Reviewerly/exHarmony huggingface dataset:
Integrating Keywords into BERT4Rec for Sequential Recommendation

A crucial part of recommender systems is to model the user's preference based on her previous interactions. Different neural networks (e.g., Recurrent Neural Networks), that predict the next item solely based on the sequence of interactions have been successfully applied to sequential recommendation. Recently, BERT4Rec has been proposed, which adapts the BERT architecture based on the Transformer model and training methods used in the Neural Language Modeling community to this task. However, BERT4Rec still only relies on item identifiers to model the user preference, ignoring other sources of information. Therefore, as a first step to include additional information, we propose KeBERT4Rec, a modification of BERT4Rec, which utilizes keyword descriptions of items. We compare two variants for adding keywords to the model on two datasets, a Movielens dataset and a dataset of an online fashion store. First results show that both versions of our model improves the sequential recommending task compared to BERT4Rec.
Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

Abstract The current approaches for image retrieval are more concentrating on numerous image features. Texture, shape, spatial information, and color are the fundamental features to deal with flexible image datasets. This paper aims to develop new Content‐Based Image Retrieval System based on Optimal Weighted Hybrid Pattern. Two relevant patters like Local Vector Pattern and Local Derivative Pattern are intended to develop a novel Content‐Based Image Retrieval system. The optimal weighted hybrid pattern is implemented to derive a new feature vector, so that the weight is optimized by a modified optimization algorithm called Improved Local Leader‐based Spider Monkey Optimization to maximize the precision and recall of the retrieved images. The retrieval of the image is done by measuring the similarity based on Mean Square Distance between the features of query image as well as training image. Finally, the performance comparison of the proposed and the traditional patterns shows its reliable performance.
Can Machines Tell Stories? A Comparative Study of Deep Neural Language Models and Metrics

Massive textual content has enabled rapid advances in natural language modeling. The use of pre-trained deep neural language models has significantly improved natural language understanding tasks. However, the extent to which these systems can be applied to content generation is unclear. While a few informal studies have claimed that these models can generate `high quality' readable content, there is no prior study on analyzing the generated content from these models based on sampling and fine-tuning hyperparameters. We conduct an in-depth comparison of several language models for open-ended story generation from given prompts. Using a diverse set of automated metrics, we compare the performance of transformer-based generative models - OpenAI's GPT2 (pre-trained and fine-tuned) and Google's pre-trained TransformerXL and XLNet to human-written textual references. Studying inter-metric correlation along with metric ranking reveals interesting insights - the high correlation between the readability scores and word usage in the text. A study of the statistical significance and empirical evaluations between the scores (human and machine-generated) at higher sampling hyperparameter combinations (t = {0.75, 1.0}, k = {100, 150, 250}) reveal that the top pre-trained and fine-tuned models generated samples condition well on the prompt with an increased occurrence of unique and difficult words. The GPT2-medium model fine-tuned on the 1024 Byte-pair Encoding (BPE) tokenized version of the dataset along with pre-trained Transformer-XL models generated samples close to human written content on three metrics: prompt-based overlap, coherence, and variation in sentence length. A study of overall model stability and performance shows that fine-tuned GPT2 language models have the least deviation in metric scores from human performance.
Meta-path Augmented Sequential Recommendation with Contextual Co-attention Network

It is critical to comprehensively and efficiently learn user preferences for an effective sequential recommender system. Existing sequential recommendation methods mainly focus on modeling local preference from users’ historical behaviors, which largely ignore the global context information from the heterogeneous information network. This prevents a comprehensive user preference representation. To address these issues, we propose a joint learning approach to incorporate global context with local preferences efficiently. The proposed approach introduces meta-paths from a heterogeneous information network to capture the global context information, and the position-based self-attention mechanism is adopted to model the local preference representation efficiently. Compared with the methods that only consider the local preference, our proposed method takes the advantages of incorporating global context information, which extracts structural features that captures relevant semantics to construct users’ global preference representation for the sequential recommendation. We further adopt a co-attention mechanism to model complex interactions between global context and users’ historical behaviors for better user representations. Quantitative and qualitative experimental evaluations are conducted on nine large-scale Amazon datasets and a multi-modal Zhihu dataset. The promising results demonstrate the effectiveness of the proposed model.
Understanding pet scams: A case study of advance fee and non-delivery fraud using victims’ accounts

Advance fee and non-delivery frauds have become very common with the growing preference for online shopping and the new opportunities this brings for online offenders. This article uses unique access to a volunteer group’s database focused on preventing pet scams to explore this type of crime. Distances, among other factors, make the purchase of pets online common in countries such as the USA, Australia and South Africa. This modality of purchase has been exploited by organized criminals largely based in Cameroon to conduct advance fee and non-delivery frauds. The article uses data from the volunteer group Petscams.com to provide unique insights on the techniques of the offenders with particular reference to the strategies used to maximize victimization by using real accounts of victims of such frauds. It also briefly notes how the COVID-19 crisis has been used to adapt this type of scam. The article’s discussion identifies the need for a more nuanced assessment into the role of victim oriented voluntary organizations.
Security Threat Modeling

Traditional threat modeling approaches such as Microsoft's STRIDE rely on Data Flow Diagrams (DFDs) as the main input. As DFDs are constructed from only five distinct model element types, these system models are deliberately kept simple. While this lowers the bar for practical adoption, there are a number of significant drawbacks.
A non-intrusive optical (NIO) approach to characterize heliostats in utility-scale power tower plants: Methodology and in-situ validation

No reliable in-situ optical characterization tool is available that can efficiently and accurately measure various optical errors of individual heliostats in a utility-scale power-tower plant. Here, an innovative non-intrusive optical (NIO) approach is proposed to measure mirror surface slope error, mirror facet canting error, and heliostat tracking error based on reflection images using the natural target—the tower—in a heliostat field. The NIO approach adopts various techniques in photogrammetry, reflectometry, and geometrical optics to first determine the camera position and the actual tower position with respect to the heliostat. Then, Snell’s law is applied to find the surface normal at the reflection point or line of a reflection image and derive the surface slope error, mirror facet canting error, and heliostat tracking error accordingly. In addition, an in-situ validation is performed to demonstrate the feasibility of the NIO approach. Analysis with multiple sets of reflection images on one single heliostat of interest demonstrates that the NIO method can achieve a measurement uncertainty of 0.22 mrad for mirror facet slope error (root-mean-square) at the given test conditions. The sensitivity of measurement uncertainty to various parameters will be presented in a separate work. In the future, state-of-the-art unmanned aerial systems (UAS) are planned to perform the efficient image collection with a goal to complete the survey across a utility-scale field of over 10,000 heliostats within one or a few days depending on the number of UASs. The development of the NIO approach is expected to fill the critical gap for successful solar-field operation and maintenance, which is necessary for the global growth of the solar power-tower technology. The NIO approach can also be applied to any relevant industry requiring high-precision mirror surface shape characterization.
A low-power asynchronous hardware implementation of a novel SVM classifier, with an application in a speech recognition system

Machine Learning (ML) has been applied in so many areas in reason of its robustness, usability, and reliability, mainly in hardware implementation. One of its well-known algorithms is the Support Vector Machine (SVM), the simplest to be applied in hardware because of its mathematical modeling. In this study, we propose the implementation in hardware of SVM multi-class classifiers within the asynchronous paradigm (i.e., without clock signal application) in a 4-stage pipeline architecture. With the purpose to evaluate the proposed architecture behavior, we used a Field Programmable Gate Array (FPGA) device to prototype the circuit. The proposed asynchronous SVM classifier is applied in a Speech Recognition system of 30 classes where a hybrid training algorithm was processed in the Matlab software, known as PSO-SVM training algorithm. Therefore, the training phase was processed in software in reason of its computational load. For the SVM classification phase, we propose, for the first time to the best of our knowledge, an asynchronous pipeline architecture of four stages with Multiply-Accumulator (MAC) unit application and three different control circuits described from Extended Burst-Mode (XBM) and State Transition Graph (STG) specifications, leading to energy-efficient design. In order to validate the SVM recognition results in the speech recognition application, the tests are from 60 speeches and 20 speakers, so it is a diversified and reliable data set of tests. The main goal here was to design a machine learning hardware implementation in a low power application and, through that, to prove that the asynchronous paradigm reduced power. As a result, we obtained a reduced power consumption of 5.72 mW, a fast average response time which was 0.61μs and the most area-efficient circuit (1315 LUTs); the accuracy in recognition success rate was another preoccupation, and it was very successful, 98% of success. Besides, we present comparisons with an asynchronous version of the same SVM datapath and with different synchronous architectures from literature to prove that our novelty is better in power consumption and area size. For hardware applications where low power and high performance are the sought features, the presented architecture revealed the best position when compared to similar works from the recent technical literature for pattern recognition systems.
What We Talk About When We Talk About Data Physicality

Data physicalizations “map data to physical form,” yet many canonical examples are not based on external data sets. To address this contradiction, I argue that the practice of physicalization forces us to rethink traditional notions of data. This article proposes a conceptual framework to examine how physicalizations relate to data. This article develops a two-dimensional conceptual space for comparing different perspectives on data used in physicalization, drawing from design theory and critical data studies literature. One axis distinguishes between epistemological and ontological perspectives, focusing on the relationship between data and the mind. The second axis distinguishes how data relate to the world, differentiating between representational and relational perspectives. To clarify the aesthetic and conceptual implications of these different perspectives, the article discusses examples of data physicalization for each quadrant of the continuous space. It further uses the framework to examine the explicit and implicit assumptions about data in physicalization literature. As a theoretical article, it encourages practitioners to think about how data relate to the manifestations and the phenomena they try to capture. It invites exploration of the relationship between data and the world as a generative source of creative tension.
An efficient algorithm for unique class association rule mining

Association rule mining is one of the main means in Knowledge discovery and Machine learning. Such kind of rules present knowledge of interrelations among items in a dataset. Class Association Rules (CARs) are a subset of association rules which are always mined using labeled datasets. Simply, a typical CAR has an itemset that is associated to a class label. Mining CARs is vital for construction of pattern or rule-based classification models and has received recently increasing research interest. In this work, a complete efficient but not exhaustive CAR mining algorithm (UniqAR) is introduced. UniqAR generates always and only 100% accurate CARs which are called unique association rules using two rule search hypothesis of Subsumption and Nonsense to find unique itemsets in order to generate the Unique CARs. Unlike alternatives of CAR mining algorithms, UniqAR mined association rules aren’t based on itemset frequency or item selectivity. It can generate both frequent and rare association rules. No preferences of support, coverage, or item participant in itemsets are required to be provided for the proposed mining process. The main contribution of this work to CARs’ state of the art is describing unique itemsets and class association rules and providing an efficient mining process for them. Unlike the other unique rule mining alternatives in the literature, the proposed novel mining process depends on a complete but not exhaustive search that employs rules inter-relations. UniqAR has been modeled with computational analysis and extended evaluation. It is shown that UniqAR can extract all unique itemsets for unique association mining with no need to setup any user preferences, template or any constraints. Moreover, it describes accurately the effects of different dataset criteria like number of attributes/features, feature values, cases, and class labels on UniqAR unique itemset extraction mining process in an efficient way that avoids a huge number of itemsets/cases comparisons. Results have shown that the proposed UniqAR algorithm is feasible and promising.
Liability and artificial intelligence in the EU: Assessing the adequacy of the current Product Liability Directive

The European Union selected achieving a leadership position in the AI sector as one of the priorities for the future of the bloc as a whole. Economic reasons are behind this decision, but they are not the exclusive motive behind this objective. Undeniably, AI will have an enormous impact on world’s economy and if the EU falls behind, the standards of living that citizens of the Union currently enjoy may be at risk. Furthermore, there is also the question of keeping European values, principles and ethical standards alive in this technological transition. To achieve the leadership position it desires, it is essential for the EU to possess an updated, producer-friendly legal framework, that manages, at the same time, to ensure consumer protection and safe development of AI. One of the legal instruments that may need to be amended is the Product Liability Directive. In this paper we will study the Product Liability Directive and its shortcomings on AI regulation, along with the possible solutions to adapt the EU product liability legislation to this new technological challenge. We will assess what solutions are best suited to apply in the EU and fulfil the objective of achieving leadership in the sector.
FASSD: A Feature Fusion and Spatial Attention-Based Single Shot Detector for Small Object Detection

Deep learning methods have significantly improved object detection performance, but small object detection remains an extremely difficult and challenging task in computer vision. We propose a feature fusion and spatial attention-based single shot detector (FASSD) for small object detection. We fuse high-level semantic information into shallow layers to generate discriminative feature representations for small objects. To adaptively enhance the expression of small object areas and suppress the feature response of background regions, the spatial attention block learns a self-attention mask to enhance the original feature maps. We also establish a small object dataset (LAKE-BOAT) of a scene with a boat on a lake and tested our algorithm to evaluate its performance. The results show that our FASSD achieves 79.3% mAP (mean average precision) on the PASCAL VOC2007 test with input 300 × 300, which outperforms the original single shot multibox detector (SSD) by 1.6 points, as well as most improved algorithms based on SSD. The corresponding detection speed was 45.3 FPS (frame per second) on the VOC2007 test using a single NVIDIA TITAN RTX GPU. The test results of a simplified FASSD on the LAKE-BOAT dataset indicate that our model achieved an improvement of 3.5% mAP on the baseline network while maintaining a real-time detection speed (64.4 FPS).
Boundary Coding Representation for Organ Segmentation in Prostate Cancer Radiotherapy

Accurate segmentation of the prostate and organs at risk (OARs, e.g., bladder and rectum) in male pelvic CT images is a critical step for prostate cancer radiotherapy. Unfortunately, the unclear organ boundary and large shape variation make the segmentation task very challenging. Previous studies usually used representations defined directly on unclear boundaries as context information to guide segmentation. Those boundary representations may not be so discriminative, resulting in limited performance improvement. To this end, we propose a novel boundary coding network (BCnet) to learn a discriminative representation for organ boundary and use it as the context information to guide the segmentation. Specifically, we design a two-stage learning strategy in the proposed BCnet: 1) Boundary coding representation learning. Two sub-networks under the supervision of the dilation and erosion masks transformed from the manually delineated organ mask are first separately trained to learn the spatial-semantic context near the organ boundary. Then we encode the organ boundary based on the predictions of these two sub-networks and design a multi-atlas based refinement strategy by transferring the knowledge from training data to inference. 2) Organ segmentation. The boundary coding representation as context information, in addition to the image patches, are used to train the final segmentation network. Experimental results on a large and diverse male pelvic CT dataset show that our method achieves superior performance compared with several state-of-the-art methods.
Efficient and accurate structural fusion of Bayesian networks

Bayesian Network (BN) fusion provides a precise theoretical framework for aggregating the graphical structure of a set of BNs into a consensus network. The fusion process depends on a total ordering of the variables, but both the problem of searching for an optimal consensus structure (according to the standard problem definition) as well as the one of looking for the optimal ordering are NP-hard. In this paper we start with this theoretical framework and extend it from a practical point of view. The two main methodological contributions we make are: (1) an adaptation of the well-known BN learning algorithm GES (Greedy Equivalence Search) to the case of having a set of BNs as input instead of data (we prove the correctness of the adapted algorithm, i.e., under certain standard assumptions the optimal solution for the BN fusion process is obtained); and (2) a heuristic method for identifying a suitable order of the variables, which allows us to obtain consensus BNs having (far) fewer edges than those obtained by using random orderings. Finally, we design several computational experiments to test our proposals. From the results, we can conclude that the consensus network can be efficiently obtained by using the proposed heuristic to compute the total order of the variables, with this DAG being very close to the optimal one.
KubCG: A dynamic Kubernetes scheduler for heterogeneous clusters

Summary Container platforms are increasingly being used to deploy cloud‐based services. Nevertheless, many cloud services are also demanding graphics processing units (GPUs) to accelerate different applications that make use of their parallel architecture, such as deep learning or just video processing. Thus, different container technologies, such as Docker and Kubernetes, are implementing GPU support. Some effort is being devoted to design algorithms to schedule applications into heterogeneous computing systems that use CPUs and GPUs together. This article is part of this effort, and we describe how to build a dynamic scheduling platform for Kubernetes that is able to manage the deployment of Docker containers in a heterogeneous cluster, which we call KubCG. This platform implements a new scheduler that optimizes the deployment of new containers by taking into account the Kubernetes Pod timeline and the historical information about the execution of the containers. We have performed different tests to validate this new algorithm, and KubCG was able to reduce the time to complete different tasks down to a 64% of the original time in our different experiments.
Regularized Matrix Factorization for Multilabel Learning With Missing Labels

This article tackles the problem of multilabel learning with missing labels. For this problem, it is widely accepted that label correlations can be used to recover the ground-truth label matrix. Most of the existing approaches impose the low-rank assumption on the observed label matrix to exploit label correlations by decomposing it into two matrices, which describe the latent factors of instances and labels, respectively. The quality of these latent factors highly influences the recovery of ground-truth labels and the construction of the multilabel classification model. In this article, we propose recovering the ground-truth label matrix by regularized matrix factorization. Specifically, the latent factors of instances are regularized by the local topological structure derived from the feature space, which can be further used to induce an effective multilabel model. Moreover, the latent factors of labels and the label correlations are mutually adapted via label manifold regularization. In this way, the recovery of the ground-truth label matrix and the construction of the multilabel classification model are optimized jointly and can benefit from the regularized matrix factorization. Extensive experimental studies show that the proposed approach significantly outperforms the state-of-the-art algorithms on both full-label and missing-label data.