Reviewerly/exHarmony huggingface dataset:
Image Semantic Segmentation Based on Encoder-Decoder Network

Semantic segmentation is an extremely important task in computer vision. At present, the related methods have achieved high performance. Nevertheless, Semantic segmentation still faces the challenge of localization accuracy due to DCNN invariance and existence of objects at multi-scale. In order to improve the accuracy of segmentation, this paper proposes a U-SEM encoder-decoder network. Firstly, in the encoding stage, it down-samples through the ResNet. Secondly, in the decoding stage, in order to filter and utilize the useful features, the SE-Mobile Block is proposed and fused to the network. The SE block adopts the idea of attention mechanism to focus on useful features and ignore those redundant features. Mobile blocks use deep separable convolutions to replace traditional convolutions, speeding up operations and reducing parameters. Finally, it adopts the skip structure where the feature information of different scales are merged to produce accurate and detailed segmentation. Experimental results show that the proposed network achieves good performance on multiple datasets which reaches the accuracy of 78.4% mIOU on PASCAL VOC 2012 and 75.7% mIOU on Cityscapes dataset.
Processing Grid-format Real-world Graphs on DRAM-based FPGA Accelerators with Application-specific Caching Mechanisms

Graph processing is one of the important research topics in the big-data era. To build a general framework for graph processing by using a DRAM-based FPGA board with deep memory hierarchy, one of the reasonable methods is to partition a given big graph into multiple small subgraphs, represent the graph with a two-dimensional grid, and then process the subgraphs one after another to divide and conquer the whole problem. Such a method (grid-graph processing) stores the graph data in the off-chip memory devices (e.g., on-board or host DRAM) that have large storage capacities but relatively small bandwidths, and processes individual small subgraphs one after another by using the on-chip memory devices (e.g., FFs, BRAM, and URAM) that have small storage capacities but superior random access performances. However, directly exchanging graph (vertex and edge) data between the processing units in FPGA chip with slow off-chip DRAMs during grid-graph processing leads to limited performances and excessive data transmission amounts between the FPGA chip and off-chip memory devices. In this article, we show that it is effective in improving the performance of grid-graph processing on DRAM-based FPGA hardware accelerators by leveraging the flexibility and programmability of FPGAs to build application-specific caching mechanisms, which bridge the performance gaps between on-chip and off-chip memory devices, and reduce the data transmission amounts by exploiting the localities on data accessing. We design two application-specific caching mechanisms (i.e., vertex caching and edge caching ) to exploit two types of localities (i.e., vertex locality and subgraph locality ) that exist in grid-graph processing, respectively. Experimental results show that with the vertex caching mechanism, our system (named as FabGraph) achieves up to 3.1× and 2.5× speedups for BFS and PageRank, respectively, over ForeGraph when processing medium graphs stored in the on-board DRAM. With the edge caching mechanism, the extension of FabGraph (named as FabGraph+) achieves up to 9.96× speedups for BFS over FPGP when processing large graphs stored in the host DRAM.
Intention classification in multiturn dialogue systems with key sentences mining

Abstract The multiturn dialogue system has been prevalently used in e‐commerce websites and modern information systems, which significantly improves the efficiency of problem solving and further promotes the service quality. In a multiturn dialogue system, the problem of intention classification is a core task, as the intention of a customer is the basis of subsequent problems handling. However, traditional related methods are unsuitable for the classification of multiturn dialogues. Because traditional methods do not distinguish the importance of each sentence and concatenate all sentences in the text, which is likely to generate a model with low prediction accuracy. In this paper, we propose a method of multiturn dialogue classification based on key sentences mining. We design a keywords extraction algorithm, mining key sentences from the dialogue text. We propose an algorithm finishing the computation of the weights of each sentence. According to the sentence weight and the sentence vector, the dialogue text is transformed to a dialogue vector. The dialogue text is classified by a classifier, and the input is the dialogue vector. We conducted sufficient experiments on a real‐world dataset, evaluating the performance of the proposed method. The experimental results show that our method outperforms the related methods on a series of evaluation metrics.
Variational Bayesian Sparsification for Distillation Compression

Model compression is a critical technique for cumbersome models to reduce memory consumption and accelerate inference. Here, we propose a novel method, called Variational Bayesian Sparsification, for distilling large models into small and sparse models while maintaining accuracy. Different from prior work, our approach innovatively embeds Bayesian sparsification into distillation. The core contributions are in two folds. First, the minibatch re-weighting method is proposed to dynamically balance the hard and soft knowledge, which can largely boost distillation accuracy. Then, the Bayesian deep sparse method presents to leverage the group sparseness and element sparseness simutaneously to reduce the parameter redundancy of student networks. We validate our method in MNIST, CIFAR10 and CIFAR100 datasets. Our method achieves 98.86% compression ratio with minor accuracy loss in MNIST. It is also evaluated through a compact model with only 1.99M weights on CIFAR10 and performed favorably against state-of-the-art compression methods on CFAR100, to verify the algorithm's generality.
Effective Exploitation of Posterior Information for Attention-Based Speech Recognition

End-to-end attention-based modeling is increasingly popular for tackling sequence-to-sequence mapping tasks. Traditional attention mechanisms utilize prior input information to derive attention, which then conditions the output. However, we believe that knowledge of posterior output information may convey some advantage when modeling attention. A recent technique proposed for machine translation called the posterior attention model (PAM) demonstrates that posterior output information can be used in that way for machine translation. This paper explores the use of posterior information for attention modeling in an automatic speech recognition (ASR) task. We demonstrate that direct application of PAM to ASR is unsatisfactory, due to two deficiencies; Firstly, PAM adopts attention based weighted single-frame output prediction by assuming a single focused attention variable, whereas wider contextual information from acoustic frames is important for output prediction in ASR. Secondly, in addition to the well-known exposure bias problem, PAM introduces additional mismatches in attention training and inference calculations. We present extensive experiments combining a number of alternative approaches to solving these problems, leading to a high performance technique which we call extended PAM (EPAM). To counter the first deficiency, EPAM modifies the encoder to introduce additional context information for output prediction. The second deficiency is overcome in EPAM through a two part solution of a mismatch penalty term and an alternate learning strategy. The former applies a divergence-based loss to correct the mismatch bias distribution, while the latter employs a novel update strategy which relies on introducing iterative inference steps alongside each training step. In experiments with both WSJ-80hrs and Switchboard-300hrs datasets we found significant performance gains. For example, the full EPAM system model achieved a word error rate (WER) of 10.6% on the WSJ eval92 test set, compared to 11.6% for traditional prior-attention modeling. Meanwhile, on the Switchboard eval2000 test set, we achieved 16.3% WER compared to the traditional method WER of 17.3%.
Inferring Cloud-Network Slice’s Requirements from Non-Structured Service Description

To support future 5G computing and communication scenarios, cloud-network management tools should deploy cloud-network services adopting uncomplicated ways, reducing not only the time to market but also broadening the community capable of deploying new services. In this paper, we present the support of NECOS Platform, an EU-Brazil jointly funded project, towards slice-as-a-service creation from non-structured service description. We describe how NECOS architecture allows such functionality during the slice creation loop, and we present the initial efforts we took for structuring such a mechanism.
Naïve bayes classifier models for cerebral infarction classification

Abstract Cerebral infarction is a condition of tissue damage in the brain caused by inadequate oxygen supply caused by obstruction of the flow of regions to the area (ischemia). Brain ischemia is more common than hemorrhagic, but surgery is most often performed in hemorrhagic strokes. This condition is also called stroke infarction. In stroke infarction does not occur bleeding. Changes in brain blood vessel walls can be primary due to congenital or degenerative abnormalities and secondary processes caused by other processes such as inflammation, arteriosclerosis, hypertension, diabetes mellitus and many other processes, so the cause of stroke is very multifactorial. The final sign of cell damage due to ischemia is marked by the nucleus which becomes picnotic and fragmented. Research shows that stroke infarction can occur at the age of 15-55 years. To diagnose the presence or absence of cerebral infarction in the brain is not enough just to use a CT scan, therefore machine learning will also be used to diagnose the presence or absence of cerebral infarction in the brain. For this reason, the authors propose the Naïve Bayes Classifier method as a classification method that has good accuracy, good precision, good memory, and a good F1-score in calcifying a patient whose brain has cerebral infarction or not. In this proposed method, Naïve Bayes Classifier is a probabilistic machine learning model used to classify. Naïve Bayes Classifier is a simple probability technique based on the Bayes theorem with the assumption of independence among predictors. In simple terms, Naïve Bayes Classifier assumes that the presence of certain features in a class is not related to the presence of other features. This method can achieve an accuracy value of up to 92.43%, so this method can be an efficient classification tool.
Receptor: A Platform for Exploring Latent Relations in Sensitive Documents

Many government and public organisations have a requirement to release their official documents to the public and therefore need to review such documents to identify and protect any sensitive information that they contain. When reviewing a document for sensitivity, reviewers often use information from other documents within the collection to assist in their decisions. It can be difficult for the reviewers to find related documents in large digital collections when they are performing sensitivity review. Receptor is a new solution that aims to provide sensitivity reviewers with the ability to explore a collection of documents to discover latent relations, between for example entities and events, that can be a reliable indicator of sensitive information. The system provides novel scalable graph search and exploration functionalities as well as interactive visualisations of the latent relations between related entities, events, and documents to enable users to identify hidden patterns of sensitivity.
An Effective Feature Selection Based Classification model using Firefly with Levy and Multilayer Perceptron based Sentiment Analysis

Recently, Sentimental Analysis (SA) attains most important attention while making a decision when it used for extracting and classifying the sentiments existing in web-based reviews which are written by a customer. Here, SA is used as a sentiment classification (SC) issue where the reviews are classified as positive and negative factors which are based on internet reviews. This work projects an efficient SA technique for online reviews by the combination of Feature Selection (FS) as well as classification. The Firefly (FF) and Levy flights (FFL) models are used to extract features from web-based review and Multilayer Perceptron (MLP) is employed to classify the sentiments. For experimentation, a benchmark DVD dataset is employed to test the effectiveness of the presented model. The final outcome reveals that the proposed FF-MLP method attains effective classifying operation from every dataset on behalf of various performance metrics.
A history of the Oz multiparadigm language

Oz is a programming language designed to support multiple programming paradigms in a clean factored way that is easy to program despite its broad coverage. It started in 1991 as a collaborative effort by the DFKI (Germany) and SICS (Sweden) and led to an influential system, Mozart, that was released in 1999 and widely used in the 2000s for practical applications and education. We give the history of Oz as it developed from its origins in logic programming, starting with Prolog, followed by concurrent logic programming and constraint logic programming, and leading to its two direct precursors, the concurrent constraint model and the Andorra Kernel Language (AKL). We give the lessons learned from the Oz effort including successes and failures and we explain the principles underlying the Oz design. Oz is defined through a kernel language, which is a formal model similar to a foundational calculus, but that is designed to be directly useful to the programmer. The kernel language is organized in a layered structure, which makes it straightforward to write programs that use different paradigms in different parts. Oz is a key enabler for the book Concepts, Techniques, and Models of Computer Programming (MIT Press, 2004). Based on the book and the implementation, Oz has been used successfully in university-level programming courses starting from 2001 to the present day.
Who is Liable if a Public Cryptocurrency Protocol Fails?

Cryptocurrencies have entered the economy as alternative money, speculation objects, and utility tokens for digital platforms. Cryptocurrencies are based on cryptography-based asset disposals broadcasted peer-to-peer to be validated decentrally according to consensus mechanisms in compliance with consented protocols. There several risks associated with the protocols, which may eventually result in the failure of a cryptocurrency. The question is then if anyone can be held liable for such a failure. Such liability is important for both victim compensation as well as the promotion of robust protocols. The paper discusses who can be held liable, possible legal bases for liability, and some legal gaps are identified.
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles

Auto-annotation by an ensemble of models is an efficient method of learning on unlabeled data. However, wrong or inaccurate annotations generated by the ensemble may lead to performance degradation of the trained model. We propose filtering the auto-labeled data using a trained model that predicts the quality of the annotation from the degree of consensus between ensemble models. Using semantic segmentation as an example, we demonstrate the advantage of the proposed auto-annotation filtering over training on data contaminated with inaccurate labels. We show that the performance of a state-of-the-art model can be achieved by training it with only a fraction (30%) of the original manually labeled samples, and replacing the rest with auto-annotated, quality filtered labels.
Knowledge Discovery within ADS-B Data from Routine Helicopter Tour Operations

Knowledge discovery or data mining techniques are widely used for anomaly detection in the commercial aviation domain to retrospectively improve operational safety.However, in the general aviation domain, especially for rotorcraft, analyses of flight data records for anomaly detection are not as prevalent.In this study, ADS-B data from a helicopter tour operator will be used to develop a prototype framework for uncovering patterns from routine flights.The ADS-B data contains two types of information: 1) time series of various flight parameters and 2) trajectory parameters.Various knowledge discovery techniques able to handle the aforementioned data types are explored and a few promising methods are applied to the ADS-B data of a helicopter tour operator in Hawaii.From the clustering results, patterns in the flight data records can be observed and can then be used by Subject-Matter Experts (SMEs) to facilitate the detection of anomalies.With this framework in place, rotorcraft operators will be able to analyze their routine flight data to not only monitor the safety of their operations but also to acquire knowledge on their operational patterns.
Side searching and object improving algorithms for Images

Image improvement and edge discovery are very important in the digital globe to discover the knowledge related to an entity similar to strength, level, contrast, range, limitations, missing and hidden areas or boundaries etc. There are very less methods and calculations to exhibit the better details of a goal. In this paper primarily, an edge finding method i.e. Side Searching Algorithm (SSA) has been discussed that discovers the unseen boundaries from images and it is an extremely easy approach. The other future technique of enhancement finds its path to depiction and enhances the darkest observation of an aim. For this an Object Improving Algorithm has been developed (OIA) for enhancing the darkest area of image.
Metadata Matters in User Engagement Prediction

Predicting user engagement (e.g., click-through rate, conversion rate) on the display ads plays a critical role in delivering the right ad to the right user in online advertising. Existing techniques spanning Logistic Regression to Factorization Machines and their derivatives, focus on modeling the interactions among handcrafted features to predict the user engagement. Little attention has been paid on how the ad fits with the context (e.g., hosted webpage, user demographics). In this paper, we propose to include the metadata feature, which captures the visual appearance of the ad, in the user engagement prediction task. In particular, given a data sample, we combine both the basic context features, which have been widely used in existing prediction models, and the metadata feature, which is extracted from the ad using a state-of-the-art deep learning framework, to predict user engagement. To demonstrate the effectiveness of the proposed metadata feature, we compare the performance of the widely used prediction models before and after integrating the metadata feature. Our experimental results on a real-world dataset demonstrate that the metadata feature is able to further improve the prediction performance.
Towards Explainable Retrieval Models for Precision Medicine Literature Search

In professional search tasks such as precision medicine literature search, queries often involve multiple aspects. To assess the relevance of a document, a searcher often painstakingly validates each aspect in the query and follows a task-specific logic to make a relevance decision. In such scenarios, we say the searcher makes a structured relevance judgment, as opposed to the traditional univariate (binary or graded) relevance judgment. Ideally, a search engine can support searcher's workflow and follow the same steps to predict document relevance. This approach may not only yield highly effective retrieval models, but also open up opportunities for the model to explain its decision in the same "lingo" as the searcher. Using structured relevance judgment data from the TREC Precision Medicine track, we propose novel retrieval models that emulate how medical experts make structured relevance judgments. Our experiments demonstrate that these simple, explainable models can outperform complex, black-box learning-to-rank models.
Tukey’s robust M-estimator for phase demodulation of interferograms with nonuniform shifts

In this paper, we introduce an iterative scheme for phase demodulation of interferograms with nonuniformly spaced phase shifts. Our proposal consists of two stages: first, the phase map is obtained through a least squares fitting; second, the phase steps are retrieved using a statistical robust estimator. In particular, we use Tukey’s biweighted M-estimator because it can cope with both noisy data and outliers in comparison with the ordinary least squares estimator. Furthermore, we provide the frequency description of the algorithm and the phase demodulation allowing us to analyze the procedure and estimation according to the frequency transfer function (FTF) formalism for phase-shifting algorithms. Results show that our method can accurately retrieve the phase map and phase shifts, and it converges by the 10th iteration.
Perfect Anonymity

For 35 years, the cryptographic community has created the impression that anonymous communication is always possible. Chaum's dining cryptographer's solution is regarded as achieving unconditional security. Chaum's MIX approach, namely, applying a uniformly random permutation on the plaintexts to be sent, is often given as a definition for anonymity. However, people working in law enforcement know that attempts by whistle-blowers and criminals to remain at large, often fail. The content of the message may reveal the name of the sender or may leak for whom the message is really intended. To understand this issue, we give a definition of perfect anonymity. We contrast our definition with Chaum's approach and discuss the impact on Chaum's dining cryptographer's solution.
Multimodal Video Saliency Analysis With User-Biased Information

Video saliency is widely used in various video understanding and processing related applications. Despite the fact that studies have indicated the influence of user preferences on visual attention when watching videos, current researches on saliency are based on visual contents and have not taken viewer-related information into account. In this paper, we propose a learning-based multimodal framework to predict video saliency aided by social data analysis. We introduce a popularity assisted attention mechanism into a content-specific neural network to extract spatio-motion features, and utilize a convolutional long short-term memory (ConvLSTM) network to discover temporal characteristics. Experiments demonstrate that our approach outperforms the state-of-the-art video saliency analysis methods, which validates the effectiveness of incorporating external user-biased information into saliency prediction.