Reviewerly/exHarmony huggingface dataset:
A Proposed Model to Solve Cold Start Problem using Fuzzy User-Based Clustering

In online environments, a vast amount of data is explored daily on the internet, such as news, movies, audios, and books. The interest of the target user is the greatest demand for recommender systems, and getting suitable information is a challenge. To develop a recommender system, collaborative filtering (CF) approach considers users who have similar ratings. Therefore, can compute the similarity of users when there are enough rated by users to items. A significant challenge of the collaborative filtering approach is cold start problem, which is how to make a recommendation for users who have few ratings than others. This work proposes a collaborative filtering model based on applying fuzzy c-means clustering on user's truthfulness information. A new fuzzy user-based similarity measure formula is suggested which combines user's rating with fuzzy truthfulness information using a combination coefficient. The experimental results using Movie Lens data set have shown an improvement in recommendation under sparsity and cold start condition.
Across-Sensor Feature Learning for Energy-Efficient Activity Recognition on Mobile Devices

In this paper we propose across-sensor representation learning framework for improving power-accuracy trade-off in multi-sensor human activity recognition (HAR). The goal of the study is to achieve the level of performance comparable to one of multi-sensor HAR systems by using fewer or even single sensor. Such performance is achieved by learning relations between these sensors at training time and utilizing them at test time. These relations are learned by supervised deep models which use multi-sensor data during training only. The absence of need for having multiple sensors during test time allows turning these sensors off and replacing them with a single sensor coupled with learned across-sensor relations. These across-sensor relations make up for the information lost from the turned-off sensors. Using fewer sensors reduces energy consumption of HAR systems deployed on a smartphone. Moreover, it allows building HAR systems for situations when collection of multi-sensor data is possible only during training. This work presents preliminary results achieved with the proposed approach on the SHL dataset. Obtained results show an improvement of up to 14% in classification accuracy of single-sensor HAR.
Parsimonious generalization of fuzzy thematic sets in taxonomies applied to the analysis of tendencies of research in data science

This paper proposes a novel method, referred to as ParGenFS, for finding a most specific generalization of a query set represented by a fuzzy set of topics assigned to leaves of the rooted tree of a taxonomy. The query set is generalized by “lifting” it to one or more “head subjects” in the higher ranks of the taxonomy. The head subjects should cover the query set, with the possible addition of some “gaps”, taxonomy nodes covered by the head subject but irrelevant to the query set. To decrease the numbers of gaps, we admit some “offshoots”, nodes belonging to the query set but not covered by a head subject. The method globally minimizes the total number of head subjects, gaps and offshoots, each suitably weighted. Our algorithm is applied to the structural analysis and description of a collection of 17,685 abstracts of research papers published in 17 Springer journals related to Data Science for the 20-year period 1998–2017. Our taxonomy of Data Science (TDS) is extracted from the Association for Computing Machinery Computing Classification System 2012 (ACM-CCS), a six-level hierarchical taxonomy manually developed by a team of ACM experts. The TDS also includes a number of additional leaves that we added to cater for recent developments not represented in the ACM-CCS taxonomy. We find fuzzy clusters of leaf topics over the text collection, using specially developed machinery. Three of the clusters are indeed thematic, relating to the Data Science sub-areas of (a) learning, (b) information retrieval, and (c) clustering. These three clusters are then lifted in the TDS using ParGenFS, which allows us to draw some conclusions about tendencies in developments in these areas.
Continuous Gesture Recognition through Selective Temporal Fusion

Gesture recognition is an important task with the potential to revolutionize human/computer interfaces (HCI). Gestures, however, are dynamic. While a few gestures may be static poses, most gestures are complex temporal sequences of motions. For most HCI applications, gestures must be recognized in real-time in streaming data. Therefore, most recognition systems analyze each frame as it comes in, fusing data across time to detect gestures. This paper presents results of the first systematic study of temporal fusion techniques for streaming gesture recognition. These results show that the choice of the best fusion strategy depends on whether the input is global (i.e. full-frame) or a spatially focused window, and on whether the input is unprocessed RGB or depth depth versus a flow field.This conclusion is then used to extend a state-of-the-art architecture for isolated gesture recognition, FOANet [24], to continuous gesture recognition. The result is a system that established a new state-of-the-art for recognition performance on the ChaLearn ConGD data set, with a mean Jaccard Index of 0.77 compared to the previous best result of 0.61. This paper also establishes a baseline of performance for the newer, continuous version of the NVIDIA dataset.
Extreme learning machine‐based alleviation for overloaded power system

The present study proposes a corrective method of electric system overload provided that the wind farm is integrated into the distribution system, taking into account the congestion cost. The authors attempted to mitigate the overload and to monitor flow over transmission lines. Unified power flow controller device was the first suggestion utilised to solve this problem, then due to its extremely fast training and the excellent generalisation performance, extreme learning machine algorithm is employed. The fundamental point is the transmission line alleviation. In addition, other targets are realised: the load shedding avoidance, minimisation of losses and congestion cost. This study is also designed to utilise PowerWorld Simulator and MATLAB software to demonstrate methods for relieving transmission overloads. The accuracy of the proposed approach has been tested for Algerian (Adrar) 22-bus system. Obtained results showed an improvement in power system behaviour. Simulation results are exposed, discussed and compared at the end of this study.
Real-Time Object Detection in Remote Sensing Images Based on Visual Perception and Memory Reasoning

Aiming at the real-time detection of multiple objects and micro-objects in large-scene remote sensing images, a cascaded convolutional neural network real-time object-detection framework for remote sensing images is proposed, which integrates visual perception and convolutional memory network reasoning. The detection framework is composed of two fully convolutional networks, namely, the strengthened object self-attention pre-screening fully convolutional network (SOSA-FCN) and the object accurate detection fully convolutional network (AD-FCN). SOSA-FCN introduces a self-attention module to extract attention feature maps and constructs a depth feature pyramid to optimize the attention feature maps by combining convolutional long-term and short-term memory networks. It guides the acquisition of potential sub-regions of the object in the scene, reduces the computational complexity, and enhances the network’s ability to extract multi-scale object features. It adapts to the complex background and small object characteristics of a large-scene remote sensing image. In AD-FCN, the object mask and object orientation estimation layer are designed to achieve fine positioning of candidate frames. The performance of the proposed algorithm is compared with that of other advanced methods on NWPU_VHR-10, DOTA, UCAS-AOD, and other open datasets. The experimental results show that the proposed algorithm significantly improves the efficiency of object detection while ensuring detection accuracy and has high adaptability. It has extensive engineering application prospects.
BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender Systems

Collaborative recommender systems are vulnerable to shilling attacks. To address this issue, many methods including supervised and unsupervised have been proposed. However, supervised detection methods require training classifiers and they only apply to detect known types of attacks. The existing unsupervised detection methods need to know the prior knowledge of attacks, otherwise they suffer from low detection precision. In this paper, we present BS-SC, an unsupervised approach for detecting shilling profiles, which does not need to know the attack size or to label the candidate spammers. BS-SC starts from an in-depth analysis of user behaviors and uses two key mechanisms (i.e., behavior features extraction and behavior similarity matrix clustering) to distinguish shilling profiles from genuine ones. The behavior features reflect the behavior difference between genuine and shilling profiles, and the behavior similarity matrix clustering is to cluster shilling profiles based on their highly similar behaviors. Experimental results on the MovieLens and the sampled Amazon review datasets indicate that BS-SC outperforms the baseline unsupervised approaches, even when the prior knowledge is given for them.
Deep neural network architecture search using network morphism

The paper presents the results of the research on neural architecture search (NAS) algorithm. We utilized the hill climbing algorithm to search for well-performing structures of deep convolutional neural network. Moreover, we used the function preserving transformations which enabled the effective operation of the algorithm in a short period of time. The network obtained with the advantage of NAS was validated on skin lesion classification problem. We compared the parameters and performance of the automatically generated neural structure with the architectures selected manually, reported by the authors in previous papers. The obtained structure achieved comparable results to hand-designed networks, but with much fewer parameters then manually crafted architectures.
Control-Flow Integrity: Attacks and Protections

Despite the intense efforts to prevent programmers from writing code with memory errors, memory corruption vulnerabilities are still a major security threat. Consequently, control-flow integrity has received significant attention in the research community, and software developers to combat control code execution attacks in the presence of type of faults. Control-flow Integrity (CFI) is a large family of techniques that aims to eradicate memory error exploitation by ensuring that the instruction pointer (IP) of a running process cannot be controlled by a malicious attacker. In this paper, we assess the effectiveness of 14 CFI techniques against the most popular exploitation techniques, including code reuse attacks, return-to-user, return-to-libc, and replay attacks. We also classify these techniques based on their security, robustness, and implementation complexity. Our study indicates that the majority of the CFI techniques are primarily focused on restricting indirect branch instructions and cannot prevent all forms of vulnerability exploitation. We conclude that the performance overhead introduced, jointly with the partial attack coverage, is discouraging the industry from adopting most of them.
Same Stats, Different Graphs: Exploring the Space of Graphs in Terms of Graph Properties

Data analysts commonly utilize statistics to summarize large datasets. While it is often sufficient to explore only the summary statistics of a dataset (e.g., min/mean/max), Anscombe's Quartet demonstrates how such statistics can be misleading. We consider a similar problem in the context of graph mining. To study the relationships between different graph properties, we examine low-order non-isomorphic graphs and provide a simple visual analytics system to explore correlations across multiple graph properties. However, for larger graphs, studying the entire space quickly becomes intractable. We use different random graph generation methods to further look into the distribution of graph properties for higher order graphs and investigate the impact of various sampling methodologies. We also describe a method for generating many graphs that are identical over a number of graph properties and statistics yet are clearly different and identifiably distinct.
Learning a strong detector for action localization in videos

We address the problem of spatio-temporal action localization in videos in this paper. Current state-of-the-art methods for this challenging task rely on an object detector to localize actors at frame-level firstly, and then link or track the detections across time. Most of these methods commonly pay more attention to leveraging the temporal context of videos for action detection while ignoring the importance of the object detector itself. In this paper, we prove the importance of the object detector in the pipeline of action localization, and propose a strong object detector for better action localization in videos, which is based on the single shot multibox detector (SSD) framework. Different from SSD, we introduce an anchor refine branch at the end of the backbone network to refine the input anchors, and add a batch normalization layer before concatenating the intermediate feature maps at frame-level and after stacking feature maps at clip-level. The proposed strong detector have two contributions: (1) reducing the phenomenon of missing target objects at frame-level; (2) generating deformable anchor cuboids for modeling temporal dynamic actions. Extensive experiments on UCF-Sports, J-HMDB and UCF-101 validate our claims, and we outperform the previous state-of-the-art methods by a large margin in terms of frame-mAP and video-mAP, especially at a higher overlap threshold.
Right Ventricle Segmentation in Short-Axis MRI Using a Shape Constrained Dense Connected U-Net

Segmentation of right ventricle (RV) in short-axis MRI is an essential step for evaluating the structure and function of RV. In this paper, a shape constrained deep learning network based on dense connectivity and dilated convolutions is proposed, which aims to strengthen feature propagation and have more diversified features through dense connections and skip connections. In the meantime, dilated convolution is used to expand the receptive fields and enhance the connectivity of segmentation results. The shape constraint of RV is introduced into the loss function to improve the prediction accuracy. Transfer learning is employed to strengthen the generalization of the network to the RV shape constraint. Finally, post-processing is performed by analysing boundary curvature of segmentation results and shape correlation of endocardium and epicardium of RV. Our network is validated on the MICCAI2012 public dataset, and the evaluation results show that our network outperforms the state-of-the-art methods in several evaluation metrics.
Empirical study on the usage of graph query languages in open source Java projects

Graph data models are interesting in various domains, in part because of the intuitiveness and flexibility they offer compared to relational models. Specialized query languages, such as Cypher for property graphs or SPARQL for RDF, facilitate their use. In this paper, we present an empirical study on the usage of graph-based query languages in open-source Java projects on GitHub. We investigate the usage of SPARQL, Cypher, Gremlin and GraphQL in terms of popularity and their development over time. We select repositories based on dependencies related to these technologies and employ various popularity and source-code based filters and ranking features for a targeted selection of projects. For the concrete languages SPARQL and Cypher, we analyze the activity of repositories over time. For SPARQL, we investigate common application domains, query use and existence of ontological data modeling in applications that query for concrete instance data. Our results show, that the usage of graph query languages in open-source projects increased over the last years, with SPARQL and Cypher being by far the most popular. SPARQL projects are more active in terms of query related artifact changes and unique developers involved, but Cypher is catching up. Relatively few applications use SPARQL to query for concrete instance data: A majority of those applications employ multiple different ontologies, including project and domain specific ones. Common application domains are management systems and data visualization tools.
Contextualising and aligning security metrics and business objectives: A GQM-based methodology

Pre-defined security metrics suffer from the problem of contextualisation, i.e. a lack of adaptability to particular organisational contexts – domain, technical infrastructure, stakeholders, business process, etc. Adapting metrics to an organisational context is essential (1) for the metrics to align with business requirements (2) for decision makers to maintain relevant security goals based on measurements from the field. In this paper we propose Symbiosis, a methodology that defines a goal elicitation and refinement process mapping business objectives to security measurement goals via the use of systematic templates that capture relevant context elements (business goals, purpose, stakeholders, system scope). The novel contribution of Symbiosis is the well-defined process, which enforces that (1) metrics align with business objectives via a top-down derivation that refines top-level business objectives to a manageable granularity (2) the impact of metrics on business objectives is explicitly traced via a bottom-up feedback mechanism, allowing an incremental approach where feedback from metrics influences business goals, and vice-versa. In this paper, we discuss the findings from applying Symbiosis to three case studies of known security incidents. Our analysis shows how the aforementioned pitfalls of security metrics development processes affected the outcome of these high-profile security incidents and how Symbiosis addresses such issues.
Comparison of 3D 360-Degree Video Compression Performance Using Different Projections

3D and multi-view 360-degree videos offer 3D immersive visual experience of the real world on head-mounted displays (HMDs). They are used in extended reality applications (including virtual reality, augmented reality and mixed reality) such as gaming, entertainment, education and medicine. However, these applications require a very high rate of compression and a great deal of computational resources. Compression efficiency and coding complexity are highly influenced by 360 projections used to map spherical frames to rectangular frames. To study the effect of this mapping process, in this paper, we evaluate the performance of various projections in 3D and multi-view 360-degree videos in terms of coding efficiency, quality and complexity. The evaluation is conducted for two coding scenarios: stereo coding scenario where the encoder considers the interview dependency for disparity estimation and simulcast coding scenario where two views are encoded separately. This performance evaluation approach provides valuable insights on using 360-degree projections in 3D and multi-view coding.
A Comparative Study on Missing Data Handling Using Machine Learning for Human Activity Recognition

In mobile computing era, human activity recognition is an important research area for eldercare and healthcare center. Activity recognition performance decrease while partial data are lost due to various reasons, such as power limitation, sensors data transmission capacity, hardware failures, and network issues (e.g., packet collision, unreliable link, and unexpected damage). Therefore, data loss or missing is a realistic challenge. In this research, we evaluate the performance of algorithms to perform their ability to handling missing data. It is required to investigate the optimal missing percentage in the dataset and its effect on accuracy. In this case, missing data are implemented randomly in the dataset with incremental scaling. We evaluate the performance using two benchmark datasets named HASC dataset and Single Chest-Mounted accelerometer dataset with data missing at random pattern. Here, we classified using support vector machine and random forest classifiers with eleven statistical features. Initially in the original dataset, there are no missing data in the dataset. However, to evaluate the performance, we added various levels of missing data randomly and studied the performances. Our study demonstrated that over 8% missing data in the dataset greatly reduce recognition results. Moreover, this recognition result has impact on sampling rate during data collection time.
Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.
Performance Evaluation of Dimensionality Reduction Techniques on High Dimensional Data

With a large amount of data being generated each day, the task on analyzing and making inferences from data is becoming an increasingly challenging task. One of the major challenges is the curse of dimensionality which is dealt with by using several popular dimensionality reduction techniques such as ICA, PCA, NMF etc. In this work, we make a systematic performance evaluation of the efficiency and effectiveness of various dimensionality reduction techniques. We present a rigorous evaluation of various techniques benchmarked on real-world datasets. This work is intended to assist data science practitioners to select the most suitable dimensionality reduction technique based on the trade-off between the corresponding effectiveness and efficiency.