Reviewerly/exHarmony huggingface dataset:
Notice of Violation of IEEE Publication Principles: HopNet based Associative Memory as FC layer in CNN for Odia Character Classification

Notice of Violation of IEEE Publication Principles <br><br>“HopNet based Associative Memory as FC Layer in CNN for Odia Character Classification” <br> by Ramesh Chandra Sahoo, Sateesh Kumar Pradhan, Poonam Tanwar <br> in the Proceedings of the 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), January 2020 <br><br> After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <br><br> This paper contains copied content from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. <br><br> “Unsupervised Learning using Pretrained CNN and Associative Memory Bank” <br> by Qun Liu and Supratik Mukhopadhyay <br> in the Proceedings of the International Joint Conference on Neural Networks (IJCNN), July 2018 <br><br> <br/> A deep neural network such as convolutional neural network is a popular and most commonly applied technique in image processing for classification for the last few years. The overhead of the feature extraction step will be avoided due to the implicit feature extraction nature of convolutional neural network (CNN) and these extracted features contain substantial information that could be sufficient for an image classification problem. Fully connected (FC) layers in CNN take the results of the last convolution and/or pooling layer and then use them to recognize or classifying images into labels. In this paper, we present an associative memory-based model named Hopfield network as a fully connected layer to store patterns for classification in CNN architecture like LeNet-5. The main purpose of using Hopfield network is to avoid backpropagation as it is a fully connected recurrent network as the state-of-art results which we have obtained are comparable with other models. To measure the performance of the new architecture, we used NIT, Rourkela, Odia characters dataset and compared it with other models for classification.
Transfer learning meets sales engagement email classification: Evaluation, analysis, and strategies

Abstract Enterprise email classification in the sales engagement platform is a challenge due to its evolving asynchronous conversational context during the sales process and differences across industries and organizations. This is further exacerbated by the limited amount of labeled emails due to security and privacy constraints. The leaderboard success of using pretrained language models (LMs) such as BERT and various transfer learning techniques promises a paradigm shift to natural language processing, yet the recipe for applying high performance transfer learning (HPTL) in practical applications remains unclear. This article investigates applying HPTL to sales engagement email classification through a series of experiments and analysis. The experiment datasets include two different organizations' emails. The contribution of this paper is 4‐fold: (a) analysis and characterization of the email corpora from different organizations; (b) identification of the best combinations of pre‐trained LMs under different modeling architectures; (c) study of the impact and trade‐off of limited labeled data on the model accuracy and training time; and (d) characterization and study of the impact of different orgs' datasets on the model accuracy. Our results showed that a practical winning recipe that uses BERT‐finetuning with as few as 500 labeled training examples can consistently outperform significantly with reasonable training time among all models evaluated.
PPSI: Practical Private Set Intersection Over Large-Scale Datasets

Private Set Intersection (PSI) is a fundamental building block in data analytics, which has extensive practical applications including Genome matching, Botnet detection, Social networking, etc. The continuous increase in large scale datasets makes traditional PSI protocols no longer scalable and efficient enough in practice. Thus, it becomes a promising problem to design an efficient private set intersection protocol over largescale datasets in a privacy-preserving manner. Unfortunately, some existing solutions in traditional two-party setting are not suitable for the weak computational capability clients due to multiple interactions and several solutions in server-aided (or outsourced) setting are not efficient enough for large-scale datsets (e.g., million or billion elements set size). In this paper, we construct two basic secure and practical PSI (PPSI) protocols over large-scale datasets in the server-aided setting based on Bloom filter. The first protocol is secure against a semi-honest server, while the second one is even secure against a malicious server who is able to arbitrarily add or remove some elements from the computed intersection without revealing intersection size to the server. Experimental results show that our two basic protocols only need around 15 seconds and 24 seconds (128-bit security in parallel mode) over one millionelement datasets respectively. Finally, we propose a multi-round mechanism for our two basic protocols, which as results show, can double the efficiency.
Text Multi-classification Based on Word Embedding and Multi-Grained Cascade Forest

As an important research field in data mining and machine learning, text classification has been widely used in news classification, sentiment analysis, opinion mining, and so on. It has become an important basic subtask in the field of natural language processing. In this paper, we propose a text multi-classification algorithm based on word embedding and multi-grained cascade forest, which use word embedding model to represent text, sliding windows to scan the word vectors and cascade forest to classify texts. Experiments show that this text multi-classification algorithm can greatly improve the classification accuracy compared with other traditional machine learning models. Compared with the deep neural network classification models, it can greatly improve efficiency with achieving highly competitive performance.
21.2 A 1.5μJ/Task Path-Planning Processor for 2D/3D Autonomous Navigation of Micro Robots

Autonomous micro robots have been deployed for various applications, ranging from unmanned package delivery to smart aerial surveillance. These robots possess intelligence for perception, make decisions based on the collected information, and take actions automatically [1], as illustrated in Fig. 21.2.1. Due to the limited battery lifetime of micro robots, energy-efficient processing for path planning is critical, especially in dynamic environments. In [2], a low-power path planning processor for 2D indoor navigation tasks through the iterative-deepening A * (IDA *) algorithm is proposed. IDA * is a grid-based planning algorithm, which finds a deterministic path on a pre-defined graph converted from a grid map. However, IDA * requires massive memory usage to store the entire map, leading to exponential complexity in memory storage and search time as the map size increases. For maps with an increased dimension and resolution, such as for navigation in large 3D space, IDA* usually fails to find a solution with an acceptable latency.
On the nature and number of isomorphism classes of the minimal ring extensions of a finite commutative ring

Let (A, M) be any finite local (nonzero commutative unital) ring. Then the set of A-algebra isomorphism classes represented by (equivalently, consisting of) rings B such that A⊂B is a decomposed (minimal ring) extension is in one-to-one correspondence with the collection of sets {I1,I2} of ideals I1andI2 of A such that M=I1⊕I2 (internal direct sum), with any such {I1,I2} corresponding to the class represented by the ring B=A/I1×A/I2. If A is not a field, then the (finite) set of A-algebra isomorphism classes represented by (equivalently, consisting of) rings B such that A⊂B is a ramified (minimal ring) extension has cardinality at least 2; known examples show that this bound cannot be improved. If X denotes an indeterminate over the field F4, then R:=F2+XF4[X]/(X2) is of minimal cardinality in the class of finite local rings A such that A is not a field and the A-algebra isomorphism classes represented by a ramified extension of A constitute fewer than half of the (finitely many) A-algebra isomorphism classes represented by a minimal ring extension of A.
Imbalanced Learning with Parametric Linear Programming Support Vector Machine for Weather Data Application

Imbalanced learning is a challenging task in predictive modeling and machine learning that has inspired many researchers to attempt to improve the existing algorithms for more accurate predictions of imbalanced data sets. Due to the nature of rare events, developing reliable and efficient classification models for imbalanced data has not been easily accomplished, and over the past two decades, various methods have been proposed. To this end, we propose a Linear Programming Support Vector Machine (LP-SVM) model to address the issue of imbalanced learning in weather applications. To further improve the model’s predictive accuracy, we have implemented a parameter selection method based on the multi-objective parametric simplex approach for parameter tuning of LP-SVM. For numerical tests, we have used a real data set consisting of weather observations made by the Bureau of Meteorology’s (BM) system in Australia. The results obtained from training and testing the model demonstrate the effectiveness of our proposed model on the tested examples.
Binary codes from $ m $-ary $ n $-cubes $ Q^m_n $

<p style='text-indent:20px;'>We examine the binary codes from adjacency matrices of the graph with vertices the nodes of the <inline-formula><tex-math id="M4">\begin{document}$ m $\end{document}</tex-math></inline-formula>-ary <inline-formula><tex-math id="M5">\begin{document}$ n $\end{document}</tex-math></inline-formula>-cube <inline-formula><tex-math id="M6">\begin{document}$ Q^m_n $\end{document}</tex-math></inline-formula> and with adjacency defined by the Lee metric. For <inline-formula><tex-math id="M7">\begin{document}$ n = 2 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M8">\begin{document}$ m $\end{document}</tex-math></inline-formula> odd, we obtain the parameters of the code and its dual, and show the codes to be <inline-formula><tex-math id="M9">\begin{document}$ LCD $\end{document}</tex-math></inline-formula>. We also find <inline-formula><tex-math id="M10">\begin{document}$ s $\end{document}</tex-math></inline-formula>-PD-sets of size <inline-formula><tex-math id="M11">\begin{document}$ s+1 $\end{document}</tex-math></inline-formula> for <inline-formula><tex-math id="M12">\begin{document}$ s &lt; \frac{m-1}{2} $\end{document}</tex-math></inline-formula> for the dual codes, i.e. <inline-formula><tex-math id="M13">\begin{document}$ [m^2,2m-1,m]_2 $\end{document}</tex-math></inline-formula> codes, when <inline-formula><tex-math id="M14">\begin{document}$ n = 2 $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M15">\begin{document}$ m\ge 5 $\end{document}</tex-math></inline-formula> is odd.
Using a Sequence-to-sequence Model for Large-scale Automated ICD Coding

In the domain of NLP (Natural Language Processing), there are studies on automated ICD coding from text data. Due to the sparsity of the training data, it is difficult to process codes that represent rare diseases. Therefore, in many previous studies, the number of targeted codes was often limited by the frequency in the training data. In this work, we propose a method for generating ICD-10 codes by using sequence-to-sequence (seq2seq) model. A seq2seq model can automatically learn the regularity, more specifically hierarchical structure, found on the character sequence of ICD codes, so that it makes use of the interactions and relations among the codes during training, which conventional classifier fails to use. As a result, the proposed method can predict even the codes not appeared in training data. In our experiment, we randomly divided the all pairs of ICD-10 code and its description written in natural language into equal halves. By using the first half, we trained a seq2seq model that convert a token sequence of ICD-10 description into a character sequence representing ICD-10 code. Then, we tested the model on the latter half, which are not included in the training data. We found it successfully assigned the corresponding correct code to 80% of the descriptions, while the conventional classifier trained on the same data did not assign any correct code. In addition, we experimented ICD code assignment with medical test data. Our proposed model showed higher accuracy score than the conventional classifier model. Moreover, we implemented a seq2seq model with attention. Investigating the attended tokens during prediction, we analyzed the performance of our seq2seq model.
Optimization of Energy Efficiency for FPGA-Based Convolutional Neural Networks Accelerator

Abstract Convolutional neural network (CNN) is widely applied to image recognition with high recognition accuracy. CNN has a wider implementation in general-purpose processors and can be accelerated on FPGA. CNN has a unique way of computing, but general-purpose processors are not efficient for CNN and cannot meet energy efficiency requirements. And the previous studies on FPGA did not involve an energy-efficient implementation on FPGA. We innovatively propose energy efficiency models and implement high energy efficiency CNN on FPGA. We implemented the LeNet-5 network model on the GENESYS 2 board and compared it to the traditional processor and previous studies. By comparison, the computing throughput of CPU, GPU and FPGA are 3.831GFLOPS, 27.143GFLOPS and 19.61GFLOPS respectively, and their powers are 32.15W, 52W, 4.152W respectively. The final energy efficiency (GFLOPS/W) is 0.119GFLOPS/W, 0.522 GFLOPS/W, 4.723 GFLOPS/W, so the energy efficiency of FPGA are far superior to that of CPU and GPU. Since the energy efficiency we achieved on FPGA is also higher than that of FPL2009 and FPGA2015, and we have achieved good experimental results in energy efficiency.
A Survey of Workload Management Difficulties in the Public Cloud

Distributed computing is referring to both the applications and the services provided over the web or Internet and the hardware and system programming in data centers that provide its services. The cloud computing gives an expansive pool of shared resources, programming bundles, data, storage, and a wide range of applications according to client requests at any time. Cloud computing is developing rapidly; an extensive number of clients are pulled in toward cloud administrations for more fulfillment. Adjusting the heap has turned out to be an additionally intriguing exploration zone in this field. Better load adjusting calculation in the cloud framework builds the execution and resource use by increasingly dispersing work stack among various nodes in the system. With the approach of a public cloud domain, numerous other solicitations and administrations across geologies that need to run. A few workloads might be changeless and need to run continuously, for example, an online trade webpage or a control framework that deals with basic conservation processing. Virtualized workloads include another level of many-sided quality. This paper aims to investigate the difficulties of load adjusting in the public cloud environment.
Recognizing the Data Type of Firmware Data Segments With Deep Learning

Data segment analysis is of great value for firmware analysis. The data segment contains abundant information such as pointers and strings which is helpful for accelerating the process of code segment analysis. In this paper, we propose a novel approach of applying deep learning to solve the problem of data type identification in data segments, that is a fundamental problem in data segment analysis. We define 3 data types of data segment, then design several data segment byte feature extraction methods to construct feature sequences, and finally present a deep learning-based approach with feature sequences as input to recognize the data type byte by byte. Then, the recognized type can be further corrected efficiently by prior knowledge. Based on the data segment of a firmware, we built a dataset that included 18,032,352 samples (in bytes of data segment). We implement a prototype system and evaluate it with our dataset, then determine reasonable models and hyperparameters through several experiments, and eventually confirm that deep learning techniques are suitable for identifying the data type in data segment. Kappa coefficient of our data type recognition reached 0.96 and the models can be retained quickly. Using 131,072 samples in our dataset for 32 seconds of training, the accuracy can reach 90%; the accuracy can reach 97% with 273 seconds of training and 950,272 samples. Furthermore, our approach has higher accuracy than IDA in string recognition. In experiments, the recall and precision of our approach reached 96.5% and 90% respectively, whereas corresponding results of IDA is 92.9% and 85.7%. In addition, we selected 8 open source software to compile and test, and compared the detection results with TypeMiner. Experiments show that our method has certain cross-platform and operating system capabilities, and performs better than TypeMiner on some software.
FPGA-based Implementation of a Tree-based Classifier using HW-SW Co-design

FPGA-based implementation of tree-based classifiers has been a very promising approach due to the ability of FPGA to execute tasks in parallel, which speeds up the processing tasks and saves power. The challenge with FPGA implementation is the need to build all classifier components in hardware, structure format, which is time-consuming and does not follow the best practices in creating modular design architecture. This paper addresses this issue by proposing a hardware-software codesign implementation of a tree-based classifier that uses ID3 algorithm for classification tasks. We used the Xilinx microblaze processor inside the FPGA to execute ID3 algorithm functions, while using hardware modules for pre-processing and feature engineering tasks.
Depth Estimation From Single Image Through Multi-Path-Multi-Rate Diverse Feature Extractor

Convolutional neural networks can effectively learn features and predict the depth by considering different scene types. However, previous studies have not accurately predicted the depth in cases wherein the objects or scenes were small and the background was complex. These studies have used the bilinear up-sampling method to enlarge the feature maps during training, or to disable the transfer of multiscale information to the end of the network. However, this has resulted in blurred regions in the depth maps and contour loss.This paper proposes a multi-path-multi-rate feature extractor, which can effectively extract multi-scale information to make accurate depth predictions. We used the U-NET [1] architecture to obtain depth maps with high resolution, and also used the proposed multi-path-multi-rate feature extractor to translate useful features from the encoder to the decoder. Dilated convolutions with different rates can provide different types of field-of-view information, which increases the precision of depth estimation and maintains the object contours. Finally, we conducted experiments using an indoor scene (NYUv2 [2]). The results show that the proposed framework achieved an improvement of 12.9% in RMSE, 9.9% in REL, and 9.3% in log10, and it requires approximately 0.048 seconds to predict a depth map from a single image.
Receptive Field Pyramid Network for Object Detection

Current state-of-the-art methods usually utilize feature pyramid to provide various receptive fields for detecting objects at different scales. However, the feature maps from low- to high-level layers have large semantic gaps and are with different spatial resolutions, so that their representational capacity differs and noise is introduced when fusing them. To overcome this limitation and carry out better object detection, we design a novel network named Receptive Field Pyramid Network (RFPN). The proposed method is derived based on a receptive field pyramid through dilated convolutions, such that all of the extracted feature maps are with strong semantics and the same resolution. Moreover, we propose a pyramid attention mechanism by iteratively leveraging information from previous receptive fields to give higher responses for objects of interest. Experimental results on publicly available datasets show that the proposed method achieves better results than existing methods for comparison.
Multi-Task Learning in Autonomous Driving Scenarios Via Adaptive Feature Refinement Networks

Many deep learning applications benefit from multi-task learning with several related objectives. In autonomous driving scenarios, being able to accurately infer motion and spatial information is essential for scene understanding. In this paper, we combine an adaptive feature refinement module and a unified framework for joint learning of optical flow, depth and camera pose estimation in an unsupervised manner. The feature refinement module is embedded into motion estimation and depth prediction sub-networks, which can exploit more channel-wise relationships and contextual information for feature learning. Given a monocular video, our network firstly estimates depth and camera motion, and calculates rigid optical flow. Then, we design an auxiliary flow network for inferring non-rigid flow fields. In addition, a forward-backward consistency check is adopted for occlusion reasoning. Extensive experiments on KITTI dataset demonstrate that the proposed method achieves potential results comparing to recent deep learning networks.
On Modeling ASR Word Confidence

We present a new method for computing ASR word confidences that effectively mitigates the effect of ASR errors for diverse downstream applications, improves the word error rate of the 1-best result, and allows better comparison of scores across different models. We propose 1) a new method for modeling word confidence using a Heterogeneous Word Confusion Network (HWCN) that addresses some key flaws in conventional Word Confusion Networks, and 2) a new score calibration method for facilitating direct comparison of scores from different models. Using a bidirectional lattice recurrent neural network to compute the confidence scores of each word in the HWCN, we show that the word sequence with the best overall confidence is more accurate than the default 1-best result of the recognizer, and that the calibration method can substantially improve the reliability of recognizer combination.
Improved Bilingual Sentiment Analysis Lexicon Using Word-level Trigram

In social networks, Senegalese citizen freely express their opinions using French and Wolof Languages, which often leads to a lot of spelling mistakes in their writing. Furthermore, the complexity of Wolof language in orthography and French language in grammar and conjugation makes the analysis of Senegalese citizen's opinions more challenging. FWLSA-score [1], has been recently proposed as an effective solution for overcoming the complexities of this bilingual sentiment analysis. This solution was based on word-level similarity. But, it does not deal perfectly with verbs' conjugation or some words' declination in both languages. This paper proposes an improvement of FWLSA-score [1] to address these issues. The new contribution is based on the word-level trigrams (list of consecutive three letters). Experimental results conducted on the same data [2], show that it successfully predicts sentiment polarity with an accuracy of 96.83% (implying an increase of 5.93% compared to the previous work).