Reviewerly/exHarmony huggingface dataset:
Privacy-preserving Feature Extraction via Adversarial Training

Deep learning is increasingly popular, partly due to its widespread application potential, such as in civilian, government and military domains. Given the exacting computational requirements, cloud computing has been utilized to host user data and model. However, such an approach has potential privacy implications. Therefore, in this paper, we propose a method to protect user’s privacy in the inference phase of deep learning workflow. Specifically, we use an intermediate layer to separate the entire neural network into two parts, which are respectively deployed on the user device and the cloud server. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> , deployed on the user device, is used for raw data transformation, which removes the need for users to upload raw data to the cloud directly. However, we also demonstrate there exists potential for privacy leakage in the intermediate features of the neural network through two concrete experiments. In other words, the encoder on its own does not provide adequate privacy protection. Therefore, we also propose an approach to achieve <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Privacy-preserving Feature Extraction based on Adversarial Training (P-FEAT)</i> , where the goal of privacy attacking tasks and the goal of target tasks are adversarial in terms of sensitive attributes. By imposing privacy constraints during the feature extraction, we can reduce the contribution of the extracted features to the privacy leakage. In this way, privacy protection capability of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> can be further strengthened. We then demonstrate the effectiveness of P-FEAT using a large number of experiments, whose findings show that P-FEAT can significantly reduce the threats of privacy attacking tasks while maintaining high accuracy of the target tasks.
IBM Db2 Graph: Supporting Synergistic and Retrofittable Graph Queries Inside IBM Db2

To meet the challenge of analyzing rapidly growing graph and network data created by modern applications, a large number of graph databases have emerged, such as Neo4j and JanusGraph. They mainly target low-latency graph queries, such as finding the neighbors of a vertex with certain properties, and retrieving the shortest path between two vertices. Although many of the graph databases handle the graph-only queries very well, they fall short for real life applications involving graph analysis. This is because graph queries are not all that one does in an analytics workload of a real life application. They are often only a part of an integrated heterogeneous analytics pipeline, which may include SQL, machine learning, graph, and other analytics. This means graph queries need to be synergistic with other analytics. Unfortunately, most existing graph databases are standalone and cannot easily integrate with other analytics systems. In addition, many graph data (data about relationships between objects or people) are already prevalent in existing non-graph databases, although they are not explicitly stored as graphs. None of existing graph databases can retrofit graph queries onto these existing data without transferring or transforming data. In this paper, we propose an in-DBMS graph query approach, IBM Db2 Graph, to support synergistic and retrofittable graph queries inside the IBM Db2 relational database. It is implemented as a layer inside Db2, and thus can support integrated graph and SQL analytics efficiently. Db2 Graph employs a novel graph overlay approach to expose a graph view of the relational data. This approach flexibly retrofits graph queries to existing graph data stored in relational tables, without expensive data transfer or transformation. In addition, it enables efficient execution of graph queries with the help of Db2 relational engine, through sophisticated compile-time and runtime optimization strategies. Our experimental study, as well as our experience with real customers using Db2 Graph, showed that Db2 Graph can provide very competitive and sometimes even better performance on graph-only queries, compared to existing graph databases. Moreover, it optimizes the overall performance of complex analytics workloads.
Asymptotics of Moore exponent sets

Let n be a positive integer and I a k-subset of integers in [0,n−1]. Given a k-tuple A=(α0,⋯,αk−1)∈Fqnk, let MA,I denote the matrix (αiqj) with 0≤i≤k−1 and j∈I. When I={0,1,⋯,k−1}, MA,I is called a Moore matrix which was introduced by E. H. Moore in 1896. It is well known that the determinant of a Moore matrix equals 0 if and only if α0,⋯,αk−1 are Fq-linearly dependent. We call I that satisfies this property a Moore exponent set. In fact, Moore exponent sets are equivalent to maximum rank-distance (MRD) code with maximum left and right idealizers over finite fields. It is already known that I={0,⋯,k−1} is not the unique Moore exponent set, for instance, (generalized) Delsarte-Gabidulin codes and the MRD codes recently discovered in [5] both give rise to new Moore exponent sets. By using algebraic geometry approach, we obtain an asymptotic classification result: for q>5, if I is not an arithmetic progression, then there exists an integer N depending on I such that I is not a Moore exponent set provided that n>N.
Multimodal Retrieval through Relations between Subjects and Objects in Lifelog Images

With the development of wearable devices, people nowadays record their life experiences much easier than before. Lifelog retrieval becomes an emerging task. Because of the semantic gap between visual data and textual queries, retrieving lifelog images with text queries could be challenging. This paper proposes an interactive lifelog retrieval system that is aimed at retrieving more intuitive and accurate results. Our system is divided into the offline and the online parts. In the offline part, we aim to incorporate original visual and textual concepts from images into our system utilizing pre-trained word embedding. Moreover, we encode the information of relationships between subjects and objects in images by using a pre-trained relation graph generation model. In the online part, We provide an intuitive frontend with various metadata filters, which not only provides users with a convenient interface, but also a mechanism to exploit detail memory recall to users. In this case, users would clearly know the difference between the concepts in the clusters and efficiently browse the retrieved images clusters in a short time.
Thinging-Oriented Modeling of Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in both practical and research applications of unmanned aerial vehicles (UAVs).According to the literature, there is a need in this area to develop a more refined model of UAV system architecture-in other words, a conceptual model that defines the system's structure and behavior.The existing models mostly are fractional and do not account for the entire important dynamic attributes.Progress in this area could reduce ambiguity and increase reliability in the design of such systems.This paper aims to advance the modeling of UAV system architecture by adopting a conceptual model called a thinging (abstract) machine in which all of the UAV's software and hardware components are viewed in terms of the flow of things and five generic operations.We apply this model to a real case study of a drone.The resultsan integrated conceptual representation of the drone-support the viability of this approach.
Comparison between client-side and server-side rendering in the web development

Abstract Mandatory servers for universal applications that is accessible to number of users may be a deterrent for the corporation and excessive for small applications even though it could bring the compatibility advantages. Knowing that demand of web application increases to provide convenience and ease of use to the users, client side rendering comes to create software more fast and efficient. It has been done by redirecting the request towards an HTML file then the server will give messages without any content or a loading screen until the device takes all JavaScript to allow the browser compiling everything before displaying the content. Therefore, the purpose of this paper is to analyse the comparison between client side and server side method in the respect of technical aspects in term of first content paint, speed index, time to interactive, first meaningful paint, first idle CPU and estimated input latency that present better performance with 2.1s, 2.0s, 2.2s, 2.1s, 2.2s and 20ms respectively on server side. It also provide better result based on Google Audit with 100% performance, 48% accessibility, 93% best practice and 89% of search engine optimization (SEO).
Unveiling Optical Properties in Underwater Images

The appearance of underwater scenes is highly governed by the optical properties of the water (attenuation and scattering). However, most research effort in physics-based underwater image reconstruction methods is placed on devising image priors for estimating scene transmission, and less on estimating the optical properties. This limits the quality of the results. This work focuses on robust estimation of the water properties. First, as opposed to previous methods that used fixed values for attenuation, we estimate it from the color distribution in the image. Second, we estimate the veiling-light color from objects in the scene, contrary to looking at background pixels. We conduct an extensive qualitative and quantitative evaluation of our method vs. most recent methods on several datasets. As our estimation is more robust our method provides superior results including on challenging scenes.
Comparative Analysis of Convolution Neural Network Models for Continuous Indian Sign Language Classification

Classification of continuous sign language is essential for development of a sign language to spoken language translator. In this paper, classification of continuously signed sentences from the Indian Sign Language is considered using data from one inertial measurement unit placed on each hand of the signer. The recorded accelerometer and gyroscope data are used in tracking the position of hand in three-dimension, which are used as input to the classifier. The time-LeNet and multi-channel deep convolutional neural network (MC-DCNN) are employed for classification of sentences from raw position data of both hands. Moreover, a modified time-LeNet architecture is proposed to address the issue of over-fitting observed in the time-LeNet. The three models are compared for performance in terms of model complexity, loss and classification accuracies. MC-DCNN has large number of trainable parameters and provides an overall accuracy of 83.94%, while time-LeNet yields an average accuracy of 79.70%. The modified time-LeNet yields a classification accuracy of 81.62 % with just sixteenth of trainable parameters as compared to MC-DCNN.
Spontaneous Gaze Gesture Interaction in the Presence of Noises and Various Types of Eye Movements

Gaze gesture is a desirable technique for a spontaneous and pervasive gaze interaction due to its insensitivity to spatial accuracy. Unfortunately, gaze gesture-based object selection utilizing correlation coefficient is prone to a low object selection accuracy due to presence of noises. In addition, effect of various types of eye movements that present in gaze gesture-based object selection has not been tackled properly. To overcome these problems, we propose a denoising method for gaze gesture-based object selection using First Order IIR Filter and an event detection method based on the Hidden Markov Model. The experimental results show that the proposed method yielded the best object selection accuracy of . The result suggests that a spontaneous gaze gesture-based object selection is feasible to be developed in the presence of noises and various types of eye movements.
Unsupervised Multi-View Constrained Convolutional Network for Accurate Depth Estimation

Accurate depth estimation from images is a fundamental problem in computer vision. In this paper, we propose an unsupervised learning based method to predict high-quality depth map from multiple images. A novel multi-view constrained DenseDepthNet is designed for this task. Our DenseDepthNet can effectively leverage both the low-level and high-level features of input images and generate appealing results, especially with sharp details. We employ the public datasets KITTI and Cityscapes for training in an end-to-end unsupervised fashion. A novel depth consistency loss based on multi-view geometry constraint is also applied to the corresponding points across pairwise images, which helps to improve the quality of predicted depth maps significantly. We conduct comprehensive evaluations on our DenseDepthNet and our depth consistency loss function. Experiments validate that our method outperforms the state-of-the-art unsupervised methods and produce comparable results with supervised methods.
An Improved Single Shot Multibox for Video-Rate Head Pose Prediction

Head pose estimation plays a crucial role in attention detection, behavior analysis, human-computer interaction, and eye tracking, etc. The existing landmark-to-pose methods require two steps, not only to detect the key points of the human face, but also to solve the 2D to 3D mapping problem usually by the average head model. We propose an efficient and robust method to monitor the driver's attention. Firstly, the prevailing object detection algorithm SSD which has inherent capabilities of simultaneous classify and regress, is used to create a lightweight network, which avoids the shortcomings of high coupling and time-consuming of the existing methods. Then, single-scale anchors, which have a less computational cost than multi-scale anchors, are adopted for vehicle environments where the ambient light changes dramatically. Finally, by binning continuous angles into specific classes, the 3D angle regression problem is converted into angle classification and face box regression, and our model directly outputs Euler angles (Yaw, Pitch, and Roll) without detecting face landmarks. Experiments on YawDD result that our approach can efficiently perform detection tasks and estimation tasks under the actual driving environment of various luminosity. The mean average errors of prediction in AFLW2000 and 300W-LP are 6.01° and 2.38°, which demonstrates the accuracy of the proposed algorithm.
Long-Term Video Prediction via Criticization and Retrospection

Video prediction refers to predicting and generating future video frames given a set of consecutive frames. Conventional video prediction methods usually criticize the discrepancy between the ground-truth and predictions frame by frame. As the prediction error accumulates recursively, these methods would easily become out of control and are often confined to the short-term horizon. In this paper, we introduce a retrospection process to rectify the prediction errors beyond criticizing the future prediction. The introduced retrospection process is designed to look back what have been learned from the past and rectify the prediction deficiencies. To this end, we build a retrospection network to reconstruct the past frames given the currently predicted frames. A retrospection loss is introduced to push the retrospection frames being consistent with the observed frames, so that the prediction error is alleviated. On the other hand, an auxiliary route is built by reversing the flow of time and executing a similar retrospection. These two routes interact with each other to boost the performance of retrospection network and enhance the understanding of dynamics across frames, especially for the long-term horizon. An adversarial loss is employed to generate more realistic results in both prediction and retrospection process. In addition, the proposed method can be used to extend many state-of-the-art video prediction methods. Extensive experiments on the natural video dataset demonstrate the advantage of introducing the retrospection process for long-term video prediction.
Using Social Dependence to Enable Neighbourly Behaviour in Open Multi-Agent Systems

Agents frequently collaborate to achieve a shared goal or to accomplish a task that they cannot do alone. However, collaboration is difficult in open multi-agent systems where agents share constrained resources to achieve both individual and shared goals. In current approaches to collaboration, agents are organised into disjoint groups and social reasoning is used to capture their capabilities when selecting a qualified set of collaborators. These approaches are not useful when agents are in multiple, overlapping groups; depend on each other when using shared resources; have multiple goals to achieve simultaneously; and have to share the overall costs and benefits. In this article, agents use social reasoning to enhance their understanding of other agents’ goals and their dependencies, and self-adaptive techniques to adapt their level of self-interest in a collaborative process, with a view to contributing to lowering shared costs or increasing shared benefits. This model aims at improving the extent to which agents’ goals are met while improving shared resource usage efficiency. For example, in a public transport system where each mode of transport has limited capacity, commuters will be enabled to make choices that avoid over-capacity in different modes, or in a smart energy grid with limited capacity, users can make choices as to when they increase their demand. The model simultaneously helps avoid overloading a shared resource while allowing users to achieve their own goals. The proposed model is evaluated in an open multi-agent system with 100 agents operating in multiple overlapping groups and sharing multiple constrained resources. The impact of agents’ varying levels of social dependencies, mobility, and their groups’ density on their individual and shared goal achievement is analysed.
Detection of Spliced Image Forensics Using Texture Analysis of Median Filter Residual

In the image forensics, detection of Cut-Paste manipulation is complicated computing.In this paper, the texture analysis of the spliced image is used to detect image forensics.From the local entropy of the median filter residual (MFR) of the forged image, the feature set is extracted for the ground truth mask 'Find Gray level regional Maxima (FGM), ' and 'Entropy-based Edge (EbE).'Also, from the local range, the feature set is extracted for ground truth mask {'Morphological-Open Image (MOI), and 'Morphological-Erosion Image (MOE)'}.The feature vector in this paper composed of the two MOIs, two MOEs, and one EbE.The defined novel feature vector trained on a cubic support vector machine (SVM) classifier for only the performance evaluation of the proposed scheme.The performance of the proposed image forensics detection (IFD) scheme was measured with the five transformed types of image: median filtered (window size: {3×3, 5×5}), JPEG compressed (quality factor: {90, 70}) and average filtered (window size: 3×3).For the detection of the spliced image forensics, the region of Cut-Paste is classified by an input image only to the proposed scheme without the need for the trained SVM classifier.Throughout the experiment, the accuracy of median filtering detection was 98% over.Also, The area under the curve by sensitivity (TP: true positive rate) and 1-specificity (FP: false positive rate) results of the proposed IFD scheme approached to '1' with the trained cubic SVM classifier.Experimental results show high efficiency and performance to the spliced image.Therefore, the grade evaluation of the proposed scheme is ''Excellent (A).''
Backtracking Tool Root-Tracker to Identify True Source of Cyber Crime

The main goal of the network forensic tools is to collect legal evidence of cybercrime from the network environment by capturing, recording and analyzing the network packets. Such tools aim to determine the source of network security attacks. Majority of existing network forensic tools are based on the concept of IP traceback that helps the investigator to reach up to the source of a cyberattack. However, the limitation of these existing techniques is that the investigation ends at the edge router or ISP of the attacker. But the original attacker is hiding somewhere behind the ISP. Reaching up to them is the most critical aspect of network forensics. This paper broadly aims at building secure and forensically sound framework called Root-tracker for Identification of real source of cybercrime by reaching beyond the ISP. The approach of device fingerprinting is used for unique identification of criminal device which will act as legal and infallible evidence in court of law. Root Tracker has been implemented as a prototype and deployed on AWS. Testing is done in real time environment and the results shows that Root-Tracker is able to verify the evidences partially. Even if the attacker tries to format the system or modifies any of device parameter Root-Tracker can still identify the attacker device and generates partial evidence match report.
A Novel Neural Network Method for Face Recognition With a Single Sample Per Person

Face Recognition (FR) problem is one of the significant fields in computer vision.FR is used to identify the faces that appear over distributed cameras over the network.The problem of face recognition can be divided into two categories, the first is recognition with more than one sample per person, which can be called traditional face recognition problem.The second is the recognition of faces using only a Single Sample Per Person (SSPP).The efficiency of face recognition systems decreases because of limited references especially (SSPP) and faces taken in the Operational Domain (OD) different from faces in the Enrollment Domain (ED) in illumination, pose, low-resolution, and blurriness.This paper proposed a method that deals with all problems related to face recognition with SSPP.3D face reconstruction is used to increase the reference gallery set with different poses and generate a design domain dictionary to overcome the problem of limited reference.Besides, the design domain dictionary is used to feed different deep learning models.Face illumination transfer techniques are utilized to overcome the illumination problem.Labeled Faces in the Wild (LFW) dataset is used to train Super-Resolution Generative Adversarial Network (SRGAN) to overcome the low-resolution problem.Deblur Generative Adversarial Network (DeblurGAN) is trained on the LFW dataset to overcome the problem of blurriness.The proposed method is evaluated using the Chokepoint dataset and COX-S2V dataset.The final results confirm an overall enhancement in accuracy compared to techniques that use SSPP for face recognition (generic learning and face synthesizing approaches).Also, the proposed method outperforms of Traditional and Deep Learning (TDL) method accuracy, which uses SSPP for face recognition.