Reviewerly/exHarmony huggingface dataset:
Modelling digital avatars: A tuple space approach

The development of the Internet of Things (IoT) came with the manufacturing of a huge amount of smart things equipped with sensors for making them aware of their environment, and with network connection for allowing remote interaction with them. However, most smart things still lack enough autonomy and context-awareness, hindering them from being people-friendly and actually useful for their users' everyday tasks. IoT devices should take advantage of their sensors and smartness to react automatically to the needs of their users and to provide seamless interactions with them. Within this field, the authors work on the design of Digital Avatars, a mobile computing framework for dynamically programming interactions among smart devices. The framework is based on the virtual profile of the user, which is inferred, stored, and shared by their smartphone. The profile provides a personalized context for running scripts which interact with IoT devices. This way, smartphones become a digital avatar of the user, capable of acting as a personal and seamless interface with their IoT environment. In this work, we present a formalization of Digital Avatars by means of a Linda-based approach with multiple shared tuple spaces. By means of a case study, we show how properties of the systems can be proved, and we briefly describe an implementation of both the Digital Avatars framework and the case study.
Neural Model Stealing Attack to Smart Mobile Device on Intelligent Medical Platform

To date, the Medical Internet of Things (MIoT) technology has been recognized and widely applied due to its convenience and practicality. The MIoT enables the application of machine learning to predict diseases of various kinds automatically and accurately, assisting and facilitating effective and efficient medical treatment. However, the MIoT are vulnerable to cyberattacks which have been constantly advancing. In this paper, we establish a MIoT platform and demonstrate a scenario where a trained Convolutional Neural Network (CNN) model for predicting lung cancer complicated with pulmonary embolism can be attacked. First, we use CNN to build a model to predict lung cancer complicated with pulmonary embolism and obtain high detection accuracy. Then, we build a copycat model using only a small amount of data labeled by the target network, aiming to steal the established prediction model. Experimental results prove that the stolen model can also achieve a relatively high prediction outcome, revealing that the copycat network could successfully copy the prediction performance from the target network to a large extent. This also shows that such a prediction model deployed on MIoT devices can be stolen by attackers, and effective prevention strategies are open questions for researchers.
Improving the Cold Start Problem in Music Recommender Systems

Abstract The music recommender system can help us extract valuable music from a huge amount of raw information, due to the increasingly severe data sparsity and cold start problems in music recommendation system, the recommendation results will be inaccurate. But the traditional algorithm cannot effectively solve these problems; the existing improved algorithm still requires specific parameters in advance due to its poor stability. In this paper, a proposition for a cold start based on community detection algorithm is proposed. By projecting the bipartite network, calculating the similarity between the User and the Item, the Louvain algorithm is used to perform community detection on the projected One-mode network, so that the new record can be updated to the original community, and then the user group is recommended for music. With higher accuracy, solid stability, and shorter running time (when compared with other ease cold start algorithms), the algorithm in the larger community can be safely applied to the music recommendation system or prediction field.
Cycling-Net

Cycling, as a green transportation mode, provides an environmentally friendly transportation choice for short-distance traveling. However, cyclists are also getting involved in fatal accidents more frequently in recent years. Thus, understanding and modeling their road behaviors is crucial in helping improving road safety laws and infrastructures. Traditionally, people understand road user behavior using either purely spatial trajectory data, or videos from fixed surveillance camera through tracking or predicting their paths. However, these data only cover limited areas and do not provide information from the cyclist's field of view. In this paper, we take advantage of geo-referenced egocentric video data collected from the handlebar cameras of cyclists to learn how to predict their behaviors. This approach is technically more challenging, because both the observer and objects in the scene might be moving, and there are strong temporal dependencies in both the behaviors of cyclists and the video scenes. We propose Cycling-Net, a novel deep learning model that tracks different types of objects in consecutive scenes and learns the relationship between the movement of these objects and the behavior of the cyclist. Experiment results on a naturalistic trip dataset show the Cycling-Net is effective in behavior prediction and outperforms a baseline model.
Graph Neural Networks for Human-Aware Social Navigation

Autonomous navigation is a key skill for assistive and service robots. To be successful, robots have to comply with social rules, such as avoiding the personal spaces of the people surrounding them, or not getting in the way of human-to-human and human-to-object interactions. This paper suggests using Graph Neural Networks to model how inconvenient the presence of a robot would be in a particular scenario according to learned human conventions so that it can be used by path planning algorithms. To do so, we propose two automated scenario-to-graph transformations and benchmark them with different Graph Neural Networks using the SocNav1 dataset [1]. We achieve close-to-human performance in the dataset and argue that, in addition to its promising results, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered and easily embedded in code in comparison with model-based approaches. The code used to train and test the resulting graph neural network is available in a public repository.
Tourism Object Detection Around Monumen Nasional (Monas) Using YOLO and RetinaNet

Tourism sector has become one of the most potential income for some countries. One of the ways to increase income from tourism sector is to implement information technology so it can attract more tourists to come. The technology that can be implemented is smart tourism. One of the smart tourism implementations for Indonesia tourism, especially for Monas tourism destination is mobile based Visual Question Answering (VQA) application that can provide detailed information about tourism object from mobile phone camera. Focus of this thesis is to produce training model with good detection accuracy. The result of the model training process will be used as model for object detection model that will be used for doing VQA. Dataset that will be used for this research are 600 pictures containing Monas and 25 surrounding objects called class. The methods that will be used for object detection is using YOLO and RetinaNet, where both of these methods will be compared each other by searching the accuracy from evaluation metric from both method. By using original dataset, in YOLO the mean average precision (mAP) score is between 60.77% to 71.99% with 0.1 to 0.9 confidence level threshold range and in RetinaNet the mAP score is between 72.18% to 92.98%. By using augmented dataset, in YOLO the mAP score is between 52.51% to 93.72% and in RetinaNet the mAP score is between 23,8% to 56,19%. The Area Under Curve (AUC) score for original dataset is 0.99 and 0.96 for augmented dataset using YOLO method. Based on the evaluation result, YOLO can detect objects better than RetinaNet and augmented dataset can produce better detection than original dataset.
Data Mining for Fraud Detection

With the increased use online and electronic resources both by the companies and the customers the problem of fraud has been rising in the last decade. Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data, that may signify interesting patterns, including those related to fraud. This chapter aims to introduces to the concepts of fraud, processes and tools involved in data mining techniques, as well as the importance, challenges, and use cases.
Modular Moose: A New Generation of Software Reverse Engineering Platform

Advanced reverse engineering tools are required to cope with the complexity of software systems and the specific requirements of numerous different tasks (re-architecturing, migration, evolution). Consequently, reverse engineering tools should adapt to a wide range of situations. Yet, because they require a large infrastructure investment, being able to reuse these tools is key. Moose is a reverse engineering environment answering these requirements. While Moose started as a research project 20 years ago, it is also used in industrial projects, exposing itself to all these difficulties. In this paper we present ModMoose, the new version of Moose. ModMoose revolves around a new meta-model, modular and extensible; a new toolset of generic tools (query module, visualization engine, ...); and an open architecture supporting the synchronization and interaction of tools per task. With ModMoose, tool developers can develop specific meta-models by reusing existing elementary concepts, and dedicated reverse engineering tools that can interact with the existing ones.
Autonomous Recognition of Collective Behaviour in Robot Swarms

Developmental evolution of collective swarm behaviours promises new ways to evolve swarms with different movement characteristics. Preliminary work has developed value functions that can recognize emergent swarm behaviour and distinguish it from random behaviour in point-mass boid simulations. This paper examines the performance of several variants of such functions recognizing the emergent behaviour of simulated robots, which have different movement properties to point-mass boid simulations as they are constrained by the manoeuvrability of the physical robot. We designed two boid guidance algorithms for controlling Pioneer3DX robots. Five value functions were then examined and compared for their ability to distinguish swarming behaviour from unstructured behaviour. Results show that four of these can be used to distinguish structured collective behaviours of the robots and distinguish such behaviour from random movement patterns.
Notes on notebooks: is Jupyter the bringer of jollity?

As the interactive computational notebook becomes a more prominent code development medium, we examine advantages and disadvantages of this particular source code format. We specify the structure of a coding notebook layout. We describe complexities in notebook programming; some of these are incidental whereas others may be inherent complexities. We outline how we envisage research and development might proceed to advance the cause of notebook programming.
Sensor fusion of camera and LiDAR raw data for vehicle detection

Autonomous vehicles are expected to save almost half-million lives between 2035 to 2045. Moreover, since 90% of the accidents are caused by humans, 9% by weather and road conditions, and only 1% by vehicular failures, autonomous vehicles will provide much safer traffic, drastically decreasing the number of accidents. To perceive the surrounding objects and environment, autonomous vehicles depend on their sensor systems such as cameras, LiDARs, radars, and sonars. Traditionally, decision fusion is performed, implying into first individually processing each sensor's data and then combining the processed information of the different sensors. In contrast to the traditional decision fusion of the processed information from each sensor, the raw data fusion extracts information from all sensors' raw data providing higher reliability and accuracy in terms of object and environment perception. This paper proposes an improved sensor fusion framework based on You Only Look Once (YOLO) that jointly processes the raw data from cameras and LiDARs. To validate our framework, the dataset of the Karlsruhe Institute of Technology (KITTI) in partnership with Toyota Technical University generated using two cameras, and a Velodyne laser scanner is considered. The proposed raw data fusion framework outperforms the traditional decision fusion framework with a gain of 5% in terms of vehicle detection performance.
Discrete Mother Tree Optimization for the Traveling Salesman Problem

The Mother Tree Optimization (MTO) algorithm is a new swarm intelligence technique that we have recently proposed for solving continuous optimization problems. MTO is built on an offspring topology and a set of cooperating agents. In this paper, we first present a discrete version of MTO, that we call Discrete MTO (DMTO), for solving the Traveling Salesman Problem (TSP). DMTO is based on a new swap operation that is best suited for TSPs. We also used this swap operation to introduce an updated version of the Discrete Particle Swarm Optimization (DPSO) algorithm. With a careful application of our new swap operation, we will show that our Updated DPSO (UDPSO) is more effective than DPSO. In order to assess the performance of our proposed methods, DMTO and UPSO, we conducted several experiments comparing both to DPSO and an exact method (Branch and Bound), on ten TSP instances taken from the well-known TSPLIB dataset. The results clearly show that DMTO produces solutions of much better quality than in DPSO, and superior to those in UDPSO.
Depth Image Inpainting via Single Depth Features Learning

The holes in depth images, which are one of the main factors affecting image quality and applications. In order to fill holes effectively for the depth images, this paper proposes a single depth image inpainting method based on deep learning. First, features are learned and extracted from a single depth image by U-Net network. Then, the similarity measurement loss between the output and ground truth is back-propagated to update network parameters. Finally, the depth image inpainting result is created by combing the output hole filling result and original image. The NYUv2 dataset was used for the training and evaluation. Experimental results show that results of our method which only learns feature information from single depth image are similar to the optimal results of various hole filling methods which using color image information.
On the Distribution of Speaker Verification Scores: Generative Models for Unsupervised Calibration

Speaker verification systems whose outputs can be interpreted as log-likelihood ratios (LLR) allow for cost-effective decisions by comparing the system outputs to application-defined thresholds depending only on prior information. Classifiers often produce uncalibrated scores, and require additional processing to produce well-calibrated LLRs. Recently, generative score calibration models have been proposed, which achieve calibration performance close to that of state-of-the-art discriminative techniques for supervised scenarios, while also allowing for unsupervised training. The effectiveness of these methods, however, strongly depends on their capabilities to correctly model the target and non-target score distributions. In this work we propose theoretically grounded and accurate models for characterizing the distribution of scores of speaker verification systems. Our approach is based on tied Generalized Hyperbolic distributions and overcomes many limitations of Gaussian models. Experimental results on different NIST benchmarks, using different utterance representation front-ends and different back-end classifiers, show that our method is effective not only in supervised scenarios, but also in unsupervised tasks characterized by very low proportion of target trials.
Comparative Study of Classification Algorithms to Analyze and Predict a Twitter Sentiment in Apache Spark

Nowadays, the major sources of information exchange are Twitter, Facebook, WordPress, etc. The tweets can be considered as the source of the public opinion on an event, a product, or a topic. Consequently, it contains large volumes of natural data. Enormous dataset contains a huge volume and variety of information. Therefore, it cannot be prepared utilizing normal conventional tools. It can be processed by building up distributed environment or by contracting cloud based isolated infrastructure. Therefore, better approaches and instruments are required to bring the respect of the information. Apache spark is actually appropriate for performing machine learning on large-scale information. To discover out how rapidly Spark processes of huge information, we make an approach that utilize Machine Learning library (MLlib) classification algorithms in Apache Spark. We implement Logistic Regression, Multilayer Perceptron (MLP), Random Forest and Support Vector Machine (SVM) and compare among them. The models are for analysis and predicting the sentiment based on a corona tweets. A sentiment analysis based on tweets is a challenging issue. The classification algorithm is assessed by precision, recall, f-measure, accuracy and time consumed. The results show that logistic regression algorithm has higher speed in doing enormous information processing than other chosen algorithms. Moreover, according to the obtained results, apache spark has exceptionally great speed in handling big data. The classification algorithm is assessed by precision, recall, f-measure, accuracy and time consumed.
Knowledge Transfer Graph for Deep Collaborative Learning

Knowledge transfer among multiple networks using their outputs or intermediate activations have evolved through manual design from a simple teacher-student approach to a bidirectional cohort one. The major components of such knowledge transfer framework involve the network size, the number of networks, the transfer direction, and the design of the loss function. However, because these factors are enormous when combined and become intricately entangled, the methods of conventional knowledge transfer have explored only limited combinations. In this paper, we propose a novel graph representation called knowledge transfer graph that provides a unified view of the knowledge transfer and has the potential to represent diverse knowledge transfer patterns. We also propose four gate functions that control the gradient and can deliver diverse combinations of knowledge transfer. Searching the graph structure enables us to discover more effective knowledge transfer methods than a manually designed one. Experimental results show that the proposed method achieved performance improvements.
Attributional Robustness Training Using Input-Gradient Spatial Alignment

Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust. Recently, it has been shown that the explanations could be manipulated easily by adding visually imperceptible perturbations to the input while keeping the model’s prediction intact. In this work, we study the problem of attributional robustness (i.e. models having robust explanations) by showing an upper bound for attributional vulnerability in terms of spatial correlation between the input image and its explanation map. We propose a training methodology that learns robust features by minimizing this upper bound using soft-margin triplet loss. Our methodology of robust attribution training (ART) achieves the new state-of-the-art attributional robustness measure by a margin of $$\approx $$ 6–18 $$\%$$ on several standard datasets, ie. SVHN, CIFAR-10 and GTSRB. We further show the utility of the proposed robust training technique (ART) in the downstream task of weakly supervised object localization by achieving the new state-of-the-art performance on CUB-200 dataset.
Multidimensional segment trees can do range updates in poly-logarithmic time

Updating and querying on a range is a classical algorithmic problem with a multitude of applications. The Segment Tree data structure is particularly notable in handling the range query and update operations. A Segment Tree divides the range into disjoint segments and merges them together to perform range queries and range updates elegantly. Although this data structure is remarkably potent for 1-dimensional problems, it falls short in higher dimensions. Lazy Propagation enables the operations to be computed in O(logn) time in a single dimension. However, the concept of lazy propagation could not be translated to higher-dimensional cases, which imposes a time complexity of O(nk−1logn) for operations on k-dimensional data. In this work, we have made an attempt to emulate the idea of lazy propagation differently so that it can be applied for 2-dimensional cases. Moreover, the proposed modification should be capable of performing most general aggregate functions similar to the original Segment Tree, and can also be extended to even higher dimensions. Our proposed algorithm manages to perform range sum queries and updates in O(log2⁡n) time for a 2-dimensional problem, which becomes O(logd⁡n) for a d-dimensional situation.
Protection of qubits by nonlinear resonances

We show that quantized superconducting circuits are non-integrable at the classical level of description, adorned by nonlinear resonances amidst stochastic sea. The stable (elliptic) and unstable (hyperbolic) points occur in a way that by choosing the parameters of a system close to elliptic points, the dynamics is stable. Quantum mechanically, any disturbance has to tunnel the separatrix to reach the elliptic point. Thus, nonlinearity of the system provides protection. Based on these fundamental considerations from the Kolmogorov–Arnold–Moser theorem, we propose criteria for protection of qubits from any disturbance.
Continuous-domain ant colony optimization algorithm based on reinforcement learning

Improved ant colony optimization (ACO) algorithms for continuous-domain optimization have been widely applied in recent years, but these improved methods have a weak perception of environmental information changes and only rely on the residues of the pheromones in the path to guide colony evolution. In this paper, we propose an ant colony algorithm based on the reinforcement learning model (RLACO). RLACO can acquire more environmental information by calculating the diversity of the ant colony, and, uses the diversity and other basic information of the ant colony to establish a reinforcement learning model. At different stages of evolution, the algorithm chooses an optimal strategy that can maximize the reward to improve the global search ability and convergence speed of the colony. The experimental results on CEC 2017 test functions show that the proposed algorithm is superior to other algorithms for continuous-domain optimization in convergence speed, accuracy and global search ability.