Reviewerly/exHarmony huggingface dataset:
From the lab to production: A case study of session-based recommendations in the home-improvement domain

E-commerce applications rely heavily on session-based recommendation algorithms to improve the shopping experience of their customers. Recent progress in session-based recommendation algorithms shows great promise. However, translating that promise to real-world outcomes is a challenging task for several reasons, but mostly due to the large number and varying characteristics of the available models. In this paper, we discuss the approach and lessons learned from the process of identifying and deploying a successful session-based recommendation algorithm for a leading e-commerce application in the home-improvement domain. To this end, we initially evaluate fourteen session-based recommendation algorithms in an offline setting using eight different popular evaluation metrics on three datasets. The results indicate that offline evaluation does not provide enough insight to make an informed decision since there is no clear winning method on all metrics. Additionally, we observe that standard offline evaluation metrics fall short for this application. Specifically, they reward an algorithm only when it predicts the exact same item that the user clicked next or eventually purchased. In a practical scenario, however, there are near-identical products which, although they are assigned different identifiers, they should be considered as equally-good recommendations. To overcome these limitations, we perform an additional round of evaluation, where human experts provide both objective and subjective feedback for the recommendations of five algorithms that performed the best in the offline evaluation. We find that the experts' opinion is oftentimes different from the offline evaluation results. Analysis of the feedback confirms that the performance of all models is significantly higher when we evaluate near-identical product recommendations as relevant. Finally, we run an A/B test with one of the models that performed the best in the human evaluation phase. The treatment model increased conversion rate by 15.6% and revenue per visit by 18.5% when compared with a leading third-party solution.
Reverse-engineering bar charts using neural networks

Reverse-engineering bar charts extract textual and numeric information from the visual representations of bar charts to support application scenarios that require the underlying information. In this paper, we propose a neural network-based method for reverse-engineering bar charts. We adopt a neural network-based object detection model to simultaneously localize and classify textual information. This approach improves the efficiency of textual information extraction. We design an encoder-decoder framework that integrates convolutional and recurrent neural networks to extract numeric information. We further introduce an attention mechanism into the framework to achieve high accuracy and robustness. Synthetic and real-world datasets are used to evaluate the effectiveness of the method. To the best of our knowledge, this work takes the lead in constructing a complete neural network-based method of reverse-engineering bar charts.
Mining high utility itemsets using extended chain structure and utility machine

High utility itemsets are sets of items that have a high utility (e.g. a high profit or a high importance) in a transaction database. Discovering high utility itemsets has many important applications in real-life such as market basket analysis. Nonetheless, mining these patterns is a time-consuming process due to the huge search space and the high cost of utility computation. Most of previous work is devoted to search space pruning but pay little attention to utility computation. Factually, not only search space pruning but also high utility itemset identification have to resort to the computation of various utilities. This paper proposes a novel algorithm named REX (Rapid itEmset eXtraction), which extends the classic d2HUP algorithm with an improved structure, a k-item utility machine, and an efficient switch strategy. The structure can significantly reduce the time complexity of utility computation compared with the original structure used in d2HUP. The machine can quickly merge identical transactions and applies an efficient procedure for computing the utilities of extensions of a given itemset. The strategy derived from trial and error drastically gives rise to performance improvement on some databases and is also competitive with the switch strategy used in d2HUP on other databases. Experimental results show that REX achieves a speedup of from fifty percent to three orders of magnitude over d2HUP even though they use identical pruning techniques and that REX considerably outperforms state-of-the-art algorithms on real-life and synthetic databases.
Gender and age prediction from real time facial images using CNN

The face image can provide both age and gender information that serve as vital attributes in intelligent applications, such as access control, human–computer interaction and visual surveillance, etc. Further, extensive use of social media has given rise to the need for real time automatic age and gender detection. Though a number of researches related to face recognition has been done, there is a gap as far as the performance of existing methods on real-world images is considered, especially when compared to the tremendous leaps in performance recently reported for the related task of face recognition. This paper attempts to estimate age and gender from a single face real-time image using CNN. Face detection is done using Haar Cascades and Caffenet is used for age and gender recognition.
Solving complex eigenvalue problems on a quantum annealer with applications to quantum scattering resonances

Quantum computing is a new and rapidly evolving paradigm for solving chemistry problems. In previous work, we developed the Quantum Annealer Eigensolver (QAE) and applied it to the calculation of the vibrational spectrum of a molecule on the D-Wave quantum annealer. However, the original QAE methodology was applicable to real symmetric matrices only. For many physics and chemistry problems, the diagonalization of complex matrices is required. For example, the calculation of quantum scattering resonances can be formulated as a complex eigenvalue problem where the real part of the eigenvalue is the resonance energy and the imaginary part is proportional to the resonance width. In the present work, we generalize the QAE to treat complex matrices: first complex Hermitian matrices and then complex symmetric matrices. These generalizations are then used to compute a quantum scattering resonance state in a 1D model potential for O + O collisions. These calculations are performed using both a software (classical) annealer and hardware annealer (the D-Wave 2000Q). The results of the complex QAE are also benchmarked against a standard linear algebra library (LAPACK). This work presents the first numerical solution of a complex eigenvalue problem of any kind on a quantum annealer, and it is the first treatment of a quantum scattering resonance on any quantum device.
Gaussian Mixture Model and Self-Organizing Map Neural-Network-Based Coverage for Target Search in Curve-Shape Area

This article focuses on the target search problem in a curve-shape area using multiple unmanned aerial vehicles (UAVs), with the demand for obtaining the maximum cumulative detection reward, as well as the constraint of maneuverability and obstacle avoidance. First, the prior target probability map of the curve-shape area, generated by Parzen windows with Gaussian kernels, is approximated by the 1-D Gaussian mixture model (GMM) in order to extract some high-value curve segments corresponding to Gaussian components. Based on the parameterized curve segments from GMM, the self-organizing map (SOM) neural network is then established to achieve the coverage search. The step of winner neuron selection in SOM will prioritize and allocate the curve segments to UAVs, with the comprehensive consideration of multiple evaluation factors and allocation balance. The following step of neuron weight update will plan the UAV paths under the constraint of maneuverability and obstacle avoidance, using the modified Dubins guidance vector field. Finally, the good performance of GMM-SOM is evaluated on a coastline map.
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies

Deep learning has become an increasingly common technique for various control problems, such as robotic arm manipulation, robot navigation, and autonomous vehicles. However, the downside of using deep neural networks to learn control policies is their opaque nature and the difficulties of validating their safety. As the networks used to obtain state-of-the-art results become increasingly deep and complex, the rules they have learned and how they operate become more challenging to understand. This presents an issue, since in safety-critical applications the safety of the control policy must be ensured to a high confidence level. In this paper, we propose an automated black box testing framework based on adversarial reinforcement learning. The technique uses an adversarial agent, whose goal is to degrade the performance of the target model under test. We test the approach on an autonomous vehicle problem, by training an adversarial reinforcement learning agent, which aims to cause a deep neural network-driven autonomous vehicle to collide. Two neural networks trained for autonomous driving are compared, and the results from the testing are used to compare the robustness of their learned control policies. We show that the proposed framework is able to find weaknesses in both control policies that were not evident during online testing and therefore, demonstrate a significant benefit over manual testing methods.
Adopting industry agile practices in large-scale capstone education

This paper presents the practice and experience in adopting an agile organizational model for a final-year capstone program in Software Engineering. The model developed is motivated by having real (and developing) software artifacts with incrementally changing team members working on a product-line. This in turn results in more sophisticated capstone student-project outcomes. The model proposed supports student mentoring and promotes, through its internal organization, leadership and personal responsibility. The students are supported by professional software engineers, up-skilling workshops, and academic supervisors who act as a personalized reporting and grading point for the team. The academic supervisors are themselves supported by a tribe leader, a faculty member who assumes overall responsibility for a product-line, and who acts as a report to an external industry client/sponsor. This paper describes the motivation for the capstone model, its adoption, and some preliminary observations.
Establishing multilevel test-to-code traceability links

Test-to-code traceability links model the relationships between test artefacts and code artefacts. When utilised during the development process, these links help developers to keep test code in sync with tested code, reducing the rate of test failures and missed faults. Test-to-code traceability links can also help developers to maintain an accurate mental model of the system, reducing the risk of architectural degradation when making changes. However, establishing and maintaining these links manually places an extra burden on developers and is error-prone. This paper presents TCtracer, an approach and implementation for the automatic establishment of test-to-code traceability links. Unlike existing work, TCtracer operates at both the method level and the class level, allowing us to establish links between tests and functions, as well as between test classes and tested classes. We improve over existing techniques by combining an ensemble of new and existing techniques and exploiting a synergistic flow of information between the method and class levels. An evaluation of TCtracer using four large, well-studied open source systems demonstrates that, on average, we can establish test-to-function links with a mean average precision (MAP) of 78% and test-class-to-class links with an MAP of 93%.
Privacy and Security of IoT based Skin Monitoring System using Blockchain Approach

Remote patient monitoring is a system that focuses on patients care and attention with the advent of the Internet of Things (IoT). The technology makes it easier to track distance, but also to diagnose and provide critical attention and service on demand so that billions of people are safer and more safe. Skincare monitoring is one of the growing fields of medical care which requires IoT monitoring, because there is an increasing number of patients, but cures are restricted to the number of available dermatologists. The IoT-based skin monitoring system produces and store volumes of private medical data at the cloud from which the skin experts can access it at remote locations. Such large-scale data are highly vulnerable and otherwise have catastrophic results for privacy and security mechanisms. Medical organizations currently do not concentrate much on maintaining safety and privacy, which are of major importance in the field. This paper provides an IoT based skin surveillance system based on a blockchain data protection and safety mechanism. A secure data transmission mechanism for IoT devices used in a distributed architecture is proposed. Privacy is assured through a unique key to identify each user when he registers. The principle of blockchain also addresses security issues through the generation of hash functions on every transaction variable. We use blockchain consortiums that meet our criteria in a decentralized environment for controlled access. The solutions proposed allow IoT based skin surveillance systems to privately and securely store and share medical data over the network without disturbance.
A coarse-to-fine user preferences prediction method for point-of-interest recommendation

Point-of-interests (POIs) recommendations aim at recommending locations to users on social platforms by analyzing their histories or combining other information. At present, the different granularity of factors (i.e. time, geography and sociability) are not thoroughly studied in existing works. To deal with this problem, we propose a two-stage coarse-to-fine POI recommendation algorithm based on tensor factorization and weighted distance kernel density estimation (KDE). At first stage, we take account of not only long-term preferences with sequential context, but also the crowd’s preferences to estimate the coarse user-category interest. And then a specific-designed weighted KDE with consideration of spatial distance is employed to determine the fine-grained user-location interest. To evaluate the proposed method, experiments are conducted on two real benchmark location-based social network (LBSN) datasets. And the results show that the proposed method outperforms the state-of-the-art methods and produces better POI recommendation.
A Systematic Review of the Application of Maturity Models in Universities

A maturity model is a widely used tool in software engineering and has mostly been extended to domains such as education, health, energy, finance, government, and general use. It is valuable for evaluations and continuous improvement of business processes or certain aspects of organizations, as it represents a more organized and systematic way of doing business. In this paper, we only focus on college higher education. For this reason, we present a novel approach that allows detecting some gaps in the existing maturity models for universities, as they are not models that address the dimensions in their entirety. To identify these models and their validities, as well as a classification of models that were identified in universities, we carried out a systematic literature review on 27,289 articles retrieved with respect to maturity models and published in peer-reviewed journals between 2007 and 2020. We found 23 articles that find maturity models applied in universities, through exclusion and inclusion criteria. We then grouped these items into nine categories with specific purposes. We concluded that maturity models used in Universities move towards agility, which is supported by the semantic web.
Medical Fraud and Abuse Detection System Based on Machine Learning

It is estimated that approximately 10% of healthcare system expenditures are wasted due to medical fraud and abuse. In the medical area, the combination of thousands of drugs and diseases make the supervision of health care more difficult. To quantify the disease–drug relationship into relationship score and do anomaly detection based on this relationship score and other features, we proposed a neural network with fully connected layers and sparse convolution. We introduced a focal-loss function to adapt to the data imbalance and a relative probability score to measure the model’s performance. As our model performs much better than previous ones, it can well alleviate analysts’ work.
A Design Implementation and Comparative Analysis of Advanced Encryption Standard (AES) Algorithm on FPGA

As the technology is getting advanced continuously the problem for the security of data is also increasing. The hackers are equipped with new advanced tools and techniques to break any security system. Therefore people are getting more concern about data security. The data security is achieved by either software or hardware implementations. In this work Field Programmable Gate Arrays (FPGA) device is used for hardware implementation since these devices are less complex, more flexible and provide more efficiency. This work focuses on the hardware execution of one of the security algorithms that is the Advanced Encryption Standard (AES) algorithm. The AES algorithm is executed on Vivado 2014.2 ISE Design Suite and the results are observed on 28 nanometers (nm) Artix-7 FPGA. This work discusses the design implementation of the AES algorithm and the resources consumed in implementing the AES design on Artix-7 FPGA. The resources which are consumed are as follows-Slice Register (SR), Look-Up Tables (LUTs), Input/Output (I/O) and Global Buffer (BUFG).
Micrometer-level 3D measurement techniques in complex scenes based on stripe-structured light and photometric stereo

Compared with existing depth cameras, such as RGB-D, RealSense and Kinect, stripe-based structured light (SL) has the potential for micrometer-level 3D measurement; this can be attributed to its higher coding capacity. While surface texture, high-reflective region, and occlusion remain some of the main sources leading to degraded reconstruction quality in complex objects, methods that are only based on SL cannot completely solve such problems in complex object reconstruction. In this paper, we developed an advanced fusion strategy for the reconstruction of complex objects in micrometer-level 3D measurement. This includes solving the above-mentioned inherent problems of a stripe-based SL system with the aid of photometric stereo (PS). Firstly, to improve the robustness of decoding and eliminate the effects of noise and occlusion on stripe detection, a novel scene-adaptive decoding algorithm based on a binary tree was proposed. Further, a robust and practical calibration method for area light sources in the PS system, which utilizes the absolute depth information from SL system, was introduced. A piecewise integration algorithm, which is based on a subregion divided by Gray code, was proposed by combining the depth values from SL with the normal information from PS. Remarkably, this method eliminates the effects of surface texture and high-reflective region on the reconstruction quality and improves the resolution to camera-level resolution. In experimental parts, a regular cylinder was reconstructed to demonstrate micrometer-level measurement accuracy and resolution enhancement by the proposed method. Then, improvement of the reconstruction accuracy for objects with surface texture was validated with a regular pyramid that had textures on it and a white paper with characters printed on it. Lastly, a complex object containing multiple phenomena was reconstructed with the newly proposed method to show its effectiveness for micrometer-level 3D measurement in complex objects. Evaluation of our proposed method shows the improvement of the proposed method on the existing methods being used for micrometer-level 3D measurement in complex objects.
Federated Machine Learning in Vehicular Networks: A summary of Recent Applications

Future Intelligent Transportation Systems (ITS) can improve on-road safety and transportation efficiency and vehicular networks (VNs) are essential to enable ITS applications via information sharing. The development of 5G introduces new technologies providing improved support for connected vehicles through highly dynamic heterogeneous networks. Machine Learning (ML) can capture the high dynamics of VNs but the distributed data cause new challenges for ML hence requires distributed solutions. Federated learning (FL), a distributed ML framework, gives a distributed ML framework while ensuring information privacy protection and is an exciting area to explore in VNs. This article provides a detailed summary of recent FL applications in VNs and gives insights on current research challenges. The included research topics are resource management, performance optimization and applications based on VNs.
Direct NMPC for Post-Stall Motion Planning with Fixed-Wing UAVs

Fixed-wing unmanned aerial vehicles (UAVs) offer significant performance advantages over rotary-wing UAVs in terms of speed, endurance, and efficiency. However, these vehicles have traditionally been severely limited with regards to maneuverability. In this paper, we present a nonlinear control approach for enabling aerobatic fixed-wing UAVs to maneuver in constrained spaces. Our approach utilizes full-state direct trajectory optimization and a minimalistic, but representative, nonlinear aircraft model to plan aggressive fixed-wing trajectories in real-time at 5 Hz across high angles-of-attack. Randomized motion planning is used to avoid local minima and local-linear feedback is used to compensate for model inaccuracies between updates. We demonstrate our method in hardware and show that both local-linear feedback and re-planning are necessary for successful navigation of a complex environment in the presence of model uncertainty.
Deep Active Learning for Anomaly Detection

Anomalies are intuitively easy for human experts to understand, but they are hard to define mathematically. Therefore, in order to have performance guarantees in unsupervised anomaly detection, priors need to be assumed on what the anomalies are. By contrast, active learning provides the necessary priors through appropriate expert feedback. Thus, in this work we present an active learning method that can be built upon existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data effectively. We introduce a new layer that can be easily attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method. We report results on both synthetic and real anomaly detection datasets, using multi-layer perceptrons and autoencoder architectures empowered with the proposed active layer, and we discuss their performance on finding clustered and low density anomalies.