Reviewerly/exHarmony huggingface dataset:
Towards Concept Based Software Engineering for Intelligent Agents

The development of AI and machine learning applications at an industry mature level while maintaining quality and productivity goals is one of today's major challenges. Research in the field of intelligent agents has achieved many successes in recent years, especially due to various reinforcement learning techniques, and promises a high benefit in times of automation and autonomous systems. Bringing them into production, however, requires optimization against many other criteria than just accuracy. This leads to the emerging field of machine teaching. We already know many of the objectives used there from software engineering research, which has led to many well-established principles in recent decades. One of them is the component-based development whose idea finds an interesting counterpart in hierarchical reinforcement learning. We show that both areas can benefit from each other and introduce our approach of Concept Based Software Engineering, which is focused on supporting productivity and quality goals during the development of such systems.
Three-Dimensional Interval-Valued Intuitionistic Fuzzy Appointment Model

The paper exploresTraneva, Velichka the process of appointment of positions in an organization in conditions of uncertainty. We extend the optimization problem [28] of the process of appointment and reappointment, based onAtanassova, Vassia partial knowledge about the values of evaluation criteria of the human resources over time. Here, the 3-dimensional optimal appointment problem is formulated and an algorithm for its optimalTranev, Stoyan solution is proposed, where the evaluations of candidates against criteria formulated by several experts at a fixed time, are interval-valued intuitionistic fuzzy pairs (IVIFPs). The proposed algorithm for the solution takes into account the ratings of the experts and the weight coefficients of the assessment criteria according to their priority for the respective position.
EmoMix: Building an Emotion Lexicon for Compound Emotion Analysis

Building a high-quality emotion lexicon is regarded as the foundation of research on emotion analysis. Existing methods have focused on the study of primary categories (i.e., anger, disgust, fear, happiness, sadness, and surprise). However, there are many emotions expressed in texts that are difficult to be mapped to primary emotions, which poses a great challenge in emotion annotation for big data analysis. For instance, “despair” is a combination of “fear” and “sadness,” and thus it is difficult to divide into each of them. To address this problem, we propose an automatic building method of emotion lexicon based on the psychological theory of compound emotion. This method could map emotional words into an emotion space, and annotate different emotion classes through a cascade clustering algorithm. Our experimental results show that our method outperforms the state-of-the-art methods in both word and sentence-level primary classification performance, and also offer us some insights into compound emotion analysis.
XMIAR: X-ray Medical Image Annotation and Retrieval

The huge development of the digitized medical image has been steered to the enlargement and research of the Content Based Image Retrieval (CBIR) systems. Those systems retrieve and extract the images by their own low level features, like texture, shape and color. But those visual features did not aloe the users to request images by the semantic meanings. The image annotation or classification systems can be considered as the solution for the limitations of the CBIR, and to reduce the semantic gap, this has been aimed annotating or to make the classification of the image with few controlled keywords. In this paper, we suggest a new hierarchal classification for the X-ray medical image using the machine learning techniques, which are called the Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN). Hierarchy classification design was proposed based on the main body region. Evaluation was conducted based on ImageCLEF2005 database. The obtained results in this research were improved compared to the previous related studies.
Designing Picturebook Apps

This article examines picturebook apps as sites of value negotiation and design in support of community and culture, illustrated through the ongoing design and development of an O-Pipon-Na-Piwin Cree community-centred picturebook app, Pīsim Finds Her Miskanow (Pīsim Miskam Omiskanow). We introduce the C&T community to early research on electronic picturebooks, and illustrate how picturebook theory is expanded with the remediation of print texts into multimodal narrative experiences. We turn to the case of Pīsim to further enrich and diversify these conceptions, weaving elements of culture into earlier models of picturebook apps. We draw on design theory, specifically the concept of values as hypotheses, to offer storied insights on how the design process of the Pīsim picture book app is enriched through its commitment to the originating community. We conclude with implications for the community-grounded design of interactive cultural belongings, not limited to picturebooks, including museum exhibits, educational installations, and digitized cultural heritage pieces.
Spatio-Temporal Scheduling of Heterogeneous Delay-Constrained Tasks in Geo-Distributed Green Clouds

The skyrocketing growth in types and number of heterogeneous applications dramatically increases the amount of energy consumed by distributed green data centers (DGDCs). The spatial and temporal variations in prices of power grid and availability of renewable energy make it highly challenging to minimize the energy cost by intelligently scheduling arriving tasks of heterogeneous applications among green data centers while meeting their expected delay bound constraints. Unlike existing studies, this work proposes a Spatio-Temporal Task Scheduling (STTS) algorithm to minimize the energy cost by cost-effectively scheduling all arriving tasks to meet their delay bound constraints. It well uses spatial and temporal variations to achieve DGDC cost reduction and throughput improvement. In each time slot, the energy cost minimization problem for DGDC providers is formulated as a nonlinear constrained optimization one, and addressed with the proposed Genetic Simulated-annealing-based Particle swarm optimization. Trace-driven experiments show that STTS achieves larger throughput and lower energy cost than several typical task scheduling approaches while strictly meeting all tasks' delay bound constraints.
Automatic Code Generation System for Transactional Web Applications

Every day new applications appear, but several of these applications usually share a lot of features. Some applications are based on frameworks; however, most features introduce a lot of source code impacting the performance of an application especially when the application is web-based. Thus, automatic code generators have gained attention in the last years because they provide the required elements to create automatically final applications without introducing a source code that is not part of the application model. In this paper, we present an approach to generate transactional web applications based on a conceptual model as the unique input. Through the conceptual model, modelers can specify the entities, attributes, and relations of the application. Then, the approach is able to generate: (a) the source code separated in UI, business, and persistence layers, (b) the DDL (Data Definition Language) scripts for the corresponding relational database, and a script to create corresponding UML diagrams.
Development of Wimba 3 Dimension Interactive Animation Media on Plant Anatomy

The purpose of this research is to develop an interactive animated 3D media using Wimba. This interactive animation created media to facilitate student understanding about the structure and function of cells and tissues in plants through the 3D medium to increase the ability of spatial and high order thinking skills. The media is created using the Blender game engine. The method used is the software development life cycle or classic method can be called also waterfall. Research procedures, namely analysis, design, coding, and tests. Tests conducted several test stages, blackbox, test validation expert lecturers and student response. The result is a decent software has to be used, the appropriateness of materials, materials have represented cells and tissues of plants, precision 3D images, the shape of the cell has a precision and detail and aesthetics of software is already good.
Frequency Graphs for Travelling Salesman Problem Based on Ant Colony Optimization

Traveling salesman problem (TSP) is a typical combinatorial optimization problem. A heuristic model called frequency graph is introduced for TSP. It is computed with a set of optimal i-vertex paths (OP) in a weighted graph. The frequencies on the edges are enumerated from the set of OPs. The OPs have more intersections of edges with the optimal Hamiltonian cycle (OHC) than they do with the other Hamiltonian cycles. Thus, the frequencies of the OHC edges are generally bigger than those of most of the other edges. They are taken as the heuristic information instead of edges’ weights for TSP. The ant colony optimization is used to find an approximation or OHC based on the frequency graph. The solutions are compared with those using weighted graphs for certain TSP instances. The experimental results show that the frequency graph is better than weighted graph (WG) for most TSPs under the same preconditions.
SA-SPM: an efficient compiler for security aware scratchpad memory (invited paper)

Scratchpad memories (SPM) are often used to boost the performance of application-specific embedded systems. In embedded systems, main memories are vulnerable to external attacks such as bus snooping or memory extraction. Therefore it is desirable to guarantee the security of data in a main memory. In software-managed SPM, it is possible to provide security in main memory by performing software-assistant encryption.
Sockpuppet Detection in Social Network via Propagation Tree

Sockpuppet detection is a valuable and challenging issue in social network. Current works are continually making efforts to detect sockpuppet based on verbal, non-verbal or network-structure features. However, they do not consider the propagation characteristic and propagation structure of sockpuppet. With our observation, the propagation trees of sockpuppet and ordinary account are different. Sockpuppet’ propagation tree is evidently wider and deeper than that of the ordinary one. Based on these observations, we propose a propagation-structure based method to tackle sockpuppet detection problem. The experiment on two real-world datasets of Sina Weibo demonstrates that our method is more robust and outperforms previous methods.
CASFinder: Detecting Common Attack Surface

Code reusing is a common practice in software development due to its various benefits. Such a practice, however, may also cause large scale security issues since one vulnerability may appear in many different software due to cloned code fragments. The well known concept of relying on software diversity for security may also be compromised since seemingly different software may in fact share vulnerable code fragments. Although there exist efforts on detecting cloned code fragments, there lack solutions for formally characterizing their specific impact on security. In this paper, we revisit the concept of software diversity from a security viewpoint. Specifically, we define the novel concept of common attack surface to model the relative degree to which a pair of software may be sharing potentially vulnerable code fragments. To implement the concept, we develop an automated tool, CASFinder, in order to efficiently identify common attack surface between any given pair of software with minimum human intervention. Finally, we conduct experiments by applying our tool to real world open source software applications. Our results demonstrate many seemingly unrelated software applications indeed share significant common attack surface.
Benchmarking open-endedness in minimal criterion coevolution

Minimal criterion coevolution (MCC) was recently introduced to show that a very simple criterion can lead to an open-ended expansion of two coevolving populations. Inspired by the simplicity of striving to survive and reproduce in nature, in MCC there are few of the usual mechanisms of quality diversity algorithms: no explicit novelty, no fitness function, and no local competition. While the idea that a simple minimal criterion could produce quality diversity on its own is provocative, its initial demonstration on mazes and maze solvers was limited because the size of the potential mazes was static, effectively capping the potential for complexity to increase. This paper overcomes this limitation to make two significant contributions to the field: (1) By introducing a completely novel maze encoding with higher-quality mazes that allow indefinite expansion in size and complexity, it offers for the first time a viable, computationally cheap domain for benchmarking open-ended algorithms, and (2) it leverages this new domain to show for the first time a succession of mazes that increase in size indefinitely while solutions continue to appear. With this initial result, a baseline is now established that can help researchers to begin to mark progress in the field systematically.
Diachronic frame analysis

Abstract Frame Semantics offers a valuable perspective on mechanisms of semantic change, particularly metonymy. However, corpus-based frame analysis has rarely been applied to diachronic data. The potential of this approach is illustrated with a diachronic description of the Purpose frame in French, based on 1,429 tokens of 17 frame-evoking words. Metonymic mappings in the frame allow Means and Medium to replace Agent. A multinomial logistic regression model shows that usage of these mappings has increased since 1600 and is conditioned by genre and the frequency and grammatical category of the frame-evoking word. The approach may inform how metonymy leads to lexicalized semantic change.
Types for Progress in Actor Programs

Properties in the actor model can be described in terms of the message-passing behavior of actors. In this paper, we address the problem of using a type system to capture liveness properties of actor programs. Specifically, we define a simple actor language in which demands for certain types of messages may be generated during execution, in a manner specified by the programmer. For example, we may want to require that each request to an actor eventually results in a reply. The difficulty lies in that such requests can be generated dynamically, alongside the associated requirements for replies. Such replies might be sent in response to intermediate messages that never arrive, but the property may also not hold for more trivial reasons; for instance, when the code of potential senders of the reply omit the required sending command in some branches of a conditional statement. We show that, for a restricted class of actor programs, a system that tracks typestates can statically guarantee that such dynamically generated requirements will eventually be satisfied.
Convergence rates of kernel density estimates in particle filtering

Bounds on convergence rates of kernel density estimates in particle filtering are specified. The kernel density estimates are shown to be efficient for the Sobolev class of filtering densities. The upper bounds are established using Fourier analysis whilst the lower ones rely on tools of information theory.
Improving deep image denoising using pseudo‐ground‐truth images

Deep neural networks, especially convolutional neural networks, have been successfully applied to image denoising tasks. Along with advances in network architectures, many attempts have been made to find an alternative loss function to the widely used L1-loss and L2-loss. However, the perception-distortion tradeoff was recently demonstrated; thus, advanced loss functions such as adversarial loss from generative adversarial networks can only improve the perceptual image quality at the expense of distortion. This Letter shows that distortion can be further decreased when an image denoising network is trained using modified versions of ground-truth (GT) (defined as pseudo-ground-truth (PGT)) images that are obtained by combining the original GT images and initially denoised images. Experimental results show that the proposed denoising network that is trained to predict both PGT and GT images produces denoised images closer to GT images.
Smart Contracts are More than Objects: Pro-activeness on the Blockchain

In this paper we focus on the expressiveness of smart contracts (SC) and its role in blockchain technologies (BCT), by presenting Tenderfone, a prototypical blockchain platform providing SC as pro-active, time-aware, and asynchronous entities.
Empirical Study in using Version Histories for Change Risk Classification

Many techniques have been proposed for mining software repositories, predicting code quality and evaluating code changes. Prior work has established links between code ownership and churn metrics, and software quality at file and directory level based on changes that fix bugs. Other metrics have been used to evaluate individual code changes based on preceding changes that induce fixes. This paper combines the two approaches in an empirical study of assessing risk of code changes using established code ownership and churn metrics with fix inducing changes on a large proprietary code repository. We establish a machine learning model for change risk classification which achieves average precision of 0.76 using metrics from prior works and 0.90 using a wider array of metrics. Our results suggest that code ownership metrics can be applied in change risk classification models based on fix inducing changes.
Transferable Environment Model With Disentangled Dynamics

Foresight is a manifestation of human-level intelligence. In model-based reinforcement learning, obtaining a perfect environment model is essential and significant. Although recent research has achieved impressive advances in improving prediction accuracy, existing environment models are unable to generalize well across environments, mainly because appearance-independent dynamics learning completely relies on appearance-specific representation learning. In this paper, we propose a novel predictive model named transferable environment model (TEM) which disentangles state representation and dynamics. The disentanglement allows the model to deal with different observation distributions and meanwhile share a cross-environment latent dynamics. In a 3D visual platform, we show that our model has good generalization performance in target environments with very few data. Furthermore, the TEM is able to continually adapt to a sequence of target environments without forgetting the knowledge for previous environments. To the best of our knowledge, this paper is the first to endow a predictive model with the ability to work across multiple environments.
Adaptive Thresholding Method for Speckle Reduction of Echocardiographic Images

Speckle noise is a granular disturbance, which is an inherent property in echocardiographic images. One of the most powerful and efficient tools for despeckling of images is wavelet transform. A literature survey conducted reveals many wavelet-based thresholding techniques are developed based on universal or level adaptive methods. To find the optimum threshold is a key point in speckle reduction. This paper shows the comparison of different thresholding techniques with proposed adaptive thresholding technique in terms of peak signal to noise ratio (PSNR), structural similarity measure index (SSIM), image quality index (IQI) and edge preservation index (EPI). The qualitative evaluation is done by using residuals. The processing time is also measured for different algorithms. The obtained results indicate that the proposed thresholding technique gives better performance in most of performance measurement parameters.
Deep Residual Texture Network for Terrain Recognition

In terrain recognition, the terrain image not only has texture features but also contains spatial features.However, the traditional feature descriptors mainly focus on the texture features of terrain image, ignoring the spatial features of the image, and the convolutional neural network (CNN) can extract the spatial features of the image well due to the role of convolutional layer and the pooling layer.So how to extract these two features at the same time is a challenging problem.In this paper, we introduce a deep residual texture network (DrtNet) that builds a texture detail layer in the residual convolution network and becomes an end-to-end learning network.DrtNet, which simultaneously extracts the spatial geometric features and texture detail features of the terrain image, can successfully combine the traditional texture feature descriptor with the convolutional neural network.The experimental results show that the DrtNet achieves the accuracy of 97.85% on the SDU_Terrain16 dataset that was created by us and outperforms other traditional methods and current popular deep convolutional networks.In addition, DrtNet has achieved good results on two other material/texture datasets (GTOS and DTD).
Original Content Verification Using Hash-Based Video Analysis

Nowadays, the internet makes it possible for us to upload and share content online. However, the problem is that copying online content has become very easy and has put copyright content at risk. State-of-the-art tools have been designed to detect plagiarised images or texts through the detection of similarities in them. However, there has not yet been a tool for the identification of plagiarised videos which are made up of the fragments of other original videos, that may be legally protected by their authors. This paper presents a tool that has been developed to identify videos created from the fragments of other existing content. The system has been evaluated using videos from the World Cup held in Russia in 2018, some had original content while others were made up of copied fragments. In this way we have been able to verify the feasibility of the system in correctly matching original videos with the plagiarised ones. The results have been satisfactory.
Neural Network Based Approach for Learning Planning Action Models

Artificial Intelligence (AI) planning technology represents one of the most efficient solutions to guide goal driven agents in domain problems which are mostly characterized by problem solving features. The current proliferation of physical and virtual autonomous agents have therefore triggered a growing interest in the automatic acquisition of action models, which can take advantage of the observation capabilities of agents, which sense world states through their sensors. In this work we present a Neural Network (NN) based approach for learning AI planning action models by observation in noisy environments. The system learns by observing a set of execution patterns of the same action in different contexts. The perceptions of both pre/post action execution states are used to train the NN learning component and are assumed to be affected by noise, which could be due to inaccurate reading or malfunctioning of the sensors. Preliminary experiments shows that the proposed NN learning module seems to be more resilient to unforeseen situations with respect to traditional propositional-based approaches to action model learning.