Reviewerly/exHarmony huggingface dataset:
Human activity recognition using multi-input CNN model with FFT spectrograms

An activity recognition method developed by Team DSML-TDU for the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge was descrived. Since the 2018 challenge, our team has been developing human activity recognition models based on a convolutional neural network (CNN) using Fast Fourier Transform (FFT) spectrograms from mobile sensors. In the 2020 challenge, we developed our model to fit various users equipped with sensors in specific positions. Nine modalities of FFT spectrograms generated from the three axes of the linear accelerometer, gyroscope, and magnetic sensor data were used as input data for our model. First, we created a CNN model to estimate four retention positions (Bag, Hand, Hips, and Torso) from the training data and validation data. The provided test data was expected to from Hips. Next, we created another (pre-trained) CNN model to estimate eight activities from a large amount of user 1 training data (Hips). Then, this model was fine-tuned for different users by using the small amount of validation data for users 2 and 3 (Hips). Finally, an F-measure of 96.7% was obtained as a result of 5-fold-cross validation.
Camera Based Barcode Localization and Decoding in Real-World Applications

In the last decades, many approaches were presented to localize and decode barcodes in images from off the shelf cameras. However, all proposed solutions usually only deal with one type of image artifacts, such as a poorly illuminated or noisy image, or an image that suffers from motion or out-of-focus blur. In this paper, we present a complete, fully automatic pipeline, which allows the localization and decoding of barcodes in real-world scenarios. Our method is capable of localization and decoding the correct barcode information even if the input image is noisy, poorly exposed, and blurred at the same time. We can also decode the correct information from barcode images whose resolution is actually too low, i.e., where the width of the smallest bar depicted is smaller than the width of a single pixel.
Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data

Spaceborne data with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Short Wave Infrared (SWIR) bands have been used worldwide in discriminating different clay minerals. However, due to wide bandwidth of ASTER sensors, it is difficult to distinguish between pure and impure variety of talc mineral. The present study highlights the efficacy of airborne hyperspectral data Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) for spectral analysis in talc mineral in the Jahazpur Belt of Bhilwara district in Rajasthan, India. Due to its better spectral and spatial resolution, AVIRIS-NG is found to be extremely useful in identifying talc mineral in SWIR region. Talc is characterized by doublet shaped spectral pattern in AVIRIS-NG image with strong absorption dips at 2289.75 nm and 2314.80 nm which could not be picked up in ASTER image. Spectral matching of talc pixels is verified with the field spectra of samples and available spectral library of United States Geological Survey, Jet Propulsion Laboratory, and International Geological Correlation Programme. Variation in reflectance percentage of different talc pixels is attributed to variation in chemical composition of talc. However, a large number of mixed pixels with spectral pattern of both talc and kaolinite minerals have been identified in AVIRIS-NG image which are found to be an impure variety of talc. Whole-pixel to sub-pixel classification techniques with Spectral Angle Mapper, Spectral Feature Fitting, Mixture Tuned Matched Filtering, and Linear Spectral Unmixing (LSU) techniques have been applied for talc abundance mapping in Jahazpur area. LSU is found to be the most useful and accurate technique in classifying spectrally pure to impure variety of talc.
Deep-Diving into Documentation to Develop Improved Java-to-Swift API Mapping

Application program interface (API) mapping is the key to the success of code migration. Leveraging API documentation to map APIs has been explored by previous studies, and recently, code-based learning approaches have become the mainstream approach and shown better results. However, learning approaches often require a large amount of training data (e.g., projects implemented using multiple languages or API mapping datasets), which are not widely available. In contrast, API documentation is usually available, but we have observed that much information in API documentation has been underexploited. Therefore, we develop a deep-dive approach to extensively explore API documentation to create improved API mapping methods. Our documentation exploration approach involves analyzing the functional description of APIs, and also considers the parameters and return values. The results of this analysis can be used to generate not only one-to-one API mapping, but also compatible API sequences, thereby enabling one-to-many API mapping. In addition, parameter-mapping relationships, which have often been ignored in previous approaches, can be produced. We apply this approach to map APIs from Java to Swift, and the experimental results indicate that our deep-dive analysis of API documentation leads to API mapping results that are superior to those generated by existing approaches.
Ranking list preservation for feature matching

Feature matching plays a very important role in many computer vision and pattern recognition tasks. The spatial neighborhood relationship (representing the topological structures of some key feature points of an image scene) is generally well preserved between two feature points of an image pair. Several mismatch-removing methods that maintain the local neighborhood structures of potential true matches have been proposed. Defining local neighborhood structures is a crucial issue in the feature matching problem. In this paper, we propose a robust and efficient topological structure measurement called top K rank preservation (TopKRP) for mismatch removal from given putative point set. We transform feature points from the feature space to the ranking list space. Thus, the topological structure similarity of two feature points can be simply calculated by comparing their ranking lists, which are measured by the top K ranking similarity based on the spatial Euclidean distance as well as the angle correlation. TopKRP is validated on 10 public image pairs with typical scenes and 2 artificially established datasets, namely, MI52 and RS153. Experimental results demonstrate that the proposed approach outperforms several state-of-the-art feature matching methods, especially when the number of mismatches is large.
Efficient and Stable Graph Scattering Transforms via Pruning

Graph convolutional networks (GCNs) have well-documented performance in various graph learning tasks, but their analysis is still at its infancy. Graph scattering transforms (GSTs) offer training-free deep GCN models that extract features from graph data, and are amenable to generalization and stability analyses. The price paid by GSTs is exponential complexity in space and time that increases with the number of layers. This discourages deployment of GSTs when a deep architecture is needed. The present work addresses the complexity limitation of GSTs by introducing an efficient so-termed pruned (p)GST approach. The resultant pruning algorithm is guided by a graph-spectrum-inspired criterion, and retains informative scattering features on-the-fly while bypassing the exponential complexity associated with GSTs. Stability of the novel pGSTs is also established when the input graph data or the network structure are perturbed. Furthermore, the sensitivity of pGST to random and localized signal perturbations is investigated analytically and experimentally. Numerical tests showcase that pGST performs comparably to the baseline GST at considerable computational savings. Furthermore, pGST achieves comparable performance to state-of-the-art GCNs in graph and 3D point cloud classification tasks. Upon analyzing the pGST pruning patterns, it is shown that graph data in different domains call for different network architectures, and that the pruning algorithm may be employed to guide the design choices for contemporary GCNs.
A theoretical framework for designing smart and ubiquitous learning environments for outdoor cultural heritage

Cultural heritage is constantly attracting researchers and developers to introduce services that could support informal learning and enhance visitors' experience at sites, through the use of technology. Few studies introduced models and frameworks for developing learning technologies, however, none of them were developed for supporting informal learning on-the-move at outdoor cultural heritage. This paper introduces a theoretical Framework for designing Smart and ubiquitous Learning Environments (FoSLE) for outdoor cultural heritage sites, which was formulated based on the results of three field studies that were conducted for gathering user requirements. A user-centred design approach using sequential mixed methods was adopted with data being gathered using focus group, questionnaire and interview techniques. A set of general requirements was extracted from the framework to inform the design of a smart and ubiquitous learning environment proof-of-concept, SmartC, for which mobile and wearable technologies were utilised.
Multi-utility framework: blockchain exchange platform for sustainable development

Purpose The purpose of this paper is to design a sustainable development platform for water and energy peer-to-peer trading that is financially and economically feasible. Water and other resources are becoming scarcer every day, and developing countries are the neediest for an immediate intervention. Water, as a national need, is considered to be one of the most precious commodities, but it is also one of the main causes for conflicts in the 21st century. Rainwater harvesting and peer-to-peer trading of the harvested water is one of the most convenient, scalable and sustainable solutions but faces organization challenges such as the absence of suitable business models motivating normal users to sell their generated resources (such as water and energy), currency and financial settlement complexities and single utility markets. Design/methodology/approach This paper proposes a multi-utility trading platform based on the blockchain technology which can address the challenges faced by peer-to-peer trading for resources such as energy and water. Findings This paper presents a peer-to-peer multi-utility trading platform that solves the shortcomings of existing utility frameworks reported in the current literature. Originality/value This proposed platform meets the needs of developing countries as well as rural areas of developed countries. The open nature of the proposed design makes it suitable for adoption and use by various stakeholders.
Multi-robot Coordination with Agent-Server Architecture for Autonomous Navigation in Partially Unknown Environments

In this work, we present a system architecture to enable autonomous navigation of multiple agents across user-selected global interest points in a partially unknown environment. The system is composed of a server and a team of agents, here small aircrafts. Leveraging this architecture, computation-ally demanding tasks, such as global dense mapping and global path planning can be outsourced to a potentially powerful central server, limiting the onboard computation for each agent to local pose estimation using Visual-Inertial Odometry (VIO) and local path planning for obstacle avoidance. By assigning priorities to the agents, we propose a hierarchical multi-robot global planning pipeline, which avoids collisions amongst the agents and computes their paths towards the respective goals. The resulting global paths are communicated to the agents and serve as reference input to the local planner running onboard each agent. In contrast to previous works, here we relax the common assumption of a previously mapped environment and perfect knowledge about the state, and we show the effectiveness of the proposed approach in photo-realistic simulations with up to four agents operating in an industrial environment.
Video action recognition with visual privacy protection based on compressed sensing

Action recognition in video involves interpreting complex semantic information, which remains a challenge in computer vision. Research on the use of deep neural networks for video action recognition has made significant progress in recent years, but most available recognition frameworks cannot satisfactorily perform the video action recognition task in privacy scene. In light of this issue, this paper proposes a method for video action recognition that employs a compressed sensing-based visual privacy protection framework. The proposed method protects visual privacy by balancing operational efficiency with the accuracy of recognition. It consists of three steps. First, compressed sensing is used to realize visual privacy protection and significantly enhance efficiency at the same time. Second, the convolutional 3D network model is used due to its speed to represent features of video actions in the compressed sensing data, and PCA is used to reduce the dimensionality of the extracted feature vectors to reduce temporal complexity. Finally, a sparse representation-based classification algorithm is integrated into the AdaBoost architecture to improve the recognition performance of the proposed method. Experiments were performed on three commonly used video action datasets to verify the proposed method, and the results show that it is robust in video action recognition tasks, and adequately protects visual privacy.
Risk management for cyber-infrastructure protection: A bi-objective integer programming approach

Information and communication technology supply chains present risks that are complex and difficult for organizations to manage. The cost and benefit of proposed security controls must be assessed to best match an organizational risk tolerance and direct the use of security resources. In this paper, we present integer and stochastic optimization models for selecting a portfolio of security controls within an organizational budget. We consider two objectives: to maximize the risk reduction across all potential attacks and to maximize the number of attacks whose risk levels are lower than a risk threshold after security controls are applied. Deterministic and stochastic bi-objective budgeted difficulty-threshold control selection problems are formulated for selecting mitigating controls to reflect an organization’s risk preference. In the stochastic problem, we consider uncertainty as to whether the selected controls can reduce the risks associated with attacks. We demonstrate through a computational study that the trade-off between the two objectives is important to consider for certain risk preferences and budgets. We demonstrate the value of the stochastic model when a relatively high number of attacks are desired to be secured past a risk threshold and show the deterministic solution provides near optimal solutions otherwise. We provide an analysis of model solutions.
A low latency modular-level deeply integrated MFCC feature extraction architecture for speech recognition

In this paper, a low-complex chip to extract the Mel Frequency Cepstral Coefficient for a speech recognition system is presented. The architecture can operate in a continuous-flow manner to process streaming or the stored speech signal at high speed. The frame-overlap Hamming window, DFT and Mel-filter bank computations are deeply integrated to share memory buffers and avoid bit-reversal circuit to reduce area and latency. Moreover, normalised energy consumption and area delay product are reduced by 32%, and speed is increased by 5.2 times compared to prior works. Further, the fixed-point word-length is optimised to minimise the area without affecting the accuracy.
Energy-efficient auto-scaling of virtualized network function instances based on resource execution pattern

To scale virtualized network functions (VNF), information about computing resource utilization plays an important role. With the usage of both virtualized and physical resources, introduced new challenges that need to be solved by virtual network functions manager (VNFM), which are responsible for managing the lifecycle of VNFs. These challenges demand an auto-scaling mechanism that relies on proper factors. The proposed system considers the execution time, thresholds of a VNF, and weight factor reflecting virtualization overhead as the practical factors. An auto-scaling application, along with the monitoring application, is developed. These two applications communicate with each other via application programming interfaces (API) to generate the auto-scaling configurations of resources. In the light of mentioned factors, mechanism, and later provided results, the proposed system proves to be useful in solving the challenges related to energy and allocation of resources for both virtual and physical infrastructures.
Non-monotonic Spatial Reasoning for Safety Analysis in Construction

We present a new approach based on spatial reasoning in Answer Set Programming (ASP), and a prototype software tool, for automatically evaluating construction safety compliance of real-world Building Information Models (BIM) that have both a geometric component and temporal component in the form of a construction plan and schedule (4D BIM). In the 4D BIM domain, geometries of building objects are large and complex making it highly impractical to represent geometries as ASP facts, unoptimised spatial reasoning can be prohibitively slow, and rounding errors in floating point arithmetic often result in logical contradictions. Our novel framework addresses these challenges by integrating a specialised geometry database, built-in spatial optimisations, and support for real arithmetic solving. We empirically evaluate our prototype software tool on two large 4D BIM models from real buildings to demonstrate the practicality and scalability of our new framework to real-world workplace hazard prevention tasks in construction safety-in-design analysis.
The security implications of quantum cryptography and quantum computing

Classical cryptography relies on the assumption that nobody can solve a certain difficult mathematical problem in a realistic amount of time or rely on information theory arguments. Quantum cryptography relies instead on fundamental quantum physics laws. Using large quantum computers, one could break all classical asymmetric algorithms currently used for key distribution and digital signatures. Quantum computing seems to threaten many of the encryption systems in use today, which assume that nobody can solve a difficult mathematical problem in a realistic amount of time. Fabio Cavaliere, John Mattsson and Ben Smeets of Ericsson Research provide an overview of the technologies and protocols for Quantum key distribution (QKD) systems, discuss their security implications and examine standardisation activities for QKD networks. They also introduce quantum random number generators (QRNGs) as an important building block for both classical and quantum encryption systems, and address the security challenges posed by the advent of quantum computers.
Stable Computation of Hahn Polynomials for Higher Polynomial Order

In this paper, we propose a new algorithm for computing Hahn polynomial coefficients (HPCs) for higher polynomial order, which greatly reduces the spread of numerical defects associated with Hahn polynomials (HPs) using conventional methods. The proposed method is used to reconstruct large 2D images. The reliability and effectiveness of the new approach were often linked to standard repetition algorithms. The findings show that the method proposed is efficient and effective in terms of calculation accuracy and stability of high order Hahn moments.
The trace kernel bandwidth criterion for support vector data description

Support vector data description (SVDD) is a popular anomaly detection technique. The computation of the SVDD classifier requires a kernel function, for which the Gaussian kernel is a common choice. The Gaussian kernel has a bandwidth parameter, and it is important to set the value of this parameter correctly to ensure good results. A small bandwidth leads to overfitting, and the resulting SVDD classifier overestimates the number of anomalies, whereas a large bandwidth leads to underfitting and an inability to detect many anomalies. In this paper, we present a new, unsupervised method for selecting the Gaussian kernel bandwidth. Our method exploits a low-rank representation of the kernel matrix to suggest a kernel bandwidth value. Our new technique is competitive with the current state of the art for low-dimensional data and performs extremely well for many classes of high-dimensional data. This method is also applicable to one-class support vector machines (OCSVM).
The Human Mental Search Algorithm for Solving Optimisation Problems

The performance of most data science algorithms, and in particular machine learning algorithms, is largely dependent on the performance of their optimisation algorithm. In other words, without an effective optimisation algorithm there is no effective data science algorithm. Conventional optimisation algorithms suffer from drawbacks such as a tendency to get stuck in local optima and sensitivity to the initial conditions. To tackle these, population-based metaheuristic algorithms, which work on a population of candidate solution and incorporate stochastic behaviour, can be used. In this chapter, we the present the Human Mental Search (HMS) algorithm, population-based metaheuristic algorithm that is inspired by bid exploration in online auctions. HMS comprises three main operators:, mental search, grouping, and movement. The mental search operator is responsible for exploring the vicinity of a candidate solution, the grouping operator employs an unsupervised clustering technique, k-means, to partition candidate solutions, while the movement operator moves candidate solutions towards a promising area identified by the grouping operator. To evaluate the efficacy of the HMS algorithm, a set of experiments on different benchmark functions with diverse characteristics as well as both normal and large-scale problems. The obtained results clearly show the merit of HMS compared to other algorithms.
<i>Combining Textual Cues with Social Clues</i>: Utilizing Social Features to Improve Sentiment Analysis in Social Media

ABSTRACT Traditional sentiment analysis methods do not perform well when applied to social media data. In this study, we propose an approach to improve sentiment analysis performance in the context of social media. Our approach utilizes three types of additional information that can be collected from social media platforms— personal preference, friend influence, and herding effect —to enrich the input features of a supervised sentiment classification model. We implement the approach on data sets collected from Twitter across two industries (airlines and wireless service providers) and present the performance improvement attained by combining social features with pure text‐based features. To further investigate the operational implications of this improvement, we develop a stylized service recovery model for customer relationship management in social media. Our work has implications for automating social media monitoring and, more broadly, for improving customer relationship management in organizations.