Reviewerly/exHarmony huggingface dataset:
A Recommender System for Complex Real-World Applications with Nonlinear Dependencies and Knowledge Graph Context

Most latent feature methods for recommender systems learn to encode user preferences and item characteristics based on past user-item interactions. While such approaches work well for standalone items (e.g., books, movies), they are not as well suited for dealing with composite systems. For example, in the context of industrial purchasing systems for engineering solutions, items can no longer be considered standalone. Thus, latent representation needs to encode the functionality and technical features of the engineering solutions that result from combining the individual components. To capture these dependencies, expressive and context-aware recommender systems are required. In this paper, we propose NECTR, a novel recommender system based on two components: a tensor factorization model and an autoencoder-like neural network. In the tensor factorization component, context information of the items is structured in a multi-relational knowledge base encoded as a tensor and latent representations of items are extracted via tensor factorization. Simultaneously, an autoencoder-like component captures the non-linear interactions among configured items. We couple both components such that our model can be trained end-to-end. To demonstrate the real-world applicability of NECTR, we conduct extensive experiments on an industrial dataset concerned with automation solutions. Based on the results, we find that NECTR outperforms state-of-the-art methods by approximately 50% with respect to a set of standard performance metrics.
Fostering good coding practices through individual feedback and gamification: an industrial case study

Code quality is a constant challenge faced by today’s software industry. To ensure that developers follow good coding practices, a variety of program analysis and test coverage tools are routinely deployed. However, these tools often fail to engage and change the practices of developers when applied to legacy systems as they output a huge number of warnings, quickly overwhelming the developers. In this article, we explore how individual feedback and gamification can motivate developers to pay more attention to good coding practices. To that extent, we implemented these two concepts in a tool that we deployed at two large companies where we conducted a case study. We find out that individual feedback is essential for motivating developers. We also find that gamification can be useful but has to be used with caution as it can frustrate some developers. Finally, we reflect on some lessons learned during our case studies, and conclude that the promising approach of our tool needs to be supported by longitudinal studies as well as comparative studies.
Globally Optimal Inlier Set Maximization for Atlanta World Understanding

In this work, we describe man-made structures via an appropriate structure assumption, called the Atlanta world assumption, which contains a vertical direction (typically the gravity direction) and a set of horizontal directions orthogonal to the vertical direction. Contrary to the commonly used Manhattan world assumption, the horizontal directions in Atlanta world are not necessarily orthogonal to each other. While Atlanta world can encompass a wider range of scenes, this makes the search space much larger and the problem more challenging. Our input data is a set of surface normals, for example, acquired from RGB-D cameras or 3D laser scanners, as well as lines from calibrated images. Given this input data, we propose the first globally optimal method of inlier set maximization for Atlanta direction estimation. We define a novel search space for Atlanta world, as well as its parametrization, and solve this challenging problem using a branch-and-bound (BnB) framework. To alleviate the computational bottleneck in BnB, i.e., the bound computation, we present two bound computation strategies: rectangular bound and slice bound in an efficient measurement domain, i.e., the extended Gaussian image (EGI). In addition, we propose an efficient two-stage method which automatically estimates the number of horizontal directions of a scene. Experimental results with synthetic and real-world datasets have successfully confirmed the validity of our approach.
Data visualization heuristics for the physical sciences

Data visualization – that is, the graphical representation of numerical information – is foundational to the scientific enterprise. A broad literature base is available providing rules, guidelines, and heuristics for authors of scientific literature to assist in the production of scientific graphics that are readable and intuitive. However, most of the available recent publications are in the bio-, psycho-, or climate sciences literature. In this paper, we address this deficiency and provide data visualization heuristics tuned to the specific needs of the physical sciences, and particularly materials sciences, community. We enumerate six general rules and provide examples of bad and improved data graphics, and provide source code to illustrate the generation of the improved figures. The six rules we enumerate are: (1) Generate figures programmatically; (2) Multivariate data calls for multivariate representation; (3) Showing the data beats mean ± standard deviation; (4) Choose colormaps that match the nature of the data; (5) Use small multiples; and (6) Don't use vendor exports naïvely.
On the Importance of Distinguishing Word Meaning Representations: A Case Study on Reverse Dictionary Mapping

Mohammad Taher Pilehvar. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Auto-balancing and robust interferometer designs for polarization entangled photon sources

A new implementation of a Mach-Zehnder interferometer is presented. Aimed at facilitating coherent optical wavelength conversion, the interferometer utilizes a novel double displacement technique that eliminates dispersion induced phase discrepancies between its input and output arms. To demonstrate the design, the interferometer was incorporated into a source of polarization entangled photon pairs. The source produced on average 2-3 million photon pairs per second per mW of pump power, the pairs emitted being maximally entangled in the polarization degree of freedom with a fidelity of ≥98%_. The new interferometer implementation is simple and robust and promises to become a design benchmark for polarization entangled photon sources.
Haptically-Enabled VR-Based Immersive Fire Fighting Training Simulator

Firefighting is a physically demanding task that requires extensive training. With the rising risks of global warming and its evident effects on spawning bush fire, there is an increasing need for recruiting new fire fighters. This imposes an unprecedented challenge of fast-tracking training procedures, especially in rural environments where most bush fires occur. Additionally, the current manual training procedures do not take into consideration the immersion factor, without which a novice fire fighter may be overwhelmed when facing a bush fire for the first time. This challenge has motivated us to harness the power of virtual reality and develop a portable firefighting training system. The developed firefighting training system, presented in this paper, is haptically enabled to allow the trainees to experience the jet reaction forces from the hose. The system also features realistic water dispersion and interaction with fire and smoke particles via accurate particle physics modelling.
Deep Collaborative Discrete Hashing with Semantic-Invariant Structure

Existing deep hashing approaches fail to fully explore semantic correlations and neglect the effect of linguistic context on visual attention learning, leading to inferior performance. This paper proposes a dual-stream learning framework, dubbed Deep Collaborative Discrete Hashing (DCDH), which constructs a discriminative common discrete space by collaboratively incorporating the shared and individual semantics deduced from visual features and semantic labels. Specifically, the context-aware representations are generated by employing the outer product of visual embeddings and semantic encodings. Moreover, we reconstruct the labels and introduce the focal loss to take advantage of frequent and rare concepts. The common binary code space is built on the joint learning of the visual representations attended by language, the semantic-invariant structure construction and the label distribution correction. Extensive experiments demonstrate the superiority of our method.
Determination of Stress Intensity Factors and J integral based on Digital Image Correlation

Digital image correlation (DIC) is a technique in experimental mechanics to acquire full-field measurement data of displacements and deformations from the surface of specimens or components. Especially for the investigations of cracks it provides additional benefits. The actually present deformation field in the vicinity of the crack tip can be obtained which directly reflects for example crack closure effects or plasticity. Against this background the paper summarizes a procedure to compute the J integral and the stress intensity factors KI and KII based on DIC data. For this purpose the J and interaction integral are computed as line and domain integrals. Through experiments it is shown that the domain integral is less affected by scatter of the DIC data. Furthermore, the specific domain, facet sizes and facet distances slightly influence the results.
A nonparametric ensemble binary classifier and its statistical properties

In this work, we propose an ensemble of classification trees (CT) and artificial neural networks (ANN). Several statistical properties including universal consistency and upper bound of an important parameter of the proposed classifier are shown. Numerical evidence is also provided using various real-life data sets to assess the performance of the model. Our proposed nonparametric ensemble classifier does not suffer from the "curse of dimensionality" and can be used in a wide variety of feature selection cum classification problems. Performance of the proposed model is quite better when compared to many other state-of-the-art models used for similar situations.
SAF-BAGE: Salient Approach for Facial Soft-Biometric Classification - Age, Gender, and Facial Expression

How can we improve the facial soft-biometric classification with help of the human visual system? This paper explores the use of saliency which is equivalent to the human visual system to classify Age, Gender and Facial Expression soft-biometric for facial images. Using the Deep Multi-level Network (ML-Net) [1] and off-the-shelf face detector [2], we propose our approach - SAF-BAGE, which first detects the face in the test image, increases the Bounding Box (B-Box) margin by 30%, finds the saliency map using ML-Net, with 30% reweighted ratio of saliency map, it multiplies with the input cropped face and extracts the Convolutional Neural Networks (CNN) predictions on the multiplied reweighted salient face. Our CNN uses the model AlexNet [3], which is pre-trained on ImageNet. The proposed approach surpasses the performance of other approaches, increasing the state-of-the-art by approximately 0.8% on the widely-used Adience [28] dataset for Age and Gender classification and by nearly 3% on the recent AffectNet [36] dataset for Facial Expression classification. We hope our simple, reproducible and effective approach will help ease future research in facial soft-biometric classification using saliency.
Two new families of quantum synchronizable codes

In this paper, we present two new ways of quantum synchronization coding based on the $$(\varvec{u}+\varvec{v}|\varvec{u}-\varvec{v})$$ construction and the product construction, respectively, and greatly enrich the varieties of available quantum synchronizable codes. The circumstances where the maximum synchronization error tolerance can be reached are explained for both constructions. Furthermore, repeated-root cyclic codes derived from the $$(\varvec{u}+\varvec{v}|\varvec{u}-\varvec{v})$$ construction are shown to be able to provide better Pauli error-correcting capability than BCH codes.
Hypotheses Engineering: First Essential Steps of Experiment-Driven Software Development

Recent studies have proposed the use of experiments to guide software development in order to build features that the user really wants. Some authors argue that this approach represents a new way to develop software that is different from the traditional requirement-driven one. In this position paper, we propose the discipline of Hypotheses Engineering in comparison to Requirements Engineering, highlighting the importance of proper handling hypotheses that guide experiments. We derive a set of practices within this discipline and present how the literature has tackled them up to now. Finally, we propose a set of research questions that could guide future work towards helping practitioners.
Holographic Mixed Reality System for Air Traffic Control and Management

Based on a long-term prediction by the International Civil Aviation Organization indicating steady increases in air traffic demand throughout the world, the workloads of air traffic controllers are expected to continuously increase. Air traffic control and management (ATC/M) includes the processing of various unstructured composite data along with the real-time visualization of aircraft data. To prepare for future air traffic, research and development intended to effectively present various complex navigation data to air traffic controllers is necessary. This paper presents a mixed reality-based air traffic control system for the improvement of and support for air traffic controllers’ workflow using mixed reality technology that is effective for the delivery of information such as complex navigation data. The existing control systems involve difficulties in information access and interpretation. Therefore, taking notice of the necessity for the integration of air traffic control systems, this study presents the mixed reality (MR) system, which is a new approach, that enables the control of air traffic in interactive environments. This system is provided in a form usable in actual operational environments with a head-mounted see-through display installed with a controller to enable more structured work support. In addition, since this system can be controlled first-hand by air traffic controllers, it provides a new experience through improved work efficiency and productivity.
A Fixed-Point Quantization Technique for Convolutional Neural Networks Based on Weight Scaling

In order to make convolutional neural networks (CNNs) usable on smaller or mobile devices, it is necessary to reduce the computing, energy and storage requirements of these networks. One can achieved this by a fixed-point quantization of weights and activations of a CNN, which are usually represented by 32-bit floating-point. In this paper, we present an adaption of convolutional and fully connected layers in order to obtain a high usage of the given value range of activations and weights. Therefore, we introduce scaling factors obtained by moving average to limit the weights and activations. Our model, quantized to 8 bit, outperforms the 7-layer baseline model from which it is derived and the naive quantization by several percentage points. Our method does not require any additional operations in the inference and both the weights and activations have a fixed radix point.
Multi-Stream Scale-Insensitive Convolutional and Recurrent Neural Networks for Liver Tumor Detection in Dynamic Ct Images

Convolutional neural networks (CNNs) have achieved great success in numerous challenging vision tasks, and have great potential for object detection in natural images. Compared with the natural images, medical images exhibit some unique characteristics. Therefore, substantial challenges still remain in this field. The first challenge is to develop a method for effectively distilling enhancement patterns from the dynamic CT images. Moreover, since tumor sizes vary greatly and small lesions are important for early liver tumor detection, lesion detection with a widely variable scale is another challenge. In this paper, we propose a multi-stream scale-insensitive convolutional and recurrent neural network (MSCR) for liver tumor detection. Specifically, we propose the use of grouped convolutional long short-term memory (GCLSTM) to extract enhancement patterns, which is developed as a plug-and-play module. Experiments show that the MSCR framework exhibits superior performance over state-of-the-art approaches, achieving an average precision of 77.06% for detection of focal liver lesions. We have released the code of MSCR in <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Majorana representation for mixed states

We generalize the Majorana stellar representation of spin-$s$ pure states to mixed states, and in general to any Hermitian operator, defining a bijective correspondence between three spaces: the spin-density matrices, a projective space of homogeneous polynomials of four variables, and a set of equivalence classes of points (constellations) on spheres of different radii. The representation behaves well under rotations by construction, and also under partial traces where the reduced density matrices inherit their constellation classes from the original state $\ensuremath{\rho}$. We express several concepts and operations related to density matrices in terms of the corresponding polynomials, such as the anticoherence criterion and the tensor representation of spin-$s$ states described in Giraud et al. [Phys. Rev. Lett. 114, 080401 (2015)].
Robustness of Maximal α-Leakage to Side Information

Maximal α-leakage is a tunable measure of information leakage based on the accuracy of guessing an arbitrary function of private data based on public data. The parameter α determines the loss function used to measure the accuracy of a belief, ranging from log-loss at α = 1 to the probability of error at α = ∞. To study the effect of side information on this measure, we introduce and define conditional maximal α-leakage. We show that, for a chosen mapping (channel) from the actual (viewed as private) data to the released (public) data and some side information, the conditional maximal α-leakage is the supremum (over all side information) of the conditional Arimoto channel capacity where the conditioning is on the side information. We prove that if the side information is conditionally independent of the public data given the private data, the side information cannot increase the information leakage.
Deep Angular Embedding and Feature Correlation Attention for Breast MRI Cancer Analysis

Accurate and automatic analysis of breast MRI plays a vital role in early diagnosis and successful treatment planning for breast cancer. Due to the heterogeneity nature, precise diagnosis of tumors remains a challenging task. In this paper, we propose to identify breast tumor in MRI by Cosine Margin Sigmoid Loss (CMSL) with deep learning (DL) and localize possible cancer lesion by COrrelation Attention Map (COAM) based on the learned features. The CMSL embeds tumor features onto a hyper-sphere and imposes a decision margin through cosine constraints. In this way, the DL model could learn more separable inter-class features and more compact intra-class features in the angular space. Furthermore, we utilize the correlations among feature vectors to generate attention maps that could accurately localize cancer candidates with only image-level labels. We build the largest breast cancer dataset involving 10,290 DCE-MRI scan volumes for developing and evaluating the proposed methods. The model driven by CMSL achieved a classification accuracy of 0.855 and AUC of 0.902 on the testing set, with sensitivity and specificity of 0.857 and 0.852, respectively, outperforming competitive methods overall. In addition, the proposed COAM accomplished more accurate localization of the cancer center compared with other state-of-the-art weakly supervised localization method.
Secure Online Voting System Using Biometric and Blockchain

Elections play an important role is democracy. If the election process is not transparent, secure and tamper proof then the reliability and authenticity of whole process is at stake. In this paper, we have discussed online voting system which fulfills all the above system requirements. We have addressed the issue of user authentication through iris recognition. We have used One Time Password (OTP) to have additional security check. We have also taken care that one valid user should not cast multiple votes. Use of Blockchain is the another security measure implemented in order to provide decentralized, tamper proof storage of data related to users biometric, personal details and votes casted by them. Thus we are not only focusing on user authenticity but also data security is also taken into consideration. The performance of the system has been tested for users from different age group and different background and its inference is presented.
Artificial Flora Optimization Algorithm for Task Scheduling in Cloud Computing Environment

Cloud computing is a relatively new computing paradigm that enables provision of storage and computing resources over a network to end-users. Task scheduling represents the allocation of tasks to be executed to the available resources. In this paper, we propose a scheduling algorithm, named artificial flora scheduler, with an aim to improve task scheduling in the cloud computing environments. The artificial flora belongs to the category of swarm intelligence metaheuristics that have proved to be very effective in solving NP hard problems, such as task scheduling. Based on the obtained simulation results and comparison with other approaches from literature, a conclusion is that the proposed scheduler efficiently optimizes execution of the submitted tasks to the cloud system, by reducing the makespan and the execution costs.
Markov versus quantum dynamic models of belief change during evidence monitoring

Abstract Two different dynamic models for belief change during evidence monitoring were evaluated: Markov and quantum. They were empirically tested with an experiment in which participants monitored evidence for an initial period of time, made a probability rating, then monitored more evidence, before making a second rating. The models were qualitatively tested by manipulating the time intervals in a manner that provided a test for interference effects of the first rating on the second. The Markov model predicted no interference, whereas the quantum model predicted interference. More importantly, a quantitative comparison of the two models was also carried out using a generalization criterion method: the parameters were fit to data from one set of time intervals, and then these same parameters were used to predict data from another set of time intervals. The results indicated that some features of both Markov and quantum models are needed to accurately account for the results.