Miracle-dz/UniFAR-FTUs huggingface dataset:
THE CONCEPT OF INTEGRATION OF INTELLECTUAL CAPITAL AND KNOWLEDGE MANAGEMENT AND ITS RELATIONSHIPWITH BUSINESS PERFORMANCE

The objective of this research is to develop the concept of integration of Intellectual Capital (IC), Knowledge Management (KM) and its relationship with the pharmaceutical company's business performance. The basic theory underlying this integration is the resource-based theory and the knowledge-based theory. Both theories are important in this research because of the increasing role of intangible resources in the era of knowledge economy nowadays. This research is conducted with the interpretive accounting research approach. The data are collected by in depth interviews, Focus Group Discussion (FGD), and documentation. The validity of the data is tested by credibility and transferability tests. The data analysis is carried out during the data collection process with the stages of data collection, data reduction, the data display, and conclusion. The results show that there are two concepts of integration of IC and KM on business performance. The first concept is a theoretical concept that is constructed from a variety of theories and preliminary research, and the second concept is applicable concepts which are prevalent in a variety of pharmaceutical companies. The first begins with the concept of Human Capital (HC) as the central role which will form the Structural Capital (SC), Relational Capital (RC), Knowledge Management enablers (KME), and business performance. Furthermore, the SC will contribute to the RC and business performance. RC will play a role on business performance. KME will establish Knowledge Management Process (KMP) and business performance. Finally, KMP will contribute to business performance. The second concept begins with KME which will form the next KMP and KMP will be significant to the successful establishment of HC, SC and RC. Furthermore, HC will play a big role to SC, RC, and business performance. SC will be crucial to RC and business performance, as well as the RC will be instrumental in the success of the business performance. Keywords: Intellectual Capital, Knowledge Management, Business Performance, Interpretive Accounting Research.
Spatial pattern of beta-amyloid accumulation in Alzheimer’s disease as measured with positron emission tomography

Understanding the natural course of beta-amyloid accumulation over time is important for tracking the progression of Alzheimer’s disease (AD), prediction of outcomes as well as designing stage-specific clinical anti-amyloid drug trials. In this thesis, the spatial and temporal pattern of beta-amyloid accumulation as measured with positron emission tomography (PET) was investigated in subjects with preclinical and manifest sporadic and autosomal dominant AD. Overall, these results provide meaningful references for planning anti-amyloid clinical trials with PET as biomarker.
Sistem Klasifikasi Kondisi Pita Suara dengan Metode Decision Tree

This paper presents the study of pathological vocal cords classification using digital image processing. There are six classifications of vocal cords, including the normal vocal cords condition. Before the classification process, image vocal cords are extracted to obtain the characteristics or information of objects in the image. In this study, shape measurement is used to extract the glottis contour of the vocal cords that can be analyzed and classified. The process of measuring the glottis contour of vocal cords requires the vocal image in the binary image. To get the binary image, this study uses a method to automatically obtain the glottis area segmentation without user initialization. The segmentation is mainly based on active contour, which is Chan-Vese algorithm. The result of this study can optimize glottis contour extraction and results of the classification training process using Decision Tree algorithm obtains an accuracy of 98.3%.
Detection of Building Shadow in Remote Sensing Imagery of Urban Areas With Fine Spatial Resolution Based on Saturation and Near-Infrared Information

Because of the satellite orbit height and solar elevation angle, remote sensing imagery of urban areas with fine spatial resolution inevitably contains shadows cast by buildings, which thus decreases the accuracy of target recognition and information extraction. This paper proposes a new normalized-difference shadow detection index based on the saturation and near-infrared information of imagery to detect building shadows. The method was validated using typical images of urban areas from the WorldView-2, TripleSat-2, and RapidEye satellites and compared with the C3 component, multispectral band shadow detection, principal component analysis shadow detection (PCAS), normalized-difference umbra index, and GeoEye-1 shadow detection index methods. The results indicated that the proposed method effectively distinguishes building shadows from other dark ground objects and provides higher detection accuracy. The recall rate, precision, and F1 score exceeded 85%, 80%, and 85%, respectively, and their average values for four representative images were 91%, 89%, and 90%, respectively. Our method provides greater usability and better performance in the detection of building shadows in remote sensing imagery with fine spatial resolution.
The Diver System: Multimedia Communication and Localization Using Underwater Acoustic Networks

This paper concerns the design and implementation of a novel system for diving operations based on an efficient underwater acoustic network. The proposed system is composed of both static underwater nodes and divers that are equipped with acoustic modems and underwater tablets. The SUNSET Software Defined Communication Stack (S-SDCS) has been used to provide multi-hop networking capabilities. Novel image compression and transmission modules have been designed and implemented in S-SDCS. In addition, the localization module has been enhanced to increase the safety of the divers whose positions can be locally and remotely monitored. These modules interact with an innovative mobile application running on the underwater tablet thus enabling divers communication and remote position estimation and monitoring. In addition, divers can acquire and share pictures with other operators in real-time. The user interface of the application has been designed according to diver needs and feedback thus making it perfectly suitable for several diving scenarios. The system has been tested in several at-sea campaigns considering different application scenarios. The achieved results confirm that the proposed system is suitable for several diving experiences, enabling novel application scenarios while increasing divers safety.
High efficiency multiplexing scheme for multi-channel A/D conversion

Multiplexing technique is frequently used in multichannel analog to digital (A/D) conversion, thus reducing the system complexity and cost. However, if the sampling frequencies of the input channels are not equal, obtaining high A/D converter utilization is rather complicated. A new rule-based synchronous multiplexing scheme for multi-channel A/D conversion with different channel sampling frequencies is proposed. It is optimal in the sense that the required A/D conversion frequency is a minimum. The channel sequencing is based on the earliest deadline principle, similar to the one used in real-time dynamic scheduling algorithms.
The development of the ‘self-directed learning readiness scale in biology’ (SDLRSbio) for pre-university biology students

In past research, the measurement of students’ SDL readiness focused on self-motivation, self-management, and self-monitoring. These measurements were found to be too general to conclude one as a self-directed learner. Self-directedness should be specific for each subject area. This paper will discuss the pilot study of a larger research, where the SDLRSbio was developed using the Delphi technique. Specific biology laboratory skills readiness, experimental skills readiness, and data analysis and interpretation skills readiness were embedded in the SDLRSbio to ensure specificity in measuring biology students’ SDL readiness. The SDLRSbio developed consisted of 46 items in a 5 point Likert-scale. This scale was pilot tested with 30 students in Kuala Lumpur. The total scores were superimposed on a readiness continuum to reflect students’ SDL readiness. The Cronbach-alpha reading for the 46 items was 0.869. Results showed that, 73% of the biology students in the pilot study were moderately ready for SDL. 17% was less ready for SDL, and 10% was ready for SDL. This result has implications for planning and developing curricula of biology related fields.
Free fare policy as a tool for sustainable development of public transport services

This article focuses on free fare public transport policy (FFPT) as an example of the sustainable mobility paradigm FFPT is one of a number of instruments through which a balance between the economic costs and efficiency in the public transport system can be reached. Even though the strategic approach of FFPT systems is used worldwide, their implementation is somewhat sporadic. By investigating examples of currently or formerly existing free fare public transport schemes, this study analyses the overall ability of FFPT to reach the strategic targets required. The study identifies four key areas that the municipalities are trying to target (reducing car use externalities, social justice/benefits provision, increasing the efficiency of public transport, and promoting sustainable means of transportation). As the specific conditions of each locality generate particular issues, the strategy of each transport system authority differs from one to another. It is, therefore, necessary for the transport planning authorities to implement various tools (both supportive and repressive) whose synergies will target the main objectives. A systematic and conceptual approach is what underpins the successful development of the urban transport system in the long-term.
The Performance of Chirp Signal Used in LEO Satellite Internet of Things

The technology of chirp spread spectrum (CSS) has been around for decades, widely used in the fields of radar and sonar. In recent years, CSS has been used in the IEEE 802.15. 4a and Long Range (LoRa) Internet of Things (IoT), called LoRa modulation. On the other hand, CSS has never been used in low-Earth-orbit (LEO) satellite communication systems for low-data-rate transmission. CSS applied in LoRa as a kind of successful commercial IoT technology will promote research on its application in satellite communication for IoT. Recently, new types of chirp signals with the same chirp rate have become available to realize multiple accesses. This letter examines those types of chirp signal and the revised SCS called Asymmetry Chirp Signal (ACS) is proposed for better performance in LEO satellite IoT.
Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.
Model Following Reconfigurable Flight Control System Design Using Direct Adaptive Scheme

Reconfigurable flight control system provides better survivability through the automatic reconfiguration of the control system, even though faults occur during flight. The indirect adaptive control methods have been successfully applied to the reconfigurable flight control system design. To deal with insufficient input excitation problem which is common in adaptive control system, the identification algorithm of the indirect adaptive control system usually imposes constraints on the past input-output data. However, this kind of constraints may deteriorate the reconfiguration performance. Thus, in order to achieve better performance, a new model following reconfigurable controller with direct adaptive rule is proposed. In the proposed controller, the timescale separation principle is applied to control the inner-loop states as well as the outer-loop states simultaneously. To evaluate the reconfiguration performance of the proposed control method, numerical simulation is performed using six degree-of-freedom nonlinear dynamics.
Populism, blame shifting and the crisis: communication strategies in Portuguese political parties

Riding the wave of the economic crisis and the refugee emergency, populist parties have been gaining ground in European political systems. Their claims are not simply anti-elitist or people-centred: these parties defy the tenets of democratic liberalism by presenting themselves as the only ‘true’ representatives of the ‘pure’ people. Southern Europe is no exception. Five-stars movement, Podemos and Syriza have all hit the headlines in recent years with their unique kind of rhetoric. Against this background, Portugal stands out as the only southern European country where traditional mainstream parties have not had to face the strong rise of populist challengers. Yet, this does not imply that the political rhetoric has not been affected by the populist zeitgeist. This paper starts from the premise that it is more meaningful to talk of degrees of populism (Jagers & Walgrave, 2007) and that elements of the populist rhetoric can be systematically detected in the political
Higher Education Curriculum in a Global Village

Technology has become ubiquitous in the twenty-first century learning. Students in higher education are learning collaboratively across the globe as technology has made it possible for university students to blur boundaries of borders. It is imperative in the twenty-first century for a higher education curriculum to have technology embedded throughout. The purpose of this study is to explore technology in a higher education curriculum where academics have different views. The study draws on technology in higher education from three fundamental perspectives: technology critics’ perspective, technology enthusiasts’ perspective and technology from an intellectual perspective. The study ends with a con - clusion that was drawn from the three main perspectives of technology. The author clearly supports the view of technology from an intellectual point of view as it enhances students’ learning experiences.
Increased Cyclist Safety Using an Embedded System

Increased Cyclist Safety Using an Embedded System Matthew Ryan Heydorn Department of Electrical and Computer Engineering, BYU Master of Science In order to reduce bicycle-vehicle collisions, we design and implement a cost effective embedded system to warn cyclists of approaching vehicles. The system uses an Odroid C2 single board computer (SBC) to do vehicle and lane detection in real time using only vision. The system warns cyclist are warned of approaching cars using both a smartphone app and an LED indicator. Due to the limited performance of the Odroid C2 and other low power and low cost SBCs, we found that existing detection algorithms run either too slowly or do not have sufficient accuracy to be practical. Our solution to these limitations is to create a custom fully convolutional network (FCN) which is small enough to run at real time speeds on the Odroid C2 but robust enough to have decent accuracy. We show that this FCN runs significantly faster than Tiny YOLOv3 and MobileNetv2 while getting similar accuracy when all are trained on a limited dataset. Since no dataset exists that separates the fronts of vehicles from other poses and is in the context of city and country roads, we create our own. Creating a dataset to train any detector has traditionally been time consuming. We present and implement a way to efficiently do this using minimal hand annotation by generating semi-synthetic images by cropping relatively few positive images into many background images. This creates a wider background class variance than would otherwise be possible.
Model-guided empirical optimization for memory hierarchy

We are facing an increasing performance gap between processor and memory speed on today's architectures. To bridge this performance gap, various architectural features, such as SIMD and scalar registers, and multiple levels of cache, are commonly found on today's high-performance computers. Exploiting such features and managing their complex interactions pose a serious challenge for software running on these architectures to achieve the best performance. Therefore, it has been increasingly difficult for compilers to statically select the best optimizations among a large number of code transformations and parameter choices. The result is that compiler-optimized codes often achieve performance well below the best manually-tuned codes. Moreover, today's compilers are ineffective in transforming complex loop nests. Existing approaches are either complicated to apply or difficult to integrate with other loop transformations. As a result, compilers often cannot generate code with the same quality as manually tuned when optimizing for such loop nests. We propose in this dissertation a new compiler approach for optimizing for the complete memory hierarchy. Our approach combines compiler analyses and models with guided empirical search to take advantage of their complementary strengths. The analyses and models limit the search to a small number of candidate optimized codes, and the empirical results provide the most accurate information to the compiler to select among candidates and tune optimization parameter values. This research makes the following contributions. First, to support complex loop constructs, we develop a loop transformation framework that can automatically generate high-quality codes. Second, we combine this framework with required analyses and optimization strategies targeting multiple levels of the memory hierarchy. To facilitate the empirical search, each code variant generated from compiler analyses is expressed as a script, in which transformation parameters like tile sizes can be adjusted. Then the transformed code is generated from the script and run on the target machine empirically. Finally, we have implemented the above compiler framework. Experimental results on the Pentium M and SGI R10000 show that our approach can achieve performance comparable with the best manually-tuned codes, and significantly better (up to 11x speedup) than existing compiler approaches.
Chaos based Secure Storage and Transmission of Digital Medical Images

With the rapid and progressive development of medical images and data exchange in digital formats, information security and image encryption are becoming more important in data storage and transmission. Because of the widespread use of medical images in hospitals and healthcare communities, it is important to protect them from unauthorized access. In this paper, we propose a medical image encryption algorithm based on a chaotic technique. A chaotic logistic map is used to scramble the pixel values of a medical image. In order to achieve a high level of security, a simplified version of the Data Encryption Standard (DES) is used. In order to verify the higher level of security and performance with the proposed algorithm, an experiment analysis has been performed.
SqueezeBall: Enabling Intuitive Copy and Paste using Soft Ball-Type Device as Mobile Clipboard

This paper presents a novel interface driven by squeezing forces, and its application as a mobile clipboard for intuitive copy-and-paste operation using soft ball-type device, termed SqueezeBall. We designed and developed a ball-shaped interface with handheld size. Sensors and communication modules are embedded in the soft ball to react to the squeezing force of the user and to interact with multiple devices. The user can conduct a copy-and-paste operation among the several devices more intuitively by squeezing and rolling it by hands.
AC Power Flow Data in MATPOWER and QCQP Format: iTesla, RTE Snapshots, and PEGASE

In this paper, we publish nine new test cases in MATPOWER format. Four test cases are French very high-voltage grid generated by the offline plateform of iTesla: part of the data was sampled. Four test cases are RTE snapshots of the full French very high-voltage and high-voltage grid that come from French SCADAs via the Convergence software. The ninth and largest test case is a pan-European ficticious data set that stems from the PEGASE project. It complements the four PEGASE test cases that we previously published in MATPOWER version 5.1 in March 2015. We also provide a MATLAB code to transform the data into standard mathematical optimization format. Computational results confirming the validity of the data are presented in this paper.
Semantic Concept and Weighted Visual Feature Based Image Retrieval

The performance of content-based image retrieval is degraded because of the existence of the semantic gap. In order to address this drawback, this paper proposes an image retrieval method fusing with the semantic concept and the weighted visual feature. In the approach, each image is segmented using the normalized cut (N-cut), and the visual characteristics are extracted. After that, the semantic concept is achieved based on the mapping from keywords to image low-level characteristics. The distinctive proportion of the same concept in different images may lead to the priority retrieval of the image which has the same similarity and smaller correlated regions with others. Therefore, we use the weighted visual features to sort the retrieved images. Extensive experiments show the retrieval performance of the proposed method is superior to the traditional content-based image retrieval methods.