Reviewerly/exHarmony huggingface dataset:
Improving Human-computer Interaction in Low-resource Settings with Text-to-phonetic Data Augmentation

Off-the-shelf speech recognition systems can yield useful results and accelerate application development, but general-purpose systems applied to specialized domains can introduce acoustically small-but semantically catastrophic-errors. Furthermore, sufficient audio data may not be available to develop custom acoustic models for niche tasks. To address these problems, we propose a concept to improve performance in text classification tasks that use speech transcripts as input, without any in-domain audio data. Our method augments available typewritten text training data with inferred phonetic information so that the classifier will learn semantically important acoustic regularities, making it more robust to transcription errors from the general purpose ASR. We successfully pilot our method in a speech-based virtual patient used for medical training, recovering up to 62% of errors incurred by feeding a small test set of speech transcripts to a classification model trained on typescript.
Low Power Ultrasonic Gesture Recognition for Mobile Handsets

A novel approach for gesture recognition on mobile handsets which does not require any additional transducers is presented. The method is based on transmitting ultrasonic pulses from the earpiece and the loudspeaker and receiving with two microphones, usually located at the top and the bottom of the handset. Signal to Noise Ratio estimates are computed from the reflected signals on each microphone from which statistical moments are extracted and used for training a Support Vector Machine classifier along with hyperparameter optimization. The accuracy achieved is 77.5% on a database of 400 observations using 5-fold cross-validation.
Quantum Program Synthesis: Swarm Algorithms and Benchmarks

In the two decades since Shor's celebrated quantum algorithm for integer factorisation, manual design has failed to produce the anticipated growth in the number of quantum algorithms. Hence, there is a great deal of interest in the automatic synthesis of quantum circuits and algorithms. Here we present a set of experiments which use Ant Programming to automatically synthesise quantum circuits. In the proposed approach, ants choosing paths in high-dimensional Cartesian space are analogous to transformation of qubits in Hilbert space. In addition to the proposed algorithm, we introduce new evaluation criteria for searching the space of quantum circuits, both for classical simulation and simulation on a quantum computer. We demonstrate that the proposed approach significantly outperforms random search on a suite of benchmark problems based on these new measures.
Improving the sensitivity of long read overlap detection using grouped short k-mer matches

Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than second-generation sequencing technologies such as Illumina. The increased read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and characterize the intra-species variations. It also holds the promise to decipher the community structure in complex microbial communities because long reads help metagenomic assembly. One key step in genome assembly using long reads is to quickly identify reads forming overlaps. Because PacBio data has higher sequencing error rate and lower coverage than popular short read sequencing technologies (such as Illumina), efficient detection of true overlaps requires specially designed algorithms. In particular, there is still a need to improve the sensitivity of detecting small overlaps or overlaps with high error rates in both reads. Addressing this need will enable better assembly for metagenomic data produced by third-generation sequencing technologies.In this work, we designed and implemented an overlap detection program named GroupK, for third-generation sequencing reads based on grouped k-mer hits. While using k-mer hits for detecting reads' overlaps has been adopted by several existing programs, our method uses a group of short k-mer hits satisfying statistically derived distance constraints to increase the sensitivity of small overlap detection. Grouped k-mer hit was originally designed for homology search. We are the first to apply group hit for long read overlap detection. The experimental results of applying our pipeline to both simulated and real third-generation sequencing data showed that GroupK enables more sensitive overlap detection, especially for datasets of low sequencing coverage.GroupK is best used for detecting small overlaps for third-generation sequencing data. It provides a useful supplementary tool to existing ones for more sensitive and accurate overlap detection. The source code is freely available at https://github.com/Strideradu/GroupK .
Indigenous peoples in public media: A critical discourse analysis of the human zoo case

This study analyses the legal action taken for defamation in the case of the ‘human zoo’, an alleged defamatory portrayal of the Paiwan people, a Taiwanese indigenous group, by Japan’s public broadcaster, NHK. The analysis of this case is presented in two stages, the first of which employs a critical discourse analysis (CDA) to examine the covert racist discourse regarding the Paiwan people in a four-episode NHK documentary series on the history of the modernization of Japan. Second, the study evaluates the discursive patterns of the legal documents resulting from the subsequent lawsuits. The analysis incorporates the findings of interviews with key stakeholders in the human zoo case in its investigation of the presence of insidious racial prejudice against the Paiwan people, combined with a lack of sensitivity towards indigenous peoples in general, both in the documentary and in the responses from NHK in the subsequent lawsuits.
Adversarial Evolution and Deep Learning – How Does an Artist Play with Our Visual System?

We create artworks using adversarial coevolution between a genetic program (hercl) generator and a deep convolutional neural network (LeNet) critic. The resulting artificially intelligent artist, whimsically named Hercule LeNet, aims to produce images of low algorithmic complexity which nevertheless resemble a set of real photographs well enough to fool an adversarially trained deep learning critic modeled on the human visual system. Although it is not exposed to any pre-existing art, or asked to mimic the style of any human artist, nevertheless it discovers for itself many of the stylistic features associated with influential art movements of the 19th and 20th Century. A detailed analysis of its work can help us to better understand the way an artist plays with the human visual system to produce aesthetically appealing images.
Defining Agile Culture: A Collaborative and Practitioner-Led Approach

Agile transformation requires more than the adoption of new practices, it also requires a change of behaviours, norms and mindset across the organisation. This suggests that organisations need to change their culture to become more agile. But what is Agile culture? In this paper we explore how a group took a collaborative and practitioner-led approach to developing a definition of Agile Culture and a set of tools to aid cultural assessment. We tell the story of the process, discuss the artefacts created, and analyse the approach taken using a model of interdisciplinary collaboration.
Two new approaches for the visualisation of models for network meta-analysis

Meta-analysis is a useful tool for combining evidence from multiple studies to estimate a pooled treatment effect. An extension of meta-analysis, network meta-analysis, is becoming more commonly used as a way to simultaneously compare multiple treatments in a single analysis. Despite the variety of approaches available for presenting fitted models, ascertaining an intuitive understanding of these models is often difficult. This is especially challenging in large networks with many different treatments. Here we propose two visualisation methods, so that network meta-analysis models can be more easily interpreted. Our methods can be used irrespective of the statistical model or the estimation method used and are grounded in network analysis. We define three types of distance measures between the treatments that contribute to the network. These three distance measures are based on 1) the estimated treatment effects, 2) their standard errors and 3) the corresponding p-values. Then, by using a suitable threshold, we categorise some treatment pairs as being "close" (short distances). Treatments that are close are regarded as "connected" in the network analysis theory. Finally, we group the treatments into communities using standard methods for network analysis. We are then able to identify which parts of the network are estimated to have similar (or different) treatment efficacy and which parts of the network are better identified. We also propose a second method using parametric bootstrapping, where a heat map is used in the visualisation. We use the software R and provide the code used. We illustrate our new methods using a challenging dataset containing 22 treatments, and a previously fitted model for this data. Two communities of treatments that appear to have similar efficacy are identified. Furthermore using our methods we can identify parts of the network that are better (and less well) identified. Our new visualisation approaches may be used by network meta-analysts to gain an intuitive understanding of the implications of their fitted models. Our visualisation methods may be used informally, to identify the most salient features of the fitted models that can then be reported, or more formally by presenting the new visualisation devices within published reports.
Drawing Order Recovery for Handwriting Chinese Characters

Recover drawing orders from a Chinese handwriting image is a challenge issue. Most of English drawing order recovery(DOR) methods perform unsatisfactorily in Chinese. This paper proposes a novel image-to-sequence algorithm to deal with Chinese DOR problem. The proposed method utilizes two regression convolution neural network(CNN) models to generate two corresponding pen-tip movement heat-maps. To estimate pen-tip movement for most of the normal states in writing process, the algorithm analyzes the above two heat-maps with a specifically designed framework. Then the drawing order is restored through a simple iteration process based on the proposed framework. Experiments on public online handwriting database show that our method have got a remarkable result for Chinese DOR tasks. In addition, for English tasks, our method performs superiorly among state-of-the-art methods.
Common Randomized Shortest Paths (C-RSP): A Simple Yet Effective Framework for Multi-view Graph Embedding

Real-world data sets often provide several types of information about the same set of entities, showing us how they interact from different viewpoints. These data sets are well represented by multi-view graphs, which consist of multiple edge sets across the same set of nodes. Combining multiple views improves the quality of inferences drawn from the underlying data, which has led to increased interest in developing efficient multi-view graph embedding methods. We propose an algorithm, C-RSP, that generates a common (C) embedding of a multi-view graph using Randomized Shortest Paths (RSP). This algorithm generates a dissimilarity measure between nodes by minimizing the expected cost of random walks between any two nodes across all views of the graph, in doing so encoding both the local and global structure of the graph. We test C-RSP on both real and synthetic data and show that it outperforms benchmark algorithms at embedding and clustering tasks.
Super-resolution from unregistered aliased astronomical images

Images captured from astronomical telescopes always have aliasing artifacts due to atmospheric turbulence, which typically conveys additional high-frequency content containing extra details of the astronomical scene. Super-resolution (SR) reconstruction can exploit the information existing in the aliasing to create a higher-resolution astronomical image, where the precise registration of input observations is generally regarded as a necessary premise. We consider SR reconstruction as a multichannel sampling scheme with uncertain deviations mathematically and present a frequency domain technique to achieve accurate alignment of aliased astronomical images by utilizing their low-frequency and aliasing-free part of the spectrum. Moreover, a planar motion model including three parameters is allowed to describe the relative uncontrolled motions between the acquisition system and the astronomical scene. Finally, a sharp high-resolution astronomical image can be reconstructed by utilizing the adaptive normalized convolution algorithm. The technique proposed is tested in simulations and practical experiments and shows very excellent SR reconstruction capabilities in the case of aliased astronomical images.
Why the Fence Is the Seat of Reason When Experts Disagree

In order to properly understand how expert disagreement should be dealt with, it is essential to grasp how expert opinion is used in the reasoning process by which humans reach conclusions and make decisions. This paper utilises the tools of argumentation theory, specifically Douglas Walton's argument schemes, and variations upon them, in order to examine how patterns of reasoning are affected by the presence of conflicting testimony. This study suggests that although it may be supplemented with the construction of epistemic hierarchies, the scheme of reasoning for the appeal to expert opinion provides no mechanism for establishing a preference for one expert statement over another, making it impossible to reach any rational conclusion as to the likely truth of the matter. Further, it is also argued that other schemes for presumptive argumentation are either unhelpful in themselves or lead to unwanted consequences.
Condition-transforming Variational Autoencoder for Conversation Response Generation

This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs). In conventional CVAEs , the prior distribution of latent variable z follows a multivariate Gaussian distribution with mean and variance modulated by the input conditions. Previous work found that this distribution tends to become condition-independent in practical application. In our proposed CTVAE model, the latent variable z is sampled by performing a non-linear transformation on the combination of the input conditions and the samples from a condition-independent prior distribution N(0,I). In our objective evaluations, the CTVAE model outperforms the CVAE model on fluency metrics and surpasses a sequence-to-sequence (Seq2Seq) model on diversity metrics. In subjective preference tests, our proposed CTVAE model performs significantly better than CVAE and Seq2Seq models on generating fluency, informative and topic relevant responses.
The Standardised Digital Forensic Investigation Process Model (SDFIPM)

The field of digital forensics still lacks formal process models that courts can employ to determine the reliability of the process followed in a digital investigation. The existing models have often been developed by digital forensic practitioners, based on their own personal experience and on an ad-hoc basis, without attention to the establishment of standardisation within the field. This has prevented the institution of the formal processes that are urgently required. Moreover, as digital forensic investigators often operate within different fields of law enforcement, commerce and incident response, the existing models have often tended to focus on one particular field and have failed to consider all the environments. This has hindered the development of a generic model that can be applied in all the three stated fields of digital forensics. To address these shortcomings, this chapter makes a novel contribution by proposing the Advanced Investigative Process Model (the SDFIPM) for Conducting Digital Forensic Investigations, encompassing the 'middle part' of the digital investigative process, which is formal in that it synthesizes, harmonises and extends the existing models, and which is generic in that it can be applied in the three fields of law enforcement, commerce and incident response.
Recognition of Handwritten Meitei Mayek and English Alphabets Using Combination of Spatial Features

Handwritten character recognition is an exciting and challenging topic in the field of pattern recognition because of massive variation in writing style and similar looking characters. Combining two different scripts boost the challenge to another level as each language has a unique peculiarity. The choice of distinguishing feature enhances the accuracy and efficiency of a recognition system. In this paper, we present spatial features based recognition of handwritten Manipuri (Meitei Mayek) and English alphabets. Background directional distribution, projection histogram, and uniform local binary pattern features have been used for extracting distinct feature for recognition by KNN classifier. The highest accuracy achieved in the proposed methodology is 92.40%.
Recurrent 3D Convolutional Network for Rodent Behavior Recognition

Animal, specially rodent, studies are critical in understanding human health, disease and treatments. Behavior is an important observed outcome in many such studies. Thus, quantifying rodent behaviors is key. This is typically done by trained human observers, making the process very slow and subjective. This has has led to a growing interest in developing automated assessment tools. Existing approaches commonly rely on hand-crafted features which are often obtained through a tracking process. Motivated by state of the art results in image and video analysis using deep learning, we propose a deep architecture which is a combination of recurrent network and 3D convolutional network to learn long and short-term video representations. We test the proposed solution with the dataset collected by [1] and demonstrate that our framework can obtain accuracy on par with human assessment.
Colormapping resources and strategies for organized intuitive environmental visualization

Visualizations benefit from the use of intuitive organized color application, enabling a clearer understanding and communication. In this paper, we apply the concept of semantic color association to the generation of thematic colormaps for the environmental sciences in combination with principals of artistic color theory to expand feature resolution and create visual hierarchies within a visualization. In particular, we provide sets of color scales, colormaps and color organization guidance for semantically aligned water, atmosphere, land, and vegetation visualization. Strategies for directing attention via saturation levels and saturation sets of colormaps enable deployment of these techniques. All are publicly available online and accompanied by tools and strategy guidance.
Style-Oriented Personalized Landmark Recommendation

Personalized travel recommendation has attracted lots of research attention in both academic and industry communities. Although a great progress has been achieved so far, existing travel recommender systems have not well-exploited users' style-oriented preference on landmarks and local preference on the targeted city. Typically, users have their own preferences on the styles of landmarks (e.g., natural scenes or historic sites). When visiting a city, their preferences will be affected by the characteristics of this city (e.g., historic or scenic) or the “must-go” landmarks, as well as local contexts such as distance and time constraints. In this paper, we propose a novel style-oriented recommender system, which considers all the above factors to facilitate personalized landmark recommendation. Specifically, we first propose a unified classifier to detect landmark styles based on domain adaptation by leveraging web-photos in the source domain and landmark-image in the target domain. The detected landmark styles are then utilized to learn users' style-oriented preferences based on users' travel records in the past. Next, given a targeted city, the influence of users' landmark style preferences and the characteristics of the must-go landmarks of this city are simultaneously considered by a proposed style-oriented recommender system to make optimal recommendations. In addition, we further study the effects of local contexts, such as landmark popularity or location, on the performance of landmark recommendation. Extensive experiments on the real-world travel data of six cities demonstrate the effectiveness of the proposed style-oriented landmark recommendation strategy.
NLP-Driven Ensemble-Based Automatic Subtitle Generation and Semantic Video Summarization Technique

This paper proposes an automatic subtitle generation and semantic video summarization technique. The importance of automatic video summarization is vast in the present era of big data. Video summarization helps in efficient storage and also quick surfing of large collection of videos without losing the important ones. The summarization of the videos is done with the help of subtitles which is obtained using several text summarization algorithms. The proposed technique generates the subtitle for videos with/without subtitles using speech recognition and then applies NLP-based text summarization algorithms on the subtitles. The performance of subtitle generation and video summarization is boosted through ensemble method with two approaches such as intersection method and weight-based learning method. Experimental results reported show the satisfactory performance of the proposed method.
Analysis and Mitigation of Vocal Effort Variations in Speaker Recognition

In this work, we assess the impact of vocal effort on discrimination and calibration performance of a state-of-the-art speaker recognition system. We analyze three levels of vocal effort (low, normal, and high) from the SRI-FRTIV corpus. We use a deep neural network (DNN) speaker embeddings system with probabilistic linear discriminant analysis (PLDA) and find that vocal effort variation significantly degrades system performance. We apply both mixture PLDA (mix-PLDA) and trial-based calibration with condition PLDA similarity (TBC-CPLDA) to improve system robustness. Our proposed approaches resulted in 18% and 33% relative improvement in discrimination and calibration performance respectively on the SRI-FRTIV corpus.
Wan2vec: Embeddings learned on word association norms

Word embeddings are powerful for many tasks in natural language processing. In this work, we learn word embeddings using weighted graphs from word association norms (WAN) with the node2vec algorithm. Although building WAN is a difficult and time-consuming task, training the vectors from these resou rces is a fast and efficient process. This allows us to obtain good quality word embeddings from small corpora. We evaluate our word vectors in two ways: intrinsic and extrinsic. The intrinsic evaluation was performed with several word similarity benchmarks, WordSim-353, MC30, MTurk-287, MEN-TR-3k, SimLex-999, MTurk-771 and RG-65, and different similarity measures achieving better results than those obtained with word2vec, GloVe, and fastText, trained on a huge corpus. The extrinsic evaluation was done by measuring the quality of sentence embeddings using transfer tasks: sentiment analysis, paraphrase detection, natural language inference, and semantic textual similarity. The word vectors learned from the WAN are available on our Github page.