Miracle-dz/UniFAR-FTUs huggingface dataset:
A Critical Evaluation of Performance Measurement Models in Saudi Arabian Charities

Performance measurement has a pivotal role in developing and improving a performance of an organization, a great deal of previous research into performance measurement has focused on initiating and developing models and frameworks to successfully carry on this task. However, there remains a paucity of evidence on the adequate models that could apply to the non-profit sector. This thesis enriches the existing literature in a distinctive respect, as it provides a holistic view of performance measurement models in various sectors and the critical success factors that influence them. In addition, this study specifically investigates the approaches that Saudi charity organizations conduct their performance evaluation and examines the alternative means that could be appropriate to measure the performance. The result of the thesis theoretical base: Governance Theory highlight the interchange and connection between non-profit governance theory that presents standards and best practices and the performance measurement as a key role of the charity board of directors. The empirical part of this study thoroughly describes the performance measurement practices in Saudi charities, by utilizing a quantitative approach and a questionnaire, the discussion of results provides the researcher with important insights not only to the Saudi charities practices of the evaluation performance but also to the different management aspects of their organizations. The findings of the quantitative and statistical analysis highlight significant features of this kind of organizations, as well their critical factors, challenges and the current attempts to encounter these difficulties, and the advanced plans to develop and improve the charities. The discussion and findings of the examining the attitudes towards research queries demonstrate that the charities are prepared and capable to achieve excellent and modern performance measurement models.
Empirical Research on Usage Intentention and Behavior of "Toutiao" Users

In recent years, the algorithm recommendation system has received attention and respect from the Internet industry. Today, as the representative platform of the algorithm recommendation technology news client, "Toutiao" has risen rapidly in the Internet information platform industry. Based on the technology acceptance model (TAM), this study incorporates the "cognitive entertainment" variables to reconstruct the theoretical models needed for this study. Through statistical analysis of the information obtained from the data research of "Toutiao" users, the purpose is to explore the factors influencing the intention of "Toutiao" users and to find the relationship between the intention of usage and the behavior of usage. Through research, it is found that the popularity of "Toutiao" is currently high among the people who use the new media information platform to obtain information in China, and there is a strong stickiness with users. From the perspectives of users' gender, age, education, etc., there are significant differences in intention and behavior of usage. The perceived usefulness has a significant correlation with intention of usage, and intention of usage has a significant influence on behavior of usage. Keywords—algorithm recommendation; "Toutiao"; technology acceptance model; usage intention; usage behavior
Emotion Classification Using Combinations of Texture Descriptors

We present an automated new approach for facial expression recognition of seven emotions. The main objective of this thesis is building a model that can classify the spontaneous facial expressions, rather than the acted ones, and apply this model images and videos. Moreover, we will investigate if a combination of more than one image feature descriptor will improve the classification rate, and the efficacy of the texture descriptors on videos sequences. Three types of texture features from static images were combined: Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG) and Dense Speeded Up Robust Features (D-SURF). The resulting features are classified using random forests. The use of random forests allows for the identification of the most important feature types and facial locations for emotion classification. Regions around the eyes, forehead, sides of the nose and mouth are found to be most significant. We classified the important features with random forest and Support Vector Machines. We also found that the classification performance became better than using all of the extracted facial features. We achieved better than state-of-the-art accuracies using multiple texture feature descriptors. Current emotion recognition datasets comprise posed portraits of actors displaying emotions. To evaluate the recognition algorithms on spontaneous facial expressions, we introduced an unposed dataset called the “Emotional Faces in the Wild” (eLFW), a citizen-labelling of 1310 faces from the Labelled Faces in the Wild data. To collect this data, we built a website and asked citizens to label photos according to the emotion displayed. The citizens were also asked to label a selection of KDEF faces. We evaluated the common misclassification of the faces, similar to what people do; machine algorithms perform worst regarding distinguishing between sad, angry and fearful expressions. We describe a new weighted voting algorithm for multi-calcification, in which the predictions of the classifiers trained on pairs of classes are combined with weights learned using an evolutionary algorithm. This method yields superior results, particularly for the hard-to-distinguish emotions. The method was applied to the DynEmo video database. We investigated some methods to smooth the classifier predictions in order to exploit temporal continuity emotions and therefore classification error. Several smoothing techniques were investigated and optimised, and we found that the simple moving average and linear fit Lowess smoothing performed best.
R programs for splitting abridged fertility data into a fine grid of ages using the neural network method

Technical reports of the Max Planck Institute for Demographic Research receive only limited review. Views or opinions expressed in technical reports are attributable to the authors and do not necessarily reflect those of the Institute. Konrad-Zuse-Strasse 1  D-18057 Rostock  Germany  Tel +49 (0) 3 81 20 81 0  Fax +49 (0) 3 81 20 81 202  www.demogr. mpg.de Max-Planck-Institut für demografische Forschung Max Planck Institute for Demographic Research
Smart I/O Modules for Mitigating Cyber-Physical Attacks on Industrial Control Systems

Cyber-physical systems (CPSs) are implemented in many industrial and embedded control applications. Where these systems are safety-critical, correct and safe behavior is of paramount importance. Malicious attacks on such CPSs can have far-reaching repercussions. For instance, if elements of a power grid behave erratically, physical damage and loss of life could occur. Currently, there is a trend toward increased complexity and connectivity of CPS. However, as this occurs, the potential attack vectors for these systems grow in number, increasing the risk that a given controller might become compromised. In this article, we examine how the dangers of compromised controllers can be mitigated. We propose a novel application of runtime enforcement that can secure the safety of real-world physical systems. Here, we synthesize enforcers to a new hardware architecture within programmable logic controller I/O modules to act as an effective line of defence between the cyber and the physical domains. Our enforcers prevent the physical damage that a compromised control system might be able to perform. To demonstrate the efficacy of our approach, we present several benchmarks, and show that the overhead for each system is extremely minimal.
Flow insensitive relational static analysis

Static analysis of a program allows to predict the properties of its executions without actually executing the program. Abstract interpretation provides the mathematical theory to design such analysis. In particular, it helps design relational analyses, which keep track of the relations between variables. These analyses are costly because they usually require computations at every program points (they are flow-sensitive). To reduce these computations, we design a flow insensitive static analysis that can provide a relational invariant on the variables. This invariant is global, there is only one for the program analysed, but it must have the same precision as if we used a flow-sensitive one. A specific representation of the program, namely the Static Single Information (SSI) form, allows us to preserve precision thanks to the indexing of the variables. This report presents the concepts of abstract interpretation and the SSI form then details the designed analysis, ensuring its soundness at each step.
Static duplicate bug-report identification for compilers

Compiler bug reports are important for guaranteeing compiler quality; however, duplicate bug reports tend to incur extra costs. To identify duplicate bug reports for compilers, we propose a static approach (IdenDup) to identifying duplicate bug reports for compilers. This method effectively identifies duplicate bug reports for compilers in two scenarios (fuzz testing and the bug-management system) by utilizing static text and program information, including lexical features, syntax features, and proposed dataflow features that describe variable-usage path features (i.e., how variables are used and their order). We conducted empirical evaluations of the effectiveness of IdenDup based on the use of GCC and LLVM, with our results demonstrating that IdenDup effectively identified duplicate bug reports in the two scenarios for compilers and outperformed existing approaches.
Binaural Direct-to-Reverberant Energy Ratio and Speaker Distance Estimation

This article addresses the problem of distance estimation using binaural hearing aid microphones in reverberant rooms. Among several distance indicators, the direct-to-reverberant energy ratio (DRR) has been shown to be more effective than other features. Therefore, we present two novel approaches to estimate the DRR of binaural signals. The first method is based on the interaural magnitude-squared coherence whereas the second approach uses stochastic maximum likelihood beamforming to estimate the power of the direct and reverberant components. The proposed DRR estimation algorithms are integrated into a distance estimation technique. When based solely on DRR, the distance estimation algorithm requires calibration where naturally the critical distance is a good calibration point. We thus propose two approaches for the calibration of the distance estimation algorithm: Informed calibration using the critical distance of the reverberant room and blind calibration using the listener's own voice. Results across various acoustical environments show the benefit of the proposed algorithms for the estimation of sound source distances up to 3 m with an estimation error of about 35 cm using informed calibration and about 1 m using the fully blind calibration strategy.
Gaussian Product Sampling for Rendering Layered Materials

To increase diversity and realism, surface bidirectional scattering distribution functions (BSDFs) are often modelled as consisting of multiple layers, but accurately evaluating layered BSDFs while accounting for all light transport paths is a challenging problem. Recently, Guo et al. [GHZ18] proposed an accurate and general position‐free Monte Carlo method, but this method introduces variance that leads to longer render time compared to non‐stochastic layered models. We improve the previous work by presenting two new sampling strategies, pair‐product sampling and multiple‐product sampling. Our new methods better take advantage of the layered structure and reduce variance compared to the conventional approach of sequentially sampling one BSDF at a time. Our pair‐product sampling strategy importance samples the product of two BSDFs from a pair of adjacent layers. We further generalize this to multiple‐product sampling, which importance samples the product of a chain of three or more BSDFs. In order to compute these products, we developed a new approximate Gaussian representation of individual layer BSDFs. This representation incorporates spatially varying material properties as parameters so that our techniques can support an arbitrary number of textured layers. Compared to previous Monte Carlo layering approaches, our results demonstrate substantial variance reduction in rendering isotropic layered surfaces.
IT-leveraged network value cocreation: a case study of the value cocreation process and value capture in the South Korean broadcast advertising industry

ABSTRACT Organisations operate in increasingly dynamic environments, internetworked in cooperative arrangements that cocreate value. They pursue the cocreation of value through collaborative networks, rather than in isolation. However, there is insufficient understanding of how networked organisations cocreate value in the network through the innovative use of information systems (IS): existing multi-firm studies are largely concerned with dyadic relationships. In terms of capturing value from the cocreation process, many studies have reported either enhanced organisational efficiencies (exploitative capability) or resulting innovations (explorative capability), but rarely both aspects simultaneously. This study attempts to draw a comprehensive picture of IS-based value leverage by reporting both the value cocreation process (network level) and value capture (organisational level) through a case study of innovative IS use in a large business network. This network links a public hub organisation with a large number of firms in the South Korean broadcast advertising industry. Drawing on Grover and Kohli’s framework for value cocreation, the case study investigates the detailed process by which network-level value cocreation occurs in the four layers of relational arrangements. It also highlights, from an organisational ambidexterity perspective, how the cocreated value is appropriated through ambidextrous activities by the networked organisations.
Emerging business models for the cultural commons. Empirical evidence from creative cultural firms

ABSTRACT Cultural assets can be seen as a particular type of commons as they belong to a group or society that inherits them from past generations, maintains them in the present and preserves them for the benefit of future generations. The cultural commons are exposed to specific forms of the tragedy; however, little is known about how the tragedy of cultural commons can be managed and overcome. This study argues that cultural and creative firms could play a pivotal role in addressing this issue, and it inductively develops a business model for Cultural and Creative Firms to manage cultural assets as commons that are threatened with neglect or misuse. In doing this, a multiple-case study is employed to develop a new business model. This study contributes to knowledge about how creative and cultural firms can implement cooperative and participative business models to address the fragility of cultural commons.
Video-based learning assistant scheme for sustainable education

ABSTRACT Recently, owing to the development of information technology, there have been many changes in learning activities and equipment. For instance, compared with traditional textbook-based learning, e-learning has freed learners from spatial-temporal restrictions, and other educational media are replacing textbooks. Specifically, educational videos have become very popular, as they are effective at conveying their content or meaning. Furthermore, their accessibility has been improved because of the emergence of platforms that enable the sharing and delivery of such videos. However, when learners use such platforms, they tend to face difficulty in finding videos they want, as these platforms have a lot of videos. They also experience the inconvenience of performing additional searches because of the lack of information relevant to the video. To solve these problems, we propose a learning assistant system for educational video platforms. Our system helps learners find videos based on their readability level and contents. Moreover, it provides learners with useful information such as keywords and named entities for further learning activities. To demonstrate the effectiveness, we implemented a prototype system on a website dealing with speech videos and performed various experiments. By reporting some results, we show that our scheme can be used to implement sustainable education.
Fog - Applying blockchain to secure a distributed set of clusters

5G has already been presented and shown in major congresses, thus it’s just a matter of time that this technology comes to the enterprise and public usages. Therefore, we can affirm that we are moving towards a world where everything will be connected (i.e. our cars, our houses, our wearable devices). Consequently, the number of devices connected to the Fog will be around the billions. This drastic increase in connected devices at the Fog layer promotes a change in the Internet architecture, that requires new technologies to manage the newly Fog devices. Despite the cloud being a powerful model, at the end it’s a centralized architecture, thus it fails to scale with the addition of millions of Fog devices. The Fog presents a great amount of architectural and management challenges, such as who will run the Fog infrastructure, or how the Fog nodes will provide services by themselves, and a sometimes forgotten but critical aspect, the security. The current centralized security architectures do not scale well enough in order to be applied on the Fog. Those models such as Certificate Authorities (CA’s) are centralized, usually on cloud providers, and offer a much more static security (i.e. a website secured with a CA, that barely changes IP and doesn’t move). At the end this kind of security approaches are invalid in environments where devices are moving and changing networks constantly, like the fog scenario, because the same nature of the approach makes it invalid for such scenarios. Therefore, we require new and completely distributed security architectures, capable of being flexible and scalable, while at the same time providing fault proof security to the Fog. A new technology that has been growing lately is the blockchain, this technology really shines on completely distributed systems. The blockchain is capable of keeping an immutable set of data distributed across multiple peers on a network. Then the peers can use that data and update it through a consensus procedure, performed following a consensus algorithm criteria. The main objective of this project is the proposal of a novel blockchain architecture that will contain all the Fog session information. This information will be used to provide security to the Fog devices, and it will be capable of providing authentication and verification mechanisms to those devices, ensuring the integrity of the data provided. To accomplish this objective a Fog profile will be used. Each new device will be able to register its profile in the Fog session, with a required set of public keys, and by providing an extensible Rule-set field that will contain all the required information in order to identify a device (i.e. the device specs such as CPU or RAM), to later on execute services on the Fog, based on such specs and information.
Design and Failure Analysis of Two-Stage Reduction Gearbox for an All-Terrain Vehicle

: The automobile sector demands an all-0Qterrain vehicle. Wherein, single seater and moderated weight are desirable by considering the service environment. In this context, high torque is being employed while designing this kind of automobile systems. Hence, two stage reduction gearbox is a vital part of transmission in order to obtain the necessary speed and torque. Henceforth, sustainable gear design is must for such application. Therefore, the present study is exposing the failure modes of the gear box considering the dynamic condition of all-terrain vehicles. Furthermore, failure has been eliminated through simulations carried out by Ansys 18.2 and KissSYS software. wherein, the obtained results of the simulation are compared with those of theoretical calculations. On the other hand, the designed gear box has been assessed on the scale of reliability while in the dynamic condition in conjunction with assembly components. Moreover, FoS is increased from 0.77 of the previous level to 2.52 existing level due to the alteration of gear material, face width, module and number of gear teeth. This, in turn, eliminates the gear hunting phenomenon resulting in premature failure, one of the reasons for the gearbox.
Comparing the impact of alliance-learning activities on alliance performance across small and large firms

ABSTRACT Firms in the high-technology industry often form alliances to access or share knowledge with their alliance partners. This paper hypothesises that small and large firms differ in their learning needs; it also develops hypotheses that examine how three different types of alliance-learning activities increasee alliance performance within small and large firms. To test the hypotheses, survey data were collected from a sample of 173 strategic alliances formed by Taiwanese high-technology firms. Structural Equation Modelling was then performed to analyse the survey data. Among the three types of alliance-learning activities, seeking knowledge about a partner and coordinating with a partner were positively related to alliance performance. Moreover, the firm size moderated such relationships: specifically, while coordinating with a partner led to the highest alliance performance for small firms, seeking knowledge about a partner led to the highest alliance performance for large firms.
A Data-Driven Cost-Effective Session-Oriented Cognitive Radio Transmission Scheme Under Spectrum Uncertainty

Due to the emerging Internet of Things (IoT) services, the spectrum shortage problem becomes more and more serious. To tackle this challenge, many research works have been conducted to employ the cognitive radio technology to exploit under-utilized spectrums for IoT services. However, the operation of a cognitive radio transmission system is usually time-energy-consuming due to the requirement on the wideband sensing and spectrum switching, which might be hardly supportable by the light-weighted IoT devices. In this paper, we propose a data-driven cost-effective session-oriented cognitive radio transmission scheme, where the bands are directly selected based on the historical data and a “transmit-wait-transmit” mode is employed to reduce the cost. For the spectrum selection, we first attempt to determine the bands with minimal total bandwidth that could make the session accomplished with certain confidence level by modeling the available duration of a band within the session period as a random variable. Then, from the historical data, we develop a distributionally robust approach, where Kullback-Leibler divergence is used to capture the distributional ambiguity. Finally, based on the real data we collected using USRP-2922, we evaluate the effectiveness of our proposed scheme.