Reviewerly/exHarmony huggingface dataset:
A 9.0-TOPS/W Hash-Based Deep Neural Network Accelerator Enabling 128× Model Compression in 10-nm FinFET CMOS

A 10-nm DNN inference accelerator compresses model size with tabulation hash-based line-grained weight sharing and increases 8bcompute density by 3.4× to 1.6 TOPS/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The compressed model DNN implements lightweight hashing circuits to compress fully connected and recurrent neural networks. Optimized shared weight address generation reduces MUX tree area overhead by 40%. Runtime hash table generation and weight mapping circuits enable a peak energy efliciency of 9.0 TOPS/W at 450 mV, 25°C. A 128×-compressed 3-layer long shortterm memory classilies TIMIT phonemes with 85.6% accuracy for a total energy of 14 μJ/classilication, with <; 0.5% degradation in accuracy over an uncompressed network.
An Image Segmentation Method for the blind sidewalks recognition by using the convolutional neural network U-net

The image segmentation for the blind sidewalks recognition is important for the blind guide system, which can help the visually impaired people to go out freely. In the paper, we have proposed an image segmentation method for blind sidewalks recognition by using the convolutional neural network (CNN) U-Net. It is theoretically analyzed and numerically proved that the blind sidewalk region can be effectively segmented without requiring a large number of the labeled training samples by using the proposed method. And the mean value for the dice similarity coefficient (DSC) is more than 90% in the proposed method. Besides, the running time for random image segmentation is less than 0.5 second, which can easily satisfy the request for real-time image recognition.
Vulnerability Assessment, Risk, and Challenges Associated with Automated Vehicles Based on Artificial Intelligence

Artificial intelligence is the future of technology and due to the presence of artificial intelligence in the vehicle industry the growth rate has increased exponentially. With this enormous growth in the technology, various vulnerabilities also emerged that are generally neglected by the manufacturers during the manufacturing stage. The vulnerabilities are not only from the manufacturer’s side but also it can be from the developer’s side who is developing the hardware for Internet connectivity and also the software which is being used to operate the hardware which will ultimately control the vehicle. These vulnerabilities can prove to be a hacker’s paradise that is looking to infiltrate into a system like this and it will make things complicated as human lives will be at risk. This paper deals with a discussion on various security features and challenges that are emerging with the development of this technology. The paper also discusses the various risk associated with the vulnerabilities of automated vehicles and it also discuss the possible security and safety measures that can be opted to produce a safe automated vehicle. This paper also discusses the security models that are implemented by various manufacturers of the automated vehicles and it also measures the precision of those security models. The paper also deals with various intrusion detection and prevention methodologies that can be followed to control the attack on the target machine.
Pest Control and Palm Oil Plant Disease by Application of Expert System

Pests and diseases of oil palm can affect the growth and productivity of fresh fruit bunches. Therefore, it is necessary to have experts who can quickly conclude the types of diseases and how to deal with diseases that attack oil palm plants. Based on research conducted in September, the highest number was attacked by pests and diseases, which amounted to 41.33% this figure is the highest number in 1 year. The cause of the growth of pests and diseases in oil palm plants is caused by changes in climate temperature from summer to winter. From the results of monitoring humidity in September, the average temperature in the month was 32.9 degrees Celsius. The application of an expert system that is applied to a smartphone can help farmers diagnose the symptoms caused by seawit coconut plants early on. So that the spread of pests and diseases can soon be overcome.
Calculating correct compilers II: Return of the register machines

Abstract In ‘Calculating Correct Compilers’ (Bahr &amp; Hutton, 2015), we developed a new approach to calculating compilers directly from specifications of their correctness. Our approach only required elementary reasoning techniques and has been used to calculate compilers for a wide range of language features and their combination. However, the methodology was focused on stack-based target machines, whereas real compilers often target register-based machines. In this article, we show how our approach can naturally be adapted to calculate compilers for register machines.
Two-sided regularization model based on probabilistic matrix factorization and quantum similarity for recommender systems

Nowadays, with the advent of the age of Web 2.0, several social recommendation methods that use social network information have been proposed and achieved distinct developments. However, the most critical challenges for the existing majority of these methods are: (1) They tend to utilize only the available social relation between users and deal just with the cold-start user issue. (2) Besides, these methods are suffering from the lack of exploitation of content information such as social tagging, which can provide various sources to extract the item information to overcome the cold-start item and improve the recommendation quality. In this paper, we investigated the efficiency of data fusion by integrating multi-source of information. First, two essential factors, user-side information, and item-side information, are identified. Second, we developed a novel social recommendation model called Two-Sided Regularization (TSR), which is based on the probabilistic matrix factorization method. Finally, the effective quantum-based similarity method is adapted to measure the similarity between users and between items into the proposed model. Experimental results on the real dataset show that our proposed model TSR addresses both of cold-start user and item issues and outperforms state-of-the-art recommendation methods. These results indicate the importance of incorporating various sources of information in the recommendation process.
General Mixed State Quantum Data Compression with and without Entanglement Assistance

We consider the most general (finite-dimensional) quantum mechanical information source, which is given by a quantum system A that is correlated with a reference system R. The task is to compress A in such a way as to reproduce the joint source state ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AR</sup> at the decoder with asymptotically high fidelity. This includes Schumacher's original quantum source coding problem of a pure state ensemble and that of a single pure entangled state, as well as general mixed state ensembles. Here, we determine the optimal compression rate (in qubits per source system) in terms of the Koashi-Imoto decomposition of the source into a classical, a quantum, and a redundant part. The same decomposition yields the optimal rate in the presence of unlimited entanglement between compressor and decoder, and indeed the full region of feasible qubitebit rate pairs. Full version at arXiv:1912.08506 [1].
Brand Review Prediction using User Sentiments: Machine Learning Algorithm

As the innovation is increasing exponentially, the manner in which individual expresses their opinions in several website is also changing. There are plenty of reviews and opinions on platforms like Facebook, Twitter, Yelp. Typically scores are on a range of 1-5 levels or stars. Identification of sentiments by investigating textual data has played a crucial role in studies on analytics as it provides valuable alternatives for sentiment mining. A review is an analysis by an individual about items or service that has acquired using the service or item, or had expertise with it. Any ecommerce site's ranking is strongly based on its user's perception. This paper aims to report operation of Machine Learning (ML) algorithm on Yelp's database to evaluate, anticipate, and suggest brand. We implemented Naive Bayes, Random Forest, Decision Tree, Support Vector Machines, K- Nearest Neighbor and Multilayer Perceptron classifiers with sentimental analysis algorithm. Multilayer perceptron classifier achieves highest accuracy of 93.40 %.
Optical Remote Sensing Images Feature Extraction of Forest Regions

The forest region saliency extraction technology based on optical remote sensing image plays an important role in forest fire risk monitoring and forest area protection in the process of urbanization. In this paper, the large-area forest area in the optical remote sensing image is highlighted in the feature map, and the salient map is further obtained through the generated feature map to achieve accurate extraction of the optical remote sensing image forest area. Feature extraction includes two parts: adaptive color region extraction through DC (Definition Circle Model) model and corner feature extraction including suppression mechanism through edge detection model. After a series of experiments, the feature-significant extraction technique is more adaptive and accurate than other unsupervised target detection models.
Non-local Second-Order Attention Network for Single Image Super Resolution

Single image super-resolution is a ill-posed problem which aims to characterize the texture pattern given a blurry and low-resolution image sample. Convolution neural network recently are introduced into super resolution to tackle this problem and further bringing forward progress in this field. Although state-of-the-art studies have obtain excellent performance by designing the structure and the way of connection in the convolution neural network, they ignore the use of high-order data to train more power model. In this paper, we propose a non-local second-order attention network for single image super resolution, which make the full use of the training data and further improve performance by non-local second-order attention. This attention scheme does not only provide a guideline to design the network, but also interpretable for super-resolution task. Extensive experiments and analyses have demonstrated our model exceed the state-of-the-arts models with similar parameters.
Domain-Aware Recurrent Neural Network for Cross-Domain Sentiment Classification

Cross-domain sentiment classification aims at transferring the knowledge of the source domain with rich annotation resource to the scarcely labeled target domain. Existing models mainly relying on single terms or pairwise tokens that fail to capture the context within the sentence. Some others depend on external knowledge to achieve sentiment knowledge transfer by using a lexicon or heavily pre-trained model. We propose a new domain-aware recurrent neural network leveraging the customized domain adversarial technique to minimize the domain discrepancy to solve the problem of scarce or even no labeled data in the target domain. Our model can capture the contextualized domain invariant sentiment information. We design tailor-made loss functions and back propagation to achieve a better indistinguishability for domains. Experiments on a real-world product review dataset shows that our model outperforms the state-of-the-art model.
Fast Hyperspectral Image Encoder Based on Supervised Multimodal Scheme

Many compression methods, lossy or lossless, were developed for 3D hyperspectral images, and various standards have emerged and applied to these amounts of data in order to achieve the best rate-distortion performance. However, high-dimensional data volume of hyperspectal images is problematic for compression and decompression time. Nowadays, fast compression and especially fast decompression algorithms are of primary importance in image data applications. In this case, we present a lossy hyperspectral image compression based on supervised multimodal scheme in order to improve the compression results. The supervised multimodal method is used to reduce the amount of data before their compression with the 3D-SPIHT encoder based on 3D wavelet transform. The performance of the Supervised Multimodal Compression (SMC-3D-SPIHT encoder) has been evaluated on AVIRIS hyperspectral images. Experimental results indicate that the proposed algorithm provides very promising performance at low bit-rates while reducing the encoding/decoding time.
Building a Wikipedia N-GRAM Corpus

In this paper, we introduce a set of approaches to building a n-gram corpus from the Wikipedia monthly XML dumps. We then apply these to build a 1 to 5-g corpus data set, which we then describe in detail, explaining its benefits as a supplement to larger n-gram corpora like Google Web 1T 5-g corpus. We analyze our algorithms and discuss efficiency in terms of space and time. The dataset is publicly available at www.unlv.edu.
An Advanced BERT-Based Decomposition Method for Joint Extraction of Entities and Relations

Joint extraction of entities and relations is an important task in the field of natural language processing and the basis of many NLP high-level tasks. However, most existing joint models cannot solve the problem of overlapping triples well. We propose an efficient end-to-end model for joint extraction of entities and overlapping relations. Firstly, the BERT pre-training model is introduced to model the text more finely. Next, We decompose triples extraction into two subtasks: head entity extraction and tail entity extraction, which solves the problem of single entity overlap in the triples. Then, We divide the tail entity extraction into three parallel extraction sub-processes to solve entity pair overlap problem of triples, that is the relation overlap problem. Finally, We transform each extraction sub-process into sequence tag task. We evaluate our model on the New York Times (NYT) dataset and achieve overwhelming results compared with most of the current models, Precise =0.870, Recall = 0.851, and F1 = 0.860. The experimental results show that our model is effective in dealing with triples overlap problem.
SNAD Arabic Dataset for Deep Learning

Natural language processing (NLP) captured the attention of researchers for the last years. NLP is applied in various applications and several disciplines. Arabic is a language that also benefited from NLP. However, only few Arabic datasets are available for researchers. For that, applying the Arabic NLP is limited in these datasets. Hence, this paper introduces a new dataset, SNAD. SNAD is collected to fill the gap in Arabic datasets, especially for classification using deep learning. The dataset has more than 45,000 records. Each record consists of the news title, news details, in addition to the news class. The dataset has six different classes. Moreover, cleaning and preprocessing are applied to the raw data to make it more efficient for classification purpose. Finally, the dataset is validated using the Convolutional Neural Networks and the result is efficient. The dataset is freely available online.
Two-phase A*: A real-time global motion planning method for non-holonomic unmanned ground vehicles

This paper presents a search-based global motion planning method, called the two-phase A*, with an adaptive heuristic weight. This method is suitable for planning a global path in real time for a car-like vehicle in both indoor and outdoor environments. In each planning cycle, the method first estimates a proper heuristic weight based on the hardness of the planning query. Then, it finds a nearly optimal path subject to the non-holonomic constraints using an improved A* with a weighted heuristic function. By estimating the heuristic weight dynamically, the two-phase A* is able to adjust the optimality level of its path based on the hardness of the planning query. Therefore, the two-phase A* sacrifices little planning optimality, and its computation time is acceptable in most situations. The two-phase A* has been implemented and tested in the simulations and real-world experiments over various task environments. The results show that the two-phase A* can generate a nearly optimal global path dynamically, which satisfies the non-holonomic constraints of a car-like vehicle and reduces the total navigation time.
Free Viewpoint Video Synthesis Based on DIBR

The Free Viewpoint Video (FVV) system is an advanced visual media mode, which could satisfy the audience's demand of watching from any viewpoints. It has aroused great interests in the field of multimedia and vision. This paper proposes a set of solutions, including depth estimation, virtual view interpolation and post processing, to synthesize and optimize the virtual view images. Specifically, deep learning based method is proposed to compute depth images, which is more efficient than traditional stereo matching algorithm. The virtual view can be synthesized with the help of DIBR algorithm in an arbitrary position between two reference cameras. To remove the noise caused by rendering, some post processing techniques, such as median filtering and hole filling, are employed. The extensive experimental results demonstrate that the quality of the synthesized free viewpoint video has been significantly improved using our method.
Learning Domain-Specific Features From General Features for Person Re-Identification

Person re-identification (re-id) plays a vital role in surveillance and forensics application. Since the labeled images for person re-id task is limited, the generalization ability of existed person re-id models is poor. On the other hand, images of different classes (pedestrian and non-pedestrian images) share some general features. To this end, this paper aims to improve the performance of person re-id by designing a relearning network which can learn domain-specific features and general features simultaneously. The proposed relearning network consists of a pretrained backbone network which provides the general features, and several attention-based subnetworks that learn domain-specific features from general features of different levels. Besides, we propose a coarse-fine loss to improve the generalization of person re-id model by making full use of the massive labeled non-pedestrian images. Experimental results on the publicly available Market-1501, DukeMTMC-reID and CUHK03 pedestrian re-id datasets demonstrate the effectiveness of the proposed relearning network and coarse-fine loss.
Transforming RDF to Property Graph in Hugegraph

The data form in the graph data is divided into RDF and property graph. RDF appeared earlier, but it is generally larger and the data is more redundant. In the property graph, a graph is defined by properties, nodes and edges. It is easier to set property to the edges, which is more helpful for the description of the graph, so it is of great research significance to transform RDF graph to property graph. However, because the structure of RDF and property graph are naturally different, transforming RDF to property graph needs to solve the distinction of nodes and properties while ensuring the uniqueness of the nodes, supporting multiple labels and empty labels in a single-label graph database and other difficulties. In this paper, through analyzing a variety of serialized data, we proposed a specific mapping mechanism to solve the first issue. Through the method of storing the label information of the node with additional fields, we have successfully solved the second issue. Finally, through experimental verification of a specific dataset, we found that the converted data volume was reduced by about 10% to 20%, and it can successfully complete the query requirements in different scenarios.
Parameter Tuning onto Recurrent Neural Network and Long Short-Term Memory (RNN-LSTM) Network for Feature Selection in Classification of High-Dimensional Bioinformatics Datasets

Feature selection helps with the selection of relevant features that are present in large number of features and ignores the remaining features that have little value on output feature set. Deep learning methods have been applied to select relevant features in the classification problem; however, the current approach (i.e., search strategies) to the learning of a parameter can either grow out of bound or shrink (they decay exponentially in the number of layers) at each time step (iteration) with the subsequent effect of inaccurate classification of features. To address this challenge of the current search strategies, we proposes an approach to the learning of a parameter for the classification problem based on the behavior of birds (i.e., kestrel bird). The proposed approach, bio-inspired approach, is modeled as a search algorithm which is then integrated with deep learning method. The integration enables learning of optimum parameter for feature selection in a classification problem. A benchmark dataset (i.e., bioinformatics dataset with continuous data attributes) from the Arizona State University was chosen because of its high dimensionality and its continuous data attribute nature. This dataset was used to test the proposed algorithm. The algorithm proposed was evaluated against comparative bio-inspired algorithms namely PSO, ACO, WSA-MP and BAT. The findings indicate that KSA produces minimum learning rate in five datasets out of nine datasets. While on the classification accuracy, KSA produces the highest accuracy of classification in four out of nine dataset. In terms of comparison of classification accuracy using “Wilcoxon signed-rank test,” the finding indicates that “there is no statistically significant differences between the comparative algorithm and the proposed algorithm.” This indicates that KSA could be used as an alternative approach to feature selection for a classification problem.
Construction of Word Segmentation Model Based on HMM + BI-LSTM

Chinese word segmentation plays an important role in search engine, artificial intelligence, machine translation and so on. There are currently three main word segmentation algorithms: dictionary-based word segmentation algorithms, statistics-based word segmentation algorithms, and understanding-based word segmentation algorithms. However, few people combine these three methods or two of them. Therefore, a Chinese word segmentation model is proposed based on a combination of statistical word segmentation algorithm and understanding-based word segmentation algorithm. It combines Hidden Markov Model (HMM) word segmentation and Bi-LSTM word segmentation to improve accuracy. The main method is to make lexical statistics on the results of the two participles, and to choose the best results based on the statistical results, and then to combine them into the final word segmentation results. This combined word segmentation model is applied to perform experiments on the MSRA corpus provided by Bakeoff. Experiments show that the accuracy of word segmentation results is 12.52% higher than that of traditional HMM model and 0.19% higher than that of BI-LSTM model.
Closing the Loop on Collections Review

Resource cancelations happen all the time; however, assessing the outcomes of this process isn’t necessarily a priority. As our profession continues to move toward data supported decisions, how can we connect new data to decisions made 2–4 years ago? Western Carolina University will outline their approach to assessing their most recent cancelation decisions using interlibrary loan and historical journal price analysis data and discuss their plans for using key findings to plan for future collections reviews and to make budget asks.