Reviewerly/exHarmony huggingface dataset:
Estimator Vectors: OOV Word Embeddings based on Subword and Context Clue Estimates

Semantic representations of words have been successfully extracted from unlabeled corpuses using neural network models like word2vec. These representations are generally high quality and are computationally inexpensive to train, making them popular. However, these approaches generally fail to approximate out of vocabulary (OOV) words, a task humans can do quite easily, using word roots and context clues. This paper proposes a neural network model that learns high quality word representations, subword representations, and context clue representations jointly. Learning all three types of representations together enhances the learning of each, leading to enriched word vectors, along with strong estimates for OOV words, via the combination of the corresponding context clue and subword embeddings. Our model, called Estimator Vectors (EV), learns strong word embeddings and is competitive with state of the art methods for OOV estimation.
Arbitrary Chinese Font Generation from a Single Reference

Generating a new Chinese font from a multitude of references is an easy task, while it is quite difficult to generate it from a few references. In this paper, we investigate the problem of arbitrary Chinese font generation from a single reference and propose a deep learning based model, named One-reference Chinese Font Generation Network (OCFGNet), to automatically generate any arbitrary Chinese font from a single reference. Based on the disentangled representation learning, we separate the representations of stylized Chinese characters into style and content representations. Then we design a neural network consisting of the style encoder, the content encoder and the joint decoder for the proposed model. The style encoder extracts the style features of style references and maps them onto a continuous Variational Auto-Encoder (VAE) latent variable space while the content encoder extracts the content features of content references and maps them to the content representations. Finally, the joint decoder concatenates both representations in layer-wise to generate the character which has the style of style reference and the content of content reference. In addition, based on Generative Adversarial Network (GAN) structure, we adopt a patch-level discriminator to distinguish whether the received character is real or fake. Besides the adversarial loss, we not only adopt L1-regularized per-pix loss, but also combine a novel loss term Structural SIMilarity (SSIM) together to further drive our model to generate clear and satisfactory results. The experimental results demonstrate that the proposed model can not only extract style and content features well, but also have good performance in the generation of Chinese fonts from a single reference.
Research on Sentiment Analysis of Financial Text Based on Semantic Matching

With the development of the economy, more and more people on social platforms share financial-related information and hope to understand the development of financial markets through relevant financial data. Aiming at this problem, this thesis proposes a sentiment analysis algorithm of financial texts based on semantic matching. First, preliminary text vectorization is performed on financial text data. Then further training and fine-tuning are performed through pre-trained models in order to fully tap the associations between text contexts and better grasp the semantic focus, so as to better model the text content and obtain higher-level financial text semantic representation. Next, through the improved Siamese network semantic matching model, financial text vectors are trained for semantic matching, so that the distance between financial text vectors with the same emotional category is closer and the distance between different classes is farther, which further optimizes the semantic representation of financial text. Finally, support vector machines are used as classifier to perform sentiment classification of financial texts. Experimental results show that compared with other classic sentiment analysis algorithms, the proposed algorithm has the best sentiment analysis effect on financial text.
VLSI Architecture of Polynomial Multiplication for BGV Fully Homomorphic Encryption

Fully homomorphic encryption (FHE) has attracted much attention because computations can be directly performed on ciphertexts. This work explores the hardware architecture of polynomial multiplication defined in BGV-FHE, targeting the applications of aggregate plaintext using cyclotomic polynomials. We show how to effectively combine the characteristics of cyclotomic polynomials and the prime-factor FFT algorithm to obtain a novel design derived by the concept of Chinese Remainder Theorem. Experimental results reveal that a significant speedup in terms of operation reduction can be achieved by adopting the proposed schemes as compared to existing works assuming a comparable security level. For example, about 2.44 and 6.34 times improvement in the total number of required modular addition and multiplication, respectively, can be obtained by using 32 one-bit aggregate slots as compared to Chen's work when the 21845-th cyclotomic polynomial is considered. The improvement could be huge if all of the available slots are involved in applications.
A basic quasi-Boolean logic of intuitionistic character

The logic BM is Sylvan and Plumwood's minimal De Morgan logic. The aim of this paper is to investigate extensions of BM endowed with a quasi-Boolean negation of intuitionistic character included in 3-valued logic G3 and/or 3-valued logic S5G3. Unreduced Routley-Meyer ternary relational semantics are provided for all the logics defined in the paper.
Differential Privacy and Its Applicability for Official Statistics in Japan – A Comparative Study Using Small Area Data from the Japanese Population Census

As part of its preparations for the 2020 U.S. Population Census, the U.S. Census Bureau uses the methodology of differential privacy to create privacy-preserved official microdata. It is expected that the use of differential privacy for official statistical data will become a topic in Japan in the future.In this paper, we survey the current discussion on the use of differential privacy for creating official statistical data. We describe the differential privacy method used by the U.S. Census Bureau, develop a method to apply differential privacy to Japanese population grid data, and conduct a comparison between different differential privacy methods by applying them to Japanese small area data.Results demonstrate that for population grid data, both the non-negative Wavelet method (and its public-n variant) and the US census top-down method preserve the non-negativity and sparsity of the original data, but each results in a different degree of error. This provides an important criterion for choosing the most appropriate method among the three options.
Towards an Experimental Testbed to Study CyberWorm Behaviors in Large Scale Networks

Currently, there is very limited information in cybersecurity research regarding worm behavior across real large scale networks. This poster positions an experimental testbed that can be used for studying worm behaviors in large scale networks. The testbed infrastructure will be used to empirically study worm generation, propagation, attacks, policies and antidote (intervention) mechanisms. As a preliminary step towards this goal, this poster presents a 10 node case study into the behavior of a worm that attacks through IP address routing in a campus network.
Multi-Class Triplet Loss With Gaussian Noise for Adversarial Robustness

Deep Neural Networks (DNNs) classifiers performance degrades under adversarial attacks, such attacks are indistinguishably perturbed relative to the original data. Providing robustness to adversarial attacks is an important challenge in DNN training, which has led to extensive research. In this paper, we harden DNN classifiers under the adversarial attacks by regularizing their deep internal representation space with Multi-class Triplet regularization method. This method enables DNN classifier to learn a feature representation that detects similarities between adversarial and clean images and brings similar images close to their original class and pushes dissimilar images away from their false classes. This training process with our Multi-class Triplet regularization method in combination with Gaussian noise injection proves to be more robust in detecting adversarial attacks exceeding that of adversarial training on strong iterative attacks.
Facilitating and Managing Machine Learning and Data Analysis Tasks in Big Data Environments Using Web and Microservice Technologies

Driven by the current advances of machine learning in a wide range of application areas, the need for developing easy to use frameworks for instrumenting machine learning effectively for non data analytics experts as well as novices increased dramatically. Furthermore, building machine learning models in the context of Big Data environments still represents a great challenge. In the present article, those challenges are addressed by introducing a new generic framework for efficiently facilitating the training, testing, managing, storing and retrieving of machine learning models in the context of Big Data. The framework makes use of a powerful Big Data software stack platform, web technologies and a microservice architecture for a fully manageable and highly scalable solution. A highly configurable user interface hiding platform details from the user is introduced giving the user the ability to easily train, test and manage machine learning models. Moreover, the framework automatically indexes and characterizes models and allows flexible exploration of them in the visual interface. The performance and usability of the new framework is evaluated on state-of-the-arts machine learning algorithms: it is shown that executing, storing and retrieving machine learning models via the framework results in a well acceptable low overhead demonstrating that the framework can provide an efficient approach for facilitating machine learning in Big Data environments. It is also evaluated, how configuration options (e.g. caching of RDDs in Apache Spark) affect runtime performance. Furthermore, the evaluation provides indicators for when the utilization of distributed computing (i.e. parallel computation) based on Apache Spark on a cluster outperforms single computer execution of a machine learning model.
One-step Predictive Encoder - Gaussian Segment Model for Time Series Anomaly Detection

Unsupervised anomaly detection for time series is of great importance for various applications, such as Web monitoring, medical monitoring, and device fault diagnosis. Time series anomaly detection (TSAD) aims to find the observations that most different from others in a sequence of observations. With the development of deep learning, deep-autoencoder-based methods achieve state-of-the-art performance. These methods are usually able to find single anomaly points but fail to detect the anomaly segment and the change point. To tackle this problem, this paper proposes a novel TSAD method, which consists of a bidirectional LSTM (BiLSTM) autoencoder and a subsequent Gaussian segmentation model. BiLSTM encodes a time series in a predictive format from both positive and negative time directions, then outputs the latent feature vectors and restructured errors. After that, the latent features are used to find anomaly segments by the Gaussian segment model; the restructured errors are used to find change points and extreme single anomaly by a scoring function. In this way, our method can find all three kinds of anomaly points. Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.
Modified Grey Wolf Optimizer based Maximum Entropy Clustering Algorithm

In this paper, we propose a new maximum entropy clustering algorithm by modified grey wolf optimizer (GWO), which modify the traditional GWO from twofold: First, nonlinear decay factor is constructed, which leads to flexible refined search; Second, proportional weights, which render the positions of distinct grey wolves, are adjusted adaptively according to the social hierarchy of them. Based on these two modifications, the rectified GWO could greatly improve both the accuracy and convergence rate. Experimental results on 12 benchmark functions demonstrate the effectiveness of those modifications. Furthermore, we utilize modified GWO to maximum entropy based fuzzy clustering problems. Experiments on 5 real datasets indicate the high performance and efficiency of the proposed approach.
Performance Optimization for Feature Extraction Section of DeepChem

Based on the popular deep learning technique, the authors at Stanford implement DeepChem as an open source methods for the research in the fields of drug discovery, biology and so on. For the performance problem of training process of DeepChem neural network, this paper rebuilds the original serial feature extraction algorithm of DeepChem and optimizes the rebuilt serial algorithm based on the multiple processes algorithm. The experiment results show that the parallel algorithm achieves 15.38\(\times \) speedup at the best compared with the serial algorithm. For the future work, first, in addition to the multiprocessing package, the other packages such as concurrent, subprocess and so on could be considered to optimize the feature extraction algorithm; Second, the serial and parallel algorithms run slower when the data block size is 150 compared with the other block sizes, optimization of training process performance for the smaller data block size is the second direction in future work.
Optimal Measurement Budget Allocation For Particle Filtering

Particle filtering is a powerful tool for target tracking. When the budget for observations is restricted, it is necessary to reduce the measurements to a limited amount of samples carefully selected. A discrete stochastic nonlinear dynamical system is studied over a finite time horizon. The problem of selecting the optimal measurement times for particle filtering is formalized as a combinatorial optimization problem. We propose an approximated solution based on the nesting of a genetic algorithm, a Monte Carlo algorithm and a particle filter. Firstly, an example demonstrates that the genetic algorithm outperforms a random trial optimization. Then, the interest of non-regular measurements versus measurements performed at regular time intervals is illustrated and the efficiency of our proposed solution is quantified: better filtering performances are obtained in 87.5% of the cases and on average, the relative improvement is 27.7%.
Medical Image Segmentation Techniques and Their Relevance in Contemporary Imaging

Image segmentation is a method where the image is partitioned into multiple segments. Segmentation of the image is typically used for locating objects and boundaries in images and produces a set of segments that include the entire image or a series of contours drawn from the image together. Similarly, Medical Image Segmentation focusses on the automated or semi-automatic method of detecting boundaries within a 2D or 3D image. However, the major problem with segmenting the medical image is the high variability in medical images, to overcome which various segmentation techniques may be applied on these images. This paper presents the two most effective segmentation techniques i.e. fuzzy c means clustering and ant colony optimisation-canny on 3D CT scan of human body parts which further shows a comparative analysis of entropy values of the resultant images, with FCM providing drastic rise in the values. This may be highly useful to the medical practitioners for better understanding and visualisation of the images to provide effective diagnosis of the disease.
Arboreous Neighborhood Structures

Framed neighborhood structures are digraphs with variables associated with vertices or arcs. These structures are a convenient tool of formalizing the technological scheme of the simulated process: the mathematical model of the process is a static or dynamic neighborhood system associated with a framed neighborhood structure. In the article, we define the arboreous (tree-like) neighborhood structures and the corresponding arboreous neighborhood systems. These structures and systems can be considered as one of the possible versions of the general concept of a hierarchical control system. The problems of parametric identification of equations and synthesizing regulators for arboreous neighborhood systems are discussed. It is proved that for arboreous systems, the connections available in the underlying arboreous structure are always sufficient for the synthesis of stabilizing regulators.
BIBNet: An Efficient Super Resolution with Bottleneck-In-Bottleneck

Deep Neural Networks have enabled remarkable progress in the field of single image super resolution (SR). However, these models are often large and complex to be applied for real-world applications with limited resources as in mobile and embedded systems. We investigate whether the typical low latency models as MobileNet can be expected of comparable efficiency at SR tasks with recently reported SR performance in the literature. To this end, a moderate and effective architecture, Bottleneck-In-Bottleneck (BIB), is introduced in this paper. The BIB uses multiple expansion factors of the residual blocks in the form of a bottleneck, reducing computation complexity while utilizing advantageous factors of large feature dimensions. We also propose BIBNet with multiple BIB blocks, which can easily adjust its size and computational cost to create a variety of efficient and high-performance models. Extensive experiments show that, with fewer parameters and computations, BIBNet achieves highly competitive performance compared to other conventional SR methods with more complex architectures.
Social Network based Sentiment and Network Analysis to Predict Elections

The General Elections in India is a grand event with over 900 million eligible voters. India has a population of around 1.3 billion and it is the world’s largest democracy. One of the features of a democratic nation is the election. In election general sentiment and opinion of people plays an important role. Social media is abuzz with opinions and discussions taking place all over India especially twitter. We tried to predict election results and general mindset using sentiment and network analysis. Previously, some work has been done in the sphere of gauging the mindset of people using social media, specifically Sentiment Analysis of Twitter posts. Using sentiment analysis, we can classify a document into a host of categories like positive, negative, neutral, angry, happy, sad, etc. and this is especially helpful when we are trying to gauge the opinion of people based on what they wrote about it. We used Twitter data for our work and tried to apply sentiment analysis techniques to find the consensus of the public about different political leaders and political parties. The results were pretty accurate when compared to actual results and this model and analysis could prove useful in gauging the mindset of the general public about a specific topic, product, or idea. Furthermore, we also carry out a detailed network analysis of dataset using the network formed by taking tweets as the edges of the graph. This gave us some really interesting insights and resulted in the formation of 4 major communities confirming the fact that online communities are close-knitted when it comes to political orientations.
Development of a Search System for Similar Images

This paper discusses the search for images based on their content. Measuring visual similarity between two or more instances is a fundamental task of computer vision. For a person, evaluating the similarity of images is a natural task. However, in computer vision this is a complex problem that arises mainly due to the semantic gap. This paper discusses the use of a neural network, which allows you to get an estimate of the similarity between a pair of visual representations extracted from input data. The difference between the hashes of the two images obtained at the output of the neural network allows us to evaluate their similarity. The results of the developed algorithm and its software implementation are compared with existing solutions and show the best result on various data sets.
Exploiting group shuffling dynamics to convey the importance of good software design

Analysis, modeling and design activities are integral parts of any modern software development project. However, educators often struggle in convincing undergraduate students of their value. Grasping the importance of good software design requires a certain maturity that is not yet attained in the first years of a computer science curriculum. In this study, we introduce group shuffling dynamics to a formal software design course to convey the importance of these concepts by exposing students to the experience of switching groups mid-semester. This grants a whole new perspective on the matter as the cost of managing technical debt is felt when the respective developers leave, and modeling artifacts become pivotal in aiding the incorporation of new team members. This short paper presents our first experience while implementing this methodology and shares our preliminary results derived from a first set of interviews with our students.
Weight Isolation-Based Binarized Neural Networks Accelerator

In this paper, we introduce a binary neural network accelerator which using a new binarization method and hardware sparse. We propose the weights and the activations to either 1 or 0 instead of +1 or −1, which makes the convolution process simplified and more suitable for hardware implementation. To decrease the data access from off-chip memory, we propose a novel data reuse method, which can reduce 58.8% data access, while the weight isolation logic is designed to reduce power consumption. Based on the weight isolation and the retiming technique, the proposed BNN accelerator achieves low power consumption at 500MHz clock by the VC709 Evaluation Kit. Experimental results show that the proposed accelerator achieves a throughput of 3378 GOPS and 1624 GOPS/W energy efficiency.
Research on the Construction of Information Platform of Employment and Entrepreneurship for College

This year, the number of college graduates across the country will reach a new high. For a long time, college graduates are still the core talents and backbone of the recruitment of enterprises and employers, and also the main force of the whole social development and modernization construction. In the new era, facing the increasing global competition, the competition of talents among enterprises is becoming more and more fierce. As an enterprise, the proportion of knowledge-based employees is growing. The college graduates trained by the state are also the knowledge workers who will be sent to the enterprises in the future. The strength of this group is constantly expanding. They not only promote the development of enterprises, but also play an inestimable role in social and economic development. Based on the current economic situation, the competition among enterprises is also the competition among talents. From the perspective of the whole society and economic development situation, the employment and Entrepreneurship of college graduates are facing new challenges.