Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring structure penetration loss and indoor propagation at 700 MHz : Spatio-temporal data acquisition system to perform a Wireless Channel Reconstruction Based on Perimeter Channel Measurements

Overview of Proposed Work Guaranteeing reliable and ubiquitous signal reception is one of the current challenges faced by wireless communications systems. Ideally, wireless transmissions suffer only from propagation losses. In reality, the landscape through which wireless information travels often contains structures that contribute to significant losses in signal reception. This is largely a result of reflections and refractions that contribute to excess transmission losses. While increasing broadcast power will help signal integrity at the receiving end, it will invariably contribute to higher co-channel interference [1]. The high demand for ubiquitous signal reception, coupled with the aforementioned challenges, have motivated research in the development of propagation prediction models [1-6]. These prediction models are usually developed and tested through channel sounding methods. Channel sounding is a technique used for learning the characteristics and behavior of the radio channel, for building a more accurate model of the wireless channel, and for improving testing algorithms for wireless systems [11]. A wireless channel usually has multiple propagation paths; as a result, a wireless channel may become frequency dependent, time dependent, or position dependent. Frequency and time dependency are usually used to study a wireless channel. Current efforts to understand electromagnetic propagation in the radio spectrum have relied predominantly on two types of modeling: stochastic and deterministic. Rigorous channel measurements are essential in developing and evaluating these models. However, current techniques used in field measurements are often complicated by the need to measure antenna positioning. Some solutions include setting up a grid, as illustrated in Figure 1, or using track measurements. These strategies tend to be impractical and inefficient because they are time consuming and require much more equipment to be carried to the field.
Persistence Images: A Stable Vector Representation of Persistent Homology

Many datasets can be viewed as a noisy sampling of an underlying space, and tools from topological data analysis can characterize this structure for the purpose of knowledge discovery. One such tool is persistent homology, which provides a multiscale description of the homological features within a dataset. A useful representation of this homological information is a persistence diagram (PD). Efforts have been made to map PDs into spaces with additional structure valuable to machine learning tasks. We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs. The discriminatory power of PIs is compared against existing methods, showing significant performance gains. We explore the use of PIs with vector-based machine learning tools, such as linear sparse support vector machines, which identify features containing discriminating topological information. Finally, high accuracy inference of parameter values from the dynamic output of a discrete dynamical system (the linked twist map) and a partial differential equation (the anisotropic Kuramoto-Sivashinsky equation) provide a novel application of the discriminatory power of PIs.
Particle swarm optimization identification of IPMC actuator using fuzzy NARX model

In this paper, a novel inverse fuzzy NARX model is used for modeling and identifying the IPMC-based actuator's inverse dynamic model. The highly nonlinear features of the IPMC-based actuator are thoroughly modeled based on the inverse fuzzy NARX model-based identification process using experimental input-output training data. This paper proposes the novel use of a modified particle swarm optimization (MPSO) to generate the inverse fuzzy NARX (IFN) model for a highly nonlinear IPMC actuator system. The results show that the novel inverse fuzzy NARX model optimized by MPSO yields outstanding performance and perfect accuracy.
Lung Segmentation by New Curve Stopping Function Using Geodesic Active Contour Model

To detect lung area, this paper proposes curve stopping function that is based on CT coefficient of area of lung parenchyma instead of existing edge indication function. The proposed method was compared numerically using various measures and this method can detect better lung parenchyma region than existing methods. In addition, detecting procedure of the area of lung parenchyma was visually verified in lung images.
Improving Application Performance by Dynamically Balancing Speed and Complexity in a GALS Microprocessor

Microprocessors are traditionally designed to provide “best overall” performance across a wide range of applications and operating environments. Several groups have proposed hardware techniques that save energy by “downsizing” hardware resources that are underutilized by particular applications. We explore the converse: “upsizing” hardware resources in order to improve performance relative to an aggressively clocked baseline processor. Our proposal depends critically on the ability to change frequencies independently in separate domains of a globally asynchronous, locally synchronous (GALS) microprocessor. We use a variant of our multiple clock domain (MCD) processor, with four independently clocked domains. Each domain is streamlined with modest hardware structures for very high clock frequency. Key structures can then be upsized on demand to exploit more distant parallelism, improve branch prediction, or increase cache capacity. Although doing so requires decreasing the associated domain frequency, other domain frequencies are unaffected. Measuring across a broad suite of application benchmarks, we find that configuring just once per application increases performance by an average of 17.6% compared to the best fully synchronous design. When adapting to application phases, performance improves by over 20%.
HIV-1 Integrates Widely throughout the Genome of the Human Blood Fluke Schistosoma mansoni

Schistosomiasis is the most important helminthic disease of humanity in terms of morbidity and mortality. Facile manipulation of schistosomes using lentiviruses would enable advances in functional genomics in these and related neglected tropical diseases pathogens including tapeworms, and including their non-dividing cells. Such approaches have hitherto been unavailable. Blood stream forms of the human blood fluke, Schistosoma mansoni, the causative agent of the hepatointestinal schistosomiasis, were infected with the human HIV-1 isolate NL4-3 pseudotyped with vesicular stomatitis virus glycoprotein. The appearance of strong stop and positive strand cDNAs indicated that virions fused to schistosome cells, the nucleocapsid internalized and the RNA genome reverse transcribed. Anchored PCR analysis, sequencing HIV-1-specific anchored Illumina libraries and Whole Genome Sequencing (WGS) of schistosomes confirmed chromosomal integration; >8,000 integrations were mapped, distributed throughout the eight pairs of chromosomes including the sex chromosomes. The rate of integrations in the genome exceeded five per 1,000 kb and HIV-1 integrated into protein-encoding loci and elsewhere with integration bias dissimilar to that of human T cells. We estimated ~ 2,100 integrations per schistosomulum based on WGS, i.e. about two or three events per cell, comparable to integration rates in human cells. Accomplishment in schistosomes of post-entry processes essential for HIV-1replication, including integrase-catalyzed integration, was remarkable given the phylogenetic distance between schistosomes and primates, the natural hosts of the genus Lentivirus. These enigmatic findings revealed that HIV-1 was active within cells of S. mansoni, and provided the first demonstration that HIV-1 can integrate into the genome of an invertebrate.
The many faces of optimism - Extended version

The exploration-exploitation dilemma has been an intriguing and unsolved problem within the framework of reinforcement learning. “Optimism in the face of uncertainty” and model building play central roles in advanced exploration methods. Here, we integrate several concepts and obtain a fast and simple algorithm. We show that the proposed algorithm finds a near-optimal policy in polynomial time, and give experimental evidence that it is robust and efficient compared to its ascendants.
An Implement of Cache Structure in LE-OLAP

Pre-computing is an ordinary way to improve the response time in an OLAP application. With the extension of main memory, establishing a cache structure and pre-loading the data into memory with the free memory can further speed up the query response time. The paper gives out an implement of a cache structure in the LE-OLAP, and describes the query and the elimination algorithm of the cache in detail.
1 Learning , Risk , and Credit in Households ’ New Technology Investments : The Case of Aquaculture in Rural Indonesia +

This study examines the factors that influence poor Indonesian households to invest in floating net aquaculture after being relocated due to a reservoir construction project. We take this relocation and introduction of a new technology as a natural experiment in order to identify the determinants of technology adoption. To compare three primary decision factors, social learning, risk attitudes, and credit accessibility, we analyze 16 year household panel data collected exclusively for this study. Our analysis reveals that credit accessibility is the most important factors that influence the rate of new technology investment. Also, social learning as well as risk attitude influence the investment decision significantly. Our results suggest that development projects that involve voluntary investments by the poor will be more successful if complementary services to promote credit and insurance accessibility and/or local information sharing are made available especially at the early phase.
Functionality-based clustering using short textual description: helping users to find apps installed on their mobile device

In recent years, we have witnessed the incredible popularity and widespread adoption of mobile devices. Millions of Apps are being developed and downloaded by users at an amazing rate. These are multi-feature Apps that address a broad range of needs and functions. Nowadays, every user has dozens of Apps on his mobile device. As time goes on, it becomes more and more difficult simply to find the desired App among those that are installed on the mobile device. In spite of several attempts to address the problem, no good solution for this increasing problem has yet been found. In this paper we suggest the use of unsupervised machine learning for clustering Apps based on their functionality, to allow users to access them easily. The functionality is elicited from their description as retrieved from various App stores and enriched by content from professional blogs. The Apps are clustered and grouped according to their functionality and presented hierarchically to the user in order to facilitate the search on the small screen of the mobile device.
Exploring academic achievement in mathematics and attitudes towards mathematics: The role of Bourdieu's elusive habitus.

This paper reports on the quantitative part of my doctoral study carried out in three state-funded secondary schools in England. Using Bourdieu’s trilogy of habitus, embodied cultural capital and field, I have explored the interaction of social class, gender, ethnicity and attitudes towards mathematics, and their impact on mathematical achievement. The findings suggest that although cultural capital correlates positively with mathematical achievement, social class and habitus, which has been operationalised as mathematics-related career aspirations, have a more significant impact on mathematical achievement.
Working towards sincere encounters in volunteer tourism: an ethnographic examination of key management issues at a Nordic eco-village

ABSTRACT This article explores host–guest dynamics at Sólheimar eco-village, Iceland to contribute to the conceptualization of transformative learning in volunteer tourism. At the eco-village, the host and volunteers come together to share similar goals and meaningful experiences. This interaction gets complicated, however: the eco-village exists within the global capitalist system and must operate using market norms. The idealist and educational expectations of the volunteers often clash with the practical short-term goals of the community: there are also cultural and experiential differences between the parties. This clash is used to discuss the importance of sincerity in volunteer tourism at the eco-village. Data were collected through fieldwork, primarily including participant observations and interviews, to help interpret the patterns of behaviors and perceptions of both parties in relation to the aim. Ultimately, the experience that binds host and guests cannot solely be about learning to do things alternatively and sustainably; it requires sincerity, using Taylor's 2001 sincerity concept, to tackle the difficulties in working alternatively and sustainably to attain this experience. It is argued that transformative learning during the volunteer experience in alternative spaces should be conceptualized to include the promotion of sincere encounters, and adjusted to concern both the host and its guests.
Teachers’ self-reported knowledge regarding English learners: perspectives on culturally and linguistically inclusive instruction and intervention

ABSTRACT Student diversity within U.S. schools has steadily increased in recent years. This trend has driven significant interest, on the part of schools and school systems, in supporting students who are English learners (ELs) and who are culturally diverse. It has also highlighted, for those schools and school systems, the importance of professional development to prepare teachers to support those students effectively. Teacher experience and knowledge regarding this population is particularly important to the cause of inclusive education, as appropriate supports, instruction and inclusion for these learners are highly dependent on teachers’ knowledge and skills. This study surveyed teachers in three highly diverse elementary schools with high percentages of ELs, students of minority backgrounds, and students receiving free and reduced-price meals. Results indicated that, despite reporting significant previous professional development on cultural and language diversity, teachers lacked familiarity with basic concepts related to cultural and language diversity and to factors considered in the interventions process for diverse learners. These findings, if replicated, may call into question the efficacy of ongoing professional development efforts and underscore the need for more consistent design, delivery, and evaluation of professional development initiatives or programmes.
Behavioural Response to a Sudden Health Risk: Dengue and Educational Outcomes in Colombia

Abstract Epidemics tend to have a debilitating influence on the lives of directly afflicted families. However, the presence of an epidemic can also change the behaviour and outcomes of those not directly affected. This paper makes use of a short, sharp, unexpected epidemic to examine the behavioural response of the public to a sudden shift in the perceived risk to one’s health and mortality. Our analysis finds that unafflicted school students change their behaviour substantially, affecting important life outcomes. In particular, we find that between 1.9 and 4.7 fewer students, out of a typical cohort of 47 pupils, sit their school leaving examination for every additional 10 cases of severe Dengue per 10 000 inhabitants in a municipality. We rule out several possible mechanisms, leaving an increase in the salience of the disease’s risks as a plausible explanation for our findings.
Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons

Reservoir computing (RC) systems are powerful models for online computations on input sequences. They consist of a memoryless readout neuron that is trained on top of a randomly connected recurrent neural network. RC systems are commonly used in two flavors: with analog or binary (spiking) neurons in the recurrent circuits. Previous work indicated a fundamental difference in the behavior of these two implementations of the RC idea. The performance of an RC system built from binary neurons seems to depend strongly on the network connectivity structure. In networks of analog neurons, such clear dependency has not been observed. In this letter, we address this apparent dichotomy by investigating the influence of the network connectivity (parameterized by the neuron in-degree) on a family of network models that interpolates between analog and binary networks. Our analyses are based on a novel estimation of the Lyapunov exponent of the network dynamics with the help of branching process theory, rank measures that estimate the kernel quality and generalization capabilities of recurrent networks, and a novel mean field predictor for computational performance. These analyses reveal that the phase transition between ordered and chaotic network behavior of binary circuits qualitatively differs from the one in analog circuits, leading to differences in the integration of information over short and long timescales. This explains the decreased computational performance observed in binary circuits that are densely connected. The mean field predictor is also used to bound the memory function of recurrent circuits of binary neurons.
Data Reduction and Parallelization for Human Detection System

HOG (Histogram of Oriented Gradients) is one of the effective ways for extracting feature values. Also, Real Adaboost algorithm has high recognition ratio, and it is adequate to hardware implementation. Many researches on human detection systems adopted these two algorithms and had achieved progress. However, data volume of HOG feature is still a problem in the whole system. Data volume from only one frame could be over 1 GB, and this data volume causes some difficulties from the view point of both sending data to a server and execution speed. Especially, since much data volume presses also internal data communication between modules in hardware execution, much data volume could be a bottle-neck of the whole system operating speed. Here, a high speed and small memory consuming implementation of human detection system using Hardware-Software Co-design is proposed. For the executing speed of the system, HOG feature values are accelerated by an FPGA, and Real Adaboost detection is executed only by accessing ROM data in the FPGA. As a result, HOG+Real Adaboost part was accelerated about 23.1 times faster compared to the software execution. Whole system had been implemented on a single board, and it achieved 3.22 times speed up from camera input to VGA display output. Also we tried to reduce feature data volume, and achieved 93.75% of data compression compared to double precision calculation, with only 2.68% loss of the recognition accuracy.
Sequential circuit verification using symbolic model checking

The temporal logic model algorithm of E.M. Clarke et al. (ACM Trans. Prog. Lang. Syst., vol.8, no.2, p.244-63, 1986) is modified to represent a state graph using binary decision diagrams (BDDs). Because this representation captures some of the regularity in the state space of sequential circuits with data path logic, one is able to verify circuits with an extremely large number of states. This new technique is demonstrated on a synchronous pipelined design with approximately 5*10/sup 20/ states. The logic that is used to specify circuits is a propositional temporal logic of branching time, called CTL or Computation Tree Logic. The model checking algorithm handles full CTL with fairness constraints. Consequently. it is possible to handle a number of important liveness and fairness properties. which would otherwise not be expressible in CTL. The method presented is not necessarily a replacement for brute-force state-enumeration methods but an alternative that may work efficiently when the brute force methods fail. <<ETX>>
Speech Processing , Transmission and Quality Aspects ( STQ ) ; Test Methodologies for ETSI Test Events and Results ; Part 3 : 2 nd ETSI Plugtests Speech Quality Test Event Report

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Computation of bounds and performance guarantees for client-server systems using a queueing network model

Multiclass queueing networks have been suggested by a number of researchers for the performance engineering of client-server systems. A number of implementation mechanisms, such as software servers, locks in transaction processing and critical sections, make the application of existing analytical methods for queueing networks difficult for the performance analysis of client-server software and systems. Moreover, real systems are unlikely to meet the underlying assumptions required by the exact solution methods that are available. We present a performance bounds technique which is apt for modelling client-server systems and is based on relaxed assumptions about the stochastic behaviour of the system. Models can be solved to give upper and lower bounds on throughputs for different classes of clients. Of particular interest is the lower bound on throughput that gives a performance guarantee that is useful in the conservative design of systems.
Comparative Assessment of Performance for Home Energy Management Controller in Smart Grid

This paper, provides comparative assessment of performance for home energy management (HEM) controller which categories the household appliances into three different categories 1) Fixed appliances 2) Interrupt able appliances and 3)Non-interrupt able appliances on the bases of their load profiles and user preference. It is designed on the bases of two bio-inspired algorithms, genetic algorithm (GA), bacterial for aging algorithm (BFA) and two nature-inspired algorithms binary particle swarm optimization algorithm (BPSO) and ant colony optimization algorithm (ACO). Demand side management system(DSM) is also inaugurate. Real time pricing (RTP) model is used for energy price calculation. The objectives of minimize electricity cost consumption and peak to average (PAR) ratio are achieve successfully, as simulations validates. Simulations perform foraforemention heuristic algorithms, ACO perform best amongall four algorithms. Average cost for schedule algorithms GA, BFA, BPSO and ACO are 95.58%, 81%, 90.4% and 76.48%respectively.