and differences between the standards. Analysis of individual sequences of the standards showed that the British model is the closest to a therapeutic algorithms applicable in clinical practice in Poland.
Detecting Recurrent Copy Number Variations from aCGH Data via Smoothness-Constrained Models

Increasing number of genomic studies have associated copy number variations (CNVs) with several diseases such as cancer, autism, Alzheimer, and many autoimmune diseases. Therefore, developing reliable computational tools for recurrent CNV detection is crucial to understand the development of such diseases. A widely used microarray technology for measuring DNA copy number is array-based Comparative Genomic Hybridization (aCGH). Identifying concurrent CNVs is challenging due to the presence of noise and sample-specific variations. In this paper, we propose two matrix decomposition-based approaches, Smooth Regularization Decomposition (SRD) and Smooth Regularization Sparse Decomposition (SRSD) for reliable recurrent CNV detection from aCGH data. The essence of the two techniques is to model the aCGH profiles as smooth signals. However, the SRSD algorithm extends the SRD model to account for sample-specific variations. We also propose an algorithm to efficiently solve the SRSD model. Our simulations, using synthetic and realistic datasets, show that our proposed models achieve better accuracies when compared to the state-of-the-art models.
Near-Field Focus Radiation from Phased Antenna Array for Best Reception by Relatively Large Antennas

This paper examines the behavior of near-field focus (NFF) radiation of a transmitting antenna (TXA) array for an enhanced power transfer to a relatively large receiving antenna (RXA). In addition to the conventional consideration of focusing power intensity, the phase behavior is also examined. It is discovered that the power transfer effectiveness of NFF radiation to RXAs highly depends on the size of RXA and the phase behavior of the TXA’s NF distribution. Based on the examination of the reaction equation between the TXA and RXA, the maximum power transfer results in a concept of conformal phase matching between the TXA and RXA’s co-polarized components of radiation. This new type of NFF antenna design is more general, and can be referred to a conformal NFF antenna (CNFFA) design.
An Alternative Measurement Approach to Sweep Frequency Response Analysis (SFRA) for Power Transformers Fault Diagnosis

Power transformer failures can lead to power outages and significant financial loss, particularly at high voltage and medium voltage levels. Frequency response analysis (FRA) is used to diagnose transformer faults and particularly so for detecting mechanical displacements in windings and core. However, such displacements or faults affect significantly the electrical impedance of the transformer windings and challenges remain in the correlation of FRA signature and fault type. Furthermore, other factors such as the adopted measurement method and practical test setup influence the FRA signature. This paper investigates FRA responses of small laboratory test transformers measured using an IEEE standard recommended method and proposes a new generic test setup having additional current measurement points and using precision lock-in amplifiers. The results show the effect of change in the magnitude of applied voltage levels on FRA signatures.
EFFECT OF SOME INACTIVATION PHYSICAL AND CHEMICAL AGENTS ON VIABILITY, DNA POLYMORPHISM AND GENE TRANSFER ABILITY OF TWO Bacillus thuringiensis BACTERIOPHAGES

The inactivation behavior of two Bacillus thuringiensis bacteriophages (A8 and R3) was studied to evaluate their potential as viral indicators for disinfection by heat, UV-C, gamma rays, chlorine, isopropanol and ethanol. The heat effect was highly efficient, PFU/ ml was decreased from 12 log (nontreated) to 1 log at 55 ̊C for 90 min, while 60 ̊C was completely inactivated at 20 min. It was suggested that this killing effect may due to nucleic acid denatured. UV-C and Gamma irradiation were also dramatically decreased of PFU/ml. The lowest effect of tested biocides was for chlorine which supplied as sodium hypochlorite, isopropanol come in the second order. The ethanol at 80% concentration was inactivated all phage A8 population in 90 min and phage R3 in 15 min. In general, phage R3 was sensitive to tested agents than phage A8. The ability of two inactivated phages to transfer ampicillin resistance gene was studied, this ability was decreased with temperature increased. It was totally inactivated with 60 ̊C. Two phages were able to transfer the marker gene at all UV-C, Gamma and chlorine treatments. Isopropanol and ethanol were completely inactivated the process, it was suggested that this effect due to alteration of nucleic acid and structure of phages. The effects of some physical agents (UV-C and gamma rays) only were studied to identify DNA polymorphism through RAPD-PCR analysis in comparison to the control of the two selected bacteriophages. The result of RAPD analysis indicated the appearance of DNA polymorphic bands in response to treatments with doses of all gamma irradiation. Some bands present in the control disappeared in treated bacteriophages or vice versa. Keyword: Inactivation, Bacillus thuringiensis, bacteriophage, radiation, RAPD-PCR
Research on Kinetic Energy Optimization from the Perspective of Financial Friction Threshold Effect

This paper aims to analyze the financial friction coefficient in the kinetic energy of the manufacturing industry by using panel data threshold regression. The results show that: the endogenous financial friction of old kinetic energy is more limited by the cash constraint of investment. The financial friction manifestation of old and new kinetic energy is unstable and in the new kinetic energy shows a fluctuating spiral structure, while the financial friction in the old kinetic energy shows an upward trend. The financial friction caused by currency issuance shows a gradual upward trend, while bad loans and interest rates decrease gradually.
Simulation of Curative Congestion Management in Large-Scale Transmission Grids

Curative actions within operational congestion management seem to be a promising concept for realizing an efficient utilization of the transmission grid infrastructure and reducing cost-intensive redispatch measures. In contrast to today's preventive (pre-fault) use of market- and network-related measures to fulfill the N-1 security criterion, curative actions are implemented post-fault and hence allow higher utilizations in prefault situations. In this paper, the use of curative actions in large-scale transmission grids is analyzed. First, the model framework to identify preventive and curative congestion management measures is presented. A detailed model of the European transmission grid is used to analyze the potential of curative actions to reduce preventive congestion management effort. To evaluate the impact of the conceptual design of curative actions, multiple parameter sets are analyzed regarding considered market-and network-related options and technologies (generation units, HVDC systems, phase-shifting transformers), the limitation of curative adjustments and the set of considered contingencies. The results show that the use of curative actions allows to substantially reduce cost-intensive preventive measures by up to 69.2%, but requires large curative power adjustments. The limitation of curative adjustments lowers this potential significantly, which should be considered when developing operational strategies for congestion management.
Machine Learning based Link State Aware Service Function Chaining

Service Function Chaining (SFC) can be a basic deployment unit that composes a chaining order of required network functions to provide a network service. With the proliferation of Software-Defined Networking (SDN) and cloud computing, such virtualized network functions can be dynamically deployed in different sites, depending on SFC requests. While offering the advantages of flexibility and efficiency, this also leaves management complexity and room for optimization where Machine Learning (ML) can be applicable to solve the problems based on monitoring data. In this paper, we treat SFC as a problem of finding a best source-to-destination routing path from multiple candidates with different link costs and a required traversal order of network functions. There are many mathematical approaches that ensure best optimum but not scalable to the problem size, whereas our approach hides underlying considerations by applying ML technique on measured SFC data to quickly find suboptimal routing paths on a new SFC request, based on their predicted network performance such as the number of successful requests or end-to-end delay. So, we developed a measurement system that records the performance and path costs of SFCs in emulated networks with different per link costs and chain lengths. Then, we evaluate four different ML models for the approach described above.
A Feature Extraction Method Based on Sparse Filtering With Local Structure Preserved and Its Applications to Bearing Fault Diagnosis

Unsupervised feature learning, as a promising tool for extracting features automatically, overcomes shortcomings of traditional feature extraction methods which generally take plenty of effort on designing features. Among various unsupervised feature learning methods, sparse filtering is an efficient and popular one owing to its simplicity with fewer hyper-parameters. However, the standard sparse filtering fails to consider the local structure of input samples, which may affect and restrict its feature learning ability. To address this deficiency, a new feature extraction approach named sparse filtering with local structure preserved (SF-Local) is proposed. The objective function of SF-Local is composed by the standard sparse filtering and a local structural regularization (LSR) which is formulated to preserve the local structure of the input samples. To design LSR, we adopt complexity-invariant distance to select neighbors for each sample rather than the widely used Euclidean distance, which is beneficial to establishing a reliable $K$ -nearest graph. Based on SF-Local, an intelligent fault diagnosis method is developed by leveraging a supervised classifier. The developed diagnostic approach is applied to diagnose the bearing faults as well as its performance is evaluated on a bearing data set with a constant operating condition and a data set with variable operating conditions. Experimental results demonstrate the effectiveness and superiority of the proposed SF-Local.
The Roles of Customers, Utilities and Companies in Accelerating Smart Grid Implementation

This study addresses current situations and challenges surrounding the full implementation of the Smart Grid and presents wholistic ways going forward. Considering the challenges of environmental degradation, ageing infrastructures, increasing energy demands, high energy costs, sustainability, transparency and partial participation in the vertical power systems, a shift in the energy paradigm has been looked upon by the various stakeholders. An intelligent-based grid (Smart Grid) development is considered as sustainable alternative - with the confidence that the challenges concerned with traditional power systems will alleviate, and by including large renewable energy resources, energy cost will reduce, and system operation will become more secure and reliable by leveraging the various technologies that support full implementation of the new grid. Despite these promises and the availability of the important technologies for its realization, Smart Grid (SG) development has been slow and full implementation has not been realized in most countries where it is adopted. This paper presents a way forward by expositing the roles that Customers, Utilities and Companies could take as pacesetters in accelerating the implementation of the new technology solution.
Hybrid Excited Claw-Pole Machines: Comparison between Different Topologies

The paper is devoted to a 3D finite element analysis (FEA)-based comparison between three claw-pole synchronous machines equipped with a dual excitation: dc and permanent magnet (PM) ones, yielding the so-called hybrid excitation. The topologies under comparison are: (i) the inter-pole PM hybrid excited claw-pole machine, (ii) the hybrid excited brushless claw-pole machine, and (iii) the PM-assisted hybrid excited brushless claw-pole machine. The study is initiated by a description of the different topologies and an analysis of their main flux paths. Then, a 3D-FEA is carried out with emphasis on the investigation of the torque production capability of the conventional claw-pole machine with a single dc excitation in the rotor. The 3D-FEA based investigation of the torque production capability is extended to the ones of the three hybrid excited topologies which are compared to the one of the conventional claw-pole machine, taken as a reference.
300 GHz Plasmonic Mixer

A wireless receiver concept for the direct conversion of terahertz signals to the optical domain is introduced. The receiver combines two terahertz antenna-coupled phase modulators into a silicon photonics based Mach-Zehnder interferometer. The technology is demonstrated by the direct detection of a 300 GHz wireless signal on an optical fiber. The concept provides an ultra-compact and efficient terahertz to optical conversion particularly useful for next-generation wireless systems.
Combining Deep Neural Network with Traditional Classifier to Recognize Facial Expressions

Facial expressions are important in people’s daily communications. Recognising facial expressions also has many important applications in the areas such as healthcare and e-learning. Existing facial expression recognition systems have problems such as background interference. Furthermore, systems using traditional approaches like SVM (Support Vector Machine) have weakness in dealing with unseen images. Systems using deep neural network have problems such as requirement for GPU, longer training time and requirement for large memory. To overcome the shortcomings of pure deep neural network and traditional facial recognition approaches, this paper presents a new facial expression recognition approach which has image pre-processing techniques to remove unnecessary background information and combines deep neural network ResNet50 and a traditional classifier-- the multiclass model for Support Vector Machine to recognise facial expressions. The proposed approach has better recognition accuracy than traditional approaches like Support Vector Machine and doesn’t need GPU. We have compared 3 proposed frameworks with a traditional SVM approach against the Karolinska Directed Emotional Faces (KDEF) Database, the Japanese Female Facial Expression (JAFFE) Database and the extended Cohn-Kanade dataset (CK+), respectively. The experiment results show that the features extracted from the layer 49Relu have the best performance for these three datasets.
A Hardware-Efficient Parallel Architecture for HEVC Deblocking Filter

The deblocking filter (DBF) constitutes an important part of the High Efficiency Video Coding (HEVC) standard. In this paper, a novel hardware architecture of the HEVC DBF is proposed for all block boundaries within a luma $32 \times 32$ coding block (CB) to reduce visual artifacts. The proposed hardware architecture employs a high degree of parallelism and includes pipeline structure in order to improve the throughput. Experimental results demonstrate that the proposed DBF architecture can reach a high operating clock frequency of 250 MHz and can support $3840\times 2160@50fps$ real-time applications on the Zynq system-on-chip (SoC).