Reviewerly/exHarmony huggingface dataset:
A Unified Speaker Adaptation Approach for ASR

Transformer models have been used in automatic speech recognition (ASR) successfully and yields state-of-the-art results.However, its performance is still affected by speaker mismatch between training and test data.Further finetuning a trained model with target speaker data is the most natural approach for adaptation, but it takes a lot of compute and may cause catastrophic forgetting to the existing speakers.In this work, we propose a unified speaker adaptation approach consisting of feature adaptation and model adaptation.For feature adaptation, we employ a speakeraware persistent memory model which generalizes better to unseen test speakers by making use of speaker i-vectors to form a persistent memory.For model adaptation, we use a novel gradual pruning method to adapt to target speakers without changing the model architecture, which to the best of our knowledge, has never been explored in ASR.Specifically, we gradually prune less contributing parameters on model encoder to a certain sparsity level, and use the pruned parameters for adaptation, while freezing the unpruned parameters to keep the original model performance.We conduct experiments on the Librispeech dataset.Our proposed approach brings relative 2.74-6.52%word error rate (WER) reduction on general speaker adaptation.On target speaker adaptation, our method outperforms the baseline with up to 20.58% relative WER reduction, and surpasses the finetuning method by up to relative 2.54%.Besides, with extremely low-resource adaptation data (e.g., 1 utterance), our method could improve the WER by relative 6.53% with only a few epochs of training.
CPSAM: Channel and Position Squeeze Attention Module

In deep neural networks, how to model the remote dependency on time or space has always been a problem for scholars. By aggregatingpioneering method of capturing remote dependencies. However, the NL network faces many problems; 1) For different query positions in the image, the long-range dependency modeled by the NL network is quite similar so that it’s a wates of computation cost to build pixel-level pairwise relations. 2) The NL network only focuses on capturing spatial-wise lo a ng-range dependencies and neglects channel-wise attention. Therefore, in response to thesquery-specific global context of each query location, Non-Local (NL) networks propose e problems, we propose the Channel and Position Squeeze Attention Module (CPSAM). Specifically, for a feature map of the middle layer, our module infers attention maps along channel and spatial dimensions in parallel. The Channel Squeeze Attention Module selectively joins the feature of different position by a query-independent feature map. Meanwhile, the Position Squeeze Attention Module uses both avg and max pooling to compress the spatial dimension and Integrate the correlation characteristics between all channel maps. Finally, the outputs of two attention modules are combine together through the conv layer to further enhance feature representation. We have achieved higher accuracy and fewer parameters on the cifar100 and ImageNet1k compared to the NL network. The code will be publicly available soon.
Multiple Cascading Algorithms to Evaluate Performance of Face Detection

This paper intends to evaluate the previous works done on different cascading classifiers for human face detection of image data. This paper includes the working process, efficiency, and performance comparison of different cascading methods. These methods are dynamic cascade, Haar cascade, SURF cascade, and Fea-Accu cascade. Each cascade classifier is described in this paper with their working procedure and mathematical induction as well. Each technique is backed with proper data and examples. The accuracy rate of the method is given with comparison with analyze the performance of the methods. In this literature, the human face detection process using cascading classifiers from image data is studied. From the study, the performance rate and comparison of different cascading techniques are highlighted. This study will also help to determine which methods are to be used for achieving an accurate accuracy depending on the data and circumstances.
GPUGraphX: A GPU-Aided Distributed Graph Processing System

There are two major challenges for large-scale graph analytic processing, computational intensiveness caused by complex graph primitives and distributed data management caused by data of massive scales. Existing works on graph data management with CPU-based distributed systems or GPU-based single-node systems only partially solve the problem. Hence, it is desired to have a general graph processing system for both scaling out and scaling up. In this paper, we demonstrate GPUGraphX, a GPU-aided distributed graph processing system which utilizes computation capacities of GPUs for efficiency while taking the advantages of distributed systems for scalability. Results on representative graph algorithms on real datasets evaluate our proposals.
Event Graph based Sentence Fusion

Sentence fusion is a conditional generation task that merges several related sentences into a coherent one, which can be deemed as a summary sentence.The importance of sentence fusion has long been recognized by communities in natural language generation, especially in text summarization.It remains challenging for a state-of-the-art neural abstractive summarization model to generate a well-integrated summary sentence.In this paper, we explore the effective sentence fusion method in the context of text summarization.We propose to build an event graph from the input sentences to effectively capture and organize related events in a structured way and use the constructed event graph to guide sentence fusion.In addition to make use of the attention over the content of sentences and graph nodes, we further develop a graph flow attention mechanism to control the fusion process via the graph structure.When evaluated on sentence fusion data built from two summarization datasets, CNN/DaliyMail and Multi-News, our model shows to achieve state-of-the-art performance in terms of Rouge and other metrics like fusion rate and faithfulness.
Detecting Health Advice in Medical Research Literature

Health and medical researchers often give clinical and policy recommendations to inform health practice and public health policy.However, no current health information system supports the direct retrieval of health advice.This study fills the gap by developing and validating an NLP-based prediction model for identifying health advice in research publications.We annotated a corpus of 6,000 sentences extracted from structured abstracts in PubMed publications as "strong advice", "weak advice", or "no advice", and developed a BERT-based model that can predict, with a macro-averaged F1score of 0.93, whether a sentence gives strong advice, weak advice, or not.The prediction model generalized well to sentences in both unstructured abstracts and discussion sections, where health advice normally appears.We also conducted a case study that applied this prediction model to retrieve specific health advice on COVID-19 treatments from LitCovid, a large COVID research literature portal, demonstrating the usefulness of retrieving health advice sentences as an advanced research literature navigation function for health researchers and the general public.
Socializing in Interference$$:$$ Quantum-Inspired Topic Model with Crystal-Like Structure Grid for Microblog Topic Detection

Capturing highly coherent topics from large-scale informal social media messages has always been a challenging task. Existing topic models fail to model the nonlinear dependence between text and structure, which is called the interference effect in quantum cognition. Therefore, we propose a Quantum-inspired Topic Model (QTM), which naturally admits a non-linear context composition. Specifically, based on the iterative-deepening random walks, we design a Crystal-like Structure Grid (CSG) to obtain the user structural features. Then, we propose a quantum density operator based network embedding. Such a density operator is essentially a nonlinear joint representation of the user structure and text information. The resulting user sequence embeddings are fed into the Neural Variational Inference (NVI) for topic detection. Extensive experimental results on three real-world microblog datasets demonstrate that QTM outperforms state-of-the-art models.
Vernacular Mythologies: Instagram, Starbucks and Meaning-Making by Non-Elites at Paris Orly Airport

In the 1950s, the French philosopher, critic, and semiotician Roland Barthes wrote a series of texts, published subsequently as the collection <em>Mythologies</em> (1957), that constitute a dissection of French popular culture of the time. Barthes used theories embraced in linguistics, and his approach has been replicated over the years, but here I argue that the participatory web, and in particular social network services, offers us a perspective to rethink mythmaking by non-elites, thanks to the networked language and semiotic practices of Instagram users. In other words, and by invoking the Ancient Greek understanding of mythologies as the telling of stories, I look at how so-called ordinary citizens create a new set of myths by analysing the discursive presentations of a range of ‘things’ that individuals draw on at Paris Orly Airport. I explore how these ‘things’, and in particular Starbucks coffee, are explicitly made to carry meaning, according to the captions, hashtags, and emojis given by the original poster.
Platform Economy and Product Liability: Old Rules for New Markets

According to statistics, Amazon is one of the most-used online marketplaces worldwide. The COVID-19 pandemic and the ensuing lockdowns to reduce the spread of the virus have shown how critical online marketplaces are to enable e-commerce and keep commercial transactions alive, especially in such times when regular commerce is disrupted. However, when we buy online, we have no chance of examining whether the product works or whether it is defective. If something goes wrong when we buy a product from a third-party seller through Amazon, as consumers, we then face the challenge of trying to file a claim for the damages that might have arisen due to the defectiveness of the product. This article explores Amazon’s position in this scenario, with reference to the case law from both US and EU courts and regulations, not solely from the point of view of Product Liability Law, but also according to the E-Commerce Directive liability.
SmartWatch for Wall Writing: Real-time Transcription of Wall Writing from Inertial Sensing

This paper presents a smartwatch-based application, called WatchScribe, to convert any wall writing to virtual board-works. Users can scribe anything over this virtual whiteboard with zero-bootstrapping by mimicking the writing on the wall with a pen. Transcribing wall-writing with a smartwatch is challenging because of two reasons. (a) As the smartwatch's orientation changes continuously due to wrist movements, the pen's locus is different from that of the smartwatch. (b) There are events when the user lifts the pen from the whiteboard and starts at a different position to draw the next stroke. WatchScribe leverages locomotive data from a smartwatch to estimate the pen's locus using an unsupervised method. Further, it uses a lightweight approach to extract the writing micro-gestures from the estimated pen's locus and reproduces a real-time transcription of the wall-writing. We apply qualitative human-driven and novel quantitative metric-based evaluation techniques for checking the correctness of WatchScribe over the data collected from 10 participants in a very diverse setup. Compared to the closest baseline that uses a smartphone's corner as a pen, we achieve an average 9% reduction in disparity score and 27% improvement in average accuracy in terms of decipherability of the reproduced transcripts.
A Bitcoin Wallet for the Masses: Square simplified credit-card transactions. Now it wants to build cryptocurrency hardware

A Wallet to hold bitcoins―or other cryptocurrencies―is not at all a new idea. Basically a souped-up flash drive, these gadgets hold a private key, protected by a pin or passcode, that allows a user to securely access cryptocurrency data; the data itself lives in the blockchain. Some of these “wallets” have their own displays, some require use with a computer or phone.
Evidence-Based Approach in Management: Evidence-Based Management and Policy

Objective. Review of the concepts of “evidence-based approach in management”, “evidence-based management”, and “evidence-based policy”. Systematization of factors that inhibit and contribute to the dissemination of evidence-based management practices in the social sphere. Identification of priority measures to support the development of an evidence-based approach in the management in the social sphere.Background. Executives in general, and also in the social sphere, are facing challenges of an increasing level of uncertainty, which brings difficulties to make reliable forecasts and make decisions with guaranteed results. At the same time, there is a systemic crisis of resources, requiring the choice of strategies and tactics that provide the highest level of performance. An additional challenge is the increasing amount of operations and the emerging need for managers to make quick management decisions. Against this background, the evidence-based approach acquires particular importance, based on the idea of the need to rely on the findings of research and expertise, providing managers with relevant data for choosing certain areas of action.Methodology. Literature review.Conclusions. The evidence-based approach in management is a range of practices with various severity of requirements, connected by the need for evidence-based and expert data for decision-making. Evidence-based management is a process of implementing an evidence-based approach within the framework of the activities of different level managers. Evidence-based policy is the implementation of evidence-based principles in making political decisions. Promotion of the evidence-based approach in management encounters several barriers such as assessment, used methodology, finance. However, some conditions can positively influence its promotion among practitioners: the involvement of senior managers, specialized training, the convergence of practitioners and researchers, and other factors. The funding of research and its accessibility remains critical.
FQM-GC: Full-reference Quality Metric for Colored Point Cloud Based on Graph Signal Features and Color Features

Colored Point Cloud (CPC) is often distorted in the processes of its acquisition, processing, and compression, so reliable quality assessment metrics are required to estimate the perception of distortion of CPC. We propose a Full-reference Quality Metric for colored point cloud based on Graph signal features and Color features (FQM-GC). For geometric distortion, the normal and coordinate information of the sub-clouds divided via geometric segmentation is used to construct their underlying graphs, then, the geometric structure features are extracted. For color distortion, the corresponding color statistical features are extracted from regions divided with color attribution. Meanwhile, the color features of different regions are weighted to simulate the visual masking effect. Finally, all the extracted features are formed into a feature vector to estimate the quality of CPCs. Experimental results on three databases (CPCD2.0, IRPC and SJTU-PCQA) show that the proposed metric FQM-GC is more consistent with human visual perception.
LPMA - An Efficient Data Structure for Dynamic Graph on GPUs

There is a growing interest to offload dynamic graph computation to GPU and resort to its high parallel processing ability and larger memory bandwidths compared with CPUs. The existing GPU graph systems usually use compressed sparse row (CSR) as de-facto structure. However, CSR has a critical weakness for dynamic change due to the large overhead of re-balance process after update. GPMA+ is the state-of-art dynamic CSR-oriented structure that uses PMA structure and optimized segment-oriented parallel update procedure to address the dynamic weakness of CSR but still has a bottleneck on the array expansion. In this paper, we propose an optimized dynamic structure LPMA, which is a leveled structure instead of continues array to retain low time complexity and high parallel update and lift the expansion bottleneck of GPMA+. Theoretical analysis and extensive experiments on four real-life large graphs prove the superiority of LPMA.
Editorial Foreword to the Special Issue on Recent Advances in Multitemporal Remote-Sensing Data Processing

This special issue of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS) contains 11 papers both from the extended outcomes of the Multitemp 2019 presented papers, and from the submissions by following a general Call-for-Papers of this special issue. These papers focus on the interesting and relevant topics in the multitemporal data analysis.
Review on Dark Web and Its Impact on Internet Governance

Cyber attackers use the Dark Web, a collection of facilities that are not visible to search engines and normal users, to explore a variety of illegal products and services. In this paper, the Dark Web and its impact on internet governance were analyzed. The findings of a review of the literature provide in-depth knowledge on the increasing number of crimes committed on the Dark Web, considering the economic, social, along ethical consequences of cybercrime on the Dark Web, as well as analyzing the consequences and methods for locating the criminals, as well as their drawbacks. Fraudsters, militants, and government-sponsored secret agents used the Dark Web where is among the most popular difficult together with unidentifiable channels to achieve their illicit goals. Crimes that were committed on the Dark Web are similar to criminal offenses committed in the real world. Nevertheless, the sheer size of the Dark Web, the unpredictability of the ecosystem, as well as the privacy and confidentiality afforded by Dark Web services, were also critical challenges in tracing criminals. Measuring the yachting Dark Web crime risks is a critical step in discovering alternative approaches to cybercrime. The study reveals that Dark Web services are available to arrest criminals, as well as digital facts and evidence, should be analyzed and applied in a way that allows Internet Governance.
Blockchain: A New Way to Revolutionize CRM Systems

Customer relationship management (CRM) is a process of tracking, storing, and analyzing all the interactions a business has with its customers and prospects to earn profits. In the current CRM Model, the customer data is hosted on cloud which leads to security issues and problems like data duplication and corruption. The major concern that traditional CRM encompasses is that it being a centralized system has a single point of failure. This chapter proposes the use of Blockchain in CRM to overcome the problems related to the traditional CRM systems.
Robust Anomaly Detection from Partially Observed Anomalies with Augmented Classes

Anomaly detection is becoming increasingly ubiquitous in the society of data mining. Prominent anomaly detection works have achieved great success in theory and practice. However, they cannot handle the generalized semi-supervised scenario where there are only a handful of labeled anomalies, and plentiful unlabeled data that may bring in some instances of augmented anomaly classes but which are hard to be sampled. To solve this new problem, we propose a method called ACAD (Augmented Classes Anomaly Detection), which consists of three components. ACAD firstly suggests an augmented anomaly class discovery module that connects the isolation score and the similarity score to excavate the instances of hidden anomaly classes from unlabeled data accurately. ACAD then uses a specific cluster approach to compute useful similarity scores to separate reliable anomalous and normal instances among unlabeled data, respectively. ACAD finally builds a robust anomaly detector based on mined examples, successfully performing anomaly detection from partially observed anomalies with augmented classes. A series of empirical studies show that our algorithm remarkably outperforms state of the art on almost twenty datasets.
Relationship between Relative Humidity and the Dew Point Temperature in Abuja, Nigeria

The analysis of the trend in the Relative humidity and Dew point temperature, together with the assessment of the statistical significance between the two parameters received great attention due to its importance to global warming and climate change.The role that the Atmospheric water vapor plays as a greenhouse gas makes it one of the most significant determinants in the discussions of weather and climate.In the current study, the relationship between relative humidity and Dew point temperature at a location in the northern part of Nigeria (Abuja) for a whole year was analyzed.The results showed a very strong linear relationship in the two parameters especially in the dry season.The results also revealed a unique characteristic in the Dew point Temperature values of June, July and August that appeared at this station.The role the movement of the Inter-Tropical Convergence Zone ITCZ played in the results was also discussed.
Comparison of computer-generated sentiment analysis to traditional meta-analysis: an infographic

Sentiment analysis is a growing tool that uses artificial intelligence to analyze text and succinctly describe the tone and overall state of written documents. Recently, it has been applied to biomedical literature. The purpose of this study by Myszewski et al [1][1] was to compare the results of
Benchmarking of Quantization Libraries in Popular Frameworks

Quantization is a technique to reduce the size and computation time of machine learning models by reducing the precision of model parameters. However, quantization may reduce the accuracy of the model—popular ML frameworks such as Pytorch and Tensorflow support the quantization process. A quantization process may differ in reducing the number of bits (8, 16, or 32) of model parameters, static, dynamic, and quantization aware training. In this paper, we evaluate the various features of the quantization process supported in Pytorch and Tensorflow on CNN and GNN based Recommendation models. We have compared Pytorch and Tensorflow quantization libraries for the memory efficiency and accuracy of the quantized models. The paper also presents and discusses an additional challenge of quantization in GNN based Recommendation models having embedding layers.
GAN-based clustering solution generation and fusion of diffusion

In this paper, we propose a framework to generate diverse clustering solutions and conduct solution retrieval to improve performance. Specifically, we first project unlabelled data from multiple domains into a shared space while preserving the respective semantics. This space allows that representations of samples in a hard domain are recovered by a linear combination of those of others in the easy domains. Meanwhile, a clustering algorithm is adopted to provide pseudo labels for a conditional generative adversarial network to synthesize representations that in turn promote the learning of the above space. Second, we conduct the joint learning of feature projection and partition matrices on batches of representations, where the former ones are considered as clustering solutions and input into another generative adversarial network to generate more solutions. Third, we utilize the fusion of diffusion to effectively retrieve and extract the knowledge in multiple solutions to obtain the final clustering. We perform comparative experiments against other methods on multiple benchmark data sets. Experimental results demonstrate the effectiveness and superiority of our proposed method.