Reviewerly/exHarmony huggingface dataset:
Convergence to Pure Steady States of Linear Quantum Systems

We study the convergence of density operators of single-mode linear quantum systems towards their steady states in trace norm. We give compact and intuitive necessary and sufficient conditions for such linear quantum systems to have pure Gaussian steady states in terms of the Hamiltonian and coupling operators.
Priming Deep Pedestrian Detection with Geometric Context

We investigate the role of geometric context in deep neural networks to establish better pedestrian detectors that are more robust to occlusions. Notwithstanding their demonstrated successes, deep object detectors under-perform in crowded scenes with high intra-category occlusions. One brute-force solution is to collect a large number of labeled training samples under occlusion, but the combinatorial increase in the labeling effort makes it an unaffordable solution. We argue that a promising and complementary direction to solve this problem is to bring geometric context to modulate feature learning in a DNN. We identify that an effective way to leverage geometric context is to induce it in two steps - through early fusion, by guiding region proposal generation to focus on occluded regions, and through late fusion, by penalizing misalignments of bounding boxes in both 2D and 3D. Our experiments on multiple state-of-the-art DNN detectors and detection benchmarks clearly demonstrates that our proposed method outperforms strong baselines by an average of 5%.
PHILOSOPHICAL IMAGE OF CLASSICAL AND NON-CLASSICAL SCIENCE: CHANGE OF KEY METAPHORS

The article considers the expression of the generalized philosophical image of science in various periods of its development through the key metaphors used by philosophers to describe scientific cognition. The authors believe that this way it is possible to trace both the change in the character of science itself during the transition from the classical to the non-classical stage of its development, and the change in the paradigm of philosophy of science. The main lines of changes are transitions from reality reflection metaphors to metaphors of new reality construction and from free game metaphors to metaphors of work carried out in the organizational forms of production activity.
Embedding Big Data in Graph Convolutional Networks

Deep learning architectures and Convolutional Neural Networks (CNNs) have made a significant impact in learning embeddings of high-dimensional datasets. In some cases, and especially in the case of high-dimensional graph data, the interlinkage of data points may be hard to model. Previous approaches in applying the convolution function on graphs, namely the Graph Convolutional Networks (GCNs), presented neural networks architectures that encode information of individual nodes along with their connectivity. Nonetheless, these methods face the same issues as in traditional graph-based machine learning techniques i.e. the requirement of full matrix computations. This requirement bounds the applicability of the GCNs on the available computational resources. In this paper, the following assumption is evaluated: the training of a GCN with multiple subsets of the full data matrix is possible and converges to the full data matrix training scores, thus lifting the aforementioned limitation. Following this outcome, different subset selection methodologies are also examined to evaluate the impact of the learning curriculum in the performance of the trained model in small as well as very large scale graph datasets.
Flexible Statistical Learning Model for Unsupervised Image Modeling and Segmentation

We propose in this work to improve the tasks of image segmentation and modeling through an unsupervised flexible learning approach. Our focus here is to develop an alternative mixture model based on a bounded generalized Gaussian distribution, which is less sensitive to over-segmentation and offers more flexibility in data modeling than the Gaussian distribution which is certainly not the best approximation for image segmentation. A maximum likelihood- (ML) based algorithm is applied for estimating the resulted model parameters. We investigate here the integration of both a spatial information (a prior information between neighboring pixels) and a minimum description length (MDL) principle into the model learning step in order to deal with the major problems of finding the optimal number of classes and also selecting the best model that describes accurately the dataset. Therefore, the proposed model has the advantage to maintain the balance between model complexity and goodness of fit. Obtained results on a large database of medical MR images confirm the effectiveness of the proposed approach and demonstrate its superior performance compared to some conventional methods.
RetroTracker: Upgrading Existing Virtual Reality Tracking Systems

Virtual reality systems often make use of spatially tracked handheld props in the form of controllers or specialized objects to add realism and interaction. Tracking these objects today relies on the use of expensive, bulky, and power-consuming trackers that must be attached to an object. We propose a passive tracking technique that works with existing low-cost, off-the-shelf optical tracking components and is capable of turning any object into a tracked virtual reality prop. Our method utilizes paper-thin retro-reflective markers that can be placed in any free-form on everyday objects. The proof-of-concept prototype acts as a simple add-on for an existing tracking system and requires only a minimal amount of compute overhead. We demonstrate that our method allows bringing physical real-world objects to virtual worlds with ease, and provides an object identification technique using patterned retro-reflective markers.
Batmen X The Puzzler - Escaping AR's Drawbacks with Augmented Virtuality and Low Cost Sensors

We present an augmented virtuality system using low-cost or outdated devices as sensors, attached to tangible objects. Five interactions were implemented, featuring rotation, pressing, pulling, pushing and insertion movements, including passive mechanical resistance. Interactions were tied together by a puzzling narrative. Concerning storytelling, in contrast to AR, pure virtual reality offers unlimited visual possibilities and higher flexibility. Augmenting VR with physical objects allows ignoring most details of the physical environment and exploring ‘magical’ interactions. Precise registration was accomplished using wireless tracking system. The main challenge was the absent physical counterparts of the scenario, which was tackled by limiting user's reach to available interactions. Despite Leap Motion's erratic hand tracking, mouse and tablet sensors were precise enough and the augmented environment allowed a high sense of presence.
Textile Touch-Sensor Array based on Circular and Non-Circular Polymer Optical Fibers

Textiles and flexible textile surfaces can be found in many industrial goods. Due to trends like smart textiles and consumer electronics touchpads and flexible touch-sensitive sensor arrays that can be integrated in these textile surfaces are increasingly in demand. A multi-touch capable textile touchpad based on polymer optical fibers (POF) is presented. For the sensing fibers, different fiber cross sections - from circular to trilobal - are studied for maximum sensitivity and minimum cross talk. Results show that the trilobal cross section improves the sensitivity and the strength of the sensor signal by about 50%.
Generalization of Multi-bit Encoding Function Based Data Hiding Scheme

In this investigation, a generalizeSanyal, Piyali dataJana, Sharmistha embeddingJana, Biswapati scheme with satisfactory quality has been suggested using multi-bit encoding procedure. Using a multi-bit encoding procedure, Kuo, Wen-Chung, et al. achieve high data hiding capacity with acceptable stego image quality, but security was another major issue which is possible to enhance. We have proposed a secure general form of multi-bit encoding function using an Adder-Element in which we achieve PSNR 42 (dB) while maintaining the same data hiding capacity with Kuo, Wen-Chung, et al. scheme. Two major achievements of our work are (1) it does not involve any complicated calculation to insert the secret message and (2) only Adder-Element is required to retrieve the hidden message. We have observed that the developed technique accomplishes high data hiding capacity while keep image quality better than other existing schemes.
Identity Extraction from Clusters of Multi-modal Observations

In this paper, we present a method for identity extraction from TV News Broadcasts. We define the identity as a set of multi-modal observations. In our case it is the face of a person and a name of a person. The method is based on agglomerative clustering of observations. The resulting clusters represent individual identities, that appeared in the broadcasts. To evaluate the accuracy of our system, we hand labelled approximately one year worth of TV News broadcasts. This resulted in total of $$10\,301$$ multi-modal observations and 2563 unique identities. Our method achieved a coverage measure of 90.69 % and precision measure of 94.69 %. Given the simplicity of the proposed algorithm, these results are very satisfactory. Furthermore, the designed system is modular and new modalities can be easily added.
Imitation Learning for Sentence Generation with Dilated Convolutions Using Adversarial Training

In this work, we consider the sentence generation problem as an imitation learning problem, which aims to learn a policy to mimic the expert. Recent works have showed that adversarial learning can be applied to imitation learning problems. However, it has been indicated that the reward signal from the discriminator is not robust in reinforcement learning (RL) based generative adversarial network (GAN), and estimating state-action value is usually computationally intractable. To deal with this problem, we propose to use two discriminators to provide two different reward signals for constructing a more general imitation learning framework that can be used for sequence generation. Monte Carlo (MC) rollout is therefore not necessary to make our algorithm computationally tractable for generating long sequences. Furthermore, our policy and discriminator networks are integrated by sharing another state encoder network constructed based on dilated convolutions instead of recurrent neural networks (RNNs). In our experiment, we show that the two reward signals control the trade-off between the quality and the diversity of the output sequences.
Second International Workshop on Experiences and Empirical Studies on Software Reuse (WEESR 2019)

The Workshop on Experiences and Empirical Studies on Software Reuse (WEESR) aims, on the one hand, to allow researchers and practitioners discuss in-progress research regarding experiences and empirical studies on applying reuse techniques in non-academic environments. On the other hand, it aims for providing feedback on how these studies are planned, designed, conducted, and reported. The second edition of this workshop, the WEESR 2019, was co-located with the Software Product Lines Conference at 2019 (SPLC'19). There, attendants discussed six papers presenting case studies, GQM experiments, empirical studies and evaluations regarding proposals on topics such as reuse in companies not aware of software product lines, companies adopting product lines, defining the scope of a product line, modeling variability in self-adaptive systems, and reusing software for implementing artificial neural networks.
Visualization of Users Raking in Online Dating Service

Data visualization represents an important tool in data analysis. In this paper, we focus on the visualization of data from online dating service - Libimseti. Libimseti is a rarity among online dating services because it offers a user’s raking system. It means that every user can evaluate other users on the scale from one to ten. We present a modification of a circular plot to represent general data from the raking system. The introduced visualization provides both aesthetically pleasing and readable graph. It shows a general overview of people’s behavior, opinion, and personal preferences in the given service without deeper understating of the data. It helps to discover users who can suffer from other users negativity or users who provoke others and decrease the popularity of the web itself. Moreover, the presented graph can help to find influencers, who can popularize the website among its users.
KolmoLD

KolmoLD is a framework for content-addressable networking with a programmable layer: the data objects distributed across the network can contain program code to decode the data that is specified to be executed in a sandboxed environment and references other data objects based on a cryptographic hash function naming scheme.
Intelligent Home Communication Design Based on Home Computer

Abstract Along with the continuous development of computer technology, the increase in the number of computer popularization, especially the microcomputer used in the home environment, has brought a lot of convenience to human life. In recent years, intelligent home has developed rapidly, and intelligent home environment provided higher spiritual material experience for users. However, in the remote control of smart homes, most of them use network communication chips such as 3G and 4G, which wastes computer resources in the home environment. Therefore, this paper proposes an intelligent home design based on home computer, hoping to reduce the development cost of existing smart home technology and eventually accelerate the popularization of smart home.
Mike’s Labels

Mike is a homeless chronic alcoholic. I know this because I wrote his labels right here on his medical record. These labels help us help Mike, but when we layer these labels and others on top of each other, finding Mike the person becomes difficult, if not impossible.
MMCTR: A Multi-Task Model for Short Video CTR Prediction with Multi-Modal Video Content Features

This paper presents a multi-task model called MMCTR for short video CTR prediction. The input to this model consists of four parts: category features and long-term preference features from user behavior interaction data, multi-modal short video content features, and other features that are manually extracted. In this model, a weighted loss function is employed and the parameters of our model are updated by joint training. By this way, one of the tasks can utilize information of other tasks for further improving accuracy. In addition, a new hash mapping function is defined to compresses the number of category ids and optimizes the memory space. Experimental results on the dataset from the ICME 2019 Short Video Understanding Challenge show that MMCTR outperforms the tested state-of-the-art models.
Grouping Semantically Related Change-Sets to Enhance Identification of Logical Coupling

Identifying dependency between various artifacts in a large scale software system is a non-trivial task.As the software evolves, multiple artifacts like files, docs, classes, database scripts, etc., are likely to undergo change concurrently.Such artifacts tend to have a dependency between them, otherwise referred to as logical coupling.Researchers have used Support and Confidence as an association rule based measurement to predict the levels of logical coupling among the software artifacts.However, employing a single change on a software artifact can span across various closely related changes when many code contributors are working on the same change.Thus it is important to preprocess and group these semantically related change-sets before identifying logical coupling.In this paper, we propose a method to identify logical coupling and group semantically related change sets.We evaluate our method on real-world git repositories and document our observations.
Semantic Knowledge Matching Based on BERT for Dispatching Fault Disposal Information

Power grid dispatching fault disposal documents are essential for dispatching operators while it is a challenge for them to use these documents efficiently and promptly due to unstructured text data. Recently a new language representation model, BERT (Bidirectional Encoder Representations from Transformers) has created state-of-the-art models for a wide variety of natural language processing tasks with fine-tuning. In this work, we propose a semantic knowledge matching approach based on redesigned fine-tuned BERT model for dispatching fault disposal documents. The experimental results demonstrate that our proposed approach achieves significant sentence-level semantic accuracy for the offline knowledge such as previous fault handling records and fault disposal documentations, The semantic knowledge matching result can also be referred for online fault handling automatically and promptly to improve grid disposal handling efficiency. This approach can be deployed as common one for natural language proceeding tasks of power grid.
Time Optimal Tour Through a Class of Points for a Double Integrator Traveling Salesman

In this paper, a double integrator traveling salesman visiting a given set of points in phase plane and coming back to the starting point is considered. Also, each point is visited only once during a tour. The problem of finding the time optimal tour through a particular distribution of points in the phase plane is considered. An expression for optimal time of travel for time optimal tour through a “particular” class of distribution of tour points in the phase plane is obtained. The distribution of points for which the method is valid is characterized in terms of a family of parabolas in phase plane.
State Estimation for Stochastic Nonlinear Systems with Applications to Viral Infections

State estimation of biological systems is a difficult task due to their complexity and stochasticity. In particular, bilinear and Michaelis-Menten terms are the base for many biological models such as in infectious diseases, cancer, diabetes, and many others. In this paper, mentioned non-linear terms are formulated into a polynomial form with state-dependent matrices driven by additive white Gaussian noises over linear observations. To show the effectiveness of the approach, two different models widely used for modeling viral infectious diseases are considered and compared with the extended Kalman filter (EKF) algorithm. Numerical results show the applicability of the polynomial approach.
Are you sure your tool does what it is supposed to do? Validating Arabic root extraction

Abstract Although in the digital humanities, researchers use software tools to conduct their research, and often apply these tools to data the software was not developed for, there has been little attention for investigating tool performance on this data. This is strange because in order to be able to appraise the results of digital humanities research, it is important to understand to what extent the tool output is correct. To illustrate the importance of the validation of tools, this article presents a case study of validating Arabic root extraction tools. Arabic words are based on root letters; three root letters usually demarcate a semantic field. Thus, roots can be used for studying semantic fields. For example, researchers can gain insight into the relative importance of the different senses (i.e. seeing, hearing, touching, smelling, and tasting) in Arabic jurisprudence (fiqh) by extracting and counting roots. A problem is that there are only a few usable tools available. We take three root extraction tools, Khoja (Khoja and Garside, 1999, Stemming Arabic Text. Lancaster, England: Lancaster University), ISRI (Taghva et al., 2005, Arabic stemming without a root dictionary. In International Conference on Information Technology: Coding and Computing (ITCC’05). Vol. 2. Las Vegas, NV, April 2005 pp. 152–57), and AlKhalil (Boudlal et al., 2010, Alkhalil morpho sys1: a morphosyntactic analysis system for Arabic texts. In International Arab Conference on Information Technology. New York, NY: Elsevier Science Inc., April 2017, pp. 1–6), and create manually annotated gold standard data consisting of three samples of approximately 1,000 words from important books of Islamic jurisprudence. We show that Khoja is the best root extraction tool for our data. We also demonstrate that the relative counts of individual roots differ among tools, which leads to a different interpretation depending on which tool is chosen. This means that findings based on automatically extracted roots should always be interpreted with care.
An Effective Retrieval Approach for Documents Related to Past Civil Engineering Projects

During the practice of a public administration or a company, a large volume of documents are typically produced. The recent dematerialization efforts have been pushing the way for a digital form of these documents, so as to make their management more efficient and cost-saving. But, traditional document management systems entail many non-value adding activities, such as printing, transport and archiving these documents, making the retrieval of information extremely cumbersome, especially by users without a computer science degree. The research community is putting a lot of attention to realize effective and easy-to-use search capabilities over these large number of digital/digitalized documents. This paper investigates such a problem within the context of a document management system for past civil engineering projects, and proposes an applied solution to offer advanced document retrieval capabilities without demanding a strong background in computer science by presenting a proof-of.concept implemented within the context of the PROBIM project.
Virtual Crafting Experience: Hand Motion and Scent Stimulation in Conjunction with a Promotional Video for Improving Interest

Crafting workshops are useful methods for the promotion of products because it can let customers know about the technical and social appeal of the product. In this study, we have developed simple, portable, and active multisensory VR systems of crafting experiences for product promotion. It comprises a video of crafting a wooden object with scent and hand motion synchronized with the video. The result of the product evaluations demonstrated positive effects, wherein the participants were found to be more attracted to the product in the video, where only scent or haptic stimulations were presented. However, the result also indicated that there is a negative synergetic effect when two stimulations were simultaneously presented.
Learning One-class Support Vector Machine by Using Artificial Bee Colony Algorithm and Its Application for Disease Classification

The one-classification support vector (OCSVM) is a variant of SVM which only uses the positive class sample set in training stage. It has been widely used in the applications of disease diagnose, handwritten signature verification, remote sensing and document classification. However, there are many parameters needed to regulate. The mistake of parameter setting makes OCSVM it to be not effectiveness. Therefore, in this paper we proposed a learning algorithm based on the artificial bee colony algorithm to select the parameters. The construction algorithm of OSCVM is called the artificial bee colony based OSCVM (ABC-OCSVM) algorithm. Experimental results of two medical datasets of UCI data repository showed that our proposed ABC-OCSVM method outperforms the conventional LIBSVM package.