Reviewerly/exHarmony huggingface dataset:
Heat Rate Prediction of Combined Cycle Power Plant Using an Artificial Neural Network (ANN) Method

Heat rate of a combined cycle power plant (CCPP) is a parameter that is typically used to assess how efficient a power plant is. In this paper, the CCPP heat rate was predicted using an artificial neural network (ANN) method to support maintenance people in monitoring the efficiency of the CCPP. The ANN method used fuel gas heat input (P1), CO2 percentage (P2), and power output (P3) as input parameters. Approximately 4322 actual operation data are generated from the digital control system (DCS) in a year. These data were used for ANN training and prediction. Seven parameter variations were developed to find the best parameter variation to predict heat rate. The model with one input parameter predicted heat rate with regression R2 values of 0.925, 0.005, and 0.995 for P1, P2, and P3. Combining two parameters as inputs increased accuracy with regression R2 values of 0.970, 0.994, and 0.984 for P1 + P2, P1 + P3, and P2 + P3, respectively. The ANN model that utilized three parameters as input data had the best prediction heat rate data with a regression R2 value of 0.995.
HammerDrive: A Task-Aware Driving Visual Attention Model

We introduce HammerDrive, a novel architecture for task-aware visual attention prediction in driving. The proposed architecture is learnable from data and can reliably infer the current focus of attention of the driver in real-time, while only requiring limited and easy-to-access telemetry data from the vehicle. We build the proposed architecture on two core concepts: 1) driving can be modeled as a collection of sub-tasks (maneuvers), and 2) each sub-task affects the way a driver allocates visual attention resources, i.e., their eye gaze fixation. HammerDrive comprises two networks: a hierarchical monitoring network of forward-inverse model pairs for sub-task recognition and an ensemble network of task-dependent convolutional neural network modules for visual attention modeling. We assess the ability of HammerDrive to infer driver visual attention on data we collected from 20 experienced drivers in a virtual reality-based driving simulator experiment. We evaluate the accuracy of our monitoring network for sub-task recognition and show that it is an effective and light-weight network for reliable real-time tracking of driving maneuvers with above 90% accuracy. Our results show that HammerDrive outperforms a comparable state-of-the-art deep learning model for visual attention prediction on numerous metrics with ~13% improvement for both Kullback-Leibler divergence and similarity, and demonstrate that task-awareness is beneficial for driver visual attention prediction.
A Classification on Different Aspects of User Modelling in Personalized Web Search

In the context of personalization has recently been doing a lot of researches and applications. A common component of all research in the field of personalization is user modeling that also called user profiling. The main work of the user modeling in the field of personalization in the first step is capturing information about users and in the next step is to identify the user's preferences and interests and efficient use this information for increasing the retrieval performance. How to collect information about the user, user model structures and the used techniques to create a user model is different in each of personalized applications. In the previous studies, there was not a complete classification on the major dimensions of user models. In this research, we present an appropriate classification on the major dimensions of user models. We aim to present a survey on applications and techniques of user modeling and make a classification of user modeling by considering the existing literature and research and we hope can help to researchers in better-developing on the area.
A Lightweight Authentication and Inter-Cloud Payment Protocol for Edge Computing

In this paper, we propose a lightweight mutual authentication and inter-cloud redeemable payment protocol which allows IoT devices to subscribe with their home cloud service providers for roaming coverage. More precisely, such devices acquire authenticated payment tokens in order to benefit from the computation offloading services from edge nodes deployed by foreign cloud service providers. Hence, IoT devices are continuously serviced even when outside of their home cloud providers coverage. The protocol makes use of tree of secrets, hash chains, and Merkle trees. It requires sharing a Merkle tree root and a 128-bit secret key for constructing the tree of secrets among cloud admins. Our protocol provides mutual authentication, confidentiality, and easy charge redemption from the home server. For N subscribed IoT devices, the storage at the hosting clouds is limited to 2 ×(Ns +1)×16 bytes and 32×LogN bytes for the IoT device, where Ns is the maximum number of devices served by the IoT gateway per payment redemption period.
Transform Domain Pyramidal Dilated Convolution Networks for Restoration of Under Display Camera Images

Under-display camera (UDC) is a novel technology that can make digital imaging experience in handheld devices seamless by providing large screen-to-body ratio. UDC images are severely degraded owing to their positioning under a display screen. This work addresses the restoration of images degraded as a result of UDC imaging. Two different networks are proposed for the restoration of images taken with two types of UDC technologies. The first method uses a pyramidal dilated convolution within a wavelet decomposed convolutional neural network for pentile-organic LED (P-OLED) based display system. The second method employs pyramidal dilated convolution within a discrete cosine transform based dual domain network to restore images taken using a transparent-organic LED (T-OLED) based UDC system. The first method produced very good quality restored images and was the winning entry in European Conference on Computer Vision (ECCV) 2020 challenge on image restoration for Under-display Camera - Track 2 - P-OLED evaluated based on PSNR and SSIM. The second method scored $$4^{th}$$ position in Track-1 (T-OLED) of the challenge evaluated based on the same metrics.
Learnable Weighting of Intra-attribute Distances for Categorical Data Clustering with Nominal and Ordinal Attributes

The success of categorical data clustering generally much relies on the distance metric that measures the dissimilarity degree between two objects. However, most of the existing clustering methods treat the two categorical subtypes, i.e., nominal and ordinal attributes, in the same way when calculating the dissimilarity without considering the relative order information of the ordinal values. Moreover, there would exist interdependence among the nominal and ordinal attributes, which is worth exploring for indicating the dissimilarity. This paper will therefore study the intrinsic difference and connection of nominal and ordinal attribute values from a perspective akin to the graph. Accordingly, we propose a novel distance metric to measure the intra-attribute distances of nominal and ordinal attributes in a unified way, meanwhile preserving the order relationship among ordinal values. Subsequently, we propose a new clustering algorithm to make the learning of intra-attribute distance weights and partitions of data objects into a single learning paradigm rather than two separate steps, whereby circumventing a suboptimal solution. Experiments show the efficacy of the proposed algorithm in comparison with the existing counterparts.
Bandwidth Efficient IoT Traffic Shaping Technique for Protecting Smart Home Privacy from Data Breaches in Wireless LAN

The automation of the home through Internet of Things (IoT) devices presents security challenges for protecting the safety and privacy of its inhabitants. In spite of standard wireless communication security protocols, an attacker inside the wireless communication range of the smart home can extract identifier and statistical information, such as the MAC address and packet lengths, from the encrypted wireless traffic of IoT devices to make inferences about the private activities of the user. In this paper, to prevent this breach on privacy in the wireless LAN, we accomplish the following three items. First, we demonstrate that performing traffic shaping simultaneously on the upload and download node is necessary; second, we demonstrate that traffic shaping by random packet generation is impracticable due to the excessive bandwidth requirement; third, we propose traffic shaping by variable padding durations to reduce the bandwidth requirement for injecting dummy traffic during periods of user activity and inactivity to decrease the confidence of the local attacker from identifying genuine user activity traffic. From our performance evaluation, we decreased the data generated on several WiFi and ZigBee-enabled IoT devices by over 15% by our proposal of variable padding durations compared to the conventional method of fixed padding durations at low attacker confidence.
Evaluation of the effectiveness and efficiency of state-of-the-art features and models for automatic speech recognition error detection

Abstract Speech based human-machine interaction and natural language understanding applications have seen a rapid development and wide adoption over the last few decades. This has led to a proliferation of studies that investigate Error detection and classification in Automatic Speech Recognition (ASR) systems. However, different data sets and evaluation protocols are used, making direct comparisons of the proposed approaches (e.g. features and models) difficult. In this paper we perform an extensive evaluation of the effectiveness and efficiency of state-of-the-art approaches in a unified framework for both errors detection and errors type classification. We make three primary contributions throughout this paper: (1) we have compared our Variant Recurrent Neural Network (V-RNN) model with three other state-of-the-art neural based models, and have shown that the V-RNN model is the most effective classifier for ASR error detection in term of accuracy and speed, (2) we have compared four features’ settings, corresponding to different categories of predictor features and have shown that the generic features are particularly suitable for real-time ASR error detection applications, and (3) we have looked at the post generalization ability of our error detection framework and performed a detailed post detection analysis in order to perceive the recognition errors that are difficult to detect.
PanoNet3D: Combining Semantic and Geometric Understanding for LiDAR Point Cloud Detection

Visual data in autonomous driving perception, such as camera image and LiDAR point cloud, can be interpreted as a mixture of two aspects: semantic feature and geometric structure. Semantics come from the appearance and context of objects to the sensor, while geometric structure is the actual 3D shape of point clouds. Most detectors on LiDAR point clouds focus only on analyzing the geometric structure of objects in real 3D space. Unlike previous works, we propose to learn both semantic feature and geometric structure via a unified multi-view framework. Our method exploits the nature of LiDAR scans - 2D range images, and applies well-studied 2D convolutions to extract semantic features. By fusing semantic and geometric features, our method outperforms state-of-the-art approaches in all categories by a large margin. The methodology of combining semantic and geometric features provides a unique perspective of looking at the problems in real-world 3D point cloud detection.
Efficient reservoir computing using field programmable gate array and electro-optic modulation

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system’s performance and versatility, three benchmark tests are performed. The first is the 10 th order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE’s) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73 × 10 −3 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.
Sentiment Analysis for POI Recommender Systems

In recent years, researchers have explored a new way of providing recommendations to a user based on the existing reviews. Nowadays, users search for most of the details about the various tourist Point Of Interest (POI) to make their choices according to their interests. Many times, it is difficult for the user to make the right choices on POI due to a large amount and redundant information. This is commonly known as information overloading and recommendation systems are most useful to address these issues.The recommendation system helps the user to suggest tourist locations as per their interests. Most of the research is based on the numerical rating given by users on respective visited POI's. We use reviews data to recommend the POI's using the Sentiment Intensity Aanalyzer (SIA). SIA helps to get the review's weightage which suggest the top places based on the score.
Barriers and Incentives to Cybersecurity Threat Information Sharing in Developing Countries: A Case Study of Saudi Arabia

Threat information sharing practices have the potential to improve cyber security. However, participation in sharing communities is not widely adopted. The existing literature finds a variety of benefits and challenges that either promote or deter organisations from engaging in such practices. Many of these findings relate to developed countries. A gap exists between developed and developing countries in terms of cyber threat information sharing. In this paper, we describe a case study to identify the barriers and incentives for implementing threat information sharing in a developing country: Saudi Arabia. Our results offer insight into the successful implementation of threat information sharing initiatives in Saudi Arabia, showing that socio-cultural barriers and technological incentives for sharing threat information are important factors. Our survey tool can be applied in other countries both developed and developing.
Reversible Computing with Fast, Fully Static, Fully Adiabatic CMOS

To advance the energy efficiency of general digital computing far beyond the thermodynamic limits that apply to conventional digital circuits will require utilizing the principles of reversible computing. It has been known since the early 1990s that reversible computing based on adiabatic switching is possible in CMOS, although almost all the "adiabatic" CMOS logic families in the literature are not actually fully adiabatic, which limits their achievable energy savings. The first CMOS logic style achieving truly, fully adiabatic operation if leakage was negligible (CRL) was not fully static, which led to practical engineering difficulties in the presence of certain nonidealities. Later, "static" adiabatic logic families were described, but they were not actually fully adiabatic, or fully static, and were much slower.In this paper, we describe a new logic family, Static 2-Level Adiabatic Logic (S2LAL), which is, to our knowledge, the first CMOS logic family that is both fully static, and truly, fully adiabatic (modulo leakage). In addition, S2LAL is, we think, the fastest possible such family (among fully pipelined sequential circuits), having a latency per logic stage of one tick (transition time), and a minimum clock period (initiation interval) of 8 ticks. S2LAL requires 8 phases of a trapezoidal power-clock waveform (plus constant power and ground references) to be supplied. We argue that, if implemented in a suitable fabrication process designed to aggressively minimize leakage, S2LAL should be capable of demonstrating a greater level of energy efficiency than any other semiconductor-based digital logic family known today.
An improved multi-node Newton iteration search method based on PSO

When using the search algorithm to locate the vibrating target, Newton iteration method has strict requirement on the selection of initial value. To solve this problem, an improved Newton iterative search algorithm for vibration source location is proposed based on the analysis of Newton iterative search algorithm for multi-node co-location. The algorithm uses the estimation characteristics of POS algorithm to get the initial position of vibration source. Then the position of vibration target is determined by Newton iterative search method. The experimental results show that this method can effectively solve the problem of initial value selection of Newton iteration method and improve the localization accuracy of vibration source.
An improved ORB feature extraction and matching algorithm based on affine transformation

As the key technology of image processing, image feature extraction and matching are widely used in face recognition, image stitching, and visual SLAM. Among them, ORB algorithm is widely adopted because of its advantage in real-time processing. However, the matching accuracy of the feature points extracted by ORB algorithm is still a concern for further applications. To address the problem, an improved ORB algorithm based on affine transformation was proposed. After FAST feature points are detected, the descriptors under different affine transformations are extracted from the feature points, and then the stable bits of the descriptor are extracted for feature matching. To further improve the accuracy of feature matching, an improved F-SORT algorithm is employed after feature matching. The improved F-SORT algorithm first groups the matched features in sequence order, and then further refines the matched feature pairs based on the angle, scale, and distance of the feature points. Experimental results verify the effectiveness of the algorithm regarding time efficiency and matching accuracy.
Speaker Recognition Based on Long Short-Term Memory Networks

Speaker recognition is the process of identifying the speaker's identity by extracting the acoustic features in the speaker's audio for recognition. It is mainly the perception and simulation of the speaker's vocal tract information and the human ear's auditory information, which has profound significance in the fields of human daily life and military affairs. In order to solve this problem, this paper proposes a speaker recognition algorithm based on Long Short-Term Memory Networks (LSTM). The feature uses a method that combines Linear Predictive Coding (LPC) with Log-Mel spectrum. The d-vector output through the LSTM network is classified using the Softmax loss function. This method is applied to the VCTK audio data set. The experimental results show that the recognition rate of this method reaches 94.9%.
Neural-Aided Statistical Attack for Cryptanalysis

Abstract In Crypto’19, Gohr proposed the first deep learning-based key recovery attack on 11-round Speck32/64, which opens the direction of neural-aided cryptanalysis. Until now, neural-aided cryptanalysis still faces two problems: (i) the attack complexity estimations rely purely on practical experiments; (ii) it does not work when there are not enough neutral bits. To the best of our knowledge, we are the first to solve these two problems. In this paper, we propose a Neural-Aided Statistical Attack (NASA) that has the following advantages: (i) NASA supports estimating the theoretical complexity. (ii) NASA does not rely on any special properties including neutral bits. Moreover, we propose three methods for reducing the complexity of NASA. One of the methods, which is based on a newly proposed concept named Informative Bit that reveals an important phenomenon, makes NASA applicable to large-size ciphers. We have performed a series of experiments on round reduced Speck32/64, DES, and Speck96/96. These experiments do not only verify the correctness of NASA, but also further highlight the advantage and potential of NASA. Our work arguably raises a new direction for neural-aided cryptanalysis.
Human Posture Recognition Based On Convolutional Neural Network

Posture recognition is an important part of human behavior recognition, and also an important research content of human behavior recognition system. Its application value is increasingly extensive. In recent years, it has become a research hotspot in the field of computer vision.However, due to the high complexity of the human body, most of the images need to be preprocessed before feature extraction, which leads to difficulty in feature extraction and low recognition efficiency.Therefore, this paper proposes a research method of human posture recognition based on convolutional neural network.The model has 11 layers. Convolution and pooling operations are performed on the five human poses in the sampled data set, and finally enters the fully connected layer for classification to complete the training and recognition of the data set. The results show that, compared with traditional machine learning methods, this model allows the network to extract features for recognition and classification, which not only eliminates complex feature extraction methods, but also has better recognition performance and better results.
Sentic Computing for Aspect-Based Opinion Summarization Using Multi-Head Attention with Feature Pooled Pointer Generator Network

Neural sequence to sequence models have achieved superlative performance in summarizing text. But they tend to generate generic summaries that under-represent the opinion-sensitive aspects of the document. Additionally, the sequence to sequence models are prone to test-train discrepancy (exposure-bias) arising from the differential summary decoding processes in the training and testing phases. The models use ground truth summary words in the decoder training phase and predicted outputs in the testing phase. This inconsistency leads to error accumulation and substandard performance. To address these gaps, a cognitive aspect-based opinion summarizer, Feature Pooled Pointer Generator Network (FP2GN), is proposed which selectively attends to thematic and contextual cues to generate sentiment-aware review summaries. This study augments the pointer generator framework with opinion feature extraction, feature pooling, and mutual attention mechanism for opinion summarization. The proposed model FP2GN identifies the aspect terms in review text using sentic computing (SenticNet 5 and concept frequency-inverse opinion frequency) and statistical feature engineering. These aspect terms are encoded into context embeddings using weighted average feature pooling, which is processed in a pointer-generator framework inspired stacked Bi-LSTM encoder–decoder model with multi-head self-attention. The decoder system uses temporal and mutual attention mechanisms to ensure the appropriate representation of input-sequence. The study also proffers the use of teacher forcing ratio to curtail the exposure-bias-related error-accumulation. The model achieves ROUGE-1 score of 86.04% and ROUGE-L score of 88.51% on the Amazon Fine Foods dataset. An average gain of 2% over other methods is observed. The proposed model reinforces pointer generator network architecture with opinion feature extraction, feature pooling, and mutual attention mechanism to generate human-readable opinion summaries. Empirical analysis substantiates that the proposed model is better than the baseline opinion summarizers.