Reviewerly/exHarmony huggingface dataset:
Using Fully Connected and Convolutional Net for GAN-Based Face Swapping

The following topics are dealt with: low-power electronics; CMOS integrated circuits; learning (artificial intelligence); analogue-digital conversion; cryptography; field programmable gate arrays; neural nets; feature extraction; flip-flops.
Haze-robust image understanding via context-aware deep feature refinement

Image understanding under the foggy scene is greatly challenging due to inhomogeneous visibility deterioration. Although various image dehazing methods have been proposed, they usually aim to improve image visibility (such as, PSNR/SSIM) in the pixel space rather than the feature space, which is critical for the perception of computer vision. Due to this mismatch, existing dehazing methods are limited or even adverse in facilitating the foggy scene understanding. In this paper, we propose a generalized deep feature refinement module to minimize the difference between clear images and hazy images in the feature space. It is consistent with the computer perception and can be embedded into existing detection or segmentation backbones for joint optimization. Our feature refinement module is built upon the graph convolutional network, which is favorable in capturing the contextual information and beneficial for distinguishing different semantic objects. We validate our method on the detection and segmentation tasks under foggy scenes. Extensive experimental results show that our method outperforms the state-of-the-art dehazing based pretreatments and the fine-tuning results on hazy images.
Machine learning based Synthetic Data Generation using Iterative Regression Analysis

Machine learning has made a drastic impact in today's world. Developments in machine learning are happening every day at an exponential rate. However, there are still some fields that are comparatively untouched by its impact. Areas like the medical and sports sector that could benefit immensely by utilizing the advancements in Machine Learning and still are lagging solely because of one crucial reason: unavailability of data. The unavailability of data results in scarcity of data used for training the machine learning models, which directly affects the accuracy of the models, making them less reliable for real-time usage. To counter this roadblock, this paper is proposing a solution to generating synthetic data in this paper. As the name suggests, a synthetic dataset is a repository of data that is generated programmatically. So, it is not collected by any real-life survey or experiment. It's the primary purpose; therefore, it is to be flexible and rich enough to help a Machine Learning practitioner conduct fascinating experiments with various classification, regression, and clustering algorithms. Thus, using this approach, iterative regression analysis was applied to generate synthetic data using a data set that was used in the field of sports. The generated data was then used along with the original dataset to train a new model that brought about a significant increase in the accuracy of the model to predict features.
3D human model creation on a serverless environment

The creation of realistic 3D human model is traditionally timeconsuming and cumbersome, and is typically done by professionals. In recent years computer vision technologies can assist in generating human models from controlled environments, we demonstrate a different but easy capturing scenario with less constraints on the subject or the environmental setup. The reconstruction process for 3D human model consists of various intermediate process such as semantic human segmentation, human skeletal keypoint detection, and texture generation. In order to achieve easy, scalable, and flexible deployment to different cloud environments, we have chosen the serverless architecture to offload some common service functionalities to the cloud infrastructure but focused on the core task,which is the reconstruction itself. The event-driven serverless architecture eases the building of such multimedia web services with minimal coding efforts, but simply defines the APIs and declares the APIs with correspondent lambda functions. The proposed approach in this paper allow anyone with a mobile phone to generate 3D models easily and quickly in the scale of few 2-3 minutes, rather than hours.
Pig Datasets of Livestock for Deep Learning to detect Posture using Surveillance Camera

This paper proposes the pig Datasets for deep learning to detect a posture using a surveillance camera. The proposal aims to develop the best quality Datasets that can be input to the object detection pipeline for building a deep learning model. The proposed Datasets have two types with 7 and 9 categories(each called Class 7 and Class 9), and each evaluated. In Class 9, one of the categories labels to the ground- truth bounding box as a fake data assuming that the hidden head of a pig. The hidden head of pig shows to decrease performance. When training YOLOv2 and SSD against proposed Datasets, the performance an average precision (AP) of each training result evaluates the trained model with three types of optimization algorithms: Adam, SGDM, and RMSProp. The detection accuracy for proposed Datasets Class 7 reaches 97%. YOLOv2 using the proposed Datasets of Class 7 shows seven times better performance than SSD.
Translating Natural Language Instructions to Computer Programs for Robot Manipulation

It is highly desirable for robots that work alongside humans to be able to understand instructions in natural language. Existing language conditioned imitation learning models directly predict the actuator commands from the image observation and the instruction text. Rather than directly predicting actuator commands, we propose translating the natural language instruction to a Python function which queries the scene by accessing the output of the object detector and controls the robot to perform the specified task. This enables the use of non-differentiable modules such as a constraint solver when computing commands to the robot. Moreover, the labels in this setup are significantly more informative computer programs that capture the intent of the expert rather than teleoperated demonstrations. We show that the proposed method performs better than training a neural network to directly predict the robot actions.
Online Handwritten Bangla and Devanagari Character Recognition by using CNN: A Deep Learning Concept

The present experiment deals with online handwriting recognition for two regional languages Bangla and Devanagari using CNN (convolution neural network) a deep learning concept. Our proposed model consists of two convolution and pooling layers and a fully connected network. CNN model give relaxation of producing handcrafted features manually rather generates features, reduce feature dimension automatically and pass the features to a fully connected network for classification purpose. The current experiment is performed on 10000 Bangla basic characters and 1800 Devanagari characters. We have achieved 99.65% and 98.87% recognition accuracy for Bangla and Devanagari character datasets respectively.
Ontology for OpenAPI REST Services Descriptions

OpenAPI Specification (OAS) defines a description format for REST APIs. In order for a machine to understand the meaning of REST services, OpenAPI service descriptions must be unambiguous. In a previous work we analysed the reasons that cause ambiguities in OpenAPI and showed that, in order to eliminate ambiguities, OpenAPI properties must be semantically annotated and mapped to a semantic model. Leveraging latest results for hypermedia-based construction of Web APIs (i.e. Hydra), the present work forwards this approach and proposes a reference ontology for REST services along with a formal procedure for converting OpenAPI service descriptions to instances of this ontology.
Drafting in Collectible Card Games via Reinforcement Learning

Collectible card games are played by tens of millions of players worldwide. Their intricate rules and diverse cards make them much harder than traditional card games. To win, players must be proficient in two interdependent tasks: deck building and battling. In this paper, we present a deep reinforcement learning approach for deck building in arena mode - an understudied game mode present in many collectible card games. In arena, the players build decks immediately before battling by drafting one card at a time from randomly presented candidates. We investigate three variants of the approach and perform experiments on Legends of Code and Magic, a collectible card game designed for AI research. Results show that our learned draft strategies outperform those of the best agents of the game. Moreover, a participant of the Strategy Card Game AI competition improves from tenth to fourth place when coupled with our best draft agent.
Performance Prediction for Data-driven Workflows on Apache Spark

Spark is an in-memory framework for implementing distributed applications of various types. Predicting the execution time of Spark applications is an important but challenging problem that has been tackled in the past few years by several studies; most of them achieving good prediction accuracy on simple applications (e.g. known ML algorithms or SQL-based applications). In this work, we consider complex data-driven workflow applications, in which the execution and data flow can be modeled by Directly Acyclic Graphs (DAGs). Workflows can be made of an arbitrary combination of known tasks, each applying a set of Spark operations to their input data. By adopting a hybrid approach, combining analytical and machine learning (ML) models, trained on small DAGs, we can predict, with good accuracy, the execution time of unseen workflows of higher complexity and size. We validate our approach through an extensive experimentation on real-world complex applications, comparing different ML models and choices of feature sets.
Highly Secured Blockchain Based Electronic Voting System Using SHA3 and Merkle Root

Abstract Over the course of time, electronic voting has evolved as a substitute for paper ballot voting to decrease redundancies and inconsistencies. Due to the many security and privacy vulnerabilities experienced over time, the past results of e-voting in the last three decades indicate that it has not been very successful. All cryptocurrencies are actually based on block chain. Block chain is based on the principle of distribution and decentralization. It is a continuous and continuously growing ledger that holds, in a secure, chronological and immutable way, a permanent record of all the transactions that have taken place. Value in a block chain can be anything. In the case of cryptocurrencies, it takes the meaning of money or currency. While in a game it takes the form of points or score. The value can take the form of a vote or a ballot while e-voting. The blockchain can be used in this article to pass votes between two parties. In our case, the electorate is one party and the candidate who earns the vote is the other. Without having a controlling central authority body, the block chain can be implemented in a more stable manner in mass electoral voting practise. A voting system that uses a more stable, tamper-proof block chain (immutable to voting modifications either by other voters or by any third party) and is cost-effective.
A method for constructing a dataset to reveal the industrial behaviour of big data

Abstract Dataset is a set of data that becomes the standard for showing the behaviour of something. In this case, the industry. For this situation, the industry. Be that as it may, each dataset consistently has restrictions to help the prediction. The prediction is about the current conduct of the industry, where yet just exists in enormous information like in big data. This paper aims to construct a concept that describes a method for developing a dataset.
AutoCite: Multi-Modal Representation Fusion for Contextual Citation Generation

Citing comprehensive and correct related work is crucial in academic writing. It can not only support the author's claims but also help readers trace other related research papers. Nowadays, with the rapid increase in the number of scientific literatures, it has become increasingly challenging to search for high-quality citations and write the manuscript. In this paper, we present an automatic writing assistant model, AutoCite, which not only infers potentially related work but also automatically generates the citation context at the same time. Specifically, AutoCite involves a novel multi-modal encoder and a multi-task decoder architecture. Based on the multi-modal inputs, the encoder in AutoCite learns paper representations with both citation network structure and textual contexts. The multi-task decoder in AutoCite couples and jointly learns citation prediction and context generation in a unified manner. To effectively join the encoder and decoder, we introduce a novel representation fusion component, i.e., gated neural fusion, which feeds the multi-modal representation inputs from the encoder and creates outputs for the downstream multi-task decoder adaptively. Extensive experiments on five real-world citation network datasets validate the effectiveness of our model.
Towards Efficient Neural Network on Edge Devices via Statistical Weight Pruning

Convolutional neural network (CNN) becomes more and more popular as it has demonstrated great success in many visual content-oriented applications such as computer vision and image/video processing. Inevitably, massive computation resources and storage spaces are required for the hardware implementations of CNN. While “model compression” can significantly reduce parameters (e.g., weights) used in CNN so as corresponding computations by suitable pruning, this paper proposes a pruning approach based on statistical analysis of weight distribution. Experimental results show that substantial redundant weights of a demonstrated CNN were effectively removed through carefully tracing weight distributions for each layer. Meanwhile, saving weight is expected to save up to 30% of storage space and computation complexity.
JNR: Joint-Based Neural Rig Representation for Compact 3D Face Modeling

In this paper, we introduce a novel approach to learn a 3D face model using a joint-based face rig and a neural skinning network. Thanks to the joint-based representation, our model enjoys some significant advantages over prior blendshape-based models. First, it is very compact such that we are orders of magnitude smaller while still keeping strong modeling capacity. Second, because each joint has its semantic meaning, interactive facial geometry editing is made easier and more intuitive. Third, through skinning, our model supports adding mouth interior and eyes, as well as accessories (hair, eye glasses, etc.) in a simpler, more accurate and principled way. We argue that because the human face is highly structured and topologically consistent, it does not need to be learned entirely from data. Instead we can leverage prior knowledge in the form of a human-designed 3D face rig to reduce the data dependency, and learn a compact yet strong face model from only a small dataset (less than one hundred 3D scans). To further improve the modeling capacity, we train a skinning weight generator through adversarial learning. Experiments on fitting high-quality 3D scans (both neutral and expressive), noisy depth images, and RGB images demonstrate that its modeling capacity is on-par with state-of-the-art face models, such as FLAME and Facewarehouse, even though the model is 10 to 20 times smaller. This suggests broad value in both graphics and vision applications on mobile and edge devices.
New Techniques in Replica Encodings with Client Setup

A proof of replication system is a cryptographic primitive that allows a server (or group of servers) to prove to a client that it is dedicated to storing multiple copies or replicas of a file. Until recently, all such protocols required fine-grained timing assumptions on the amount of time it takes for a server to produce such replicas. Damgård, Ganesh, and Orlandi (CRYPTO’ 19) [11] proposed a novel notion that we will call proof of replication with client setup. Here, a client first operates with secret coins to generate the replicas for a file. Such systems do not inherently have to require fine-grained timing assumptions. At the core of their solution to building proofs of replication with client setup is an abstraction called replica encodings. Briefly, these comprise a private coin scheme where a client algorithm given a file m can produce an encoding $$\sigma $$ . The encodings have the property that, given any encoding $$\sigma $$ , one can decode and retrieve the original file m. Secondly, if a server has significantly less than $$n \cdot |m|$$ bit of storage, it cannot reproduce n encodings. The authors give a construction of encodings from ideal permutations and trapdoor functions. In this work, we make three central contributions.
Convolutional Neural Network Design for Breast Cancer Medical Image Classification

For computer-aided diagnosis of medical issues, image classification plays an important role to ensure high prediction accuracy. Convolutional neural network (CNN) is an important aspect of deep learning because it returns a higher performance rate than other traditional machine learning methods and solves end-to-end classification problems Recently, machine learning and artificial intelligence have rapidly advanced in different fields, especially in the health industry. One of the main medical problems in the world today arises from diseases such as brain tumors, breast cancer, and lung cancer. Therefore, this paper aims at employing image classification and medical image segmentation based on deep learning to detect such medical issues on time. In this paper, a 15-layer CNN model is designed to classify and recognize medical images; especially, breast cancer cells, by using relevant CNN classification techniques. The experimental results and supporting data show that the proposed model achieves better performance in terms of higher validation accuracy and training accuracy, as well as lower losses in training and testing, in comparison with other traditional CNN models including AlexNet and VGGNet.
GALB: Load Balancing Algorithm for CP-ABE Encryption Tasks in E-Health Environment

Security of personal data in the e-healthcare has always been challenging issue. The embedded and wearable devices used to collect these personal and critical data of the patients and users are sensitive in nature. Attribute-Based Encryption is believed to provide access control along with data security for distributed data among multiple parties. These resources limited devices do have the capabilities to secure the data while sending to the cloud but instead it increases the overhead and latency of running the encryption algorithm. On the top of if confidentiality is required, which will add more latency. In order to reduce latency and overhead, we propose a new load balancing algorithm that will distribute the data to nearby devices with available resources to encrypt the data and send it to the cloud. In this article, we are proposing a load balancing algorithm for E-Health system called (GALB). Our algorithm is based on Genetic Algorithm (GA). Our algorithm (GALB) distribute the tasks that received to the main gateway between the devices on E-health environment. The distribution strategy is based on the available resources in the devices, the distance between the gateway and the those devices, and the complexity of the task (size) and CP-ABE encryption policy length. In order to evaluate our algorithm performance, we compare the near optimal solution proposed by GALB with the optimal solution proposed by LP.
Knowledge Based Word Sense Disambiguation with Distributional Semantic Expansion for the Persian Language

Word Sense Disambiguation (WSD) can be the key component of downstream NLP applications. Existing WSD methods and systems are mostly developed and evaluated on English and low-resource languages such as Persian have not been well studied. In this paper, we propose a new knowledge-based method for Persian WSD. Using a pre-trained LDA model, we retrieve the topics of each document and assign each ambiguous content word to one of the topics. For each possible sense s of a given word w, we compute the similarity between the FarsNet (the Persian WordNet) gloss of s and the words of the assigned topic of w. We then choose the sense with the highest score as the most probable one. We evaluated our method on a Persian all-words WSD dataset and show that, compared to other knowledge-based methods, we could achieve state-of-the-art performance.
A Practical Defense against Attribute Inference Attacks in Session-based Recommendations

When users in various web and mobile applications enjoy the convenience of recommendation systems, they are vulnerable to attribute inference attacks. The accumulating online behaviors of users (e.g., clicks, searches, ratings) naturally brings out user preferences, and poses an inevitable threat of privacy that adversaries can infer one's private profiles (e.g., gender, sexual orientation, political view) with AI-based algorithms. Existing defense methods assume the existence of a trusted third party, rely on computationally intractable algorithms, or have impact on recommendation utility. These imperfections make them impractical for privacy preservation in real-life scenarios. In this work, we introduce BiasBooster, a practical proactive defense method based on behavior segmentation, to protect user privacy against attribute inference attacks from user behaviors, while retaining recommendation utility with a heuristic recommendation aggregation module. BiasBooster is a user-centric approach from client side, which proactively divides a user's behaviors into weakly related segments and perform them with several dummy identities, then aggregates real-time recommendations for user from different dummy identities. We estimate its effectiveness of preservation on both privacy and recommendation utility through extensive evaluations on two real-world datasets. A Chrome extension is conducted to demonstrate the feasibility of applying BiasBooster in real world. Experimental results show that compared to existing defenses, BiasBooster substantially reduces the averaged accuracy of attribute inference attacks, with minor utility loss of recommendations.
CNN-Based Anomaly Detection For Face Presentation Attack Detection With Multi-Channel Images

Recently, face recognition systems have received significant attention, and there have been many works focused on presentation attacks (PAs). However, the generalization capacity of PAs is still challenging in real scenarios, as the attack samples in the training database may not cover all possible PAs. In this paper, we propose to perform the face presentation attack detection (PAD) with multi-channel images using the convolutional neural network based anomaly detection. Multi-channel images endow us with rich information to distinguish between different mode of attacks, and the anomaly detection based technique ensures the generalization performance. We evaluate the performance of our methods using the wide multi-channel presentation attack (WMCA) dataset.