Reviewerly/exHarmony huggingface dataset:
A Simulator to Support Machine Learning-Based Wearable Fall Detection Systems

People’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.
360$$^{\circ }$$ Camera Alignment via Segmentation

Panoramic 360 $$^{\circ }$$ images taken under unconstrained conditions present a significant challenge to current state-of-the-art recognition pipelines, since the assumption of a mostly upright camera is no longer valid. In this work, we investigate how to solve this problem by fusing purely geometric cues, such as apparent vanishing points, with learned semantic cues, such as the expectation that some visual elements (e.g. doors) have a natural upright position. We train a deep neural network to leverage these cues to segment the image-space endpoints of an imagined “vertical axis”, which is orthogonal to the ground plane of a scene, thus levelling the camera. We show that our segmentation-based strategy significantly increases performance, reducing errors by half, compared to the current state-of-the-art on two datasets of 360 $$^{\circ }$$ imagery. We also demonstrate the importance of 360 $$^{\circ }$$ camera levelling by analysing its impact on downstream tasks, finding that incorrect levelling severely degrades the performance of real-world computer vision pipelines.
SoccER: Computer graphics meets sports analytics for soccer event recognition

Automatic event detection from images or wearable sensors is a fundamental step towards the development of advanced sport analytics and broadcasting software. However, the collection and annotation of large scale sport datasets is hindered by technical obstacles, cost of data acquisition and annotation, and commercial interests. In this paper, we present the Soccer Event Recognition (SoccER) data generator, which builds upon an existing, high quality open source game engine to enable synthetic data generation. The software generates detailed spatio-temporal data from simulated soccer games, along with fine-grained, automatically generated event ground truth. The SoccER software suite includes also a complete event detection system entirely developed and tested on a synthetic dataset including 500 min of game, and more than 1 million events. We close the paper by discussing avenues for future research in sports event recognition enabled by the use of synthetic data.
Quantizing Oriented Object Detection Network via Outlier-Aware Quantization and IoU Approximation

In recent years, a large number of quantization schemes have been proposed for compressing convolutional neural networks (CNN). However, most of them have the following problems: 1) when there are outliers in the weight, post-training quantization cannot obtain the ideal effect, and the accuracy loss is unavoidable; 2) quantizing the non-maximum suppression (NMS) stage of oriented object detection networks is non-trivial so that such networks are difficult to deploy on edge computing devices that only support integer operations. In this letter, we propose the outlier-aware quantization (OAQ) to boost the robustness of the post-training quantization method. Besides, we design a multilayer perceptron network to approximate the intersection-over-union (IoU) of rotated boxes, making the NMS stage can be deployed on integer-arithmetic-only devices. The experiment results demonstrate that our solution outperforms the widely used post-training quantization method. Meanwhile, to the best of our knowledge, this is the first study that focuses on the optimization and quantization of the NMS stage of oriented object detection networks.
Defenses Against Byzantine Attacks in Distributed Deep Neural Networks

Large scale deep learning is trending recently, since people find that complicated networks can sometimes reach high accuracy on image recognition, natural language processing, etc. With the increasing complexity and batch size of deep neural networks, the training for such networks is more difficult because of the limited computational power and memory. Distributed machine learning or deep learning provides an efficient solution. However, with the concern of untrusted machines or hardware failures, the distributed system may suffer Byzantine attacks. If some workers are attacked and just upload malicious gradients to the parameter server, they will lead the total training process to a wrong model or even cannot converge. To defend the Byzantine attacks, we propose two efficient algorithms: FABA, a Fast Aggregation algorithm against Byzantine Attacks, and VBOR, a Variance Based Outlier Removal algorithm. FABA conducts the distance information to remove outliers one by one. VBOR uses the variance information to remove outliers with one-pass iteration. Theoretically, we prove the convergence of our algorithms and give an insight of the correctness. In the experiment, we compare FABA and VBOR with the state-of-the-art Byzantine defense algorithms and show our superior performance.
Privacy-Preserving Collaborative Analytics on Medical Time Series Data

Medical time series data analytics based on dynamic time warping (DTW) greatly benefits modern medical research. Driven by the distributed nature of medical data, the collaboration of multiple healthcare institutions is usually necessary for a sound medical conclusion. Among others, a typical use case is disease screening for public health, where multiple healthcare institutions wish to collaboratively detect over their joint datasets the patients whose medical records have similar features to the given query samples. However, sharing the medical data faces critical privacy obstacles with the increasingly strict legal regulations on data privacy. In this article, we present the design of a novel system enabling privacy-preserving DTW-based analytics on distributed medical time series datasets. Our system is built from a delicate synergy of techniques from both cryptography and data mining domains, where the key idea is to leverage observations on the advancements in plaintext DTW analytics (e.g., clustering and pruning) to facilitate the scalable computation in the ciphertext domain, through our tailored security design. Extensive experiments over real medical time series datasets demonstrate the promising performance of our system, e.g., our system is able to process a secure DTW query computation over 15K time series sequences in 34 minutes.
A Light-weight Blockchain Architecture for V2V Knowledge Sharing at Vehicular Edges

Autonomous vehicles (AV) utilize various machine learning (ML) models for performing tasks such as pedestrian detection, charging station prediction, routing path prediction, intrusion detection, to name a few. To improve the robustness of such applications, decentralized collaboration is imperative in a vehicular network. Moreover, decentralized collaboration poses threats, such as forged message injection, forged identity, and repudiatory activities, as most vehicles are stranger to each other. In recent years, blockchain technology has been widely used in the vehicular network to enhance trust, provenance, and eliminate unauthorized access to the vehicular services. However, most of the existing vehicular blockchain suffers from issues related to scalability, efficiency, and transaction verification. In this paper, we propose a lightweight vehicular blockchain architecture for distributed model sharing to enhance trust, verifiability, and non-repudiation in distributed vehicular collaboration. We propose a novel PoVS-BFT protocol and an effective two-step transaction verification mechanism for model sharing applications. Finally, simulation results are presented to conform to the efficacy of our proposed architecture. Simulation results show that the proposed PoVS-BFT protocol can minimize the size of consensus committee up to 62.5% which in turn reduces communication complexity during the consensus process.
Mining Frequent Patterns Partially Devoid of Dissociation with Automated MMS Specification Strategy

Mining frequent patterns with single minimum support is quite unpredictable as it may miss rare itemsets depending upon the high value of minimum threshold. The phenomenon is commonly known as "rare item problem" dilemma. In this connection, frequent pattern mining under multiple minimum supports (MMS) is considered as an adequate solution of the problem. Existing approaches in this domain require user-defined minimum item support (MIS), and consider only the support in recognizing the importance of the items and ignore other factors including the inter-item relationships. The generation of huge number of itemsets including the itemsets with low associability or high dissociation is another problem in the existing MMS-based approaches. Keeping view of the above issues, this paper introduces AutoMMS-FPM, an automated MMS specification approach for frequent pattern mining with partial devoid of dissociation. The proposed approach not only takes account of support but also some other factors such as influence (inf), dissociation (d) and length importance factor (ρ) to boost up the mining process. Influence refers to the inter-item relationship and it is formulated based on the concept of degree centrality of the corresponding item network. MIS for the items are calculated automatically using MBS and RIF factors where the first one relates to the item support and the second one relates to the item influence. Experimental results on both of the synthetic and real datasets show that the proposed algorithm outperforms than existing approaches in terms of number of generated itemsets, run time, memory usages, dissociation and scalability.
Fostering More-than-Human Imaginaries: Introducing DIY Speculative Fabulation in Civic HCI

Amidst the epistemological turbulence of the Anthropocene, forging new narratives has considerable importance. Departing from human-centred thinking and world-building, a shift in perspective towards a more-than-human worldview requires a continuous process of increasing awareness through various media and education. In our research on DIY tools and methodologies for urban civic interventions, we examine how applying a speculative fabulation method facilitates probing, informing and engaging citizens on a variety of human and more-than-human urban issues. Drawing on the theoretical backdrop from the environmental humanities, through an ethnographic account of two preliminary participatory design tracks, we describe how we infused our workshop interventions and participatory protocols with embodied and material storytelling that probes towards fostering more-than-human imaginaries. We discuss the potentials and pitfalls of using such an approach for tackling urban issues, and end the paper with a propositional outline for integrating the speculative fabulation method in civic HCI.
Robust Generation of Logical Qubit Singlet States with Reverse Engineering and Optimal Control with Spin Qubits

Abstract A protocol is proposed to generate singlet states of three logical qubits constructed by pairs of spins. Single and multiple operations of logical qubits are studied for the construction of an effective Hamiltonian, with which robust control fields are derived with invariant‐based reverse engineering and optimal control. Moreover, systematic errors are further compensated by periodic modulation for better robustness. Furthermore, resistance to random noise and decoherence of the protocol is also shown with numerical simulations. Therefore, the protocol may provide useful perspectives for the generation of logical qubit entanglement in spin systems.
Don’t Bother Me: How to Handle Content-Irrelevant Objects in Handheld Augmented Reality

In this paper, we applied the concept of diminished reality to remove content-irrelevant pedestrian (i.e., real object) in the context of handheld augmented reality (AR). We prepared three view conditions: in Transparent (TP) condition, we removed the pedestrian entirely; in Semi-transparent (STP) condition, the pedestrian became semi-transparent; lastly, in Default (DF) condition, the pedestrian appeared as is. We conducted a user study to compare the effects of the three conditions on users' engagement and perception of a virtual pet in the AR content. Our findings revealed that users felt less distracted to the AR content in TP and STP conditions, compared to the DF condition. Furthermore, users felt the virtual pet as more life-like, its behavior more plausible, and felt a higher spatial presence in the real environment, in the TP condition.
Classification of Bangla News Articles Using Bidirectional Long Short Term Memory

Classification is a method of assigning input vectors to one of the discrete classes. This problem can be used to identify related content such as E-commerce, news agencies, content curators, blogs, directories, and likes can use automated technologies. In this paper, we have proposed a method of classification using bi-directional LSTM to classify the Bangla news headline. We have used Bangla stop word corpus to removing stop words to get a better result in our method of classification. We have used Gensim and fastText model to vectorized our text to compatible with our machine learning model. We have built a dataset that contains around 10 lakh articles from the different renowned newspapers of Bangladesh and 8 different categories. Then we trained this data in 3 different models. Among those models, Bi-LSTM has achieved 85.14 percent accuracy, which is better than any other method.
Exaggerated Error Handling Hurts! An In-Depth Study and Context-Aware Detection

Operating system (OS) kernels frequently encounter various errors due to invalid internal states or external inputs. To ensure the security and reliability of OS kernels, developers propose a diverse set of mechanisms to conservatively capture and handle potential errors. Existing research has thus primarily focused on the completeness and adequacy of error handling to not miss the attention. However, we find that handling an error with an over-severe level (e.g., unnecessarily terminating the execution) instead hurts the security and reliability. In this case, the error-handling consequences are even worse than the error it attempts to resolve. We call such a case Exaggerated Error Handling (EEH). The security impacts of EEH bugs vary, including denial-of-service, data losses, broken control-flow integrity, memory leaks, etc. Despite its significance, detecting EEH remains an unexplored topic.
An Efficient Implementation of NTRU Encryption in Post-Quantum Internet of Things

The Internet of Things (IoT) or the Internet of Everything (IoE) goes even further beyond, not only it affects the way of exchanging data but also touches the physical lives. On the other side, security challenges of end-to-end device communication need to be addressed i.e., compliance & regulation, protocols, remediation, impacts & risks, threat diversities, and new applications. This research demonstrates impacts, risks, and threat diversities on IoT devices that use either conventional cryptography or post-quantum cryptography. This research also provides proof of concepts of security infrastructure for end-to-end communication among the IoT devices. Moreover, this research proposes and implements lightweight post-quantum cryptography for communication between the publisher to the subscriber in Raspberry Pi3 B+ e.g., $N^{\mathfrak{k}h}$ degree Truncated Polynomial Ring (NTRU) lattice-based cryptography. The experimentation compares NTRU with AES, Fernet, and RSA respectively on encryption time analysis, decryption time analysis, and network layer analysis. The results suggest some critical points that should be considered for the future development of smart homes, smart factories, smart cities, smart health, etc.
Prospecting for gold mineralization with the use of remote sensing and GIS technology in North Kordofan State, central Sudan

The main objective of this study is to carry out regional geological mapping and gold prospecting in the poor savannah Northern Kordofan State, central Sudan. The selection of the study area was based on its favorable gold mineralization geological setting. Syntheses of the various forms of imagery, digital image processing, spectral analysis and office mapping were conducted before going into the field. Field observations, chemical analysis and petrographic investigations have all been integrated with the help of Geographic Information Systems (GIS) to come out with reliable interpretation that can be implemented in similar geologic setting and climatic conditions. A package of digital image processing techniques was applied to Landsat7 Enhanced Thematic Mapper Plus (ETM+) image in order to increase the visual interpretability of the various lithological units for the purpose of geological mapping and delineation of alteration zones, which represent possible target zones for gold mineralization. Different color composite images in addition to the result of image transformation (including Red Green Blue, RGB to Intensity Hue Saturation, IHS; Principal Component Analysis, PCA) were prepared to facilitate the delineation of the main rock groups. Several ratio images were prepared, combined together and displayed in RGB color composites. The outcome of the image processing is the discovery of many alteration and shear zones which trend north-northeast to south-southwest affecting most of the rock types. 139 chip samples were collected from the alteration and shear zones, which were delineated by remote sensing data interpretation and actual field work. They include samples from quartz veins and rock alteration zones which were analyzed by fire assay. All the collected samples proved to be gold-bearing. The Au values range from 0.1 to 84.9 g/ton. Artisanal mining activity in the study area coincided with the remotely sensed data interpretation results conducted in this work, i.e. during the field checkup artisanal miners were found in the places where anomalous signals appeared in the satellite image, thus reinforcing the reliability of the employed approach of remote sensing and GIS for gold prospecting in poor savannah zones.
Specifying and computing causes for query answers in databases via database repairs and repair-programs

There is a recently established correspondence between database tuples as causes for query answers in databases and tuple-based repairs of inconsistent databases with respect to denial constraints. In this work, answer-set programs that specify database repairs are used as a basis for solving computational and reasoning problems around causality in databases, including causal responsibility. Furthermore, causes are introduced also at the attribute level by appealing to an attribute-based repair semantics that uses null values. Corresponding repair-programs are introduced, and used as a basis for computation and reasoning about attribute-level causes. The answer-set programs are extended in order to capture causality under integrity constraints.
Neural Spectrum Alignment: Empirical Study

Expressiveness and generalization of deep models was recently addressed via the connection between neural networks (NNs) and kernel learning, where first-order dynamics of NN during a gradient-descent (GD) optimization were related to gradient similarity kernel, also known as Neural Tangent Kernel (NTK) []. In the majority of works this kernel is considered to be time-invariant [, ]. In contrast, we empirically explore these properties along the optimization and show that in practice top eigenfunctions of NTK align toward the target function learned by NN which improves the overall optimization performance. Moreover, these top eigenfunctions serve as basis functions for NN output - a function represented by NN is spanned almost completely by them for the entire optimization process. Further, we study how learning rate decay affects the neural spectrum. We argue that the presented phenomena may lead to a more complete theoretical understanding behind NN learning.
DigiGlo

The shape and function of the human hand are intimately linked to our interaction with the physical world and sets us apart from our evolutionary ancestors. In the digital age, our hands still represent our main form of interaction. We use our hands to operate keyboards, mice, trackpads, and game controllers. This modality, however, separates content display and interaction. In this paper, we present Digital Gloves (DigiGlo), a system designed to evaluate the benefits of a unified hand display and interaction system. We explore this symbiosis in the context of gaming where users control games using hand gestures while the content is displayed on their bare hand. Building on established learning principles, we explore different hand gestures and other specially tailored interactions, through three carefully designed activities and two user studies. From these we show that this is an idea that has the potential to bring more intuitive, enjoyable, and effective gaming and learning experiences, and offer recommendations regarding how to better design such systems.
Extracting User's Food Preferences by Sentiment Analysis

With the growth and development of websites and social networks, the number of user comments on these platforms has grown significantly. These comments contain rich information, which can be analyzed to discover individuals' preferences in various areas, including food. Extracting individuals' preferences can be useful for many applications of the Internet of Things paradigm. This paper proposes a method for extracting individuals' food preferences from their comments. The method includes extracting foods names from individual comments, clustering them, and performing sentiment analysis for each name. Comments on the Trip Advisor website have been used in experiments. In this regard, 100 users have been chosen and their comments from January to September of 2018 have been collected. Data from the first six months has been used for training the proposed method, while the data from the last 3 months has been used for testing. The results indicate the high precision of the proposed method in extracting users' food preferences.
Soft Compression: An Approach to Shape Coding for Images

In this letter, we propose soft compression, an lossless compression approach to shape coding for images using location index and codebook of designed shapes with various sizes. This method is different from traditional image compression methods, as it aims at finding the basic shape blocks of pictures to improve the compression ratio from the perspective of information theory and frequency. In particular, the triplets including location and codeword are stored when encoding an image instead of the entire array. It is shown that soft compression can greatly reduce the bandwidth and storage space needed in the process of transmitting and storing the same kind of images.