Reviewerly/exHarmony huggingface dataset:
How Secure are our Computer Systems Courses?

Introductory computer systems courses teach students how a single program is executed inside a computer, providing them with their first exposure to the logical internals of computing systems. This is one of the first introductory courses where students can learn about security and the need for robust coding. However, currently, these courses are taught with a focus on functionality and efficiency only, ignoring security almost entirely.
Distant supervision for medical concept normalization

We consider the task of Medical Concept Normalization (MCN) which aims to map informal medical phrases such as "loosing weight" to formal medical concepts, such as "Weight loss". Deep learning models have shown high performance across various MCN datasets containing small number of target concepts along with adequate number of training examples per concept. However, scaling these models to millions of medical concepts entails the creation of much larger datasets which is cost and effort intensive. Recent works have shown that training MCN models using automatically labeled examples extracted from medical knowledge bases partially alleviates this problem. We extend this idea by computationally creating a distant dataset from patient discussion forums. We extract informal medical phrases and medical concepts from these forums using a synthetically trained classifier and an off-the-shelf medical entity linker respectively. We use pretrained sentence encoding models to find the k-nearest phrases corresponding to each medical concept. These mappings are used in combination with the examples obtained from medical knowledge bases to train an MCN model. Our approach outperforms the previous state-of-the-art by 15.9% and 17.1% classification accuracy across two datasets while avoiding manual labeling.
Feature Selection Based on Naive Bayes for Caesarean Section Prediction

Abstract Data mining using machine learning algorithms can be used to help analyze historical data to predict the need for a caesarean section. The dataset used for predicting caesarean section has many features, but those features have the possibility of redundancy or irrelevance that can cause a decrease in classifier performance. This research proposes a model that implements feature selection to select relevant features and can provide improved performance predictions for caesarean section. Some proposed feature selection techniques are Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Sequential Forward Floating Selection (SFFS), Sequential Forward Floating Selection (SBFS), Sequential Backward Floating Selection (SBFS), and selectKBest. The classification algorithm used to classify is Naive Bayes. The model that gives the best performance value is the model that applies the SelectKbest as feature selection.
Lr-Stream: Using latency and resource aware scheduling to improve latency and throughput for streaming applications

Low latency and high throughput are two of the most critical performance requirements for big data stream computing systems. As multi-source high-speed data streams arrive in real time, it is essential to study latency-aware and resource-aware scheduling to reduce latency and increase throughput. In this paper, we propose a latency- and resource-aware scheduling framework (Lr-Stream) targeting stream-oriented big data applications. Our key contributions can be summarized as follows: (1) a stream topology model and resource scheduling model Lr-Stream are proposed, aiming at optimizing latency and throughput; (2) a latency-aware scheduling strategy and a resource-aware scheduling strategy are proposed; (3) Lr-Stream together with monitor, calculator, and deployment function modules are implemented, and integrated into Apache Storm; (4) system metrics are thoroughly evaluated from latency- and resource-aware perspective on a typical distributed stream computing platform. Experimental results demonstrate that the proposed Lr-Stream yields significant performance improvements in terms of reducing system latency and increasing system throughput.
On the benefits of correcting brightness and contrast in global digital image correlation: Monitoring cracks during curing and drying of a refractory castable

The gray level conservation is the underlying hypothesis of Digital Image Correlation (DIC). However, it may be challenging to enforce in some experimental configurations. Brightness and contrast corrections (BCCs) may be added to the registration procedure. Different types of BCCs were implemented for global DIC, and their benefits were analyzed for localized and diffuse sources of brightness changes. As a case study to apply BCCs, a refractory castable was placed inside a climatic chamber, and cracks were generated due to localized expansions during its curing and drying. To choose the best BCC for this case, two sets of images were considered. The first one allowed the noise floor levels to be evaluated. The second one dealt with the development of a crack network. The BCCs significantly reduced gray level residuals enabling cracks with small openings to be detected. The coarse discretization was effective in correcting lighting changes and avoided its coupling with the measured kinematic fields and other local phenomena.
Location-Aware and Regularization-Adaptive Correlation Filters for Robust Visual Tracking

Correlation filter (CF) has recently been widely used for visual tracking. The estimation of the search window and the filter-learning strategies is the key component of the CF trackers. Nevertheless, prevalent CF models separately address these issues in heuristic manners. The commonly used CF models directly set the estimated location in the previous frame as the search center for the current one. Moreover, these models usually rely on simple and fixed regularization for filter learning, and thus, their performance is compromised by the search window size and optimization heuristics. To break these limits, this article proposes a location-aware and regularization-adaptive CF (LRCF) for robust visual tracking. LRCF establishes a novel bilevel optimization model to address simultaneously the location-estimation and filter-training problems. We prove that our bilevel formulation can successfully obtain a globally converged CF and the corresponding object location in a collaborative manner. Moreover, based on the LRCF framework, we design two trackers named LRCF-S and LRCF-SA and a series of comparisons to prove the flexibility and effectiveness of the LRCF framework. Extensive experiments on different challenging benchmark data sets demonstrate that our LRCF trackers perform favorably against the state-of-the-art methods in practice.
Semantic Approach to Data Integration for an Internet of Things Supporting Apparel Supply Chain Management

The rapid development of the Internet of Things (IoT) and the huge growth of valuable data produced by decentralising information processing along global apparel supply chain have led to a persuasive appeal for a semantic approach to integrating distributed data facilities in the field of self-determining collaborating logistics services. This paper describes a framework, Apparel Business Decentralised Data Integration (ABDDI), which exploits knowledge representation techniques and languages (e.g. Description Logics – DLs) to annotate relevant business activities, movements of products within the manufacturing network to provide value-added services. More specifically the paper discusses the DLs formalisms, which are used for knowledge representation in a decidable fragment of First Order Logic; and ALN (D) (Attributive Language with unqualified Number restrictions and concrete Domains) related issues. The paper presents an algorithm to demonstrate the DLs based entity concept similarity assessment to facilitate semantic web service. Finally, a business scenario is used to present some of the knowledge representation formalisms and concept similarity assessment in ABDDI.
Leveraging personality information to improve community recommendation in e‐learning platforms

Abstract E‐learning platforms are becoming more and more important and they are gradually changing people’s learning ways. In the e‐learning platforms, users actively create and join their favorite communities to share their questions and ideas. With the increase of users of e‐learning platforms, the number of communities is increasing dramatically. In this context, it has become difficult for users to find learning communities that match their interests and preferences. Therefore, how to effectively recommend the learning community for users has become an urgent need. However, compared to learning item recommendation, there is relatively limited work on learning community recommendation, and the existing research on community recommendation often ignores the personality information. Personality is considered one of the primary factors that influence human behavior and social relationships, as it affects how people react and interact with others. Several studies have demonstrated that people with similar personality tend to have similar interests. Furthermore, homophily theory also states that social interactions between similar individuals occur at a higher rate than among dissimilar ones. Since interests and interactions are important driving forces for users to join the learning communities, personality has an important impact on users’ choices of communities. Therefore, this paper aims at shedding some light on the impact of personality information on the accuracy of community recommendations. Particularly, we propose three enhanced matrix factorization models based on the Big Five personality framework. To evaluate the effectiveness of our proposed models, we conducted extensive experiments on myPersonality datasets. The results prove that the personality information can improve the performance of the learning community recommendation model and alleviate the data sparsity problem.
Federated Learning with Proximal Stochastic Variance Reduced Gradient Algorithms

Federated Learning (FL) is a fast-developing distributed machine learning technique involving the participation of a massive number of user devices. While FL has benefits of data privacy and the abundance of user-generated data, its challenges of heterogeneity across users’ data and devices complicate algorithm design and convergence analysis. To tackle these challenges, we propose an algorithm that exploits proximal stochastic variance reduced gradient methods for non-convex FL. The proposed algorithm consists of two nested loops, which allow user devices to update their local models approximately up to an accuracy threshold (inner loop) before sending these local models to the server for global model update (outer loop). We characterize the convergence conditions for both local and global model updates and extract various insights from these conditions via the algorithm’s parameter control. We also propose how to optimize these parameters such that the training time of FL is minimized. Experimental results not only validate the theoretical convergence but also show that the proposed algorithm outperforms existing Stochastic Gradient Descent-based methods in terms of convergence speed in FL setting.
INHIBITOR: An intrusion tolerant scheduling algorithm in cloud-based scientific workflow system

With the development of cloud computing technologies, more and more scientific workflows have been executed in clouds. However, cloud-based scientific workflows face many threats due to the resource sharing. Adversaries in clouds can directly or indirectly destroy them by means of side channels, virtual machine escape, and so on, which will cause interruption or produce incorrect outputs. Cloud-based scientific workflows are often applied in important scientific research fields, their failures will bring huge losses. Therefore, we propose an Intrusion toleraNt scHeduling algorIthm in cloud-Based scIenTific wORkflow system (INHIBITOR) to enhance the security. This algorithm constructs three replicas for each sub-task and designs a voting mechanism to realize the result verification. Based on this framework, INHIBITOR studies how to schedule these sub-task replicas and deduces the constraints which should be satisfied for intrusion tolerant scheduling. Furthermore, an elastic resource provisioning strategy is presented to improve resource utilization. To verify the effectiveness of INHIBITOR, we conduct experiments with WorkflowSim toolkit and use success rate, task completion rate and execution costs to evaluate it. Experimental results demonstrate that, compared with existing methods, INHIBITOR can not only increase the success rate by around 12.54%, but also improve the efficiency by about 4.7% and reduce execution costs by around 11.29%.
A particular upper expectation as global belief model for discrete-time finite-state uncertain processes

To model discrete-time finite-state uncertain processes, we argue for the use of a global belief model in the form of an upper expectation that is the most conservative one under a set of basic axioms. Our motivation for these axioms, which describe how local and global belief models should be related, is based on two possible interpretations for an upper expectation: a behavioural one similar to Walley's, and an interpretation in terms of upper envelopes of linear expectations. We show that the most conservative upper expectation satisfying our axioms, that is, our model of choice, coincides with a particular version of the game-theoretic upper expectation introduced by Shafer and Vovk. This has two important implications: it guarantees that there is a unique most conservative global belief model satisfying our axioms; and it shows that Shafer and Vovk's model can be given an axiomatic characterisation and thereby provides an alternative motivation for adopting this model, even outside their game-theoretic framework. Finally, we relate our model to the upper expectation resulting from a traditional measure-theoretic approach. We show that this measure-theoretic upper expectation also satisfies the proposed axioms, which implies that it is dominated by our model or, equivalently, the game-theoretic model. Moreover, if all local models are precise, all three models coincide.
Guess Me If You Can: A Visual Uncertainty Model for Transparent Evaluation of Disclosure Risks in Privacy-Preserving Data Visualization

Minimization of disclosure risks is a key challenge in publicly available visualizations that can potentially reveal personal information. Such risks are inherently dependent on the amount of information that adversaries can gain by manipulating visual representations and by using their background knowledge. Conventional risk quantification models proposed in the field of privacy-preserving data mining suffer from a lack of transparency in letting data owners control privacy parameters and understand their implications for disclosure risks. To fill this gap, we propose a visual uncertainty model for letting data owners understand the relationships between privacy parameters and vulnerable visualization configurations. Our main contribution is a probabilistic analysis of the disclosure risks associated with vulnerabilities in privacy-preserving parallel coordinates and scatter plots. We quantify the relationship among attack scenarios, adversarial knowledge, and the inherent uncertainty in cluster-based visualizations that can act as defense mechanisms. We present examples and a case study to demonstrate the effectiveness of the model.
JomImage: Weight Control with Mobile SnapFudo

Tracking calorie of nourishment expended has become more difficult when eating outside. With numerous decisions of inexpensive food accessible, the utilization of fast food is high among Malaysians. In 2014, WHO has revealed that Malaysia is the fattest country of South East Asia. These days, there is numerous calories following applications accessible in-store to help with weight reduction. In any case, these applications may not be reasonable as it requires numerous user inputs for food logging. This study is proposed to improve the smart dieting propensity and advance weight reduction a mobile app for android. This study will involve image recognition of food to minimize user input in food logging. From the survey result, most respondents are aware of their number of body mass index but find a difficulty to track their calorie intake when dining out. The opinion concluded from the interview, it is difficult to know every fixing and how much of the culinary expert put in their meals served. In this manner, the mobile application comes out with the solution of image recognition and simple interface to ease the burden of inputs requirements that most calories following applications are battling now. As the outcome, the executed apps can offer information on the most proficient method to get thinner and propel them to accomplish their weight objective.
Identification of intrusive lithologies in volcanic terrains in British Columbia by machine learning using random forests: The value of using a soft classifier

Identifying the location of intrusions is a key component in exploration for porphyry Cu ± Mo ± Au deposits. In typical porphyry terrains, in the absence of outcrop, intrusions can be difficult to discriminate from the compositionally similar volcanic and volcanoclastic sedimentary rocks in which they are emplaced. The ability to produce lithological maps at an early exploration stage can significantly reduce costs by assisting in planning and prioritization of detailed mapping and sampling. Additionally, a data-driven strategy provides opportunity for the discovery of intrusions not identified during conventional mapping and interpretation. We used random forests (RF), a supervised machine-learning algorithm, to classify rock types throughout the Kliyul porphyry prospect in British Columbia, Canada. Rock types determined at geochemical sampling sites were used as training data. Airborne magnetic and radiometric data, geochemistry, and topographic data were used in classification. Results were validated using First Quantum Minerals’ geologic map, which includes additional detail from targeted location and transect mapping. The petrophysical and compositional similarity of rock types resulted in a noisy classification. Intrusions, particularly the more discrete, were inconsistently predicted, likely due to their limited extent relative to data sampling intervals. Closer examination of class membership probabilities (CMPs) identified locations where the probability of an intrusion being present was elevated significantly above the background. Indeed, a large proportion of mapped intrusions correspond to areas of elevated probability and, importantly, areas were highlighted as potential intrusions that were not identified in geologic mapping. The RF classification produced a reasonable lithological map, if lacking in resolution, but more significantly, great benefit comes from the insights drawn from the RF CMPs. Mapping the spatial distribution of elevated intrusion CMP, a soft classifier approach, produced a map product that can target intrusions and prioritize detailed mapping for mineral exploration.
Cryptanalysis of a Pairing-free Certificateless Signcryption scheme

Signcryption is a very useful cryptographic primitive that aims to achieve authentication and confidentiality in an efficient manner. We cryptanalyze the signcryption scheme of Wei and Ma (2019) which is claimed to be secure. Further, we propose a corresponding modification to show how their signcryption scheme can be made more secure in our proposed signcryption scheme. The security analysis is also applicable to other signcryption schemes with similar design.
Motion Prediction and Robust Tracking of a Dynamic and Temporarily-Occluded Target by an Unmanned Aerial Vehicle

A tracking controller for unmanned aerial vehicles (UAVs) is developed to track moving targets in the presence of occlusion. The controller can track moving targets based on a bounding box of the target detected by a deep neural network using the you-only-look-once (YOLO) method. The features generated from the YOLO approach relaxes the assumption of continuous availability of the feature points for applications, which facilitates estimation using an unscented Kalman filter (UKF) and the design of image-based tracking controller in this work. The challenge is that when occlusion is present, the bounding box of the moving target becomes unobtainable and makes the estimation diverge. To solve this, a motion model derived by quadratic programming is employed as a process model in the UKF, wherein the estimated velocity is implemented as a feedforward term in the developed tracking controller in order to enhance the tracking performance. Since no motion constraint is assumed for the target, the developed controller can be applied to track various moving targets. Simulations are used to demonstrate the performance of the developed estimator and controller in the presence of occlusion. Experiments are also conducted to verify the efficacy of the developed estimator and controller.
A heuristic approach for load balancing the FP-growth algorithm on MapReduce

Frequent itemset discovery is an important step in Association Rule Mining. The Frequent Pattern (FP) growth algorithm, often used for discovering frequent itemsets, cannot scale directly to today’s Big Data, especially for large sparse datasets. Hence there is a need to distribute and parallelize the FP-growth algorithm. Parallel FP-growth (PFP) is a parallel implementation of the FP-growth algorithm on Hadoop’s MapReduce execution framework. Though PFP scales to large datasets, it suffers from imbalanced load across processing units. In this paper we propose a heuristic based, lower order of complexity, load balancing strategy for the PFP algorithm, called Heuristic Based PFP (HBPFP). Our results show that HBPFP distributes the load more evenly across the Hadoop cluster nodes, runs faster than the PFP algorithm, and uses cluster resources more efficiently, especially for large sparse datasets.
Cross-Validation Approach to Evaluate Clustering Algorithms: An Experimental Study Using Multi-Label Datasets

Clustering validation is one of the most important and challenging parts of clustering analysis, as there is no ground truth knowledge to compare the results with. Up till now, the evaluation methods for clustering algorithms have been used for determining the optimal number of clusters in the data, assessing the quality of clustering results through various validity criteria, comparison of results with other clustering schemes, etc. It is also often practically important to build a model on a large amount of training data and then apply the model repeatedly to smaller amounts of new data. This is similar to assigning new data points to existing clusters which are constructed on the training set. However, very little practical guidance is available to measure the prediction strength of the constructed model to predict cluster labels for new samples. In this study, we proposed an extension of the cross-validation procedure to evaluate the quality of the clustering model in predicting cluster membership for new data points. The performance score was measured in terms of the root mean squared error based on the information from multiple labels of the training and testing samples. The principal component analysis (PCA) followed by k-means clustering algorithm was used to evaluate the proposed method. The clustering model was tested using three benchmark multi-label datasets and has shown promising results with overall RMSE of less than 0.075 and MAPE of less than 12.5% in three datasets.