Reviewerly/exHarmony huggingface dataset:
Image Interpolation via Gradient Correlation-Based Edge Direction Estimation

This paper introduces an image interpolation method that provides performance superior to that of the state-of-the-art algorithms. The simple linear method, if used for interpolation, provides interpolation at the cost of blurring, jagging, and other artifacts; however, applying complex methods provides better interpolation results, but sometimes they fail to preserve some specific edge patterns or results in oversmoothing of the edges due to postprocessing of the initial interpolation process. The proposed method uses a new gradient-based approach that makes an intelligent decision based on the edge direction using the edge map and gradient map of an image and interpolates unknown pixels in the predicted direction using known intensity pixels. The input image is subjected to the efficient hysteresis thresholding-based edge map calculation, followed by interpolation of low-resolution edge map to obtain a high-resolution edge map. Edge map interpolation is followed by classification of unknown pixels into obvious edges, uniform regions, and transitional edges using the decision support system. Coefficient-based interpolation that involves gradient coefficient and distance coefficient is applied to obvious edge pixels in the high-resolution image, whereas transitional edges in the neighborhood of an obvious edge are interpolated in the same direction to provide uniform interpolation. Simple line averaging is applied to pixels that are not detected as an edge to decrease the complexity of the proposed method. Applying line averaging to smooth pixels helps to control the complexity of the algorithm, whereas applying gradient-based interpolation preserves edges and hence results in better performance at reasonable complexity.
Cyberbullying Detection for Online Games Chat Logs using Deep Learning

Cyberbullying is a form of harassment that takes place in the internet where a bully sends a harsh message to harass the receiver. In this study, a learning model is developed using Convolutional Neural Network (CNN), which is usually used for image, and is then used to create a system for detecting cyberbullying in online game chat logs. Chat logs collected from Dota and Ragnarok were preprocessed and annotated. 60% of the data were used for training, 30% were used for testing the generated model and the remaining 10% were used for validating the developed model. After validation, the accuracy of the CNN model yielded to 99.93%. Based on the results of validation, the CNN model tends to overfit, even with regularizations applied, and was not able to generalize well. For comparison, another model was created using Naive Bayes and the accuracy yielded to 92.23%. It can be concluded that detecting cyberbullying using Naive Bayes is already possible, however, the accuracy is still not comparable to existing DNN models. While the use of CNN model results to overfitting, it is recommended to explore on other DNN architectures for online games chat logs.
Towards Creating Public Key Authentication for IoT Blockchain

Besides confidentiality and privacy, trust is an important factor for any IoT system. When a sensor send data that is signed with its private key, the receiving nodes verify it using the public key of the sensor. Hence, it is understood that authenticating the public key of the system is part of creating trust within the system. Traditionally, trust is maintained using Public Key Infrastructure (PKI) where a centralized Certificate Authority (CA) is used for authenticating the public keys. However, a centralized system can result in single point of failure where CA can be compromised or can act maliciously. Decentralizing this system using blockchain and by automating the process of certificate authentication without the need for a central third party can overcome the abovementioned limitations. We identify the challenges in creating such a system and propose a generic framework for PKI in IoT infrastructure using blockchain that can provide the functions of a CA.
Direction and distance sensors and sensing system for elderly people

Elderly people need either a caretaker or a cart to carry their belongings in and around their house or to/from nearby places, like a shopping mall, bus stop etc. This paper reports the development of a direction-cum-distance sensing system (DDSS) using ultrasonic sensors for a cart to assist elderly people. The DDSS can convert an ordinary assistive cart into a person-following assistive robot cart that would follow its user/target. Instead of using infrared or vision-based tracking systems for sensing direction and distance, as reported by other researchers, the DDSS reported here uses ultrasonic beam/pulses. The DDSS integrates the two tasks of finding the direction and measuring the distance in a single unit. For finding the direction of the target (user), an ultrasonic transmitter is placed on the back of the target, whereas two ultrasonic sensors are placed on the front of the cart. The distance of the target from the cart is measured using the classical technique of measuring the time taken by ultrasonic pulses to travel from the cart up to the target and back from the target to the cart after its reflection from the target. Performance of the DDSS in direction mode has been evaluated experimentally, which is very gratifying. Since the DDSS works with ultrasonic beam/pulses, the assistive robot cart can work not only indoor but also outdoor (where infrared emissions from the sun and other sources may cause mal-operation of infrared-based tracking systems) and in low-visibility areas (where camera-based tracking systems may fail to work). The new DDSS has additional advantages of simplicity, ready availability of components and low cost. Some suggestions for further work are included in the last section of the paper.
Toward GDPR Compliance in IoT Systems

The General Data Protection Regulation (GDPR) allow citizens to control their data. For that, they must define and update their security data policies that are generally more sophisticated and more dynamic than classical access control policies managed by system administrators. Consequently, GDPR implementation in modern scalable and dynamic systems like IoT is still a challenge. We propose a security model for data privacy and an original solution where a GDPR consent manager is integrated using Complex Event Processing (CEP) system and following the edge computing. We show, through a smart home IoT system, the efficiency of our approach in terms of flexibility and scalability.
A model based on dual-layer attention mechanism for semantic matching

In community question and answering (Q&A) systems, due to the diversity of words and syntactic structure, matching question pairs representing similar meaning is a challenging task. A novel model based on dual-layer attention mechanism for semantic matching is proposed in this work. Firstly, an attention-based preprocessing method is used on the word representation layer to reduce redundant information. Secondly, a bilateral multiple perspectives attention mechanism is utilized on the context representation layer to obtain more interactive information. Finally, the obtained information is passed through a Bi-directional Long Short Term Memory Network (BiLSTM). Then the obtained final time steps of the two sequences are combined for prediction. The experimental results show that the accuracy of the proposed model in our self-defined Chinese dataset is up to 95.54% and also 88.91% with Quora dataset. It outperforms the existing advanced benchmark models. The model also provides stability and scalability to natural language inference tasks with the accuracy of 87.4% in the Stanford Natural Language Inference (SNLI) dataset.
The estimation of low and high-pass active filter parameters with opposite charged system search algorithm

Algorithms are frequently used to solve problems that have a large search space and take a long time to be mathematically solved. They can later be improved with different improvement methods based on the structure and the type of the problem. In this study, the charged system search algorithm (CSS), which has been successfully implemented in the solutions of numerous engineering problems studied within the literature, was improved by introducing opposition-based learning method (OBL) to it in two different methods. With these improved algorithms, solutions were developed for 30-dimensional multimodal test functions in the first place and the results were discussed. In the second place, the parameters of active filters given below were determined from E24 standard series with developed approaches. Filters are electronic circuits that enhance the wanted frequency components of electric signals applied on their inputs and remove harmonics and interferences from these signals. They are divided into two types; active and passive filters. Active filters are produced with transistors or op-amps. They are financially more advantageous compared to passive filters. These filters are preferred especially in low frequencies due to their low costs. Adjustable for a large frequency domain, active filters are very convenient in terms of size and weight and their designs are highly simple. They can easily be connected successively without affecting one another. In this study, the parameter values of Sallen-Key topology Butterworth low and high-pass active filters, which have an extensive area of use, were determined through improved algorithms. My suggestions for the future studies are these: the effects of the opposite position learning approach on other heuristic algorithms can be analysed solving test functions and different engineering problems; different approaches other than the two approaches proposed in this study, can be developed for the opposite position learning concept; LPF and HPF designs solved in the study can be solved for different degrees and stages and finally new designs can be done for different filter types and different resistor and capacitor series that haven’t been handled in this study.
Multiple Choice Question Answer System using Ensemble Deep Neural Network

Question answer (QA) system is closely related to NLP and IR tasks. An automated QA system should understand the semantics of question and derive answers relevant to it. In case of MCQ system this tasks becomes difficult as the model needs to understand the semantics and select an answer from a given choice. In this paper we propose a ensemble approach to predict answers to Multiple choice question using LSTM model, hybrid LSTM -Convolution NN model and Multilayer Perception (MLP) model. Firstly, by using LSTM and hybrid LSTM-CNN models are trained parallel. Multilayer Perception is used to predict option to training dataset separately. The 8thGr-NDMC datasets are selected for model evaluation and comparison. The 8 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> GR-NDMC is used for experimentation purpose. The observed results demonstrate that the proposed approach performs better than some other single forecasting models.
Using Inception-Resnet V2 for Face-based Age Recognition in Scenic Spots

In recent years, face recognition technology has been applied in many famous scenic spots. However, it is still difficult to use this technology to simplify the process of buying tickets, especially when many fake ID cards are illegally used for buying tickets that do not conform to real age groups. In this paper, we propose to incorporate the face-based age recognition technology to ticket inspection to solve this kind of problem, which could also greatly improve the efficiency of ticket checking. In this paper, we try to fine tuning the Inception ResnetV2 model, by replacing the original simple convolutional neural network for the facial feature extraction and recognition. The experiment results suggest that the accuracy of age recognition has been significantly improved.
Analysis of local binary pattern for emphysema classification in lung CT image

Medical digital images and methods for their processing and automatic analysis have been used for faster and more precise diagnosis. Computer-aided diagnosis systems are widely used by specialists as help for detecting and analyzing suspicions regions in medical digital images. Various types of medical digital images and numerous diseases that can be detected on them make this wide research field. One of the diseases that can be detected in lung CT images is chronic obstructive pulmonary disease or emphysema. In this paper we analyzed the capabilities of texture descriptors, local binary pattern, for detecting and classification of emphysema. Three different types of local binary pattern are used. Instead of using a whole local binary pattern operator output, statistical measurements have been used. Support vector machine optimized by elephant herding optimization algorithm was used for classification. Based on the obtained results, it can be concluded that six statistical information of uniform local binary pattern achieve the best classification accuracy.
A 44.1TOPS/W Precision-Scalable Accelerator for Quantized Neural Networks in 28nm CMOS

Supporting variable precision for computing quantized neural network in a hardware accelerator is an efficient way to reduce overall computation time and energy. However, in the previous precision-scalable hardware, bit-reconfiguration logic increases the chip area significantly. In this paper, we demonstrate a compact precision-scalable accelerator chip using bitwise summation and channel-wise aligning schemes. The measurement results show that the peak performance per compute area is improved by 5.1-7.7x and system-level energy-efficiency is improved by up to 64% compared to previous precision-scalable accelerators.
Flagging Implausible Inspection Reports of Distribution Transformers via Anomaly Detection

Distribution transformer is the key equipment in the distribution power grid, and sampling inspection is the main method used to ensure quality control. Inspection comprises many testing processes. Due to various reasons, the testing results may occasionally have errors and be implausible. Only a few errors can be detected empirically by inspectors. To solve this problem, anomaly detection is proposed in this paper to determine implausible inspection reports and assist in re-inspecting. The well-known and representative anomaly detection algorithms, which are now used in many application domains are introduced. These algorithms include single Gaussian distribution and multivariate Gaussian distribution, local outlier factor, one-class SVM. Based on the distribution transformer inspection reports collected, anomaly samples are constructed manually. Train datasets and test datasets are then formulated. By comparing the testing results, the one-class SVM algorithm can detect anomaly samples in testing datasets correctly. Thus, it can be used to distinguish the abnormal samples (i.e., reports with measurement errors suspected) in the inspection reports of distribution transformer, which can help inspectors complete their inspecting work correctly and effectively.
A Fuzzy-Cluster based Semantic Information Retrieval System

Due to the increasing number of digital document repositories there is a heavy demand for information retrieval systems and therefore, information retrieval is still appearing as an emerging area of research. The information retrieval technology these days focuses on achieving better performance under different context by extracting documents most appropriate to the user's query. Majority of the classical keyword based retrieval techniques does not focus on semantic meanings and therefore, are found to be less effective in reconstructing the actual information conveyed in the context. Also, retrieval of the relevant documents depends on appropriate analysis of the query terms. As words are polysemic, their actual meanings are influenced by their relationships with other words and their syntactic roles in the sentence. This work presents a fuzzy-cluster based semantic information retrieval model that considers these relationships to determine the exact meaning of the user query and extracts relevant documents as per their relevance scores.
Review of cyber security threats and proposed trustworthy memory acquisition mechanism

Due to the major exponential leap in the amount of digital data generated, processed, transferred and stored in a daily life brings challenges to the digital forensic field as like it does in other research and technical area. Digital forensics has become rapidly important as a method to investigate a cyber and mobile-assisted crime. While multiple tools are present and much research & development are being undertaken, but huge uncertainties and challenges exist regarding the future of cyber security. In this literature a detailed survey is discussed on security threats in cyber security and analyzing some network with existing tools and proposes a trustworthy memory acquisition management based security solution. The crucial challenges that exist within the domain, including increased volume of data as per platforms and systems that exist, security mechanism for securing the data on existing systems, analyzing the unwanted cyber threats and forensic investigation on stored database.
A Multivariate Public Key Encryption Scheme With Equality Test

The public key encryption with equality test (PKEET) allows the cloud server to judge whether two different ciphertexts are generated by the same message without decryption.Through this technique, PKEET provides an effective solution for building secure outsourced databases, and has made some rich achievements.This paper combines multivariate public key encryption and equality test, and proposes the first multivariate public key encryption scheme with equality test (MPKEET), which inherits the advantages of both primitives.Moreover, the equality test algorithm proposed in this paper is based on a straight line.Compared with the schemes based on bilinear pairing, it is simpler and easier to implement.And our MPKEET scheme achieves desirable security, which can resist linearization equation attacks, differential attacks, XL attacks, Gröbner basis attacks and the attack of quantum computer, when appropriate parameters are selected.
Deep Convolutional Neural Network Accelerator Featuring Conditional Computing and Low External Memory Access

This paper presents an ASIC accelerator for deep convolutional neural networks (DCNNs) featuring a novel conditional computing scheme that synergistically combines precision-cascading with zero-skipping. To reduce many redundant convolution operations that are followed by max-pooling operations, we propose precision-cascading, where the input features are divided into a number of low-precision groups and approximate convolutions with only the most significant bits (MSBs) are performed first. Based on this approximate computation, the full-precision convolution is performed only on the maximum pooling output that is found. This way, the total number of bit-wise convolutions can be reduced by ~2× without affecting the output feature values and with <; 0.8% degradation in final ImageNet classification accuracy. Precision-cascading provides the added benefit of increased sparsity per low-precision group, which we exploit with zero-skipping to eliminate clock cycles as well as external memory access that involve zero inputs. By jointly optimizing the conditional computing scheme and hardware architecture, the 40nm prototype chip demonstrates a peak energy-efficiency of 8.85 TOPS/W at 0.9V supply and low external memory access of 55.31 MB (or 0.0018 access/MAC) for ImageNet classification with VGG-16 CNN.
DGPose: Deep Generative Models for Human Body Analysis

Abstract Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without specific training for such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose, have different characteristics. In the first, body pose labels are taken as conditioners, from a fully-supervised training set. In the second, our structured semi-supervised approach allows for pose estimation to be performed by the model itself and relaxes the need for labelled data. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images. It is not only capable of mapping images to interpretable latent representations but also able to map these representations back to the image space. We compare our models with relevant baselines, the ClothNet-Body and the Pose Guided Person Generation networks, demonstrating their merits on the Human3.6M, ChictopiaPlus and DeepFashion benchmarks.
Knowing the price of success

If an agent, or a coalition of agents, knows that it has a strategy to achieve a certain outcome, it does not mean that the agent knows what the strategy is. Even if the agent knows what the strategy is, she might not know the price of executing this strategy. The article considers modality “the coalition not only knows the strategy, but also knows an upper bound on the price of executing the strategy”. The main technical result is a sound and complete bimodal logical system that describes the interplay between this modality and the distributed knowledge modality.
Seismic spectrum decomposition based on sparse time-frequency analysis

Time-frequency analysis is widely used in many engineering fields. However, the traditional time-frequency analysis methods suffer from issues such as low resolution or the interference of cross terms. To solve the above issues, this paper proposes a sparse time-frequency analysis by using an L1-norm constraint, fitting the sparse prior of a signal's spectrum. This process begins with the relationship between the sparse spectrum and the short-time measurement in order to propose the short-time sparse spectrum inversion model. Then, the first-order primal-dual method is employed to solve the proposed model. In this way, the reconstructed spectrum is constrained to be sparse. On the one hand, the concentration of the proposed algorithm is high due to the adoption of the L1-norm constraint. On the other hand, cross terms are avoided because the proposed method is based on the short-time Fourier transform and convex optimization technology. To show the performance of the proposed method, experiments based on both the theoretical signal and the real seismic signal are then conducted and compared with state-of-the-art time-frequency methods. The results show that the proposed method can obtain more accurate time-frequency distributions than other algorithms. Finally, we apply the proposed method in the decomposition of a seismic signal spectrum. The experiments show that the proposed method is capable of obtaining high-resolution frequency slices and more precisely exploring the spatial distribution of a reservoir than traditional time-frequency methods.