Reviewerly/exHarmony huggingface dataset:
Categorization of Cooking Actions Based on Textual/Visual Similarity

In this paper, we propose a method of automatically categorizing cooking actions appearing in recipe data. We extract verbs from textual descriptions of cooking procedures in recipe data, and vectorize them by using word embedding. These vectors provide a way to compute contextual similarity between verbs. We also extract images associated with each step of the procedures, and vectorize them by using a standard feature extraction method. For each verb, we collect images associated with the steps whose description includes the verb, and calculate the average of their vectors. These vectors provide a way to compute visual similarity between verbs. However, one type of action is sometimes represented by several types of images in recipe data. In such cases, the average of the associated image vectors is not appropriate representation of the action. To mitigate this problem, we propose a yet another way to vectorize verbs. We first cluster all the images in the recipe data into 20 clusters. For each verb, we calculate the ratio of each cluster within the set of images associated with the verb, and create a 20-dimensional vector representing the distribution over the 20 classes. We calculate similarity of verbs by using these three kinds of vector representations. We conducted a preliminary experiment for comparing these three ways, and the result shows that each of them are useful for categorizing cooking actions.
Hanasu

The Japanese language is not only one of the most favorite foreign languages in Indonesian higher education, but it is also considered a difficult foreign language to learn. This fact creates many mobile learning (m-learning) applications that were developed to help learners learn the Japanese language independently. Existing Japanese m-learning apps concentrate only on improving vocabulary, writing and reading skills. Although improving vocabulary and reading skills are important, practicing Japanese pronunciation is also crucial in fundamental Japanese learning. This study introduces the idea of utilizing the listening and speaking exercise in the M-learning implementation to provide learners with a new experience in term of Japanese language learning method. In addition, this paper also describes preliminary learning outcomes from a small-scale assessment of the learners when they are using the speaking input in m-learning. Evaluation result indicates that the listening and speaking method is feasible for future implementation of m-learning with promising learning outcome enhancement compared to the conventional learning method.
Absorption-Based Query Answering for Expressive Description Logics

Conjunctive query answering is an important reasoning task for logic-based knowledge representation formalisms, such as Description Logics, to query for instance data that is related in certain ways. Although many knowledge bases use language features of more expressive Description Logics, there are hardly any systems that support full conjunctive query answering for these logics. In fact, existing systems usually impose restrictions on the queries or only compute incomplete results. In this paper, we present a new approach for answering conjunctive queries that can directly be integrated into existing reasoning systems for expressive Description Logics. The approach reminds of absorption, a well-known preprocessing step that rewrites axioms such that they can be handled more efficiently. In this sense, we rewrite the query such that entailment can dynamically be checked in the dominantly used tableau calculi with minor extensions. Our implementation in the reasoning system Konclude outperforms existing systems even for queries that are restricted to the capabilities of these other systems.
NeuroManagement and Intelligent Computing Method on Multimodal Interaction

In recent years, the research of multimodal interaction has made rapid progress owing to the development of artificial intelligence and big data technology, as well as the new findings of human psychology and behavior's study. Nevertheless, the human- computer's multimodal interaction is actually a dynamic and evolutionary process, so there are still some difficult issues that need to be further explored such as how to comprehensively deal with the multimodal fusion data, adaptively consider the user's personalized habits, and precisely understand the expresser's intentions. NeuroManagement, an emerging interdisciplinary subject to study human's management based on neural mechanism, provides new theory and methodology for probing into the above issues. From the perspective of NeuroManagement, this keynote paper analyzes the user's neural mechanism of multimodal interactions, discusses the characteristics of neural activities in that process, and proposes a framework of intelligent computation method to understand and measure the user's intentions as well as his/her psychological states. In order to promote the interdisciplinary development of artificial intelligence and neuroscience, Neuroartificial Intelligence, a new subject field is proposed by this paper.
A Bayesian Approach to Recurrence in Neural Networks

We begin by reiterating that common neural network activation functions have simple Bayesian origins. In this spirit, we go on to show that Bayes's theorem also implies a simple recurrence relation; this leads to a Bayesian recurrent unit with a prescribed feedback formulation. We show that introduction of a context indicator leads to a variable feedback that is similar to the forget mechanism in conventional recurrent units. A similar approach leads to a probabilistic input gate. The Bayesian formulation leads naturally to the two pass algorithm of the Kalman smoother or forward-backward algorithm, meaning that inference naturally depends upon future inputs as well as past ones. Experiments on speech recognition confirm that the resulting architecture can perform as well as a bidirectional recurrent network with the same number of parameters as a unidirectional one. Further, when configured explicitly bidirectionally, the architecture can exceed the performance of a conventional bidirectional recurrence.
Optimum Point of Intersection Selection in Horizontal Highway Alignment Design: A Comparative Study Using Path Planner Method and Ant Algorithm

This paper proposes the application of the path planner method (PPM) for optimizing horizontal alignment. The method operates on the principle of rapidly exploring random tree (RRT) [1, 2] and is capable in efficiently searching non-convex high dimension space. In this method, the entire search space is explored by the randomly generated point of intersections (PIs) which are connected to develop a tree-like path by expanding from start to end point, iteratively. These paths would meet the highway geometric guidelines and minimize associated cost. The potential sets of PIs are connected to generate the tangent sections, thus in developing an alignment. The alignment is checked for encroachment towards restricted areas and any violation leads to further refinement of the PI locations. Various researchers have extensively used artificial intelligence (AI) based heuristics algorithms for optimization of horizontal alignment. The proposed PPM is compared with one such algorithm, i.e. the ant algorithm (AA). Further, the suitability of the PPM in optimizing the horizontal alignment is reviewed. The efficiency of both the methods is verified through two case studies, one being a hypothetical and another using a real topographical map of a place in Gujarat, India.
Time-Dependent Strategies in Games of Timing

Timing, a central aspect of decision-making in security scenarios, is a subject of growing academic interest; frequently in the context of stealthy attacks, or advanced persistent threats (APTs). A key model in this research landscape is FlipIt [1]. However, a limiting simplifying assumption in the FlipIt literature is that costs and gains are not subject to discounting, which contradicts the typical treatment of decision-making over time in most economically relevant contexts. Our recent work [2] introduces an adaptation of the FlipIt model that applies time-based exponential discounting to the value of a protected resource, while allowing players to choose from among the same canonical strategies as in the original game. This paper extends the study of games of timing by introducing two new classes of strategies that are fundamentally motivated by a time-discounted world view. Within our game model, we compute player utilities, best responses and give a partial characterization of the game’s Nash equilibria. Our model allows us to re-interpret the APT model using a finite total valuation, and a finite time horizon. By applying time-based discounting to the entire decision-making framework, we increase the level of realism as well as applicability to organizational security management.
Pedagogical opportunities of information and communication technologies at lessons of geography

In connection with the reduction of cognitive interest among learners to study geography, information and communication technologies are called upon to help in solving this problem. At a modern lesson of geography important is a maintenance of process of studying of geography the sound moments and a visual row, such combination of means of education promotes creation of effective productive educational activity. Information and communication learning tools are the main learning tools in the classroom, and the teacher is designed to organize learning activities with learning tools in the classroom and after school hours. Use of information and communication technologies helps the teacher to organize activity of pupils with various sources of information: Internet resources, electronic educational resources which make a basis of components of educational activity: motivational, operational and technical and control and estimated. At lessons of geography it is possible to use the following electronic educational resources: electronic textbooks; electronic collections of additional and evident materials to lessons (posters, interactive maps, schemes, illustrations); multimedia educational presentations; electronic control and measuring materials (KIMY); electronic files tasks; databases of educational appointment (encyclopedias, reference books, dictionaries, glossaries, exercise machines, games); catalogs of the digital educational resources (DER); audiobooks and digital videos; digital photo albums, photo galleries, virtual excursions. The electronic textbook includes animation, audio fragments, video fragments and also interactive maps; the main destination of this textbook consists in demonstration of training materials. Software of control and correction of knowledge allow to process quickly and objectively received results. Using the test constructor, the necessary verification tasks on various topics of the course are compiled. The choice of information and communication tutorials for a lesson of geography is carried out taking into account goals and tasks, the maintenance of a training material, the used methods of training at each stage of educational activity.
Reconstruction Error Aware Pruning for Accelerating Neural Networks

This paper presents a pruning method, Reconstruction Error Aware Pruning (REAP), to reduce the redundancy of convolutional neural network models for accelerating the inference. REAP is an extension of one of the state-of-the-art channel pruning methods. Our method takes 3 steps, (1) evaluating the importance of each channel based on the reconstruction error of the outputs in each convolutional layer, (2) pruning less important channels, (3) updating the remaining weights by the least squares method so as to reconstruct the outputs. By pruning with REAP, one can produce a fast and accurate model out of a large pretrained model. Besides, REAP saves us lots of time and efforts required for retraining the pruned model. As our method requires a large computational cost, we have developed an algorithm based on biorthogonal system to conduct the computation efficiently. In the experiments, we show that REAP can conduct pruning with smaller sacrifice of the model performances than several existing state-of-the-art methods such as CP [9], ThiNet [17], DCP [25], and so on.
Authoring Formats and Their Extensibility for Application Profiles

Metadata application profiles act as a key element in interoperability of metadata instances. There are various accepted formats to express application profiles. Most of these expression formats such as RDF, OWL, JSON-LD, SHACL, and ShEx are machine-actionable, and formats like spreadsheets or web pages act as human-readable documentation. Due to limited options in the mutual conversion of these expression formats, they are often created independent of each other and thus makes the process of application profile creation and maintenance an expensive process requiring sophisticated skills and time. Proposals for convertible authoring formats to create application profiles have received less acceptance, mainly due to their inability in addressing various use cases and requirements. The authors describe a new authoring format based on YAML and evaluate the scope of extensibility of authoring formats by comparing this with the capabilities of two already available proposals; Simple DSP, Bibframe Editor. The comparison includes an attempt to recreate real-world application profiles with an extensible format. This study argues that the extensible authoring formats are suitable for creating application profiles with custom requirements and different use cases. This would promote the acceptance of application profiles by reducing the associated cost and skill requirements in creation, maintenance, and publishing of application profiles.
Budget Constraint Bag-of-Task Based Workflow Scheduling in Public Clouds

Bag-of-Tasks (BoT) workflows have appeared in distributed computing platforms such as Spark, MapReduce, and Pegasus. Budget constraints usually exist for these applications. It is crucial to design scheduling algorithms to minimize makespans under budget constraints for BoT workflows. However, most existing workflow algorithms are tailored for general workflows without considering batch structures of Bot-workflows. The main challenge for scheduling BoT workflows is to distribute the budget to different BoTs appropriately considering BoT structures. In this paper, a configuration-and-serialization iterative adjusting based heuristic algorithm (CSIA) is proposed to minimize the makespans under budget constraints. CSIA allocates VM configurations and serial degrees to different BoTs appropriately to decrease the makespan. Experimental results illustrate that the proposal gets shorter makespans on several types of workflow instances than existing algorithms under budget constraints.
Hybrid Entanglement Swapping for Satellite-Based Quantum Communications

Hybrid entanglement swapping supports the teleportation of any arbitrary states, regardless of whether the quantum information in the state is encoded in Discrete Variables (DV) or Continuous Variables (CV). In this work, we study the CV teleportation channel created between two ground receivers via direct lossy-distribution from a low-Earth-orbit (LEO) satellite. Such a flexible teleportation protocol has the potential to interconnect a global array of quantum-enabled devices regardless of the different intrinsic technology upon which the devices are built. However, past studies of hybrid entanglement swapping have not accounted for channel transmission loss. Here we derive the general framework for teleporting an arbitrary input mode over a lossy CV teleportation channel. We investigate the specific case where the input modes are part of DV states entangled in the photon number basis, then identify the optimal teleportation strategy. Our results show that, relative to DV photon-number entanglement sourced directly from the satellite, there are circumstances where our teleported DV states retain higher entanglement quality. We discuss the implications of our new results in the context of generating a global network of ultra-secure communications between different quantum-enabled devices which possess line-of-sight connections to LEO satellites. Specifically, we illustrate the impact the teleportation process has on the key rates from a Quantum Key Distribution protocol.
No-Reference Image Quality Assessment via Multi-order Perception Similarity

No-reference image quality assessment (NR-IQA) aims to develop models that can predict the quality of distorted image automatically and accurately without the reference. Lack of reference makes NR-IQA based on feature learning difficult to avoid the impact of image contents on features. In this paper, we follow an innovative strategy and present a novel NR-IQA approach based on multi-order perception similarity to overcome the difficulty. The key to our strategy is that the high and low order feature maps can be the reference to each other to reduce the dependence of features on image content. In our framework, the similarity relies on describing the retention of fine structures that is extremely sensitive to distortion in distorted image to predict quality degradation, which is monotonic with respect to the distortion level. The similarities of features from structure to texture between high and low order feature maps are utilized to predict image quality. Finally, a regression model is trained to learn the mapping from feature similarities to perceptual quality. Extensive experiments demonstrate the effectiveness and superiority of our approach against compared methods.
YNU-junyi in BioNLP-OST 2019: Using CNN-LSTM Model with Embeddings for SeeDev Binary Event Extraction

We participated in the BioNLP 2019 Open Shared Tasks: binary relation extraction of SeeDev task.The model was constructed using convolutional neural networks (CNN) and long short term memory networks (LSTM).The full text information and context information were collected using the advantages of C-NN and LSTM.The model consisted of two main modules: distributed semantic representation construction, such as word embedding, distance embedding and entity type embedding; and CNN-LSTM model.The F1 value of our participated task on the test data set of all types was 0.342.We achieved the second highest in the task.The results showed that our proposed method performed effectively in the binary relation extraction.
Adaptive AR visual output security using reinforcement learning trained policies

Augmented reality (AR) technologies have seen significant improvement in recent years with several consumer and commercial solutions being developed. New security challenges arise as AR becomes increasingly ubiquitous. Previous work has proposed techniques for securing the output of AR devices and used reinforcement learning (RL) to train security policies which can be difficult to define manually. However, whether such systems and policies can be deployed on a physical AR device without degrading performance was left an open question. We develop a visual output security application using a RL trained policy and deploy it on a Magic Leap One head-mounted AR device. The demonstration illustrates that RL based visual output security systems are feasible.
Analysis of the Construction and Implementation of the New Mode of School-Enterprise Cooperation in Higher Vocational Colleges from the Perspective of Educational Reform

In January 2019, the State Council of the People's Republic of China issued the implementation plan of national vocational education reform, which clearly stated that "guided by promoting employment and adapting to the needs of industrial development, China's vocational education should be transformed into a type of education with social participation of enterprises and distinct professional characteristics".This paper takes the image file technology major of Shanghai Publishing and Printing College as an example to analyze the construction of the major in order to adapt to the social and economic development as the guide, to the integration of industry and education as the guarantee, and to explore the construction and path of the new mode of school enterprise cooperation under the background of education reform.
Continuous support for rehabilitation using machine learning

Abstract Providing a suitable rehabilitation at home after an acute episode or a chronic disease is a major issue as it helps people to live independently and enhance their quality of life. However, as the rehabilitation period usually lasts some months, the continuity of care is often interrupted in the transition from the hospital to the home. Relieving the healthcare system and personalizing the care or even bringing care to the patients’ home to a greater extent is, in consequence, the superior need. This is why we propose to make use of information technology to come to participatory design driven by users needs and the personalisation of the care pathways enabled by technology. To allow this, patient rehabilitation at home needs to be supported by automatic decision-making, as physicians cannot constantly supervise the rehabilitation process. Thus, we need computer-assisted patient rehabilitation, which monitors the fitness of the current patient plan to detect sub-optimality, proposes personalised changes for a patient and eventually generalizes over patients and proposes better initial plans. Therefore, we will explain the use case of patient rehabilitation at home, the basic challenges in this field and machine learning applications that could address these challenges by technical means.
Automatic Choice of Denoising Parameter in Perona-Malik Model

In this work, we propose a no-reference method for automatic choice of the parameters of Perona-Malik image diffusion algorithm for the problem of image denoising. The idea of the approach it to analyze and quantify the presence of structures in the difference image between the noisy image and the processed image as the mutual information value. We apply the proposed method to photographic images and to retinal images with modeled Gaussian noise with different parameters and analyze the effects of no-reference parameter choice compared to the optimal results. The proposed algorithm shows the effectiveness of no-reference parameter choice for the problem of image denoising.