Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors Influencing Expatriate Academics' Knowledge Sharing: A Framework

Knowledge is believed to be a strategic asset which contributes to innovation, competency and sustainable development of an organisation. Since the number of expatriate academics in Chinese universities grows unprecedently fast, there is a need to study knowledge sharing issues of this particular group. However, the existing literature fails to provide decision-makers and relevant stakeholders with a clear understanding of what factors motivate foreign academics to share knowledge or prevent them from knowledge sharing behaviours. This paper is aiming at synthesising previous literature on knowledge sharing in the business field, knowledge sharing of academics, and knowledge sharing of expatriates to build the theoretical foundation that underpins future researches on the emerging topic.
Operationalizing the integration of user interaction specifications in the synthesis of modeling editors

A long shortcoming in the automatic generation of domain-specific modeling (DSM) editors has been the lack of user experience, in particular, user interaction adapted to its user. The current practice relies solely on the abstract and concrete syntax of the language, restricting the user interaction with the editor to a set of generic interactions built-in the tool. To increase the user experience with DSM editors, we propose to specify the different viewpoints of interactions (e.g., I/O devices, component of the interface, behavior of the editor) each modeled at the right level of abstraction for its user expert. The goal of this paper is to demonstrate the feasibility of the approach, by anchoring the operational semantics of all these viewpoints in a Statecharts model that controls the DSM editor. We report on the complex transformation that takes as input different viewpoints are expressed in at distinct levels of abstraction, to produce a custom interaction with the DSM editor based on a RETE algorithm. Our implementation shows that we can produce correct and responsive results by emulating existing DSM editors.
An approach for persistent time-varying values

As reactive systems, such as cyber-physical systems and the Internet of Things, become increasingly important, time-varying values, also known as signals, are playing an important role in software development. Although reactive systems require the change histories of some signals to be stored for various purposes such as post analysis and simulation, current programming languages do not provide a way to declare that signals are persistent. This paper proposes a method that realizes persistent signals in a reactive programming language, where (1) every update to each persistent signal is recorded in a time-series database, which can be seen as a part of the programming language runtime; and (2) persistent signals support a convenient time-oriented query mechanism. In this approach, each signal in the reactive programming language is seamlessly connected with the time-series database. This method is implemented as an extension of SignalJ, a Java-based reactive programming language that supports signals. In the implementation, the persistent signal mechanism is integrated with TimescaleDB, a PostgreSQL-based time-series database. In preliminary performance evaluations, our implementation had good responsiveness on most tests, indicating its feasibility for use in many applications.
Detection of False Weibo Repost Based on XGBoost

This paper focuses on the false repost problem in Weibo, and the XGBoost classification algorithm is introduced to detect the true or false weibo repost and compared with some classic approaches and neural network. The model results show that XGBoost algorithm has higher classification performance than support vector machine, random forest, logistic regression and MLP. The two most important classification features of false repost are the number of weibos and the number of followers.
Interaction can hurt - Exploring gesture-based interaction for users with Chronic Pain

Chronic Pain is a universal disorder affecting millions of people, influencing even the most basic decisions in their lives. With the computer becoming such an integral part of our society and the ever-expanding interaction paradigm, the need to explore potential computer interactions for people with Chronic Pain has only increased. In this paper we explore the used of gesture-based interaction as a medium with which these users can perform the base operations of computer interaction. We show that, for gestural pointing and selection, modeling users’ interaction space and multimodel interaction performed the best in terms of throughput.
A symmetry-based n-body solver compiler

N-body simulation is a classic application in high-performance computing. Although compiler optimizations designed for other classics such as matrix multiplication and FFT were well developed, the ones designed for N-body problems have been little studied. Hand optimizations for N-body solvers are thus rampant. To remedy this complication, we design a domain-specific language (DSL) embedded in Python for describing N-body problems and develop a DSL compiler that exploits the symmetric nature of N-body problems. It brings efficient code comparably with hand-written code.
Alleviating Irregularity in Graph Analytics Acceleration: a Hardware/Software Co-Design Approach

Graph analytics is an emerging application which extracts insights by processing large volumes of highly connected data, namely graphs. The parallel processing of graphs has been exploited at the algorithm level, which in turn incurs three irregularities onto computing and memory patterns that significantly hinder an efficient architecture design. Certain irregularities can be partially tackled by the prior domain-specific accelerator designs with well-designed scheduling of data access, while others remain unsolved. Unlike prior efforts, we fully alleviate these irregularities at their origin---the data-dependent program behavior. To achieve this goal, we propose GraphDynS, a hardware/software co-design with decoupled datapath and data-aware dynamic scheduling. Aware of data dependencies extracted from the decoupled datapath, GraphDynS can elaborately schedule the program on-the-fly to maximize parallelism. To extract data dependencies at runtime, we propose a new programming model in synergy with a microarchitecture design that supports datapath decoupling. Through data dependency information, we present several data-aware strategies to dynamically schedule workloads, data accesses, and computations. Overall, GraphDynS achieves 4.4× speedup and 11.6× less energy on average with half the memory bandwidth compared to a state-of-the-art GPGPU-based solution. Compared to a state-of-the-art graph analytics accelerator, GraphDynS also achieves 1.9× speedup and 1.8× less energy on average using the same memory bandwidth.
TunnelScatter: Low Power Communication for Sensor Tags using Tunnel Diodes

Due to extremely low power consumption, backscatter has become the transmission mechanism of choice for battery-free devices that operate on harvested energy. However, a limitation of recent backscatter systems is that the communication range scales with the strength of the ambient carrier signal~(ACS). This means that to achieve a long-range, a backscatter tag needs to reflect a strong ACS, which in practice means that it needs to be close to an ACS emitter. We present TunnelScatter, a mechanism that overcomes this limitation. TunnelScatter uses a tunnel diode-based radio frequency oscillator to enable transmissions when there is no ACS, and the same oscillator as a reflection amplifier to support backscatter transmissions when the ACS is weak. Our results show that even without an ACS, TunnelScatter is able to transmit through several walls covering a distance of 18m while consuming a peak biasing power of \SI57 \micro\watt. Based on TunnelScatter, we design battery-free sensor tags, called TunnelTags, that can sense physical phenomena and transmit them using the TunnelScatter mechanism.
Mind the gap: lessons learned from translating grammars between MontiCore and Xtext

Model-driven systems engineering relies on software languages that support different stakeholders. These languages often operate in different technological spaces. Checking consistency, tracing, and change propagation of models developed by different stakeholders, thus demands methods to bridge the gaps between these spaces. Research on the integration of heterogeneous software languages often considers heterogeneity within specific technological spaces only. We outline a systematic method to translate grammars between the technological spaces of the MontiCore and Xtext language workbench (LWB) and report observations on general grammar translation challenges. We have realized this translation in an automated toolchain and present lessons learned along the way. This can significantly facilitate bridging different technological spaces and, thus, improve model-driven systems engineering
Hosting OpenMP programs on Java virtual machines

To leverage existing virtual machine infrastructures is attractive for programming language implementors because competitive runtime performance may be achieved with a reduced effort. For example, the Truffle framework has enabled Ruby (TruffleRuby), and C (Sulong)guest language implementations to be hosted on a Java Virtual Machine(JVM). In this paper, we present Sulong-OpenMP, the first Truffle-based implementation to support parallel programs written in C/C++ and OpenMP. Our implementation adds OpenMP support to Sulongthat executes LLVM Intermediate Representation (LLVM IR) for C/C++ programs on a JVM. We outline the challenges faced in supporting OpenMP execution semantics, and the current limitations of Sulong-OpenMP. The geometric mean overhead of 1 thread Sulong-OpenMP compared to sequential Sulong execution was 2.6% for the NAS Parallel Benchmark suite, at peak runtime performance. Although this paper focuses on the correctness of our implementation concerning the OpenMP memory model, we also highlight the diminishing performance gap between the native execution with clang -O2 and our Sulong-OpenMP as only 1.2x in the best case using 4 OpenMP threads.
Developing Best Practices for Air Quality Sensor Deployments Through Testing

Lower cost air quality sensors have the potential to provide air pollution information at higher spatial resolutions than traditional monitoring. Such information could be used to evaluate project and planning impacts plus disparities in air pollution health impacts across populations. However, lower cost air quality sensors have technical limitations, hardware sustainability considerations, and resource issues associated with deploying and managing a network in the public right of way. To help improve understanding about the uses and limitations, the City of Portland Bureau of Planning and Sustainability tested three types of low cost air quality sensor devices across various field and lab deployments in collaboration. The project supported multi-disciplinary partnership development between the City, Oregon Department of Environmental Quality (DEQ), Portland State University (PSU) researchers, Green Electronics Council (GEC) and multiple private sector partners. This paper details findings about sensor performance in various deployment configurations. Results highlight challenges and recommendations that can be used to improve interoperability for air quality sensor networks. Findings about maintenance planning and sensor sustainability to minimize electronics waste are also discussed. Through this testing design, it was established that lower cost air quality sensors are capable of measuring local roadside pollution signals in addition to urban background pollution. Establishing this pattern is key to using sensors to support additional planning and assessment questions.
Packet Access Control Mechanism Based on Cipher Identification in Software-defined Network

Software defined networking (SDN) decouples the controller plane from the data plane, offering flexible network configure and management. Because of this architecture, the SDN network is vulnerable to threats caused by user identity forgery, such as illegal intrusion and DDoS attacks. In this paper, we propose a control and forwarding mechanism based on cipher identification in SDN. All packets are encapsulated with cipher identification and signed by private keys based on cipher identification. In order to prevent the forged packets, mechanism verifies the signature at the entrance and exit of the network. The cipher identifier is designed as a matching field recognized by the SDN switch, and the network forwarding behavior is defined based on the cipher identifier.
Learning and Predicting User Pairwise Preferences from Emotions and Gaze Behavior

Most people believe that their choices are based only on a rational analysis of available alternatives. Actually, emotions affect our choices and are activated as feedback during the decision process. In Recommender Systems research, preference learning is an important element to produce good recommendations. Preferences can be acquired by explicitly asking the user or by analyzing his implicit feedback that can be inferred from his behavior. Following the latter vein of research, we developed a novel approach to automatically extract these preferences by analyzing the gaze behavior and the facial expressions of the users while looking at item’s images form making a choice between them. Specifically, we focused on pairwise image comparisons in a preference elicitation experiment and exploited a Process Mining approach to learn preferences. In particular, we investigated whether behavior patterns can be learned and used to predict the user choice. We evaluated strategies based on gaze behavior and experienced emotions and results obtained so far show promising prediction performance.
A vision of miking: interactive programmatic modeling, sound language composition, and self-learning compilation

This paper introduces a vision of Miking, a language framework for constructing efficient and sound language environments and compilers for domain-specific modeling languages. In particular, this language framework has three key objectives: (i) to automatically generate interactive programmatic modeling environments, (ii) to guarantee sound compositions of language fragments that enable both rapid and safe domain-specific language development, (iii) to include first-class support for self-learning compilation, targeting heterogeneous execution platforms. The initiative is motivated in the domain of mathematical modeling languages. Specifically, two different example domains are discussed: (i) modeling, simulation, and verification of cyber-physical systems, and (ii) domain-specific differentiable probabilistic programming. The paper describes the main objectives of the vision, as well as concrete research challenges and research directions.
'This was a great project!': reflections on a 'successful' mental health promotion project in a remote Indigenous school

Issue addressed: The complexities encountered in an Indigenous community when a white project support team assisted a school (Bwgcolman on Palm Island, Queensland) to implement MindMatters, a centralised, national project aiming to promote the psychosocial health of young Australians through the development of a comprehensive, school- based mental health promotion program. Approach: The MindMatters consortium offered pilot schools curriculum materials, professional development for staff, funding and ongoing support at a local level in return for their participation in the project. The support team flew to the island on two occasions to provide support. Conclusion: Whether or not MindMatters constituted a community project at Bwgcolman is debatable. Nevertheless, the project at Bwgcolman was considered a 'success' by key players since initial aims identified by the school were tangible (eg, professional development, curriculum development) and met in a way that the school could take ownership of. Additionally, behavioural management policy was implemented in a manner that was cognisant of a history of coercive relations with Indigenous communities. So what? : It is important in the telling of the success story at Bwgcolman that even though MindMatters endeavoured to be culturally sensitive, it was nevertheless a centralist mental health promotion program. Future mental health promotion initiatives need to be aware that the approach of the support team in attempting to hand back some community control at the local level may have played a role in the school succeeding. (author abstract)
Targeted Nanoparticles for Cancer Therapy

The World Health Organization (WHO) estimates that 84 million people will die of cancer between 2005-2015 [1]. According using the WHO mortality database it has been estimated the total number of cancer deaths in the European Union (EU) and in 2012 is predicted to be 1283101, of which 717398 men and 565703 women [2]. The most common types of cancer that will be diagnosed are lung (C33-C34), intestine (colon and rectum; C18C21) and prostate (C61) for men, and breast (C50), intestine (C18-C21) and lung (C33-C34) for women.
The socio-emotional impact of malnutrition : a comparison of infants hospitalised for malnutrition and infants hospitalised for surgical reasons.

The present study looks at the socio-emotional impact of malnutrition. This is done by comparing infants who are hospitalised for malnutrition, and infants hospitalised for surgical reasons. Twenty infants were selected from the medical ward (malnutrition ward), and twenty from the surgical ward were selected as a control group. Observation was used as a method of collecting data on socio-emotional behaviour. The results of analysis of variance indicated that there was a significant difference between the two groups of children in social and emotional responsiveness. Malnourished children showed less responsiveness, which was suggested by less activity, less affect, and low reactions to situations compared to the control group. The results also indicated that females are more sensitive than males. The results of this study invite possibilities for further investigations.
Perinatal Mesenchymal Stem Cell Banking for Umbilical Cord Blood Transplantation and Regenerative Medicine

Traditionally, bone marrow ( BM ) and mobilized peripheral blood ( MPB ) have been the predominant source of autologous and allogeneic hematopoietic stem cell s ( HSC s) for transplantation of patients diagnosed with BM and blood disorders. Over the past several decades, however, umbilical cord blood ( UCB ) has been established as an alternative source to BM and MPB for HSC transplantations. HSCs found in the human UCB have demonstrated clinical benefi t, as exemplifi ed by their use in over 20,000 transplantations worldwide in humans for the treatment of a host of hematopoietic and nonhematopoietic diseases. As a result of their clinical potential, over the past 20 years, UCB from newborns have been cryopreserved and banked, both publicly and privately, for clinical use. However, the transplantation of UCB cells is limited by the fi nite number of derivable HSCs from each unit. Strategies to overcome this shortcoming have included ex vivo expansion of HSCs, as well as, multiple UCB unit transplantations, both with limited success. Specifi cally, there has been no clinical evidence to date for the successful ex vivo expansion of HSCs, and multiple UCB unit transplantations are limited by unit quality, availability, and the appropriate HLA match. A more realistic path to overcome the clinical limitations of UCB is the cotransplantation of mesenchymal stem cell s ( MSC s), specifi cally those derived from the umbilical cord (UC) tissue, along with UCB. Recent studies have shown an increase in hematopoietic engraftment when UC derived MSCs are coinfused with UCB, compared with UCB infusions alone. As a result, technologies to process and cryopreserve MSCs found within the UC tissue have been established. Thus, both MSCs and
Automated Optic Disc Localization Algorithm by Combining A Blob of Corner Patterns, Brightness and Circular Structures Models

Automatic localization of optic disc (OD) is an important step of diabetic retinopathy (DR) detection. This paper presents a methodology to locate the optic disc within the field of view (FOV) of retina image. The information taken from corner patterns of branching and cross-over points of blood vessels, the brightness and circular structure are combined to identify the location of OD. Moreover, a step of the FOV background estimation is designed to increase robustness of the proposed method due to the incomplete circular shape of OD caused by vascular tortuosity effect. The method was evaluated on the three public datasets as DIARETDB0, DIARETDB1, and MESSIDOR. The accuracy rate was 99.23%, 100%, and 99.25%, respectively. It obtained valid locations of OD in 615 out of the 619 images of the three datasets.
SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations

Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as an effective means to avoid storing zeros and performing unnecessary computation on zero elements. However, compression techniques like Compressed Sparse Row (CSR) that are widely used today introduce significant instruction overhead and expensive pointer-chasing operations to discover the positions of the non-zero elements. In this paper, we identify the discovery of the positions (i.e., indexing) of non-zero elements as a key bottleneck in sparse matrix-based workloads, which greatly reduces the benefits of compression. We propose SMASH, a hardware-software cooperative mechanism that enables highly-efficient indexing and storage of sparse matrices. The key idea of SMASH is to explicitly enable the hardware to recognize and exploit sparsity in data. To this end, we devise a novel software encoding based on a hierarchy of bitmaps. This encoding can be used to efficiently compress any sparse matrix, regardless of the extent and structure of sparsity. At the same time, the bitmap encoding can be directly interpreted by the hardware. We design a lightweight hardware unit, the Bitmap Management Unit (BMU), that buffers and scans the bitmap hierarchy to perform highly-efficient indexing of sparse matrices. SMASH exposes an expressive and rich ISA to communicate with the BMU, which enables its use in accelerating any sparse matrix computation. We demonstrate the benefits of SMASH on four use cases that include sparse matrix kernels and graph analytics applications. Our evaluations show that SMASH provides average performance improvements of 38% for Sparse Matrix Vector Multiplication and 44% for Sparse Matrix Matrix Multiplication, over a state-of-the-art CSR implementation, on a wide variety of matrices with different characteristics. SMASH incurs a very modest hardware area overhead of up to 0.076% of an out-of-order CPU core.