Reviewerly/exHarmony huggingface dataset:
Energy-efficient Acceleration of Convolutional Neural Networks using Computation Reuse

• Most convolutional neural networks can undergo low-precision quantization. • A quantized network has millions of weights, but very limited unique weight values. • Limited unique weight values yields many repetitive arithmetic operations. • A coding method to eliminate storing redundant weight values. • A computation reuse-aware architecture to skip repetitive arithmetic operations. The large and ever-increasing size of state-of-the-art convolutional neural networks (CNNs) poses both throughput and energy challenges to the underlying hardware, especially in embedded and mobile computing platforms. Weight quantization is a promising method to reduce the computational complexity of neural networks without losing significant accuracy. In this paper, we show that the value locality of quantized filter weights brings about a high amount of computation redundancy in CNNs. In order to leverage this computational redundancy to reduce neural network complexity, this paper proposes CORN C, a computation reuse-aware accelerator for CNNs. CORN C leverages two opportunities to eliminate repetitive computations: (1) the specific algorithmic structure of the convolutional neural networks in which each element of the input data (and intermediate feature maps) is multiplied by tens to thousands of CNN filter weights, and (2) the data value locality in the network filter weights when low-precision quantization is applied. Experimental results show that by eliminating the considerable amount of repetitive multiplication, computation reuse offers up to 10.5%–20.9% energy reduction and 13.4%–41.6% latency reduction over state-of-the-art accelerators across a set of widely-used CNN benchmarks.
Implementation of agglomerative hierarchical clustering algorithm for earthquake areas in Sulawesi Island

Earthquakes are vibrations that suddenly occur on the earth’s surface caused by a shift in the earth’s plates. Sulawesi Island is one of the earthquake-prone areas in Indonesia. The impact caused by an earthquake varies depending on its magnitude and depth. This research aims on grouping of earthquake-prone areas on the island of Sulawesi based on their magnitude and depth. This grouping has been done can be done using the data mining techniques, namely clustering. The clustering method used in this research is agglomerative hierarchical clustering. The data used in this study were obtained from United States Geological Survey (USGS). Based on grouping 751 earthquake data from January 1971 to December 2020 using average linkage the research found that there are 2 clusters on Sulawesi Island with a Silhouette coefficient of 0.68.
Using Pectoral Muscle Removers in Mammographic Image Process to Improve Accuracy in Breast Cancer

Cancer is a disease that attacks almost any organ or tissue of the body when abnormal cells grow uncontrollably and invade adjacent parts of the body. The second highest incidence of cancer in Indonesia is breast cancer with 42.1 cases per 100,000 population with an average mortality rate of 17 per 100,000 population. Mammography is a special imaging modality with x-rays to produce detailed breast images with at least 2 viewpoints, namely Craniocaudal (CC) or top view and Medio Lateral Oblique (MLO) or side view. The chest muscle area on the MLO display often interferes with the cancer identification process on mammography images because it has a dominant density and is similar to the density of cancer tissue. This research proposes a framework consisting of pectoral muscle detection on MLO display, image enhancement process, segmentation, and feature extraction. This study succeeded in increasing the accuracy of the MLO display mammography image after using the pectoral muscle remover using gradual edge detection and Hough lines Transform with the ratios of accuracy, precision, specificity, and sensitivity for images without pectoral muscle removers respectively were 33.59%, 30%, 11.49% and 80.48%. As for the images with pectoral muscle removers, the accuracy, precision, specificity, and sensitivity values respectively ​​were 68.67%, 64.71%, 57.14%, and 80.49%. For future projects, this research can be developed using Convolutional Neural Network (CNN) to improve accuracy. This is expected to help doctors and radiologists in the process of reading patient mammography so it can reduce mortality from breast cancer.
Measuring and assessing indeterminacy and variation in the morphology-syntax distinction

Abstract We provide a discussion of some of the challenges in using statistical methods to investigate the morphology-syntax distinction cross-linguistically. The paper is structured around three problems related to the morphology-syntax distinction: (i) the boundary strength problem; (ii) the composition problem; (iii) the architectural problem. The boundary strength problem refers to the possibility that languages vary in terms of how distinct morphology and syntax are or the degree to which morphology is autonomous. The composition problem refers to the possibility that languages vary in terms of how they distinguish morphology and syntax: what types of properties distinguish the two systems. The architecture problem refers to the possibility that languages vary in terms of whether a global distinction between morphology and syntax is motivated at all and the possibility that languages might partition phenomena in different ways. This paper is concerned with providing an overarching review of the methodological problems involved in addressing these three issues. We illustrate the problems using three statistical methods: correlation matrices, random forests with different choices for the dependent variable, and hierarchical clustering with validation techniques.
An Intermediate-level Attack Framework on The Basis of Linear Regression

This article substantially extends our work published at ECCV (Li et al., 2020), in which an intermediate-level attack was proposed to improve the transferability of some baseline adversarial examples. Specifically, we advocate a framework in which a direct linear mapping from the intermediate-level discrepancies (between adversarial features and benign features) to prediction loss of the adversarial example is established. By delving deep into the core components of such a framework, we show that a variety of linear regression models can all be considered in order to establish the mapping, the magnitude of the finally obtained intermediate-level adversarial discrepancy is correlated with the transferability, and further boost of the performance can be achieved by performing multiple runs of the baseline attack with random initialization. In addition, by leveraging these findings, we achieve new state-of-the-arts on transfer-based l∞ and l2 attacks. Our code is publicly available at https://github.com/qizhangli/ila-plus-plus-lr.
End-to-end lossless compression of high precision depth maps guided by pseudo-residual

Facing the explosion of massive amount of high precision depth maps, we pro-pose a novel end-to-end lossless compression method for high precision depth maps. The whole process is comprised of two sub-processes, named pre-processing of depth maps and deep lossless compression of processed depth maps. The deep lossless compression network consists of two sub-networks, named lossy compression net-work and lossless compression network. We leverage the concept of pseudo-residual to guide the generation of distribution for residual and avoid introducing context models. Our end-to-end lossless compression network outperforms tested learned and non-learned codecs with at least 7&#x0025; reduction on real world datasets (DIODE and SementicKITTI) and has low computational cost. The visualization of the whole operations is shown as Figure 1. More details and code are available at https://git.openi.org.cn/OpenCompression/DeepLosslessCompression.
Fringe visibility and correlation in Mach–Zehnder interferometer with an asymmetric beam splitter

We study the wave-particle duality in a general Mach-Zehnder interferometer with an asymmetric beam splitter from the viewpoint of quantum information theory. The correlations(including the classical correlation and the quantum correlation) between the particle and the which-path detector are derived when they are in pure state or mixed state at the output of Mach-Zehnder interferometer. It is found that the fringe visibility and the correlations are effected by the asymmetric beam splitter and the input state of the particle. The complementary relations between the fringe visibility and the correlations are also presented.
Incremental User Embedding Modeling for Personalized Text Classification

Individual user profiles and interaction histories play a significant role in providing customized experiences in real-world applications such as chatbots, social media, retail, and education. Adaptive user representation learning by utilizing user personalized information has be-come increasingly challenging due to ever-growing his-tory data. In this work, we propose an incremental user embedding modeling approach, in which embeddings of user’s recent interaction histories are dynamically integrated into the accumulated history vectors via a trans-former encoder. This modeling paradigm allows us to create generalized user representations in a consecutive manner and also alleviate the challenges of data management. We demonstrate the effectiveness of this approach by applying it to a personalized multi-class classification task based on the Reddit dataset, and achieve 9% and 30% relative improvement on prediction accuracy over a baseline system for two experiment settings through appropriate comment history encoding and task modeling.
Learning Spatio-Temporal Downsampling for Effective Video Upscaling

Downsampling is one of the most basic image processing operations. Improper spatio-temporal downsampling applied on videos can cause aliasing issues such as moiré patterns in space and the wagon-wheel effect in time. Consequently, the inverse task of upscaling a low-resolution, low frame-rate video in space and time becomes a challenging ill-posed problem due to information loss and aliasing artifacts. In this paper, we aim to solve the space-time aliasing problem by learning a spatio-temporal downsampler. Towards this goal, we propose a neural network framework that jointly learns spatio-temporal downsampling and upsampling. It enables the downsampler to retain the key patterns of the original video and maximizes the reconstruction performance of the upsampler. To make the downsamping results compatible with popular image and video storage formats, the downsampling results are encoded to uint8 with a differentiable quantization layer. To fully utilize the space-time correspondences, we propose two novel modules for explicit temporal propagation and space-time feature rearrangement. Experimental results show that our proposed method significantly boosts the space-time reconstruction quality by preserving spatial textures and motion patterns in both downsampling and upscaling. Moreover, our framework enables a variety of applications, including arbitrary video resampling, blurry frame reconstruction, and efficient video storage.
PHO-LID: A Unified Model Incorporating Acoustic-Phonetic and Phonotactic Information for Language Identification

We propose a novel model to hierarchically incorporate phoneme and phonotactic information for language identification (LID) without requiring phoneme annotations for training. In this model, named PHO-LID, a self-supervised phoneme segmentation task and a LID task share a convolutional neural network (CNN) module, which encodes both language identity and sequential phonemic information in the input speech to generate an intermediate sequence of phonotactic embeddings. These embeddings are then fed into transformer encoder layers for utterance-level LID. We call this architecture CNN-Trans. We evaluate it on AP17-OLR data and the MLS14 set of NIST LRE 2017, and show that the PHO-LID model with multi-task optimization exhibits the highest LID performance among all models, achieving over 40% relative improvement in terms of average cost on AP17-OLR data compared to a CNN-Trans model optimized only for LID. The visualized confusion matrices imply that our proposed method achieves higher performance on languages of the same cluster in NIST LRE 2017 data than the CNN-Trans model. A comparison between predicted phoneme boundaries and corresponding audio spectrograms illustrates the leveraging of phoneme information for LID.
Multi-Objective Virtual Machine Placement using Order Exchange and Migration Ant Colony System algorithm

Cloud computing is one among the most crucial commercial technologies nowadays. It offers a diverse range of services. One of the most exciting and important procedures in cloud computing is virtual machine installation (VMP). Virtual Machine Placement uses evolutionary computing to lower energy consumption while lowering the total number of physical servers that are currently in use. By examining the ant colony system’s (ACS) promising performance for combinatorial issues, Order Exchange and Ant Colony System OEMACS, an approach based on ACS finds solution by combining order exchange and migration local search strategies, was developed (Order exchange and Migration Ant Colony System). From a global optimization standpoint, The OEMACS algorithm is capable of significantly lowering the active servers in number and is used for virtual machine assignment. It also aids in the reduction of the number of active servers that are underutilized. In OEMACS, artificial ants are guided to the best feasible solution using the pheromone deposition method. It also arranges virtual machines in such a way that resource waste and power consumption are reduced. On servers with homogenous and heterogeneous VM sizes, this strategy is used. OEMACS surpasses some of the previously utilized algorithms, such as standard heuristics and other evolutionary-based techniques, according to the findings.
Depth Estimation from Monocular Infrared Images for Autonomous Flight of Drones

In this work, we propose a depth estimation method from an image of an infrared camera to avoid collision for night flights of a drone. The highest flight speed of a drone is generally approximate 22.2 m/s, and long-distant depth information is crucial for night flights since if long-distance information is not available, the drone flying at high speeds is prone to collisions. However, depth cameras with long measurable distance are too heavy to equip on a drone. This work applies Pix2Pix, which is a kind of Conditional Generative Adversarial Networks (CGANs), and it generates depth images from an infrared camera. The models are trained with taking advantage of AirSim, which is one of the flight simulators. Airsim can take both infrared and depth images over a hundred meters in the virtual environment, and our model generates a depth image that provides longer distance information than the images taken by a common depth camera. We evaluate the effectiveness of our proposed method in terms of PSNR and SSIM using test images in AirSim. In addition, the proposed method is utilized for flight simulation to evaluate the effectiveness to collision avoidance.
Dual-biprism-based coaxial fringe projection system

Fringe projection profilometry (FPP) has been widely used for three-dimensional shape measurement because of its simple hardware and high measurement accuracy. However, the use of most FPP systems to measure complex surfaces such as stepped height objects can result in severe occlusion and shadow. To alleviate this problem, a coaxial measurement method based on dual biprisms is presented in this paper. The system is low-cost and easy to implement, and does not require the introduction of mechanical moving parts. Only dual biprisms are introduced into the coaxial system to provide the geometric constraints required for reconstruction. Experimental results on the reconstruction of a surface with significant height changes demonstrate that the proposed method can achieve shadow-free measurements.
Stroboscopic video microscopy with sub-nanometer accuracy for characterizing and monitoring MEMS

Current state-of-the-art systems for measuring movements at a microscopic scale in MEMS mostly rely on laser Doppler vibrometry (LDV). However, a major downside of LDV is that only one point at a time can be tracked and only in the direction of the incident laser beam. On the other hand, stroboscopic video microscopy (SVM) allows monitoring the inplane displacements of all points in the field of view simultaneously. Commercially available vibrometry systems often provide an SVM mode. However, their resolution typically ranges from several to tens of nanometers. In contrast, some experimental SVM systems described in literature have achieved resolutions down to tens of picometers. Here we compare the performance of our self-built SVM setup to a modern commercial LDV device in characterizing piezoelectric actuators made from sintered lead zirconate titanate (PZT). The samples were stimulated with sinusoidal signals to induce surface strain in all three directions of space. Maps of the induced strain fields were recorded in-plane with SVM and out-of-plane with LDV. Our measurements prove that SVM, as realized in our setup, can be a cost-effective alternative to LDV for monitoring and characterizing of MEMS with sub-nanometer accuracy. Especially at low frequencies and when applied to challenging samples, SVM can outperform LDV in terms of accuracy and time efficiency.
Recording Performances of Some File Types for Pandas Data

Scientists, researchers, engineers, etc. almost everyone who works with data crosses paths with Pandas at some point. It is so powerful library that allows for easy, rapid and efficient manipulation of data. It can convert data it represent into various file types. Among these file types, the determination of the one which records the same Pandas data with the smallest size on the disk is an important issue considering the abundance of today's data. In this study, the file types that can save Pandas data with minimum size has been experimentally investigated from various perspectives. In this respect, the CSV, HDF, JSON, Excel and Pickle file types are involved in the experiments. The sizes of these files were benchmarked under several conditions such as the completeness or lack of data and type of variables that are contained in data. In addition, it was also examined that how file sizes vary as data increases.
Survey on Abstractive Transcript Summarization of YouTube Videos

Thousands of video recordings are created and shared on the internet every day. It is becoming increasingly difficult to spend time to watch such videos, which may take longer than anticipated, and our efforts may go in vain if we are unable to extract meaningful information from them. Summarizing transcripts of such videos helps us to quickly search for relevant patterns in the video without having to go through the entire content. Abstractive transcript summarization model is very useful in extracting YouTube video transcripts and generates a summarized version. An automatic summarizer's purpose is to shorten the time of reading, enable easier selection, be less prejudiced compared to humans, and portray content that is compressed while preserving the important material of the actual document. Extractive and abstractive approaches are the two most common ways to summarise text. Extractive approaches choose phrases or sentences from input text, whereas Abstractive methods generate new words from input text, making the task much more difficult.
An Effective Construction of Multiple Viewpoint based Panorama Design using Digital Image Processing Scheme

Panoramic stitching from a group of photos under wide baselines is the primary goal of this system, which uses SIFTS and SURF characteristics to achieve this goal. In this method, pictures and non-planar scene structures can have broad baselines. A mesh-based framework for 2D alignment and regularity is used instead of a 3D reconstruction. Composition should be improved, and the misaligned area should perform better. As a result, we have developed a new method called Novel image stitching approach for stitching together several photos collected from diverse perspectives. Our solution is unique in that it allows for large baselines between photos and non-planar scene structures. A mesh-based framework for 2D alignment and regularity is used instead of a 3D reconstruction. We may create panoramic pictures that are as perspective-preserving locally as feasible while yet being virtually orthogonal from a global perspective by solving an alignment-and-prior-constraint-based global objective function. On a misaligned area, we enhance composition and perform well. The proposed strategy has been proven to be successful in the face of difficult data sets.
Text Revision By On-the-Fly Representation Optimization

Text revision refers to a family of natural language generation tasks, where the source and target sequences share moderate resemblance in surface form but differentiate in attributes, such as text formality and simplicity. Current state-of-the-art methods formulate these tasks as sequence-to-sequence learning problems, which rely on large-scale parallel training corpus. In this paper, we present an iterative in-place editing approach for text revision, which requires no parallel data. In this approach, we simply fine-tune a pre-trained Transformer with masked language modeling and attribute classification. During inference, the editing at each iteration is realized by two-step span replacement. At the first step, the distributed representation of the text optimizes on the fly towards an attribute function. At the second step, a text span is masked and another new one is proposed conditioned on the optimized representation. The empirical experiments on two typical and important text revision tasks, text formalization and text simplification, show the effectiveness of our approach. It achieves competitive and even better performance than state-of-the-art supervised methods on text simplification, and gains better performance than strong unsupervised methods on text formalization. Our code and model are released at https://github.com/jingjingli01/OREO.
Design and Implementation of Encryption Tool using Advanced Encryption Standard (AES)

The growth of the internet has led to the development of today’s data-centric world where information can be exchanged from one part of the world to another with the click of a button. The most commonly used communication device is the mobile phone, with the ability of a portable computer. A large number of chatting applications have been developed that help people to communicate. The information typed inside these chatting applications must pass through the public infrastructure to reach the destination which is its most vulnerable phase of data transfer. A malicious hacker can gain access to messages or alter the messages thus compromising the data security. To maintain data integrity and confidentiality data encryption is used. Data encryption is used which convert our messages into encrypted form so that only people with the secret key can read them. Many systems now claim to guarantee safe and secret messages, bolstered by recent allegations of pervasive state surveillance of personal conversations. This covers a huge number of new projects as well as numerous well-known security-enhancing tools.We investigate and arrange current secure message solutions, as well as present a test framework for their security, usability, and ease of use. We sketch out each design area for three important challenges: trust establishment, conversation security, and transit privacy. Trusted methods of providing secure security and privacy features do not work well in use and detection, while other mixed methods that have not been properly processed in academic textbooks may provide better trade performance.