Reviewerly/exHarmony huggingface dataset:
Hierarchical Matching and Reasoning for Action Localization via Language Query

This paper strives for temporal localization of actions in untrimmed videos via natural language queries. Prevailing methods represent both query sentence and video as a whole and perform sentence-video matching via global features, which neglects local correspondence between sentence and video. In this work, we aim to move beyond this limitation by delving into the fine-grained local sentence-video matching, such as phrase-motion matching and word-object matching. We propose a hierarchical matching and reasoning method based on deep conditional random field to integrate hierarchical matching between visual concepts and textual semantics for temporal action localization via query sentence. Our method decomposes each sentence into textual semantics (i.e., phrases and words), obtains multi-level matching results between the textual semantics and the visual concepts in a video (i.e., results of phrase-motion matching and word-object matching), and then reasons relations between multi-level matching via pairwise potentials of conditional random field to achieve coherence in hierarchical matching. By minimizing the overall potential, the final matching score between a sentence and a video is computed as the conditional probability of the conditional random field. Our proposed method is evaluated on public Charades-STA dataset and the experimental results verify its superiority over the state-of-the-art methods.
Optimal Visual Cues for Smartphone Earthquake Alert Systems: Preliminary Data from Lab and Field Experiments

Earthquake alerts must rapidly capture attention and trigger efficient reactions, yet little is known about their optimal visual characteristics. To this end, an interdisciplinary effort (design-technology-emotion psychology) focused on one of the early warning key factors: the visual properties of the alert. We first conducted a controlled lab experiment which directly compared the efficiency of different alert cues (text, alert icons, fear faces) in capturing peripheral attention while participants were preoccupied with an engaging activity. Results supported an advantage for icons and faces over text. Next, we conducted a controlled field experiment examining the impact of the alert stimuli (icons vs. faces), personalization (i.e., choosing one’s alert), and the effect of practice by mass-alerting subjects with a custom-made app. Our results suggest dramatic effects of practice, and furthermore, within the practice conditions, participants responded faster to personally chosen faces than icons. Importantly, face alerts received higher user experience ratings of trust and safety. These preliminary results tentatively point to novel future directions in the development of efficient and trustworthy alert interfaces. Furthermore, we join a growing body of studies and technological trends geared at using faces as a means of identification, data collection and analysis.
On the Wilsonian Meaning of Quantum Error Correction

Abstract We sketch a recipe to define renormalization group transformations based on Kadanoff‐Wilson block packing using a quantum error correction code. In such a case the RG transformations of the couplings are determined by the error matrix of the QEC code. In order to define the RG transformation of couplings we use Weinberg's sum rule for an error Källen Lehman function. We define an error beta function that for holographic AdS codes is conjectured to be zero. For holographic codes the relation between Weinberg's sum rule for the error Källen Lehman function and bulk locality is briefly discussed.
Sword: An Opaque Blockchain Protocol

I describe a blockchain design that hides the transaction graph from Blockchain Analyzers. The design is based on the realization that today the miner creating a block needs enough information to verify the validity of transactions, which makes details about the transactions public and thus allows blockchain analysis. Some protocols, such as Mimblewimble, obscure the transaction amounts but not the source of the funds which is enough to allow for analysis. The insight in this technical note is that the block creator can be restricted to the task of ensuring no double spends. The task of actually verifying transaction balances really belongs to the receiver. The receiver is the one motivated to verify that she is receiving a valid transaction output since she has to convince the next receiver that the balances are valid, otherwise no one will accept her spending transaction. The bulk of the transaction can thus be encrypted in such a manner that only the receiver can decrypt and examine it. Opening this transaction allows the receiver to also open previous transactions to allow her to work her way backward in a chain until she arrives at the coin generation blocks and completely verify the validity of the transaction. Since transactions are encrypted on the blockchain a blockchain analyzer cannot create a transaction graph until he is the receiver of a transaction that allows backward tracing through to some target transaction.
From Technical to Aesthetics Quality Assessment and Beyond

Every day 1.8+ billion images are being uploaded to Facebook, Instagram, Flickr, Snapchat, and WhatsApp [6]. The exponential growth of visual media has made quality assessment become increasingly important for various applications, from image acquisition, synthesis, restoration, and enhancement, to image search and retrieval, storage, and recognition.
Optimizing Quantum Models of Classical Channels: The Reverse Holevo Problem

Given a classical channel—a stochastic map from inputs to outputs—the input can often be transformed into an intermediate variable that is informationally smaller than the input. The new channel accurately simulates the original but at a smaller transmission rate. Here, we examine this procedure when the intermediate variable is a quantum state. We determine when and how well quantum simulations of classical channels may improve upon the minimal rates of classical simulation. This inverts Holevo’s original question of quantifying the capacity of quantum channels with classical resources: We determine the lowest-capacity quantum channel required to simulate a classical channel. We also show that this problem is equivalent to another, involving the local generation of a distribution from common entanglement.
Constrained Linear Curvature Image Registration Model and Its Numerical Algorithm

We propose a constrained linear curvature image registration model to explicitly control the deformation according to the transformed Jacobian matrix determinant using point-by-point inequality constraints in this paper. In addition, an effective numerical method is proposed to solve the resulting inequality constrained optimization model. Finally, some numerical examples are given to prove the obvious advantages of the curvature image registration model with inequality constraints.
Research of Communication Coverage and Terrain Masking for Path Planning

Recent complex battle field demands Network Centric Warfare(NCW) ability to control various parts into a cohesive unit.In path planning filed, the NCW ability increases complexity of path planning algorithm, and it has to consider a communication coverage map as well as traditional parameters such as minimum radar exposure and survivability.In this paper, pros and cons of various propagation models are summarized, and we suggest a coverage map generation method using a Longley-Rice propagation model.Previous coverage map based on line of sight has significant discontinuities that limits selection of path planning algorithms such as Dijkstra and fast marching only.If there is method to remove discontinuities in the coverage map, optimization based path planning algorithms such as trajectory optimization and Particle Swarm Optimization(PSO) can also be used.In this paper, the Longley-Rice propagation model is used to calculate continuous RF strengths, and convert the strength data using smoothed leaky BER for the coverage map.In addition, we also suggest other types of rough coverage map generation using a lookup table method with simple inputs such as terrain type and antenna heights only.The implemented communication coverage map can be used various path planning algorithms, especially in the optimization based algorithms.
Solving complicated advanced mathematical problems based on matlab software

By solving a series of complicated problems using the function and the loop statement provided by Matlab software, we found that the Matlab software is a powerful and convenient software. It not only requires fewer commands but also gives results in a short time. It is suitable to help researchers to solve any problems in higher mathematics, both simple problems, and complicated problems. However, it has some shortcomings also.
Robot on the shoulders of humans

Abstract Recent trends in artificial intelligence (AI) development have led to a proliferation of studies on the ability of AI systems generate artistic works. Debate continues about the general benefit of society from artistic and scientific works made by AI systems. Questions have been raised about infringement of copyright by AI. The present article examines the modern approaches to regulation of AI and the role of humans in the creation of artistic work. The main purpose of the research is to prevent unfair use of original works created by human and discuss originality in artistic work generated by AI. Overall, these studies highlight the needs for the protection of human copyrights in the event of commercial exploitation by AI system and the protection of the general public from mass distribution of artificial pseudo‐scientific works.
Improving the Efficiency of QoE Crowdtesting

Crowdsourced testing is an increasingly popular way to study the quality of experience (QoE) of applications, such as video streaming and web. The diverse nature of the crowd provides a more realistic assessment environment than laboratory-based assessments allow. Because of the short life-span of crowdsourcing tasks, each subject spends a significant fraction of the experiment time just learning how it works. We propose a novel experiment design to conduct a longitudinal crowdsourcing study aimed at improving the efficiency of crowdsourced QoE assessments. On Amazon Mechanical Turk, we found that our design was 20% more cost-effective than crowdsourcing multiple one-off short experiments. Our results showed that subjects had a high level of revisit intent and continuously participated in our experiments. We replicated the video streaming QoE assessments in a traditional laboratory setting. Our study showed similar trends in the relationship between video bitrate and QoE, which confirm findings in prior research.
Efficient Single Shot Object Detector Towards More Accurate and Faster Prediction

Although a series of refinements on single shot object detector (SSD) are presented recently for better precision, most come at the cost of increased inference time. Inspiringly, we propose a very efficient SSD (ESSD) towards more accurate and faster detection. It could not only significantly optimize the detection accuracy, but also greatly improve the computational efficiency. The core lies in the proposed neighbor-scale feature fusion, a simple but effective way, to reduce the channel dimensionality followed by integrating neighbor-scale feature layers in the network. That is the key to characterize the principal component expression of objects in fewer dimensions and strengthen the abstraction of objects on both the current and higher feature layers, which evidently decrease the inference time while improving the performance. Experimental results show that the mean average precision (mAP) of ESSD on Pascal VOC 2007 and 2012 evaluations achieves 81.2 and 79.1, respectively, as well as the average speed reaches 52.9 fps for a 512 × 512 input, which actually outperform many state-of-the-art methods.
First Impression: AI Understands Personality

When you first encounter a person, a mental image of that person is formed. First impression, an interactive art, is proposed to let AI understand human personality at first glance. The mental image is demonstrated by Beijing opera facial makeups, which shows the character personality with a combination of realism and symbolism. We build Beijing opera facial makeup dataset and semantic dataset of facial features to establish relationships among real faces, personalities and facial makeups. First impression detects faces, recognizes personality from facial appearance and finds the matching Beijing opera facial makeup. Finally, the morphing process from real face to facial makeup is shown to let users enjoy the process of AI understanding personality.
Soft-Root-Sign: A New Bounded Neural Activation Function

This paper proposes a new activation function, namely “Soft-Root-Sign” (SRS), which is smooth, non-monotonic, and bounded. The bounded property of SRS distinguishes itself from most state-of-the-art activation functions. It prevents the distribution of the output from being scattered in the non-negative real number space and corrects it to the positive real number space, providing a more stable distribution of output during network training. We evaluated SRS on deep networks applied to image classification task. Experimental results show that the proposed activation function SRS is superior to state-of-the-art nonlinearities.
Fatal Lack of Information Transparency in Public Health Emergency: Lessons from the COVID-19 Outbreak in China

This article examines the lack of information transparency on the part of the Chinese government as revealed in the COVID-19 outbreak. Based on the evidence of the lack of information transparency in the initial stage of this public health emergency, the article reviews how the Chinese public health emergency information system, which had been established in response to the 2003 SARS crisis, was implemented. It further analyses the fundamental reasons for the lack of information transparency despite the reporting, disseminating and early warning mechanisms that existed in the country. It finds that powerless centres for disease control and prevention, prioritisation of the political concern of social stability and harmonisation over public health, extremely tight governance of public opinions and inadequacies of the public health emergency information system with respect to new and emerging infectious diseases are the four major factors that combined to result in the lack of information transparency in the COVID-19 outbreak in China. The article identifies big lessons to be learned to promote information transparency in public health emergencies.
Parameter estimation of weak space‐based ADS‐B signals using genetic algorithm

Space-based automatic dependent surveillance-broadcast (ADS-B) is an important emerging augmentation of existing ground-based ADS-B systems. In this paper, the problem of space-based ultra-long-range reception processing of ADS-B signals is described. We first introduce a header detection method for accurately determining the pulse position of a weak ADS-B signal. We designed a signal encoding method, shaping method, and fitness function. We then employed a genetic algorithm to perform high-precision frequency and phase estimations of the detected weak signal. The advantage of this algorithm is that it can simultaneously estimate the frequency and phase, meaning a direct coherent demodulation can be implemented. To address the computational complexity of the genetic algorithm, we improved the ratio algorithm for frequency estimation and raised the accuracy beyond that of the original ratio algorithm with only a slight increase in the computational complexity using relatively few sampling points.
Prospects and Challenges of Web 3.0 Technologies Application in the Provision of Library Services

The chapter examines the prospects and challenges of the application of Web 3.0 technologies as they relate to semantic web, federated search, mobile application, and their impact on library services. The principles, features, application, potentiality, and challenges of the technologies vis-à-vis library services form the broad objectives that guided the chapter. Following a brief retrospective review of the developments of web technologies, the chapter discusses Web 3.0 from the context of semantic web, cloud computing, federated search and virtual reference services. It broadens the prospects of Web 3.0 as it affects the provision of web-based services like its flexibility as solution to digital content volatility and ability to widen cloud-based services using open source electronic library software among others. Having expatiated the challenges Web 3.0 portends for web-based library services, the chapter concludes with the need for librarians and users to co-create value for participatory librarianship.
Penggunaan dan Fungsi Kalimat Kondisional Bahasa Jepang “to”, “tara”, “reba” dan “nara” Berdasarkan Modalitas dan Teori Teritori Informasi

In Japanese, four conjugate particles are usually used to form conditional sentence patterns, namely “to”, “tara”, “reba” and “nara”. Based on the use of their modality and information territory, they have different uses and functions. Data analysis of Japanese newspaper sentences using qualitative method showed that Japanese conditional sentences can be classified into two types. The first type is Repeated Factual Conditional Sentence that uses the conjugate particle “to”. This sentence is used when the speaker and listener are considered to know the contents of the information in it, thus expressing a recurring event or habit. The modality that is widely used is the epistemic modality. The second type is Hypotheses or Assumptions Conditional Sentences that use the conjugate particles of “tara”, “reba”, and “nara”. This conditional sentence is used when the contents of the information is the speaker’s desire and only exists in the territory of the speaker. Therefore, its functions is to show a desire, request, suggestion, order, and prohibition, all of which are hypotheses or assumptions of the speaker. The modality used is the intentional modality, such as the modality of desires, requests, suggestions, commands, prohibitions, and so on.
Path Aggregation and Dual Supervision Network for Scene Text Detection

In recent years, instance segmentation-based scene text detection has been widely concerned by academics and industry. However, these segmentation methods based on the coding-decoding paradigm are limited by the loss of information caused by subsampling, which is the root cause of pixel misclassification in the instance segmentation task. In this paper, we propose an effective approach for scene text detection, which named Path Aggregation and Dual Supervision Network (PADSNet). To introduce the from coarse to fine detection idea into the one-stage segmentation algorithm, a single-task multi-level supervision method is designed. Meanwhile, deformable convolution is used to break through the limits of CNN’s rectangular receptive field, so that it can better adapt to arbitrary shape scene text. The experimental results show that our method can effectively reduce pixel misclassification, and achieve f-measure 85.4% and 83.19% on the ICDAR2015 dataset and CTW1500 dataset respectively.
Economy Dimension of Sustainable Development Index of Solar Powered Electricity Project in Pilolaheya And Pelita Hijau Villages of Bonebolango Regency

Several villages in Bone Bolango Regency are isolated because of geographical conditions and conservation reasons. There are no access road from paved road which can be used by PLN to installed grid. Several programs have been launched, including supply of electricity using renewable energy such as Micro hydro electric and solar cell, to help the villagers from blackout. Unfortunately most of installed power plants did not operate continuously since the human resources in the village is too weak to handle and maintain the sophisticated electrical instrument. Story of in Bone Bolango is not unique there are many similar stories in the entire Indonesia. To address why the ideal program of government is failing, we proposed this research which has ultimate aim to solve the existing problem in the society through research using transdisciplinary approach. Our starting location is Bone Bolango regency where we have been in contact for years. The first year of research is focused on framing the problems in the society through eyes of researchers (academics), government and people. The second year of research will be focused on transferring knowledge from each side to the others with aim to create goal oriented solution. Ones we have it in the third year, we will delivered that together (co delivered) and see what happen.The output research in every year is article published in peer reviewed and Scopus indexed journals and reputable international conference in Indonesia and Southeast Asia. Keywords: Isolated, sustainable development, transdisciplinary approach.
A Quantum Scheme for Calculating the Inner Product of Unsigned Vectors

We propose a quantum scheme for calculating the inner product of unsigned vectors. Numerical simulations are used to test the accuracy and successful probability of the scheme and verify its feasibility.
Effective Data Access Provision with Advanced Security Features in Cloud Computing

The paper discusses the standard frameworks and conceptual system architecture suitable for cloud computing environment. The major focus of this paper is to provide a clear description to advanced security features that supports better access provision to the cloud data users. This is accomplished through extending the support for the properties such as confidentiality, integrity, security factors. This paper does not provide any description of cryptographic phases and cloud deployment models associated with system frame work establishment. It provides a clear description to integrity and security assurance over the outsourced data. It acts as a supporting factor to access control models defined in previous papers of the domain.
Cross-Domain Disentangle Network for Image Manipulation

Deep image-to-image translation networks have shown satisfying results on domain adaptive image generation tasks. In this paper, we make further progress on a new task of cross-domain image manipulation. We aim at generating a manipulated image that has a specified factor of the source-domain image and other factors of the target-domain image. We present a novel cross-domain disentangle network, which is capable of learning disentangled representation from data across multiple domains. This network extracts the latent representation of images on source and target domains, and disentangles the specified and unspecified factors of latent representation simultaneously on both domains. Image manipulation within and across domain can be realized by manipulating on the latent space. Empirical experiments show that our method is able to generate convincing intra-domain and cross-domain manipulated images.
TEACHER’S OCCUPATION THROUGH THE PRISM OF LEGISLATION, THEORY AND OPERATION

Teacher’s occupation is a research challenge for each author. It is the most intensely changing, in its specificity and nuance profession. It is an occupation - a mirror of social development. That is why it is quite often the subject of research and diverse and mixed comments. This article is a reflection of the question from Prof. V. Gyurova’s article : Why is only pedagogical competence not enough for the 21st century teacher? It is an attempt to continue the discussion. The article raises questions about how, what and why. Regular, theoretical and operational structures related to teacher qualifications are considered. Modern pragmatic views on teacher training and their stimulation for the development of methodical competence as an operational structure of research skills are offered.