Reviewerly/exHarmony huggingface dataset:
Robotic Object Sorting via Deep Reinforcement Learning: a generalized approach

This work proposes a general formulation for the Object Sorting problem, suitable to describe any non-deterministic environment characterized by friendly and adversarial interference. Such an approach, coupled with a Deep Reinforcement Learning algorithm, allows training policies to solve different sorting tasks without adjusting the architecture or modifying the learning method. Briefly, the environment is subdivided into a clutter, where objects are freely located, and a set of clusters, where objects should be placed according to predefined ordering and classification rules. A 3D grid discretizes such environment: the properties of an object within a cell depict its state. Such attributes include object category and order. A Markov Decision Process formulates the problem: at each time step, the state of the cells fully defines the environment's one. Users can custom-define object classes, ordering priorities, and failure rules. The latter by assigning a non-uniform risk probability to each cell. Performed experiments successfully trained and validated a Deep Reinforcement Learning model to solve several sorting tasks while minimizing the number of moves and failure probability. Obtained results demonstrate the capability of the system to handle non-deterministic events, like failures, and unpredictable external disturbances, like human user interventions.
Traffic sign detection optimization using color and shape segmentation as pre-processing system

One of performance indicator of an Autonomous Vehicle (AV) is its ability to accomodate rapid environment changing; and performance of traffic sign detection (TSD) system is one of them. A low frame rate of TSD impacts to late decision making and may cause to a fatal accident. Meanwhile, adding any GPU to TSD will significantly increases its cost and make it unaffordable. This paper proposed a pre-processing system for TSD which implement a color and a shape segmentation to increase the system speed. These segmentation systems filter input frames such that the number of frames sent to AI system is reduced. As a result, workload of AI system is decreased and its frame rate increases. HSV threshold is used in color segmentation to filter frames with no desired color. This algorithm ignores the saturation when performing color detection. Further, an edge detection feature is employed in shape segmentation to count the total contours of an object. Using German Traffic Sign Recognition Benchmark dataset as model, the pre-processing system filters 97% of frames with no traffic sign objects and has an accuracy of 88%. TSD system proposed allows a frame rate improvement up to 32 FPS when YOLO algorithm is used.
Ontological Analysis of Outcomes of Non-formal and Informal Learning for Agro-Advisory System AdvisOnt

Agricultural advisory systems are widely used now for fast dissemination of agricultural knowledge and information, introduction of modern scientific research and technologies in production, mobility and constant advanced training of agricultural specialists. Their implementation becomes an important factor in competitiveness of rural economy. We consider advisory systems used by various countries and regions, their specifics and aims, and knowledge bases that can be used as bedrock for agro-advisory applications – the European Multilingual Classifier of Skills, Competences, Qualifications and Occupations ESCO ontology, ontologies of learning resources (on example of MOOCs), user profile ontologies. Analyses of the intelligent Web-oriented informational technologies shows an expedience of advisory development based on the Semantic Web standards of knowledge representation and processing. An important factor of advisory efficiency deals with semantic documentation and validation of non-formal and informal learning outcomes typical for agriculture. We propose semantic advisory system AdvisOnt based on ontological representation of knowledge about competencies, vacancies, training courses, user profiles, employers and companies. AdvisOnt services match available vacancies and applicant profiles to recommend employment or further learning of wanting competences by analysis of their formal, non-formal and informal learning outcomes.
Shift and Blend

In the context of human-agent interaction, we see the emergence of computational artefacts that display hybridity because they can be experienced as tools and agents. In this paper we propose a tool-agent spectrum as an analytical lens that uses 'intention' as a central concept. This spectrum aims to clarify how a computational object can change from being conducive to the intentions of others ('tool') to appearing to have intentions of its own ('agent'), or vice versa. We have applied this analytical lens to unravel people's experiences in two hybrid cases; guide dogs as a living mobility aid for the visually impaired and an experimental wearable object named 'BagSight' as a rudimentary artificial counterpart. We compared both cases through the lens of a tool-agent spectrum and elaborate on these results by discussing some of the principles by which computational artefacts can shift across the spectrum. We conclude by discussing the limitations of this study and provide suggestions for future work.
A Reinforcement Learning Based Adaptive Mutation for Cartesian Genetic Programming Applied to the Design of Combinational Logic Circuits

The design of digital logic circuits involves transforming logical expressions into an electronic circuit seeking to minimize certain attributes such as the number of transistors. There are deterministic algorithms for this task, but they are limited to small problems. On the other hand metaheuristics can be used for solving this type of problem and Cartesian Genetic Programming (CGP) is widely adopted in the literature. In CGP, mutation is commonly the only operator for generating new candidate solutions. Thus, the performance of this metaheuristic is dependent on the proper choice of mutation and its parameters. Normally, CGP mutates the candidate solutions according to a uniform distribution. Thus, any modification has the same chance to occur. In order to improve the performance of CGP an adaptive mutation operator using an \(\epsilon \)-greedy strategy for bias the selection of gates is proposed here. The proposal is evaluated using problems of a benchmark from the literature. The results obtained indicate that the proposed adaptive mutation is promising and that its relative performance increases with the size of the problem.
IMAGE SALIENT OBJECT DETECTION ALGORITHM BASED ON ADAPTIVE MULTI-FEATURE TEMPLATE

Salient object detection affected by area edge blurring and scene complexity has various problems, such as incomplete edge extraction and blurry salient maps. Fusing of multiple salient features improves the detection performance, but an inappropriate fusion algorithm may reduce the results of detection. A salient object detection algorithm based on adaptive multi-feature template was proposed to solve the ineffective fusion of various salient features. First, salient edge features were obtained using Conv1 and Conv2 in the Resnet50 model by combining local edge information with high-level global location information. Second, while some attributes such as texture, color contrast, spatial features, and salient edge features were input into the adaptive multi-feature template, these features were spread to every layer of cellular automata. The final saliency map was obtained by calculating the histogram of the target, background, and entire area of the image and automatically generating weight coefficients of different features according to the intersection of the histogram. Results show that the average absolute error (MAE) of the proposed algorithm is only 0.044, while the comprehensive evaluation index (F-score) reaches 0.899. Thus, this algorithm achieves better accuracy and higher recall rate. The adaptive multi-feature template effectively solves the fusion problem of multiple salient features and can accurately obtain the salient areas of the image. This study provides references for image segmentation, image classification, object tracking, and other fields in computer vision. Keywords: Salient object detection, Salient edge, Cellular automata, Adaptive multi-Feature template
Refactoring and Active Object Languages

Refactorings are important for object-oriented (OO) programs. Actor- and active object programs place an emphasis on concurrency. In this article, we show how well-known OO refactorings such as Hide Delegate, Move Method, and Extract Class interact with a concurrency model that distinguishes between local and remote objects. Refactorings that are straightforward in Java suddenly force the developers to reflect on the underlying assumptions of their actor system. We show that this reflection is primarily necessary for refactorings that add or remove method calls, as well as constructor invocations. We present a general notion of correctness of refactorings in a concurrent setting, and indicate which refactorings are correct under this notion. Finally, we discuss how development tools can assist the developer with refactorings in languages with rich semantics.
Towards Silent Paralinguistics: Deriving Speaking Mode and Speaker ID from Electromyographic Signals

Silent Computational Paralinguistics (SCP) -the assessment of speaker states and traits from non-audibly spoken communication -has rarely been targeted in the rich body of either Computational Paralinguistics or Silent Speech Processing.Here, we provide first steps towards this challenging but potentially highly rewarding endeavour: Paralinguistics can enrich spoken language interfaces, while Silent Speech Processing enables confidential and unobtrusive spoken communication for everybody, including mute speakers.We approach SCP by using speech-related biosignals stemming from facial muscle activities captured by surface electromyography (EMG).To demonstrate the feasibility of SCP, we select one speaker trait (speaker identity) and one speaker state (speaking mode).We introduce two promising strategies for SCP: (1) deriving paralinguistic speaker information directly from EMG of silently produced speech versus (2) first converting EMG into an audible speech signal followed by conventional computational paralinguistic methods.We compare traditional feature extraction and decision making approaches to more recent deep representation and transfer learning by convolutional and recurrent neural networks, using openly available EMG data.We find that paralinguistics can be assessed not only from acoustic speech but also from silent speech captured by EMG.
Detection of Voicing and Place of Articulation of Fricatives with Deep Learning in a Virtual Speech and Language Therapy Tutor

Children with fricative distortion errors have to learn how to correctly use the vocal folds, and which place of articulation to use in order to correctly produce the different fricatives.Here we propose a virtual tutor for fricatives distortion correction.This is a virtual tutor for speech and language therapy that helps children understand their fricative production errors and how to correctly use their speech organs.The virtual tutor uses log Mel filter banks and deep learning techniques with spectral-temporal convolutions of the data to classify the fricatives in children's speech by place of articulation and voicing.It achieves an accuracy of 90.40% for place of articulation and 90.93% for voicing with children's speech.Furthermore, this paper discusses a multidimensional advanced data analysis of the first layer convolutional kernel filters that validates the usefulness of performing the convolution on the log Mel filter bank.
Code Property Graph-Based Vulnerability Dataset Generation for Source Code Detection

Most existing deep learning-based source code vulnerability detection methods focus on the design of different deep learning algorithms to improve the accuracy of source code vulnerability detection, ignoring the obvious problems: firstly, lack of sufficient source code vulnerability data; secondly, lack of high-quality code data for deep learning algorithms. Therefore, we propose a code attribute graph-based data generation method for deep learning based source code vulnerability detection. The proposed method tries to represent source code based on code attribute graph, which is used to extract the control flow information and data flow information of source code sequentially. And the code data can be generated by retrieval-matching method. The advantage of this method is that it can extract rich semantic information of source code, and the generated code slices can be used for deep learning algorithms directly. Experimental results show that the proposed method can generate a large number of high-quality code data, which can provide data for deep learning-based source code vulnerability detection.
Automatic summarising of user stories in order to be reused in future similar projects

User stories play an important role in agile development systems. In this study, a method of summarising user stories is proposed to reuse them in the future. To enhance the results, quality improvement should be made on user stories. It would help developers build better results, and it may also lead to omitting some essential information. To avoid such issues, user stories are duplicated in two exact similar groups, and quality improvement is made on one set while the other set remains unattained. With the help of a modified bag of words and a verb parser, a collection of keywords and key verbs are extracted for both groups. Afterwards, automatic user stories are made, and then an expert improves them. Next, some experts choose between the results and select the better ones. The result is evaluated by applying different experiments on the framework and prototype implementation on 14 data sets of a user story from industry and a fake data set from Duke University. The result showed 97% of micro F-measure and 93% of macro F-measure, which are promising. These new user stories can be used as the base user stories in future similar projects.
SFU-store-nav: A multimodal dataset for indoor human navigation

This article describes a dataset collected in a set of experiments that involves human participants and a robot. The set of experiments was conducted in the computing science robotics lab in Simon Fraser University, Burnaby, BC, Canada, and its aim is to gather data containing common gestures, movements, and other behaviours that may indicate humans' navigational intent relevant for autonomous robot navigation. The experiment simulates a shopping scenario where human participants come in to pick up items from his/her shopping list and interact with a Pepper robot that is programmed to help the human participant. We collected visual data and motion capture data from 108 human participants. The visual data contains live recordings of the experiments and the motion capture data contains the position and orientation of the human participants in world coordinates. This dataset could be valuable for researchers in the robotics, machine learning and computer vision community.
Publishing and Using Legislation and Case Law as Linked Open Data on the Semantic Web

Legislation and case law are widely published on the Web as documents for humans to read. In contrast, this paper argues for publishing legal documents as Linked Open Data (LOD) on top of which intelligent legal services for end users can be created in addition to just providing the documents for close reading. To test and demonstrate this idea, we present work on creating the Linked Open Data service Semantic Finlex for Finnish legislation and case law and the semantic portal prototype LawSampo for serving end users with legal data. Semantic Finlex is a harmonized knowledge graph that is created automatically from legal textual documents and published in a SPARQL endpoint on top of which the various applications of LawSampo are implemented. First applications include faceted semantic search and browsing for 1) statutes and 2) court decisions, as well as 3) a service for finding court decisions similar to a given one or free text. A novelty of LawSampo is the provision of ready-to-use tooling for exploring and analyzing legal documents, based on the “Sampo” model.
LiveQA: A Question Answering Dataset Over Sports Live

In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu ( https://nba.hupu.com/games .) website. Derived from the characteristics of sports games, LiveQA can potentially test the reasoning ability across timeline-based live broadcasts, which is challenging compared to the existing datasets. In LiveQA, the questions require understanding the timeline, tracking events or doing mathematical computations. Our preliminary experiments show that the dataset introduces a challenging problem for question answering models, and a strong baseline model only achieves the accuracy of 53.1% and cannot beat the dominant option rule. We release the code and data of this paper for future research. (code: https://github.com/PKU-TANGENT/GAReader-LiveQA ), (data: https://github.com/PKU-TANGENT/LiveQA ).
The Path Planning of Synchronous Cooperative Motion Control between Robot and Positioner for Complex Space Curve Processing

In this paper, we propose a synchronous cooperative path planning (SCPP) algorithm for the robot and the positioner to process complex space curve workpieces. The specific algorithm design is illustrated by using the intersecting line welding as an example. The robot and positioner are regarded as an 8-degree-of-freedom (DOF) system to plan the whole synchronous cooperative motion path, and the constraint for the Y-axis of the welding torch coordination system is added for solving the intersecting line orientation information. SCPP is used to process the intersecting line. The changes of welding torch orientation and robot joint rotation angles during welding of the intersecting line by using the improved method and compared to the traditional method. The experimental results show that 8-DOF keeps synchronous cooperative in the whole movement. There is no interference happening during the entire cooperative movement and the welding torch’s orientation remains basically unchanged during the welding intersecting line.
Learning Global Transparent Models consistent with Local Contrastive Explanations

There is a rich and growing literature on producing local contrastive/counterfactual explanations for black-box models (e.g. neural networks). In these methods, for an input, an explanation is in the form of a contrast point differing in very few features from the original input and lying in a different class. Other works try to build globally interpretable models like decision trees and rule lists based on the data using actual labels or based on the black-box models predictions. Although these interpretable global models can be useful, they may not be consistent with local explanations from a specific black-box of choice. In this work, we explore the question: Can we produce a transparent global model that is simultaneously accurate and consistent with the local (contrastive) explanations of the black-box model? We introduce a natural local consistency metric that quantifies if the local explanations and predictions of the black-box model are also consistent with the proxy global transparent model. Based on a key insight we propose a novel method where we create custom boolean features from sparse local contrastive explanations of the black-box model and then train a globally transparent model on just these, and showcase empirically that such models have higher local consistency compared with other known strategies, while still being close in performance to models that are trained with access to the original data.
Disentangling Human Error from Ground Truth in Segmentation of Medical Images

Recent years have seen increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depends on the quality of labels. This problem is particularly pertinent in the medical image domain, where both the annotation cost and inter-observer variability are high. In a typical label acquisition process, different human experts provide their estimates of the ‘true’ segmentation labels under the influence of their own biases and competence levels. Treating these noisy labels blindly as the ground truth limits the performance that automatic segmentation algorithms can achieve. In this work, we present a method for jointly learning, from purely noisy observations alone, the reliability of individual annotators and the true segmentation label distributions, using two coupled CNNs. The separation of the two is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the noisy training data. We first define a toy segmentation dataset based on MNIST and study the properties of the proposed algorithm. We then demonstrate the utility of the method on three public medical imaging segmentation datasets with simulated (when necessary) and real diverse annotations: 1) MSLSC (multiple-sclerosis lesions); 2) BraTS (brain tumours); 3) LIDC-IDRI (lung abnormalities). In all cases, our method outperforms competing methods and relevant baselines particularly in cases where the number of annotations is small and the amount of disagreement is large. The experiments also show strong ability to capture the complex spatial characteristics of annotators’ mistakes.
Advanced Web Crawlers

In this chapter, we will discuss a crawling framework called Scrapy and go through the steps necessary to crawl and upload the web crawl data to an S3 bucket.
A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses

Research in adversarial learning follows a cat and mouse game between attackers and defenders where attacks are proposed, they are mitigated by new defenses, and subsequently new attacks are proposed that break earlier defenses, and so on. However, it has remained unclear as to whether there are conditions under which no better attacks or defenses can be proposed. In this paper, we propose a game-theoretic framework for studying attacks and defenses which exist in equilibrium. Under a locally linear decision boundary model for the underlying binary classifier, we prove that the Fast Gradient Method attack and the Randomized Smoothing defense form a Nash Equilibrium. We then show how this equilibrium defense can be approximated given finitely many samples from a data-generating distribution, and derive a generalization bound for the performance of our approximation.