Reviewerly/exHarmony huggingface dataset:
End-to-End Session-Based Recommendation on GPU

In recent years, several deep learning-based algorithms have been proposed for recommendation systems while its adoption in industry deployments have been steeply growing. In particular, NLP-inspired approaches have been successfully adapted for sequential and session-based recommendation problems, which are important for many domains like e-commerce, news and streaming media. In this regard, this hands-on tutorial will offer to the participants: (I) an introduction on the main concepts and algorithms for session-based recommendation, (II) how to build, train and evaluate a session-based recommendation model based on RNN and Transformer architectures, and (III) how to speed up with GPUs the entire RecSys pipeline which encompasses feature engineering, preprocessing, training, evaluation and inference using NVIDIA Merlin - an open source ecosystem for large-scale deep learning recommender systems.
Constrained Policy Optimization Algorithm for Autonomous Driving via Reinforcement Learning

Reinforcement Learning (RL) has achieved great success in many complicated and difficult problems in recent years. In the field of autonomous driving, RL promises to make road transportation safer, more efficient and more sustainable through intelligent sensing, decision and control techniques in driving tasks. Due to above reasons it is expected that RL will provide a huge role in autonomous driving as well. In this paper, we present the methodology of constrained policy optimization and Markov modeling. In order to ensure the learning process, this paper chooses to explore an algorithm called the constrained policy optimization (CPO) followed by trust region policy optimization and Markov modeling. CPO algorithm ensures the policy by modifying the policy update. In order to fully verify the learning function of the CPO algorithm, many test fields are selected from straight road to slightly complex geometry.
Quantum algorithm for quantum state discrimination via partial negation and weak measurement

The quantum state discrimination problem is to distinguish between non-orthogonal quantum states. This problem has many applications in quantum information theory, quantum communication and quantum cryptography. In this paper, a quantum algorithm using weak measurement and partial negation will be proposed to solve the quantum state discrimination problem using a single copy of an unknown qubit. The usage of weak measurement makes it possible to reconstruct the qubit after measurement since the superposition will not be destroyed due to measurement. The proposed algorithm will be able to determine, with high probability of success, the state of the unknown qubit and whether it is encoded in the Hadamard or the computational basis by counting the outcome of the successive measurements on an auxiliary qubit.
Support structure representation learning for sequential data clustering

Sequential data clustering is a challenging task in data mining (e.g., motion recognition and video segmentation). For good performance in dealing with complex local correlation and high-dimensional structure of sequential data, representation based methods have become one of the hot topics for sequential data clustering, in which subspace clustering is a representative tool. Subspace clustering methods divide the sequence into disjoint segments according to a locally continuous and connected representation of raw data. Although the subspace clustering methods maintain the successive property of sequential data well, there exist redundant connections in the intersection of two subsequences, which will destroy the integrity of a cluster and easily cause the chained partition of the sequence. So it is necessary to learn a more specific structure representation of a sequence to preserves both sequential information and efficient connections. Besides, the representation that conducive to clustering should have sparsity and connectivity under some assumptions. To this end, we propose a novel method to learn the support structure representation of sequence, which can extract sufficient information about instances and get the compact structure of sequential data. Furthermore, a new subspace clustering method is proposed based on the representation based method. Theoretical analysis and experimental results show the effectiveness of the proposed method.
Applying Artificial Bee Colony Algorithm to Interactive Evolutionary Computation

In this study, we apply an artificial bee colony (ABC) algorithm to the interactive evolutionary computation (IEC) method for the multimodal retrieval of candidate solutions. Previous works have proposed IEC systems using a parallel interactive tabu search algorithm (PITS) that generates multiple tabu search (TS) retrievals and a hybrid genetic algorithm (GA) involving a global retrieval method and a TS involving a local retrieval method for multimodal retrieval. However, the PITS cannot efficiently retrieve candidate solutions and it has a complicated algorithm. The hybrid GA–TS also finds it hard to retrieve candidate solutions if the user has a more multimodal preference. We propose herein an IEC method with the ABC algorithm for the multimodal and simultaneous retrieval of candidate solutions. We perform a numerical simulation with a pseudo user that imitates multimodal preferences as target candidate solutions instead of a real user. The results show that the proposed method can retrieve multimodal candidate solutions in conditions with limited numbers of candidate solutions and bees.
Comparing Strategies for Post-Hoc Explanations in Machine Learning Models

Most of the machine learning models act as black boxes, and hence, the need for interpreting them is rising. There are multiple approaches to understand the outcomes of a model. But in order to be able to trust the interpretations, there is a need to have a closer look at these approaches. This project compared three such frameworks—ELI5, LIME and SHAP. ELI5 and LIME follow the same approach toward interpreting the outcomes of machine learning algorithms by building an explainable model in the vicinity of the datapoint that needs to be explained, whereas SHAP works with Shapley values, a game theory approach toward assigning feature attribution. LIME outputs an R-squared value along with its feature attribution reports which help in quantifying the trust one must have in those interpretations. The R-squared value for surrogate models within different machine learning models varies. SHAP trades-off accuracy with time (theoretically). Assigning SHAP values to features is a time and computationally consuming task, and hence, it might require sampling beforehand. SHAP triumphs over LIME with respect to optimization of different kinds of machine learning models as it has explainers for different types of machine learning models, and LIME has one generic explainer for all model types.
Towards Consistent Predictive Confidence through Fitted Ensembles

Deep neural networks are behind many of the recent successes in machine learning applications. However, these models can produce overconfident decisions while encountering out-of-distribution (OOD) examples or making a wrong prediction. This inconsistent predictive confidence limits the integration of independently-trained learning models into a larger system. This paper introduces separable concept learning framework to realistically measure the performance of classifiers in presence of OOD examples. In this setup, several instances of a classifier are trained on different parts of a partition of the set of classes. Later, the performance of the combination of these models is evaluated on a separate test set. Unlike current OOD detection techniques, this framework does not require auxiliary OOD datasets and does not separate classification from detection performance. Furthermore, we present a new strong baseline for more consistent predictive confidence in deep models, called fitted ensembles, where overconfident predictions are rectified by transformed versions of the original classification task. Fitted ensembles can naturally detect OOD examples without requiring auxiliary data by observing contradicting predictions among its components. Experiments on MNIST, SVHN, CIFAR-10/100, and ImageNet show fitted ensemble significantly outperform conventional ensembles on OOD examples and are possible to scale.
A Proposed Software Protection Mechanism for Autonomous Vehicular Cloud Computing

Cars are becoming smarter every day. They are being equipped with sensors, communications interfaces, and more powerful processing capabilities. The primary purpose was to enable these cars to drive themselves without any human interventions and become so-called Autonomous Vehicles (AVs). But why stop there, why not harness all that computing power for a greater collective purpose. That’s how the idea of Autonomous Vehicular Cloud Computing (AVCC) was born. Nonetheless, this is not a trivial task, the mobile and dynamic nature of vehicles poses a significant challenge in the formation and management of this cloud computing model and yet a more substantial challenge in terms of security and privacy of all the parties involved in this system. In this paper, we focus on protecting software running on AVCC. We use dynamic obfuscated compilation to complicate programs’ execution paths and hinder information leakage via side channels attacks. Relying on compilers offers advantages, such as the independence of architecture and support for a variety high-level programming languages and application simplicity with minimal set-up cost. Here, we introduce our system in the realm of ARM processor, which power AVCC. Then, we present execution statistics for simple standard programs. The results show tangible timing variations in diversified code versions for the same program, which may disrupt side-channel attacks.
Continuous-Action Reinforcement Learning for Portfolio Allocation of a Life Insurance Company

The asset management of an insurance company is more complex than traditional portfolio management due to the presence of obligations that the insurance company must fulfill toward the clients. These obligations, commonly referred to as liabilities, are payments whose magnitude and occurrence are a byproduct of insurance contracts with the clients, and of portfolio performances.In particular, while clients must be refunded in case of adverse events, such as car accidents or death, they also contribute to a common financial portfolio to earn annual returns. Customer withdrawals might increase whenever these returns are too low or, in the presence of an annual minimum guaranteed, the company might have to integrate the difference. Hence, in this context, any investment strategy cannot omit the inter-dependency between financial assets and liabilities.To deal with this problem, we present a stochastic model that combines portfolio returns with the liabilities generated by the insurance products offered by the company. Furthermore, we propose a risk-adjusted optimization problem to maximize the capital of the company over a pre-determined time horizon.Since traditional financial tools are inadequate for such a setting, we develop the model as a Markov Decision Process. In this way, we can use Reinforcement Learning algorithms to solve the underlying optimization problem. Finally, we provide experiments that show how the optimal asset allocation can be found by training an agent with the algorithm Deep Deterministic Policy Gradient.
DS-TAGCN: A Dual-Stream Topology Attentive GCN for Node Classification in Dynamic Graphs

With the rapid growth of information, large amounts of graph-structured data have been generated. As an important task in graph-structured data research, node classification, which aims to classify nodes into different categories, has attracted a lot of attention from researchers in recent years. Real-life graphs are often dynamic whose graph topology and node attributes are constantly evolving. However, most of the studies focus on static graphs which can not capture the evolution of dynamic graphs. Node classification in dynamic graphs mainly has the following two challenges. First, it is difficult to effectively integrate modeling spatial and temporal features. Second, the evolution of dynamic graphs is located not only in node attributes but also in the graph topology. It is hard to learn the evolution of both aspects in the meantime. Besides, existing methods focus only on topological relations connected by explicit edges, while ignoring implicit topological relations that act in non-edge form. Implicit topological relations can help aggregate neighborhood features and further refine the modeling of node evolution patterns. To address these challenges and problems, we propose DS-TAGCN, a dual-stream topology attentive GCN for dynamic graph node classification. DS- TAGCN learns spatial-temporal features simultaneously by using a combination of GCN and LSTM. A dual-stream framework is designed to focus on the evolution of node attributes and graph topology, respectively. To mine the implicit topology, we propose TAGCN instead of GCN to model the implicit topological relations. Additionally, we incorporate a hierarchical attention mechanism in the network to automatically model the importance of different dimensional features. Extensive experiments demonstrate the effectiveness of DS-TAGCN.
Effect of Applied Design Thinking Courses

This study examines learning outcomes by applying design thinking to logic and writing in the area of education, educational philosophy and in history classes. It also provides basic data on whether to expand the application to other classes. The results of the study are as follows: First, in creative thinking competency, the average score of the students was higher than before in all divergent thinking abilities, adventure and curiosity, intellectual inquiry, but not for original flexibility. The difference between the means was statistically significant (p &lt; .05). Second, in emotional intelligence improvement, the average was higher than before in all forms of emotion recognition and expression, empathy, thinking promotion, and emotion utilization. Only with emotional regulation did the average score not increase. The difference between the means was statistically significant (p &lt; .05). Third, design thinking was effective in five categories of learning outcomes. From this study, we found that using design thinking is meaningful in improving the ability of students to solve problems related to creative thinking and emotional intelligence.
Less is more! Support of Parallel and Time-critical Assembly Tasks with Augmented Reality

Manual assembly tasks require workers to precisely assemble parts in 3D space in parallel steps. Often, additional time pressure further increases the complexity of these parallel tasks (e.g. in adhesive bonding processes). The performance in parallel tasks is heavily influenced by the capacity of the working memory, which is often overwhelmed if rules and states of too many tasks have to be remembered or perceived. Therefore, we propose to use Augmented Reality (AR) to investigate how visual assistance with parallel tasks can affect worker performance in time and spatial dependent process steps. In a user study, we compare three conditions: AR instructions presented (a) One task, (b) Two tasks, and (c) Four tasks per step on a tablet. For instructions we used selected work steps from a standardized adhesive bonding process as a representative for common time-critical assembly tasks. Our results show that instructions with multiple displayed tasks simultaneously per step can improve the process time but also increase the error rate and task load. The work instructions with less displayed tasks per work step showed better subjective results among participants, which may increase motivation, especially among less experienced workers. Our results help designers and developers to design assistance systems for time-critical and simultaneously executable assembly tasks, while considering process times, error rate and task load.
A Novel Effort Measure Method for Effort-Aware Just-in-Time Software Defect Prediction

Just-in-time software defect prediction (JIT-SDP) is a fine-grained software defect prediction technology, which aims to identify the defective code changes in software systems. Effort-aware software defect prediction is a software defect prediction technology that takes into consideration the cost of code inspection, which can find more defective code changes in limited test resources. The traditional effort-aware defect prediction model mainly measures the effort based on the number of lines of code (LOC) and rarely considers additional factors. This paper proposes a novel effort measure method called Multi-Metric Joint Calculation (MMJC). When measuring the effort, MMJC takes into account not only LOC, but also the distribution of modified code across different files (Entropy), the number of developers that changed the files (NDEV) and the developer experience (EXP). In the simulation experiment, MMJC is combined with Linear Regression, Decision Tree, Random Forest, LightGBM, Support Vector Machine and Neural Network, respectively, to build the software defect prediction model. Several comparative experiments are conducted between the models based on MMJC and baseline models. The results show that indicators ACC and [Formula: see text] of the models based on MMJC are improved by 35.3% and 15.9% on average in the three verification scenarios, respectively, compared with the baseline models.
Learning spatial self‐attention information for visual tracking

Visual object tracking has been a fundamental topic in computer vision and many convolutional neural network (CNN) based trackers proposed in recent years have achieved state-of-the-art performance, multi-domain convolutional neural network (MDNet) is one of the most representative CNN-based algorithms with high performance. In order to significantly improve the robustness and accuracy of the MDNet algorithm, a multi-domain convolutional neural network based on spatial self-attention information learning (SAMDNet) is proposed in this study. The authors use the spatial self-attention module for the spatial information learning of the model. The spatial self-attention module selectively aggregates the feature at each position by a weighted sum of the features at all positions. Under the control of self-learning parameters in this module, spatial attention information can be flexibly acquired. The authors also propose a novel interval loss term to solve the problem of different classes with the same semantics in the training data. Finally, an anomaly detection module is carefully designed for relocation after the algorithm completely lost the target. Extensive experiments on the object tracking benchmark (OTB) and the visual object tracking challenge (VOT) benchmarks show that the proposed tracker outperforming most of the state-of-art trackers.
Valid Statements by the Crowd: Statistical Measures for Precision in Crowdsourced Mobile Measurements

Crowdsourced network measurements (CNMs) are becoming increasingly popular as they assess the performance of a mobile network from the end user’s perspective on a large scale. Here, network measurements are performed directly on the end-users’ devices, thus taking advantage of the real-world conditions end-users encounter. However, this type of uncontrolled measurement raises questions about its validity and reliability. The problem lies in the nature of this type of data collection. In CNMs, mobile network subscribers are involved to a large extent in the measurement process, and collect data themselves for the operator. The collection of data on user devices in arbitrary locations and at uncontrolled times requires means to ensure validity and reliability. To address this issue, our paper defines concepts and guidelines for analyzing the precision of CNMs; specifically, the number of measurements required to make valid statements. In addition to the formal definition of the aspect, we illustrate the problem and use an extensive sample data set to show possible assessment approaches. This data set consists of more than 20.4 million crowdsourced mobile measurements from across France, measured by a commercial data provider.
Efficient computation of contrastive explanations

With the increasing deployment of machine learning systems in practice, transparency and explainability have become serious issues. Contrastive explanations are considered to be useful and intuitive, in particular when it comes to explaining decisions to lay people, since they mimic the way in which humans explain. Yet, so far, comparably little research has addressed computationally feasible technologies, which allow guarantees on uniqueness and optimality of the explanation and which enable an easy incorporation of additional constraints. Here, we will focus on specific types of models rather than black-box technologies. We study the relation of contrastive and counterfactual explanations and propose mathematical formalizations as well as a 2-phase algorithm for efficiently computing (plausible) pertinent positives of many standard machine learning models.
Blending of Learning-based Tracking and Object Detection for Monocular Camera-based Target Following

Deep learning has recently started being applied to visual tracking of generic objects in video streams. For the purposes of robotics applications, it is very important for a target tracker to recover its track if it is lost due to heavy or prolonged occlusions or motion blur of the target. We present a real-time approach which fuses a generic target tracker and object detection module with a target re-identification module. Our work focuses on improving the performance of Convolutional Recurrent Neural Network-based object trackers in cases where the object of interest belongs to the category of familiar objects. Our proposed approach is sufficiently lightweight to track objects at 85-90 FPS while attaining competitive results on challenging benchmarks.
A Survey on Anomaly Detection Strategies

Anomaly detection can be defined as the process to find out the outliers of a dataset, where outliers or anomalies are the instances that don't belong to that particular dataset. These anomalies might point to a wide range of things such as unusual network traffic, uncover a faulty sensor, or simply identify data for cleaning, before analysis, etc. Detecting anomalous behavior beforehand can prevent malignant abuse, data breaches, and intellectual property theft. Various anomaly detection techniques have recently become available and commonly used. Based on availability of techniques, one can yield better results for a specific user or dataset than others. This paper provides a concise overview of the most broadly used strategies for detecting anomalies. For this purpose, we present recent research works briefly, along with their established methodology. Finally, we outline some challenges to be dealt with while working with these methods.