Reviewerly/exHarmony huggingface dataset:
Exploration and Generation of Efficient FPGA-based Deep Neural Network Accelerators

Convolutional Neural Networks (CNNs) have emerged as an answer to next-generation applications such as complex image recognition and object detection. Embedding such compute-intensive and memory-hungry algorithms on edge systems will lead to smarter high-value applications. However, the algorithmic innovations in the CNN field leave the hardware accelerators one step behind. Reconfigurable hardware (e.g. FPGAs) allows designing custom accelerators adapted to new algorithms. Furthermore, new design approaches such as high-level synthesis (HLS) enable to generate RTL code based on high-level function descriptions. This paper presents a high-level CNN accelerator generation framework for FPGAs. A first phase of the framework characterizes CNN descriptions using hardware-aware metrics. These metrics then drive a hardware generation phase which builds the proper C source code implementation for each layer of the network. Finally, an HLS tool outputs the synthesizable RTL code of the accelerator. This approach aims at reducing the gap between the evolving applications based on artificial intelligence and hardware accelerators, thus reducing time-to-market of new systems.
Embedding Semantic Anchors to Guide Topic Models on Short Text Corpora

Documents on the social media platform Twitter are formulated in short and simple style, instead of being written extensively and elaborately. Further, the core message of a post is often encoded into characteristic phrases called hashtags. These hashtags illustrate the semantics of a post or tie it to a specific topic. In this paper, we propose multiple approaches of using hashtags and their surrounding texts to improve topic modeling of short texts. We use transfer learning by applying a pre-trained word embedding of hashtags to derive preliminary topics. These function as supervising information, or seed topics and are passed to Archetypal LDA (A-LDA), a recent variant of Latent Dirichlet Allocation. We demonstrate the effectiveness of our approach using a large corpus of posts exemplarily on Twitter. Our approaches improve the topic model's qualities in terms of various quantitative metrics. Moreover, the presented algorithms used to extract seed topics can be utilized as form of lightweight topic model by themselves. Hence, our approaches create additional analytical opportunities and can help to gain a more detailed understanding of what people are talking about on social media. By using big data in terms of millions of tweets for preprocessing and fine-tuning, we enable the classification algorithm to produce topics that are very coherent to the reader.
Matching The Statements: A Simple and Accurate Model for Key Point Analysis

Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic.Furthermore, KPA allows quantifying the coverage of each summary by counting its matched arguments.With the aim of creating high-quality summaries, it is necessary to have an in-depth understanding of each individual argument as well as its universal semantic in a specified context.In this paper, we introduce a promising model, named Matching the Statements (MTS) that incorporates the discussed topic information into arguments/key points comprehension to fully understand their meanings, thus accurately performing ranking and retrieving best-match key points for an input argument.Our approach 1 has achieved the 4 th place in Track 1 of the Quantitative Summarization -Key Point Analysis Shared Task by IBM, yielding a competitive performance of 0.8956 (3 rd ) and 0.9632 (7 th ) strict and relaxed mean Average Precision, respectively.
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?

Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physically realizable, yet most work has focused on 2D domain with recent forays into 3D. Characterizing the robustness properties of patch attacks and their invariance to 3D pose is important, yet not fully elucidated, and is the focus of this paper. To this end, several contributions are made here: A) we develop a new metric called mean Attack Success over Transformations (mAST) to evaluate patch attack robustness and invariance; and B), we systematically assess robustness of patch attacks to 3D position and orientation for various conditions; in particular, we conduct a sensitivity analysis which provides important qualitative insights into attack effectiveness as a function of the 3D pose of a patch relative to the camera (rotation, translation) and sets forth some properties for patch attack 3D invariance; and C), we draw novel qualitative conclusions including: 1) we demonstrate that for some 3D transformations, namely rotation and loom, increasing the training distribution support yields an increase in patch success over the full range at test time. 2) We provide new insights into the existence of a fundamental cutoff limit in patch attack effectiveness that depends on the extent of out-of-plane rotation angles. These findings should collectively guide future design of 3D patch attacks and defenses.
An Efficient Parallel Architecture for Resource-Shareable Reed-Solomon Encoder

Reed-Solomon (RS) codes are adopted in many digital communication and storage systems to ensure data reliability. For many of these systems, the encoder and decoder are not active at the same time. In previous designs, RS encoders implemented as linear feedback shift registers in a concatenated structure are reused to compute the syndromes so that the decoder complexity is reduced. However, the parallel versions of such encoders have very long critical path and hence can not achieve high speed. This paper proposes a new parallel resource-shareable RS encoder architecture based on the Chinese Remainder Theorem (CRT). The generator polynomial of RS codes is decomposed into factors of degree one and state transformation is developed to enable the sharing of the hardware units for syndrome computation. As a result, the critical path is reduced to only one multiplier and one adder, regardless of the parallelism. Additionally, by utilizing the property that the degrees of the decomposed polynomial factors are one, optimizations are also developed to greatly simplify the CRT-based encoder. For example encoders of a (255, 229) RS code over GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> ), our proposed design can achieve at least 29% higher efficiency in terms of area-time product for moderate or higher parallelisms compared to the previous resource-shareable RS encoder and traditional parallel RS encoders combined with syndrome computation units that implement the same functionality.
Incentive-driven Edge Cooperation for Service Provision

Edge co-operations around us are playing an increasingly crucial role in both personal and business activities, because they can bring us higher-quality services. However, more networks take part in an edge co-operation, more privacy protections the co-operation need to be supported to stimulate edge networks' participation. Edge co-operation has great potential in providing a high-performance, low-latency, and high-bandwidth service environment but requires strong incentives to stimulate more networks to participate in. To tackle this challenge, we design a novel privacy-preserving incentive mechanism (PIM) for edge co-operation at a fully distributed edge. The proposed mechanism allows the Internet Service Provider (ISP) to select suitable edge networks for service provision under a budget constraint while guaranteeing differential privacy, approximate truthfulness, computational efficiency, individual rationality. Through several simulations, we evaluate the performance and validate the properties of our mechanism.
Detection of agglomerate fog based on a shallow convolutional neural network

Abstract As a kind of frequent bad weather, Agglomerate fog is a serious danger to people's safe driving, especially on the highway. Therefore, the research on the detection of fog is of great practical significance to ensure the safety of pedestrians. This paper proposes a shallow convolutional neural network for agglomerate fog detection in images, including the framework of the network and the detailed design of each component. Firstly, the image is divided into several sub-images; and then a shallow convolutional neural network is constructed and employed to identify the existence of fog for each of the sub-area images; lastly, the decision results of each sub-area images were integrated to determine whether the whole image contained agglomerate fog. A large quantity of simulation data and real data were used to test the performance of the proposed method, the experimental results show that the presented method can achieve more than 90% detection accuracy, which demonstrated that the advantage of the proposed method comparing with several existed methods.
Construction and Application of Knowledge Graph of Weapon and Equipment System of Systems

In the context of diversified types of weapon and equipment and massive data quantification, how to model and store, manage and apply the data of various types of weapon and equipment is a problem that needs to be solved. The article analyzes the characteristics of the weapon and equipment system and proposes a method to construct the knowledge graph of the weapon and equipment system based on this method, which first constructs the ontology in the top-down order, then defines the edge nodes and fills in the structured data. Finally, the application is carried out according to the constructed knowledge graph, and the future development direction is proposed.
Lane Detection and Traffic Sign Recognition from Continuous Driving Scenes using Deep Neural Networks

Increasing analysis on automatic driving technology predicts that by upcoming years it will outnumber the manual vehicles. Ensuring safety and reducing road accidents is the primary concern in automated vehicles. Among many challenging and complex tasks to build an automated vehicle, Lane detection and Traffic sign recognition are very crucial. Though these are challenging tasks due to various road conditions, environmental or weather conditions that drivers can encounter while driving, researches have shown that advancement in deep learning and computer vision has gone beyond imagination. This paper proposes and analyses deep learning techniques compared with the existing different approaches to implement an efficient Lane detection and Traffic sign recognition model that acts as a driver support system to operate in real time. It is a significant module for unmanned vehicles and Advanced Driver Assistance Systems (ADAS). This system is aimed to operate in a real time environment for enhanced safety by faster acquisition of traffic signs and detecting the lanes to assist the driver in controlling and performing scrutinized operations. For this purpose, Convolutional Neural Network (CNN) based on SegNet architecture for lane detection and YOLOv4 (You Only Look Once), a clever CNN algorithm meant for object detection in real time can be used to detect and segment the sign boards along with recognising them.
Noisy medieval data, from digitized manuscript to stylometric analysis: Evaluating Paul Meyer’s hagiographic hypothesis

Abstract Stylometric analysis of medieval vernacular texts is still a significant challenge: the importance of scribal variation, be it spelling or more substantial, as well as the variants and errors introduced in the tradition, complicate the task of the would-be stylometrist, by inducing noise and perhaps even interferences in the authorship signal. Basing the analysis on the study of the copy from a single hand of several texts can partially mitigate these issues (Camps and Cafiero, 2013, Setting bounds in a homogeneous corpus: a methodological study applied to medieval literature. Revue Des Nouvelles Technologies de l’information (RNTI), SHS-1, pp. 55–84), but the limited availability of complete diplomatic transcriptions might make this difficult. In this article, we use a workflow combining handwritten text recognition and stylometric analysis, applied to the case of the hagiographic works contained in MS BnF, fr. 412. We seek to evaluate Paul Meyer's hypothesis about the constitution of groups of hagiographic works, as well as to examine potential authorial groupings in a vastly anonymous corpus.
Towards Representing Time-Cost Tradeoffs for Service Compositions

Service Level Agreements have to contain the time and cost of using a service. We propose time-cost tables for representing the relationship between time and cost. Basically there can be a direct or indirect correlation between duration and cost of a service. We introduce algorithms for computing the time-cost relationship of services composed as an orchestration of other services. These services may have directly and indirectly proportional cost with respect to time resulting in more complex relationships between duration and cost. These representations allow to infer the minimum cost of a service given temporal constraints, or for checking which durations are affordable for an available budget. The time-cost tables thus offer a basis for various optimization of composed services.
3D-Adv: Black-Box Adversarial Attacks against Deep Learning Models through 3D Sensors

The combination of deep learning techniques and commercial 3D sensors reveal a bright future as they provide a low cost and convenient method to collect and analyze depth information from the environment for various applications ranging from industrial modeling to mobile face recognition. Despite the abundant research devoted to the development of more accurate, flexible and efficient machine learning schemes as well as 3D sensors, security concerns related to these techniques remain largely untouched. In this paper, we propose a novel adversarial attack against this combination by showing that deep learning models with popular 3D sensors may misclassify real objects in the physical environment. Comparing to the existing attack algorithms against deep learning models developed for 3D data analysis that only consider digital point cloud data and single deep learning model, our attacks target popular commercial 3D sensors combined with various deep learning schemes in the black-box setting. Experimental results demonstrate that our 3D printed adversarial objects stay effective after scanned by a 3D sensor.
Improved Elliptic Curve Digital Signature Algorithm in Wireless Sensor Networks

The Wireless Sensor Network (WSN) has an evolving technology and is able to be used in critical situation such as commercial applications like traffic surveillance, buildings, smart homes and habitat monitoring, etc. One of the biggest challenges today for wireless sensor networks is security. Wireless Sensor Networks (WSN) security models required the following services like integrity and authentication and confidentiality. A well-designed cryptographic algorithm is the secret to developing a riskless WSN scheme in order to fulfil the requirement for sensor nodes with less resource and low power consumption. Public-key cryptography algorithm, namely elliptical curve cryptography (ECC) & elliptic curve digital signature algorithm (ECDSA), have attracted interest from the many researchers in various institutions because of the security and high performance of these algorithms. Algorithms based on the elliptical curve (EC) have proved computationally efficient and provide more security in comparison to traditional crypto-algorithms in the specified key size. ECDSA is first successful ECC- based algorithm. This paper includes descriptions of ECDSA using the SHA-l. We analyse and propose a improve ECDSA algorithm. The main objective of this improved algorithm is to be provide more security as compared to previous algorithm.
AI-Driven Intelligent Text Correction Techniques for Mobile Text Entry

Current text correction processes on mobile touch devices are laborious: users either extensively use backspace, or navigate the cursor to the error position, make a correction, and navigate back, usually by employing multiple taps or drags over small targets. In this chapter, we present two techniques, Type, Then Correct and JustCorrect, that utilize the power of artificial intelligence to improve the text correction experience on mobile devices. All of the techniques skip error-deletion and cursor-positioning procedures, and instead allow the user to type the correction first, and then apply that correction to a previously committed error. We evaluated these techniques in and the results show that correction with the new techniques was faster than de facto cursor and backspace-based correction.
Distribution-Aware Weight Compression for Federated Averaging Learning Over Wireless Edge Networks

Recently, federated learning (FL) over wireless edge networks has aroused much research interest due to its merits in mitigating the privacy risks. On the basis of the standard FL, a federated averaging (FedAvg) learning algorithm emerges to reduce the communication rounds between the edge nodes and the central server. Even though the number of communication rounds of FedAvg learning is significantly reduced, exchanging all model parameters is still of heavy communication cost. To reduce the communication cost, this paper proposes a model compression method for FedAvg learning that adapts to the model weights distribution, namely distribution-aware weight compression (DAWC). In the proposed DAWC, we propose a parameter-oriented quantization algorithm (POQA) according to the distribution properties of different parameters of the model weights to iterate out the optimal quantization intervals, with the target of minimizing the mean square quantization errors. When the quantization is finished, Huffman coding is used to minimize the average code length. It is analyzed that FedAvg using the proposed DAWC converges at a fast speed. Experiment results show that DAWC exhibits the optimal performance in comparison with existing benchmarks.
Augmented Reality Mobile Application For Malay Heritage Museum

Lack of staffs and language barriers are two factors that limit the Malay Heritage Museum UPM staffs to handle visitors especially during peak seasons. Therefore, an augmented reality mobile application is proposed to assists and entertain visitors while touring the museum. In this work, the augmented reality will be imposed at the Malay traditional cloth and textiles section. To develop the propose application, the methodology used in this work is agile model due to its capability to ensure that the overall process can be done within a time given and also its ability to increase the standard software instantly. Agile model consists of six phases such as requirements, design, development, testing, deployment and review. The experiment was conducted on the same day and three hours were allocated for the respondent because they were having classes. The time required for one respondent to experience the MTRACR is approximately 10 minutes per person. The survey is used as a platform to get feedbacks from users. Based on the survey, it shows that all users were satisfied with the proposed application and they suggested others to use it.
AC2M: An Automated Consent Management Model for Blockchain Financial Services Platform

There is a trend in Open APIs and data sharing to provide better and innovative financial services. Data regulations aim to improve consumers’ privacy and control over their data. However, it still confronts the challenges of properly managing and protecting users’ privacy in such an open data sharing paradigm. This paper proposes an automated consent management model based on blockchain technology for various coming financial services. We employ the Proof of Authority consensus mechanism to balance the business flexibility and efficiency with client privacy control. Our model enables automated, transparent, and accountable management for consumer consents in financial services. We implement a proof of concept prototype to demonstrate the feasibility and applicability of our model. Performance testing is also conducted to demonstrate the feasibility and efficiency from the performance viewpoint.
Stochastic stability of decentralized Kalman filter for nonlinear systems

Summary In this paper, stochastic stability of a decentralized Kalman filter (DKF) for nonlinear systems and a bound on the sum of square of predicted estimation error are examined of a single node. A Lyapunov analysis approach is used to show that the predicted estimation error of the nonlinear DKF is stochastically stable and exponentially bounded in mean square, if the system is observable, controllable and the initial estimation error is bounded. Moreover, it is validated by the numerical simulations. The numerical results show that the sum of square of the predicted estimation error is bounded above.
Parameter Estimation and Classification via Supervised Learning in the Wireless Physical Layer

Emerging wireless networks possess the potential to achieve levels of connectivity and Quality-of-Service (QoS) that are orders of magnitude higher than today’s networks. Realizing the potential of these networks will require flexible, low cost, and accurate Digital Signal Processing (DSP). Supervised Learning (SL) models employing unknown parameter estimation and classification techniques have experienced widespread use in physical (PHY) layer wireless communication systems since they can achieve low costs via inexpensive forward-pass computations, attain flexible operations due to trainable parameters, and yield accurate results based on the universal approximator attribute. In this tutorial paper, we present a methodical explanation of how SL can be applied to unknown parameter estimation and classification across several different PHY layer components of a wireless communications system. Additionally, via a survey and comparison of popular methods, this paper provides insights on how to perform weight training, weight initialization, loss function regularization, data pre-processing, input feature design, ensemble training, and hyper-parameter validation. In our review of state-of-the-art works, we found significant use of SL algorithms in the following PHY layer applications: Dynamic Spectrum Access (DSA), channel corrections, Automatic Gain Control (AGC), Multiple Input Multiple Output (MIMO) control, Analog to Digital (ADC) conversion, and Automatic Coding and Modulation (ACM). The overarching goal of this survey and tutorial paper is to assist the reader in understanding the motivation and methodologies associated with various SL algorithms applied to PHY layer DSP operations, as well as to provide the reader with the necessary tools and techniques needed for addressing open challenges to be experienced by future wireless networks.