Miracle-dz/UniFAR-FTUs huggingface dataset:
A new model for requirements to code traceability to support code coverage analysis

Requirements traceability is one of the most important and challenging tasks in the industry to ensure that software has been developed according to all user requirements. Requirements need to be specified and traced throughout the software development life cycle in order to map links between different software artifacts. This paper proposes a new model for finding traceability of functional requirements into artifacts such as methods, classes and packages. In this approach, requirements traceability links to code remains updated every time even after changes in code and requirements. This can be applied in updated coverage data and analysis to know which part of code has been tested and which has not been tested yet. Management can visualize the progress and quality of the software by knowing the traced code.
Real-time detection of small faces in HD video

In this paper, we propose a novel approach for real-time face detection in HD videos. The major goal is to detect small faces at a distance for HD videos as well as to preserve low false-alarm rate. The proposed method firstly fast scan face candidates in the down-sampled chrominance channels by using skin color features. Given the candidates, we precisely filter true facial objects from the candidates which location and size are subjected to be mapped into the original luminance channel by using the binary texture features. In our experiment, we showed that the proposed face detection method archived about 20ms/frame in HD videos. It means that the proposed method would be more than 30 times faster than the baseline Viola-Jones one in processing time for 720P-sized video. The gain of processing time will be higher for larger-sized video such as 1080P. And we also see that the false-alarm rate was extremely reduced as about 1/20 of the baseline approach.
Automated text analysis in psychology: methods, applications, and future developments*

abstract Recent years have seen rapid developments in automated text analysis methods focused on measuring psychological and demographic properties. While this development has mainly been driven by computer scientists and computational linguists, such methods can be of great value for social scientists in general, and for psychologists in particular. In this paper, we review some of the most popular approaches to automated text analysis from the perspective of social scientists, and give examples of their applications in different theoretical domains. After describing some of the pros and cons of these methods, we speculate about future methodological developments, and how they might change social sciences. We conclude that, despite the fact that current methods have many disadvantages and pitfalls compared to more traditional methods of data collection, the constant increase of computational power and the wide availability of textual data will inevitably make automated text analysis a common tool for psychologists.
Resources and Destination Competitiveness Factors Important for Planning Sport Tourism Supply

The aim of this paper is to determine resources and competitive factors of sport tourism suitable to tourism business circumstances. For this paper and according to accessible and processed sources, had not been found research that determine and treats concept of sport comprehensively, by the sub-areas of sport. Sport tourism in theory represents a selective form of tourism and should be based, in accordance with contemporary trends, on comparative competitiveness and sustainable development. Sustainable development includes a wide range of relevant platforms. These platforms influence the sustainable planning of sport tourism supply. The question is what are the resources and factors that are important for the planning of sport-tourism supply and fit into comparative competitiveness and sustainable development? The practical and theoretical purpose of this paper are the determination of resources and factors important for the planning and development of sport tourism in those destinations that want to develop this form of selective tourism. In these destinations such created supply of sports and recreational amenities and facilities should resulted with increase in consumption. Previously defined resources and factors should be the basis for the planning of the overall development of a particular destination and, consequently, for decision-making process on potential investment engagements. Such structural changes should also reflected on the structure of the supply and potentially define changes in the guests structure/profile and their demand or consumption. Thus, in this paper determined resources and factors, if applied, should affect directly or indirectly the planning and decisions development in each particular destination.
World scientific production on biodiesel

Tecnica en Bibliotecologia Medica. Red de Estudios Cienciometricos para la Educacion Superior. Direccion de Organizacion y Control de la Actividad Cientifica. Centro Nacional de Investigaciones Cientificas (CNIC). La Habana. Cuba. Master en Bibliotecologia y Ciencia de la Informacion. Red de Estudios Cienciometricos para la Educacion Superior. Direccion de Organizacion y Control de la Actividad Cientifica. Centro Nacional de Investigaciones Cientificas (CNIC). La Habana. Cuba. Ingeniero Quimico. Direccion de Produccion. Centro Nacional de Investigaciones Cientificas (CNIC). La Habana. Cuba. Master en Ciencias de la Informacion. Red de Estudios Cienciometricos para la Educacion Superior. Direccion de Organizacion y Control de la Actividad Cientifica. Centro Nacional de Investigaciones Cientificas (CNIC). La Habana. Cuba.
Study of Map Reduce Over Different Cube Computation Approaches: Survey Paper

MapReduce is a programing poser and an related effort for processing and generating super aggregation sets. Users determine a map operate that processes a key/value place to make a set of medium key/value pairs, and a Reduce function work that merges all middle values associated with the very mediate key. Efficient extract of aggregations very significant role in Data wearhouse Store systems. Multidimensionalaccumulation psychotherapy applications data crosswise much dimensions designer for anomalies or unusual patterns. The SQL mass functions and the GROUP BY operator are utilised for accumulation. But Data psychotherapy applications requirement the N-dimensional generality of these operators. Data cube is introduced which is a way of structuring information in N-dimensions so as to spread analysis over few measure. Datawearhouse implementation for the essential part of data cube computation. The precomputation of all or endeavor of a data cube can greatly restrict the salutation quantify and deepen the executing of online analytical processing. Various strategies to Cube Materlization, there are various methods for cube computation and specific computation algorithms, namely Star Cubing, BUC, Multiway array aggregation, parallel algorithms, the computation of shell fragments and. But these techniques some rule so new MapReduce based approach is used.
Heart Rate Monitoring System using Finger Tip through Arduino and Processing Software

- Technological innovations in the field of disease prevention and maintenance of patient health have enabled the evolution of fields such as monitoring systems. Heart rate is a very vital health parameter that is directly related to the soundness of the human cardiovascular system. Heart rate is the number of times the heart beats per minute, reflects different physiological conditions such as biological workload, stress at work and concentration on tasks, drowsiness and the active state of the autonomic nervous system. It can be measured either by the ECG waveform or by sensing the pulse - the rhythmic expansion and contraction of an artery as blood is forced through it by the regular contractions of the heart. The pulse can be felt from those areas where the artery is close to the skin. This paper describes a technique of measuring the heart rate through a fingertip and Arduino. It is based on the principal of photophelthysmography (PPG) which is non-invasive method of measuring the variation in blood volume in tissue using a light source and detector. While the heart is beating, it is actually pumping blood throughout the body, and that makes the blood volume inside the finger artery to change too. This fluctuation of blood can be detected through an optical sensing mechanism placed around the fingertip. The signal can be amplified and is sent to arduino with the help of serial port communication. With the help of processing software heart rate monitoring and counting is performed. The sensor unit consists of an infrared light-emitting-diode (IR LED) and a photo diode. The IR LED transmits an infrared light into the fingertip, a part of which is reflected back from the blood inside the finger arteries. The photo diode senses the portion of the light that is reflected back. The intensity of reflected light depends upon the blood volume inside the fingertip. So, every time the heart beats the amount of reflected infrared light changes, which can be detected by the photo diode. With a high gain amplifier, this little alteration in the amplitude of the reflected light can be converted into a pulse.
Road Quality and Ghats Complexity analysis using Android sensors

Importance of the road infrastructure for the society could be the same as importance of blood vessels for humans. Road surface quality should be monitored and repaired on a regular basis. It is very difficult to design a optimal system which gathers the road condition data and processes it. Participatory sensing approach can be mostly used for such data collection.The paper is describes a mobile sensing system (android application) for road irregularity detection using Android OS based smart phone sensors. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.Continuously keeping track on road and traffic conditions in a city is a problem widely studied. Many methods have available towards addressing this problem. But this methods proposed require dedicated hardware such as GPS devices and accelerometers in vehicles or cameras on roadside and near traffic signals. All such proposed are unaffordable tothe common man regarding of monetary cost and human effort required. We extend a prior study to improve the algorithm based on using accelerometer, GPS and magnetometer sensor readings for trafficand road conditions detection. We are specifically made research in identifying braking events - frequent braking indicates congested traffic conditions - and bumps on the roads to characterize the type of road.
Confidence of Conditional Functional Dependencies Using Reservoir Sampling

Conditional functional dependencies (CFD), are extensions of classical functional dependencies that apply to certain subset of the relations, as specified in pattern tableau. The support and confidence of a given tableau are key properties in understanding and exploring data quality. Consider a data warehouse containing large quantities of historical data. Careful analysis of a particular relation over time may have revealed various tableaux that have high support and confidence, checking the support and confidence on new data as they arrive hour by hour is an important quality check, and can reveal new trends if the support or confidence change suddenly. Given sufficient computational resources, it is straightforward to compute the confidence of a CFD by first sorting the input on attributes of the relation. For each group in the support set, the number of matching rows and the frequency of the most common consequent that satisfies all applicable assertions, can be found, allowing direct computation of the confidence. So if proper samples of data are provided, calculating the confidence is easier. Our focus lies in coming up with providing some technique of providing sampled data. Existing sampling schemes are not sufficient as they do not provide us with reliable sampled data for which the confidence is to be calculated. We have proposed an algorithm in its naïve version, which follows the concept of reservoir sampling. The algorithm proposes a skip random variable S(n, t), which is generated in constant time by generating its continuous counterpart and then correcting it so that it has exactly the desired distribution function F(s), by a modification of von Neumann’s rejectionacceptance method. Proposed Algorithm is significantly faster than other reservoir sampling algorithms. Keywords-Conditional Functional Dependencies (CFD), Functional Dependencies, pattern tableau, Support, Confidence, Reservoir Sampling, Random variable, rejection-acceptance method.
Hierarchical Control of Linear Systems from the Abstraction Feedback Gain

In this paper, the problem of hierarchical control for a class of linear systems is addressed. Given a large‐scale linear system, we obtain a stabilizing feedback gain for it from that of an abstract system. And then, we employ this feedback gain to design a new interface between the original system and the abstraction. Furthermore, the synthesis of the new interface can be allowed by a new simulation function characterized by a low‐order matrix associated to the abstraction. Finally, an example is given to illustrate the proposed approach.
Accurate and fast taxonomic profiling of microbial communities

With the advent of next generation sequencing there has been an explosionof the size of data that needs to be processed, where next generationsequencing yields basepairs of DNA in the millions. The rate atwhich the size of data increases supersedes Moores law therefore there isa huge demand for methods to nd meaningful labels of sequenced data.Studies of microbial diversity of a sample is one such challenge in the eldof metagenomics. Finding the distribution of a bacterial community hasmany uses for example, obesity control. Existing methods often resort toread-by-read classication which can take several days of computing timein a regular desktop environment, excluding genomic scientists withoutaccess to huge clusters of computational units.By using sparsity enforcing methods from the general sparse signal processingeld (such as compressed sensing), solutions have been found tothe bacterial community composition estimation problem by a simultaneousassignment of all sample reads to a pre-processed reference database.The inference task is reduced to a general statistical model based onkernel density estimation techniques that are solved by existing convexoptimization tools. The objective is to o er a reasonably fast communitycomposition estimation method. This report proposes, clustering asa means of aggregating data to improve existing techniques run-time andbiological delity. Use of convex optimization tools to increase the accuracyof mixture model parameters are also explored and tested. Thework is concluded by experimentation on proposed improvements withsatisfactory results.The use of Dirichlet mixtures is explored as a parametric model ofthe sample distribution where it is deemed that the Dirichlet is a goodchoice for aggregation of k-mer feature vectors but the use of ExpectationMaximization is unt for parameter estimation of bacterial 16s rRNAsamples.Finally, a semi-supervised learning method found on distance basedclassication of taxa has been implemented and tested on real biologicaldata with high biological delity.
Human-computer interaction issues in a battlefield reasoning system

We present a methodology for developing intelligent computer agents and blackboards to assist planners in grasping the situation quickly and making high quality decisions. We illustrate this methodology in the context of a specific military planning task. Military planners have many cognitive challenges when creating and revising battle plans. At all stages of planning planners must contend with being overloaded with information, while having little time in which to process it. We illustrate the use of the methodology in the design of an intelligent agent and human-computer interface for the planning task of Course of Action (COA) generation.
Phosphorene: what can we know from computations?

The past year has witnessed the fast growth of investigations on monolayer and few‐layer black phosphorous, termed as ‘phosphorene. ’ The intrinsic mechanical, electronic, thermal, and optical properties of phosphorene, which have mainly been revealed by computations, endow it with significant potential applications to the fields of electronics, optoelectronics, thermoelectrics, catalysis, and energy storage. In this overview, we summarize the computational investigations on phosphorene from aspects of inherent quality, properties, potential applications, and new allotropes, and manage to interpret what we can know about phosphorene from computations. We hope that this overview would help better understand phosphorene and provide guidance for experimental and computational colleagues to further investigate phosphorene and other novel two‐dimensional materials. WIREs Comput Mol Sci 2016, 6:5–19. doi: 10.1002/wcms.1234
LTE-advanced random access mechanism for M2M communication: A review

Machine Type Communications (MTC) enables one or more self-sufficient machines to communicate directly with one another without human interference. MTC applications include smart grid, security, e-Health and intelligent automation system. To support huge numbers of MTC devices, one of the challenging issues is to provide a competent way for numerous access in the network and to minimize network overload. In this article, the different control mechanisms for overload random access are reviewed to avoid congestion caused by random access channel (RACH) of MTC devices. However, past and present wireless technologies have been engineered for Human-to-Human (H2H) communications, in particular, for transmission of voice. Consequently the Long Term Evolution (LTE) –Advanced is expected to play a central role in communicating Machine to Machine (M2M) and are very optimistic about H2H communications. Distinct and unique characteristics of M2M communications create new challenges from those in H2H communications. I.
Building theories of creative industry managers: Challenges, perspectives, and future directions

This chapter addresses the challenges in developing broad theoretical paradigms for studying media managers, given scholars' lack of access to these professionals, the role of “spin” in framing managerial decisions, and the ever-shifting nature of cultural production. Project-by-project changes in players, objectives, and pitfalls require managers to adopt new managerial strategies in each new context. As a result, the chapter struggles with media studies' need to develop a coherent set of meanings for media management. It recognizes that while this goal is often hindered by the managerial cultures scholars seek to explain, the project itself can still be productive in terms of generating multiple discourses about media management. The chapter challenges media scholars to go beyond individual case studies and identify the broader applicability and significance of their findings.
Effective Set Covering Approach for Vehicle Routing Based on Artificial Immune

In this paper, a heuristic algorithm to solve the Vehicle Routing Problem (VRP) is proposed based on the Artificial Immune System (AIS). By introducing the route covering methodology, a new encoding and heuristic structure are developed. Routes are constructed primarily by the cluster-first-route-second method, with the network renewal mechanism to generate initial antibodies and the bi-learning with balanced-opportunity approach to expand antibody population. The concentric-circle builder is to identify different customer clusters to further form routes. Upon the elite strategy (AB) and (R), worse antibodies are deleted and routes keep diversified in the pool. By further solving the set covering model, the VRP solution is improved along with increasing route choices. Further, the route combination phase is developed to add promising routes, and it brings the final optimal VRP solution by selecting optimal routes from the final route pool. Finally, experiment tests are carried out to illustrate the effectiveness of the proposed heuristic.
Video Adaptation Based on Content Characteristics and Hardware Capabilities

The subjective user satisfaction related to viewing a video on a resource constrained mobile device is modeled by using a utility function. The resulting models account for different video content characteristics and varying hardware capabilities of the viewing device. The user satisfaction is divided into two independent components, namely the temporal and the spatial satisfaction. Subjective video evaluation experiments are performed to obtain the models for each component. These components are combined by using a weighted sum. The frame-rate and the bit-rate that maximizes the total utility is then obtained by the optimization algorithms. It is further demonstrated that the utility curves obtained through subjective tests for a limited number of devices can be interpolated to generate utility curves for other devices with different hardware capabilities.
Children of Katrina

Fothergill and Peek examine three post-Katrina adaptive recovery trajectories adopted by children: declining, finding equilibrium, and fluctuating. The authors describe how they conducted in-depth interviews and observational field studies to identify these recovery trajectories and bring a child’s perspective to the field of disaster research. Their innovative approach provides unique insights into children’s ability to navigate trauma by analyzing two prevailing notions regarding children’s recovery capacity. Under the “resilience myth,” children possess a unique psychological ability “shrug off” any personal setbacks. In juxtaposition, “the helpless victim myth” holds that children lack any skills whatsoever to persevere in the face of calamity. Therefore, experts must program recovery in a post-disaster period to recreate normalcy for children. The authors frame their analysis of these two perspectives under the caveat that children’s experiences differ from each based on their social location. Thus, the “disasters as equal opportunity event myth” fails to account for preand post-matrix of social, cultural, human, and financial assets and the social contexts under which children readjust to social dislocation following a disaster.
Camera calibration, data segmentation, and fitting approaches for a visual edge inspection system

The design of edges is very important for many components. In this paper we therefore present a light-sectioning based measurement head, which is suitable for the edge inspection of different workpieces. Beyond the design we also present a new calibration technique for its camera. The calibration is mainly based on several perspective projections, which are successively executed. In each step, the linear system of homogeneous equations is solved by using singular value decomposition. Each mapping is therefore obtained in the least squares sense. Because of the novel design of the calibration device, a high number of reference points can be used for the description of these mappings. The inspection of a workpiece detail implicates a large amount of data, some of which is useless. To extract the data essential for the fitting routines, a special correlation/regression based template matching is proposed. After the description of the segmentation process we propose a measurement progression, which enables us to obtain a fast and easy perspective correction of the three-dimensional light sectioning data. Finally, a fitting method is presented. Based on singular value decomposition, the data is fitted to the corresponding form of the fillet or chamfer. As the fit is done in the least squares sense, one can obtain statistical information out of the decomposition process.