Reviewerly/exHarmony huggingface dataset:
Equational Logic and Categorical Semantics for Multi-Languages

Programming language interoperability is the capability of two programming languages to interact as parts of a single system. Each language may be optimized for specific tasks, and a programmer can take advantage of this. HTML, CSS, and JavaScript yield a form of interoperability, working in conjunction to render webpages. Some object oriented languages have interoperability via a virtual machine host (.NET CLI compliant languages in the Common Language Runtime, and JVM compliant languages in the Java Virtual Machine). A high-level language can interact with a lower level one (Apple's Swift and Objective-C). While there has been some research exploring the interoperability mechanisms (Section 1) there is little development of theoretical foundations. This paper presents an approach to interoperability based around theories of equational logic, and categorical semantics. We give ways in which two languages can be blended, and interoperability reasoned about using equations over the blended language. Formally, multi-language equational logic is defined within which one may deduce valid equations starting from a collection of axioms that postulate properties of the combined language. Thus we have the notion of a multi-language theory and much of the paper is devoted to exploring the properties of these theories. This is accomplished by way of category theory, giving us a very general and flexible semantics, and hence a nice collection of models. Classifying categories are constructed, and hence equational theories furnish each categorical model with an internal language; from this we can also establish soundness and completeness. A set-theoretic semantics follows as an instance, itself sound and complete. The categorical semantics is based on some pre-existing research, but we give a presentation that we feel is easier and simpler to work with, improves and mildly extends current research, and in particular is well suited to computer scientists. Throughout the paper we prove some interesting properties of the new semantic machinery. We provide a small running example throughout the paper to illustrate our ideas, and a more complex example in conclusion.
EgoSpace

In this work, we propose a novel wearable device to augment the user's egocentric space to a wide range. To achieve this goal, the proposed device provides bidirectional projection using a head-mounted wearable projector and two dihedral mirrors. The included angle of the mirrors were set to reflect the projected image in front of and behind the user. A prototype system is developed to explore possible applications using the proposed device in different scenarios, such as riding a bike and map navigation.
Unseen image generating domain-free networks for generalized zero-shot learning

In generalized zero-shot learning (GZSL), it is imperative to solve the bias problem due to extreme data imbalance between seen and unseen classes, i.e., unseen classes are misclassified as seen classes. We alleviate the bias problem by generating synthetic images of unseen classes. The most challenging part is that existing GAN methods are only focused on producing authentic seen images, so realistic unseen images cannot be generated. Specifically, we propose a novel zero-shot generative adversarial network (ZSGAN) which learns the relationship between images and attributes shared by seen and unseen classes. Unlike existing works that generate synthetic features of unseen classes, we can generate more generalizable realistic unseen images. For instance, generated unseen images can be used for zero-shot detection, segmentation, and image translation since images have spatial information. We also propose domain-free networks (DFN) that can effectively distinguish seen and unseen domains for input images. We evaluate our approaches on three challenging GZSL datasets, including CUB, FLO, and AWA2. We outperform the state-of-the-art methods and also empirically verify that our proposed method is a network-agnostic approach, i.e., the generated unseen images can improve performance regardless of the neural network type.
Document Retrieval Using Deep Learning

Document Retrieval has seen significant advancements in the last few decades. Latest developments in Natural Language Processing have made it possible to incorporate context and complex lexical patterns to document representations. This opens new possibilities for developing advanced retrieval systems. Traditional approaches for indexing documents suggest averaging word and sentence encoding to form fixed-length document embeddings. However, the common bag-of-word approach fails to incorporate the semantic context, which can be critical for understanding document-query relevancy. We address this by leveraging Bidirectional Encoder Representations from Transformers (BERT) to create semantically rich document embeddings. BERT compensates the limitations of the Term Frequency Inverse Document Frequency (TF-IDF) by incorporating contextual embeddings. In this paper, we propose an ensemble of BERT and TF-IDF for a document retrieval system, where TFIDF and BERT together score the documents against a query, to retrieve a final set of top K documents. We critically compare our model against the standard TF-IDF method and demonstrate a significant performance improvement on MS MARCO data (Microsoft-curated data of Bing queries).
Heterogeneous Siamese Tracking System Based on PYNQ Framework

Deep neural network models have been gradually applied to the field of visual tracking due to their excellent feature expression capabilities. However, the model is large, which is expressed in the calculation of model parameters. As a result, the implementation platforms of visual tracking algorithm are usually limited by computing power, power consumption, portability, etc. In this paper, we propose a Siamese network tracking scheme based on PYNQ framework, which is deployed on ZYNQ platform. By optimizing the calculation process, Siamese Network accelerated IP core and Region Proposal Network accelerated IP core are designed. The double buffer structure is adopted to effectively call different feature map calculations and reduce off-chip memory access. Python is used to call the accelerated IP core at the top level as the hardware coprocessor to realize the data interaction from the bottom level to the top level and update the system running results asynchronously in Jupyter notebook. We achieve an average 36.7FPS on Xilinx ZCU104 platform, which illustrates that our method has the important practical benefit of allowing lightweight architectures to achieve good performance at high framerates.
CoFi-points

With the explosive growth of web resources, an increasingly important task in recommender systems is to provide high-quality personalized services by learning users’ preferences from historically observed information. As an effective preference learning technology, collaborative filtering has been widely extended to model the one-class or implicit feedback data, which is known as one-class collaborative filtering (OCCF). For a long time, pairwise ranking-oriented learning scheme has been viewed as a superior solution than the pointwise scheme for OCCF due to its higher accuracy in most cases. However, we argue that with appropriate model design, pointwise preference learning can achieve comparable or even better performance than the counterpart, i.e., pairwise preference learning. In particular, we propose a new preference assumption, i.e., pointwise preference on user/item-set. Based on this new assumption, we develop a novel, simple, and flexible solution called collaborative filtering via pointwise preference learning on user/item-set (CoFi-points). Furthermore, we derive two specific algorithms of CoFi-points with respect to the involved user-set and item-set, i.e., CoFi-points(u) and CoFi-points(i), referring to preference assumptions defined on user-set and item-set, respectively. Finally, we conduct extensive empirical studies on four real-world datasets with the state-of-the-art methods, and find that our solution can achieve very promising performance with respect to several ranking-oriented evaluation metrics.
Fitness rate-based rider optimization enabled for optimal task scheduling in cloud

Not long ago, there has been a dramatic augment in the attractiveness of cloud computing systems that depends computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same physical infrastructure. It is considered as an essential pool of resources, which are offered to users through Internet. Without troubling the fundamental infrastructure, pay-per-use computing resources are provided to the users by the cloud computing technology. Scheduling is a significant dilemma in cloud computing as a cloud provider has to serve multiple users in cloud environment. This proposal plans to implement an optimal task scheduling model in cloud sector as a challenge over the existing technologies. The proposed model solves the task scheduling problem using an improved meta-heuristic algorithm called Fitness Rate-based Rider Optimization Algorithm (FR-ROA), which is the advanced form of conventional Rider Optimization Algorithm (ROA). The objective constraints considered for optimal task scheduling are the maximum makespan or completion time, and the sum of the completion times of entire tasks. Since the proposed FR-ROA has attained the advantageous part of reaching the convergence in a small duration, the proposed model will outperform the other conventional algorithms for accomplishing the optimal task scheduling in cloud environment.
A Comparison of a Transition-based and a Sequence-based Analysis of AOI Transition Sequences

Several visual analytics (VA) systems are used for analyzing eye-tracking data because they synergize human-in-the-loop exploration with speed and accuracy of the computer. In the VA systems, the choices of visualization techniques could afford discovering certain types of insights while hindering others. Understanding these affordances and hindrances is essential to design effective VA systems. In this paper, we focus on two approaches for visualizing AOI transitions: the transition-based approach (exemplified by the radial transition graph, RTG) and the sequence-based approach (exemplified by the Alpscarf). We captured the insights generated by two analysts who individually use each visualization technique on the same dataset. Based on the results, we identify four phases of analytic activities and discuss opportunities that the two visualization approaches can complement each other. We point out design implications for VA systems that combine these visualization approaches.
Cloud Computing Curriculum: Developing Exemplar Modules for General Course Inclusion

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that are practical and compatible with this rapidly changing field. This is especially relevant as cloud services are becoming common computing resources for many new computational approaches and advanced subjects such as machine learning and data science. The ability to incorporate specific components of cloud computing teaching content into a variety of courses has become important. However, the lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts continues to be a challenge for instructors. This working group will try to address this challenge.
Go-Through

Social Virtual Reality (VR) offers new opportunities for designing social experiences, but at the same time, it challenges the usability of VR as other avatars can block paths and occlude one's avatar's view. In contrast to designing VR similar to the physical reality, we allow avatars to go through and to see through other avatars. In detail, we vary the property of avatars to collide with other avatars. To better understand how such properties should be implemented, we also explore multimodal feedback when avatars collide with each other. Results of a user study show that multimodal feedback on collision yields to a significantly increased sensation of presence in Social VR. Moreover, while the loss of collision (the possibility to go through other avatars) causes a significant decrease of felt co-presence, qualitative feedback showed that the ability to walk through avatars can ease to access spots of interest. Finally, we observed that the purpose of Social VR determines how useful the possibility to walk through avatars is.
Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

We present the first empirical study of the impact of loss-related errors on TV viewing engagement across disparate platforms, delivery technologies and performance measures. Our dataset comprises anonymized video viewing sessions and data about quality of delivery from a content service provider with a nationwide customer base. We study buffering events on streaming apps, mild and severe packet loss errors on multicast-delivered IPTV to a Set-Top-Box (STB) and signal strength errors on Digital Terrestrial TV. Since these metrics cannot be directly compared to each other, we use engagement as our proxy measure. We first characterize the relationship between each impairment and viewing engagement, investigating confounding factors such as type of content, asset length and connection type. We conclude that the loss of engagement due to poor quality delivery is incurred immediately for on-demand content and in the long-term for live content. We rank impairments across platforms by their impact on engagement.
A Framework for Paper Submission Recommendation System

Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers.
A unified information perceptron using deep reservoir computing

The delay feedback reservoir, as a branch of reservoir computing, has attracted a wide range of research interests because of its training efficiency and its simplicity for hardware implementation. However, its potential for processing various kinds of data, like sequential and matrix data, has not been fully explored. In this paper, we present a unified information processing structure by fusing the convolutional or fully connected neural network with the delay feedback reservoir into a hybrid neural network model to accomplish the comprehensive information processing goal. Our experimental results show that our methodology achieves high accuracy in both image classification and speech recognition, yielding 99.03% testing accuracy on the handwritten digits dataset (MNIST) and 97.3% on Spoken Digits Command Dataset (SDCD).
Remote KYC: Attacks and Counter-Measures

Onboarding of new customers is a sensitive task for various services, like Banks who have to follow the Know Your Customer (KYC) rules. Mobile Onboarding Applications or KYC by Streaming are expanding rapidly to provide this capacity at home. Unfortunately, this leaves the authentication tools in the hand of end-users, allowing the attacker to directly tamper the video stream. With the rise of new digital face manipulation technologies, traditional face spoofing attacks such as presentation attacks or replay attacks should not be the only one to be considered. A new kind of face spoofing attacks (i.e. digital face spoofing) needs to be studied carefully. In this paper, we analyze those new kinds of attacks and propose a method to secure identity documents against both the traditional attacks and the new ones.
Agreement Study Using Gesture Description Analysis

Choosing adequate gestures for touchless interfaces is a challenging task that has a direct impact on human-computer interaction. Such gestures are commonly determined by the designer, ad-hoc, rule-based, or agreement-based methods. Previous approaches to assess agreement grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this article, we propose a generalized framework that inherently incorporates the gesture descriptors into the agreement analysis. In contrast to previous approaches, we represent gestures using binary description vectors and allow them to be partially similar. In this context, we introduce a new metric referred to as soft agreement rate (SAR) to measure the level of agreement and provide a mathematical justification for this metric. Furthermore, we perform computational experiments to study the behavior of SAR and demonstrate that existing agreement metrics are a special case of our approach. Our method is evaluated and tested through a guessability study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by SAR is 2.64 times higher than the previous metrics. Finally, we show that our approach complements the existing agreement techniques by generating an artificial lexicon based on the most agreed properties.
Forward and Backward Multimodal NMT for Improved Monolingual and Multilingual Cross-Modal Retrieval

We explore methods to enrich the diversity of captions associated with pictures for learning improved visual-semantic embeddings (VSE) in cross-modal retrieval. In the spirit of "A picture is worth a thousand words", it would take dozens of sentences to parallel each picture's content adequately. But in fact, real-world multimodal datasets tend to provide only a few (typically, five) descriptions per image. For cross-modal retrieval, the resulting lack of diversity and coverage prevents systems from capturing the fine-grained inter-modal dependencies and intra-modal diversities in the shared VSE space. Using the fact that the encoder-decoder architectures in neural machine translation (NMT) have the capacity to enrich both monolingual and multilingual textual diversity, we propose a novel framework leveraging multimodal neural machine translation (MMT) to perform forward and backward translations based on salient visual objects to generate additional text-image pairs which enables training improved monolingual cross-modal retrieval (English-Image) and multilingual cross-modal retrieval (English-Image and German-Image) models. Experimental results show that the proposed framework can substantially and consistently improve the performance of state-of-the-art models on multiple datasets. The results also suggest that the models with multilingual VSE outperform the models with monolingual VSE.
Recommendation System for Enhancing eLearning using Deep Learning

eLearning is the use of technology in providing education via various online resources. It has the potential to change the whole education system. Recommendation Systems are schemes that aim to provide the user with the most appropriate and precise products by filtering helpful data from a wide information base. A recommender system in the eLearning environment is a software agent that tries to intelligently recommend actions to a learner by various methods. This paper is a compilation of various techniques that are used by recommender systems in eLearning platforms. Also it focuses on deep learning techniques to enhance eLearning Recommendation system.
University Financial Sustainability Assessment Models

The current state for Russian higher education in the regions is aggravated by tougher requirements to higher education and outflow of potential students from towns to bigger cities. One of the aspects for reducing the negative impact of the revealed problem is to increase financial stability through knowledge production and monitor the factors influencing the sustainable development of the university. This management mechanism is to determine the factors allowing the university to remain competitive in the conditions of uncertainty and volatility. The application of the Cobb–Douglas production function and mathematical statistics for modeling the current state of financial stability of the university is represented in the article. The dependency of the intellectual factor increase on the university’s remote education and its smart environment is revealed. The function of the rate of knowledge production is determined. The suggested mathematical model is to be used for investment strategies simulations at smart university in order to make decisions on both educational and financial strategy of university development. The obtained results and the suggested models have been tested in the Institute of Economics and Management at Togliatti State University. The findings and results can be used for smart university’s development because of the achieved possibility to assess financial stability and level of the university’s adaptation to the external environment.
A Semi-automated generation of Entity-Relationship Diagram based on Morphosyntactic Tagging from the Requirements Written in a Serbian Natural Language

An Entity-Relationship (E-R) diagram is a graphical representation of interest in a specific domain of knowledge about any information system. It is a data modeling technique that can help defining business processes and plays a central role in software engineering process. Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements generated from lengthy textual specifications. The automatic generation of different software diagram from natural language requirements such as English is highly challenging, with advancements in artificial intelligence. In this paper, a semi-automated approach for the design of E-R model from the short requirements written in a Serbian natural language using morphological analysis is presented. Presented rules are heuristics and were founded suitable for conceptual model creation in the phase of requirements analysis. At the end, examples obtained by using basic learning resources are presented.
Interactivity Proposals for Surveillance Videos

This paper introduces spatio-temporal interactivity proposals for video surveillance. Rather than focusing solely on actions performed by subjects, we explicitly include the objects that the subjects interact with. To enable interactivity proposals, we introduce the notion of interactivityness, a score that reflects the likelihood that a subject and object have an interplay. For its estimation, we propose a network containing an interactivity block and geometric encoding between subjects and objects. The network computes local interactivity likelihoods from subject and object trajectories, which we use to link intervals of high scores into spatio-temporal proposals. Experiments on an interactivity dataset with new evaluation metrics show the general benefit of interactivity proposals as well as its favorable performance compared to traditional temporal and spatio-temporal action proposals.