Reviewerly/exHarmony huggingface dataset:
Bimodal-Distributed Binarized Neural Networks

Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ complexity and power consumption significantly. Binarization techniques, however, suffer from ineligible performance degradation compared to their full-precision counterparts. Prior work mainly focused on strategies for sign function approximation during the forward and backward phases to reduce the quantization error during the binarization process. In this work, we propose a bimodal-distributed binarization method (BD-BNN). The newly proposed technique aims to impose a bimodal distribution of the network weights by kurtosis regularization. The proposed method consists of a teacher–trainer training scheme termed weight distribution mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart. Preserving this distribution during binarization-aware training creates robust and informative binary feature maps and thus it can significantly reduce the generalization error of the BNN. Extensive evaluations on CIFAR-10 and ImageNet demonstrate that our newly proposed BD-BNN outperforms current state-of-the-art schemes.
Grammatical Error Analysis in Recount Texts Made By Seventh Grade

Abstract - The objective of this study is to know the most dominant grammatical error made by the seventh-grade students of SMP Trisula Perwari 2 in writing recount text and the reason for the errors. The writer used Betty Azar’s types of error theory and Richards’ sources of error theory. The method used in this study was a descriptive analysis method to describe students’ errors and analyze the data. The research findings show that add a word error holds the first position in the most dominant errors made by the seventh-grade students of SMP Trisula Perwari 2 with 154 errors or 20% followed by other types of error with the total error of verb tense is 114 errors or 15% and spelling is 93 errors or 12%. In addition, the writer found that the students’ errors caused by interlingual transfer and intralingual transfer. The most frequent source of errors affecting the students in making recount text is intralingual transfer with a total of 437 errors (57%), which is followed by interlingual transfer in the second position with a total of 328 errors (43%). Key Words: Error Analysis, Writing, Recount Text.Abstrak - Tujuan dari penelitian ini adalah untuk mengetahui jenis kesalahan yang paling dominan dibuat oleh siswa kelas tujuh SMP Trisula Perwari 2 dalam menulis teks recount dan sebab dari kesalahan-kesalahan tersebut. Penulis menggunakan teori jenis kesalahan milik Betty Azar dan teori sumber kesalahan milik Richards. Metode yang digunakan dalam penelitian ini adalah metode analisis deskriptif untuk menjelaskan kesalahan siswa dan menganalisis data. Hasil penelitian menunjukkan bahwa jenis kesalahan add a word berada di posisi pertama dalam jenis kesalahan paling dominan yang dilakukan oleh siswa kelas tujuh SMP Trisula Perwari 2 dengan 154 kesalahan atau 20% diikuti oleh jenis kesalahan lainnya dengan total kesalahan verb tense adalah 114 kesalahan atau 15% dan spelling adalah 93 kesalahan atau 12%. Selain itu, penulis menemukan bahwa kesalahan siswa disebabkan oleh interlingual transfer dan intralingual transfer. Sumber kesalahan yang paling sering mempengaruhi siswa dalam membuat teks recount adalah intralingual transfer dengan total 437 kesalahan (57%) yang diikuti oleh interlingual transfer di posisi kedua dengan total 328 kesalahan (43%).Kata Kunci : Analisis Kesalahan, Menulis, Teks Recount
Machine Learning Algorithms for Breast Cancer Diagnosis: Challenges, Prospects and Future Research Directions

Early diagnosis of breast cancer does not only increase the chances of survival but also control the diffusion of cancerous cells in the body. Previously, researchers have developed machine learning algorithms in breast cancer diagnosis such as Support Vector Machine, K-Nearest Neighbor, Convolutional Neural Network, K-means, Fuzzy C-means, Neural Network, Principle Component Analysis (PCA) and Naive Bayes. Unfortunately these algorithms fall short in one way or another due to high levels of computational complexities. For instance, support vector machine employs feature elimination scheme for eradicating data ambiguity and detecting tumors at initial stage. However this scheme is expensive in terms of execution time. On its part, k-means algorithm employs Euclidean distance to determine the distance between cluster centers and data points. However this scheme does not guarantee high accuracy when executed in different iterations. Although the K-nearest Neighbor algorithm employs feature reduction, principle component analysis and 10 fold cross validation methods for enhancing classification accuracy, it is not efficient in terms of processing time. On the other hand, fuzzy c-means algorithm employs fuzziness value and termination criteria to determine the execution time on datasets. However, it proves to be extensive in terms of computational time due to several iterations and fuzzy measure calculations involved. Similarly, convolutional neural network employed back propagation and classification method but the scheme proves to be slow due to frequent retraining. In addition, the neural network achieves low accuracy in its predictions. Since all these algorithms seem to be expensive and time consuming, it necessary to integrate quantum computing principles with conventional machine learning algorithms. This is because quantum computing has the potential to accelerate computations by simultaneously carrying out calculation on many inputs. In this paper, a review of the current machine learning algorithms for breast cancer prediction is provided. Based on the observed shortcomings, a quantum machine learning based classifier is recommended. The proposed working mechanisms of this classifier are elaborated towards the end of this paper.
The Dual Beveridge Curve

When firms decide to post a vacancy they can hire from the pool of unemployed workers or they can poach a worker from another firm.In this paper we show that if there are two different matching processes, one for unemployed workers and another one for job-to-job transitions, then implications for the Beveridge curve are potentially very different, influencing the effects of monetary policy on unemployment.We show that over the years the hiring process and how job postings are used as an input into this process have changed dramatically.
$T$-depth-optimized Quantum Search with Quantum Data-access Machine

Quantum search algorithms offer a remarkable advantage of quadratic reduction in query complexity using quantum superposition principle. However, how an actual architecture may access and handle the database in a quantum superposed state has been largely unexplored so far; the quantum state of data was simply assumed to be prepared and accessed by a black-box operation -- so-called oracle, even though this process, if not appropriately designed, may adversely diminish the quantum query advantage. Here, we introduce an efficient quantum data-access process, dubbed as quantum data-access machine (QDAM), and present a general architecture for quantum search algorithm. We analyze the runtime of our algorithm in view of the fault-tolerant quantum computation (FTQC) consisting of logical qubits within an effective quantum error correction code. Specifically, we introduce a measure involving two computational complexities, i.e. quantum query and $T$-depth complexities, which can be critical to assess performance since the logical non-Clifford gates, such as the $T$ (i.e., $\pi/8$ rotation) gate, are known to be costliest to implement in FTQC. Our analysis shows that for $N$ searching data, a QDAM model exhibiting a logarithmic, i.e., $O(\log{N})$, growth of the $T$-depth complexity can be constructed. Further analysis reveals that our QDAM-embedded quantum search requires $O(\sqrt{N} \times \log{N})$ runtime cost. Our study thus demonstrates that the quantum data search algorithm can truly speed up over classical approaches with the logarithmic $T$-depth QDAM as a key component.
ParticleNeRF: A Particle-Based Encoding for Online Neural Radiance Fields

While existing Neural Radiance Fields (NeRFs) for dynamic scenes are offline methods with an emphasis on visual fidelity, our paper addresses the online use case that prioritises real-time adaptability. We present ParticleNeRF, a new approach that dynamically adapts to changes in the scene geometry by learning an up-to-date representation online, every 200ms. ParticleNeRF achieves this using a novel particle-based parametric encoding. We couple features to particles in space and backpropagate the photometric reconstruction loss into the particles' position gradients, which are then interpreted as velocity vectors. Governed by a lightweight physics system to handle collisions, this lets the features move freely with the changing scene geometry. We demonstrate ParticleNeRF on various dynamic scenes containing translating, rotating, articulated, and deformable objects. ParticleNeRF is the first online dynamic NeRF and achieves fast adaptability with better visual fidelity than brute-force online InstantNGP and other baseline approaches on dynamic scenes with online constraints. Videos of our system can be found at our project website https://sites.google.com/view/particlenerf.
Accelerating Time Series Analysis via Processing using Non-Volatile Memories

Time Series Analysis (TSA) is a critical workload to extract valuable information from collections of sequential data, e.g., detecting anomalies in electrocardiograms. Subsequence Dynamic Time Warping (sDTW) is the state-of-the-art algorithm for high-accuracy TSA. We find that the performance and energy efficiency of sDTW on conventional CPU and GPU platforms are heavily burdened by the latency and energy overheads of data movement between the compute and the memory units. sDTW exhibits low arithmetic intensity and low data reuse on conventional platforms, stemming from poor amortization of the data movement overheads. To improve the performance and energy efficiency of the sDTW algorithm, we propose MATSA, the first Magnetoresistive RAM (MRAM)-based Accelerator for TSA. MATSA leverages Processing-Using-Memory (PUM) based on MRAM crossbars to minimize data movement overheads and exploit parallelism in sDTW. MATSA improves performance by 7.35x/6.15x/6.31x and energy efficiency by 11.29x/4.21x/2.65x over server-class CPU, GPU, and Processing-Near-Memory platforms, respectively.
A new Knowledge Inference Approach Based on Multi-headed Attention Mechanism

To address the problem of sparse relationships between entities in the current knowledge graph and the increase in complexity and computational effort in knowledge inference, this paper proposes a knowledge inference method based on a multi-headed attention mechanism - the MAtt-BiGRU model. For a given triad, the path information is encoded by a two-way gated recurrent neural network, and the confidence of the model is calculated by aggregating it with the candidate paths with the help of the multi-headed attention mechanism, while the inference ability of the model is enhanced by dynamic negative sample training. Numerical experiments are conducted on the MAtt-BiGRU model using large knowledge graph datasets NELL-995 and FB15k-237. The experimental results show that the proposed MAtt-BiGRU model has better results in Mean Average Precision (MAP) and Mean Reciprocal Rank(MRR) performance evaluation metrics when compared with existing commonly used path ranking algorithms such as Path Ranking Algorithm(PRA) and Path-RNN, and the proposed model can more accurately reason about the relations between entities.
Automatic Identification Technology of Optical Fiber based on Genetic Neural Network Algorithm

With the development of image recognition technology, many manufacturers design and manufacture different fiber cleaning machines. Using the advantages of neural network(NN) in pattern recognition, this paper proposes a genetic neural network algorithm(NNA) combining NN and genetic algorithm to study the automatic optical fiber recognition technology. Optical fiber image recognition and image preprocessing are discussed, and the hardware implementation of optical fiber automatic recognition system is studied; The Gabor extracted image texture features are analyzed, and the simulation test of optical fiber image recognition is carried out according to the Gabor extracted feature training. The test results verify the accuracy of the optical fiber automatic recognition technology proposed in this paper.
Recurrent neural network technology in wireless communication network security risk prediction

In recent years, many wireless network security risks are caused by information explosion and the interconnection of all things. Data mining technology has been applied in the field of wireless communication to solve the problems of wireless network security risks. This paper introduces the construction of wireless communication network security risk prediction model and the implementation of prediction scheme based on recurrent neural network(RNN), which provides an effective method for mobile wireless network security data prediction.
Digital Media Literacy during Covid-19 Pandemic Era among Millenials

In information and digital era, people were convenient in gain information from various media, especially online media. But this also bring threat if they don’t have the digital media literacy. This study attempt to explore digital media literacy among millennial during Covid-19 pandemic. Covid-19 pandemic indirectly force people to increase their level of digital media literacay by using technology as an impact of Work from Home policy. This study using data collected using existing statistic combined with online survey. Existing statistic used to view the trends related to the study, and online survey were used to gain more personal information about digital media use. Sampling in online survey drawn using quota sampling, to compare any difference from each millennial categories. Using multiple data collection method and several datasets were researcher’s attempt to explore this study from multiple perspective, or called triangulation. Results show that Indonesia netizens were fairly digitally literate. They become more familiar with the internet and digital services during the Covid-19 pandemic. Internet penetration also distributed evenly in every region in Indonesia, as an effect of digital transformation and infrastructure development. But to be truly digitally literate and have media literacy, Indonesian netizen need to have both hard skills (ability to access internet and understanding technology) and soft skill (creativity, persuasion, emotional intelligence, collaboration, and adaptability).
A Novel Filter-Level Deep Convolutional Neural Network Pruning Method Based on Deep Reinforcement Learning

Deep neural networks (DNNs) have achieved great success in the field of computer vision. The high requirements for memory and storage by DNNs make it difficult to apply them to mobile or embedded devices. Therefore, compression and structure optimization of deep neural networks have become a hot research topic. To eliminate redundant structures in deep convolutional neural networks (DCNNs), we propose an efficient filter pruning framework via deep reinforcement learning (DRL). The proposed framework is based on a deep deterministic policy gradient (DDPG) algorithm for filter pruning rate optimization. The main features of the proposed framework are as follows: (1) AA tailored reward function considering both accuracy and complexity of DCNN is proposed for the training of DDPG and (2) a novel filter sorting criterion based on Taylor expansion is developed for filter pruning selection. To illustrate the effectiveness of the proposed framework, extensive comparative studies on large public datasets and well-recognized DCNNs are conducted. The experimental results demonstrate that the Taylor-expansion-based filter sorting criterion is much better than the widely used minimum-weight-based criterion. More importantly, the proposed filter pruning framework can achieve over 10× parameter compression and 3× floating point operations (FLOPs) reduction while maintaining similar accuracy to the original network. The performance of the proposed framework is promising compared with state-of-the-art DRL-based filter pruning methods.
Rosenblatt’s First Theorem and Frugality of Deep Learning

The Rosenblatt's first theorem about the omnipotence of shallow networks states that elementary perceptrons can solve any classification problem if there are no discrepancies in the training set. Minsky and Papert considered elementary perceptrons with restrictions on the neural inputs: a bounded number of connections or a relatively small diameter of the receptive field for each neuron at the hidden layer. They proved that under these constraints, an elementary perceptron cannot solve some problems, such as the connectivity of input images or the parity of pixels in them. In this note, we demonstrated Rosenblatt's first theorem at work, showed how an elementary perceptron can solve a version of the travel maze problem, and analysed the complexity of that solution. We also constructed a deep network algorithm for the same problem. It is much more efficient. The shallow network uses an exponentially large number of neurons on the hidden layer (Rosenblatt's A-elements), whereas for the deep network, the second-order polynomial complexity is sufficient. We demonstrated that for the same complex problem, the deep network can be much smaller and reveal a heuristic behind this effect.
New Identified Strategies to Forge Multivariate Signature Schemes

A rogue certificate authority (RCA) is a dishonest entity that has the trust of web browsers and users to produce valid key pairs which are vulnerable. This work analyses two acknowledged post-quantum secure Multivariate Quadratic Problem (MQP) based signature schemes, namely the UOV and Rainbow signature schemes that obtain their key pair from a potential RCA methodology. We revisit two and provide a novel RCA methodology that would enable adversaries to forge UOV and Rainbow signatures. We also lay out two strategies to identify whether the public parameters are generated by the first two methodologies. To this end, strategies to identify the third strategy remain elusive. As such, the UOV and Rainbow schemes remain vulnerable to forgery if it was forged via the third methodology.
Learnable Fusion Mechanisms for Object Detection in Autonomous Vehicles

&lt;p&gt; In this work, we propose a novel deep learning based sensor fusion framework, that uses both camera and LiDAR sensors in a multi-modal and multi-view setting. In order to leverage both data streams, we incorporate two new sophisticated fusion mechanisms: element-wise multiplication and multi-modal factorized bilinear pooling. When compared to previously used fusion operators such as element-wise addition and concatenation of feature maps, our proposed fusion methods significantly increase the bird’s eye view moderate average precision score by +4.97% and +8.35% for both methods, respectively, when evaluated on KITTI dataset for object detection. Furthermore, we provide a detailed study of important design choices that contribute to the performance of deep learning based sensor fusion frameworks such as data augmentation, multi-task learning, and the design of the convolutional architecture. Finally, we provide qualitative results that showcase both success and failure cases for our proposed framework. We also discuss directions for mitigating failure cases. &lt;/p&gt;
Performance Review of Feature-Based Method in Implementation Text Steganography Approach

Steganography is part of information hiding as the knowledge in science system that covers confidential messages via text, image, audio and video. Many researchers’ effort implemented steganography in the text domain using the feature-based method concerning uniqueness letters to embed that conceal the hidden message. This paper intends to review the achievement performance that is used in feature-based method. This paper aim to concern specifically on performance of robustness, security and capacity in implementation feature-based method of text steganography. Therefore, this paper reviews the implementation some performance that achieve by previous researcher in developing feature-based method of text steganography.
A Dynamic Time-Bound Access Control for Secure Hierarchical Content Sharing

&lt;p&gt;With rapid growth of mobile users, protecting content from unauthorized users become a complex problem. The concept of temporal role-based access control reduces complexity of user management and restricts access to specified time-slots. But, content privacy is still questionable in case of system resources compromise unexpectedly. Therefore, cryptographic solution for time-bound hierarchical content management is an emerging problem. Most of the related schemes focused on individual user keys and/or revocation, but not on time-bound keys. Hence, these are not well suitable for subscription-based services like pay-TV and newspaper. In this paper, we propose a cryptographic time-bound access control with constant size time-bound keys. In our scheme, subscribed time-slots embed into individual user keys to avoid periodical broadcasting of temporal keys. We prove that our scheme is selectively secure against collision attack under chosen-ciphertext attack. We then discuss cloud-based application to show the strategies of efficient revocation and reduced computational overheads for end-users. &lt;/p&gt;
Secure Exam Storage Using RSA public key encryption

Abstract Performance, usability, and security are the focus of software systems development. Confidentiality, availability and integrity are the main goals of these systems security. One common software system that needs to be secured is exams, which is an assessment method to measure student performance in education. Sharing exams between faculty members is common task for moderation and review, but it exposed to Leakage. Exams Leakage will affect confidentiality and integrity of the exams. Loss of confidentiality will affect the reputation of the institute, where loss of integrity may have serious impact on the students’ performance. It is a troublesome for faculties to guarantee exam electronic documents confidentiality. Therefore, it is required to have secure exam storage systems. Some solutions are central server-based systems, cloud-based systems, and block chain-based systems. The cloud-based system is secure and trusted, but data may corrupt without the knowledge of the cloud service provider. Bock chain is a better solution but requires more storage. In this paper, we proposed a key management method to secure exams storage using public key Cryptosystem; it uses an RSA algorithm to provide distinctive feature of data integrity, confidentiality and authentication. It can be used in centralised server-based system or cloud-based system. The proposed framework provides security without increase in encoding and decoding time and without increase in file size.