Reviewerly/exHarmony huggingface dataset:
Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model

Currently, path planning algorithms are used in many daily tasks. They are relevant to find the best route in traffic and make autonomous robots able to navigate. The use of path planning presents some issues in large and dynamic environments. Large environments make these algorithms spend much time finding the shortest path. On the other hand, dynamic environments request a new execution of the algorithm each time a change occurs in the environment, and it increases the execution time. The dimensionality reduction appears as a solution to this problem, which in this context means removing useless paths present in those environments. Most of the algorithms that reduce dimensionality are limited to the linear correlation of the input data. Recently, a Convolutional Neural Network (CNN) Encoder was used to overcome this situation since it can use both linear and non-linear information to reduce data. This paper analyzes in-depth the performance to eliminate the useless paths using this CNN Encoder model. To measure the mentioned model efficiency, we combined it with different path planning algorithms. Next, the final algorithms (combined and not combined) are checked in a database composed of five scenarios. Each scenario contains fixed and dynamic obstacles. Their proposed model, the CNN Encoder, associated with other existent path planning algorithms in the literature, was able to obtain a time decrease to find the shortest path compared to all path planning algorithms analyzed. the average decreased time was 54.43%.
A Weighted Partial Domain Adaptation for Acoustic Scene Classification and Its Application in Fiber Optic Security System

Domain adaptation (DA) is a technology that transfers knowledge from the source domain to the target domain. General domain adaptation assume that the source and the target domain have the same label space. However, in practical application tasks, the label of the target domain is often only a subset of the source label. For this situation, partial domain adaptation is usually used as an effective solution to transfer knowledge from a large number of labeled data sets to unlabeled micro data sets. In this article, a weighted partial domain adaptation method is proposed to solve the Acoustic Scene Classification (ASC) problem. Our method establish a connection between source and target domains to do the partial domain adaptation. Experiments are carried out on TUT and ESC-50 datasets which show that our method achieves state-of-the-art results. What is more, we apply the algorithms to an optical fiber perimeter security system to complete early warning by identifying intrusion signals.
Enabling personalized decision support with patient-generated data and attributable components

Decision-making related to health is complex. Machine learning (ML) and patient generated data can identify patterns and insights at the individual level, where human cognition falls short, but not all ML-generated information is of equal utility for making health-related decisions. We develop and apply attributable components analysis (ACA), a method inspired by optimal transport theory, to type 2 diabetes self-monitoring data to identify patterns of association between nutrition and blood glucose control. In comparison with linear regression, we found that ACA offers a number of characteristics that make it promising for use in decision support applications. For example, ACA was able to identify non-linear relationships, was more robust to outliers, and offered broader and more expressive uncertainty estimates. In addition, our results highlight a tradeoff between model accuracy and interpretability, and we discuss implications for ML-driven decision support systems.
Node Pair Information Preserving Network Embedding Based on Adversarial Networks

Network embedding aims to learn the low-dimensional node representations for networks, which has attracted an increasing amount of attention in recent years. Most existing efforts in this field attempt to embed the network based on node similarity, which generally relies on edge existence statistics of the network. Instead of relying on the global edge existence statistics for every node pair, in this article, we utilize the information between a pair of nodes in a local way and propose a model, called node pair information preserving network embedding (NINE), based on adversarial networks. The main idea lies in preserving the node pair information (NI) by means of adversarial networks. The architecture of the proposed NINE model consists of three main components, namely: 1) NI embedder; 2) NI generator; and 3) NI discriminator. In the NI embedder, to avoid the complicated similarity calculation for a pair of nodes, the original NI vector calculated from the direct neighbor information of the two nodes is adopted as features, and the edge existence information is taken as labels to learn the embedded NI vector in a supervised learning manner. The second component is the NI generator, which takes the original node representation vectors of a node pair as input and outputs the generated NI vector. In order to make the generated NI vector follow the same distribution of the corresponding embedded NI vector, the generative adversarial network (GAN) is adopted, resulting in the third component, called the NI discriminator. Extensive experiments are conducted on seven real-world datasets in three downstream tasks, namely: 1) network reconstruction; 2) link prediction; and 3) node classification. Comparison results with seven state-of-the-art models demonstrate the effectiveness, efficiency, and rationality of our model.
Efficient distributed network covert channels for Internet of things environments†

Abstract Each day more and more Internet of Things (IoT) devices are being connected to the Internet. In general, their applications are diverse but from the security perspective, it is evident that they are increasingly targeted by cybercriminals and used for nefarious purposes. Network covert channels form a subgroup of the information-hiding research area where secrets are sent over communication networks embedded within the network traffic. Such techniques can be used, among others, by malware developers to enable confidential data exfiltration or stealth communications. Recently, distributed network covert channels have raised the attention of security professionals as they allow the cloaking of secret transmission by spreading the covert bits among many different types of data-hiding techniques. However, although there are many works dealing with IoT security, little effort so far has been devoted in determining how effective the covert channels threat can be in the IoT henvironments. That is why, in this article, we present an extensive analysis on how distributed network covert channels that utilize network traffic from IoT devices can be used to perform efficient secret communication. More importantly, we do not focus on developing novel data-hiding techniques but, instead, considering the nature of IoT traffic, we investigate how to combine existing covert channels so the resulting data transfer is less visible. Moreover, as another contribution of our work, we prepare and share with the community the network traffic dataset that can be used to develop effective countermeasures against such threats.
Unbiased Measurements Conversion Based Sequential Filtering for Target Tracking with Range, Range Rate and Direction Cosine Measurements

The measurements in phased array radar are obtained in the direction cosine coordinates, which include the range and direction cosine measurements. In order to realize the target tracking with nonlinear measurements and improve the tracking performance with range rate information, the unbiased measurements conversion based sequential filtering for target tracking with range, range rate and direction cosine measurements is proposed. First, the position measurements are processed by the converted measurements Kalman Filter (CMKF), where the statistical characteristics of the converted measurements error based on range and direction cosine measurements are calculated based on the predicted information to remove the correlation between the covariance matrix and the measurement noise; Based on the filtering result from position information filter, the pseudo measurement, which is constructed by the range and range rate measurements, is input to realize sequential filtering to improve the position filter result. The statistical characteristics of pseudo measurement are also calculated based on the predicted information. Simulation results demonstrate the effectiveness of the proposed algorithm.
Automatic Generation of Diegetic Guidance in Cinematic Virtual Reality

One of the advantages of Cinematic Virtual Reality over traditional movies is that viewers can freely explore the virtual space. However, such freedom leads to a problem of missing some key events during watching. Therefore, directional visual guidance in the virtual space is vital for the viewer to follow the storyline and capture key events. Generating moving objects is one of the most commonly used diegetic guidance techniques which can implicitly guide the viewer's attention in the virtual space. In this paper, we investigate the formulation of key events in Cinematic Virtual Reality with event-of-interest script. Based on the formulation, we analyze the factors influencing diegetic guidance and propose an automatic-generating approach named Dynamic Diegetic Guidance. User study showed Dynamic Diegetic Guidance has a higher degree of immersion and takes less time to redirect viewers to key events compared with fixed guidance techniques, which makes the viewing experience more informative and entertaining.
MR-BIRCH: A scalable MapReduce-based BIRCH clustering algorithm

Many classical clustering algorithms have been fitted into MapReduce, which provides a novel solution for clustering big data. However, several iterations are required to reach an acceptable result in most of the algorithms. For each iteration, a new MapReduce job must be executed to load the dataset into main memory, which results in high I/O overhead and poor efficiency. BIRCH algorithm stores only the statistical information of objects with CF entries and CF tree to cluster big data, but with the increase of the tree nodes, the main memory will be insufficient to contain more objects. Hence, BIRCH has to reduce the tree, which will degrade the clustering quality and decelerate the whole execution efficiency. To deal with the problem, BIRCH was fitted into MapReduce called MR-BIRCH in this paper. In contrast to a great number of MapReduce-based algorithms, MR-BIRCH loads dataset only once, and the dataset is processed parallel in several machines. The complexity and scalability were analyzed to evaluate the quality of MR-BIRCH, and MR-BIRCH was compared with Python sklearn BIRCH and Apache Mahout k-means on real-world and synthetic datasets. Experimental results show, most of the time, MR-BIRCH was better or equal to sklearn BIRCH, and it was competitive to Mahout k-means.
GuardSpark++: Fine-Grained Purpose-Aware Access Control for Secure Data Sharing and Analysis in Spark

With the development of computing and communication technologies, extremely large amount of data has been collected, stored, utilized, and shared, while new security and privacy challenges arise. Existing platforms do not provide flexible and practical access control mechanisms for big data analytics applications. In this paper, we present GuardSpark++, a fine-grained access control mechanism for secure data sharing and analysis in Spark. In particular, we first propose a purpose-aware access control (PAAC) model, which introduces new concepts of data processing/operation purposes to conventional purpose-based access control. An automatic purpose analysis algorithm is developed to identify purposes from data analytics operations and queries, so that access control could be enforced accordingly. Moreover, we develop an access control mechanism in Spark Catalyst, which provides unified PAAC enforcement for heterogeneous data sources and upper-layer applications. We evaluate GuardSpark++ with five data sources and four structured data analytics engines in Spark. The experimental results show that GuardSpark++ provides effective access control functionalities with a very small performance overhead (average 3.97%).
An Integrated Framework for Web Data Preprocessing Towards Modeling User Behavior

Extracting useful and relevant information from the massive amount of data by web users becomes a challenging task. This work regarding applications based on the use of Web Usage Mining (WUM). Clickstream, transaction data, and user profile data represent various sources of web usage data. Preprocessing is an essential process in WUM for understanding the user's life as a whole within a session on a website. It signifies the aggregation of consequent series of page views executed by a singular user traversing through a website. Therefore, it represents a critical task that involves converting raw web log data to obtain a suitable pattern for efficient analysis. The importance of these processes relies on the complex nature of web architecture, and it takes almost 80% of the mining process. This work introduces a complete framework for data preprocessing with a comprehensive demonstration of a real dataset.
An Unsupervised Approach for Graph-based Robust Clustering of Human Gait Signatures

Classification of gait abnormalities plays a key role in medical diagnosis, sports, physiotherapy and rehabilitation. We demonstrate the effectiveness of a new graph construction-based outlier detection method and and the applicability of a new parameter-free clustering approach on radar-based human gait signatures. Micro-Doppler radar-based human gait signatures of ten test subjects for five different gait types consisting of normal, simulated abnormal and assisted walks are clustered using five different clustering algorithms. The proposed algorithm outperforms existing methods both in cluster enumeration and partition and achieves an overall correct clustering rate of 92.8%. The developed method is promising for performing medical diagnosis in a robust unsupervised fashion.
Learning a Deep Agent to Predict Head Movement in 360-Degree Images

Virtual reality adequately stimulates senses to trick users into accepting the virtual environment. To create a sense of immersion, high-resolution images are required to satisfy human visual system, and low latency is essential for smooth operations, which put great demands on data processing and transmission. Actually, when exploring in the virtual environment, viewers only perceive the content in the current field of view. Therefore, if we can predict the head movements that are important behaviors of viewers, more processing resources can be allocated to the active field of view. In this article, we propose a model to predict the trajectory of head movement. Deep reinforcement learning is employed to mimic the decision making. In our framework, to characterize each state, features for viewport images are extracted by convolutional neural networks. In addition, the spherical coordinate maps and visited maps are generated for each viewport image, which facilitate the multiple dimensions of the state information by considering the impact of historical head movement and position information. To ensure the accurate simulation of visual behaviors during the watching of panoramas, we stipulate that the model imitates the behaviors of human demonstrators. To allow the model to generalize to more conditions, the intrinsic motivation is employed to guide the agent’s action toward reducing uncertainty, which can enhance robustness during the exploration. The experimental results demonstrate the effectiveness of the proposed stepwise head movement predictor.
Seeding-Based Multi-Objective Evolutionary Algorithms for Multi-Cloud Composite Applications Deployment

There are an increasing number of enterprises deploying their application services to multi-cloud to benefit the advantages brought by cloud computing. The multi-cloud composite applications deployment problem (MCADP) aims to select proper cloud resources from multiple cloud providers at different locations to deploy applications with shared constituent services so as to optimize application performance and deployment cost. Multi-objective evolutionary algorithms (MOEAs) can be utilized to find a set of trade-off solutions for MCADP. During population initialization of MOEAs, seeding strategies can considerably improve the algorithms' performance. For example, the seeding-based MOEAs, AO-Seed and SO-Seed, introduce a pre-optimization phase to search for solutions to be embedded into the initial population of MOEAs. With the extra optimization overhead, however, the two seeding-based MOEAs can only identify one or a limited few solutions to MCADP utilized by MOEAs. To solve MCADP effectively and efficiently, we propose new seeding-based MOEAs in this paper. The approach can construct application-specific seeds according to problem domain knowledge and build a group of diverse and high-quality solutions for the initial population of MOEAs. Extensive experiments have been conducted on a real-world dataset. The results demonstrate that the proposed seeding-based MOEAs outperform SO-Seed and AO-Seed with less computation cost for MCADP.
Faultless to a fault?

The propagation of faults in hardware devices for a Boolean circuit leaves a distinct fault template which can be used as a signature to launch a new class of attacks called Fault Template Attacks (FTA). In this paper we present a systematic methodology to develop fault templates of Boolean circuits using awell known concept in design verification, namely positive Davio's decomposition. We use this improved template called FTA* to attack a popular defence against side channel analysis called Threshold Implementations (TI), which are based on computations using non-complete shares of the secret data to hide it. The paper tries to explore whether such shares can be still combined to reveal the secret quantities by inflicting carefully crafted faults in the registers storing the shares and observing the fault propagation templates. In particular, we show as a case study that a TI-protected S-Box of the PRESENT block cipher can be circumvented by considering Double Event Upset (DEU) faults in single registers storing targeted shared bits as suggested by the derived fault templates. This shows that the TI crypto-circuits are not naturally faultless to faults and needs further explorations to thwart such powerful attack vectors.
Micropower Object Range and Bearing Sensor for Smart Contact Lenses

In this article, we present an eye-tracking system for distance ranging, developed for varifocal contact lens applications. The system comprises of a set of four ~150 µm-thick, semi-circular coils, which are patterned on flexible Kapton substrates and can easily conform to the sclera. The object-distance from the eyeballs is determined via triangulation of the eye gaze angles, which is calculated from the induced electromagnetic voltage between the coils. Preliminary experiments with the coil-system, placed on eyeball simulated polymeric spheres resulted in accurate prediction of object distance in the range of 0.1-15 D (Diopter) with an RMS error of 0.1 D and object direction in the range of -15 to 15-degree arc with 0.4 degree RMS error, respectively. Diopter is the reciprocal of the object distance (in meters). The energy required to perform a single range reading was ~ 20 µJ.
Perfectly-Secure Asynchronous MPC for General Adversaries (Extended Abstract)

We study perfectly-secure Multiparty Computation (MPC) in the asynchronous communication setting, tolerating a generalized non-threshold adversary, characterized by an adversary structure. Ashwin Kumar et al. (ACISP2002) presented a condition which is both necessary as well as sufficient for the existence of perfectly-secure MPC in this setting. However, we show that their protocol is flawed and present a new protocol (with the same necessity condition). Moreover, our protocol is conceptually simpler, and unlike their protocol, does not rely on monotone span programs (MSPs). As a sub-contribution, we also present an asynchronous Byzantine agreement protocol (tolerating a non-threshold adversary), which is used as a key component in our MPC protocol.
Verify&amp;Revive: Secure Detection and Recovery of Compromised Low-end Embedded Devices

Tiny and specialized computing platforms, so-called embedded or Internet of Things (IoT) devices, are increasingly used in safety- and privacy-critical application scenarios. A significant number of such devices offer limited or no security features, making them attractive targets for a wide variety of cyber attacks, exemplified by malware infestations. One key component in securing these devices is establishing a root of trust, which is typically attained via remote attestation (RA), a security service that aims to ascertain the current state of a remote device and detect any malicious tampering. Although several (software-based, hardware-based, and hybrid) RA approaches have been proposed to address this problem, two main issues remain, regardless of the type of RA. First, all but one of the existing RA approaches are vulnerable to Time-Of-Check Time-Of-Use (TOCTOU) attack, where a transient malware may infect the corresponding embedded device between two consecutive RA routines without being detected. Second, little attention has been devoted to efficiently and securely rescuing devices that are determined to be compromised, increasing the maintenance cost of IoT deployments, especially in industrial control systems, where (re-)deploying a new device is often a cost-sensitive operation.
Detection of housing and utility problems in districts through social media texts

Social media stores a significant amount of information which can be used for extraction of specific knowledge. A variety of topics that arise there concerns a lot of everyday life aspects, including urban-related problems. In this work, we demonstrate the way of using the texts from social media on the topic of housing and utility problems, such as litter on the streets, graffiti on a public building or noisy neighbours. Our aim is to develop an approach based on machine learning to automatically filter such citizen messages and classify them into several categories. To achieve this, we solve the classification problem with an almost unlimited number of negative categories using the One-Class approach and combine data from several resources to construct proper text embedding by combining results from the guided topic model and deep neural pretrained BERT method. Comparison with statistics taken from the official site indicates that the distributions of posts on each problem category are similar for districts of Saint-Petersburg
Improving wearable-based fall detection with unsupervised learning

Abstract Fall detection (FD) is a challenging task that has received the attention of the research community in the recent years. This study focuses on FD using data gathered from wearable devices with tri-axial accelerometers (3DACC), developing a solution centered in elderly people living autonomously. This research includes three different ways to improve a FD method: (i) an analysis of the event detection stage, comparing several alternatives, (ii) an evaluation of features to extract for each detected event and (iii) an appraisal of up to 6 different clustering scenarios to split the samples in subsets that might enhance the classification. For each clustering scenario, a specific classification stage is defined. The experimentation includes publicly available simulated fall data sets. Results show the guidelines for defining a more robust and efficient FD method for on-wrist 3DACC wearable devices.