Reviewerly/exHarmony huggingface dataset:
Attention-Based Bidirectional Long Short-Term Memory Networks for Chinese Named Entity Recognition

Named entity recognition is an important task in natural language processing and has been carefully studied in recent decades. In this paper, we investigate the problem of Chinese named entity recognition. Using attention mechanisms based on BiLSTM-CRF model, a model is proposed in this paper, which makes better use of word-based and character-based information. All the potential words that match the input characters and sentences with the dictionary are encoded, and one attention layer to control the dynamic acquisition of multiple potential characters in different paths from sequence information. A series of input characters and all potential words matched with dictionaries in sentences are encoded to measure the correlation scores between candidate characters and potential words. Another attention layer is to produce a weight vector and merge word-level features from each time step into a sentence-level feature vector by multiplying the weight vector. Then, CRF model is introduced to get the final tagging to obtain the desired result. The experimental data shows that the F1-score of our model has increased from 73.88% to 75.10% on the OntoNote 4 dataset, and from 93.18% to 94.17% on the MSRA dataset. The results show that our method has a better performance than the previous model.
Video Prediction with Temporal-Spatial Attention Mechanism and Deep Perceptual Similarity Branch

Video prediction is a challenging but worth exploring task in computer vision. Different from image analysis, the challenge of video analysis derives from more complicated dependencies in time as well as in space. In this paper, we propose a Temporal-Spatial Attention Mechanism (TSAM) to capture not only spatial appearance dependencies but also temporal dynamic dependencies in video sequence. The TSAM is transplantable for existing networks and allows the long-range dependency modeling for various video analysis tasks (we take video prediction for specific experiments in this paper). Besides, we propose an additional Deep Perceptual Similarity Branch (DPSB) to encourage a better approximation to the ground-truth in high-level feature space, laying the foundation for frame generation. Extensive experiments on KTH, Penn Action and UCF-101 datasets demonstrate that our model performs quite competitively across diverse natural visual scenes, even in long-term video prediction.
HBLast: An Open-Source FPGA Library for DNA Sequencing Acceleration

Biological sequence alignment algorithms work by identifying the degree of similarity between a newly discovered biological sequence with already known sequences. Exponential growth in the database and query sizes make pure software-based solutions inefficient in finding a solution within the time constraints. This calls for hardware acceleration-based solutions such as those based on field programmable gate arrays (FPGAs). In this work we present the design and implementation of the popular BLAST algorithm for nucleotide sequence alignment, called HBLast, targeting FPGA-based reconfigurable platforms.
Data Augmentation of CT Images of Liver Tumors to Reconstruct Super-Resolution Slices based on a Multi-Frame Approach

Improvement of tumor images have applications in the extraction of features and image retrieval algorithms. The multi-frame super-resolution technique is a chief approach in high-resolution image reconstruction. However, the acquisition of several low-resolution frames is not practical in the medical domain. In this paper, we use volumetric CT data of the abdominal region to prepare new low-resolution images and employ them in a data augmentation approach to the super-resolution image reconstruction. We showed that our method improved the results of interpolation and conventional multi-frame approaches by 0.02 and 0.03 respectively using the SSIM index.
Deep Classification Technique for Density Counting

Crowd counting, resulted from extensive analysis, is reflected by many aspects such as appearance similarity between people, background components and the inter-blocking in intense crowds. Current research is challenging these aspects by applying different types of architectures. In this paper, we propose a single conventional neural network for density counting based on four conventional layers. A comparison of our proposed network with Switched Conventional Neural Networks (Switch-CNN) approaches has been performed in order to evaluate its performance in terms of accuracy and loss. As a result, several experiments prove the effectiveness and efficiency of the proposed method. We got 94.6% and 0.2625 for both accuracy and loss respectively.
Progress on automatic annotation of speech corpora using complementary ASR systems

Deep learning techniques, requiring large amounts of training data, are currently state-of-the-art in automatic speech recognition (ASR). Corporate giants, such as Google or IBM, train English ASR systems on more than 100k hours of annotated speech, while research on under-resourced languages, such as Romanian, has to deal with as little as 300 hours. In this context, automatic annotation of speech corpora and unsupervised acoustic model training are promising directions to be explored to leverage the lack of data. This study describes the progress made by SpeeD laboratory in this research direction: using an already proven methodology, applying it on large scale (more than 700 hours of unlabeled speech) and analyzing in-depth the experimental results to identify potential future directions. Moreover, we present novel results on Romanian ASR: the methodology leads to a relative Word Error Rate (WER) improvement up to almost 10%.
Fully Unsupervised Salient Object Detection

Object detection is one of the most important components of machine vision. Today, object detection is used in a variety of areas, including guidance, driving, industry, and other key areas. For this reason, many algorithms have been proposed in this regard, with the aim of increasing the quality of detecting objects in an image. Since the correct representation of the object in the image is considered an essential requirement, in this article, a five-step algorithm is proposed for object detection. Experiments are performed on a given database in comparison with other methods in this area. In this algorithm, a color space is used to extract the feature and from self-encoder to remove the noise in the property matrix. Then, by scoring the clusters created based on the features, using the mean shift algorithm, the maximum pixels of the object are detected and separated from the background. The results of the experiments show performance of the proposed method in dealing with photos that involve challenges from multiple objects to changes the image.
Predictability of diffusion-based recommender systems

The recommendation methods based on network diffusion have been shown to perform well in both recommendation accuracy and diversity. Nowdays, numerous extensions have been made to further improve the performance of such methods. However, to what extent can items be predicted by diffusion-based algorithms still lack of understanding. Here, we mainly propose a method to quantify the predictability of diffusion-based algorithms. Accordingly, we conduct experiments on Movielens and Netflix data sets. The results show that the higher recommendation accuracy based on diffusion algorithms can still be achieved by optimizing the way of resource allocation on a density network. On a sparse network, the possibility of improving accuracy is relatively low due to the fact that the current accuracy of diffusion-based methods is very close its predictability. In this case, we find that the predictability can be improved significantly by multi-steps diffusion, especially for users with less historical information. In contrast to common belief, there are plausible circumstances where the higher predictability of diffusion-based methods do not correspond to those users with more historical recording. Thus, we proposed the diffusion coverage and item average degree to explain this phenomenon. In addition, we demonstrate the recommendation accuracy in real online system is overestimated by random partition used in the literature, suggesting the recommendation in real online system may be a harder task.
ACPNet:Anchor-Center Based Person Network for Human Pose Estimation and Instance Segmentation

We present an effective approach to tackle the multi-person pose estimation and person instance segmentation jointly. The approach based on Mask R-CNN uses a set of well-designed labels, called anchor-center based label, to learn keypoints localization in complex and crowded multi-person scenes. Combining the annotation of bounding boxes, we adopt a regression method to predict heatmaps and 2D-offset vector fields based on anchor-center for each keypoint type. Instead of using a person-detector, we use the person proposal network to predict anchors' locations in images. To generate high-quality segmentation mask, we use the ResNet-FPN with the deformable convolutions to model geometric transformations for non-rigid objects. Our method can efficiently and effectively deal with human pose estimation and instance segmentation tasks in a flexible end-to-end manner. Without bells and whistles, our method achieves a comparable result on the COCO keypoints task and the state-of-the-art accuracy on the COCO person instance segmentation task.
Real-time Traffic Congestion Detection with SIGHTA Regression Network

This work proposes a novel SIGHTA regression network to detect traffic congestion in real-time via video stream. The network inherits the architecture of YOLOv3 [1] which uses Darknet53 backbone. Referring to SPP network [2], the network structure has been adjusted so that more objects can be detected. Additionally, a novel regression algorithm and loss function are presented to regress quadrilateral objects. Experimental results demonstrate that the SIGHTA network significantly outperforms the existing object detection methods by mAP. Meanwhile the speed is faster.
Improving Image Super-Resolution via Feature Re-Balancing Fusion

Recently, benefiting from the strong ability of feature representation, deep-learning-based methods have achieved excellent performance in single image super-resolution (SR). Furthermore, skip connection and feature fusion have been demonstrated to be a commendable strategy to deal with various features in different depth for informative reconstruction. Nevertheless, cross-layer features show diverse characteristic in detail representation, blindly fusion then introduces unavoidable interference of features. In this paper, we propose a novel feature fusion unit by utilizing alternative dilated convolutions for re-balancing diverse cross-layer features, named Feature Re-Balancing Fusion Network (RBFNet), which is theoretically and experimentally demonstrated to be robust to the interference in feature fusion for SR. Extensive experiments show that the proposed method achieves excellent performances quantitatively and qualitatively against the state-of-the-art methods.
Pose-Projected Action Recognition Hourglass Network (PARHN) in Soccer

Current research on soccer action recognition does not focus on player-level actions. We introduce the pose-projected action recognition hourglass network (PARHN) for performing player-level action recognition in soccer. This network is inspired by ARHN, a network originally introduced for hockey action recognition. PARHN has two main novelties in its structure. First, it includes an embedded pose projection component that regularizes the numerical range of the player's pose vector, by applying two separate zero-phase component analysis (ZCA)-whitening. The projected pose vector is ef-fectively learned by few succeeding layers, and significantly improves the performance of the network and its generalizationability. Second, PARHN incorporates the temporal information by having a parallel structure for extracting projected pose vectors from all frames of an input sequence and also by using Long short-term memory (LSTM) layers to integrate the pose vectors across the input frames. Also, a new dataset, named SAR4 (standing for, Soccer Action Recognition for 4 action types), is generated. It includes 1292 video sequences, in which soccer players are tracked and labeled for performing four types of action (i.e., goalkeeper diving, player shooting, receiving pass and giving pass). Introduced network achieves the overall F1-score of 88.1% on the test data, which is 20.1% better than the result of the baseline network, ARHN.
Single Image De-Raining via Generative Adversarial Nets

In this paper, we propose a Generative Adversarial Network for Single Image De-raining(GAN-SID). We observe that batch normalization has side effects in the de-raining task. Therefore, we introduce instance normalization to replace the traditional batch normalization layers in both generator and discriminator. Motivated by the Squeeze-and-Excitation (SE) network that can learn the importance of channels, we introduce SE module in the generator to give different weights to the learned features. To preserve image details while removing rain streaks, we propose to utilize pixel-wise loss, perceptual loss, and adversarial loss to train the proposed network. Experiments on two synthetic datasets and real world images demonstrate that the proposed method outperforms state-of-the-art de-raining works in both objective and subjective measurements.
Development of high-speed camera with image quality evaluation

High-speed CMOS camera can capture high frame rate phenomena for a wide range of applications. However, most of the cameras currently in use are simply outputting pixel data, and cannot be adjusted in real time when the surrounding environment changes. The main idea of this paper is to develop a compact high-speed CMOS camera that can evaluate the image quality in real time while transmitting high-speed image data stably. The camera is based on the architecture of high-speed CMOS image sensor and FPGA (Field-Programmable Gate Array). The built-in sensor control, data transmission and image quality evaluation are all designed using Verilog HDL (Hardware Description Language). Our camera ensures that the processing unit at the back end of the vision system can receive high-quality images.
A reversible visible watermarking for 2D CAD engineering graphics based on graphics fusion

To visually label the copyright of 2D CAD (Computer aided design) engineering graphics, a reversible visible watermarking based on graphics fusion is proposed. For a given watermark graphics, the lines are first converted into polylines, and a random perturbation is performed to each vertex by using a random seed. Secondly, the host graphics are equally divided into multiple regions, and then the centers of these regions are calculated. The center of each watermark graphics is aligned with the center of each region. After that, the watermark graphics are scaled to be suitable for the size of these regions. Finally, the watermark graphics are iteratively embedded by fusing the host graphics and the watermark graphics. The host graphics can be completely recovered from the fused graphics by the authorized users who know the secret key. Experimental results validate its effectiveness, and the analysis demonstrates that it has a certain robustness against translation, rotation, scaling, and entity deletion.
Intelligent Ammeter Reading Recognition Method Based on Deep Learning

Aiming at the weaknesses of the current meter reading mode, this paper proposes an intelligent ammeter reading recognition method based on deep learning. A camera is first used to collect the image information of an ammeter digital dial, then the collected images are pre-processed, and finally the pre-processed images are automatically recognized by using the multi-layer convolutional neural network in this method. This method can be used for recognizing an ammeter automatically, reducing the workload of manual meter recognition and improving the recognition precision of the meter.
Neural embeddings: accurate and readable inferences based on semantic kernels

Abstract Sentence embeddings are the suitable input vectors for the neural learning of a number of inferences about content and meaning. Similarity estimation, classification, emotional characterization of sentences as well as pragmatic tasks, such as question answering or dialogue, have largely demonstrated the effectiveness of vector embeddings to model semantics. Unfortunately, most of the above decisions are epistemologically opaque as for the limited interpretability of the acquired neural models based on the involved embeddings. We think that any effective approach to meaning representation should be at least epistemologically coherent. In this paper, we concentrate on the readability of neural models, as a core property of any embedding technique consistent and effective in representing sentence meaning. In this perspective, this paper discusses a novel embedding technique (the Nyström methodology) that corresponds to the reconstruction of a sentence in a kernel space, inspired by rich semantic similarity metrics (a semantic kernel) rather than by a language model. In addition to being based on a kernel that captures grammatical and lexical semantic information, the proposed embedding can be used as the input vector of an effective neural learning architecture, called Kernel-based deep architectures (KDA). Finally, it also characterizes by design the KDA explanatory capability, as the proposed embedding is derived from examples that are both human readable and labeled. This property is obtained by the integration of KDAs with an explanation methodology, called layer-wise relevance propagation (LRP) , already proposed in image processing. The Nyström embeddings support here the automatic compilation of argumentations in favor or against a KDA inference, in form of an explanation: each decision can in fact be linked through LRP back to the real examples, that is, the landmarks linguistically related to the input instance. The KDA network output is explained via the analogy with the activated landmarks. Quantitative evaluation of the explanations shows that richer explanations based on semantic and syntagmatic structures characterize convincing arguments, as they effectively help the user in assessing whether or not to trust the machine decisions in different tasks, for example, Question Classification or Semantic Role Labeling. This confirms the epistemological benefit that Nyström embeddings may bring, as linguistically rich and meaningful representations for a variety of inference tasks.
Ebook Sharing Models in Academic Libraries

As ebooks make up an ever-larger portion of academic library catalogs, it is becoming increasingly problematic that licensing agreements, digital rights management (DRM), and technical issues make it difficult to share them between libraries. This column examines some of the various models libraries and consortia are using to find ways to share ebooks despite the existing barriers.
Drilling Into Dashboards: Responding to Computer Recommendation in Fraud Analysis

Credit card fraud analysis is almost entirely automated. However, there may be occasions when a human analyst is required to intervene. In this paper, we consider situations in which a transaction triggers an automated alert but not sufficiently to allow automated response. On such occasions, automated analysis makes a recommendation as to the fraud pattern that has been identified and the human analyst decides, if this recommendation is correct and what action to take. In order to support the analyst, a “dashboard” can be used to display information that is relevant to the fraud pattern. Thus, a computer could analyze transaction data, define this as a known fraud pattern, and then present the analyst with a dashboard to illustrate how the transaction might fit the pattern. We explore the efficiency with which people respond to the computer's recommendation, and whether computer confidence has an impact on this response. We define efficiency in terms of information search: the user will either have the relevant information on screen or will need to drill-into a dashboard (i.e., open additional windows for information). The results show that participants adapt their decision making to the confidence of the automated support, and, although they drill-down even when not required, are efficient in terms of time spent looking at relevant information.
The Model for Determining Weight Coefficients of Maintainability Criteria in Agile Software Development Process

In today's era, quality of the product is very important. This allows customers to adapt the product easily and quickly. The agile methodology is one of the most adopted methodologies for software development. At the same time, there are no defined models for the quality measurement in agile approach. The paper presents a model for estimating the maintenance value for any project developed in agile environment, as maintainability of the software product plays a vital role for defining quality of the product. For determining the weight values for each selected maintainability criteria, SWARA a decision making approach is used. The results allow determining the maintenance estimated value for any product developed using agile software development process.
Candidate Recruitment Based on Automatic Answer Evaluation Using WordNet

Any organization's future survival in the industry mainly depends on the decisions they take today and recruiting a new employee is one of those critical decisions that an organization has to take. Recruitment process of an organization should have the capability to find the right person to the right job. Most of the organizations today, interview candidates to test their job skills but not their personality level. But recruiting the most skilled person does not work out in a good manner always. Especially for the positions like leaders, they have to consider about the personality of the employee. And also, in the industries like IT industry, employees are supposed to work in project teams but not individually. That is where personality matters the most. To work in a team, all the members should be able to corporate with each other without facing any difficulty. Therefore candidate recommendation chatbot system is suggested and it is capable of evaluating both technical skills and personality traits of the candidate and providing a final recommendation based on the scores obtained to those two sections. The system mainly contains four modules; Question Generation and Dialogue Flow Maintaining Module, Technical Answers Evaluating Module, Vocabulary Based Personality Evaluating Module and Candidate Recommendation Module. This paper will be discussing about the answer evaluation module of the system which contains an ontology to store java related technical questions and answers and has used WordNet to measure the correctness of the answers.
Accurate and Efficient Object Detection with Context Enhancement Block

Recently feature pyramid composed of multi-level feature maps has been extensively used in region-free detectors to address multi-scale object detection. However, the contradiction between scale and context in the feature pyramid limits the detection performance, extraordinarily on small objects. Most works introduce an extra top-down path to overcome the limitation yet suffering from high computational burden. In this paper, we propose a novel Expansion Receptive Field Block (ERFB) to capture multiple strong contextual features at low computational cost, and then apply the Feature Attention Block (FAB) to eliminate the inconsistency between different features to generate more discriminative features. To be further, we construct an efficient and accurate detector (named CEBNet) mainly consists of Context Enhancement Blocks (CEBs), which are cascaded with ERFB and FAB. The extensive experiments on Pascal VOC and MS COCO demonstrate that CEBNet achieves state-of-the-art detection accuracy at a real-time processing speed.