Reviewerly/exHarmony huggingface dataset:
Extending a Tag-based Collaborative Recommender with Co-occurring Information Interests

Collaborative Filtering is largely applied to personalize item recommendation but its performance is affected by the sparsity of rating data. In order to address this issue, recent systems have been developed to improve recommendation by extracting latent factors from the rating matrices, or by exploiting trust relations established among users in social networks. In this work, we are interested in evaluating whether other sources of preference information than ratings and social ties can be used to improve recommendation performance. Specifically, we aim at testing whether the integration of frequently co-occurring interests in information search logs can improve recommendation performance in User-to-User Collaborative Filtering (U2UCF). For this purpose, we propose the Extended Category-based Collaborative Filtering (ECCF) recommender, which enriches category-based user profiles derived from the analysis of rating behavior with data categories that are frequently searched together by people in search sessions. We test our model using a big rating dataset and a log of a largely used search engine to extract the co-occurrence of interests. The experiments show that ECCF outperforms U2UCF and category-based collaborative recommendation in accuracy, MRR, diversity of recommendations and user coverage. Moreover, it outperforms the SVD++ Matrix Factorization algorithm in accuracy and diversity of recommendation lists.
Tighter Weighted Polygamy Inequalities of Multipartite Entanglement in Arbitrary-Dimensional Quantum Systems

We investigate polygamy relations of multipartite entanglement in arbitrary-dimensional quantum systems. By improving an inequality and using the βth (0 ≤ β ≤ 1) power of entanglement of assistance, we provide a new class of weighted polygamy inequalities of multipartite entanglement in arbitrary-dimensional quantum systems. We show that these new polygamy relations are tighter than the ones given in Kim (Phys. Rev. A 97, 042332 2018).
Pengembangan Media Pembelajaran Berbasis Aplikasi Articulate Storyline Pada Mata Pelajaran Ekonomi Kelas X SMA

The development of learning media by using the Articulate Storyline application is an effort to help students understand the message on the learning material delivered, able to create pleasant atmosphere, can support the motivation, interests and memory of students in understanding learning. The research aim produce learning media products learning media that are in accordance with the criteria for the feasibility of the media, material and effective on Economics class X subjects at Dian Andalas High School Padang.This Type of research known as Research and Development (R & D). The development models used is the development model proposed by 4-D. This research by conducted at Dian Andalas Padang High School. The product of development was assessed by one material expert and two media experts as well as practicality test on Dian Andalas Padang High School student Class X. The research subjects were 15 high school students. Documentation data collection tools and validation sheets. The product validity test results show that the product is suitable for use, for the material aspects the average validator is 4.4 in the very goodicategory. For aspects of the media are in the category of very.valid with,an averagea of validator of 4.57 and average of validator II of 4.71, and product trials for aspects of product practicality are categorized practically with an average number of 4.75. Then it can be conclude that this learning media product ispsuitable for use them learning process.
An empirical survey on the early adoption of DNS certification authority authorization

A new certification authority authorization (CAA) resource record for the domain name system (DNS) was standardized in 2013. Motivated by the later 2017 decision to enforce mandatory CAA checking for most certificate authorities, this paper surveys the early adoption of CAA by using an empirical sample collected from the Alexa’s top-million domains. According to the results, (i) the adoption of CAA is still at a modest level; only a little below two percent of the popular domains sampled have adopted CAA. Among the domains that have adopted CAA, (ii) authorizations dealing with wildcard certificates are rare compared to conventional certificates. Interestingly, (iii) the results only partially reflect the market structure of the global certificate business. With these timely results, the paper contributes to the ongoing large-scale empirical research on the use of encryption technologies.
Perancangan Validasi Permohonan Narasumber Pada Sistem Informasi Permohonan Narasumber Menggunakan Finite State Automata

The Law and Human Rights Research and Development Agency in carrying out its duties and functions provides services in the form of Providing Resource persons. Managing mail manually requires a long time and allows for document loss. This study aims to reduce the use of time, help minimize the loss of letters, and reduce errors in recording letters by storing them in a database in accordance with the specified format. In the design of the system will check the applicant's input and validate before the data is stored in the database using Finite State Automata (FSA) so that the data stored in accordance with the format specified. The implementation of this research is expected to be able to help the Law and Human Rights Research and Development Agency to reduce the use of time, help minimize the loss of letters, and reduce errors in recording letters in accordance with the specified format.
Compositional trace conversion

In order to eliminate traces as stipulated grammatical objects, syntactic movement has been reformulated in terms of multiple-merge: it is the result of the same constituent being merged into the structure multiple times, using either copies or multidominance structures. In spite of their empirical and conceptual advantages, multiple-merge theories pose known challenges for the semantic interpretation of movement, as there are no variable-denoting traces in lower positions. The most common means of resolving this conundrum is trace conversion (Fox 2002, 2003), in which either a syntactic operation makes alterations at lower merge sites in order to generate trace-like interpretations, or the semantics behaves as if such a syntactic operation had occurred. In this paper I discuss problems faced by presently formulated versions of trace conversion and propose an alternative, compositional trace conversion, in which multiple-merge structures can be directly interpreted in a straightforwardly compositional manner. This approach is shown to generalize well, extending to modals and degree phrases as well as DPs. BibTeX info
Simple primitive recognition via hierarchical face clustering

Abstract We present a simple yet efficient algorithm for recognizing simple quadric primitives (plane, sphere, cylinder, cone) from triangular meshes. Our approach is an improved version of a previous hierarchical clustering algorithm, which performs pairwise clustering of triangle patches from bottom to top. The key contributions of our approach include a strategy for priority and fidelity consideration of the detected primitives, and a scheme for boundary smoothness between adjacent clusters. Experimental results demonstrate that the proposed method produces qualitatively and quantitatively better results than representative state-of-the-art methods on a wide range of test data.
Adding Knowledge to Unsupervised Algorithms for the Recognition of Intent

Computer vision algorithms performance are near or superior to humans in the visual problems including object recognition (especially those of fine-grained categories), segmentation, and 3D object reconstruction from 2D views. Humans are, however, capable of higher-level image analyses. A clear example, involving theory of mind, is our ability to determine whether a perceived behavior or action was performed intentionally or not. In this paper, we derive an algorithm that can infer whether the behavior of an agent in a scene is intentional or unintentional based on its 3D kinematics, using the knowledge of self-propelled motion, Newtonian motion and their relationship. We show how the addition of this basic knowledge leads to a simple, unsupervised algorithm. To test the derived algorithm, we constructed three dedicated datasets from abstract geometric animation to realistic videos of agents performing intentional and non-intentional actions. Experiments on these datasets show that our algorithm can recognize whether an action is intentional or not, even without training data. The performance is comparable to various supervised baselines quantitatively, with sensible intentionality segmentation qualitatively.
Sequential nonabsorbing microwave single-photon detector

We propose a nonabsorbing microwave single-photon detector that uses an artificial atom as a coherent interaction mediator between a traveling photon and a high-Q resonator, fully exploiting the knowledge of the photon's arrival time. Our proposal can be implemented with the current level of technology and achieves distinguishability (probability of distinguishing between zero and one photon) in excess of 98% for realistic parameters. This is better than any of the similar detector proposals, even the ones using several artificial atoms.
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition

Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex handcrafted heuristics for sequential matching - which are applied on top of a pre-computed pairwise similarity matrix between reference and query image sequences of a single route - to further reduce false-positive rates compared to single-frame retrieval methods. As a result, performing multi-frame place recognition can be extremely slow for deployment on autonomous vehicles or evaluation on large datasets, and fail when using relatively short parameter values such as a sequence length of 2 frames. In this paper, we propose DeepSeqSLAM: a trainable CNN+RNN architecture for jointly learning visual and positional representations from a single monocular image sequence of a route. We demonstrate our approach on two large benchmark datasets, Nordland and Oxford RobotCar - recorded over 728 km and 10 km routes, respectively, each during 1 year with multiple seasons, weather, and lighting conditions. On Nordland, we compare our method to two state-of-the-art sequence-based methods across the entire route under summer-winter changes using a sequence length of 2 and show that our approach can get over 72% AUC compared to 27% AUC for Delta Descriptors and 2% AUC for SeqSLAM; while drastically reducing the deployment time from around 1 hour to 1 minute against both. The framework code and video are available at https://mchancan.github.io/deepseqslam
Inference Strategies for Machine Translation with Conditional Masking

Conditional masked language model (CMLM) training has proven successful for nonautoregressive and semi-autoregressive sequence generation tasks, such as machine translation.Given a trained CMLM, however, it is not clear what the best inference strategy is.We formulate masked inference as a factorization of conditional probabilities of partial sequences, show that this does not harm performance, and investigate a number of simple heuristics motivated by this perspective.We identify a thresholding strategy that has advantages over the standard "mask-predict" algorithm, and provide analyses of its behavior on machine translation tasks.
RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning

Research on continual learning has led to a variety of approaches to mitigating catastrophic forgetting in feed-forward classification networks. Until now surprisingly little attention has been focused on continual learning of recurrent models applied to problems like image captioning. In this paper we take a systematic look at continual learning of LSTM-based models for image captioning. We propose an attention-based approach that explicitly accommodates the transient nature of vocabularies in continual image captioning tasks -- i.e. that task vocabularies are not disjoint. We call our method Recurrent Attention to Transient Tasks (RATT), and also show how to adapt continual learning approaches based on weight egularization and knowledge distillation to recurrent continual learning problems. We apply our approaches to incremental image captioning problem on two new continual learning benchmarks we define using the MS-COCO and Flickr30 datasets. Our results demonstrate that RATT is able to sequentially learn five captioning tasks while incurring no forgetting of previously learned ones.
Multi-Vehicle Cooperative Target Tracking with Time-Varying Localization Uncertainty via Recursive Variational Bayesian Inference

Cooperative target tracking by multiple vehicles connected through inter-vehicle communication is a promising way to improve the estimation of target state. The effectiveness of cooperative tracking closely depends on the accuracy of relative localization between host and cooperative vehicles. However, the localization signal usually provided by the satellite-based navigation system is rather susceptible to dynamic driving environment, thus influencing the effectiveness of cooperative tracking. In order to implement reliable cooperative tracking, especially when the statistical characteristic of the relative localization noise is time-varying and uncertain, this paper presents a recursive Bayesian framework which jointly estimates the state of the target and the cooperative vehicle as well as the localization noise parameter. An online variational Bayesian inference algorithm is further developed to achieve efficient recursive estimate. The simulation results verify that our proposed algorithm can effectively boost the accuracy of target tracking when the localization noise dynamically changes over time.
The New "Covid-19" Home Office Worker: Evolving Computer-Human Interactions and the Perceived Value of Workplace Technology

The context for this paper is the ongoing Covid-19 Global Pandemic and the guidance from the majority of nation-state governments for all central office based type workers to completely shift to working from home, advice which business and organisations adopted. Recent European research has established that 85% [1] of knowledge workers up to the Covid-19 Pandemic worked 80% or more of their time in a central office environment. Suddenly these office workers had to relocate their work into their homes without any opportunities for their employers to plan this move. Part of the challenge was access to the right technology. This problem was quickly resolved by the purchase of the relevant technology but not necessarily the new human-computer interaction mode. The focus of the field research was to assess the evolving human-computer of the office-based worker before the Covid-19 Pandemic. The paper has two intended outcomes. First, to provide short term insights to better understand the impact of rapid change of physical and technology-centric workspaces, due to Covid-19 on user behaviours and experience. Second to use the research to deliver better user experience design and collaboration and how technologies such Augmented Reality could enhance that experience.
Development of Natural Science Learning Tools with Guided Inquiry Model Assisted by Real Media to Improve Students' Scientific Creativity and Science Process Skills

This study aims to develop science learning tools with guided inquiry models assisted by real media to improve scientific creativity and science process skills of grade VIII students at SMP IT Male and SMP IT Female Mataram on light and optical equipment. The development of this tool adopts the development of Dick and Carey. The resulting products are in the form of learning tools in the form of syllabus, lesson plans, LKPD, scientific creativity instruments and science process skills. Before being used, the device was validated by four expert validators and the result was that the device developed was suitable for use in learning. To test the effectiveness of the device, scientific creativity and scientific process skills were tested. The results of the scientific creativity test obtained an average N-Gain of 0.4. This shows that the device developed is quite effective in increasing the scientific creativity of students in the two schools being the subject. The second effectiveness test is the science process skills test. The average N-Gain value obtained was 0.7, this indicates that the learning device developed with a guided inquiry model assisted by real media is very effective in improving students' science process skills.
Trade-off relations for operation entropy of complementary quantum channels

The entropy of a quantum operation, defined as the von Neumann entropy of the corresponding Choi–Jamiołkowski state, characterizes the coupling of the principal system with the environment. For any quantum channel acting on a state of a given size, one defines the complementary channel, which sends the input state into the state of the environment after the operation. Making use of subadditivity of entropy, we show that for any dimension the sum of both entropies is bounded from below. This result characterizes the trade-off between the information on the initial quantum state accessible to the principal system and the information leaking to the environment. For one qubit maps we describe the interpolating family of depolarizing maps, for which the sum of both entropies gives the lower boundary of the region allowed in the space spanned by both entropies.
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training

Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, numerous gradient compression techniques have been proposed and have demonstrated high compression ratios. However, most existing methods do not scale well to large scale distributed systems (due to gradient build-up) and/or fail to evaluate model fidelity (test accuracy) on large datasets. To mitigate these issues, we propose a new compression technique, Scalable Sparsified Gradient Compression (ScaleCom), that leverages similarity in the gradient distribution amongst learners to provide significantly improved scalability. Using theoretical analysis, we show that ScaleCom provides favorable convergence guarantees and is compatible with gradient all-reduce techniques. Furthermore, we experimentally demonstrate that ScaleCom has small overheads, directly reduces gradient traffic and provides high compression rates (65-400X) and excellent scalability (up to 64 learners and 8-12X larger batch sizes over standard training) across a wide range of applications (image, language, and speech) without significant accuracy loss.
Discrete-Time Quantum Walks on Oriented Graphs

The interest in quantum walks has been steadily increasing during the last two decades.It is still worth to present new forms of quantum walks that might find practical applications and new physical behaviors.In this work, we define discrete-time quantum walks on arbitrary oriented graphs by partitioning a graph into tessellations, which is a collection of disjoint cliques that cover the vertex set.By using the adjacency matrices associated with the tessellations, we define local unitary operators, whose product is the evolution operator of our quantum walk model.We introduce a parameter, called α, that quantifies the amount of orientation.We show that the parameter α can be tuned in order to increase the amount of quantum walk-based transport on oriented graphs.
Underwater Image Restoration Based on Adaptive Color Compensation and Dual Transmission Estimation

Visibility restoration of an underwater image degraded by turbid water (due to scattering, absorption, and reflection) is a challenging task which requires careful design of computational imaging methods. The fundamental problems which limit image restoration are random diffusion and absorption of light in turbid water. Recently, the red-channel underwater image restoration method has emerged as an effective approach for visibility restoration under turbid water. However, this method fails to restore the visibility in a degraded image captured under non-uniform turbid water. The method fails due to its reliance on single transmission which cannot correctly express light propagation in an underwater environment. To overcome these problems, we propose a novel image restoration method based on adaptive color compensation and dual transmission estimation (ACDTE) to restore the visibility of underwater images degraded by non-uniform turbid water. The proposed method uses color-tone adaption to determine the hue of underwater images, and estimates global water light with quadtree decomposition. The method estimates dual transmission in the media through coefficient modification for R/G/B channels. Simulation and experimental results show that the proposed method can correct color deviations and has the advantage of visibility restoration of underwater images. The proposed method can be used for marine exploration, underwater rescue, and environmental monitoring.
Need for Computational and Psycho-linguistics Models in Natural Language Processing for Web Documents

Natural Language Processing (NLP) is an argument that provokes territory of figural manner for motorized scrutiny and depiction of humane language. Research in it unfolded from the term of punch cards till the age of intelligent examination engines which takes less than a second to Figure out millions of web pages and digital documents. In the web, pieces of evidence and end-users produced content to have formerly stimulated text. The obligation of advantageous and astute content driving is decisive forthwith. Consequently, the web is being treated as a foremost source of evidence brought about the extensive extent of a probe to perceive the compass of the machine. Considering the democratization of online content formulation, it has skeptically influenced the information resurgence and eradication. This paper pulls contemporary developments in the NLP probe to perceive the scope of machine learning in a modern era to avoid ramification of adverse instructions.