Reviewerly/exHarmony huggingface dataset:
Learning Semisupervised Multilabel Fully Convolutional Network for Hierarchical Object Parsing

This article presents a semisupervised multilabel fully convolutional network (FCN) for hierarchical object parsing of images. We consider each object part (e.g., eye and head) as a class label and learn to assign every image pixel to multiple coherent part labels. Different from previous methods that consider part labels as independent classes, our method explicitly models the internal relationships between object parts, e.g., that a pixel highly scored for eyes should be highly scored for heads as well. Such relationships directly reflect the structure of the semantic space and thus should be respected while learning the deep representation. We achieve this objective by introducing a multilabel softmax loss function over both labeled and unlabeled images and regularizing it with two pairwise ranking constraints. The first constraint is based on a manifold assumption that image pixels being visually and spatially close to each other should be collaboratively classified as the same part label. The other constraint is used to enforce that no pixel receives significant scores from more than one label that are semantically conflicting with each other. The proposed loss function is differentiable with respect to network parameters and hence can be optimized by standard stochastic gradient methods. We evaluate the proposed method on two public image data sets for hierarchical object parsing and compare it with the alternative parsing methods. Extensive comparisons showed that our method can achieve state-of-the-art performance while using 50% less labeled training samples than the alternatives.
Things2Vec: Semantic Modeling in the Internet of Things With Graph Representation Learning

The advent of fifth generation (5G) enables the Internet of Things (IoT) to connect a massive number of things. The interaction and communication among these things generate an enormous amount of context-aware data that is semantically diverse. Traditional data representation approaches, such as semantic annotation, ontology, and semantic Web technology are rule based, which lack flexibility and adaptability when applied to IoT. To address the challenge, this article mainly focuses on the problem of semantic representation, which is essential for processing and fusion of IoT data. To serve as a bridge, we propose a high-level framework, namely, Things2Vec, which aims to produce the latent semantic representations from the interaction of things through the graph embedding technique. These semantic representations benefit various IoT semantic analysis tasks, such as the IoT service recommendation and automation of things. In Things2Vec, we utilize the graph to model the function sequence relationships that are generated by the interaction of things, which is called the IoT context graph. Since these function sequence relationships are heterogeneous in terms of semantics, it causes general graph embeddings to fail to learn complete information. Thus, we propose a biased random walk procedure, which is tailored to capture the neighborhoods of nodes with different types of semantic relationships. Extensive experiments are carried out, and our results show that the proposed method can effectively capture the semantic relationships among context-aware data in IoT.
Vehicle-Related Scene Understanding Using Deep Learning

Automated driving is an inevitable trend in future transportation, it is also one of the eminent achievements in the matter of artificial intelligence. Deep learning produces a significant contribution to the progression of automatic driving. In this paper, our goal is to primarily deal with the issue of vehicle-related scene understanding using deep learning. To the best of our knowledge, this is the first time that we utilize our traffic environment as an object for scene understanding based on deep learning. Moreover, automatic scene segmentation and object detection are joined for traffic scene understanding. The techniques based on deep learning dramatically decrease human manipulations. Furthermore, the datasets in this paper consist of a large amount of our collected traffic images. Meanwhile, the performance of our algorithms is verified by the experiential results.
Selective prototype-based learning on concept-drifting data streams

Data stream mining has gained increasing attention in recent years due to its wide range of applications. In this paper, we propose a new selective prototype-based learning (SPL) method on evolving data streams, which dynamically maintains representative instances to capture the time-changing concepts, and make predictions in a local fashion. As an instance-based learning model, SPL only maintains some important prototypes (i.e., ISet) via error-driven representativeness learning. The fast condensed nearest neighbor (FCNN) rule, is further introduced to compress these prototypes, making the algorithm also applicable under memory constraints. To better distinguish noises from the instances associated with the new emerging concept, a potential concept instance set (i.e., PSet) is used to store all misclassified instances. Relying on the potential concept instance set, a local-aware distribution-based concept drift detection approach is proposed. SPL has several attractive benefits: (a) it can fit the evolving data streams very well by maintaining a small size of instance set; (b) it is capable of capturing both gradual and sudden concept drifts effectively; (c) it has great capabilities to distinguish noise/outliers from drifting instances. Experimental results show that the SPL has better classification performance than many other state-of-the-art algorithms.
Artificial Intelligence Surpassing Human Intelligence: Factual or Hoax

Abstract Artificial intelligence is one of the most trending topics in the field of Computer Science which aims to make machines and computers ‘smart’. There are multiple diverse technical and specialized research associated with it. Due to the accelerating rate of technological changes, artificial intelligence has taken over a lot of human jobs and is giving excellent results that are more efficient and effective, than humans. However, a lot of time there has been a concern about the following: will artificial intelligence surpass human intelligence in the near future? Are computers’ ever accelerating abilities to outpace human jobs and skills a matter of concern? The different views and myths on the subject have made it even a more than just a topic of discussion. In this research paper, we will study the existing facts and literature to understand the true definitions of artificial intelligence (AI) and human intelligence (HI) by classifying each of its types separately and analyzing the extent of their full capabilities. Later, we will discuss the possibilities if AI eventually can replace human jobs in the market. Finally, we will synthesize and summarize results and findings of why artificial intelligence cannot surpass human intelligence completely in the future.
Comparison of A* and RRT in real–time 3D path planning of UAVs

Unmanned Aerial Vehicles (UAVs) are being integrated into a wide range of military, industrial and commercial applications.Such applications require faultless autonomous systems to coordinate, guide, navigate and control different UAVs of different sizes, designed for different purposes with different capabilities.In this regard, different path planning algorithms were developed to ensure that UAVs are supplied with collision-free path paramount to which are the A* and the RRT algorithms, a graph-based and a samplingbased algorithm respectively.Such algorithms shall ideally operate in real-time to furnish the UAV navigation system with real-time, valid, obstacle-free paths in view of changes in the environment or other external or user-defined restrictions.Owing this need, in this paper a real-time platform to assess the performance of the A* and RRT algorithm with an associated smoothing algorithm was developed and tested using 3, 3D obstacle environment with different complexities.The salient user-defined, system-defined and internal constants were independently considered and their effect on performance assessed.Results showed that the A* outperformed the RRT algorithm in both path length and computational time for all scenarios considered with difference increasing with scenario complexity.But, both algorithms can be utilised if the associated parameters are attentively chosen based on the scenario the UAV will operate as both algorithm reached a 100% success rate for all scenario at specific parameter assignments.
Aerodynamic optimization of unmanned aerial vehicle for offshore search and rescue (SAR) operation

Abstract Workers on offshore oils rigs are constantly risking their lives due to the location of these rigs in the middle of the ocean. One of the biggest risks to these workers are falling from the rigs and drowning especially during bad weather. Operating UAVs can reduce the risks of injury, human errors and enhances inspections, surveying and emergency operations. The applications of UAVs in offshore tasks are discussed in this paper as well as the aerodynamic optimization of UAV’s propellers. Computational Fluid Dynamics (CFD) analysis and experimental procedures were conducted on multiple propeller alternatives to determine the drag and lift properties and selecting the optimal option based on the lift to drag ratio and desired output. The CFD analysis and the experimental test found that the medium sized (9.0 x 4.5 in) propellers were the optimal option with a drag to lift ratio of 1.75.
SCC++: Predicting the programming language of questions and snippets of Stack Overflow

Stack Overflow is the most popular Q&A website among software developers. As a platform for knowledge sharing and acquisition, the questions posted on Stack Overflow usually contain a code snippet. Determining the programming language of a source code file has been considered in the research community; it has been shown that Machine Learning (ML) and Natural Language Processing (NLP) algorithms can be effective in identifying the programming language of source code files. However, determining the programming language of a code snippet or a few lines of source code is still a challenging task. Online forums such as Stack Overflow and code repositories such as GitHub contain a large number of code snippets. In this paper, we design and evaluate Source Code Classification (SCC++), a classifier that can identify the programming language of a question posted on Stack Overflow. The classifier achieves an accuracy of 88.9% in classifying programming languages by combining features from the title, body and the code snippets of the question. We also propose a classifier that only uses the title and body of the question and has an accuracy of 78.9%. Finally, we propose a classifier of code snippets only that achieves an accuracy of 78.1%. These results show that deploying Machine Learning techniques on the combination of text and code snippets of a question provides the best performance. In addition, the classifier can distinguish between code snippets from a family of programming languages such as C, C++ and C#, and can also identify the programming language version such as C# 3.0, C# 4.0 and C# 5.0.
Scalable Distributed Random Number Generation Based on Homomorphic Encryption

Generating a secure source of publicly-verifiable randomness could be the single most fundamental technical challenge on a distributed network, especially in the blockchain context. Many current proposals face serious problems of scalability and security issues. We present a protocol which can be implemented on a blockchain that ensures unpredictable, tamper-resistant, scalable and publicly-verifiable outcomes. The main building blocks of our protocol are homomorphic encryption (HE) and verifiable random functions (VRF). The use of homomorphic encryption enables mathematical operations to be performed on encrypted data, to ensure no one knows the outcome prior to being generated. The protocol requires O(n) elliptic curve multiplications and additions as well as O(n) signature signing and verification operations, which permits great scalability. We present a comparison between recent approaches to the generation of random beacons.
Multi-agent sensitivity enhanced iterative best response: A real-time game theoretic planner for drone racing in 3D environments

This paper presents a real-time game theoretic motion planning approach that enables an autonomous drone to race competitively against an arbitrary number of opponent drones along a 2D or 3D racecourse. Our method computes an approximate Nash equilibrium in the space of robot trajectories to maximally advance the ego robot while taking into account the opponents' intentions and responses. The core of our solution is a "sensitivity enhanced" iterative best response algorithm that the ego robot uses to repeatedly plan its own trajectory and infer opponents' trajectories, ultimately seeking a Nash equilibrium in the joint space of trajectories for all the drones. The algorithm includes a term that allows the ego vehicle to gain advantage by exploiting the influence of the ego drone's trajectory on the adversaries' objectives through the shared collision avoidance constraints among the vehicles. We also propose two methods for accelerating this computationally intensive iterative algorithm using (i) parallel computing with multiple CPU cores, and (ii) a neural network model that learns to predict trajectories close to the Nash equilibrium through offline training examples. Extensive simulation studies are conducted to benchmark the performance of our game theoretic planner and the statistical results show that our approach largely outperforms a baseline model predictive control algorithm that does not account for the opponents' reactions. Hardware experiments with 4 quadrotor robots on a 3D racecourse are performed to show the applicability of our method in real-time robotic systems.
Implementation of RSA Signatures on GPU and CPU Architectures

This paper reports a constant-time CPU and GPU software implementation of the RSA exponentiation by using algorithms that offer a first-line defense against timing and cache attacks. In the case of GPU platforms the modular arithmetic layer was implemented using the Residue Number System (RNS) representation. We also present a CPU implementation of an RNS-based arithmetic that takes advantage of the parallelism provided by the Advanced Vector Extensions 2 (AVX2) instructions. Moreover, we carefully analyze the performance of two popular RNS modular reduction algorithms when implemented on many- and multi-core platforms. In the case of CPU platforms we also report that a combination of the schoolbook and Karatsuba algorithms for integer multiplication along with Montgomery reduction, yields our fastest modular multiplication procedure. In comparison with previous literature, our software library achieves faster timings for the computation of the RSA exponentiation using 1024-, 2048- and 3072-bit private keys.
Contextual-YOLOV3: Implement Better Small Object Detection Based Deep Learning

Small object detection is an open challenge due to its limited resolution and information. Existing object detection pipelines can't meet the requirement of accuracy for small objects. In this paper, we aim to address small object detection problem by introducing contextual information in detector. For this purpose, we propose an improved algorithm for fusing context in YOLOV3 called Contextual-YOLOV3. Specifically, in order to improve the accuracy for small objects. Contextual-YOLOV3 constructs a contextual relationship matrix combined with the classification probability of YOLOV3 to replace the original classification probability. Meanwhile we use context-based filtering algorithm for optimal window selection instead of non-maximum suppression algorithm (NMS). Extensive evaluations on the challenging Tsinghua-Tencent 100K and National Grid's intelligent inspection dataset benchmark well demonstrate the superiority of Contextual-YOLOV3 in detecting small objects.
Enhancing Blockchain Traceability with DAG-Based Tokens

Blockchain technology has positively impacted traceability across various industries such as logistics and shipping. By overcoming the problem of data silos among organizations and concentration of authority, this innovative approach allows a decentralized and tamper-resistant ledger to be built. On the other hand, the use of non-fungible tokens, a new concept for projecting digital and physical goods on a blockchain, is expected to be able to represent complicated operations in traceability systems. However, the existing token design lacks an efficient model for retrieval of past histories. The most popular way to retrieve past information on tokens is to use blockchain explorers, which are centralized trusted parties. Otherwise, so far there is only a naive way to examine all block headers, and it takes a vast amount of time. Our preliminary study shows that the processing time for retrieving all histories of 1,030 tokens on the Ethereum mainnet takes approximately 57 minutes. We propose a new token design based on a directed acyclic graph (DAG) that allows token histories to be efficiently explored without examining the whole blockchain. The model associates each state of the token from past to latest and covers expressions of relationships between tokens, such as merge and split. Our implementation and evaluation reveal that the DAG-based token design significantly improves the efficiency of exploring token transfer histories. The results show that our method can complete the exploration for 1,030 tokens in a matter of seconds and maintains performance over the long term even if the blockchain grows.
An Application of the UTAUT Model for Analysis of Adoption of Integrated License Service Information System

The use of information technology, especially information systems in government agencies is very important to improve public services. The Integrated Licensing Service Information System is a computerized system that performs licensing data processing and produces a collection of interacting information to be given to all levels of users in the Samarinda City Investment and Integrated One-Stop Service. But since the issuance of this system in 2013 there has been no system evaluation or development. This study uses the Unified Theory of Acceptance and Use of Technology (UTAUT) method with Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Behavioral Intention to Use, Gender, and Age variables. This research was conducted to determine the variables that affect users in utilizing the Integrated Licensing Service Information System and then to provide input and suggestions for Samarinda City Investment and Integrated One-Stop Service in the future implementation of the system can be better and can be accepted according to the needs of its users. Based on the results of the study, the variables that greatly influence the acceptance and utilization of this information system are performance expectations. Where the Integrated Licensing Service Information System can improve the quality of public services, especially licensing services and this system becomes a necessity in supporting work efficiency and effectiveness.
A Deep Hierarchical Reinforcement Learner for Aerial Shepherding of Ground Swarms

This paper introduces a deep reinforcement learning method to train an autonomous aerial agent acting as a shepherd to provide guidance for a swarm of ground vehicles. The learner is situated within a high-fidelity robotic-operating-system (ROS)-based simulation environment consisting of an Unmanned Aerial Vehicle (UAV) learning to guide a swarm of Unmanned Ground Vehicles (UGVs) to a target location. Our approach uses a combination of machine education, apprenticeship bootstrapping, and deep-learning-based methodologies to decompose the complex shepherding strategy into sub-problems requiring simpler skills that get fused to form the overall skills required for shepherding. The proposed methodology is effective in training the UAV agent with multiple reward designing schemes.
Leveraging Multi-Token Entities in Document-Level Named Entity Recognition

Most state-of-the-art named entity recognition systems are designed to process each sentence within a document independently. These systems are easy to confuse entity types when the context information in a sentence is not sufficient enough. To utilize the context information within the whole document, most document-level work let neural networks on their own to learn the relation across sentences, which is not intuitive enough for us humans. In this paper, we divide entities to multi-token entities that contain multiple tokens and single-token entities that are composed of a single token. We propose that the context information of multi-token entities should be more reliable in document-level NER for news articles. We design a fusion attention mechanism which not only learns the semantic relevance between occurrences of the same token, but also focuses more on occurrences belonging to multi-tokens entities. To identify multi-token entities, we design an auxiliary task namely ‘Multi-token Entity Classification’ and perform this task simultaneously with document-level NER. This auxiliary task is simplified from NER and doesn't require extra annotation. Experimental results on the CoNLL-2003 dataset and OntoNotesnbm dataset show that our model outperforms state-of-the-art sentence-level and document-level NER methods.
Motion Planning Explorer: Visualizing Local Minima Using a Local-Minima Tree

Motion planning problems often have many local minima. Those minima are important to visualize to let a user guide, prevent or predict motions. Towards this goal, we develop the motion planning explorer, an algorithm to let users interactively explore a tree of local-minima. Following ideas from Morse theory, we define local minima as paths invariant under minimization of a cost functional. The local-minima are grouped into a local-minima tree using lower-dimensional projections specified by a user. The user can then interactively explore the local-minima tree, thereby visualizing the problem structure and guide or prevent motions. We show the motion planning explorer to faithfully capture local minima in four realistic scenarios, both for holonomic and certain non-holonomic robots.
Deep skin detection on low resolution grayscale images

In this work we present a facial skin detection method, based on a deep learning architecture, that is able to precisely associate a skin label to each pixel of a given image depicting a face. This is an important preliminary step in many applications, such as remote photoplethysmography (rPPG) in which the hearth rate of a subject needs to be estimated analyzing a video of his/her face. The proposed method can detect skin pixels even in low resolution grayscale face images (64 × 32 pixel). A dataset is also described and proposed in order to train the deep learning model. Given the small amount of data available, a transfer learning approach is adopted and validated in order to learn to solve the skin detection problem exploiting a colorization network. Qualitative and quantitative results are reported testing the method on different datasets and in presence of general illumination, facial expressions, object occlusions and it is able to work regardless of the gender, age and ethnicity of the subject.