Reviewerly/exHarmony huggingface dataset:
Automatic identification of sarcasm in tweets and customer reviews

The figurative language involving sarcasm on social networks is evolving the way how the humans use computers to communicate. Consequently, artificial intelligence techniques are applied in various scenarios to make the social networking more intelligent - for instance, identification of figurative language. Identifying both literal and non-literal meaning is not easy for a machine and it is hard even for people. Therefore, novel and exact frameworks ready to identify figurative languages are important. In sarcasm detection, this is even more challenging because sarcasm changes the polarity of an evidently positive or negative expression into its inverse. To maintain a distance for a sarcastic message being comprehended in its unintended actual meaning, in micro-blogging sites, for example messages on Twitter, sarcasm is frequently set apart with a hashtag for example, '#sarcastic', '#sarcasm', '#not' etc. Moreover, the customer reviews may also contain some element of sarcasm. To contribute to this area, we gathered the data of tweets and reviews from Twitter, thesarcasmdetector.com, and Kaggle and proposed a mechanism for detecting sarcasm automatically using a classifier. A detailed experimental study was also conducted to evaluate the proposed mechanism. The results of this study were quite promising and proved the effectiveness of our approach.
I-Keyboard: Fully Imaginary Keyboard on Touch Devices Empowered by Deep Neural Decoder

Text entry aims to provide an effective and efficient pathway for humans to deliver their messages to computers. With the advent of mobile computing, the recent focus of text-entry research has moved from physical keyboards to soft keyboards. Current soft keyboards, however, increase the typo rate due to a lack of tactile feedback and degrade the usability of mobile devices due to their large portion on screens. To tackle these limitations, we propose a fully imaginary keyboard (I-Keyboard) with a deep neural decoder (DND). The invisibility of I-Keyboard maximizes the usability of mobile devices and DND empowered by a deep neural architecture allows users to start typing from any position on the touch screens at any angle. To the best of our knowledge, the eyes-free ten-finger typing scenario of I-Keyboard which does not necessitate both a calibration step and a predefined region for typing is first explored in this article. For the purpose of training DND, we collected the largest user data in the process of developing I-Keyboard. We verified the performance of the proposed I-Keyboard and DND by conducting a series of comprehensive simulations and experiments under various conditions. I-Keyboard showed 18.95% and 4.06% increases in typing speed (45.57 words per minute) and accuracy (95.84%), respectively, over the baseline.
ProUM: High Utility Sequential Pattern Mining

In recent decade, utility mining has attracted a great attention, but most of the existing studies are developed to deal with itemset-based data. Different from the itemset-based data, the time-ordered sequence data is more commonly seen in real-world situations. Current utility mining algorithms have the limitation when dealing with sequence data since they are time-consuming and require large amount of memory usage. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store necessary information of sequence-order and utility. By utilizing the projection technique in generating utility-array, ProUM can significantly improve the mining efficiency, and effectively reduce the memory consumption. Besides, we propose a new upper bound named sequence extension utility. Several pruning strategies are further applied to improve the efficiency of ProUM. Experimental results show that the proposed ProUM algorithm significantly outperforms the state-of-the-art algorithms.
Pose Based Start Intention Detection of Cyclists

In this work, we present a new approach for start intention detection of cyclists based on 3D human pose estimation to increase their safety in road traffic. Start intention detection is realized using sequences of frame-wise estimated 3D poses. The poses were obtained by image sequences recorded by a stereo camera mounted behind the windshield of a moving vehicle. For training and evaluation, a dataset with 206 starting cyclists was created in real traffic. We demonstrate the advantages of this approach by comparing it to an existing, solely head trajectory based method. In particular, we investigate the performance for different observed time horizons ranging from 0.12 s up to 1.0 s as inputs for the two methods. This is of special importance for the protection of cyclists in road traffic, as they often only become visible to approaching vehicles shortly before dangerous situations occur. With an input length of 1.0 s the solely head trajectory based approach detects starting motions on average 0.834 s after the first motion of the bicycle with an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 97.5 %. The pose based approach outperforms these results by achieving the same F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score 0.135 s earlier. The advantages of the pose based method become even more obvious with shorter input lengths. With an input length of 0.12 s, the head based approach achieves an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 93.5 % after 2.37 s, while the same score is reached after 0.668 s using poses.
Secrets of soccer: Neural network flows and game performance

Soccer is the most popular sport in the world, with currently over three billion fans. There are various reasons for this success, but a unique feature of soccer stands out: every match has a high level of unpredictability. For instance, it is not uncommon for a less skilled team to sometimes defeat a better team. Moreover, a match that is apparently decided in favor of a team can suddenly change course, even with few minutes left, ending with a completely opposite result. These highly dramatic effects have brought popularity to this sport, making every match a complex event where the outcome is far from granted. This same complexity has however challenged all attempts of data analysis: the “secrets of soccer”, that is to say the recipes for success, are still an unknown realm, defying all common statistical approaches. In this study we try to shed some light on these secrets by introducing a novel approach that uses neural network flows. We transform a team play into a corresponding brain-like structure, an abstraction that we analyze using measures of efficiency, assessing the “quality of thinking” of the brain. This way, we can view any soccer match as an alternate battle of minds and explore how far this parallelism can help to solve some fundamental open problems, like finding an effective recipe for success, and establishing the best field control strategies.
Autonomous Exploration with Expectation-Maximization

We consider the problem of autonomous mobile robot exploration in an unknown environment for the purpose of building an accurate feature-based map efficiently. Most literature on this subject is focused on the combination of a variety of utility functions, such as curbing robot pose uncertainty and the entropy of occupancy grid maps. However, the effect of uncertain poses is typically not well incorporated to penalize poor localization, which ultimately leads to an inaccurate map. Instead, we explicitly model unknown landmarks as latent variables, and predict their expected uncertainty, incorporating this into a utility function that is used together with sampling-based motion planning to produce informative and low-uncertainty motion primitives. We propose an iterative expectation-maximization algorithm to perform the planning process driving a robot's step-by-step exploration of an unknown environment. We analyze the performance in simulated experiments, showing that our algorithm maintains the same coverage speed in exploration as competing algorithms, but effectively improves the quality of the resulting map.
VLSI Implementation for an Adaptive Haze Removal Method

In the real-time outdoor application of computer vision-based systems, haze removal, a preprocessing technique that can recover clear images from foggy ones, is necessary for object detection.Therefore, in this paper, an efficient haze removal method suitable for hardware design is proposed to obtain high quality fog-free images.Based on the atmosphere scattering model and the dark channel prior method, the atmospheric light of the whole image and the transmission map could be extracted.These are important and necessary parameters of the recovery model.Instead of using single global atmospheric light to restore foggy image, a local atmospheric light estimation method is applied in the proposed design to achieve optimal results.To ensure that the overall image is consistent without block artifacts, dynamic adjustment of local atmospheric light is made based on global atmospheric light.Additionally, to obtain a transmission map, a refined estimation method is performed to ease the halo effect.In terms of both quantitative and qualitative evaluations, the simulation results indicate that the proposed design exhibits superior performance without color oversaturation and distortion.To meet the requirements of real-time applications, a six-stage very-large-scale integration (VLSI) architecture for the proposed algorithm is implemented by using TSMC 0.13-um technology.The synthesis results show that the design yields a processing rate of approximately 200 Mpixels/s, which is rapid enough to facilitate Full HD resolution at 30 fps in real time.
Interpretability of Machine Learning Solutions in Industrial Decision Engineering

The broad application of machine learning (ML) methods and algorithms in diverse range of organisational settings led to the adoption of legislation, like European Union's General Data Protection Regulation, which require firm capabilities to explain algorithmic decisions. Currently in the ML literature there does not seem to be a consensus on the definition of interpretability of a ML solution. Moreover, there is no agreement about the necessary level of interpretability of such solution and on how this level can be determined, measured and achieved. In this article, we provide such definitions based on research as well as our extensive experience of building ML solutions for various organisations across industries. We present CRISP-ML, a detailed step-by-step methodology, that provides guidance on creating the necessary level of interpretability at each stage of the solution building process and is consistent with the best practices of project management in the ML settings. We illustrate the versatility and effortless applicability of CRISP-ML with examples across a variety of industries and types of ML projects.
Pedestrian Detection and Behavior Recognition Based on Vision

In the field of autonomous driving, pedestrian detection and pedestrian behavior identification are important in ensuring pedestrian safety and reducing traffic accidents. Facing the complex traffic scene of the city, we propose a vision-based, high-accuracy and real-time pedestrian detection and pedestrian behavior recognition method for the on-vehicle environment. The method firstly uses the YOLOv3-TINY network to accurately and quickly identify pedestrians, and then uses the improved Deepsort algorithm to track the identified pedestrians. An improved Alexnet was then proposed to identify pedestrian behavior and train on data sets adapted to the study based on the mars dataset. After getting the pedestrian behavior, the behavior is corrected by multi-target tracking, and finally the real-time warning level information is given. Compared with similar algorithms, the experimental results show that the algorithm proposed in this paper improves the recognition accuracy and real-time performance, and the frame rate reaches 20frame/s.
Convolutional Neural Network for Classification of Source Codes

A method to classify source code based on convolutional neural networks is presented. The goal of the neural networks is to predict the type of algorithm that is used in the corresponding source code so that the result obtained can be used for different kinds of assistance and assessment for programming education. In the proposed method, source code is converted into a sequence that represents the structure of the code without any keywords, such as variable names or function names. In present paper, models and implementation of the proposed method are presented. An experiment considering several algorithm types is also conducted. For evaluation of the proposed method, source code accumulated in an online judge system is used. The results of the experiment demonstrate that the proposed method can predict the algorithm used in the given source code to a high degree of accuracy.
Latent Space Exploration Using Generative Kernel PCA

Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of kernel PCA in terms of hidden and visible units similar to Restricted Boltzmann Machines. This connection has led to insights on how to use kernel PCA in a generative procedure, called generative kernel PCA. In this paper, the use of generative kernel PCA for exploring latent spaces of datasets is investigated. New points can be generated by gradually moving in the latent space, which allows for an interpretation of the components. Firstly, examples of this feature space exploration on three datasets are shown with one of them leading to an interpretable representation of ECG signals. Afterwards, the use of the tool in combination with novelty detection is shown, where the latent space around novel patterns in the data is explored. This helps in the interpretation of why certain points are considered as novel.
Hierarchical Model Predictive Control for Autonomous Vehicle Area Coverage

Area coverage using autonomous vehicles receives increasing attention due to a widespread range of possible applications. Examples are surveillance and monitoring tasks or search and rescue missions. Efficient and safe area coverage in dynamic environments, however, is challenging. It requires tight integration of the planning and control task to guarantee collision avoidance and optimal coverage. We propose a combination of two coupled model predictive controllers for optimal area coverage with dynamic obstacle avoidance. The planning is based on a mixed integer programming formulation of the predictive controller. It allows to take dynamic objects, such as other autonomous vehicles into account and considers a simplified dynamic model of the autonomous vehicle. The autonomous vehicle itself is controlled by a continuous time nonlinear model predictive path following controller, which obeys detailed dynamic and kinematic constraints and follows the provided path. The design of the controllers takes the interconnections in terms of dynamic constraints and reference definitions between them into account. Simulation results for a quadcopter illustrate the performance and real-time feasibility of the proposed hierarchical predictive control strategy.
Robust Frame Synchronization Scheme for Continuous-Variable Quantum Key Distribution with Simple Process

In continuous-variable quantum key distribution (CVQKD) systems, high-quality data synchronization between two legitimate parties, Alice and Bob, is the premise of the generation of shared secret keys. Synchronization with specially designed frames is an efficient way, but it requires special modulating devices to generate these special frames. Moreover, the extra requirement of special modulating devices makes it technically impossible for some passive preparation schemes. We propose a novel approach to realize synchronization in this paper, which is different from those special-frame-based methods. In our proposed scheme, Alice publishes parts of the original signals as the synchronization frames and Bob takes these frames to perform the synchronization algorithm. Besides, a synchronization feature is applied to deal with phase shifts. The simulation results based on practical data demonstrate that the proposed synchronization scheme not only maintains a high success rate but simplifies the data processing flow at the same time, which dramatically reduces the computational complexity.
Automation of Wastewater Treatment Plant

In this project, the problem was the realization of a SCADA (Supervisory Control and Data Acquisition) system and automation system that will reduce the costs of maintenance, will increase energy efficiency and will help to improve the quality of wastewater that drains into the river and which comes directly from the sewerage system of Tbilisi and Gardabani cities. The automated station will work non-stop, with high efficiency in water quality and cleaning. The warranty of this station is 25 years without mechanical defects, from which comes the decrease on the maintenance expenses towards a non-automated station.
BUNKER: A Blockchain-based trUsted VNF pacKagE Repository

Current projects applying blockchain technology to enhance the trust of NFV environments do not consider the VNF repository. However, the blockchain's properties can enhance trust by allowing to verify a VNF package's integrity without relying (a) on a Trusted Third Party (TTP) for remote attestation or (b) a secure database. This paper presents BUNKER, a Blockchain-based trUsted VNF packagE Repository, intended to be integrated with traditional database-based package verification environments, acting as a trusted repository containing VNF package information. Moreover, BUNKER allows users to acquire VNFs without the need of a TTP using an Ethereum Smart Contract (SC). The SC automatically transfers license fees to the vendor once a VNF is acquired, and sends the VNF package's link to the buyer before verifying its integrity.
To Extend or Not to Extend? Context-Specific Corpus Enrichment

An agent in pursuit of a task may work with a corpus of documents with linked subjective content descriptions. Faced with a new document, an agent has to decide whether to include that document in its corpus or not. Basing the decision on only words, topics, or entities, has shown to not lead to a balanced performance for varying documents. Therefore, this paper presents an approach for an agent to decide if a new document adds value to its existing corpus by combining texts and content descriptions. Furthermore, an agent can use the approach as a starting point for high quality content descriptions for new documents. A case study shows the effectiveness of our approach given varying types of new documents.
Varied channels region proposal and classification network for wildlife image classification under complex environment

A varied channels region proposal and classification network (VCRPCN) is developed based on a deep convolutional neural network (DCNN) and the characteristics of the animals appearing for automatic wildlife animal classification in camera trapped images, the architecture of the network is improved by feeding different channels into different components of the network to accomplish different aims, i.e. the animal images and their background images are employed in the region proposal component to extract region candidates for the animal's location, and the animal images combined with the region candidates are fed into the classification component to identify their categories. This novel architecture considers changes to the image due to the animals' appearances, and identifies potential animal regions in images and extracts their local features to describe and classify them. Five hundred low contrast animal images have been collected. All images have low contrast due to being acquired during the night. Cross-validation is employed to statistically measure the performance of the proposed algorithm. The experimental results demonstrate that in comparison with the well-known object detection network, faster R-CNN, the proposed VCRPCN achieved higher accuracy with the same dataset and training configuration with an average accuracy improvement of 21%.
On using MapReduce to scale algorithms for Big Data analytics: a case study

Abstract Introduction Many data analytics algorithms are originally designed for in-memory data. Parallel and distributed computing is a natural first remedy to scale these algorithms to “Big algorithms” for large-scale data. Advances in many Big Data analytics algorithms are contributed by MapReduce, a programming paradigm that enables parallel and distributed execution of massive data processing on large clusters of machines. Much research has focused on building efficient naive MapReduce-based algorithms or extending MapReduce mechanisms to enhance performance. However, we argue that these should not be the only research directions to pursue. We conjecture that when naive MapReduce-based solutions do not perform well, it could be because certain classes of algorithms are not amendable to MapReduce model and one should find a fundamentally different approach to a new MapReduce-based solution. Case description This paper investigates a case study of a scaling problem of “Big algorithms” for a popular association rule-mining algorithm, particularly the development of Apriori algorithm in MapReduce model. Discussion and evaluation Formal and empirical illustrations are explored to compare our proposed MapReduce-based Apriori algorithm with previous solutions. The findings support our conjecture and our study shows promising results compared to the state-of-the-art performer with 7% increase in performance on the average of transactions ranging from 10,000 to 120,000. Conclusions The results confirm that effective MapReduce implementation should avoid dependent iterations, such as that of the original sequential Apriori algorithm. These findings could lead to many more alternative non-naive MapReduce-based “Big algorithms”.
iUC: Flexible Universal Composability Made Simple

Proving the security of complex protocols is a crucial and very challenging task. A widely used approach for reasoning about such protocols in a modular way is universal composability. A perfect model for universal composability should provide a sound basis for formal proofs and be very flexible in order to allow for modeling a multitude of different protocols. It should also be easy to use, including useful design conventions for repetitive modeling aspects, such as corruption, parties, sessions, and subroutine relationships, such that protocol designers can focus on the core logic of their protocols. While many models for universal composability exist, including the UC, GNUC, and IITM models, none of them has achieved this ideal goal yet. As a result, protocols cannot be modeled faithfully and/or using these models is a burden rather than a help, often even leading to underspecified protocols and formally incorrect proofs. Given this dire state of affairs, the goal of this work is to provide a framework for universal composability which combines soundness, flexibility, and usability in an unmatched way. Developing such a security framework is a very difficult and delicate task, as the long history of frameworks for universal composability shows. We build our framework, called iUC, on top of the IITM model, which already provides soundness and flexibility while lacking sufficient usability. At the core of iUC is a single simple template for specifying essentially arbitrary protocols in a convenient, formally precise, and flexible way. We illustrate the main features of our framework with example functionalities and realizations.