Reviewerly/exHarmony huggingface dataset:
Deep Zero-Shot Learning for Scene Sketch

We introduce a novel problem of scene sketch zero-shot learning (SSZSL), which is a challenging task, since (i) different from photo, the gap between common semantic domain (e.g., word vector) and sketch is too huge to exploit common semantic knowledge as the bridge for knowledge transfer, and (ii) compared with single-object sketch, more expressive feature representation for scene sketch is required to accommodate its high-level of abstraction and complexity. To overcome these challenges, we propose a deep embedding model for scene sketch zero-shot learning. In particular, we propose the augmented semantic vector to conduct domain alignment by fusing multi-modal semantic knowledge (e.g., cartoon image, natural image, text description), and adopt attention-based network for scene sketch feature learning. Moreover, we propose a novel distance metric to improve the similarity measure during testing. Extensive experiments and ablation studies demonstrate the benefit of our sketch-specific design.
Graded logic for decision support systems

We present the soft computing graded logic (GL) as a mathematical infrastructure for a soft computing propositional calculus, and GL aggregation. The GL aggregation of degrees of truth (or fuzzy membership) is a crucial component of complex criterion functions used in decision support systems. In the context of GL we propose aggregation functions that integrate means and t-norms/conorms, combining logic and probabilistic reasoning. We also propose a set of necessary and sufficient basic logic functions (models of simultaneity, substitutability, and complementing of degrees of truth) and their analytic forms. GL is developed as a seamless soft computing generalization of classical Boolean logic. The proposed generalizations include: (1) the continuous parameterized transition from drastic conjunction to drastic disjunction, integrating the regions of hyperconjunction, hard and soft partial conjunction, logic neutrality, soft and hard partial disjunction, and hyperdisjunction and (2) semantic generalization where we assign a degree of importance to each degree of truth, in a way that is consistent with observable properties of human intuitive reasoning. These generalizations reflect the fact that degrees of truth are not anonymous real numbers, but values that have semantic identity derived from goals and requirements of decision-maker. The basic goal of GL is to provide humancentric mathematical models of logic aggregators that can be used in the decision process of comparison and selection of alternatives based on the evaluation of each alternative. This paper is structured to present the characteristic properties of entire GL in a way that is both concise and complete.
Structure-aware person search with self-attention and online instance aggregation matching

This paper tackles the task of person search, which is a new challenging computer version task in real-world scenarios. The challenge of this task is mainly from: i) Due to viewpoints, occlusions, etc., the visual appearance of a particular person varies greatly, making re-identification difficult; ii) the bounding box where pedestrians are not available, the model needs to search for the person in the entire gallery image. The task of person search is consisted of pedestrian detection and person re-identification (re-id). Instead of completing the two tasks separately, we put the two pieces together to address these issues. We design a more suitable end-to-end framework for person search, which both improve re-id and detection at the same time. Through the change of the anchor with structural prior, the pedestrian detection can be refined faster and better. By introducing self-attention, the framework enhances the fusion of global information. An Online Instance Aggregation Matching (OIAM) loss function is proposed to train the network, and it effectively solves the problem of many categories but few samples of the same category. Extensive experiments are conducted on the PRW and CUHK-SYSU datasets, and our proposed method can outperform other person search methods in both mAP and top-1 evaluation protocols.
Meta-Learning Perspective for Personalized Image Aesthetics Assessment

Image aesthetic is a highly subjective task. Thus, generic aesthetics models may lead to inconsistent user agreements even on the same image. Personalized aesthetics models can be employed to remedy the inconsistency issue. In real situation, users shared very small number of annotated images, which makes this problem more challenging. To solve problems above, unlike previous works that focused on user interactive or extracting simple yet effective image features, we address this by meta-learning. Meta-learning is a framework designed for quick adaption of an existing model to a new task with limited labeled data samples. In this way, we can leverage a small amount of annotated data from user and generate an effective personalized aesthetics model quickly. In addition, we proposed a novel meta-learning strategy and a novel meta regularization for our task. Experimental results demonstrate that our approach can effectively learn personalized aesthetics preferences and outperform existing methods on quantitative comparisons with a strong generalization ability.
Crowdsourced Children Monitoring and Finding With Holding Up Detection Based on Internet-of-Things Technologies

In this paper, we propose a crowdsourced children monitoring and finding (CCMF) framework to detect holding-up behaviors and find missing children using wearable devices and surrounding smartphones based on Internet of Things (IoT) technologies. In the monitoring mode, the CCMF framework can prevent young children from taking away by strangers/people with bad intentions. In the finding mode, the CCMF framework can cooperatively find missing children equipped with wearable devices consisting of mobile iBeacon and 3-axis accelerometer modules through crowdsourced sensing networks formed by smartphone users with outdoor GPS and indoor IoT localization. According to our review of relevant research, CCMF is the first children monitoring and finding solution that can detect holding-up postures of a target child and provide the guiding path to a lost child through crowdsourced sensing networks. An iOS-based prototype with Arduino wearable devices and mobile/static iBeacon nodes is implemented to verify the feasibility and superiority of our framework. Experimental results show that CCMF outperforms existing methods and can significantly increase recognition success rates and efficiently reduce false alarm rates of holding up detection.
A Server-Side Optimized Hybrid Multicast-Unicast Strategy for Multi-User Adaptive 360-Degree Video Streaming

The head-mounted display (HMD) for 360-degree videos cannot be shared by multiple users, which significantly increases the bandwidth consumption when multiple users request the same video content simultaneously. This paper proposes a server-side hybrid multicast-unicast strategy for multi-user adaptive 360-degree video streaming, aiming to ensure the overall quality of experience (QoE) of the users in a bandwidth-constrained environment. A framework is established to realise this strategy, through which the delivery mode (multicast or unicast) and the corresponding bitrate for each tile can be jointly adapted to the dynamics of users' throughputs and field of views (FoVs). To maximize the overall QoE, a user clustering method is proposed to classify the users into several multicast clusters. Based on these clusters, we then formulate the joint delivery mode selection and rate adaptation problem as a non-linear integer programming problem which can be solved using the steepest ascent gradient algorithm. Finally, experiments are carried out to verify the performance of the proposed strategy.
Smarter Smart Contract Development Tools

Much recent work focuses on finding bugs and security vulnerabilities in smart contracts written in existing languages. Although this approach may be helpful, it does not address flaws in the underlying programming language, which can facilitate writing buggy code in the first place. We advocate a re-thinking of the blockchain software engineering tool set, starting with the programming language in which smart contracts are written. In this paper, we propose and justify requirements for a new generation of blockchain software development tools. New tools should (1) consider users' needs as a primary concern; (2) seek to facilitate safe development by detecting relevant classes of serious bugs at compile time; (3) as much as possible, be blockchain-agnostic, given the wide variety of different blockchain platforms available, and leverage the properties that are common among blockchain environments to improve safety and developer effectiveness.
A Behavior-cluster Based Imbalanced Classification Method for Credit Card Fraud Detection

Credit card fraud detection has been paid more and more attention by researchers. The credit card transactions are represented by highly imbalanced data sets. The number of genuine transactions is far more than fraudulent transactions, which will greatly affect the detection of fraud. Existing methods mainly consider how to balance the two classes only based on data volume, without considering the complexity of user behavior in credit card transactions, that is, the behavior noise. In this paper, we propose a behavior-cluster based imbalanced classification method. The main idea is to divide user behaviors into several group behaviors, remove behavior noise, and then hierarchical sampling. Experiments on a large scale credit card transaction data provided by a financial institution and 18 UCI data sets show that our method is superior to the existing method.
SSNet: Structure-Semantic Net for Chinese typography generation based on image translation

The abundant complex Chinese characters often lead to the high cost of time and labor in its typography production, which cannot meet various demands of typohraphies in daily life. Image translation methods are becoming the mainstream of typography generation to facilitate typography production. Nevertheless, current translation methods do not take the Chinese semantics and structure into account. In this paper, we propose a method called Structure-Semantic Net (SSNet) for Chinese typography generation, which utilizes disentangled stroke features from the structure module, pre-trained semantic features from the semantic module to generate target typographies. Furthermore, a novel loss called dual-masked Hausdorff distance is proposed to punish the incorrectly generated pixels to regularize the character contours, stabilizing the training process. Qualitative and quantitative results show that the proposed SSNet surpasses existing image translation methods in image quality, and the ablation study of SSNet verifies the effectiveness of each module and loss function.
Cloudmaskgan: A Content-Aware Unpaired Image-To-Image Translation Algorithm for Remote Sensing Imagery

Cloud segmentation is a vital task in applications that utilize satellite imagery. A common obstacle in using deep learning-based methods for this task is the insufficient number of images with their annotated ground truths. This work presents a content-aware unpaired image-to-image translation algorithm. It generates synthetic images with different land cover types from original images, while preserving the locations and the intensity values of the cloud pixels. Therefore, no manual annotation of ground truth in these images is required. The visual and numerical evaluations of the generated images by the proposed method prove that their quality is better than that of competitive algorithms.
Design of Cryptographic model for End-to-End Encryption in FPGA based systems

Digitization has transformed our world. The famous World Wide Web, various social networks and numerous e-commerce applications are responsible to generate huge volume of data regularly. The emerging data security mechanisms ensures a safe transmission of information through the internet. Symmetric key cryptography is one of the key techniques used for the security of large data transmission over the internet. Some popular symmetric key algorithms; TDES, IDEA, AES etc. The proposed model uses several symmetric key algorithms in cascaded manner. The model is first divided into 64 bits plaintext block and then into two sub-blocks of 32 bits. The left and right blocks go through a list of symmetric key cryptography techniques like TR, RPPT, TB and RPSPNC and bit rotation in one step. TR stands for Triangular Encryption, which is responsible to generate a triangle shape bit stream where different encryption-decryption techniques are achieved by the order of reading bit RPPT (Recursive Pared Parity Technique) a very simple space efficient encryption algorithm produces cipher text by performing logical OR of consecutive bits. Transformation of Bits (TB) uses swapping method of bits of different order for encryption and decryption process. Recursive Positional Substitution on Prime-Nonprime of Cluster (RPSPNC) methodology swaps bits based on prime/non prime bit position and generates intermediate bit stream as a cipher text. Finally merge the two halves sub-blocks to get the 64 bits cipher text. To test the feasibility of the proposed model it is compared with AES and another embedded encryption technique RPPT+TB for entropy, non-homogeneity, n-gram(4-gram) test, frequency distribution graph and FPGA based hardware test.
Direct 3D Detection of Vehicles in Monocular Images with a CNN based 3D Decoder

In autonomous driving, the detection of objects like surrounding vehicles based on monocular RGB images is usually performed by 2D bounding box detectors. The resulting 2D objects can be used for a first coarse 3D position estimate but for a precise location, additional sensor data has to be taken into account. For further use in sensor fusion systems and environment maps it is preferable to detect objects, their orientation and dimensions directly in 3D coordinates. To address this 3D object detection task, we propose a direct 3D bounding box estimator which is realized as CNN decoder module and can be connected to most 2D object detectors like SSD[1], OverFeat[2], YOLO[3] and RetinaNet[4] or directly to CNN feature extractors like VGG [2] and ResNet [5]. The 3D parameters of the objects such as dimension and orientation are directly predicted by the CNN module. To successfully train this complex MultiNet architecture, a combination and modification of current loss functions is proposed. The fastest of the proposed network module combinations is capable of detecting objects in 3D camera coordinates at a frame rate of 28 fps.
Doubly Stochastic Neighbor Embedding on Spheres

Stochastic Neighbor Embedding (SNE) methods minimize the divergence between the similarity matrix of a high-dimensional data set and its counterpart from a low-dimensional embedding, leading to widely applied tools for data visualization. Despite their popularity, the current SNE methods experience a crowding problem when the data include highly imbalanced similarities. This implies that the data points with higher total similarity tend to get crowded around the display center. To solve this problem, we introduce a fast normalization method and normalize the similarity matrix to be doubly stochastic such that all the data points have equal total similarities. Furthermore, we show empirically and theoretically that the doubly stochasticity constraint often leads to embeddings which are approximately spherical. This suggests replacing a flat space with spheres as the embedding space. The spherical embedding eliminates the discrepancy between the center and the periphery in visualization, which efficiently resolves the crowding problem. We compared the proposed method (DOSNES) with the state-of-the-art SNE method on three real-world datasets and the results clearly indicate that our method is more favorable in terms of visualization quality. DOSNES is freely available at http://yaolubrain.github.io/dosnes/.
Advanced processing techniques and secure architecture for sensor networks in ubiquitous healthcare systems

Ubiquitous healthcare aims to transcend all boundaries, providing healthcare services to anyone, anytime, anywhere. The implementation of such services not only increases the coverage and quality of healthcare facilities, but their adoption in disaster medical delivery could save time, money, and lives. The term Ubiquitous Computing implies the presence of interconnected hardware and software systems, whose presence is not identified. The technology is pervasive, yet invisible to enable people to go about their daily life activities and their interactions with the environment, without being distracted by the technology surrounding them. To achieve this, multiple wearable physiological monitoring devices, Internet of things-based architectures, and participatory sensing systems have been developed. However, with such constant monitoring, the healthcare data get increasingly big and more difficult to process. Moreover, since daily life activities are composite in nature, they could be part of more complex routines and behaviors, and precise, instance-wise labels are difficult to create. Instance-wise labeling for multiple activities, along with the sequence of the activities for every individual window, is required for proper predictions. This chapter explores the applications and challenges in U-healthcare systems while analyzing secure architectures to resolve certain specific issues in this area of research. Further, advanced data processing techniques are studied for accurate labeling and prediction of possible health issues based on previous data accumulated through various resources, such as prior health records and incoming data through sensor networks.
Spatial and Angular Reconstruction of Light Field Based on Deep Generative Networks

Light field (LF) cameras often have significant limitations in spatial and angular resolutions due to their design. Many techniques that attempt to reconstruct LF images at a higher resolution only consider either spatial or angular resolution, but not both. We propose a generative network using high-dimensional convolution to improve both aspects. Our experimental results on both synthetic and real-world data demonstrate that the proposed model outperforms existing state-of-the-art methods in terms of both peak signal-to-noise ratio (PSNR) and visual quality. The proposed method can also generate more realistic spatial details with better fidelity.
Combining Local and Document-Level Context: The LMU Munich Neural Machine Translation System at WMT19

We describe LMU Munich's machine translation system for English→German translation which was used to participate in the WMT19 shared task on supervised news translation.We specifically participated in the documentlevel MT track.The system used as a primary submission is a context-aware Transformer capable of both rich modeling of limited contextual information and integration of large-scale document-level context with a less rich representation.We train this model by fine-tuning a big Transformer baseline.Our experimental results show that document-level context provides for large improvements in translation quality, and adding a rich representation of the previous sentence provides a small additional gain.
An Analysis of (Non-)Use Practices and Decisions of Internet of Things

Recent market reports have suggested that adoption of the Internet of Things (IoT) does not always lead to long-term use. This paper aims to advance an understanding of the sociological process of the (non-)use of the IoT. To this end, we present results from a mixed-methods study that analyzed survey data from 834 IoT users in the U.S. Many of our participants treated these devices as co-actors for achieving their goals and continued to use them because they had developed a routine or because the devices influenced their social interactions and identity. Participants limited their use of a device when they did not feel in control, when the device failed to understand their intent, or when they did not understand the device’s behavior. We also found that excessive information offered by, disappointment due to, and the complexity of the devices led to their abandonment. Lastly, we discuss the implications of our results for understanding technology (non-)use and provide design recommendations.
Contour-based character segmentation for printed Arabic text with diacritics

Current developments in sensors open new possible uses across numerous real-life applications, including optical character recognition (OCR). An OCR system requires incorporation of text processing tools into the sensor functionality. The most critical stage in OCR systems is the segmentation stage. It refers to the challenge of subdividing a text image into characters, which can be individually processed using a classifier. The cursive nature of the Arabic script such as the existence of different shapes for each character according to its location in the word besides the existence of diacritics makes Arabic character segmentation a very challenging task. A robust offline character segmentation algorithm for printed Arabic text with diacritics is developed based on the contour extraction technique. The algorithm works through extracting the up-contour part of a word and then identifies the splitting areas of the word characters. Then a postprocessing stage is used to handle the over-segmentation problems that appear in the initial segmentation stage. The proposed scheme is benchmarked using the APTI dataset and a manually collected dataset consisting of image texts varying in font size, type, and style for more than 38,000 words. The experiments show that the proposed algorithm is able to segment Arabic words with diacritics with an average accuracy of 98.5%.
On Relating Code Smells to Security Vulnerabilities

In recent years there has been an abundance of well-known software design problems that fall under a variety of different terms such as flaws or code smells. Nowadays software systems place considerable importance to security concerns related to code flaws that lead to software vulnerabilities. In this paper, we present a study to identify the relationship between code smells and vulnerabilities in software code. Our study provides information about the impact of code smells on software security and considers open source implementation of technologies under the Apache Tomcat software. The results show the relationship between the code smells and the security vulnerabilities. While most code smells, have a slight impact, one particular smell is identified to have a more considerable impact.
An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition

Vehicle taillight recognition is an important application for automated driving, especially for intent prediction of ado vehicles and trajectory planning of the ego vehicle. In this work, we propose an end-to-end deep learning framework to recognize taillights, i.e. rear turn and brake signals, from a sequence of images. The proposed method starts with a Convolutional Neural Network (CNN) to extract spatial features, and then applies a Long Short-Term Memory network (LSTM) to learn temporal dependencies. Furthermore, we integrate attention models in both spatial and temporal domains, where the attention models learn to selectively focus on both spatial and temporal features. Our method is able to outperform the state of the art in terms of accuracy on the UC Merced Vehicle Rear Signal Dataset, demonstrating the effectiveness of attention models for vehicle taillight recognition.