Reviewerly/exHarmony huggingface dataset:
OurPlaces: Cross-Cultural Crowdsourcing Platform for Location Recommendation Services

This paper presents a cross-cultural crowdsourcing platform, called OurPlaces, where people from different cultures can share their spatial experiences. We built a three-layered architecture composed of: (i) places (locations where people have visited); (ii) cognition (how people have experienced these places); and (iii) users (those who have visited these places). Notably, cognition is represented as a paring of two similar places from different cultures (e.g., Versailles and Gyeongbokgung in France and Korea, respectively). As a case study, we applied the OurPlaces platform to a cross-cultural tourism recommendation system and conducted a simulation using a dataset collected from TripAdvisor. The tourist places were classified into four types (i.e., hotels, restaurants, shopping malls, and attractions). In addition, user feedback (e.g., ratings, rankings, and reviews) from various nationalities (assumed to be equivalent to cultures) was exploited to measure the similarities between tourism places and to generate a cognition layer on the platform. To demonstrate the effectiveness of the OurPlaces-based system, we compared it with a Pearson correlation-based system as a baseline. The experimental results show that the proposed system outperforms the baseline by 2.5% and 4.1% in the best case in terms of MAE and RMSE, respectively.
Deep Image Compression Using Decoder Side Information

We present a Deep Image Compression neural network that relies on side information, which is only available to the decoder. We base our algorithm on the assumption that the image available to the encoder and the image available to the decoder are correlated, and we let the network learn these correlations in the training phase. Then, at run time, the encoder side encodes the input image without knowing anything about the decoder side image and sends it to the decoder. The decoder then uses the encoded input image and the side information image to reconstruct the original image. This problem is known as Distributed Source Coding (DSC) in Information Theory, and we discuss several use cases for this technology. We compare our algorithm to several image compression algorithms and show that adding decoder-only side information does indeed improve results. Our code is publicly available Our code is available at: https://github.com/ayziksha/DSIN .
Visual Tracking by TridentAlign and Context Embedding

Recent advances in Siamese network-based visual tracking methods have enabled high performance on numerous tracking benchmarks. However, extensive scale variations of the target object and distractor objects with similar categories have consistently posed challenges in visual tracking. To address these persisting issues, we propose novel TridentAlign and context embedding modules for Siamese network-based visual tracking methods. The TridentAlign module facilitates adaptability to extensive scale variations and large deformations of the target, where it pools the feature representation of the target object into multiple spatial dimensions to form a feature pyramid, which is then utilized in the region proposal stage. Meanwhile, context embedding module aims to discriminate the target from distractor objects by accounting for the global context information among objects. The context embedding module extracts and embeds the global context information of a given frame into a local feature representation such that the information can be utilized in the final classification stage. Experimental results obtained on multiple benchmark datasets show that the performance of the proposed tracker is comparable to that of state-of-the-art trackers, while the proposed tracker runs at real-time speed. (Code available on https://github.com/JanghoonChoi/TACT).
Enabling AI at the edge with XNOR-networks

In recent years we have seen a growing number of edge devices adopted by consumers, in their homes (e.g., smart cameras and doorbells), in their cars (e.g., driver assisted systems), and even on their persons (e.g., smart watches and rings). Similar growth is reported in industries including aerospace, agriculture, healthcare, transport, and manufacturing. At the same time that devices are getting smaller, Deep Neural Networks (DNN) that power most forms of artificial intelligence are getting larger, requiring more compute power, memory, and bandwidth. This creates a growing disconnect between advances in artificial intelligence and the ability to develop smart devices at the edge. In this paper, we present a novel approach to running state-of-the-art AI algorithms at the edge. We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks (BWN) and XNOR-Networks. In BWN, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations (in terms of number of the high precision operations) and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a BWN version of AlexNet is the same as the full-precision AlexNet. Our code is available at: urlhttp://allenai.org/plato/xnornet.
Multiview video summarization using video partitioning and clustering

Multiview video summarization plays a crucial role in abstracting essential information form multiple videos of the same location and time. In this paper, we propose a new approach for the multiview summarization. The proposed approach uses the BIRCH clustering algorithm for the first time on the initial set of frames to get rid of the static and redundant. The work presents a new approach for shot boundary detection using frame similarity measures Jaccard and Dice. The algorithm performs effectively synchronized merging of keyframes from all camera-views to obtain the final summary. Extensive experimentation conducted on various datasets suggests that the proposed approach significantly outperforms most of the existing video summarization approaches. To state a few, a 1.5% improvement on video length reduction, 24.28% improvement in compression ratio, and 6.4% improvement in quality assessment ratio is observed on the lobby dataset.
Improving the effectiveness of traditional education based on computer artificial intelligence and neural network system

The application of artificial intelligence and machine learning algorithms in education reform is an inevitable trend of teaching development. In order to improve the teaching intelligence, this paper builds an auxiliary teaching system based on computer artificial intelligence and neural network based on the traditional teaching model. Moreover, in this paper, the optimization strategy is adopted in the TLBO algorithm to reduce the running time of the algorithm, and the extracurricular learning mechanism is introduced to increase the adjustable parameters, which is conducive to the algorithm jumping out of the local optimum. In addition, in this paper, the crowding factor in the fish school algorithm is used to define the degree or restraint of teachers’ control over students. At the same time, students in the crowded range gather near the teacher, and some students who are difficult to restrain perform the following behavior to follow the top students. Finally, this study builds a model based on actual needs, and designs a control experiment to verify the system performance. The results show that the system constructed in this paper has good performance and can provide a theoretical reference for related research.
Per-Instance Configuration of the Modularized CMA-ES by Means of Classifier Chains and Exploratory Landscape Analysis

In this paper, we rely on previous work proposing a modularized version of CMA-ES, which captures several alterations to the conventional CMA-ES developed in recent years.Each alteration provides significant advantages under certain problem properties, e.g., multi-modality, high conditioning.These distinct advancements are implemented as modules which result in 4 608 unique versions of CMA-ES.Previous findings illustrate the competitive advantage of enabling and disabling the aforementioned modules for different optimization problems.Yet, this modular CMA-ES is lacking a method to automatically determine when the activation of specific modules is auspicious and when it is not.We propose a well-performing instancespecific algorithm configuration model which selects an (almost) optimal configuration of modules for a given problem instance.In addition, the structure of this configuration model is able to capture inter-dependencies between modules, e.g., two (or more) modules might only be advantageous in unison for some problem types, making the orchestration of modules a crucial task.This is accomplished by chaining multiple random forest classifiers together into a so-called Classifier Chain based on a set of numerical features extracted by means of Exploratory Landscape Analysis (ELA) to describe the given problem instances.
Assessment of End-User Susceptibility to Cybersecurity Threats in Saudi Arabia by Simulating Phishing Attacks

Phishing attacks are cybersecurity threats that have become increasingly sophisticated. Phishing is a cyberattack that can be carried out using various approaches and techniques. Usually, an attacker uses trickery as well as fraudulent and disguised means to steal valuable personal information or to deceive the victim into running malicious code, thereby gaining access and controlling the victim’s systems. This study focuses on evaluating the level of cybersecurity knowledge and cyber awareness in Saudi Arabia. It is aimed at assessing end-user susceptibility through three phishing attack simulations. Furthermore, we elaborate on some of the concepts related to phishing attacks and review the steps required to launch such attacks. Subsequently, we briefly discuss the tools and techniques associated with each attack simulation. Finally, a comprehensive analysis is conducted to assess and evaluate the results.
A Supervised Machine Learning Approach for DASH Video QoE Prediction in 5G Networks

Future fifth generation (5G) networks are envisioned to provide improved Quality-of-Experience (QoE) for applications by means of higher data rates, low and ultra-reliable latency and very high reliability. Proving increasing beneficial for mobile devices running multimedia applications. However, there exist two main co-related challenges in multimedia delivery in 5G. Namely, balancing operator provisioning and client expectations. To this end, we investigate how to build a QoE-aware network that guarantees at run-time that the end-to-end user experience meets the end users' expectations at the same that the network's Quality of Service (QoS) varies. The contribution of this paper is twofold: first, we consider a Dynamic Adaptive Streaming over HTTP (DASH) video application in a realistic emulation environment derived from real 5G traces in static and mobility scenarios to assess the QoE performance of three state-of-art Adaptive Bitrate Streaming (ABS) algorithm categories: Hybrid - Elastic and Arbiter+; buffer-based - BBA and Logistic; and rate-based - Exponential and Conventional. Second, we propose a Machine Learning (ML) classifier to predict user satisfaction which considers network metrics, such as RTT, throughput, and number of packets. Our proposed model does not rely on knowledge about the application or specific traffic information. We show that our ML classifiers achieve a QoE prediction accuracy of 87.63 % and 79 % for static and mobility scenarios, respectively.
Determination of various Deep Learning Parameters to Predict Heart Disease for Diabetes Patients

Heart disease is one of the world's major diseases which may come due to high blood glucose, high blood pressure and weight, etc. Diabetes which occurs through insulin and blood glucose level can also be the reason for getting heart disease. Deep learning model is used to prognosis the heart disease for the diabetes patients. There are two model Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). By comparing the parameters of both methods, the GRU gives the best results. Diabetes patients have their records about their diabetes, which is took as the input for predicting the heart disease for that patients. The input involves patient's id and their timing of checking. Code and Value tell about the types of diabetes. Glycemic Index value shows about the dejection of the food. Heredity shows whether sugar comes from their generation. The input also has the details about medication, blood pressure and cholesterol. Gated Recurrent Unit is the best method; it does not have any separate memory for storing the date from previous input. It uses the relevant gate to remove the unwanted data and the needed from the input. It combines the data and needed and starts predicting the results. GRU predicts with the learning rate. Learning rate uses back propagation to test and obtain the most suitable parameters for activation, loss and optimization function. The treatment of heart disease is rendered for patients by these parameters and processes.
Smart College Chatbot using ML and Python

The days of solely engaging with a service through a keyboard are over. Users interact with systems more and more through voice assistants and chatbots. A chatbot is a computer program that can converse with humans using Artificial Intelligence in messaging platforms. Every time the chatbot gets input from the user, it saves input and response which helps chatbot with little initial knowledge to evolve using gathered responses. With increased responses, precision of the chatbot also gets increases. The ultimate goal of this project is to add a chatbot feature and API for Matrusri Engineering College. This project will investigate how advancements in Artificial Intelligence and Machine Learning technology are being used to improve many services. Specifically it will look at development of chatbots as a channel for information distribution. The program selects the closest matching response from closest matching statement that matches input utilizing WordNet, it then chooses response from known selection of statements for that response. This project aimed to implement online chatbot system to assist users who access college website, using tools that expose Artificial Intelligence methods such as Natural Language Processing, allowing users to communicate with college chatbot using natural language input and to train chatbot using appropriate Machine Learning methods so it will be able to generate a response. There are numerous applications that are incorporating a human appearance and intending to simulate human dialog, yet in most part of the cases knowledge of chatbot is stored in a database created by a human expert.
A QoE Inference Method for DASH Video Using ICMP Probing

An increase of Video on Demand (VoD) consumption has occurred in recent years. Delivering high Quality of Experience (QoE) for users consuming VoD is crucial. Many methods were proposed to estimate QoE based on network metrics, or to obtain direct feedback from video players. Recent proposals usually require monitoring tools installed in multiple network nodes, instrumentation of client devices, updates on existing network elements, among others. We propose a method based on Internet Control Message Protocol (ICMP) probing to estimate QoE for users consuming VoD. The method allows network operators to estimate which QoE level can be delivered to the user according to current network conditions using a Machine Learning (ML) model. Our method does not require installation of software at different network nodes, relying on ICMP probing which is widely supported by existing devices. Our QoE inference model estimates Mean Opinion Score (MOS) with Root Mean Square Error (RMSE) of 0.98, with additional 27 Kbps of traffic during probing. We evaluate the model's generalization capacity when estimating QoE for videos different from the one used for training, which can speed up model's creation process. In those cases MOS was estimated with RMSE of 1.01.
Online Handwritten Mathematical Symbol Segmentation and Recognition with Bidirectional Context

Discriminating ambiguous symbols in online handwritten mathematical expression is difficult without context. We propose a Bidirectional Recurrent Neural Network for segmenting and classifying online handwritten mathematical symbols. The context from forward and backward directions helps the classification model discriminate ambiguous symbols and improve recognition rates. The classification model is integrated into the Stochastic Context-Free Grammar recognition system for recognizing mathematical expressions. We show the effectiveness of the approach for improving symbol classification and segmentation on the CROHME 2016 dataset.
An Energy-Efficient Implementation of Group Pruned CNNs on FPGA

In recent years, convolutional neural network (CNN)-based artificial intelligence algorithms have been widely applied to object recognition and image classification tasks. However, the high performance of convolutional neural networks comes at the cost of high-intensity computing and enormous numbers of parameters, which pose substantial challenges to terminal implementations. An end-to-end FPGA-based accelerator is proposed in this work that efficiently processes fine-grained pruned CNNs. A group pruning algorithm with group sparse regularization (GSR) is introduced to solve internal buffer misalignments and load imbalances of the accelerator after fine-grained pruning. A mathematical model of accelerator access and transmission is established to explore the optimal design scale and calculation mode. The accelerator is optimized by designing sparse processing elements and by scheduling the on- and off-chip buffers. The proposed approach reduces the computation of a state-of-the-art large-scale CNN, VGG16, by 86.9% with an accuracy loss on CIFAR-10 of only 0.48%. The accelerator achieves 188.41 GOPS at 100 MHz and consumes 8.15 W when implemented on a Xilinx VC707, making it more energy-efficient than previous approaches.
Remote Sensing of Lake Sediment Core Particle Size Using Hyperspectral Image Analysis

Hyperspectral imaging has recently emerged in the geosciences as a technology that provides rapid, accurate, and high-resolution information from lake sediment cores. Here we introduce a new methodology to infer particle size distribution, an insightful proxy that tracks past changes in aquatic ecosystems and their catchments, from laboratory hyperspectral images of lake sediment cores. The proposed methodology includes data preparation, spectral preprocessing and transformation, variable selection, and model fitting. We evaluated random forest regression and other commonly used statistical methods to find the best model for particle size determination. We tested the performance of combinations of spectral transformation techniques, including absorbance, continuum removal, and first and second derivatives of the reflectance and absorbance, along with different regression models including partial least squares, multiple linear regression, principal component regression, and support vector regression, and evaluated the resulting root mean square error (RMSE), R-squared, and mean relative error (MRE). Our results show that a random forest regression model built on spectra absorbance significantly outperforms all other models. The new workflow demonstrated herein represents a much-improved method for generating inferences from hyperspectral imagery, which opens many new opportunities for advancing the study of sediment archives.
Knowledge embedded GCN for skeleton-based two-person interaction recognition

Two-person interaction recognition with skeleton data has attracted much attention in computer vision. Recently, graph convolutional network (GCN) based methods, which model the skeleton data in the form of graph, have achieved remarkable performance. However, the topology of graph in existing methods, denoted as naturally connected graph, is predefined based on the natural connection of each person. It ignores the correlations between two persons and cannot be suitable for different actions. In this paper, we design two graphs by exploiting the knowledge for two-person interaction recognition. A knowledge-given graph is constructed to build the direct connection between two persons. Meanwhile, a knowledge-learned graph is proposed to build the adaptive correlations, which is unique for each input sample. Moreover, we further propose the knowledge embedded graph convolution network (K-GCN) to exploit the complementarity among knowledge-given, knowledge-learned and naturally connected graphs for two-person interaction recognition. In addition, multi-level scheme is proposed to model the joint-level and part-level information simultaneously, which further enhances the performance. To verify the effectiveness of the proposed method, consecutive ablation studies are performed on two prevalence datasets, SBU and NTU Interaction. Experimental results show that our method achieves the state-of-the-art performance for two-person interaction recognition on both of them.
RGB-D Co-attention Network for Semantic Segmentation

Incorporating the depth (D) information for RGB images has proven the effectiveness and robustness in semantic segmentation. However, the fusion between them is still a challenge due to their meaning discrepancy, in which RGB represents the color but D depth information. In this paper, we propose a co-attention Network (CANet) to capture the fine-grained interplay between RGB’ and D’ features. The key part in our CANet is co-attention fusion part. It includes three modules. At first, the position and channel co-attention fusion modules adaptively fuse color and depth features in spatial and channel dimension. Finally, a final fusion module integrates the outputs of the two co-attention fusion modules for forming a more representative feature. Our extensive experiments validate the effectiveness of CANet in fusing RGB and D features, achieving the state-of-the-art performance on two challenging RGB-D semantic segmentation datasets, i.e., NYUDv2, SUN-RGBD.
A comparative study of support vector machine and neural networks for file type identification using n-gram analysis

File type identification (FTI) has become a major discipline for anti-virus developers, firewall designers and for forensic cybercrime investigators. Over the past few years, research has seen the introduction of several classifiers and features. One of these advances is the so-called n-grams analysis, which is an interpretation of statistical counting in classified fragments. Recently, n-grams based approaches were already successfully combined with computational intelligence classifiers. However, the academic body of literature is scant when it comes to a comprehensive explanation of machine learning based approaches such as neural networks (NN) or support vector machines (SVM). For example, how the input parameters, including learning rate, different values of n for n-grams, etc. influence the results. In addition, very few studies have compared the scalability of NN vs. SVM approaches. Therefore, a systematic research in comparing different approaches is needed to address these questions. Hence, this paper investigates this type of comparison, by focusing on the n-gram analysis as a feature for the two different classifiers: SVMs and NNs. This paper details our experiments with two NNs and four SVMs, using linear kernels and RBF kernels on RealDC datasets. In general, we found that SVM-based approaches performed better than the NN, but their scalability is still a challenge.