Reviewerly/exHarmony huggingface dataset:
Improving Real-Time Position Estimation Using Correlated Noise Models

We provide algorithms for inferring GPS (Global Positioning System) location and for quantifying the uncertainty of this estimate in real time. The algorithms are tested on GPS data from locations in the Southern Hemisphere at four significantly different latitudes. In order to rank the algorithms, we use the so-called log-score rule. The best algorithm uses an Ornstein-Uhlenbeck (OU) noise model and is built on an enhanced Kalman Filter (KF). The noise model is capable of capturing the observed autocorrelated process noise in the altitude, latitude and longitude recordings. This model outperforms a KF that assumes a Gaussian noise model, which under-reports the position uncertainties. We also found that the dilution-of-precision parameters, automatically reported by the GPS receiver at no additional cost, do not help significantly in the uncertainty quantification of the GPS positioning. A non-learning method using the actual position measurements and employing a constant uncertainty does not even converge to the correct position. Inference with the enhanced noise model is suitable for embedded computing and capable of achieving real-time position inference, can quantify uncertainty and be extended to incorporate complementary sensor recordings, e.g., from an accelerometer or from a magnetometer, in order to improve accuracy. The algorithm corresponding to the augmented-state unscented KF method suggests a computational cost of O(dx2dt), where dx is the dimension of the augmented state-vector and dt is an adjustable, design-dependent parameter corresponding to the length of "past values" one wishes to keep for re-evaluation of the model from time to time. The provided algorithm assumes dt=1. Hence, the algorithm is likely to be suitable for sensor fusion applications.
Robust Text Extraction in Images for Personal Event Planner

Modern smartphones are having a large number of applications for personal event scheduling. The main mode of input to those applications are text, voice-input from the user or E-Mail updates provided by certain service providers. For certain events, the details of the event would be available as hard copies of different forms like paper print outs, banners and various modes of advertisements. In such cases, the user needs to manually locate the vital information from the image then needs to update his event schedule. To minimize the load on the user, an intelligent personal scheduler has been developed which takes an image input, extracts necessary information from the image and updates the schedule of the user with appropriate alert settings. This paper discusses about an event planner based on image processing and uses Google Calendar for scheduling the events. The application makes use of Optical Character Recognition (OCR) algorithm for identification of text in the images which acts as an input to the scheduler and Google Calendar is used to update the event planner.
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop

As the complexity of deep learning (DL) modelsincreases, their compute requirements increase accordingly. De-ploying a Convolutional Neural Network (CNN) involves twophases: training and inference. With the inference task typicallytaking place on resource-constrained devices, a lot of research hasexplored the field of low-power inference on custom hardwareaccelerators. On the other hand, training is both more compute-and memory-intensive and is primarily performed on power-hungry GPUs in large-scale data centres. CNN training onFPGAs is a nascent field of research. This is primarily due tothe lack of tools to easily prototype and deploy various hardwareand/or algorithmic techniques for power-efficient CNN training. This work presentsBarista, an automated toolflow that providesseamless integration of FPGAs into the training of CNNs withinthe popular deep learning framework Caffe. To the best of ourknowledge, this is the only tool that allows for such versatile andrapid deployment of hardware and algorithms for the FPGA-based training of CNNs, providing the necessary infrastructurefor further research and development.
Text Imbalance Handling and Classification for Cross- platform Cyber-crime Detection using Deep Learning

Cyberbullying has become a very prevalent issue in recent times. Not just qualifying this as a women issue, starting from politicians to a ten-year-old kid, every person is being bullied on any social platform. It is highly essential to build an artificial intelligence based model that detects the presence of bullying in cross-platform posts. However, textual datasets which are useful for model generation are highly imbalanced in nature. In this paper, we propose two main methods to handle textual data imbalancing which are synonym replacement and artificial data generation using generative adversarial neural networks. We present a systematic analysis of our approaches using a convolutional neural network classifier. Our work shows how removing data imbalance with generative adversarial network techniques before classification improves the overall performance of the model.
MeToo: Sentiment Analysis using Neural Networks (Grand Challenge)

In the contemporary world, wide-range online social data has become a valuable tool in studying global movements and their impact on people as well as the people's impact on them. One such movement, #MeToo has led hitherto unknown victims of sexual assault to come out and openly share their personal experiences. While a lot of people have responded positively towards the movement, some have not. Our work attempts to study the data that we have gathered from Twitter and generate a better understanding of the MeToo movement's impact on society and vice versa. We use three different model architectures, a Convolutional Neural Network, a Recurrent Neural Network and a BERT transformer to classify the collected tweets on the basis of their attitude towards the people who have come out and the movement in general. The source to our GitHub project can be found at: https://github.com/ahmadkhan242/MeToo.
Temporal Pooling in Inflated 3DCNN for Weakly-supervised Video Anomaly Detection

Anomaly detection in surveillance videos requires significant attention in feature engineering to discriminate anomaly activity patterns from normal patterns. Keeping this in mind, this paper aims to extract superior quality spatio-temporal features from Inflated 3DCNN followed by a temporal pooling strategy to intensify relevant spatio-temporal feature in untrimmed anomalous videos. A superior temporal pooling strategy leads to better understanding of temporal dependency through LSTM model, which has become a necessary step for anomaly detection in surveillance videos. Thus, we propose a method consisting of an ideal temporal pooling strategy in inflated 3DCNN feature map along with LSTM model for temporal dependency encoding for weakly-supervised anomaly detection task. Our method is validated on a large scale video anomaly detection dataset, namely UCF-crime, resulting competitive performance in anomaly detection task with recent state-of-the-art methodologies.
Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval

The last few years have witnessed the rise of the big data era in which approximate nearest neighbor search is a fundamental problem in many applications, such as large-scale image retrieval. Recently, many research results have demonstrated that hashing can achieve promising performance due to its appealing storage and search efficiency. Since complex optimization problems for loss functions are difficult to solve, most hashing methods decompose the hash code learning problem into two steps: projection and quantization. In the quantization step, binary codes are widely used because ranking them by the Hamming distance is very efficient. However, the massive information loss produced by the quantization step should be reduced in applications where high search accuracy is required, such as in image retrieval. Since many two-step hashing methods produce uneven projected dimensions in the projection step, in this paper, we propose a novel dimension analysis-based quantization (DAQ) on two-step hashing methods for image retrieval. We first perform an importance analysis of the projected dimensions and select a subset of them that are more informative than others, and then we divide the selected projected dimensions into several regions with our quantizer. Every region is quantized with its corresponding codebook. Finally, the similarity between two hash codes is estimated by the Manhattan distance between their corresponding codebooks, which is also efficient. We conduct experiments on three public benchmarks containing up to one million descriptors and show that the proposed DAQ method consistently leads to significant accuracy improvements over state-of-the-art quantization methods.
Eliciting actors’ perspectives in integrated watershed management: exploring a practical tool based on a mental model approach

A profound understanding of actors' perspectives on issues to be resolved is essential for researchers and practitioners in integrated natural resource management. Existing mental model approaches to systematically elicit such perspectives have been based on qualitative interviews, which implies small sample sizes or time-consuming analyses. Few studies have investigated complex projects of practical relevance. We studied the actors' perspectives before the participatory process in a real-world integrated watershed management project through qualitative interviews and a quantitative survey based on a theoretical framework inspired by mental model approaches. We identified clear accordance and discordance among the perspectives regarding meanings of the water bodies, objectives for their management, and related beliefs. The mixed methods mental model approach proved to be a promising practical tool for the effective and efficient elicitation of actors' perspectives in decision making.
AI for Accessibility: Virtual Assistant for Hearing Impaired

Speech is one of the essential communication methods for human beings. The present solutions available for people with hearing disabilities are limited due to accessibility and expensive due to the high cost of hardware components. Sound classification methods are primarily used in smart assistants and smart home products. This technology has a lot of potential and can be inculcated in an application-based solution for deaf people. Apart from this, solutions pertaining to Sign Language Recognition are limited in usability and features as most of these products are limited to only the alphabet's recognition, which is real-world usage is inadequate. With the advancement in pose estimation algorithms, a solution can be developed which can recognize words and sentences to improve the efficiency of daily communication.
From flat file to interface: Synthesis of personas and analytics for enhanced user understanding

Abstract The persona approach is a communicative design technique for understanding users. With increasing access to analytics data, personas are more and more generated from online user statistics using big data and algorithmic approaches. This integration offers new opportunities to shift personas from flat files of data presentation to that of interactive interfaces for analytics systems. We illustrate this transition towards the concept of “persona as interface” with a persona analytics system, Automatic Persona Generation (APG). In pushing advancements of both persona and analytics conceptualization, development, and use, the APG system presents a multi‐layered “full‐stack” integration. APG affords three levels of user data presentation, which are (a) the conceptual persona, (b) the analytical user metrics, and (c) the foundational user data. Moving to a “personas as interfaces” approach offers the benefits of both personas and analytics systems and addresses some of the shortcomings of each. We provide results from user experiments of and use cases for APG. The result is a better tool than either persona or analytics along for user understanding.
PKI is key to securing a post-Covid remote workforce

In just a few short months, the Covid-19 outbreak has dramatically changed the way that global employees work. In a recent IDC survey, respondents said they expect nearly 30% of their workforce will be working from home in 2021, compared to approximately 6% of their workforce prior to the pandemic. 1 With the Covid-19 outbreak, analysts estimate that we have witnessed two years of IT digital transformation in two months, and many of the changes are here to stay. Most organisations were forced to shift employees to remote work in just weeks or even days. The switchover has opened the door to a flood of new security threats. Multifactor authentication, such as PKI certificates and passwords, is the best approach to apply flexible, scalable security to protect networks and their users in today's world, explains Brian Trzupek of DigiCert.
An Experimental Evaluation on the Dependency between One-Way Hash Functions and Salt

Passwords are barriers that protect unauthorized users from accessing personal information in any application. Protecting passwords is one of the challenging tasks in today's world. Currently, a combination of Username/Password used for authentication for a large number of applications. Malicious users might try to steal/misuse the user's data for unethical purposes. To prevent passwords from stealing, developers prefer to use one-way hash functions. One-way hash functions are theoretically irreversible functions that take as an input variable size text and output fixed-sized text. In reality, hash functions are not collision-resistant. Therefore it is recommended to use passwords and randomly generated text called salt to generate hash values and prevent rainbow tables and dictionary attacks. Passwords are hashed at the client-side and sent across the public channel/network. A salt is a randomly generated alphanumeric text used to concatenate with a password to generate a random hash value. This paper demonstrates how the random generation of salt is dependent on passwords and how hash values are dependent on salt. Further, analysis of the behaviour of passwords and hash values using various tools like Wireshark, Ettercap, and Hydra are presented in the paper.
Formal Requirements in an Informal World

With today's increasing complexity of systems and requirements there is a need for formal analysis of requirements. Although there exist several formal requirements description languages and corresponding analysis tools that target an industrial audience, there is a large gap between the form of requirements and the training in formal methods available in industry today, and the form of requirements and the knowledge that is necessary to successfully operate the analysis tools. We propose a process to bridge the gap between customer requirements and formal analysis. The process is designed to support in-house formalisation and analysis as well as formalisation and analysis as a service provided by a third party. The basic idea is that we obtain dependability and comprehensibility by assuming a senior formal requirements engineer who prepares the requirements and later interprets the analysis results in tandem with the client. We obtain scalability as most of the formalisation and analysis is supposed to be conducted by junior formal requirements engineers. In this paper, we define and analyse the process and report on experience from different instantiations, where the process was well received by customers.
Data Augmentation Using Principal Component Resampling for Image Recognition by Deep Learning

Image recognition by deep learning usually requires many sample images to train. In case of a small number of images available for training, data augmentation techniques should be applied. Here we propose a novel image augmentation technique based on a random permutation of coefficients of within-class principal components obtained after applying Principal Component Analysis (PCA). After reconstruction, newly generated surrogate images are employed to train a deep network. In this study, we demonstrated the applicability of our approach on training a custom convolutional neural network using the CIFAR-10 image dataset. The experimental results show an improvement in terms of classification accuracy and classification ambiguity.
Trail: A Blockchain Architecture for Light Nodes

In Bitcoin and Ethereum, nodes require large storage capacity to maintain all the blockchain data, such as transactions, UTXOs, and account states. As of May 2020, the storage size of the Bitcoin blockchain has expanded to 270 GB, and it will continue to increase. This storage requirement is a major hurdle to becoming a block proposer or validator. Although many studies have attempted to reduce the storage size, in the proposed methods, a node cannot keep all blocks or cannot generate a block. We propose an architecture called Trail that allows nodes to hold all blocks in a small storage and to generate and validate blocks and transactions. Trail does not depend on a consensus algorithm or fork choice rule. In this architecture, a client who issues transactions has the data to prove its own balances and can generate a transaction containing the proof of balances. The nodes in Trail do not store transactions, UTXOs and account balances: they keep only blocks. The blocksize is approximately 8 KB, which is 100 times smaller than that of Bitcoin. Further, the block size is constant regardless of the number of accounts and the number of transactions. Compared to traditional blockchains, clients who issue transactions must store additional data. However, we show that proper data archiving can keep the account device storage size small. Trail allows more users to be block proposers and validators and improves the decentralization of the blockchain.
It's Good to Chat?

We present and evaluate a deployed conversational AI system that acts as a host of a working public building on a university campus. The system combines open-domain social chat with task-based conversation regarding navigation in the building, live resource updates (e.g. available computers), and events in the building. We investigated the impact of open-domain social chat on task completion and user preferences by comparing the combined system with a task-only version. We find that there is no significant difference in task completion or several aspects of user preference between the two systems, but that users would be significantly happier to talk to the task-only system in the future. This suggests that the "walk-up" public setting and workplace nature of the environment creates a markedly different use case to the in-home, and more individual and private "companion/assistant" setting which is commonly assumed for systems like Alexa. We discuss the implications for the design of conversational systems in other public settings.
Exploring the Potential Benefits of Alternative Quantum Computing Architectures

Noisy intermediate scale quantum (NISQ) computers are becoming a reality thanks to the recent advances made by researchers who physically build such systems. In order to execute corresponding quantum algorithms (usually provided in terms of quantum circuits), certain physical constraints in the architectures need to be satisfied. More precisely, physical constraints restrict the possible interactions between qubits which frequently result in cases where qubits which are supposed to interact in a quantum circuit are not allowed to interact on the physical device. Thus far, this is addressed by dedicated methods that map the logical quantum circuit to a physical realization and satisfy the constraints by inserting further operations. This leads to additional costs which harm the fidelity of the circuit and, hence, urgently need to be avoided. Unfortunately, current state-of-the-art approaches for this mapping process take the existing architectures as invariant and only try to reduce the number of additionally needed operations. In contrast, (slight) changes in the, respectively, given architectures (which still keep the underlying physical constraints satisfied) might be possible and may allow for even better (i.e., less costly) mappings. But this potential has not been investigated yet. In this work, we explore this potential. More precisely, we introduce several schemes for generating alternative coupling graphs (and, by this, quantum computing architectures) that still might be able to satisfy physical constraints but, at the same time, allow for a more efficient realization of the desired quantum functionality. Evaluations confirm the potential of those alternative coupling graphs and demonstrate that they can reduce the mapping overhead by up to 60% in the best case and up to almost 40% on average.
Multimodal Assessment of Oral Presentations using HMMs

Audience perceptions of public speakers' performance change over time. Some speakers start strong but quickly transition to mundane delivery, while others may have a few impactful and engaging portions of their talk preceded and followed by more pedestrian delivery. In this work, we model the time-varying qualities of a presentation as perceived by the audience and use these models both to provide diagnostic information to presenters and to improve the quality of automated performance assessments. In particular, we use HMMs to model various dimensions of perceived quality and how they change over time and use the sequence of quality states to improve feedback and predictions. We evaluate this approach on a corpus of 74 presentations given in a controlled environment. Multimodal features-spanning acoustic qualities, speech disfluencies, and nonverbal behavior were derived both automatically and manually using crowdsourcing. Ground truth on audience perceptions was obtained using judge ratings on both overall presentations (aggregate) and portions of presentations segmented by topic. We distilled the overall presentation quality into states representing the presenter's gaze, audio, gesture, audience interaction, and proxemic behaviors. We demonstrate that an HMM of state-based representation of presentations improves the performance assessments.
A Unified Framework for Sparse Online Learning

The amount of data in our society has been exploding in the era of big data. This article aims to address several open challenges in big data stream classification. Many existing studies in data mining literature follow the batch learning setting, which suffers from low efficiency and poor scalability. To tackle these challenges, we investigate a unified online learning framework for the big data stream classification task. Different from the existing online data stream classification techniques, we propose a unified Sparse Online Classification (SOC) framework. Based on SOC, we derive a second-order online learning algorithm and a cost-sensitive sparse online learning algorithm, which could successfully handle online anomaly detection tasks with the extremely unbalanced class distribution. As the performance evaluation, we analyze the theoretical bounds of the proposed algorithms and conduct an extensive set of experiments. The encouraging experimental results demonstrate the efficacy of the proposed algorithms over the state-of-the-art techniques on multiple data stream classification tasks.
An Experimental Evaluation of SHA-512 for Different Modes of Operation

We have different numbers of algorithms for computation of the hash. SHA-512 is vulnerable to pre-image attacks. Two variants of SHA-512, which are SHA-512/224 and SHA-512/256, are also susceptible to collision attacks. This paper aimed to overcome the challenges of SHA-5112 and its variants. Our approach is to use the SHA-512 algorithm with different modes of operation. We evaluate each mode of operation in terms of the time required for the computation of the hash. This paper also describes two essential properties, such as pre-image resistance and collision resistance of our approach.