Reviewerly/exHarmony huggingface dataset:
IoT and Modern Marketing: Its Social Implications

The advent of the Internet of Things and digital marketing has changed the way businesses operate and their interactions with consumers. The digital economy has percolated a wide range of industries and smartphones have ingrained into the consumer's lifestyle. The demand for customized services and goods has increased. However, this leaves the consumers' privacy at stake. Current researches have been conducted on IoT and its security threats, its utility in specific retail space for customer marketing. This kind of research has been limited to certain industries and marketing paradigms. A need to summarize IoT and the marketing potential it offers and the social impact this has on consumer behavior is a research gap we aim to address. The current paper's primary purpose is to reflect upon the concept of marketing 4.0 and the fact that the ever-increasing number of IoT devices are connected and transmitting enormous personal data regularly, to determine a person's personality traits and influence their decisions. Further IoT related ethical marketing strategies are outlined.
Efficient Shallow Wavenet Vocoder Using Multiple Samples Output Based on Laplacian Distribution and Linear Prediction

This paper presents a novel way for an efficient implementation scheme of shallow WaveNet vocoder with multiple samples (segment) output based on the use of Laplacian distribution and linear prediction. In our previous work, we have proposed a shallow architecture for WaveNet vocoder that utilizes only 9 dilated convolutional layers while capable of generating high-quality speech with the use of Laplacian distribution in speech samples modeling. However, there is still a lot of room for improvements to increase the computation efficiency, such as by the inference of segment output and the use of a more compact structure. In this work, we tackle this issue by proposing a simple implementation scheme of segment output modeling, that can be easily extended into other neural vocoders, where the Laplacian distribution parameters of multiple samples are estimated simultaneously. Further, to preserve the dependencies of the samples within the segment, we also propose utilizing linear prediction (LP) to compute the distribution parameters, where data- driven LP-coefficients are estimated by the WaveNet vocoder along with locations and scales. Finally, a shallower WaveNet vocoder with 6 layers is deployed. The experimental results demonstrate that the proposed LP-based Laplacian distribution can alleviate the quality degradation caused by segment generation.
Multidocument Abstractive Summarization using Abstract Meaning Representation for Indonesian Language

Abstractive summarization based on Abstract Meaning Representation (AMR) graphs employs predicate-argument structures to generate more coherent summarization. However, this approach is designed only for single document summarization of English news article. In this paper, we develop AMR graph-based multi-document summaries for Indonesian news articles. The representation of Indonesian AMR graph is aided by a set of rules and dictionaries. AMR graph is selected as a summary graph by performing feature extraction, applying Integer Linear Programming (ILP), and determining parameters with perceptron. Multidocument summarization is implemented by selecting sentences which will be summarized with Agglomerative Hierarchical Clustering. Experiments were carried out to determine the best number of epochs carried out for weight learning used for graph selection, and determine the threshold of the distance between cluster members produced by Agglomerative Hierarchical Clustering. The results of the tests performed were ROUGE-1 and ROUGE-2. The best result for ROUGE-1 and ROUGE-2 are 0.78947 and 0.55556.
Privacy preserving online matching on ridesharing platforms

Ridesharing platforms, as typical applications of spatial crowdsourcing, are becoming more and more popular in the era of mobile internet and sharing economy. One of the most fundamental issues on ridesharing platforms is to assign orders to drivers, which can be naturally modeled as online bipartite matching problem. However, conventional online matching algorithms usually lack data privacy protection mechanisms. This has become a serious issue since the spatiotemporal data of passengers is often sensitive. New policies such as EU’s General Data Protection Regulation (GDPR) also enforce protection of sensitive data, which further exacerbate the privacy issues. To deal with the problems, in this paper we propose a framework based on differential privacy (DP) techniques to preserve the privacy of individuals on ridesharing platforms. Specifically, we devise a novel approach to perturb locations in online minimum bipartite matching problem and theoretically show that the performance of the perturbed matching algorithm has the same magnitude with the unperturbed one. Experiments conducted on real datasets have also shown the effectiveness of proposed framework.
LSTM-Based One-Pass Decoder for Low-Latency Streaming

Current state-of-the-art models based on Long-Short Term Memory (LSTM) networks have been extensively used in ASR to improve performance. However, using LSTMs under a streaming setup is not straightforward due to real-time constraints. In this paper we present a novel streaming decoder that includes a bidirectional LSTM acoustic model as well as an unidirectional LSTM language model to perform the decoding efficiently while keeping the performance comparable to that of an off-line setup. We perform a one-pass decoding using a sliding window scheme for a bidirectional LSTM acoustic model and an LSTM language model. This has been implemented and assessed under a pure streaming setup, and deployed into our production systems. We report WER and latency figures for the well-known LibriSpeech and TED-LIUM tasks, obtaining competitive WER results with low-latency responses.
Interactive Storytelling in Extended Reality: Concepts for the Design

Extended reality (XR) covers many techniques, such as virtual reality (VR) and augmented reality (AR), for creating mixtures of physical and synthetic realities. In this chapter, we present the work done in realizing interactive storytelling in XR. In addition to a literature review, we discuss how user experience (UX) and game design can be transformed to these new application areas.
Optimal professional development ICT training initiatives at flagship universities

This study explores best practices and roles of information and communication technology (ICT) in select professional development centers at 16 flagship universities. Through adopting a qualitative case study design, this study explores the strengths and weaknesses of current technology training initiatives in the selected professional development centers. As part of the research and teaching programs at flagship universities, professional development center leaders shared about the current ICT practices as well as the strengths and limitations of their own centers. The analysis section includes a critical look at ICT practices among flagship universities from a human resource theory lens. Findings indicate common successes that facilitate the ICT practices of these centers including delivery mediums, services, ideas, and goals, as well as various barriers of implementing ICT training initiatives. The paper concludes with suggestions on how professional development center leaders, senior administrators, and educational policy makers can help improve professional development processes with the assistance of optimal ICT initiatives.
Intruder Control Mode Simulation of Deterministic Quantum Cryptography Protocol for Depolarized Quantum Channel

Security and reliability of traditional cryptographic methods for ensuring confidentiality is questionable taking into account contemporary threats (HPC, Grid technologies, quantum computing et al). Alternatively, quantum cryptography has proven itself as a more reliable information security technology. It's independent from intruder computational capability, uses specific unique properties of quantum particles, and based on quantum physics laws inviolability. However, data transmission using deterministic quantum protocol with depolarizing quantum channel is not studied today (potentially, intruder can hide his probes in this channel). From this viewpoint, the model of an intruder control mode for deterministic protocol with Bell states of qutrit pairs in the noisy depolarizing channel is developed and a simulation of this protocol in such channel is fulfilled. Also, the formula for a total probability of an error, which can arise both owing to attack, and owing to natural noise, at measurement in a intruder control mode is derived.
Fully Automatic Polyp Detection Based on a Novel U-Net Architecture and Morphological Post-Process

Colorectal lesions known as polyps are one of the diagnostic symptoms for colorectal disease. So, their accurate detection and localization based on a computer-aided diagnosis can assist colonists for prescribing more effective treatments. The computer vision and machine learning methods like pattern recognition and deep learning neural networks are the most popular strategies for automatic polyp detection purpose. The proposed approach in this paper is an innovative deep learning neural network. The proposed network has a novel U-Net architecture. The architecture of proposed network includes fully 3D layers which enable the network to be fed with multi or hyperspectral images or even video streams. Moreover, there is a dice prediction output layer. This type of output layer employs probabilistic approaches and benefits from more accurate prediction abilities. The proposed method is applied to international standard optical colonoscopy datasets known as CVC-ClinicDB, CVC-ColonDB and ETIS-Larib. The implementation and evaluation results demonstrate that the proposed U-Net outperforms other competitive methods for automatic polyp detection based on accuracy, precision, recall and F-Score criteria. The proposed method can assist experts and physicians to localize colonial polyps with more accuracy and speed. In addition, the proposed network can be used on live colonoscopy observations due to its high performance and fast operability.
Deep Neural Networks Based Automatic Speech Recognition for Four Ethiopian Languages

In this work, we present speech recognition systems for four Ethiopian languages: Amharic, Tigrigna, Oromo and Wolaytta. We have used comparable training corpora of about 20 to 29 hours speech and evaluation speech of about 1 hour for each of the languages. For Amharic and Tigrigna, lexical and language models of different vocabulary size have been developed. For Oromo and Wolaytta, the training lexicons have been used for decoding. We achieved relative word error rate (WER) reductions for all the languages by using Deep Neural Networks (DNN) based acoustic models that range from 15.1% to 31.45%. The relative improvement obtained for Wolaytta speech recognition system is much higher (31.45%) than the improvement achieved for the other languages. This attributes to the weaker language model and the bigger size of training speech we used for Wolaytta.
Multi-View Clustering Via Mixed Embedding Approximation

This paper tackles multi-view clustering via proposing a novel mixed embedding approximation (MEA) method. Formally, we aim to learn a uniform orthogonal embedding based on the orthogonal pre-embeddings of each view. At first, we hope that the uniform embedding can reconstruct the affinity graph of each view. To improve the representation of learnt embedding, we perform an embedding approximation on Grassmann manifold which is famous on subspace analysis. To perform the difference of views, a hidden weights learning module is provided. Moreover, we propose an iterative algorithm to solve the proposed MEA method and provide rigorously convergence analysis. Extensive experiments demonstrate the superiorities of the proposed method.
Full-Sum Decoding for Hybrid Hmm Based Speech Recognition Using LSTM Language Model

In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should be applied in decoding. This motivates to reconsider full summation over the HMM-state sequences instead of Viterbi approximation in decoding. We explore the potential gain from more accurate probabilities in terms of decision making and apply the full-sum decoding with a modified prefix-tree search framework. The proposed full-sum decoding is evaluated on both Switchboard and Librispeech corpora. Different models using CE and sMBR training criteria are used. Additionally, both MAP and confusion network decoding as approximated variants of general Bayes decision rule are evaluated. Consistent improvements over strong baselines are achieved in almost all cases without extra cost. We also discuss tuning effort, efficiency and some limitations of full-sum decoding.
A Hybrid Structural Sparse Error Model for Image Deblocking

Inspired by the image nonlocal self-similarity (NSS) prior, structural sparse representation (SSR) models exploit each group as the basic unit for sparse representation, which have achieved promising results in various image restoration applications. However, conventional SSR models only exploited the group within the input degraded (internal) image for image restoration, which can be limited by over-fitting to data corruption. In this paper, we propose a novel hybrid structural sparse error (HSSE) model for image deblocking. The proposed HSSE model exploits image NSS prior over both the internal image and external image corpus, which can be complementary in both feature space and image plane. Moreover, we develop an alternating minimization with an adaptive parameter setting strategy to solve the proposed HSSE model. Experimental results demonstrate that the proposed HSSE-based image deblocking algorithm outperforms many state-of-the-art image deblocking methods in terms of objective and visual perception.
Incremental Learning of Object Models From Natural Human–Robot Interactions

In order to perform complex tasks in realistic human environments, robots need to be able to learn new concepts in the wild, incrementally, and through their interactions with humans. This article presents an end-to-end pipeline to learn object models incrementally during the human-robot interaction (HRI). The pipeline we propose consists of three parts: 1) recognizing the interaction type; 2) detecting the object that the interaction is targeting; and 3) learning incrementally the models from data recorded by the robot sensors. Our main contributions lie in the target object detection, guided by the recognized interaction, and in the incremental object learning. The novelty of our approach is the focus on natural, heterogeneous, and multimodal HRIs to incrementally learn new object models. Throughout the article, we highlight the main challenges associated with this problem, such as high degree of occlusion and clutter, domain change, low-resolution data, and interaction ambiguity. This article shows the benefits of using multiview approaches and combining visual and language features, and our experimental results outperform standard baselines.
Comparative Study of K-Means Clustering Using Iris Data Set for Various Distances

K-means clustering is an algorithm, which has been used to cluster the given data into k sets that are mutual exclusive of each other. The K-means algorithm is designed to work with the Euclidean distance but there are many measures to identify the dissimilarity of the dataset. The aim of this paper is to discuss the performance of K-means clustering algorithm on city block, cosine, and correlation distance which are used to get the results and further their performance has been shown in terms of accuracy. For classification, authors have chosen the IRIS data set. K means have claimed 98% accuracy on city block and correlation distance.
Learning a Subword Inventory Jointly with End-to-End Automatic Speech Recognition

Recent work has demonstrated the promise of using subword units as output targets for sequence-to-sequence automatic speech recognition (ASR) models. Our work builds on the latent sequence decomposition (LSD) framework, in which the use of subword units in ASR is dependent on both the speech input and text output. In this paper, we follow the LSD method for using subword units but introduce an updated loss function that allows the ASR model to explicitly perform unit discovery, as well. We show that our n-gram loss function outperforms standard maximum likelihood loss within the LSD framework. We also show that uniform greedy sampling of subword units, which is much faster than LSD, is also an effective decomposition strategy when combined with the n-gram loss. Along with quantitative results on the Wall Street Journal Corpus, we present an analysis of the subword inventory learned by our model.
Data Science Tools for Crime Investigation, Archival, and Analysis

Crime Tracking System (CTS) aims to facilitate the process by automating data entry, storage, and retrieval using a mobile interface and a database to efficiently store said data. Crime investigation plays a significant role in keeping the modern cities safe. The emergence of the field of data science has opened new doors that have high potential in facilitating the process of criminal investigation. Data science enables criminal investigators to create tangible and quantifiable information on criminal social network profiles then analyzes crime concurrence based on location proximity. Aiming at availing the relevance of integrating data science analysis techniques in the criminal investigation, this study examines the suitability of cluster graphs and geolocation proximity (through heat maps) as two of the data science analysis techniques to facilitate criminal investigation process. On one hand, cluster graph analyses the social network of a suspect and examines its centrality, parallel betweenness and community affiliation. On the other hand, geolocation proximity provides an investigator with previous crime occurrences through the use of the heat map visualizing technique. These concepts are tested by embedding them in a mobile application developed to facilitate investigation process. The findings of this study demonstrate that the integration of data science and investigation activities is feasible and can extend the investigation process beyond the scope of the police profile structured database based on manual interrogation activities. This study ends by concluding the limitations and future research directions.
Reproducibility is a Process, Not an Achievement: The Replicability of IR Reproducibility Experiments

This paper espouses a view of reproducibility in the computational sciences as a process and not just a point-in-time "achievement". As a concrete case study, we revisit the Open-Source IR Reproducibility Challenge from 2015 and attempt to replicate those experiments: four years later, are those computational artifacts still functional? Perhaps not surprisingly, we are not able to replicate most of the retrieval runs encapsulated by those artifacts in a modern computational environment. We outline the various idiosyncratic reasons why, distilled into a series of "lessons learned" to help form an emerging set of best practices for the long-term sustainability of reproducibility efforts.
Differentiable Branching In Deep Networks for Fast Inference

In this paper, we consider the design of deep neural networks augmented with multiple auxiliary classifiers departing from the main (backbone) network. These classifiers can be used to perform early-exit from the network at various layers, making them convenient for energy-constrained applications such as IoT, embedded devices, or Fog computing. However, designing an optimized early-exit strategy is a difficult task, generally requiring a large amount of manual fine-tuning. In this paper, we propose a way to jointly optimize this strategy together with the branches, providing an end-to-end trainable algorithm for this emerging class of neural networks. We achieve this by replacing the original output of the branches with a ‘soft’, differentiable approximation. In addition, we also propose a regularization approach to trade-off the computational efficiency of the early-exit strategy with respect to the overall classification accuracy. We evaluate our proposed design approach on a set of image classification benchmarks, showing significant gains in accuracy and inference time.
Data in Brief: Can a mega-journal for data be useful?

As part of the current move towards open science, there is increasing pressure for scientists to share their research data. In support of this, several journals only publish descriptions of data generated from research: data papers. It is not clear whether this service encourages data reuse, however. This article assesses the prevalence and impact of the largest such journal, Data in Brief, comparing it with 24 other general or specialist data journals. The results show that Data in Brief became the largest data journal in 2016 and that its papers attracted over five Mendeley readers each, within a year of publication, as well as a non-trivial amount of citations. Its papers have been cited for relevance or facts contained in them in addition to acknowledging the reuse of associated datasets in about 1% of cases. Some papers describe electronic datasets whereas other papers embedded tables or images that formed the shared data. Overall, the journal seems to make a positive contribution to science by enabling access to multiple types of data, even though its papers rarely lead to data reuse.
No-Regret Non-Convex Online Meta-Learning

The online meta-learning framework is designed for the continual lifelong learning setting. It bridges two fields: meta-learning which tries to extract prior knowledge from past tasks for fast learning of future tasks, and online-learning which tackles the sequential setting where problems are revealed one by one. In this paper, we generalize the original framework from convex to non-convex setting, and introduce the local regret as the alternative performance measure. We then apply this framework to stochastic settings, and show theoretically that it enjoys a logarithmic local regret, and is robust to any hyperparameter initialization. The empirical test on a real-world task demonstrates its superiority compared with traditional methods.