Reviewerly/exHarmony huggingface dataset:
Evaluation of Rounding Functions in Nearest Neighbor Interpolation

A novel evaluation study of the most appropriate round function for nearest neighbor image interpolation is presented. Evaluated rounding functions are selected among the five rounding rules defined by the Institute of Electrical and Electronics Engineers IEEE 754-2008 standard. Both full- and no-reference image quality assessment metrics are used to evaluate the influence of rounding functions on nearest neighbor interpolation image quality. The concept of the number of occurrences is used to determine the percentage of achieved occurrences, which is also equivalent to the sample proportion percentage used to calculate the margin of error. Inferential statistical analysis is used to deduce from a small number of images and draw a conclusion of the performance of each rounding function on a big number of images. Experimental results are provided to demonstrate with 95% confidence the minimum performance percentage of the most appropriate rounding function.
Counterfactual Generative Networks

Neural networks are prone to learning shortcuts -- they often model simple correlations, ignoring more complex ones that potentially generalize better. Prior works on image classification show that instead of learning a connection to object shape, deep classifiers tend to exploit spurious correlations with low-level texture or the background for solving the classification task. In this work, we take a step towards more robust and interpretable classifiers that explicitly expose the task's causal structure. Building on current advances in deep generative modeling, we propose to decompose the image generation process into independent causal mechanisms that we train without direct supervision. By exploiting appropriate inductive biases, these mechanisms disentangle object shape, object texture, and background; hence, they allow for generating counterfactual images. We demonstrate the ability of our model to generate such images on MNIST and ImageNet. Further, we show that the counterfactual images can improve out-of-distribution robustness with a marginal drop in performance on the original classification task, despite being synthetic. Lastly, our generative model can be trained efficiently on a single GPU, exploiting common pre-trained models as inductive biases.
Anti-corruption disclosure as a necessary evil: impact on profitability and stability of extractive firms in Africa

Purpose This study aims to examine the effect of anti-corruption disclosure on the profitability and financial stability of extractive firms in Africa. It also tests the convergence of profitability and financial stability. Design/methodology/approach The study uses an unbalanced panel data of 27 firms operating in five African countries covering the period 2006–2018. Anti-corruption assessment is done in line with GRI 205: Anti-Corruption. Profitability is measured using the return on asset and return on equity, whereas the z -score measures financial stability. The study uses the panel-corrected error regression technique for estimation. Findings There is evidence that corruption disclosure reduces the financial stability of firms. Disclosures on corruption analysis and corruption training are the main factors driving the reduction in financial stability. The effect on profitability is not significant except in the case of disclosure on corruption response, which also reduces profitability. There is strong statistical evidence to suggest that profitability and financial stability of extractive firms converge. This suggests that less-performing firms catch up with high performers. Research limitations/implications The study has relevant implications for practitioners, policymakers and the academic community. The study uses data that is skewed towards large extractive firms. Originality/value This study is premier in exploring the effect of anti-corruption disclosure on performance metrics among extractive firms in Africa. It is also unique in providing a test of both beta and sigma convergence of performance among the firms.
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech

Although early text-to-speech (TTS) models such as Tacotron 2 have succeeded in generating human-like speech, their autoregressive architectures have several limitations: (1) They require a lot of time to generate a mel-spectrogram consisting of hundreds of steps. (2) The autoregressive speech generation shows a lack of robustness due to its error propagation property. In this paper, we propose a novel non-autoregressive TTS model called BVAE-TTS, which eliminates the architectural limitations and generates a mel-spectrogram in parallel. BVAE-TTS adopts a bidirectional-inference variational autoencoder (BVAE) that learns hierarchical latent representations using both bottom-up and top-down paths to increase its expressiveness. To apply BVAE to TTS, we design our model to utilize text information via an attention mechanism. By using attention maps that BVAE-TTS generates, we train a duration predictor so that the model uses the predicted duration of each phoneme at inference. In experiments conducted on LJSpeech dataset, we show that our model generates a mel-spectrogram 27 times faster than Tacotron 2 with similar speech quality. Furthermore, our BVAE-TTS outperforms Glow-TTS, which is one of the state-of-the-art non-autoregressive TTS models, in terms of both speech quality and inference speed while having 58% fewer parameters.
Path Planning and Control of Mobile Robots Using Modified Tabu Search Algorithm in Complex Environment

An innovative modified Tabu search technique is developed for the effective navigation of mobile robots in complex environment. The method is modified to improve the efficiency of mobile robot towards having optimized path in short duration of time. Tabu search method works on the principle of getting shortest path by forming a Tabu list, which informs the mobile robot not to visit the previously visited positions and helps the robot in obtaining the obstacle negotiation angle. The modified Tabu search method helps the mobile robot to travel from any source to target location in any environment with suitable obstacle negotiation angle avoiding hurdles in its path. The developed modified algorithm has been incorporated in mobile robots and various trials were carried out in different environments to confirm the effectiveness of modified algorithm.
Blockchain as a governance mechanism for tackling dark side effects in interorganizational relationships

Purpose This paper aims to investigate how the emerging blockchain technology can tackle dark side or dysfunctional effects at different stages of the interorganizational relationship life cycle. The rationale for this study stems from the somewhat paradoxical causes of dysfunctional effects. In particular, concepts such as trust and cooperation that typically result in positive relationship outcomes may also lead to negative effects under certain conditions. This contradiction creates a governance headache for organizations in their quest for initiating, developing, maintaining and enhancing efficient interorganizational relationships. Design/methodology/approach The study draws upon multiple organizational theories (agency, signaling, transaction cost, population ecology, institutional) and develops a conceptual understanding of how blockchain can serve as a safeguard for tackling dark side effects in interorganizational relationships. Primarily, the paper outlines a set of research propositions that provides a platform for developing an actionable managerial decision framework. In addition, the authors conduct an automated textual analysis of qualitative blockchain expert opinion using the ALCESTE software and uncover salient themes about blockchain governance. Findings The blockchain ledger distributes trust among participants and keeps dark side effects at bay. Hence, blockchain can transform conventional approaches for handling dark side effects into value creating activities. The results of an automated textual analysis on a corpus of expert opinions provides preliminary support for several aspects of blockchain governance. Furthermore, the study articulates a decision framework that managers can use for optimal relationship governance and identifies several areas for future research. Research limitations/implications This paper is conceptual in nature and draws upon multiple theoretical perspectives to outline a set of research propositions. Thus, lack of empirical testing is a current limitation. However, the findings from an automated textual analysis of expert opinions provide exploratory but encouraging support for the power of blockchain to tackle dark side effects. Practical implications Managers can deploy blockchain creatively while selecting interorganizational relationship partners. For example, provenance issues in organizations’ supply chains can be efficiently managed using blockchain. Likewise, organizations may also create efficient learning around blockchain to gain efficiencies in relationship management. Originality/value Conventional approaches for managing dark side effects in interorganizational relationships rely mainly on ex post governance strategies. By contrast, this paper supplements the extant approach by discussing ex ante strategies that can be deployed at different stages of the interorganizational relationship cycle, e.g. initiation, maintenance/development and termination to better address dark side effects.
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication

We resolve the min-max complexity of distributed stochastic convex optimization (up to a log factor) in the intermittent communication setting, where $M$ machines work in parallel over the course of $R$ rounds of communication to optimize the objective, and during each round of communication, each machine may sequentially compute $K$ stochastic gradient estimates. We present a novel lower bound with a matching upper bound that establishes an optimal algorithm.
Enhancing knowledge integration from multiple experts to guiding personalized learning paths for testing and diagnostic systems

The testing and diagnostic systems have been considered to be useful for students because they can point to their learning problems to provide helpful suggestions for improving the knowledge. The previous approach shows that using the knowledge from multiple experts can develop a testing and diagnostic system that provides more accurate suggestions for learners comparing to the system using a single expert. Nevertheless, the low-quality knowledge integration method of the multi-expert approach can provide some inaccurate learning suggestions. This work proposes a practical method for enhancing knowledge integration from multiple experts to provide more effective learning suggestions. An experiment has been conducted on freshmen in university to evaluate the effectiveness of the proposed method. The results show that the proposed approach not only improves the learning achievements of the students but also decreases the number of reconsidering cases.
NANet: Nuclei-Aware Network for Grading of Breast Cancer in HE Stained Pathological Images

Automatic breast cancer grading methods based on HE stained pathological images can be summarized into two categories. The first category is to use learning-based methods to directly extract the features of the pathological image for breast cancer grading. However, unlike the coarse-grained problem of breast cancer classification, grading of breast Invasive Ductal Carcinoma (IDC) is a fine-grained classification problem. Only using general methods cannot classify IDC well. The second category is to conduct the three evaluation criteria of Nottingham Grading System (NGS) separately, and then integrate the results of the three criteria to obtain the final IDC grading result. However, NGS is only a semi-quantitative evaluation method. The inherent medical motivation of NGS is to grade IDC with the help of nuclei-related features. In this paper, we proposed a nuclei-aware network for IDC grading in pathological images. The entire network achieves an effect similar to the attention mechanism in end-to-end learning, so as to learn fine-grained and nuclei-related feature representations for IDC grading. It should to be pointed out that our method can emphasize custom areas, thus providing a way to model medical knowledge into the network structure. This is different from the general attention mechanism that cannot artificially control the area of attention. Experimental results show that the performance of proposed method is better than the state-of-the-art.
Exploratory Adversarial Attacks on Graph Neural Networks

Graph neural networks (GNNs) have been successfully used to analyze non-Euclidean network data. Recently, there emerge a number of works to investigate the robustness of GNNs by adding adversarial noises into the graph topology, where gradient-based attacks are widely studied due to their inherent efficiency and high effectiveness. However, the gradient-based attacks often lead to sub-optimal results due to the discrete structure of graph data. To this end, we design a novel exploratory adversarial attack (termed as EpoAtk) to boost the gradient-based perturbations on graphs. The exploratory strategy in EpoAtk includes three phases, generation, evaluation and recombination, with the goal of sidesteping the possible misinformation that the maximal gradient provides. In experiments, EpoAtk is evaluated on benchmark datasets for the task of semi-supervised node classification in different attack settings. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art attacks with the same attack budgets.
The Multilingual TEDx Corpus for Speech Recognition and Translation.

We present the Multilingual TEDx corpus, built to support speech recognition (ASR) and speech translation (ST) research across many non-English source languages. The corpus is a collection of audio recordings from TEDx talks in 8 source languages. We segment transcripts into sentences and align them to the source-language audio and target-language translations. The corpus is released along with open-sourced code enabling extension to new talks and languages as they become available. Our corpus creation methodology can be applied to more languages than previous work, and creates multi-way parallel evaluation sets. We provide baselines in multiple ASR and ST settings, including multilingual models to improve translation performance for low-resource language pairs.
The exact asymptotic form of Bayesian generalization error in latent Dirichlet allocation

Latent Dirichlet allocation (LDA) obtains essential information from data by using Bayesian inference. It is applied to knowledge discovery via dimension reducing and clustering in many fields. However, its generalization error had not been yet clarified since it is a singular statistical model where there is no one-to-one mapping from parameters to probability distributions. In this paper, we give the exact asymptotic form of its generalization error and marginal likelihood, by theoretical analysis of its learning coefficient using algebraic geometry. The theoretical result shows that the Bayesian generalization error in LDA is expressed in terms of that in matrix factorization and a penalty from the simplex restriction of LDA’s parameter region. A numerical experiment is consistent with the theoretical result.
GAN based efficient foreground extraction and HGWOSA based optimization for video synopsis generation

Video Synopsis is a smart and efficient solution to summarize a long duration of surveillance video into short. Most of the video synopsis techniques are not suitable to address complex situations like changes in illumination, dynamic background, camera jitter, etc. These techniques firmly depend on the preprocessing results of foreground extraction and multiple objects tracking. Further, the optimization process is a vital phase for the decrement of collision rate among moving objects, where the widely used Simulated Annealing (SA) usually suffers from the issue of slow convergence rate with a high computational overhead. Taking these aforementioned facts into account for feature extraction, we formulate a foreground extraction scheme exploring the concept of multi-frame and multi-scale in Generative Adversarial Network (mFS-GANs). Further, an optimization algorithm is proposed through the hybridization of SA and Grey Wolf Optimizer (GWO), named as, HGWOSA to ensure global optimal result with a low computing overhead. The performance of the proposed scheme is evaluated through extensive simulations and compared with that of the benchmark schemes. The experiments are carried out using some standard surveillance video dataset (ChangeDetection.Net, MIT Surveillance Dataset, and UMN Dataset) and one self-generated surveillance video at IIIT Bhubaneswar. Overall analysis and experimental evaluations demonstrate that our proposed scheme outperforms the other competing schemes in terms of both the quantitative and qualitative measures. Finally, the proposed model can be substantially employed in the generation of off-line video synopsis, which is potentially applicable to video surveillance applications for smart cities.
Contrastive Learning with Adversarial Perturbations for Conditional Text Generation

Recently, sequence-to-sequence (seq2seq) models with the Transformer architecture have achieved remarkable performance on various conditional text generation tasks, such as machine translation. However, most of them are trained with teacher forcing with the ground truth label given at each time step, without being exposed to incorrectly generated tokens during training, which hurts its generalization to unseen inputs, that is known as the exposure bias problem. In this work, we propose to solve the conditional text generation problem by contrasting positive pairs with negative pairs, such that the model is exposed to various valid or incorrect perturbations of the inputs, for improved generalization. However, training the model with naive contrastive learning framework using random non-target sequences as negative examples is suboptimal, since they are easily distinguishable from the correct output, especially so with models pretrained with large text corpora. Also, generating positive examples requires domain-specific augmentation heuristics which may not generalize over diverse domains. To tackle this problem, we propose a principled method to generate positive and negative samples for contrastive learning of seq2seq models. Specifically, we generate negative examples by adding small perturbations to the input sequence to minimize its conditional likelihood, and positive examples by adding large perturbations while enforcing it to have a high conditional likelihood. Such `hard'' positive and negative pairs generated using our method guides the model to better distinguish correct outputs from incorrect ones. We empirically show that our proposed method significantly improves the generalization of the seq2seq on three text generation tasks --- machine translation, text summarization, and question generation.
Domain Theory in Constructive and Predicative Univalent Foundations

We develop domain theory in constructive univalent foundations without Voevodsky’s resizing axioms. In previous work in this direction, we constructed the Scott model of PCF and proved its computational adequacy, based on directed complete posets (dcpos). Here we further consider algebraic and continuous dcpos, and construct Scott’s D_∞ model of the untyped λ-calculus. A common approach to deal with size issues in a predicative foundation is to work with information systems or abstract bases or formal topologies rather than dcpos, and approximable relations rather than Scott continuous functions. Here we instead accept that dcpos may be large and work with type universes to account for this. For instance, in the Scott model of PCF, the dcpos have carriers in the second universe U₁ and suprema of directed families with indexing type in the first universe U₀. Seeing a poset as a category in the usual way, we can say that these dcpos are large, but locally small, and have small filtered colimits. In the case of algebraic dcpos, in order to deal with size issues, we proceed mimicking the definition of accessible category. With such a definition, our construction of Scott’s D_∞ again gives a large, locally small, algebraic dcpo with small directed suprema.
The role of Disentanglement in Generalisation

Combinatorial generalisation — the ability to understand and produce novel combinations of familiar elements — is a core capacity of human intelligence that current AI systems struggle with. Recently, it has been suggested that learning disentangled representations may help address this problem. It is claimed that such representations should be able to capture the compositional structure of the world which can then be combined to support combinatorial generalisation. In this study, we systematically tested how the degree of disentanglement affects various forms of generalisation, including two forms of combinatorial generalisation that varied in difficulty. We trained three classes of variational autoencoders (VAEs) on two datasets on an unsupervised task by excluding combinations of generative factors during training. At test time we ask the models to reconstruct the missing combinations in order to measure generalisation performance. Irrespective of the degree of disentanglement, we found that the models supported only weak combinatorial generalisation. We obtained the same outcome when we directly input perfectly disentangled representations as the latents, and when we tested a model on a more complex task that explicitly required independent generative factors to be controlled. While learning disentangled representations does improve interpretability and sample efficiency in some downstream tasks, our results suggest that they are not sufficient for supporting more difficult forms of generalisation.
Using Design Fiction Memos to Analyze UX Professionals’ Values Work Practices

Multiple methods have been used to study how social values and ethics are implicated in technology design and use, including empirical qualitative studies of technologists' work. Recently, more experimental approaches such as design fiction explore these themes through fictional worldbuilding. This paper combines these approaches by adapting design fictions as a form of memoing, a qualitative analysis technique. The paper uses design fiction memos to analyze and reflect on ethnographic interviews and observational data about how user experience (UX) professionals at large technology companies engage with values and ethical issues in their work. The design fictions help explore and articulate themes about the values work practices and relationships of power that UX professionals grapple with. Through these fictions, the paper contributes a case study showing how design fiction can be used for qualitative analysis, and provides insights into the role of organizational and power dynamics in UX professionals' values work.
Hybrid data clustering approaches using bacterial colony optimization and k-means

Abstract Data clustering is a fashionable data analysis technique in the data mining. K-means is a popular clustering technique for solving a clustering problem. However, the k-means clustering technique extremely depends on the initial position and converges to a local optimum. On the other hand, the bacterial colony optimization (BCO) is a well-known recently proposed data clustering algorithm. However, it is a high computational cost to complete a given solution. Hence, this research paper proposes a new hybrid data clustering method for solving data clustering problem. The proposed hybrid data clustering algorithm is a combination of the BCO and K-means called BCO+KM clustering algorithm. The experimental result shows that the proposed hybrid BCO+KM data clustering algorithm reveal better cluster partitions.