Reviewerly/exHarmony huggingface dataset:
Are All Rejected Recommendations Equally Bad?

When evaluating algorithms that recommend a list of relevant items to a user, it is common to use metrics such as precision to measure the system accuracy. When computing precision, one computes the number of items that were selected by the user among the recommended items. As such, recommended items that were not selected by the user, which we call \em rejected recommendations, are all considered to be bad recommendations, resulting in no increase to the system accuracy metric. Our ultimate goal is to develop a new recommendation accuracy evaluation metric, which may assign some value to the rejected recommendations. In this paper, as a first step, we claim that some rejected recommendations are better than others. Specifically, we consider items that are similar to the item that was finally selected, as better recommendations than items that bear little similarity. We conduct a user study, showing that rejected recommendations that have high content or collaborative similarity to the selected item are perceived by users as better recommendations than items with low similarity. In addition, we study the correlations between the recommended items shown to a user and the un-recommended items that the user has selected in a real-life job posting dataset. We show that when considering item similarity rather than simple precision, the correlations are much higher. This may be attributed to the influence of the recommended items on the decisions of the user.
Executing Underspecified Actions in Real World Based on Online Projection

Plan execution on real robots in realistic environments is underdetermined and often leads to failures. The choice of action parameterization is crucial for task success. In this paper, we present a mechanism for a robot that is acting in a real-world environment to think ahead of time with fast plan projection and, thereby, choose action parameterizations that are predicted to lead to successful execution. For finding causal relationships between action parameterizations and task success, we provide the robot with means for plan introspection and propose a systematic and hierarchical plan structure to support that. We evaluate our approach by showing how a PR2 robot, when equipped with the proposed system, is able to choose action parameterizations that increase task execution success rates and overall performance of fetch and place actions in a real world setting.
Bidirectional LSTM joint model for intent classification and named entity recognition in natural language understanding

Recurrent Neural Networks (RNN) have claimed to achieve the state of the arts results in some cases, better performances than humans could have, especially RNN – Long Short Term Memory (LSTM) and RNN – Bidirectional LSTM, Attention based LSTM encoder-decoder networks in the domains of Speech Recogn ition, Sequence Labeling, Text Classification, Image Caption Generation and many more. The main focus of this paper is to present here a simple LSTM – Bidirectional LSTM joint model for Intent Classification and Named Entity Recognition (NER) with and without Convolutional Neural Network (CNN) as feature extractor. The aim of this experiment is to improve the accuracy of the model through inducing information from a well-performing model on a particular task to another model in a joint model framework and conclude if is there any correlation that might aid in syntactic and semantic structural learning of the task through the application of learned weights. The comparative results of models with and without CNN as feature extractors prepended to the models are tabulated as well.
HPM-TDP: An efficient hierarchical PatchMatch depth estimation approach using tree dynamic programming

Accurate and efficient estimation of the dense depth information from a pair of stereo images is a key step for many applications such as digital surface model production, 3D reconstruction and visualization, autonomous driving, and robotic navigation. Although great progress has been achieved in stereo matching over the past decade, the matching difficulties in poor and repetitive texture regions remain an issue. Aiming at solving the shortcomings of the current methods, this paper proposes HPM-TDP, which is an efficient hierarchical PatchMatch depth estimation approach that integrates a coarse-to-fine image pyramid strategy with a continuous Markov random field (MRF)-based global energy optimization framework, and minimizes the energy function by combining a hierarchical PatchMatch (HPM) framework and local α-expansion based tree dynamic programming (TDP). Firstly, the coarse-to-fine image pyramid strategy is integrated with the PatchMatch filter algorithm to quickly generate the hierarchical disparity plane prior for initializing each pixel’s disparity plane of the energy function optimization. Secondly, a multi-resolution cost aggregation strategy is adopted to boost the robustness of the matching cost function in the poor and repetitive texture areas. Finally, the HPM framework and local α-expansion based TDP are adopted to solve the non-submodular energy optimization problem, resulting in a globally optimized disparity plane map. Three benchmark datasets—the Middlebury 3.0, KITTI 2015, and Vaihingen datasets—were used to test the performance of HPM-TDP. The comprehensive experimental results demonstrate that HPM-TDP obtains a good performance on all datasets in terms of the (“Out-Noc”, “Avg-Noc”, “Out-All”, “Avg-All”) of (15.45%, 4.16px, 24.26%, 12.14px) and (5.46%, 1.20px, 6.55%, 1.54px) for Middlebury 3.0 and KITTI 2015 training datasets, and the (“Out-All”, “Avg-All”) of (26.32%, 4.04px) for Vaihingen dataset, respectively.
A Chatbot for Goal-Oriented Requirements Modeling

Goal-Oriented Requirements Engineering approaches, in which the KAOS framework plays a key role, have been widely used for eliciting software requirements because they provide an easier way of communicating among stakeholders. However, the goal-oriented requirements modeling is not an easy way for novice requirements engineers. These professionals need more support in creating KAOS models. Recent studies have focused on the applicability of Artificial Intelligence techniques (e.g., Natural Language Processing – NLP) to support Requirements Engineering activities. In this sense, this paper aims to describe a way to support requirements elicitation for novice requirements engineers through the use of NLP within a chatbot. The chatbot (KAOSbot) acts as a KAOS modeling assistant. To evaluate our hypotheses about perceived efficacy from the novice requirements engineers perspective, we performed a quasi-experiment concerning KAOSbot’s perceived ease of use, perceived usefulness and intention to use. The results show that KAOSbot tool is a promising approach for specifying KAOS models because it was perceived as easy to use, useful, and the participants intend to use it in the future.
Multi-negative samples with Generative Adversarial Networks for image retrieval

The task of image retrieval has received considerable attention from the visual AI community. However, collecting large-scale labeled images for training is rather laborious and expensive. Moreover, negative samples are treated equally without considering their differences when compared with the query. To overcome those problems, in this paper, we propose to utilize generated virtual images and multiple negative samples to simultaneously learn image representations for the task of image retrieval. In our method, we first utilize the Generative Adversarial Networks in a semi-supervised fashion to produce virtual images with an adversarial loss. Second, considering the neighborhood structure within negative samples, a random sampling algorithm is proposed to effectively mining the potentially hard samples. Third, we propose a multi-negative loss function with the Kullback–Leibler divergence. Finally, by optimizing the total loss the deep neural networks are trained. Then the learned networks are further used to obtain image representations. Extensive experiments are conducted on publicly available datasets. Our model demonstrates better performances in the task of image retrieval.
Designing for Openness in Making

Placing an emphasis on openness with regard to learning styles, idea generation and participation, maker culture can potentially appeal to many people. However, many makerspaces are struggling to attract representatively diverse groups of participants. In response to this observation, this qualitative study of a digital project week in a mid-size town in Germany explores constituents of openness with a focus on the gendered nature of computing. The degree to which makerspaces can or cannot transcend boundaries and impart openness for female participants and others remains vibrant, especially as gender is a construct that intersects with others demographic variables. We conducted several gender-related workshops and examined the local maker context through an intersectional lens, identifying four themes, which were consequential for openness in the context of the digital project week. Managing these themes, we suggest, can improve maker awareness of diversity issues but does not necessarily provide for a sustainable approach.
Proactive Clone Recommendation System for Extract Method Refactoring

Extract Method refactoring is commonly used for merging code clones into a single new method. In this position paper, we propose a proactive clone recommendation system for "Extract Method" refactoring. The proposed system that has been implemented as Eclipse plug-in monitors code modifications on the fly. Once the proposed system detects an "Extract Method" refactoring instance based on the analysis of code modifications, it recommends code clones of the refactored code as refactoring candidates. The preliminary user study shows that the users are able to refactor a greater number of clones in less time compared to a code clone analysis environment GemX.
Insurance ratemaking using the Exponential-Lognormal regression model

Abstract This paper is concerned with presenting the Exponential-Lognormal (ELN) regression model as a competitive alternative to the Pareto, or Exponential-Inverse Gamma, regression model that has been used in a wide range of areas, including insurance ratemaking. This is the first time that the ELN regression model is used in a statistical or actuarial context. The main contribution of the study is that we illustrate how maximum likelihood estimation of the ELN regression model, which does not have a density in closed form, can be accomplished relatively easily via an Expectation-Maximisation type algorithm. A real data application based on motor insurance data is examined in order to emphasise the versatility of the proposed algorithm. Finally, assuming that the number of claims is distributed according to the classic Negative Binomial and Poisson-Inverse Gaussian regression models, both the a priori and a posteriori, or Bonus–Malus, premium rates resulting from the ELN regression model are calculated via the net premium principle and compared to those determined by the Pareto regression model that has been traditionally used for modelling claim sizes.
Recommendation and Classification Systems: A Systematic Mapping Study

Today, recommendation algorithms are widely used by companies in multiple sectors with the aim of increasing their profits or offering a more specialized service to their customers. Moreover, there are countless applications in which classification algorithms are used, seeking to find patterns that are difficult for people to detect or whose detection cost is very high. Sometimes, it is necessary to use a mixture of both algorithms to give an optimal solution to a problem. This is the case of the ADAGIO, a R&amp;D project that combines machine learning (ML) strategies from heterogeneous data sources to generate valuable knowledge based on the available open data. In order to support the ADAGIO project requirements, the main objective of this paper is to provide a clear vision of the existing classification and recommendation ML systems to help researchers and practitioners to choose the best option. To achieve this goal, this work presents a systematic review applied in two contexts: scientific and industrial. More than a thousand papers have been analyzed resulting in 80 primary studies. Conclusions show that the combination of these two algorithms (classification and recommendation) is not very used in practice. In fact, the validation presented for both cases is very scarce in the industrial environment. From the point of view of software development life cycle, this review also shows that the work being done in the ML (for classification and recommendation) research and industrial environment is far from earlier stages such as business requirements and analysis. This makes it very difficult to find efficient and effective solutions that support real business needs from an early stage. It is therefore that the article suggests the development of new ML research lines to facilitate its application in the different domains.
Resilience of Randomized RNS Arithmetic with Respect to Side-Channel Leaks of Cryptographic Computation

In this paper, we want to promote the influence of randomized arithmetic on the leaks during a code execution. When somebody wants to extract some specific information from these leaks, one can observe different emanations of the device like power consumption. These leaks mostly come from the variations of the Hamming distances of the successive states of the system. This phenomenon is particularly critical for cryptographic devices. Our work evaluates the resilience of randomized moduli in Residue Number System (RNS) against Correlation Power Analysis (CPA), Differential Power Analysis (DPA). Our analysis is illustrated through the evaluation of scalar multiplication on an elliptic curve using the Montgomery Powering Ladder (MPL) algorithm which protects from Simple Power Analysis (SPA). We also propose an evaluation based on the Maximum Likelihood Estimator (MLE), which crosses the information of the whole state vector, instead of analysing only the current state like with CPA or DPA. Furthermore, MLE gives better performance and smooths the results allowing a better evaluation of the behaviour of the leakage. Our experimental evaluation suggests that the number of observations, needed to perform exploitable information leakage, is proportional to the number of possible RNS bases.
View-Invariant Pose Analysis for Human Movement Assessment from RGB Data

We propose a CNN regression method to generate high-level, view-invariant features from RGB images which are suitable for human pose estimation and movement quality analysis. The inputs to our network are body joint heatmaps and limb-maps to help our network exploit geometric relationships between different body parts to estimate the features more accurately. A new multiview and multimodal human movement dataset is also introduced part of which is used to evaluate the results of the proposed method. We present comparative experimental results on pose estimation using a manifold-based pose representation built from motion-captured data. We show that the new RGB derived features provide pose estimates of similar or better accuracy than those produced from depth data, even from single views only.
Realizing an Implementation Platform for Closed Loop Cyber-Physical Systems Using Blockchain

Cyber-physical Systems (CPS) comprise of a network of physically distributed embedded sensors and actuators equipped with computational and communication capability. In CPS, Internet of Things (IoT) devices communicate in a trustless environment as the data can be compromised due to the centralized database, limited power and computational constraints. At the same time, reliability and resiliency are key concerns in CPS in the face of unforeseen circumstances, often emanating from disaster based failures. In this paper, critical issues of centralized database security in CPS are addressed via a distributed blockchain based solution. The proposed system encompasses a smart contract based framework in Ethereum blockchain. It further explores the potential of blockchain in securing and offering a distributed network for the CPS in a closed loop manner. To demonstrate the realizability of the proposed framework, a testbed implementation for the proposed idea is provided. A desktop computer, a laptop, a simple temperature sensor and a Light Emitting Diode (LED) are interfaced in a Peer-to-Peer (P2P) network using Ethereum. The speedy transaction of sensor data in the blockchain at various difficulty levels and actuation through smart contracts enhance the usability of the platform for CPS and various IoT applications.
Identification of Denatured Biological Tissues Based on Time-Frequency Entropy and Refined Composite Multi-Scale Weighted Permutation Entropy during HIFU Treatment

Identification of denatured biological tissue is crucial to high intensity focused ultrasound (HIFU) treatment. It is not easy for intercepting ultrasonic scattered echo signals from HIFU treatment region. Therefore, this paper employed time-frequency entropy based on generalized S-transform (GST) to intercept ultrasonic echo signals. First, the time-frequency spectra of ultrasonic echo signal is obtained by GST, which is concentrated around the real instantaneous frequency of the signal. Then the time-frequency entropy is calculated based on time-frequency spectra. The experimental results indicate that the time-frequency entropy of ultrasonic echo signal will be abnormally high when ultrasonic signal travels across the boundary between normal region and treatment region in tissues. Ultrasonic scattered echo signals from treatment region can be intercepted by time-frequency entropy. In addition, the refined composite multi-scale weighted permutation entropy (RCMWPE) is proposed to evaluate the complexity of nonlinear time series. Comparing with multi-scale permutation entropy (MPE) and multi-scale weighted permutation entropy (MWPE), RCMWPE not only measures complexity of signal including amplitude information, but also improves the stability and reliability of multi-scale entropy. The RCMWPE and MPE are applied to 300 cases of actual ultrasonic scattered echo signals (including 150 cases in normal status and 150 cases in denatured status). It is found that the RCMWPE and MPE values of denatured tissues are higher than those of the normal tissues. Both RCMWPE and MPE can be used to distinguish normal tissues and denatured tissues. However, there are fewer feature points in the overlap region between RCMWPE of denatured tissues and normal tissues compared with MPE. The intra-class distance and the inter-class distance of RCMWPE are less and greater respectively than MPE. The difference between denatured tissues and normal tissues is more obvious when RCMWPE is used as the characteristic parameter. The results of this study will be helpful to guide doctors to obtain more accurate assessment of treatment effect during HIFU treatment.
A novel approach for unsupervised learning of software components

Clustering and classification are two important tasks in data mining and machine learning. These tasks have various applications in other related areas of research such as software engineering, text mining, image processing, and bio-informatics. Clustering is an NP-Hard problem, i.e. there is no proved polynomial time algorithm that can cluster a given set of input instances. However, approaches for evaluating cluster quality exist in the literature. This paper gives a new approach for software component learning by introducing an incremental learning approach for component clustering. Experiments are conducted by applying proposed approach on synthetic dataset and results proved the importance of proposed approach in terms of execution time and memory consumed.
Computing Quality-of-Experience Ranges for Video Quality Estimation

Typically, the measurement of the Quality of Experience for video sequences aims at a single value, in most cases the Mean Opinion Score (MOS). Predicting this value using various algorithms has been widely studied. However, deviation from the MOS is often handled as an unpredictable error. The approach in this contribution estimates intervals of video quality instead of the single valued MOS. Well-known video quality estimators are fused together to output a lower and upper border for the expected video quality, on the basis of a model derived from a well-known subjectively annotated dataset. Results on different datasets provide insight on the suitability of the well-known estimators for this particular approach.
An Empirical Study of Regression Bug Chains in Linux

Regression bugs are a type of bugs that cause a feature of software that worked correctly but stop working after a certain software commit. This paper presents a systematic study of regression bug chains, an important but unexplored phenomenon of regression bugs. Our paper is based on the observation that a commit c1, which fixes a regression bug b1, may accidentally introduce another regression bug b2. Likewise, commit c2 repairing b2 may cause another regression bug b3, resulting in a bug chain, i.e., b1 → c1 → b2 → c2 → b3. We have conducted a large-scale study by collecting 1579 regression bugs and 2630 commits from 57 Linux versions (from 2.6.12 to 4.9). The relationships between regression bugs and commits are modeled as a directed bipartite network. Our major contributions and findings are fourfold: 1) a novel concept of regression bug chains and their formulation; 2) compared to an isolated regression bug, a bug on a regression bug chain is much more difficult to repair, costing 2.4× more fixing time, involving 1.3× more developers and 2.8× more comments; 3) 85.8% of bugs on the chains in Linux reside in Drivers, ACPI, Platform Specific/Hardware, and Power Management; and 4) 83% of the chains affect only a single Linux subsystem, while 68% of the chains propagate across Linux versions.
Correlations between Human Performance in Information Seeking, Information Integration, and Overall Process in Diagnostic Tasks

Insufficient effort has been made to investigate details of the diagnostic process, specifically information seeking and information integration. In this study, participants were asked to accomplish eight diagnostic tasks of locating the leaking position of some working material in a simulated nuclear power plant. The participants' information seeking actions and possibility judgments about all potential leaking positions were recorded and then described using eight dependent variables concerning their gains and expenses. Generalized linear mixed-models were used to evaluate the effects of time pressure and the correlations. The results showed that (1) information seeking and information integration were closely correlated in multifaceted ways; (2) higher diagnostic accuracy was associated with fewer information acquiring actions but with longer average intervals between these actions; and (3) high time-pressure induced speed-and-accuracy trade-offs in the sub-processes, and the overall diagnostic speed and accuracy were negatively related between individuals, but positively related between trials