Reviewerly/exHarmony huggingface dataset:
DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking

Visual object tracking is a fundamental task in the field of computer vision. Recently, Siamese trackers have achieved state-of-the-art performance on recent benchmarks. However, Siamese trackers do not fully utilize semantic and objectness information from pre-trained networks that have been trained on image classification task. Furthermore, the pre-trained Siamese architecture is sparsely activated by the category label, which leads to unnecessary calculations and overfitting. In this paper, we propose to learn a Domain-Aware that fully utilizes semantic and objectness information while producing a class-agnostic using a ridge regression network. Moreover, to reduce the sparsity problem, we solve the ridge regression problem with a differentiable weighted-dynamic loss function. Our tracker, dubbed DomainSiam, improves the feature learning in the training phase and generalization capability to other domains. Extensive experiments are performed on five tracking benchmarks, including OTB2013 and OTB2015, for a validation set as well as VOT2017, VOT2018, LaSOT, TrackingNet, and GOT10k for a testing set. DomainSiam achieves a state-of-the-art performance on these benchmarks while running at 53 FPS.
Policy Management Technique Using Blockchain for Cloud VM Migration

In recent years, the use of the cloud has become one of the essential elements when constructing a system for deploying web services. By combining serverless cloud services, various web services can be performed without building servers. However, serverless environments have various limitations, and traditional methods of building servers on VM are indispensable. In addition, technology for migrating VM to physically different hosts was established, and it became possible to easily migrate VM between hosts under the same cloud management. However, depending on the server running on VM, there may be restrictions on the range that can be physically moved. We have solved the problem by focusing on the problem that migration can be performed without checking the information about the server running on the VM at the time of migration. Therefore, in this paper, we introduce a managing technique for VM migration policy in the cloud using blockchain, and show that it is more useful than the conventional policy management method.
VAWS: Vulnerability Analysis of Neural Networks using Weight Sensitivity

The advancement in deep learning has taken the technology world by storm in the last decade. Although, there is enormous progress made in terms of algorithm performance, the security aspect of these algorithms has not received a lot of attention from the research community. As more industries start to adopt these algorithms the issue of security is becoming even more relevant. Security vulnerabilities in machine learning (ML), especially in deep neural networks (DNN), is becoming a concern. Various techniques have been proposed, including data manipulations and model stealing. However, most of them are focused on ML algorithms and target threat models that require access to training dataset. In this paper, we present a methodology that analyzes the DNN weight parameters under the threat model that assumes the attacker has the access to the weight memory only. This analysis is then used to develop an attack that manipulates weight parameters with respect to their sensitivity. To evaluate this attack, we implemented our methodology on a MLP trained on IRIS dataset and LeNet (DNN architecture) trained on MNIST dataset. Our experimental results demonstrate that alteration of model parameters results in subtle accuracy drop of the model. Depending on the applications such subtle changes can cause significant system malfunction or disruption, for example in vision-based industrial applications. Our results show that using our methodology a subtle accuracy drop can be achieved in a reasonable amount of time with very few parameter changes.
Clustering Geo-Indistinguishability for Privacy of Continuous Location Traces

We consider privacy of obfuscated location reports that can be correlated through time/space to estimate the real position of a user. We propose a user-centric Location Privacy-Preserving Mechanism (LPPM) that protects users not only against single reports, but also over time, against continuous reports. Our proposed mechanism, designated clustering geo-indistinguishability, creates obfuscation clusters to aggregate nearby locations into a single obfuscated location. To evaluate the utility of the mechanism, we resorted to a real use-case based on geofencing. Our evaluation results have shown a suitable privacy-utility trade-off for the proposed clustering geo-indistinguishability mechanism.
On Collisions Related to an Ideal Class of Order 3 in CSIDH

CSIDH is an isogeny-based key exchange, which is a candidate for post quantum cryptography. It uses the action of an ideal class group on $$\mathbb {F}_{p}$$ -isomorphism classes of supersingular elliptic curves. In CSIDH, the ideal classes are represented by vectors with integer coefficients. The number of ideal classes represented by these vectors determines the security level of CSIDH. Therefore, it is important to investigate the correspondence between the vectors and the ideal classes. Heuristics show that integer vectors in a certain range represent “almost” uniformly all of the ideal classes. However, the precise correspondence between the integer vectors and the ideal classes is still unclear. In this paper, we investigate the correspondence between the ideal classes and the integer vectors and show that the vector $$(1, \dots , 1)$$ corresponds to an ideal class of order 3. Consequently, the integer vectors in CSIDH have collisions related to this ideal class. Here, we use the word “collision” in the sense of distinct vectors belonging to the same ideal class, i.e., distinct secret keys that correspond to the same public key in CSIDH. We further propose a new ideal representation in CSIDH that does not include these collisions and give formulae for efficiently computing the action of the new representation.
Representation learning for minority and subtle activities in a smart home environment

Daily human activity recognition using sensor data can be a fundamental task for many real-world applications, such as home monitoring and assisted living. One of the challenges in human activity recognition is to distinguish activities that have infrequent occurrence and less distinctive patterns. We propose a hierarchical classifier to perform two-phase learning. In the first phase the classifier learns general features to recognise majority classes, and the second phase is to collect minority and subtle classes to identify fine difference between them. We compare our proposal with a collection of state-of-the-art classification techniques on four real-world third-party datasets that involve different types of object sensors and are collected in different environments and on different subjects and six imbalanced datasets from the UCI-Irvine Machine Learning repository. Our results demonstrate that our hierarchical classifier approach performs better than state-of-the-art techniques including both structure- and feature-based learning techniques. The key novelty of our approach is that we reduce the bias of the ensemble classifier by training it on a subspace of data, which allows identification of activities with subtle differences, and thus provides well-discriminating features.
Magnetic Induction-based Human Activity Recognition (MI-HAR)

Human activity recognition (HAR) is a growing research area with applications in a variety of domains, such as healthcare, rehabilitation, daily life-logging, personal fitness, senior care, and assistance for people with cognitive disorders. Detection of human movements using body sensor networks is one of the most popular yet challenging approaches. There are critical challenges inherent in the sensor-based activity recognition method, such as power management, security, and coverage. Lossy mediums, such as the human body can significantly affect the electromagnetic wave propagating systems. In this paper, we introduce a novel wireless system based on magnetic induction (MI) and deep learning for human activity recognition to address the main challenges and constraints. The generated synthetic MI motion signals are also presented. The proposed MI system is integrated with machine learning techniques to model and detect human activities.
Image Processing Algorithm for Appearance-Based Gesture Recognition

Gesture detection algorithms usually involve tridimensional models of the hand, high-end equipment and time-consuming development. We propose a simple method of implementing a gesture recognition algorithm for live videos, using low resources and a web camera. For the implementation of the algorithm, the OpenCV library was used to detect the skin and calculate several geometric parameters of the hand. These parameters (or features) were further compared with some templates. The templates were experimentally determined and, based on them, nine gestures were defined. The recognition rate is influenced by a good detection of the skin. Our application lets the user sample the skin color in the image, in order to improve detection. The recognition of a gesture is reliable and fast enough to use in real-life applications, such as controlling a game with hand gestures.
Kinship verification using Deep Neural Network Models

The recent advancements in the field of image processing has led to giving the importance of kinship verification. In this paper, we propose methodology based on Deep Neural Networks (DNN) and Support Vector Machine (SVM) classifier for the blood relation identification of human faces in the images. We examined the best parameters for every feature extraction technique such as: Grey-Level Co-Occurrence Matrix (GLCM), Completed Joint Scale Local Binary Pattern (CJLBP), Alexnet, Resnet on KinFace-I And KinFace-II datasets. The method is made up of two basic stages which are; (1) Feature Extraction (2) Classification. In the proposed method we adopted Alexnet for the process of feature extraction, and the classifier used is support vector machine (SVM). The results obtained using proposed approach gives better results in comparison to many other approaches that were used previously.
Using Meta-Morph Rules to develop Morphological Analysers: A case study concerning Tamil

This paper describes a new and larger coverage Finite-State Morphological Analyser (FSM) and Generator for the Dravidian language Tamil.The FSM has been developed in the context of computational grammar engineering, adhering to the standards of the ParGram effort.Tamil is a morphologically rich language and the interaction between linguistic analysis and formal implementation is complex, resulting in a challenging task.In order to allow the development of the FSM to focus more on the linguistic analysis and less on the formal details, we have developed a system of meta-morph(ology) rules along with a script which translates these rules into FSM processable representations.The introduction of meta-morph rules makes it possible for computationally naive linguists to interact with the system and to expand it in future work.We found that the meta-morph rules help to express linguistic generalisations and reduce the manual effort of writing lexical classes for morphological analysis.Our Tamil FSM currently handles mainly the inflectional morphology of 3,300 verb roots and their 260 forms.Further, it also has a lexicon of approximately 100,000 nouns along with a guesser to handle out-of-vocabulary items.Although the Tamil FSM was primarily developed to be part of a computational grammar, it can also be used as a web or stand-alone application for other NLP tasks, as per general ParGram practice.
Effective Convolutional Neural Network Layers in Flow Estimation for Omni-Directional Images

In this paper, we investigate effective neural network layers for optical flow estimation and in particular for omni-directional optical flow. Optical flow has many applications in computer graphics, augmented reality and in 3D modeling. We create a simple dataset that enables us to efficiently assess the effectiveness of different neural network layers for optical flow. Based on this small-sized diagnostic dataset, FlowCLEVR, we conclude that a deformable convolution layer is highly effective in reducing motion and occlusion boundary blur. Based on these results, we are able to design modifications to various existing network architectures improving their performance. We demonstrate improved performance on FlowCLEVR, on standard datasets for optical flow in planar images and on a novel omni-directional optical flow dataset. We also extend our work to omni-directional stereo.
Sentiment Analysis on Tweets Using Machine Learning and Combinatorial Fusion

Sentiment analysis using social network platforms such as twitter has achieved tremendous results. However, due to its imbalanced data content and semantic context, it remains a challenge to give a full and effective sentiment labeling. In this paper, we propose a two-stage data analytic approach consisting of machine learning algorithms and combinatorial fusion. The first stage uses five machine learning algorithms: logistic regression, naive Bayes, perceptron, random forest, and support vector machine (SVM). Combinatorial fusion is then used to combine subset of these five algorithms. We conduct our investigation using a Kaggle dataset to classify each of the tweets as positive, neutral, or negative sentiment. We demonstrate that although most of the machine learning algorithms perform well, combination of these algorithms with higher performance ratio and cognitive diversity can perform even better.
Spatial Patterns of Chemical Weathering at the Basal Tertiary Nonconformity in California from Multispectral and Hyperspectral Optical Remote Sensing

Visible through shortwave (VSWIR) spectral reflectance of the geologic units across the basal Tertiary nonconformity (BTN) is characterized at three spatially disparate locations in California. At two of these sites, location-specific spectral endmembers are obtained from AVIRIS imaging spectroscopy and linear spectral mixture models are used to visualize spatial patterns in chemical weathering associated with the BTN. Weathering patterns are found to match well with traditional geologic maps of the BTN at each site, but results show more spatially detailed quantitative geologic information about the spatial variability of chemical weathering near the nonconformity than is possible in a traditional geologic map. Spectral endmembers and unmixing results are also compared across locations. At the two locations with AVIRIS coverage, strong absorptions centered near 2200 nm are observed, consistent with previous geologic publications reporting intense chemical weathering at the BTN. Information loss associated with multispectral sampling of the reflectance continuum is also examined by resampling endmembers from the Maniobra location to mimic the spectral response functions of the WorldView 3, Sentinel-2 and Landsat 8 sensors. Simulated WorldView 3 data most closely approximate the full information content of the AVIRIS observations, resulting in nearly unbiased unmixing results for both endmembers. Mean fraction differences are −0.02 and +0.03 for weathered and unweathered endmembers, respectively. Sentinel-2 and Landsat 8 are unable to distinguish narrow, deep SWIR absorptions from changes in the overall amplitude of the SWIR spectral continuum, resulting in information loss and biased unmixing results. Finally, we characterize a third location using Sentinel-2 observations only. At this site we also find spectrally distinct features associated with several lithologies, providing new information relevant to the mapping of geologic contacts which is neither present in high spatial resolution visible imagery, nor in published geologic mapping. Despite these limitations, the spatial pattern of the Sentinel-2 and Landsat 8 fraction estimates is sufficiently similar to that of the WorldView 3 and AVIRIS fraction estimates to be useful for mapping purposes in cases where hyperspectral data are unavailable.
Naive Bayes Classification based on Differential Privacy

Data mining has a wide range of applications in the real world. However, it is possible to disclose the private information of users in the process of data mining. Therefore, it is of great significance to protect the users' privacy while mining the knowledge behind the data. In this paper, we propose a Naive Bayes classification method based on differential privacy. For nominal attributes, we add Laplace noise to the count. For numerical attributes, we add Laplace noise to the mean, standard deviation, and scale parameter, and then use the noisy parameters to calculate the prior probability and conditional probability. For numerical attributes, we assume that they follow Gaussian, Laplace, or lognormal distribution, and apply our algorithms to compare utilities.
Physical e-Sports in VAIR Field system

In this study, we define physical e-sports that require physical training and show an example of such sport created using a mobile virtual reality system, the VAIR Field. Unlike conventional e-sports, physical e-sports involve physical activities, becoming the technological evolution of conventional sports. Our system uses extended reality technology without a head-mounted display and is safe for children to play, but it requires physical exercise and physical ability. It is a new kind of sport that uses multiple mobile devices and virtual weapons, providing more than just visual reality and allowing multiple players to play at the same time. By superimposing the physical world and the virtual world, physical e-sports allow the body to move with full force.
Visibility Detection in Time Series of Planetscope Images

This article addresses the problem of estimating scene visibility in time series of satellite images. We are especially focused on satellites with few spectral bands and high revisit frequency. Our approach exploits the redundancy of information acquired during these revisits. It is based on an unsupervised algorithm that tracks local ground textures across time and detects ruptures caused by opaque clouds, haze, cirrus and shadows. Experiments have been carried out on 18 PlanetScope time series covering various locations. These time series come with hand-made labeled ground truth. We compare our results with those of the PlanetScope algorithm and demonstrate the effectiveness of the proposed method : success rates of 94% and 84% are reached for the visible and occulted regions classification.
Adaptive preview control of single‐point path tracker for car‐like delivery service robot

The purpose of this research is to design a simple and intuitive path tracker to minimise the distance error of a car-like delivery service robot using only a position sensor, i.e. global positioning system in outdoor environments. The authors proposed not only a single-point preview path tracker (SPP), but also a curvature-based preview control using a quadratic curve fitting technique. This method can provide flexibility in preview distance control even at a constant speed, and reduces the burden of an actuator for maintenance. Furthermore, the performance of the proposed adaptive SPP is considerably improved compared to that when using a fixed preview distance.
CSI Classification for 5G via Deep Learning

5G communication requires continuous exchanges of channel state information (CSI) between the base station and user equipment (UE) to adjust the physical layer parameters. CSI classification in a noisy environment is challenging, since CSI can get corrupted. To address this problem, we apply a convolutional neural network (CNN) to classify several key CSI parameters. In a simulation study, our CNN method classifies the CSI parameters with accuracy ranging between 84-98%, which is approximately 24-38% higher than the 3GPP recommendations for UEs.
Adaptive Caches as a Defense Mechanism Against Cache Side-Channel Attacks

Side-channel attacks exploit architectural features of computing systems and algorithmic properties of applications executing on these systems to steal sensitive information. Cache side-channel attacks are more powerful and practical compared to other classes of side-channel attacks due to several factors, such as the ability to be mounted without physical access to the system. Some secure cache architectures have been proposed to counter side-channel attacks. However, they all incur significant performance overheads. This work explores the viability of using adaptive caches, which are conventionally used as a performance-oriented architectural feature, as a defense mechanism against cache side-channel attacks. We conduct an empirical analysis, starting from establishing a baseline for the attacker's ability to infer information regarding the memory accesses of the victim process when there is no active defense mechanism in place and the attacker is fully aware of all the cache parameters. Then, we analyze the effectiveness of the attack without complete knowledge of the cache configuration. Finally, based on the insight that the success of the attack is heavily dependent on knowledge of the cache configuration, we implement the run-time cache reconfigurations and observe their effect on the success of the attack. We observe that reconfiguring different cache parameters during a side-channel attack reduces the accuracy of the attack in detecting cache sets accessed by the victim by 44% on average, with a maximum of 90% reduction.
A Genetic-Based Ensemble Learning Applied to Imbalanced Data Classification

Imbalanced data classification is still a focus of intense research, due to its ever-growing presence in the real-life decision tasks. In this article, we focus on a classifier ensemble for imbalanced data classification. The ensemble is formed on the basis of the individual classifiers trained on supervise-selected feature subsets. There are several methods employing this concept to ensure a high diverse ensemble, nevertheless most of them, as Random Subspace or Random Forest, select attributes for a particular classifier randomly. The main drawback of mentioned methods is not giving the ability to supervise and control this task. In following work, we apply a genetic algorithm to the considered problem. Proposition formulates an original learning criterion, taking into consideration not only the overall classification performance but also ensures that trained ensemble is characterised by high diversity. The experimental study confirmed the high efficiency of the proposed algorithm and its superiority to other ensemble forming method based on random feature selection.
Repeatable Oblivious Shuffling of Large Outsourced Data Blocks

As data outsourcing becomes popular, oblivious algorithms have raised extensive attentions. Their control flow and data access pattern appear to be independent of the input data they compute on. Oblivious algorithms, therefore, are especially suitable for secure processing in outsourced environments. In this work, we focus on oblivious shuffling algorithms that aim to shuffle encrypted data blocks outsourced to a cloud server without disclosing the actual permutation of blocks to the server. Existing oblivious shuffling algorithms suffer from issues of heavy communication cost and client computation cost for shuffling large-sized blocks because all outsourced blocks must be downloaded to the client for shuffling or peeling off extra encryption layers. To help eliminate this void, we introduce the "repeatable oblivious shuffling" notation that avoids moving blocks to the client and thus restricts the communication and client computation costs to be independent of the block size. For the first time, we present a concrete construction of repeatable oblivious shuffling using additively homomorphic encryption. The comprehensive evaluation of our construction shows its effective usability in practice for shuffling large-sized blocks.