Reviewerly/exHarmony huggingface dataset:
DORi: Discovering Object Relationship for Moment Localization of a Natural-Language Query in Video

This paper studies the task of temporal moment localization in a long untrimmed video using natural language query. Given a query sentence, the goal is to determine the start and end of the relevant segment within the video. Our key innovation is to learn a video feature embedding through a language-conditioned message-passing algorithm suitable for temporal moment localization which captures the relationships between humans, objects and activities in the video. These relationships are obtained by a spatial sub-graph that contextualizes the scene representation using detected objects and human features conditioned in the language query. Moreover, a temporal sub-graph captures the activities within the video through time. Our method is evaluated on three standard benchmark datasets, and we also introduce YouCookII as a new benchmark for this task. Experiments show our method outperforms state-of-the-art methods on these datasets, confirming the effectiveness of our approach.
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding

Phrase localization is a task that studies the mapping from textual phrases to regions of an image. Given difficulties in annotating phrase-to-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision. We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations. By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance in weakly-supervised scenarios. Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weakly-supervised methods. With the help of the visually-aware language representations, we can also improve the previous best unsupervised result by 5.56%. We conduct ablation studies to show that both our novel model and our weakly-supervised strategies significantly contribute to our strong results.
Reproducibility Companion Paper: Selective Deep Convolutional Features for Image Retrieval

In this companion paper, firstly, we briefly summarize the contributions of our main manuscript: Selective Deep Convolutional Features for Image Retrieval, published in ACM MultiMedia 2017. In addition, we provide detail instructions together with pre-configured MATLAB scripts which allow experiments to be executed and to reproduce the results reported in our main manuscript effortlessly. The source code is available at https://github.com/hnanhtuan/selectiveConvFeatures_ACMMM_reproducibility.
GeoNEX: Earth observations from operational geostationary satellite systems

&amp;lt;p&amp;gt;The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort involving scientists from NASA, NOAA, JAXA/JMA, KMA/KARI exploring the feasibility of producing EOS-quality research products from operational geostationary satellite systems for climate monitoring. The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, Fengyun-4, GeoKompsat-2A) carry sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. More importantly, they provide observations as frequently as 5-15 minutes. Data from various currently operating geostationary platforms provide a geo-ring of hyper-temporal, multispectral observations. Such high frequency observations, particularly when combined with data from polar orbiters, offer exciting possibilities for improving the retrieval of geophysical variables by overcoming cloud cover, enable studies of diurnally varying phenomena over land, in the atmosphere and the oceans, and help in operational decision-making in agriculture, hydrology and disaster management. Beyond the weather-focused geo-sensors, a number of new spectrometers are scheduled to be launched in the next five years in geostationary orbit to study atmospheric pollution (GEMS, TEMPO), ocean color (GOCI) and carbon cycling (GeoCARB). This talk will highlight new research, data sets, algorithms and computational platforms that utilize data from geostationary satellites to advance our ability to monitor the environment and create climate resiliency.&amp;lt;/p&amp;gt;
Vid-ODE: Continuous-Time Video Generation with Neural Ordinary Differential Equation

Video generation models often operate under the assumption of fixed frame rates, which leads to suboptimal performance when it comes to handling flexible frame rates (e.g., increasing the frame rate of the more dynamic portion of the video as well as handling missing video frames). To resolve the restricted nature of existing video generation models' ability to handle arbitrary timesteps, we propose continuous-time video generation by combining neural ODE (Vid-ODE) with pixel-level video processing techniques. Using ODE-ConvGRU as an encoder, a convolutional version of the recently proposed neural ODE, which enables us to learn continuous-time dynamics, Vid-ODE can learn the spatio-temporal dynamics of input videos of flexible frame rates. The decoder integrates the learned dynamics function to synthesize video frames at any given timesteps, where the pixel-level composition technique is used to maintain the sharpness of individual frames. With extensive experiments on four real-world video datasets, we verify that the proposed Vid-ODE outperforms state-of-the-art approaches under various video generation settings, both within the trained time range (interpolation) and beyond the range (extrapolation). To the best of our knowledge, Vid-ODE is the first work successfully performing continuous-time video generation using real-world videos.
Relating Structure and Power: Extended Version

Combinatorial games are widely used in finite model theory, constraint satisfaction, modal logic and concurrency theory to characterize logical equivalences between structures. In particular, Ehrenfeucht-Fraisse games, pebble games, and bisimulation games play a central role. We show how each of these types of games can be described in terms of an indexed family of comonads on the category of relational structures and homomorphisms. The index $k$ is a resource parameter which bounds the degree of access to the underlying structure. The coKleisli categories for these comonads can be used to give syntax-free characterizations of a wide range of important logical equivalences. Moreover, the coalgebras for these indexed comonads can be used to characterize key combinatorial parameters: tree-depth for the Ehrenfeucht-Fraisse comonad, tree-width for the pebbling comonad, and synchronization-tree depth for the modal unfolding comonad. These results pave the way for systematic connections between two major branches of the field of logic in computer science which hitherto have been almost disjoint: categorical semantics, and finite and algorithmic model theory.
The effectiveness of videos and pocket books on the level of knowledge and attitudes towards stigma people with HIV/AIDS

Cases of human imunnodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS) in middle and low income countries were very many. One of the biggest obstacles in the prevention of HIV/AIDS is the high stigma people with HIV/AIDS (PLWHA). There are still 34% of health students stigmatizing PLWHA. Appropriate learning media is needed to provide information about HIV/AIDS to midwifery students. The purpose was to determine the effect videos and pocket books on the knowledge, attitudes towards stigma of PLWHA. This research was a quantitative study with quasi-experimental non equivalent control group design. The intervention group was given information using video, the control group was given a pocket book. The number of samples is 100 respondents. Data analysis using t-test and simple linear regression. There is a relationship between the provision of videos and pocket books on the post test knowledge with p=0.002. There is an influence of giving video to attitude with p=0.022 OR 2.731. There was a relationship between the provision of videos and pocket books on the knowledge and attitudes of respondents. There was no relationship between the source of video information on the adequacy of material about HIV AIDS and the experience of meeting PLWHA with the level of knowledge and attitude of respondents.
Prospects for digital technology in Higher education: an expert View

A conceptual analysis of the structure of educational activities in higher education is carried out and a system «map» is developed, representing groups of necessary digital technologies and solutions. Based on a survey of experts, digital technologies are positioned according to availability levels – from preliminary developments to mass-marketed products. In the future, until 2030, all the technology groups discussed will be developed to the level of locally applied products, many to mass-used products
Adaptive Circuit Learning for Quantum Metrology

Quantum metrology is an important application of emerging quantum technologies. We explore whether a hybrid system of quantum sensors and quantum circuits can surpass the classical limit of sensing. In particular, we use a circuit learning approach to search for encoder and decoder circuits that scalably improve sensitivity under given application and noise characteristics. Our approach uses a variational algorithm that can learn a quantum sensing circuit based on platform-specific control capacity, noise, and signal distribution. The quantum circuit is composed of an encoder which prepares the optimal sensing state and a decoder which gives an output distribution containing information of the signal. We optimize the full circuit to maximize the Signal-to-Noise Ratio (SNR). Furthermore, this learning algorithm can be run on real hardware scalably by using the parameter-shift rule which enables gradient evaluation on noisy quantum circuits, avoiding the exponential cost of quantum system simulation. We demonstrate a 1.69x SNR improvement over the classical limit on a 5-qubit IBM quantum computer. More notably, our algorithm overcomes the plateauing (or even decreasing) performance of existing entanglement-based protocols with increased system sizes.
Assessing the Helpfulness of Review Content for Explaining Recommendations

Despite the maturity already achieved by recommender systems algorithms, little is known about how to obtain and provide users with a proper rationale for a recommendation. Transparency and effectiveness of recommender systems may be increased when explanations are provided. In particular, identifying of helpful argumentative content from reviews can be leveraged to generate textual explanations. In this paper, we investigate the reasons why a review might be considered helpful, and show that the perception of credibility and convincingness mediates the relationship between helpfulness and the perception of objectivity and relevant aspects addressed. Our findings led us to suggest an argumentbased approach to automatically extracting helpful content from hotel reviews, a domain that differs from those that best fit classical argumentation theories.
On-Chip All-Optical Polarisation Pulling via Stimulated Brillouin Scattering

We demonstrate a tunable on-chip all-optical polarisation controller based on Polarisation Pulling via Stimulated Brillouin Scattering, achieving near-complete rotations between orthogonal linear polarisation states.
iDirector: An Intelligent Directing System for Live Broadcast

Live sports broadcasting is the live coverage of sports (e.g., a soccer match) as a television program, on various types of broadcasting media (e.g., television or internet). Directing such live sports broadcast is cost-expensive and demands experienced sports directors with sufficient broadcasting skills. In this paper, we demonstrate an end-to-end intelligent system for live sports broadcasting, namely iDirector, which aims to mimic the human-in-loop live broadcasting process by aggregating the input multi-camera video streaming into the final output program video (PGM video) for audience. We construct this system as an event-driven pipeline with three modules: video decoder, video analyzer, and broadcasting controller. Specifically, given the multi-view video streaming captured from cameras placing in the stadium, video decoder module first decodes the input video streaming into a series of frames and clips. Next, video analyzer performs multiple pre-learned models in parallel for frame- and clip-level content understanding (e.g., events localization and highlight detection). Based on all the analytic results across frames and clips, with only 30 seconds looking ahead, broadcasting controller automatically produces the broadcast videos via camera view switch, playback and slow-motion. When some high-profile events (e.g., free kick) happen, broadcasting controller will render visual effects on PGM video to enhance audiences' entertained pleasure.
Better Distractions: Transformer-based Distractor Generation and Multiple Choice Question Filtering

For the field of education, being able to generate semantically correct and educationally relevant multiple choice questions (MCQs) could have a large impact. While question generation itself is an active research topic, generating distractors (the incorrect multiple choice options) receives much less attention. A missed opportunity, since there is still a lot of room for improvement in this area. In this work, we train a GPT-2 language model to generate three distractors for a given question and text context, using the RACE dataset. Next, we train a BERT language model to answer MCQs, and use this model as a filter, to select only questions that can be answered and therefore presumably make sense. To evaluate our work, we start by using text generation metrics, which show that our model outperforms earlier work on distractor generation (DG) and achieves state-of-the-art performance. Also, by calculating the question answering ability, we show that larger base models lead to better performance. Moreover, we conducted a human evaluation study, which confirmed the quality of the generated questions, but showed no statistically significant effect of the QA filter.
Political Mission of University: A Retrospective View

Academic3 space in its different manifestations has been taking an honorable position in social structure from the earliest stages of the history of human civilization by systematizing multitude experiences of both external and internal world of humankind. At the same time, educational landscape was formulating the different ways of how to theorize about and interact with the world. Simultaneously, there was always combating with the alternative systems and, what is more, this struggle wasn’t necessarily intellectual or polemical. Little has changed in how society perceives academy and its functions in the era of accomplished digital revolution, including its role as an instrument of surveillance and social sorting – these two important elements of power. In this article, an attempt is taken to comprehend University – and speaking broadly academic space as such – as a special kind of social and political field used to perform surveillance and social control. On the example of colonial colleges in the USA, this article examines how University may serve as a surveillance mechanism on the one hand and as a mean of cultural transformation on the other hand, and what conclusions can be made regarding the present and the future of University in the digital era .
THE ADVENT OF ANTHROPOMORPHIC INTELLIGENT MACHINES: A BOON TO NURSING OR ITS NEMESIS?

N/A
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection

Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli ("Is the stimulus category A or B?"), (2) as sequence labeling of tokens ("Which tokens describe the stimulus?"), and (3) as clause classification ("Does this clause contain the emotion stimulus?"). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we aim to answer whether clause classification or sequence labeling is better suited for emotion stimulus detection in English. To accomplish that, we propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that sequence labeling is superior on three out of four datasets, in both clause-based and sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.
Singapore state strategy on development of intellectual property: normative-legal and institutional aspects

The objects of this research is the relations emerging in implementation of state strategy on development of intellectual property in Singapore. The work expounds the significance of normative-legal and institutional elements of state mechanism of protection of intellectual rights in realization of the strategy, The author highlights the key aspects of implementation of the strategy on transforming Singapore into a hub of intellectual property in Asia that determine the coordinative role of the state in this process. Research is conducted on the Intellectual Property Hub Master Plan of 2013, forming the content of state strategy for development of intellectual property in Singapore. The Singapore&amp;rsquo;s Intellectual Property Hub Master Plan intends close cooperation and interaction between all institutional elements of national mechanism of protection of intellectual rights. The normative framework for implementation of this plan consists in the legislation, which employs international standards in the area of intellectual property. Singapore&amp;rsquo;s state development strategy is aimed at attaining the leading position in the region and the world by creating the system of alternative settlement of disputes, qualitative and quantitative strengthening of the pull of specialists in the sphere of intellectual property, involvement of private sector, connecting the state to international bases and systems, reexamination of tax breaks and preferences, as well as stimulation of scientific research and proliferation of the experience of strategic planning.
Constructive heuristic for the vertex bisection problem

The Vertex Bisection Problem (VBP) consists in partitioning a generic graph into two equallysized subgraphs and such that the number of vertices in with at least one adjacent vertex in is minimized.This problem is NP-hard with practical applications in the telecommunication industry.In this article we propose a new constructive algorithm for VBP based on the Greedy Randomized Adaptive Search Procedure (GRASP) methodology.We call our algorithm CVBP.We compare CVBP with a previously published GRASP-based constructive algorithm (LIT) in order to assess the performance of our algorithm in practice.The results of the experiment showed that CVBP outperformed LIT by 75.83 % in solution quality.The validation of the experimental evidence was performed by the well-known Wilcoxon Signed Rank Sum Test.The test found statistical significance for a confidence level of 99.99 %.Therefore, we consider that our constructive heuristic is a good alternative to stochastically solve the Vertex Bisection Problem.
Relating Functional and Imperative Session Types

Imperative session types provide an imperative interface to session-typed communication. In such an interface, channel references are first-class objects with operations that change the typestate of the channel. Compared to functional session type APIs, the program structure is simpler at the surface, but typestate is required to model the current state of communication throughout. Following an early work that explored the imperative approach, a significant body of work on session types has neglected the imperative approach and opts for a functional approach that uses linear types to manage channel references soundly. We demonstrate that the functional approach subsumes the early work on imperative session types by exhibiting a typing and semantics preserving translation into a system of linear functional session types. We further show that the untyped backwards translation from the functional to the imperative calculus is semantics preserving. We restrict the type system of the functional calculus such that the backwards translation becomes type preserving. Thus, we precisely capture the difference in expressiveness of the two calculi and conclude that the lack of expressiveness in the imperative calculus is largely due to restrictions imposed by its type system.
Perceptimatic: A Human Speech Perception Benchmark for Unsupervised Subword Modelling

In this paper, we present a data set and methods to compare speech processing models and human behaviour on a phone discrimination task. We provide Perceptimatic, an open data set which consists of French and English speech stimuli, as well as the results of 91 English- and 93 French-speaking listeners. The stimuli test a wide range of French and English contrasts, and are extracted directly from corpora of natural running read speech, used for the 2017 Zero Resource Speech Challenge. We provide a method to compare humans' perceptual space with models' representational space, and we apply it to models previously submitted to the Challenge. We show that, unlike unsupervised models and supervised multilingual models, a standard supervised monolingual HMM-GMM phone recognition system, while good at discriminating phones, yields a representational space very different from that of human native listeners.
The complete hierarchical locality of the punctured Simplex code

Abstract This paper presents a new alphabet-dependent bound for codes with hierarchical locality. Then, the complete list of possible localities is derived for a class of codes obtained by deleting specific columns from a Simplex code. This list is used to show that these codes are optimal codes with hierarchical locality.
Simulation of Print-Scan Transformations for Face Images based on Conditional Adversarial Networks

In many countries, printing and scanning of face images is frequently performed as part of the issuance process of electronic travel documents, e.g., ePassports. Image alterations induced by such print-scan transformations may negatively effect the performance of various biometric sub-systems, in particular image manipulation detection. Consequently, according training data is needed in order to achieve robustness towards said transformations. However, manual printing and scanning is time-consuming and costly.In this work, we propose a simulation of print-scan transformations for face images based on a Conditional Generative Adversarial Network (cGAN). To this end, subsets of two public face databases are manually printed and scanned using different printer-scanner combinations. A cGAN is then trained to perform an image-to-image translation which simulates the corresponding print-scan transformations. The goodness of simulation is evaluated with respect to image quality, biometric sample quality and performance, as well as human assessment.