Miracle-dz/UniFAR-FTUs huggingface dataset:
Poison in the Pot: The Legacy of Lead

Feel lonely? What about reading books? Book is one of the greatest friends to accompany while in your lonely time. When you have no friends and activities somewhere and sometimes, reading book can be a great choice. This is not only for spending the time, it will increase the knowledge. Of course the b=benefits to take will relate to what kind of book that you are reading. And now, we will concern you to try reading poison in the pot the legacy of lead as one of the reading material to finish quickly.
Teaching and learning with technology in higher education: blended and distance education needs ‘joined-up thinking’ rather than technological determinism

In higher education (HE), some of the distinctions between conventional, campus-based universities and those dedicated to distance education are being eroded through the use of information and communication technology. Despite huge investments in technology to enhance teaching and learning, there has been a considerable lack of clarity about what this actually signifies in practice. Implementation decisions are frequently technology-led rather than being focused on clearly defined educational goals. This article attempts to examine key issues and synthesise a number of important debates relating to teaching and learning with technology in HE. It examines some of the key factors influencing how technology is utilised in HE teaching and learning. It argues that decision-making by managers and teachers with regard to technology use needs to be founded upon evidence that takes account of all of those factors, not just a narrow selection of them. Rather than just considering technical issues and/or the idealised promises made about technologies, ‘joined-up thinking’ is required to integrate the multiple contextual factors that all influence how technology is actually used in teaching and learning.
Intelligent Scheduling and Memory Management Techniques for Modern GPU Architectures

With the massive multithreading execution feature, graphics processing units (GPUs) have been widely deployed to accelerate general-purpose parallel workloads (GPGPUs). However, using GPUs to accelerate computation does not always gain good performance improvement. This is mainly due to three inefficiencies in modern GPU and system architectures. First, not all parallel threads have a uniform amount of workload to fully utilize GPU’s computation ability, leading to a sub-optimal performance problem, called warp criticality. To mitigate the degree of warp criticality, I propose a CriticalityAware Warp Acceleration mechanism, called CAWA. CAWA predicts and accelerates the critical warp execution by allocating larger execution time slices and additional cache resources to the critical warp. The evaluation result shows that with CAWA, GPUs can achieve an average of 1.23x speedup. Second, the shared cache storage in GPUs is often insufficient to accommodate demands of the large number of concurrent threads. As a result, cache thrashing is commonly experienced in GPU’s cache memories, particularly in the L1 data caches. To alleviate the cache contention and thrashing problem, I develop an instructionaware Control Loop Based Adaptive Bypassing algorithm, called Ctrl-C. Ctrl-C learns the cache reuse behavior and bypasses a portion of memory requests with the help of feedback control loops. The evaluation result shows that Ctrl-C can effectively improve cache utilization in GPUs and achieve an average of 1.42x speedup for cache sensitive GPGPU workloads. Finally, GPU workloads and the co-located processes running on the host chip multiprocessor (CMP) in a heterogeneous system setup can contend for memory resources in multiple levels, resulting in significant performance degradation. To maximize the system throughput and balance the performance degradation of all co-located
INVESTIGATION THE EFFECT OF PERCEIVED VALUE ON CUSTOMERS ' COMMITMENT IN THE FINANCIAL SERVICE MARKET BY CLARIFYING THE MEDIATING ROLE OF CUSTOMER RELATIONSHIP QUALITY ( CASE STUDY OF QAVAMIN BANKS OF SHIRAZ ) *

The aim of this study is to examine the effect of perceived value on customers' commitment in the financial service market by clarifying the mediating role of customer relationship quality (case study of Qavamin Banks of Shiraz). The research method is descriptive (correlational) and applied. The statistical population includes all customers of Qavamin Bank of Shiraz. The sample size was estimated using Morgan table as 384 people and clustered random sampling was used. Data collection tool was Perceived Value Scale (Musakhani et al.,) with cronbach's alpha 0.832, Customers' Commitment Scale (Musakhani et al) with alpha coefficient as 0.754, and Customers' relationship quality scale (Rahminia) with alpha coefficient 0.776 based on 5 degrees Lickert scale (completely agree, agree, no idea, disagree, and completely disagree). After conducting factor analysis, the research hypotheses were tested using related analytical methods; also, correlation test and coefficient of partial correlation were calculated using SPSS software. The results showed that a positive significant relationship exists among perceived value and customers' commitment with the mediating role of customer relationship quality in the financial services market.
Have contraction: explaining “trace effects” in a theory without movement

The English auxiliary have may reduce to the nonsyllabic form /v/ in certain environments. This phenomenon (known as have contraction) has been adopted by proponents of derivational approaches to syntax (e.g. Radford 1988: 414) as evidence for the existence of traces and of verb movement in general. However, closer examination of the data reveals that have contraction only occurs when three conditions apply: i. when the auxiliary is finite ; ii. when it follows a pronoun with an empty coda; iii. when that pronoun is the whole subject, that is, is not conjoined with any other noun or pronoun. It is not clear how the movement approach can account for conditions (ii) and (iii) above. My proposal is that the frequent occurrence of strings conforming to the above conditions is such that over time the auxiliary verb has become an affix. The solution is offered within the framework of lexical-functional grammar, a unification-based approach in which words and phrases are represented at copresent parallel levels containing functional and categorial information (f-structure and c-structure). The two individual f-structures created by the pronoun and the affix are able to unify to create the larger f-structure of an inflected pronoun. The affix v attaches to its pronominal host through morphological composition using inside-out function application. In this way the affix is able to supply information regarding TENSE and ASP to the whole sentence. This is blocked if the inflected pronoun is part of a conjoined subject, explaining the ungrammaticality of sentences with conjoined NP subjects, one of which is an inflected pronoun. This behavior would not be predicted by theories that assume that have contraction is a purely surface (phrase-structure) phonetic phenomenon
Landscape image analysis using fuzzy adaptive resonance theory

Describes an image analysis system using fuzzy methodology. Some landscapes, which are composed of natural things, are chosen as target images. In this system, the fuzzy adaptive resonance theory (fuzzy ART) algorithm is first used to cluster the constituent elements of the landscape images. The means and variance of the clusters are computed and then used to determine the membership functions of fuzzy sets. Based on the derived membership functions, a supervised learning algorithm is used to generate fuzzy rules automatically, and classification will then be facilitated through fuzzy max-min inference. Simulation on real pictures of scenery has shown that the proposed image analyzer is very successful because the result is visually confirmed by human observation.
An Improved TLD Tracking Method Using Compressive Sensing

Qiang Li, Xueshi Ge and Geng Wang School of Software, Shanghai Jiao Tong University, Shanghai, China Naval Academy of Armament, Shanghai, China Corresponding author Abstract—Visual Tracking, as an important subject in computer vision, has been widely used in surveillance, space exploration, and human-computer interaction etc. Both tracking-learningdetection (TLD) [1] and compressive tracking (CT) [2] are successful algorithms among those proposed recently. However, TLD suffers from low efficiency and CT overlooks scale change during tracking. In this paper, we propose an improved TLD tracking algorithm by using compressive sensing. The improvements include enhancing the detection method in TLD with CT, employing Kalman filter in detector to estimate the tracking region for improving the detection speed. Besides, adaptive search radius is employed to deal with object disappearance and shielding issue. Lastly, the tracking results of TLD and CT are integrated to estimate the target status and update the classifier. The experiments show that, compared to the original algorithms, the improved algorithm combines the advantages of two algorithms, conducing to accurate tracking precision, faster tracking speed and handling the object extent change.
New virtual laboratories presenting advanced motion control concepts

The paper deals with development of software framework for rapid generation of remote virtual laboratories. Client-server architecture is chosen in order to employ real-time simulation core which is running on a dedicated server. Ordinary web browser is used as a final renderer to achieve hardware independent solution which can be run on different target platforms including laptops, tablets or mobile phones. The provided toolchain allows automatic generation of the virtual laboratory source code from the configuration file created in the open- source Inkscape graphic editor. Three virtual laboratories presenting advanced motion control algorithms have been developed showing the applicability of the proposed approach.
Salinitas didefinisikan sebagai adanya garam terlarut dalam konsentrasi yang berlebihan dalam larutan tanah. Satuan pengukuran salinitas adalah konduktivitas elektrik yang dilambangkan dengan decisiemens/m pada suhu 25 C. Pengaruh utama salinitas adalah berkurangnya pertumbuhan daun yang langsung me

Screening of Soybean Cultivars Glycine max (L.) Merrill under Sodium Chloride Stress Condition. Salinity is one of the most serious and widespread agricultural problems resulting in losses of yield. Generally, as land is more intensively cultivated, the salinity problem becomes more severe. A high concentration of NaCl greatly reduces growth of both the shoot and the root. One strategy available to cope with saline soil is to choose salt-tolerance crops or to select salt-tolerance cultivars within a crop. Experiments were conducted to asses the performance of ten cultivars soybean (Glycine max [L.] Merrill) to salt stress at germination and seedling stages. Salinity treatments were begun by adding 70, 80, 90, and 100 mM NaCl to the basal nutrient solution. According to germination percentage, fresh weight/dry weight ratios, and the percentage of dead apical buds we suggest that Wilis, Malabar and Sindoro were tolerant lines, Genjah Jepang, Lokan, and Tidar were moderate and the sensitive lines were Lumut, Yellow Biloxy, Si Cinang and Sriyono.
Design and implementation of secured agent based NoC using shortest path routing algorithm

Network on chip (NoC) is a scalable interconnection architecture for every increasing communication demand between many processing cores in system on chip design. Reliability aspects are becoming an important issue in fault tolerant architecture. Hence there is a demand for fault tolerant Agent architecture with suitable routing algorithm which plays a vital role in order to enhance the NoC performance. The proposed fault tolerant Agent based NoC method is used to enhance the reliability and performance of the Multiprocessor System on Chip (MPSoC) design against faulty links and nodes. These agents are placed in hierarchical manner to collect, process, classify and distribute different fault information related to the faulty links and nodes of the network. This fault information is used for further packet routing in the network with the help of shortest path routing algorithm. In addition to this the agent will provide the security for the node by setting firewall, which then decides whether the packet has to be processed or not. This intern provides high performance, low latency NoC by avoiding deadlock and live lock with low area overhead.
EXAMINING THE PSYCHOMETRIC PROPERTIES OF THE MACHIAVELLIAN MANAGERIAL PRACTICES SCALE

This research aims to adapt and examine the validity and accuracy of the 6-item Machiavellian Managerial Practice Scale in Turkish, which is a sub-dimension of the Organizational Machiavellianism Scale developed by Kessler et al., (2010). In this context, construct validity (only in terms of convergent validity), factor structure (confirmatory factor analysis) and reliability (internal consistency) of the Machiavellian Managerial Practices Scale were tested. In order to test construct validity, narcissism, Machiavellism, psychopathy, and counterproductive work behavior scale were used. According to the results of the research, Machiavellian Managerial Practice Scale was positively identified with narcissism, Machiavellianism, psychopathy, and counterproductive work behavior. These findings supported the convergent accuracy of the Machiavellian Managerial Practice Scale. Dependent factor analysis showed that the fit index supported the factor structure of the scale. Finally, the internal consistency of the scale was calculated at an acceptable level. As a result, the psychometric properties of the Turkish version of the Machiavellian Managerial Practice Scale were found to be at an acceptable level.
Flood Forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

— The aim of the present study is to explore applicability of artificial intelligence techniques such as ANFIS (Adaptive Neuro-Fuzzy Inference System) in forecasting flood for the case study, Dharoi Dam on the Sabarmati river near village Dharoi in Kheralu Taluka of Mehsana District in Gujarat State, India. The proposed technique combines the learning ability of neural network with the transparent linguistic representation of fuzzy system. ANFIS models with various input structures and membership functions are constructed, trained and tested to evaluate the models. Statistical indices such as Root Mean Square Error (RMSE), Correlation Coefficient (R), Coefficient of Determination (R 2 ) and Discrepancy Ratio (D) are used to evaluate performance of the ANFIS models in forecasting flood. This objective is accomplished by evaluating the model by comparing ANFIS model to Statistical method like Log Pearson type-III method to forecasting flood. This comparison shows that ANFIS model can accurately and reliably be used to forecast flood in this study.
Topology and routing in overlay networks

In this age of information, new models of information exchange methodologies based on overlay networks are gaining popular attention. Overlay networks provide a logical interconnection topology over an existing physical network. Overlay networks offer benefits such as ease of implementation, flexibility, adaptability, and incremental deployability. Due to the wide range of applications and advantages, formal study of overlay networks is required to understand the various research challenges in this context. In this thesis, we study two classes of overlay networks namely peer-to-peer networks and wireless ad hoc networks. Our focus will be along two central issues in overlay networks: how to arrive at efficient topologies and how to provide efficient routing strategies. Peer-to-peer networks have gained a lot of research attention in recent years for various reasons. Despite many advances however, fundamental questions such as how to design deterministic constructions, and how to organize peers of non-uniform bandwidth have remained open. In this thesis, we answer these questions by providing a deterministic overlay topology, Pagoda, that can be used for efficient routing, data management and multicasting. Given the difficulty of arriving at good deterministic topologies in a purely decentralized manner, we also propose a unified methodology to create a large class of overlay topologies via an approach called the supervised overlay networks. We show that this approach also has other advantages such as support for rapid peer join/leave and rapid repair. For the case of wireless ad hoc networks, we start by providing a model for wireless communication that is much more realistic than the models that are being used in the theoretical community. Using this model, we show how to arrive a sparse spanner construction based on dominating sets. We then use the spanner construction to provide efficient algorithms for broadcasting and information gathering in wireless ad hoc networks. All our algorithms are simple, self-stabilizing and require only a constant amount of storage at any node. Thus, our algorithms are also applicable in a wide variety of scenarios such as simple sensor devices.
Elemental image array generation method by using optimized depth image‐based rendering algorithm for integral imaging display

We propose an elemental image array (EIA) generation method by using an optimized depth image‐based rendering (DIBR) algorithm. In this method, the EIA is synthesized by the reference and virtual viewpoint elemental images, and the virtual viewpoint elemental images at the given locations are generated by DIBR algorithm. We optimize the existing DIBR algorithm by adaptively repairing the warped depth images in the processing part and extend the generation dimension of the virtual viewpoint elemental images from one dimension to two dimensions. The optimized DIBR algorithm can effectively solve the problem: the low quality of virtual viewpoint elemental images caused by discontinuous depth values and disocclusion regions. We also implement the generations of virtual viewpoint elemental images and EIA in graph processing unit to reduce the time cost. Experimental results show that the proposed method can not only improve the quality of the virtual viewpoint images but also accelerate the generations of the virtual viewpoint elemental images and EIA.
Vibration-Based Damaged Road Classification Using Artificial Neural Network

It is necessary to develop an automated method to detect damaged road because manually monitoring the road condition is not practical. Many previous studies had demonstrated that the vibration-based technique has potential to detect damages on roads. This research explores the potential use of Artificial Neural Network (ANN) for detecting road anomalies based on vehicle accelerometer data. The vehicle is equipped with a smart-phone that has a 3D accelerometer and geo-location sensors. Then, the vehicle is used to scan road network having several road anomalies, such as, potholes, speedbump, and expansion joints. An ANN model consisting of three layers is developed to classify the road anomalies. The first layer is the input layer containing six neurons. The numbers of neurons in the hidden layer is varied between one and ten neurons, and its optimal number is sought numerically. The prediction accuracy of 84.9% is obtained by using three neurons in conjunction with the maximum acceleration data in x, y, and z-axis. The accuracy increases slightly to 86.5%, 85.2%, and 85.9% when the dominant frequencies in x, y, and z-axis, respectively, are taken into account beside the previous data.
Bulk types with class

Bulk types | such as lists, bags, sets, nite maps, and priority queues | are ubiquitous in programming. Yet many languages don't support them well, even though they have received a great deal of attention, especially from the database community. Haskell is currently among the culprits. This paper has two aims: to identify some of the technical di culties, and to attempt to address them using Haskell's constructor classes. The paper can also be read as a concrete proposal for new Haskell bulk type libraries.
Applied Regression and Modeling: A Computer Integrated Approach

This book creates a balance between the theory, practical applications, and computer implementation behind Regression—one of the most widely used techniques in analyzing and solving real world problems. It begins with a thorough explanation of prerequisite knowledge with a discussion of Simple Regression Analysis including the computer applications. Multiple Regression—a widely used tool to predict a response variable using two or more predictors, follows this. Since the analyses of regression models involve tedious and complex computations, complete computer analysis including the interpretation of multiple regression problems along with the model adequacy tests and residual analysis using widely used computer software are presented. The use of computers relieves the analyst of tedious, repetitive calculations, and allows one to focus on creating and interpreting successful models.
Rethinking the Notion of Non-Functional Requirements

Requirements standards and textbooks typically classify require- ments into functional requirements on the one hand and attributes or non-func- tional requirements on the other hand. In this classification, requirements given in terms of required operations and/or data are considered to be functional, while performance requirements and quality requirements (such as require- ments about security, reliability, maintainability, etc.) are classified as non- functional. In this paper, we present arguments why this notion of non-functional require- ments is flawed and present a new classification of requirements which is based on four facets: kind (e.g. function, performance, or constraint), representation (e.g. operational, quantitative or qualitative), satisfaction (hard or soft), and role (e.g. prescriptive or assumptive). We define the facets, discuss typical combi- nations of facets and argue why such a faceted classification of requirements is better than the traditional notion of functional and non-functional requirements.
News Category Classification Using Distinctive Bag of Words and ANN Classifier

Category classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a “yes” or “no” answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.