Reviewerly/exHarmony huggingface dataset:
PMKT: Privacy-preserving Multi-party Knowledge Transfer for financial market forecasting

While decision-making task is critical in knowledge transfer, particularly from multi-source domains, existing knowledge transfer approaches are not generally designed to be privacy preserving. This has potential legal and financial implications, particularly in sensitive applications such as financial market forecasting. Therefore, in this paper, we propose a Privacy-preserving Multi-party Knowledge Transfer system (PMKT), based on decision trees, for financial market forecasting. Specifically, in PMKT, we leverage a cryptographic-based model sharing technique to securely outsource knowledge reflected in decision trees of multiple parties, and design a secure computation mechanism to facilitate privacy-preserving knowledge transfer. An encrypted user-submitted request from the target domain can also be sent to the cloud server for secure prediction. Also, the use of decision trees allows us to provide interpretability of the predictions. We then demonstrate how PMKT can achieve privacy guarantees, and empirically show that PMKT achieves accurate forecasting without compromising on accuracy.
FSFC: An input filter-based secure framework for smart contract

Discovering vulnerabilities in smart contracts, particularly those that can be exploited, is challenging. Existing research efforts tend to focus on pre-tests or are not capable of dynamically protecting the deployed contracts without impacting on the availability of the contracts. Thus in this paper, we propose and implement a high-availability and unified input Filter-based Secure Framework for Ethereum smart Contract (hereafter referred to as FSFC). FSFC is designed to allow the deployed smart contracts to continue running normally even when faced with potential attacks (due to vulnerability exploitation). Specifically, the proposed approach allows one to dynamically identify and discard bad inputs before getting processed. In other words, the owner can protect the contract by deploying filters through FSFC, regardless of the vulnerability discovered in the deployed contract, and without suspending the contract service. We also evaluate the security of FSFC. Then, using integer vulnerability as a case study, we demonstrate how FSFC can be deployed and evaluate its utility using real-world smart contracts with known integer vulnerability. For example, a comparative summary demonstrates that in comparison to the plain Geth, FSFC only incurs minimal additional overhead for the miners and less than 2% extra gas consumption for normal users.
The Effect of Non-Monetary Rewards on Employee Performance in Massive Open Online Courses

A challenge for organizations is to increase employee performance and motivation, since the most crucial asset of every organization is manpower. Many companies and factories have started implementing online training platforms under the concept of “Massive Open Online Courses (MOOCs)” in their workplace to foster employee performance. Previously, the mobile application called “HSC MOOC” which is provided by the Halal Science Center, Chulalongkorn Univer-sity, Thailand functioned as a solution that encourages self-learning on online platforms at companies in Thailand. However, the main barrier or risk that occurs when implementing an online platform is the user’s motivation, since the dropout rate is considered as a serious issue regarding MOOCs. Thus, incentive and re-ward were added to online training programs which aimed to motivate employees. Many types of rewards were provided for employees who had met their own company’s expectations. Recently, psychology research papers have illustrated that non-monetary rewards seem to provide greater results on the side of employee’s motivation. However, not all types of non-monetary rewards provide positive impact on employee’s motivation. Therefore, the aim of this research is to present the effect of different non-monetary rewards on employee performance. &#x0D; Ninety volunteer employees from a food manufacturing company in Chiang Mai, Thailand participated in this research. The experiment was divided into two sections. The first section aimed to measure the motivation of employees which based on different non-monetary rewards. The questionnaire for measuring Valence, Instrumentality, and Expectancy variables (VIE theory) was deployed to test employee motivation in 3 different groups; “Tangible Non-Monetary Re-wards”, “Social Non-Monetary Rewards” and “Job Related Non-Monetary Re-wards”. The test consisted of 10 items using a 5-point Likert scale. The second experiment aimed to reveal which type of non-monetary reward is the most suitable for motivating employees in participating and completing the course in MOOCs. Participants in different groups were assigned to learn via MOOCs on their mobile device within a period of 30 days. Different types of non-monetary rewards were provided only for participants who had completed certain conditions in MOOCs. The overall results showed that the group of tangible non-monetary rewards reached the significant highest score on the VIE questionnaire and over 60% of participants exposed to tangible non-monetary rewards completed the course’s conditions in MOOCs.
An Empirical Study on the Identification of Driving Factors of Satisfaction with Online Learning Based on TAM*

Taking technology acceptance model (TAM) as the research framework and by means of random sampling method and questionnaire method, the author has conducted an acceptance scale survey on 340 predictive questionnaires, and made a quadratic regression analysis on the questionnaire data with the aid of SPSS24.0 analysis software, so as to discuss the perceived satisfaction of online learning, as well as the influence of behavioral attitude, perceived usefulness, perceived ease of use, and behavioral intention on the perceived satisfaction.The results shows that perceived usefulness, perceived ease of use, attitude and behavioral intention have significant positive effects on perceived satisfaction of online learning.Behavioral intention of online learning performs a partially mediating function in perceived usefulness, ease of use, attitude and satisfaction.It can be seen that online learning attitude, perceived usefulness and ease of use can positively influence online learning satisfaction through behavioral intention, while there is no significant difference between male and female on the above variables.
Normalization of face illumination with photorealistic texture via deep image prior synthesis

Illumination normalization for face recognition is of great importance when the face image is taken under a harsh lighting condition and only one single image is available. In this paper, an illumination normalization method that can preserve face identity and make the processed image have photorealistic face texture is proposed. First, an illumination regression filter is proposed to remove a large number of illumination components of the input image. Then, the accelerated proximal gradient algorithm is used for low-rank decomposition of the filtered image, which further reduces the residual illumination and noise of the face image. However, after these two operations, the face image has a peeled-off appearance. To avoid the unrealistic appearance, a deep image prior synthesis process is developed to synthesize the photorealistic face texture. To accomplish this task, the filtered image, the low-rank decomposed image, the original image and any a uniformly illuminated frontal face image as reference are used as synthetic ingredients. These ingredients, as prior knowledge, synthesize a face image that has the texture features of the reference image and retains the identity of the input image. We try to bridge the appearance gap between the synthetic ingredients and the final synthetic image by a deep neural network. Instead of training the network on a large dataset, the final synthetic image is generated by iterating the parameters of the generator-structured network according to a certain proportion of the synthetic ingredients. Systematic evaluations conducted on public databases demonstrate that the proposed method is robust to illumination with better performance than state-of-the-art methods.
Fast Quasi-Conformal Regional Flattening of the Left Atrium

Two-dimensional representation of 3D anatomical structures is a simple and intuitive way for analysing patient information across populations and image modalities.It also allows convenient visualizations that can be included in clinical reports for a fast overview of the whole structure.While cardiac ventricles, especially the left ventricle, have an established standard representation (e.g.bull's eye plot), the 2D depiction of the left atrium (LA) is challenging due to its sub-structural complexity including the pulmonary veins (PV) and the left atrial appendage (LAA).Quasiconformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the LA to place the PV and LAA in the same geometrical 2D location for different cases.Some registrationbased methods have been proposed but 3D (or 2D) surface registration is time-consuming and prone to errors if the geometries are very different.We propose a novel atrial flattening methodology where a quasi-conformal 2D map of the LA is obtained quickly and without errors related to registration.In our approach, the LA is divided into 5 regions which are then mapped to their analogue two-dimensional regions.A dataset of 67 human left atria from magnetic resonance images (MRI) was studied to derive a population-based 2D LA template representing the averaged relative locations of the PVs and LAA.The clinical application of the proposed methodology is illustrated on different use cases including the integration of MRI and electroanatomical data.
Contour-Maintaining-Based Image Adaption for an Efficient Ambulance Service in Intelligent Transportation Systems

Ambulance services play a vital role in intelligent transportation systems (ITS).In an intelligent ambulance system, the medical images can help doctors quickly and accurately understand the patients' condition during first aid.On various display devices in different kinds of ambulances, content-aware image adaption can be used to better present the medical image among different display resolutions and aspect ratios.Most existing methods mainly focus on visual protection of salient areas, such as specific organ parts of the human body, with less attention paid to the visual effect of unimportant areas.However, the human visual system is more sensitive to the edge and contour of images, which are important for ambulance services.To improve the visual effect of adapted images, a contour-maintaining-based image adaption method for an efficient ambulance service in ITS is proposed here.Firstly, the proposed method innovatively combines the weighted gradient, saliency, and edge maps into an importance map.Secondly, energy is optimized for reducing contour distortion and interruption according to the visual slope and curvature of contours and edges in non-salient areas.Finally, applying the sub-procedure of a forward seam carving method, the optimal seams can more evenly pass through the contour areas.The experimental results demonstrate that the proposed method is more effective than other similar methods.
Real-Time Pedestrian Detection Based on Improved YOLO Model

To investigate accurate and real-time pedestrian detection results is a mainstream trend in the field of intelligent security. Avoiding too many external disturbances causing the errors and omissions, in order to obtain a reliable and discriminative detection. This paper proposes a deep learning method based on improved YOLO model to efficiently detect pedestrians. It addresses two necessary above issues: (1) leverage real-time saliency region detection through surveillance camera; and (2) extract more detail discriminative feature with human parsing. The results show that our deep real-time saliency and detail discriminative feature with human parsing based on improved YOLO model, successfully learn both spatial and temporal cues, making pedestrian detection further ensures the accuracy and timeliness in practical application scenes.
Assessment of Environmental Geochemistry of Lead-Zinc Mining at Ishiagu Area, Lower Benue Trough, Southeastern Nigeria

Mining activities have long been recognized as a major source of environmental contamination associated with heavy metals and metalloids. This study evaluated the relationship between the occurrence and mining of lead-zinc sulphide ores at Ishiagu, Nigeria, and heavy metal and metalloid contamination. A comparative study of two zones in the area, with and without mining activities was also made Water, soil, stream sediment and ore samples were analyzed, after acid digestion, using atomic absorption spectrophotometer (AAS).&amp;nbsp; The concentration levels of seven heavy metals and a metalloid namely Pb, Cu, Ni, Zn, Mn, Co, Cd and as were evaluated. While the highest concentration levels of As, Co and Pb (5.20 mg/l, 0.54 mg/l and 3.40 mg/l respectively) were found in water, those of Ni and Mn (2.26 mg/l and 5.48 mg/l respectively) occurred in soil.&amp;nbsp; For Cu and Zn, highest levels of concentration (2.80 mg/l and 0.41 mg/l respectively) occurred in stream sediments. The variations in the concentration levels of these elements in varying geologic media (soil, water and sediment) indicate influence of rock types, human activities and media physiochemical characteristics. Geostatistical analyses using QQPlot, semivariogram and kriging showed normal distribution of these elements. Distribution and dispersion patterns of the heavy metals indicated increase in concentration levels in the local stream flow direction. Pb, Cu, As, Cd, Mn, and Ni concentrations had reached pollutant levels in water based on WHO standards, while Zn level is below. Since the local people use untreated surface water and groundwater for drinking and other domestic purposes, soil for farming and lead for cosmetics, long term exposure poses significant health risk for humans, animals and plants.
A TensorFlow Extension Framework for Optimized Generation of Hardware CNN Inference Engines

The workloads of Convolutional Neural Networks (CNNs) exhibit a streaming nature that makes them attractive for reconfigurable architectures such as the Field-Programmable Gate Arrays (FPGAs), while their increased need for low-power and speed has established Application-Specific Integrated Circuit (ASIC)-based accelerators as alternative efficient solutions. During the last five years, the development of Hardware Description Language (HDL)-based CNN accelerators, either for FPGA or ASIC, has seen huge academic interest due to their high-performance and room for optimizations. Towards this direction, we propose a library-based framework, which extends TensorFlow, the well-established machine learning framework, and automatically generates high-throughput CNN inference engines for FPGAs and ASICs. The framework allows software developers to exploit the benefits of FPGA/ASIC acceleration without requiring any expertise on HDL development and low-level design. Moreover, it provides a set of optimization knobs concerning the model architecture and the inference engine generation, allowing the developer to tune the accelerator according to the requirements of the respective use case. Our framework is evaluated by optimizing the LeNet CNN model on the MNIST dataset, and implementing FPGA- and ASIC-based accelerators using the generated inference engine. The optimal FPGA-based accelerator on Zynq-7000 delivers 93% less memory footprint and 54% less Look-Up Table (LUT) utilization, and up to 10× speedup on the inference execution vs. different Graphics Processing Unit (GPU) and Central Processing Unit (CPU) implementations of the same model, in exchange for a negligible accuracy loss, i.e., 0.89%. For the same accuracy drop, the 45 nm standard-cell-based ASIC accelerator provides an implementation which operates at 520 MHz and occupies an area of 0.059 mm 2 , while the power consumption is ∼7.5 mW.
Locally Guided Multiple Bi-RRT∗ for Fast Path Planning in Narrow Passages

Rapidly-exploring Random Tree Star (RRT∗) and its variants can provide a collision-free and asymptotic optimal solution for many path planning problems. However, it is inefficient for many RRT∗ based variants to rapidly find one initial solution in a clustered environment with narrow passages, since consuming high memory as well as time, due to a large number of iterations in sampling critical nodes. To overcome this problem, the paper proposes the Locally Guided Multiple Bi-RRT∗ (LGM-BRRT∗) method, which can provide a fast solution by incorporating an improved bridge-test and a novel search strategy based on local guidance. It ensures an accelerated success rate and more efficient memory utilization compared with Bidirectional RRT∗(BRRT∗), and it is easy for implementation as well. It was verified on different types of scenarios in terms of efficiency and success rate. The results demonstrated that LGM-BRRT∗ is beneficial for fast path planning in a clustered environment with narrow passages.
Modal Boolean Connexive Logics: Semantics and Tableau Approach

In this paper we investigate Boolean connexive logics in a language with modal operators: □, ◊. In such logics, negation, conjunction, and disjunction behave in a classical, Boolean way. Only implication is non-classical. We construct these logics by mixing relating semantics with possible worlds. This way, we obtain connexive counterparts of basic normal modal logics. However, most of their traditional axioms formulated in terms of modalities and implication do not hold anymore without additional constraints, since our implication is weaker than the material one. In the final section, we present a tableau approach to the discussed modal logics.
Evaluation of the complexity of treatment plans from a national IMRT/VMAT audit – Towards a plan complexity score

<h2>Abstract</h2><h3>Purpose</h3> This work aimed to characterize and compare the complexity of the plans created in the context of a national IMRT/VMAT audit. A plan complexity score is proposed to summarize all the evaluated complexity features. <h3>Materials and methods</h3> Nine complexity metrics have been computed for the audit plans, evaluating different complexity aspects. An approach based on Principal Component Analysis was followed to explore the correlation between the metrics and derive a smaller set of new uncorrelated variables (principal components, PCs). The resulting PCs were then used to calculate a plan complexity score. Plan quality was also assessed and the correlation between plan complexity, quality and deliverability investigated using the Spearman's rank correlation coefficient. <h3>Results</h3> The first two PCs explained over 90% of the total variance in the original dataset. Their representation allowed to identify patterns in the data, namely a clear separation between plans created using different technologies/techniques. The calculated plan complexity score quantified these differences. Sliding window Eclipse plans were found to be the most complex and VMAT Eclipse group presented the highest complexity variability, for the evaluated parameters. Concerning plan quality, no differences between treatment technology/technique have been identified. However, plans with larger number of monitor units tended to be associated with higher deviations between calculated and measured doses. <h3>Conclusions</h3> The proposed plan complexity score allowed to summarize the differences not only inter- but also intra-groups of technologies/techniques, paving the way for improvement of the planning strategies at the national level through knowledge sharing.
Hyper-FTT: A Food Supply-Chain Trading and Traceability System Based on Hyperledger Fabric

Building a food traceability system provides an effective way solving the problems arousing in the food safety domain. Blockchain is preferred to construct a commodity traceability system due to its innate immutability and consistency of stored data, maintained through cryptographic means and consensus mechanism. While current blockchain based traceability system just collects and stores verified commodity information in the chain, which is composed of a decentralized ledger architecture. But the information is less accurate or intact which cannot trace the real food source along the food supply-chain. We propose to establish a Hyperledger-based Food Trading and Traceability system called Hyper-FTT, by aggregating all the providers including food warehousing enterprises, food processing enterprises and food retails to reach agreements and conclude business transactions on the chain, then an unbroken food supply-chain can be formed to provide trusted food tracing. Implementation and experiments are conducted to evaluate the performance of the proposed demonstration system.
Hybrid Translation with Classification: Revisiting Rule-Based and Neural Machine Translation

This paper proposes a hybrid machine-translation system that combines neural machine translation with well-developed rule-based machine translation to utilize the stability of the latter to compensate for the inadequacy of neural machine translation in rare-resource domains. A classifier is introduced to predict which translation from the two systems is more reliable. We explore a set of features that reflect the reliability of translation and its process, and training data is automatically expanded with a small, human-labeled dataset to solve the insufficient-data problem. A series of experiments shows that the hybrid system’s translation accuracy is improved, especially in out-of-domain translations, and classification accuracy is greatly improved when using the proposed features and the automatically constructed training set. A comparison between feature- and text-based classification is also performed, and the results show that the feature-based model achieves better classification accuracy, even when compared to neural network text classifiers.
Graph-Based Relation-Aware Representation Learning for Clothing Matching

Learning mix-and-match relationships between fashion items is a promising yet challenging task for modern fashion recommender systems, which requires to infer complex fashion compatibility patterns from a large number of fashion items. Previous work mainly utilises metric learning techniques to model the compatibility relationships, such that compatible items are closer to each other than incompatible ones in the latent space. However, they ignore the contextual information of the fashion items for compatibility prediction. In this paper, we propose a Graph-based Type-Relational Neural Network (GTR-NN) framework, which first generates item representations through multi-layer ChebNet considering k-hop neighbour information, and then outputs compatibility score by predicting the binary label of an edge between two nodes under a specific type relation. Extensive experiments for two fashion-related tasks demonstrate the effectiveness and superior performance of our model.