Miracle-dz/UniFAR-FTUs huggingface dataset:
Kinematic calibration for industrial robots using articulated arm coordinate machines

To improve the position accuracy of industrial robots, a novel kinematic model and calibration method using articulated arm coordinate machines (AACMM) is proposed in this paper. The end of the industrial robot is connected to the probe of an AACMM, thus forming a closed kinematic chain. The coordinate systems of the double arms were established, based on which the mapping of the joint angles and the position of the end were derived as well as the nominal value of the kinematic parameters of the industrial robot. A two-step search method was presented for kinematic parameter identification of the industrial robot. The identified kinematic parameters were used to compensate the errors of the nominal kinematic parameters in the controller of the industrial robot. Experiments were conducted to verify the calibration method, which show that after calibration, the average position errors of the industrial robot were decreased greatly.
Managing digital infrastructures: negotiating control and drift in service provisioning

The digital infrastructure literature provides important perspectives on the intrinsic relations between information systems in today's organisations. However, little attention has been paid to the challenges involved in providing requisite digital infrastructure services to organisations. In this paper, we argue digital infrastructure service providers operate in highly complex and uncertain environments. Rather than adopting a traditional approach to control, providers must therefore continuously negotiate a balance between control and drift as two complementary strategies. Our argument is based on a retrospective longitudinal case study of a Swedish infrastructure service team within a large international firm. Using the encounter-episode process model as structuring principle and focusing on the tension between control and drift, we analyse the evolution of the provider's efforts to manage a portfolio of digital infrastructure services over a period of ten years. Based on these analyses, we uncover the involved complexities and dynamics, how control efforts and drift were constituted and how the infrastructure services were managed by continuously balancing control and drift. In conclusion, we relate the findings to extant literature to discuss new insights into provider management of digital infrastructure services.
Potential financial instruments toward sustainable urban infrastructural development in Indonesia

The development of urban infrastructures in Indonesia is a must as rapid urbanisation exerts huge pressures on basic services and infrastructures in urban areas. However, the government funds to support urban infrastructures are far from adequate. This paper, by using literatures, studies and primary data, aims at examining potential financial instruments toward sustainable urban infrastructural development in Indonesia. The study confirms that the role of the private business entities is unavoidable to sustain urban infrastructural development. Apart from the available financial instruments that have been implemented to develop urban infrastructures, other financial instruments especially private bonds in the forms of corporate bonds, project bonds and Sharia bonds instruments are potential to be taken into considerations. However, to make these private bonds well-implemented, the government needs to provide regulations, incentives and guarantees to attract the private business entities and community. Thus, much remains to be done by the government.
An efficient algorithm for extracting infrequent itemsets from weblog

Weblog data contains unstructured information. Due to this, extracting frequent pattern from weblog databases is a very challenging task. A power set lattice strategy is adopted for handling that kind of problem. In this lattice, the top label contains full set and at the bottom label contains empty set. Most number of algorithms follows bottom-up strategy, i.e. combining smaller to larger sets. Efficient lattice traversal techniques are presented which quickly identify all the long frequent itemsets and their subsets if required. This strategy is suitable for discovering frequent itemsets but it might not be worth being used for infrequent itemsets. In this paper, we propose Infrequent Itemset Mining for Weblog (IIMW) algorithm; it is a top-down breadth-first level-wise algorithm for discovering infrequent itemsets. We have compared our algorithm IIMW to Apriori-Rare, Apriori-Inverse and generated result in with different parameters such as candidate itemset, frequent itemset, time, transaction database and support threshold.
ONYA - The Wellbeing Game: How to Use Gamification to Promote Wellbeing

The Wellbeing Game uses game design elements to promote wellbeing. Players document their daily activities in the game and categorize them to one or more of five wellbeing-related factors. The users join teams and can create team events to work together and improve their wellbeing status. The present study aims to review the application and the theoretical base of ‘The Wellbeing Game’, to adapt it to the German context, and to evaluate its health effects in different settings. Additional aims are to analyze the current state of research regarding the links between health, wellbeing, and gamification and to identify crucial game design elements that have to be implemented in the application in order to address the needs of competence, autonomy, and social relatedness according to the self-determination theory.
Circuit Lower Bounds for MCSP from Local Pseudorandom Generators

The Minimum Circuit Size Problem (MCSP) asks if a given truth table of a Boolean function f can be computed by a Boolean circuit of size at most θ, for a given parameter θ. We improve several circuit lower bounds for MCSP, using pseudorandom generators (PRGs) that are local; a PRG is called local if its output bit strings, when viewed as the truth table of a Boolean function, can be computed by a Boolean circuit of small size. We get new and improved lower bounds for MCSP that almost match the best-known lower bounds against several circuit models. Specifically, we show that computing MCSP, on functions with a truth table of length N, requires • N3−o(1)-size de Morgan formulas, improving the recent N2−o(1) lower bound by Hirahara and Santhanam (CCC, 2017), • N2−o(1)-size formulas over an arbitrary basis or general branching programs (no non-trivial lower bound was known for MCSP against these models), and • 2Ω(N1/(d+1.01))-size depth-d AC0 circuits, improving the (implicit, in their work) exponential size lower bound by Allender et al. (SICOMP, 2006). The AC0 lower bound stated above matches the best-known AC0 lower bound (for PARITY) up to a small additive constant in the depth. Also, for the special case of depth-2 circuits (i.e., CNFs or DNFs), we get an optimal lower bound of 2Ω(N) for MCSP.
Parallel batch dynamic single source shortest path algorithm and its implementation on GPU based machine

In this fast changing and uncertain world, to meet the user’s requirements the computer applications based on real world data always try to give responses in the minimum possible time. Single Source Shortest Path (SSSP) calculation is a basic requirement of applications using graphs portraying real world data like social networks and road networks etc. to get useful information from them. Some of these real world data changes very frequently, so recalculation of the shortest path for all nodes of a graph depicting these real world data after small updates of graph structure is an expensive process. To minimize the cost of recalculation shortest path algorithms need to process only the affected part of a graph after any update, and to speed-up any process parallel implementation of algorithm is a frequently used technique. This paper proposes a new parallel batch dynamic SSSP calculation approach and shows its implementation on a CPUGraphic Processing Unit (GPU) based hybrid machine. The proposed algorithm is defined for positive edge weighted graphs. It accepts multiple edge weight updates simultaneously. It uses parallel modified Bellman Ford algorithm for SSSP recalculation of all affected nodes. Nvidia’s Tesla C2075 GPU is used to run the parallel implementation of the algorithm. The proposed parallel algorithm shows up to a twenty-fold speed increase as compared to best serial algorithm available in literature.
Dimensions Of Institutionalized Organizational Trust And Firm’s Innovativeness

ABSTRACT The primary goal of this article was to empirically assess how institutionalized organizational trust dimensions are linked with firm’s innovativeness. An approach to conceptualize and measure multidimensional institutionalized organizational trust as a profile model was proposed. The factor analysis, based on a survey study, was used to examine the dimensionality of the institutionalized organizational trust measure. Next, the regression analysis was used to assess the relationship between institutionalized organizational trust dimensions, such as organizational ability, integrity, benevolence, and willingness to be vulnerable, and the dependent variable of firm’s innovativeness. The findings revealed a significant positive relationship between the dimensions of institutionalized organizational trust and innovativeness. In other words, the higher the dimensions of institutionalized organizational trust, the higher the firm’s innovativeness. In addition, our results reveal that organizational willingness to be vulnerable and benevolence is of primary importance for establishing innovativeness. The practical implications for enhancing firm’s innovativeness based on findings are discussed.
Storing and Handling Complex Content for Large-scale Data

—At present, the increasing growth and pervasive development of mass data raise the challenge for big data storage. When it came to large-scale data, storage and handing method of complex data were the key problems to be settled. From big data source, we studied several problems of massive data: the big data platform framework and landing, the storage mechanism and data handling of large-scale data, the node-fault handling of mapreduce and pregel superstep, spark streaming based on the storage memory, and BlueDBM-The other forms of memory storage in massive data etc. It offered storing and handling complex content for large-scale data and support the further research of big data technical challenges, support for the application of it.
Power-aware high level evaluation model of interconnect length of on-chip memory network topology

Interconnect power is the factor that dominates the power consumption on the on-chip memory architecture. Almost all dedicated wires and buses are replaced with packet switching interconnection networks which have become the standard approach to on-chip interconnection. Unfortunately, rapid advances in technology are making it more difficult to assess the interconnect power consumption of NoC. To resolve this problem, a new evaluating methodology for interconnect power evaluation based on topology of on-chip memory (IP-ETOM) is proposed in this paper. To validate this method, two multicore architectures 2D-mesh and triplet-based architecture (TriBA) are evaluated in this research work. The on-chip memory network model is evaluated based on characteristics of on-chip architecture interconnection. MATLAB is used for conducting the experiment that evaluates the interconnection power of TriBA and 2D-mesh.
Single image fast deblurring algorithm based on hyper-Laplacian model

An improved single image fast deblurring algorithm based on hyper-Laplacian constraint is proposed. The algorithm is improved in three aspects: image blur kernel estimation sub-region selection, blur kernel precise estimation, and fast non-blind deconvolution. First, image amplitude and gradient are used as the basis of blur kernel estimation. On the basis of analysing the edge amplitude and gradient of the image, the image sub-region for blur kernel estimation is selected. Then the sparsity of the blur kernel is restricted by hyper-Laplacian, and the fast solving mode of alternately solving different variables is designed. The blur kernel information is accurately estimated. In the fast non-blind deconvolution restoration phase of the image, the regularised constraint term of the hyper-Laplacian model is improved and the image gradient distribution is constrained. The blind growth trend of the regional gradient near the strong edge can be suppressed well, and the deblurred image with clear edge structure is generated. Experimental results show that the proposed algorithm can achieve better image deblurring effect and high efficiency.
Analysis of Machine Learning Algorithms in Health Care to Predict Heart Disease

This article describes how healthcare organizations is growing increasingly and are the potential beneficiary users of the data that is generated and gathered. From hospitals to clinics, data and analytics can be a very powerful tool that can improve patient care and satisfaction with efficiency. In developing countries, cardiovascular diseases have a huge impact on increasing death rates and are expected by the end of 2020 in spite of the best clinical practices. The current Machine Learning (ml) algorithms are adapted to estimate the heart disease risks in middle aged patients. Hence, to predict the heart diseases a detailed analysis is made in this research work by taking into account the angiographic heart disease status (i.e. ≥ 50% diameter narrowing). Deep Neural Network (DNN), Extreme Learning Machine (elm), K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) learning algorithm (with linear and polynomial kernel functions) are considered in this work. The accuracy and results of these algorithms are analyzed by comparing the effectiveness among them.
EEG signals classification based on autoregressive and inherently quantum recurrent neural network

This paper shows a novel hybrid approach using an Auto-Regressive (AR) model and a Quantum Recurrent Neural Network (QRNN) for classification of two classes of Electroencephalography (EEG) signals. The QRNN-AR has been shown to be capable to capture and quantify the uncertainty inherently in EEG signals because it uses fuzzy decision boundaries to partition the feature space. Two diverse element extraction techniques were used to extract the features from EEG signals; AR coefficients are processed with Levinson-Durbin algorithm and mean square error. AR provides a better frequency resolution and good spectral data of short EEG segment. The QRNN trained by the back propagation algorithm is compared with Quantum Neural Network (QNN) and Quantum Wavelet Neural Network (QWNN). The average accuracy of the proposed QRNN model is 88.28452% at 6 seconds. The Accuracy to Time Ratio (ATR) value is 14.714086, which shows the superiority of the proposed model. Experimental results demonstrate that the QRNN-AR gives the highest overall accuracy and short processing time. In addition, the structure of the proposed method is more reliable.
Cooperative co-evolution with improved differential grouping method for large-scale global optimisation

The cooperative co-evolution (CC) framework has shown to be effective in dealing with large-scale global optimisation (LSGO). However, the performance of algorithms based on the CC framework is often affected by the chosen variable grouping method, i.e., how variables are grouped into different sub-components. In this study, an improved variable grouping strategy based on the differential grouping (DG) is proposed, namely e-based differential grouping (e-DG). The e-DG strategy can identify both direct and indirect interactions between variables. Moreover, a simple yet effective method is introduced in e-DG to identify the calculation error that is detrimental to variable grouping in the original DG method (which needs to set the threshold value appropriately). The e-DG is compared against the DG on the CEC 2010 LSGO benchmarks, and is found to perform better in terms of the grouping accuracy on almost all problems. Moreover, a CC-based differential evolution algorithm with the e-DG strategy shows good performance on the 2010 LSGO benchmarks.
The English compound stress myth

This study investigates the distribution of end-stress and fore-stress among English NN and NNN compounds. It finds that end-stress in NNs is not ‘exceptional’, as many researchers have claimed, but confined to a reasonably well defined class of attribute-head NNs within which it is (at least optionally) grammatical and often predictable. In NNNs – NNs with embedded NNs – both fore-stress and end-stress can occur in both the embedding and the embedded NN, giving rise to eight possible stress patterns, all of which are attested. Moreover, the distribution of fore-stress and end-stress in embedding and embedded NNs follows the regularities identified in free-standing NNs. There is therefore no reason to accept the generalization whereby in NNNs, the second element is always stressed under right-branching and the first element under left-branching. While such patterns are perhaps particularly frequent, all others are also grammatical: the Compound Stress Rule known in the literature for some fifty years, der.
Student Perceptions of Mobile Video Recording to Learn American Sign Language

This article reports an exploratory study of teacher candidate perception of learning, using video recording in an American Sign Language (ASL) class. Video recording for practice and reflection is relevant to learning ASL, a visual language. One advantage of recording video for learning ASL with mobile devices instead of computers is the larger physical space that can be captured. Thirteen teacher candidates in a graduate program participated, by completing three surveys regarding their experiences with mobile app assignments designed for practicing expressive skills using specific ASL grammatical features. Results showed that 10 out of 12 teacher candidates found app assignments helpful in assessing their own sign skills, and 11 out of 12 found them helpful in clarifying ASL concepts. Nine out of 12 teacher candidates reported increased interest in learning ASL, and 11 out of 13 teacher candidates indicated that their ASL improved due to use of the app.
NB-IoTtalk: A Service Platform for Fast Development of NB-IoT Applications

Narrowband Internet of Things (NB-IoT) is considered as a promising wireless communications technology for Internet of Things (IoT) especially for the outdoor environment. Many outdoor IoT applications involve large numbers of homogeneous NB-IoT devices. It is tedious to specify and accommodate these devices during application development. To resolve this issue, this paper proposes a service platform for fast development of NB-IoT applications called NB-IoTtalk. This platform utilizes a tag mechanism to provide an easy-to-manipulate graphical user interface to accommodate a large number of NB-IoT devices in an application and transparently show them in a visual map. Our approach automatically creates and parses the device profile used to interpret the payload of an NB-IoT message. We then use a smart parking lot application as an example to investigate the event-triggered reporting of NB-IoT in terms of the time-to-live (TTL) report frequency and the outage detection accuracy. This paper provides the guidelines to set the TTL interval for event-triggered NB-IoT applications.
The Effects of Stereotype Threat on MIS Students: An Initial Investigation

Stereotype threat occurs when a member of a group can be stereotyped as having certain negative characteristics. Fear of confirming the stereotype then increases apprehension, resulting in the person exhibiting lowered performance. This study examines the extent to which stereotype threats might account for performance differences between male and female students in IT-related majors when working on analytical tasks. The theory supports the contention that males may outperform females on certain tasks but attributes the reason to psycho-social factors and not to innate ability. As expected, the results of this study indicated that females who were told their performances would be lower than their male counterparts scored higher in their work quantity, but lower in their work quality. For the women where no stereotype threat was introduced, scores were comparable to the men's, lending support to this theory. The implications of stereotype threats for IT-related majors and professionals are discussed.
Sentiment analysis based on the domain dictionary: a case of analysing online apparel reviews

E-commerce offers an online shopping environment in which manufacturers, businesses, and consumers participate. The past-customer emotion outlined in their reviews plays an important role in not only the purchasing decisions of potential consumers, but also in manufacturers' production plans and in business' maintenance of their shopping environments. This paper proposes a sentiment analysis approach based on the domain dictionary for extracting subjective customer emotion and objective commodity metadata from review pages and analysing their relationships. The value of the proposed approach was demonstrated through a case study by using online apparel reviews of an anonymous brand in China. The study found that the domain dictionary can be more effective in extracting emotional information. Coats, shirts, and t-shirts garner higher emotions than pants. Sales increase as the emotion increases and decrease as the price increases. A fixed price range, in which the clothing has higher emotion and sales, can be found.
Revisiting Customer Value by Forecasting E-Service Usage

In the past, firms focused on stimulating consumption and acquiring customers while neglecting the significance of customer retention. This study delaminated customer value based on previous literature and blended the evolution of the CRM concept to build a customer value model. This work applied a Markov Chain and Bayesian theorem to forecast and recommend appropriate CRM e-services to customers, using Apple iTunes as a case study. Findings revealed that precision was the highest for 52 samples of customer behavior. The precision of a typical student or addicted worker can reach 70.6%, 60.4% for an addicted student, and 50% for a typical worker. The performance of Bayesian theorem is insignificantly influenced by customer type and sample. The adequacy was as high as 90%. The proposed framework not only revisits the value on needs, but also helps firms recommend appropriate services.
MORAVA MODULES AND THE $K(n)$-LOCAL PICARD GROUP

group is a and as a direct of cyclic to the case p we discuss the decomposition of the group of exotic elements, by studying the map from the Picard group of the K ( n )-local category to the Picard group of E hG n -modules. We ﬁnish by explaining the connection to Gross–Hopkins duality, and