Reviewerly/exHarmony huggingface dataset:
Explicit feedback based movie recommendation system: A survey

There is a significant growth of systems that provide a huge amount of data over social relationships. Technological advances nowadays allow social-based technology to grow continuously. There are many information systems through which users can share different types of information about products and/or services. Users build rich relationships based on new technology models. Today, many recommendation systems have been developed for different domains, however, they are not accurate enough to achieve users' information needs. Therefore, it is necessary to build high-quality recommendation systems. Designers face many problems and challenges that require appropriate attention in their design. ln this paper, recent literatures on the movie recommendation system are reviewed in order to envisage methods, challenges and research opportunities in developing a high-quality recommendation.
A way to increase parabolic trough plant yield by roughly 2% using all sky imager derived DNI maps

Solar fields of parabolic troughs are extensive complex thermal hydraulic facilities. Intra-hour and intra-minute variabilities of the DNI, mainly caused by passing clouds, pose an operational challenge for parabolic trough power plants. Under perfect circumstances a solar field controller would adjusts the mass flow in such a way, that the design temperature is always maintained constant with a maximized focus rate. However, heterogeneous irradiance conditions or flow distribution may cause some solar field sections to temporarily overheat while others may not reach the set point temperature, which in turn leads to an economic loss. State of the art solar field controllers have only access to incomplete information on spatial DNI variability, from DNI measurements of few pyrheliometers. Solar field controllers could be optimized with access to highly resolved DNI informations both in space and time. Such DNI information can be provided by all sky imager (ASI) based irradiance monitoring systems. In a previous study we developed and benchmarked new solar field controllers with access to spatial DNI information from an ASI system for a 50 MWe plant close to Córdoba (Spain). Significant improvements in revenue were observed. Yet, this previous study was limited to 22 days only. In this study, we estimate the potential benefit of these new solar field controllers over a 2 year period on the basis of the simulation results over 22 days. The upscaling method makes use of DNI variability classes. Using the ASI data we obtain a significant improvement in revenue up to 2% for the 2 year period.
Nearest Centroid Classification on a Trapped Ion Quantum Computer

Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
Resource prioritization and balancing for the quantum internet

Abstract The quantum Internet enables networking based on the fundamentals of quantum mechanics. Here, methods and procedures of resource prioritization and resource balancing are defined for the quantum Internet. We define a model for resource consumption optimization in quantum repeaters, and a strongly-entangled network structure for resource balancing. We study the resource-balancing efficiency of the strongly-entangled structure. We prove that a strongly-entangled quantum network is two times more efficient in a resource balancing problem than a full-mesh network of the traditional Internet.
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication

Visual storytelling aims to generate a narrative paragraph from a sequence of images automatically.Existing approaches construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content.In this paper, we propose a new way for visual storytelling by introducing a topic description task to detect the global semantic context of an image stream.A story is then constructed with the guidance of the topic description.In order to combine the two generation tasks, we propose a multi-agent communication framework that regards the topic description generator and the story generator as two agents and learn them simultaneously via iterative updating mechanism.We validate our approach on VIST dataset, where quantitative results, ablations, and human evaluation demonstrate our method's good ability in generating stories with higher quality compared to state-of-the-art methods.
JCT at SemEval-2020 Task 12: Offensive Language Detection in Tweets Using Preprocessing Methods, Character and Word N-grams

In this paper, we describe our submissions to SemEval-2020 contest.We tackled subtask 12 -"Multilingual Offensive Language Identification in Social Media".We developed different models for four languages: Arabic, Danish, Greek, and Turkish.We applied three supervised machine learning methods using various combinations of character and word n-gram features.In addition, we applied various combinations of basic preprocessing methods.Our best submission was a model we built for offensive language identification in Danish using Random Forest.This model was ranked at the 6 th position out of 39 submissions.Our result is lower by only 0.0025 than the result of the team that won the 4 th place using entirely non-neural methods.Our experiments indicate that char ngram features are more helpful than word ngram features.This phenomenon probably occurs because tweets are more characterized by characters than by words, tweets are short, and contain various special sequences of characters, e.g., hashtags, shortcuts, slang words, and typos.
Navigation Design and Library Terminology

The University Library at California State University, Stanislaus is not only undergoing a library building renovation, but a website redesign as well. The library conducted a user-centered usability study to collect data in order to best lead the library website “renovation.” A prototype was created to assess an audience-based navigation design, homepage content framework, and heading terminology. The usability study consisted of 38 student participants. It was determined that a topic-based navigation design will be implemented instead of an audience-based navigation, a search-all search box will be integrated, and the headings and menu links will be modified to avoid ambiguous library terminology. Further research on different navigation and content designs, and usability design approaches, will be explored for future studies.
Systematically Exploring Redundancy Reduction in Summarizing Long Documents

Our analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents. Yet, redundancy reduction has not been thoroughly investigated in neural summarization. In this work, we systematically explore and compare different ways to deal with redundancy when summarizing long documents. Specifically, we organize existing methods into categories based on when and how the redundancy is considered. Then, in the context of these categories, we propose three additional methods balancing non-redundancy and importance in a general and flexible way. In a series of experiments, we show that our proposed methods achieve the state-of-the-art with respect to ROUGE scores on two scientific paper datasets, Pubmed and arXiv, while reducing redundancy significantly.
Robust Secret Sharing with Almost Optimal Share Size and Security Against Rushing Adversaries

We show a robust secret sharing scheme for a maximal threshold $$t < n/2$$ that features an optimal overhead in share size, offers security against a rushing adversary, and runs in polynomial time. Previous robust secret sharing schemes for $$t < n/2$$ either suffered from a suboptimal overhead, offered no (provable) security against a rushing adversary, or ran in superpolynomial time.
Pengembangan media board game untuk pembelajaran tematik di sekolah dasar

ABSTRAKPembelajaran tematik di sekolah dasar sangat penting untuk memperkuat karakter siswa, salah satunya dengan media board game. Penelitian ini bertujuan untuk mengembangkan dan menguji kevalidan, kepraktisan, dan keefektifan media board game pada pembelajaran tematik di sekolah dasar. Metode penelitian yang digunakan adalah R&amp;D dengan desain ADDIE yang meliputi lima tahap, yakni 1) analysis (analisis), 2) design (perancangan), 3) development (pengembangan), 4) implementation (implementasi), dan 5) evaluation (evaluasi). Kevalidan produk diperoleh melalui uji pakar dengan instrumen validasi pakar. Kepraktisan produk diperoleh melalui uji coba terbatas 8 siswa dan pemberian angket untuk melihat respon siswa setelah menggunakan media board game. Keefektifan produk diperoleh melalui uji coba satu kelas dengan one grup pretest-posttest design yang hasilnya diuji menggunakan Paired Samples t-Test dengan SPSS 20.0. Hasil penelitian menunjukkan bahwa media board game dapat meningkatkan kemampuan pemecahan masalah pada pembelajaran tematik di sekolah dasar. Media pembelajaran dinyatakan valid berdasarkan hasil uji pakar media, pakar materi, dan pakar pembelajaran yang rata-ratanya mencapai 74.3% (tinggi). Media pembelajaran dinyatakan praktis berdasarkan hasil angket respon siswa mencapai 94% (praktis). Media pembelajaran juga dinyatakan efektif berdasarkan rata-rata hasil pretes dan postes serta uji Paired Samples T Test pretest-posttest dengan menunjukkan nilai Sig. (2-tailed) sama dengan 0,000. Kata kunci: Media pembelajaran, Board Game, Pemecahan Masalah, Siswa Sekolah Dasar ABSTRACTThis study aimed to develop and test the validity, practicality, and effectiveness of game board media on thematic learning in elementary schools. It employed Research and Development (R&amp;D) method with ADDIE design, including: 1) analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. The product validity was obtained through expert tests using expert validation instruments. The product practicality was verified after limited trials to 8 students. Questionnaires were given to see student responses after using the game board media. The product effectiveness was validated by a one-class trial with one group pretest-posttest design, tested using Paired Samples t-test in SPSS 20.0. Results showed that board game media improved elementary school students’ problem-solving skills in thematic learning. The learning media is valid based on the test results of media experts, material experts, and learning experts. It is also practical based on the results of the student response questionnaire. The learning media is effective based on the results of the average pretest and posttest and the Paired Samples t-test pretest-posttest test results by showing the Sig. (2-tailed) equal to 0,000. Keywords: Learning media, Board Game, Problem Solving, Elementary students
Navier-Stokes-based model of the clarinet

A model of a single reed instrument is studied in which the reed is described as an Euler-Bernoulli beam, and the air flow through the instrument is calculated using the Navier-Stokes equations. The hypothetical instrument resembles a clarinet, but is smaller than a real clarinet to keep the numerical modeling feasible on available supercomputers. This article explores the conditions under which the frequency of the reed oscillations and the emitted sound are determined by the resonant frequency of the bore of the instrument. The effect of the contact between the reed and the player's lips is also studied, and quantitative results for the air density and pressure in the mouthpiece and throughout the instrument bore are also presented.
Experimental Diagnostics of Entanglement Swapping by a Collective Entanglement Test

This paper reports on the experimental diagnostics of an entanglement-swapping protocol by means of collective entanglement witness. Our approach is suitable to detect disturbances occurring in the preparation of quantum states, quantum-communication channels, and imperfect Bell-state projections. More specifically, we demonstrate that our method can distinguish disturbances such as depolarization, phase damping, amplitude damping, and imperfect Bell-state measurement by observing four probabilities and estimating collective entanglement witness. Since entanglement swapping is a key procedure for quantum repeaters, quantum relays, device-independent quantum communications, and entanglement-assisted error correction, this can aid in faster and practical resolutions of quality of transmission related problems as our approach requires fewer measurements than other means of diagnostics.
Robustifying Sequential Neural Processes

When tasks change over time, meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning. While the standard attention has been effective in a variety of settings, we question its effectiveness in improving meta-transfer learning since the tasks being learned are dynamic and the amount of context can be substantially smaller. In this paper, using a recently proposed meta-transfer learning model, Sequential Neural Processes (SNP), we first empirically show that it suffers from a similar underfitting problem observed in the functions inferred by Neural Processes. However, we further demonstrate that unlike the meta-learning setting, the standard attention mechanisms are not effective in meta-transfer setting. To resolve, we propose a new attention mechanism, Recurrent Memory Reconstruction (RMR), and demonstrate that providing an imaginary context that is recurrently updated and reconstructed with interaction is crucial in achieving effective attention for meta-transfer learning. Furthermore, incorporating RMR into SNP, we propose Attentive Sequential Neural Processes-RMR (ASNP-RMR) and demonstrate in various tasks that ASNP-RMR significantly outperforms the baselines.
A bounded-noise mechanism for differential privacy

We present an asymptotically optimal $(\epsilon,\delta)$ differentially private mechanism for answering multiple, adaptively asked, $\Delta$-sensitive queries, settling the conjecture of Steinke and Ullman [2020]. Our algorithm has a significant advantage that it adds independent bounded noise to each query, thus providing an absolute error bound. Additionally, we apply our algorithm in adaptive data analysis, obtaining an improved guarantee for answering multiple queries regarding some underlying distribution using a finite sample. Numerical computations show that the bounded-noise mechanism outperforms the Gaussian mechanism in many standard settings.
Model-Based Visual Planning with Self-Supervised Functional Distances

A generalist robot must be able to complete a variety of tasks in its environment. One appealing way to specify each task is in terms of a goal observation. However, learning goal-reaching policies with reinforcement learning remains a challenging problem, particularly when hand-engineered reward functions are not available. Learned dynamics models are a promising approach for learning about the environment without rewards or task-directed data, but planning to reach goals with such a model requires a notion of functional similarity between observations and goal states. We present a self-supervised method for model-based visual goal reaching, which uses both a visual dynamics model as well as a dynamical distance function learned using model-free reinforcement learning. Our approach learns entirely using offline, unlabeled data, making it practical to scale to large and diverse datasets. In our experiments, we find that our method can successfully learn models that perform a variety of tasks at test-time, moving objects amid distractors with a simulated robotic arm and even learning to open and close a drawer using a real-world robot. In comparisons, we find that this approach substantially outperforms both model-free and model-based prior methods. Videos and visualizations are available here: http://sites.google.com/berkeley.edu/mbold.
A Novel Application for Game Tree Search - Exploiting Pruning Mechanisms for Quantified Integer Programs

We investigate pruning in search trees of so-called quantified integer (linear) programs (QIPs). QIPs consist of a set of linear inequalities and a minimax objective function, where some variables are existentially and others are universally quantified. A good way to solve a QIP is to apply game tree search, enhanced with non-chronological back-jumping. We develop and theoretically substantiate tree pruning techniques based upon algebraic properties. The presented Strategic Copy-Pruning mechanism allows to implicitly deduce the existence of a strategy in linear time (by static examination of the QIP-matrix) without explicitly traversing the strategy itself. We show that the implementation of our findings can massively speed up the search process.
Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge

Memes on the Internet are often harmless and sometimes amusing. However, by using certain types of images, text, or combinations of both, the seemingly harmless meme becomes a multimodal type of hate speech -- a hateful meme. The Hateful Memes Challenge is a first-of-its-kind competition which focuses on detecting hate speech in multimodal memes and it proposes a new data set containing 10,000+ new examples of multimodal content. We utilize VisualBERT -- which meant to be the BERT of vision and language -- that was trained multimodally on images and captions and apply Ensemble Learning. Our approach achieves 0.811 AUROC with an accuracy of 0.765 on the challenge test set and placed third out of 3,173 participants in the Hateful Memes Challenge.
CloudVaults: Integrating Trust Extensions into System Integrity Verification for Cloud-Based Environments

While the rapid evolution of container-based virtualization technologies, emerging as an integral part of cloud-based environments, brings forth several new opportunities for enabling the provision of distributed, mixed-criticality services, it also raises significant concerns for their security, resilience, and configuration correctness. In this paper, we present CloudVaults for coping with these challenges: a multi-level security verification framework that supports trust aware service graph chains with verifiable evidence on the integrity assurance and correctness of the comprised containers. It is a first step towards a new frontier of security mechanisms to enable the provision of Configuration Integrity Verification (CIV), during both load- and run-time, by providing fine-grained measurements in supporting container trust decisions, thus, allowing for a much more effective verification towards building a global picture of the entire service graph integrity. We additionally provide and benchmark an open-source implementation of the enhanced attestation schemes.
Referring to what you know and do not know: Making Referring Expression Generation Models Generalize To Unseen Entities

Data-to-text Natural Language Generation (NLG) is the computational process of generating natural language in the form of text or voice from non-linguistic data.A core micro-planning task within NLG is referring expression generation (REG), which aims to automatically generate noun phrases to refer to entities mentioned as discourse unfolds.A limitation of novel REG models is not being able to generate referring expressions to entities not encountered during the training process.To solve this problem, we propose two extensions to NeuralREG, a state-ofthe-art encoder-decoder REG model.The first is a copy mechanism, whereas the second consists of representing the gender and type of the referent as inputs to the model.Drawing on the results of automatic and human evaluation as well as an ablation study using the WebNLG corpus, we contend that our proposal contributes to the generation of more meaningful referring expressions to unseen entities than the original system and related work.
LIRKIS Global Collaborative Virtual Environments: Current State and Utilization Perspective

Abstract Recent advances in the field of web technologies, including the increasing support of virtual reality hardware, have allowed for shared virtual environments, reachable by just entering a URL in a browser. One contemporary solution that provides such a shared virtual reality is LIRKIS Global Collaborative Virtual Environments (LIRKIS G-CVE). It is a web-based software system, built on top of the A-Frame and Networked-Aframe frameworks. This paper describes LIRKIS G-CVE and introduces its two original components. The first one is the Smart-Client Interface, which turns smart devices, such as smartphones and tablets, into input devices. The advantage of this component over the standard way of user input is demonstrated by a series of experiments. The second component is the Enhanced Client Access layer, which provides access to positions and orientations of clients that share a virtual environment. The layer also stores a history of connected clients and provides limited control over the clients. The paper also outlines an ongoing experiment aimed at an evaluation of LIRKIS G-CVE in the area of virtual prototype testing.
Management of Academic Libraries in Poland During the COVID-19 Lockdown

Purpose/Thesis: The article considers the management strategies employed at Polish academic libraries during a crisis situation, using the lockdown imposed as a result of the COVID-19 pandemic as a case study. (The lockdown is defined here as the period between March 12th and June 2020). The study identifies the most significant obstacles to operating efficiently in the unstable VUCA environment. Approach/Methods: The author uses research methods of sociology, collecting data from the directors of Polish academic libraries by the means of an online survey, with the response rate approximating 30%. The survey included questions about the organization of information and library services during the lockdown, and about the management obstacles the libraries faced.Results and conclusions: The results suggest that both directors and staff of academic libraries did their best in the crisis situation. They adjusted the information and library service procedures, seeking to efficiently organize remote work, and manage the dispersed work environment. The most often mentioned management obstacles included the necessity for rapid adjustment, the impossibility of long-term planning, and the changing duties. However, the libraries implemented a number of solutions, which may serve them in the future if need be, such as rotational shifts, higher flexibility of organization, and task-oriented approach to professional duties. Practical implications: The study presents procedures to be applied in the case of another lockdown, identifies good practices, and relays the experiences of other academic libraries in order to improve information services at the reader's place of employment; it may inspire them to optimize information and library processes. Originality/Value: It is the first such study of the activity of Polish academic libraries during the lockdown. The results may contribute to discussions about the organizational flexibility of academic libraries and their capacity for adjustment, and well as about the future development or phaseout of certain areas of their activity.