Reviewerly/exHarmony huggingface dataset:
Image Stitching Development By Combining SIFT Detector And SURF Descriptor For Aerial View Images

Image stitching is considered as a subject that can be researched in the field of computer vision. Aerial view images are the type of images that requires image stitching because aerial images can be taken based on the flying path of a drone so that the images obtained are in the form of sequences, and the research that examines the use of different types of feature detector and descriptor in aerial view images is still lacking, therefore this research will combine two different types of feature detector and descriptor to get a better image stitching result. In the previous study the SIFT-SURF combination was used as image matching and succeeded by obtaining a small time cost and high accuracy, because of that in this study SIFT is used as a feature detector and SURF is used as a feature descriptor with input data of aerial view images. Features matching is carried out by applying a bruteforce matcher using an euclidean distance, after the initial matches or raw matches are obtained, the Lowe ratio test is done to eliminate false matches, after that Random Sample Consensus (RANSAC) is utilized as homography which is used to get correct matching that can be called inliners. The last step is image warping to adjust the obtained matches. The measurement used in this study is the number of obtained features, the number of good matches, and the time cost as the parameters to get the system performance. This research compares the proposed method with other combination methods, namely SURF-SIFT, and BRISK-ORB. This research proposed an image stitching system to stitch aerial view images by using 5 pairs of an aerial view images. The image stitching system is designed with the several steps which is preprocessing, SIFT detector and SURF description, euclidean distance matching, Lowe ratio test, RANSAC and images stitching. From the experiment that has been done, SIFT-SURF combination successfully stitch the tested images with less computational time and it also have more or the same good matching with less detected feature.
SRAGAN: Generating Colour Landscape Photograph from Sketch

Generating sketch from colour landscape photograph is very easy while it is hard to generate colour photograph from landscape sketch. In this paper, a new automatic conversion network, named Sparse Residual Attention Generative Adversarial Networks (SRAGAN), is proposed to generate landscape colour photograph from sketch. Besides of generator adversarial loss, we not only adopt L1-regularized per-pix loss, but also combine L1-regularized perceptual loss together into our model. Due to the sparsity of L1-norm, it can preserve boundary edge information very well, which makes our model can handle well the conversion task of sketch-to-photo. In addition, we proposed a ResAttention block to our network structure, which combines the residual learning blocks with attention module. Experiments show that the landscape colour photographes generated by our SRAGAN looks more natural with bright colour and clear edge information. At the same time, we integrate two models so that we can generate winter-style and summer-style photographes from the same landscape sketch. Experiments demonstrate that our method outperforms many state-of-the-arts both in quantitative and in visual performance.
Abstractive Summarization with Keyword and Generated Word Attention

Abstractive summarization is a important task in natural language processing field. In previous work, the sequence-to-sequence based models are widely used for abstractive summarization task. However, most of the current abstractive summarization models still suffer from two problems. One is that it is difficult for these models to learn an accurate source contextual representation from the redundancy and noisy source text at each decoding step. Another is the information loss problem, which is ignored in previous work. The inability of these models to effectively exploit previously generated words led to this problem. In order to address these two problems, in this paper, we propose a novel keyword and generated word attention model. Specifically, the proposed model first employs the hidden state of decoder to capture relevant keywords and previously generated words contextual at each time step. The model then utilizes obtained keywords and generated words contextual to create keywords-aware and generated words-aware source contextual, respectively. The keywords contextual contributes to learn an accurate source contextual representation, and the generated words contextual can alleviate the information loss problem. Experimental results on a popular Chinese social media dataset demonstrate that the proposed model outperforms baselines and achieves the state-of-the-art performance.
Improving Sentence Representations with Local and Global Attention for Classification

Representation learning is a key issue for text classification tasks. Few existing representation models are able to learn sufficient text information, including local semantic information and global structure information. This paper focuses on how to generate better semantic and structure representations to obtain better sentence representation with them. In detail, we propose a hierarchical local and global attention network to learn sentence representation automatically. We generate semantic and structure representations respectively with local attention. Global attention is used to get the final representation. The final representation obtained is used for training and prediction. Experimental results show that our method achieves ideal results in several text classification tasks, including sentiment analysis, subjectivity classification and question type classification. The specific accuracies are 81.6%(MR), 93.6%(SUBJ), 49.4%(SST-5) and 95.6%(TREC).
A Learning-Based Vehicle-Trajectory Generation Method for Vehicular Networking

With the rapid development of mobile applications, networking technologies have been constantly evolved to offer a more convenient way of sharing information and online-communication anytime and anywhere. Vehicular networks have the potential to become one of the important carriers of future mobile networks. The performance of current vehicular networks has been widely evaluated through simulation experiments due to the high cost and impracticality of other experimental approaches. The most paramount factors of vehicle networks are the authenticity of simulative evaluation, where the mobility of the vehicles is the first significant feature (i.e., the nodes of the vehicular network) that must be properly considered. However, generating the corresponding real mobility datasets has always been a big challenge although it is vital to the simulations of vehicular networks. Therefore, in this paper, we propose a learning-based generation method that can be used to build the vehicle-trajectory data for variety of vehicle densities. Firstly, with analyzing the road bayonet data, we obtain the hidden pattern between road traffic and time. Secondly, we deploy Vissim (a well-known traffic simulator) to generate the experimental data by considering the urban functional areas for the origins of vehicles. The generated experimental data are learned by Extreme Learning Machine (ELM), and the weight matrix of the parameters is obtained, which presents the impact of the experimental parameters on the simulation results. We prove the effectiveness of our method by comparing the generated vehicle-trajectory datasets with the vehicle density predicted by the weight matrix and the realistic traffic flow model.
Load Balancing Cloud Computing with Web-Interface Using Multi-channel Queuing Systems with Warming up and Cooling

The model based on queuing theory for a cloud computing system Web-Interface that uses pre-processing and post-processing is presented. The model with different parameters of system, like the arrival rate, service rate, service time and number of servers is analyzed. The results examined carefully and described and shown in a number of charts and figures where the effect of warming-up and cooling is clearly visible and how the effective model helps balancing the load on the servers.
Interactive Voice Technologies and the Digital Marginalization of Older Adults

The sociotechnical approach to the design of interactive systems has been seen as a means of acknowledging and understanding the benefits and risks of emerging and innovative technologies. However, implementing such an approach in practice is easier said than done, especially for technology that is as ubiquitous as mobile technologies. Yet, this is a worthwhile challenge as this approach may strengthen our understanding of users' perceptions and subsequent adoption of such mobile technologies. My previous research has indicated that there may be a link between information studies theory and the digital interface design practice surrounding mobile technologies. I plan to build upon this work by further developing and evaluating methods, practices, and approaches to bridge information theory and design practice. I am seeking advice and feedback on my research in terms of the development and evaluation of new practices of mobile design work. The implications of the sociotechnical approach to mobile voice interface design are discussed.
We Know What You Will Ask: A Dialogue System for Multi-intent Switch and Prediction

Existing task-oriented dialogue systems seldom emphasize multi-intent scenarios, which makes them hard to track complex intent switch in a multi-turn dialogue, and even harder to make proactive reactions for the user's next potential intent. In this paper, we formalize the multi-intent tracking task and introduce a complete set of intent switch modes. Then we propose ISwitch, a system that can handle complex multi-intent dialogue interactions. In this system, we design a gated controller to recognize the current intent, and a proactive mechanism to predict the next potential intent. Based on these, we use pre-defined patterns to generate proper responses. Experiments show that our model can achieve high intent recognition accuracy, and simplify the dialogue process. We also construct and release a new dataset for complex multi-turn multi-intent-switch dialogue.
Side Channel Attacks on GPUs

Graphics Processing Units (GPUs) are commonly integrated with computing devices to enhance the performance and capabilities of graphical workloads. In addition, they are increasingly being integrated in data centers and clouds such that they can be used to accelerate data intensive workloads. Under a number of scenarios the GPU can be shared between multiple applications at a fine granularity allowing a spy application to monitor side channels and attempt to infer the behavior of the victim. For example, OpenGL and WebGL send workloads to the GPU at the granularity of a frame, allowing an attacker to interleave the use of the GPU to measure the side-effects of the victim computation through performance counters or other resource tracking APIs. We demonstrate the vulnerability by implementing three end-to-end attacks. We show that an OpenGL or CUDA based spy can fingerprint websites accurately (attack I), track user activities within the website, and even infer the keystroke timings for a password text box (attack II) with high accuracy. The third attack demonstrates how a CUDA spy application can derive the internal parameters of a neural network model being used by another CUDA application on the cloud. To counter these attacks, the paper suggests mitigations based on limiting the rate of the calls, or limiting the granularity of the returned information.
On Dissimilarity Representation and Transfer Learning for Offline Handwritten Signature Verification

When compared to Writer-Dependent (WD) Handwritten Signature Verification, in which a model is trained for each individual writer, the Writer-Independent (WI) approach offers greater scalability, since only a single model is trained for all users from a dissimilarity space generated by the dichotomy transformation. However, many samples from the dissimilarity space are redundant and have little influence during the training of the verification model. This work investigates whether prototype selection (PS) preprocessing can be used in the space resulting from the dichotomy transformation without degrading the performance of the classifier. Furthermore, an investigation is also performed to examine the use of a WI classifier in a transfer learning scenario, i.e., where the classifier is trained in one dataset, and is used to verify signatures in other datasets. The experiments reported herein show that the use of prototype selection in the dissimilarity space allows a reduction in the complexity of the classifier without degrading its generalization performance. In addition, the results show that the WI classifier is scalable enough to be used in a transfer learning approach, with a resulting performance comparable to that of a classifier trained and tested in the same dataset. An analysis of the results obtained based on the instance hardness (IH) measure and dendrogram diagrams is performed in order to better understand the behavior of the resulting dichotomy transformation.
Noise-Aware Network Embedding for Multiplex Network

Network embedding aims at learning the latent representations of nodes while preserving the complex structure of the underlying graph. Real-world networks are usually related with each other via common nodes, the so-called multiplex network. To make the data mining work on the multiplex network more actionable, it become urgent and essential to transform it into low-dimension vector space. Recently, several works have been proposed to leverage the complementary information for embedding. However, they suffer from sacrificing distinct properties of the counterparts in different layers, as they preserve much noise information into embedding vectors. In this paper, we propose a Noise-Aware Network Embedding approach for Multiplex Network, namely NANE. Unlike previous works, NANE considers the roles of an identical node in different layers, and adopts a more robust and flexible strategy to rationally integrate the cross-layer information while keeping the unique characteristic of each layer. We perform extensive evaluations on several real-world datasets. The experimental results demonstrate that our NANE can achieve better performance on link prediction task and significantly outperform previous methods especially in noisy multiplex network scenarios.
Factors Influencing Empathic Behaviors for Virtual Agents

Empathy is important for smooth interaction. In this research, we focused on EQ (Empathy Quotient), which is an index to measure individuals' empathic ability, and analyzed behavior related to EQ, particularly the effect of participants' embodiment on empathic behavior. In the experiment, participants played a ball-tossing game with two agents. The experimental task was created by adapting the Cyberball game that has been used in studies of social psychology. We conducted the experiment under two conditions: an equitable scenario where the two agents toss evenly to participants and other agents; and an inequitable scenario where only one agent tosses only to participants. Then we compared the number of tosses performed by each participants. As a result, we found that the participants with high EQ tended to exclude the agent who did not receive tosses from the other agent, but that this exclusion was alleviated by embodiment.
Human Activity Recognition Using Deep Models and Its Analysis from Domain Adaptation Perspective

Human activity recognition (HAR) is a broad area of research which solves the problem of determining a user’s activity from a set of observations recorded on video or low-level sensors (accelerometer, gyroscope, etc.) HAR has important applications in medical care and entertainment. In this paper, we address sensor-based HAR, because it could be deployed on a smartphone and eliminates the need to use additional equipment. Using machine learning methods for HAR is common. However, such, methods are vulnerable to changes in the domain of training and test data. More specifically, a model trained on data collected by one user loses accuracy when utilised by another user, because of the domain gap (differences in devices and movement pattern results in differences in sensors’ readings.) Despite significant results achieved in HAR, it is not well-investigated from domain adaptation (DA) perspective. In this paper, we implement a CNN-LSTM based architecture along with several classical machine learning methods for HAR and conduct a series of cross-domain tests. The result of this work is a collection of statistics on the performance of our model under DA task. We believe that our findings will serve as a foundation for future research in solving DA problem for HAR.
DICENet: Fine-Grained Recognition via Dilated Iterative Contextual Encoding

Material Recognition is an intriguing problem in Computer Vision. While traditional approaches prefer an ensemble of networks to capture essential properties such as texture, more recent approaches leverage the power of Deep Learning to design end-to-end models. We do the same, and propose Dilated Iterative Contextual Encoding Network, a novel end-to-end framework for material recognition. As a result of gathering extensive knowledge on various characteristics of materials, our approach combines different components on the base network to address specific properties, which also helps in general recognition tasks. The traditional ResNet is replaced by a Dilated Residual Network to help capture fine-grained material information. Iterative deep aggregation helps capture and fuse global homogeneous material properties across multiple resolutions and scales. To enhance the discriminatory power of the learnt latent representation, we propose gramedial loss which is intuitively applied on a texture vector space. Spatial similarity loss is applied on strategic intermediate feature maps to effectively capture local non-homogeneous texture features from a global context, crucial for the primary classification task. Extensive experiments conducted on golden material datasets such as the FMD, MINC-2500, KTH-TIPS-2b, DTD and GTOS indicate improved performances over state of the art approaches on large datasets and two small datasets, while achieving compatible accuracies on the challenging FMD. Furthermore, our architecture also performed convincingly while categorizing general indoor and object classification datasets such as MITIndoor and CalTech-101.
A Character-Enhanced Chinese Word Embedding Model

Distributed word representation has demonstrated its advantages in many natural language processing tasks. Such as named entity recognition, entity relation extraction, and text classification. Traditional one-hot word representation represents a word as a high-dimensional and sparse vector. Instead, distributed word representation represents a word as a low-dimensional and dense vector, which are more suitable as inputs of deep neural networks. Furthermore, distributed word representation can express the semantic relatedness and syntactic regularities between different words. Word embedding is a kind of distributed word representation technology, which is very popular and useful in many natural language processing tasks. Recently, more and more researches have focused on learning word embeddings with internal morphological knowledge in words, such as character, sub-words, and other kinds of morphological information. For example, Chinese characters contain rich semantic information related to words they compose. Thus, characters can help improving the representation of words. In this paper, we present a character-enhanced Chinese word embeddings model (CCWE). In the model, we train character and word embeddings simultaneously in two parallel tasks. The framework of our model is based-on Skip-Gram. We evaluate CCWE on word similarity, analogical reasoning, text classification, and named entity recognition. The results demonstrate that our model can learn both better Chinese word and character embeddings than other baseline models.
On the Discriminative Power of Learned vs. Hand-Crafted Features for Crowd Density Analysis

Crowd density analysis is a crucial component in video surveillance mainly for security monitoring. This paper proposes a novel approach for crowd density classification, in which learned features substitute the commonly used handcrafted features. In particular, the approach consists of employing deep networks to extract useful crowd features that can further be manageable by a classifier. This process is favorable for crowd features extraction due to the large learning capability of deep networks compared to traditional methods based on handcrafted features. The proposed approach is evaluated on three challenging datasets, and the results demonstrate the effectiveness of learned features for crowd density classification. Furthermore, we include an extensive comparative study between different learned/hand-crafted features in order to investigate their discriminative power to handle such problems. Their performance is evaluated using different classifiers and strategies as well.
The Role of Visual Detail during Situated Memory Recall within a Virtual Reality Environment

Accurately eliciting long-term procedural knowledge continues to be a non-trivial challenge. In this project, we continued an exploration into a novel approach that provides users with virtual stimuli during the elicitation process. Specifically, users were placed into a virtual reality (VR) environment, representing a real-world location, and asked to role-play a familiar task while verbally describing each action they were simulating. This approach has previously shown promise when compared with context-devoid methods, but no research has yet considered whether the detail of the provided virtual stimuli may meaningfully affect recall performance.
Solving the Traveling Salesman's Problem Using Camels Herd Algorithm

This paper proposes the Camels Herd Algorithm (CHA) which presented in [1] that come from a watchful observation of the Arabian camels, in the desert ecosystem. This animal shows uncommon skills of intelligence, strategy organization, and incomparable directional cleverness in its traversal of the desert searching for food. The Camels Herd Algorithm constructs a scientific template from the performance of this animal and consume the ideal model for resolving the symmetric Traveling Salesman's Problem from the TSPLIB. This research displays that through regular communication camels have the ability to safeguard an exceptional investigation and utilization of the examination space, cooperation, and they have an upright recall of their preceding individual activities in addition to the sense of humidity in air. The results acquired by consuming the CHA to solve these TSP cases were marked against the results acquired by using other common algorithms. The results acquired using the Camel Herd algorithm were very competitive.