Reviewerly/exHarmony huggingface dataset:
A study of possible-worlds semantics of relevance-sensitive belief revision

Abstract Parikh’s relevance-sensitive axiom (P) for belief revision is open to two different interpretations, i.e. the weak and the strong version of (P), both of which are plausible depending on the context. Given that strong (P) has not received the attention it deserves, in this article, an extended examination of it is conducted. In particular, we point out interesting properties of the semantic characterization of the strong version of (P), as well as a vital feature of it that, potentially, results in a significant drop on the resources required for an implementation of a belief-revision system. Lastly, we shed light on the natural connection between global and local revision functions, via their corresponding semantic characterization, hence, a means for constructing global revision functions from local ones, and vice versa, is provided.
Toward Recommendation for Upskilling: Modeling Skill Improvement and Item Difficulty in Action Sequences

How can recommender systems help people improve their skills? As a first step toward recommendation for the upskilling of users, this paper addresses the problems of modeling the improvement of user skills and the difficulty of items in action sequences where users select items at different times. We propose a progression model that uses latent variables to learn the monotonically non-decreasing progression of user skills. Once this model is trained with the given sequence data, we leverage it to find a statistical solution to the item difficulty estimation problem, where we assume that users usually select items within their skill capacity. Experiments on five datasets (four from real domains, and one generated synthetically) revealed that (1) our model successfully captured the progression of domain-dependent skills; (2) multi-faceted item features helped to learn better models that aligned well with the ground-truth skill and difficulty levels in the synthetic dataset; (3) the learned models were practically useful to predict items and ratings in action sequences; and (4) exploiting the dependency structure of our skill model for parallel computation made the training process more efficient.
SCeVE

Authoring a collaborative, interactive Mixed Reality (MR) tour requires flexible design and development of various software modules for tasks such as managing geographically distributed participants, adaptable travel and virtual camera techniques, data logging for assessment of the incorporated techniques, as well as for evaluating the Quality of Experiences (QoE). In most cases, authors might have to develop all these software modules, instead of focusing only on the virtual environment design. In this article, we propose SCeVE, a component-based framework that supports flexible design and authoring of interactive MR tours by offering ease of access to four major design choices: (i) S ynchronization, (ii) C ollaborative e xploration, (iii) V isualization, and (iv) E valuation. Based on tour requirements, an author can access one or more components (or software libraries) of design choices via SCeVE’s API (Application Programming Interface) services , as demonstrated by the two case studies on group travel in a plant walk MR tour. SCeVE framework is innovative in the sense that it facilitates group travel in virtual environments involving “live” models of participants from geographically distributed sites. SCeVE empowers authors to focus only on the design of the required virtual environments. They can quickly build a diverse set of collaborative MR tours by utilizing the flexibility of SCeVE in terms of the various available options for traveling, rendering on multiple devices, and virtual camera viewpoint computation strategies. By providing data logs of various components, SCeVE facilitates performance evaluation of the various strategies used as well as the user experience in collaborative MR tours. SCeVE is designed in an extensible manner, allowing authors to add devices and software services as additional components.
Toward Sampling for Deep Learning Model Diagnosis

Deep learning (DL) models have achieved paradigm-changing performance in many fields with high dimensional data, such as images, audio, and text. However, the black-box nature of deep neural networks is not only a barrier to adoption in applications such as medical diagnosis, where interpretability is essential, but it also impedes diagnosis of under performing models. The task of diagnosing or explaining DL models requires the computation of additional artifacts, such as activation values and gradients. These artifacts are large in volume, and their computation, storage, and querying raise significant data management challenges. In this paper, we develop a novel data sampling technique that produces approximate but accurate results for these model debugging queries. Our sampling technique utilizes the lower dimension representation learned by the DL model and focuses on model decision boundaries for the data in this lower dimensional space.
CEC: Cluster Embedding Coding for H.264 Steganography

In this letter, we first propose a novel coding method to design content-adaptive H.264 steganography with quantized discrete cosine transform (QDCT) coefficients. Currently, the state-of-the-art steganographic methods minimize a heuristically defined distortion function using STCs and embed messages in a pixel-wise manner. When this concept is directly applied to H.264 steganographic designing, there are two factors that hinder the improvement of empirical security: interaction of embedding changes, and the block-wise characteristics of video compression. Our proposed CEC scheme performs both message embedding and video compression in the block-wise manner where a cluster of elements in a cover block are modified simultaneously, and defines a joint distortion function on the cover block to reflect the features of rate-distortion optimization. Experimental results demonstrate the effectiveness of our proposed coding method.
A Real Noise Elimination Method for CMOS Image Sensor Based on Three-Channel Convolution Neural Network

In the imaging process of CMOS image sensors, several kinds of noise will be introduced into the image. Most image denoising algorithms are developed for additive white Gaussian noise (AWGN). But the noise in the real image does not completely conform to a Gaussian distribution. The noise in the real image is complex and difficult to be modeled analysis. In this paper, a three-channel convolution neural network (TC-CNN) denoising method for real RGB image is proposed. The TC-CNN denoising method separates the real image to three images of each RGB channel. The convolution neural network is used for denoising each channel image. A new loss function and a new network architecture are proposed, this work makes the convolution neural network more suitable for denoising work. Experiment on real image datasets shows that the TC-CNN denoising method has better denoising result than the common denoising method.
Du Bois Wrapped Bar Chart: Visualizing Categorical Data with Disproportionate Values

We propose a visualization technique, Du Bois wrapped bar chart, inspired by work of W.E.B Du Bois. Du Bois wrapped bar charts enable better large-to-small bar comparison by wrapping large bars over a certain threshold. We first present two crowdsourcing experiments comparing wrapped and standard bar charts to evaluate (1) the benefit of wrapped bars in helping participants identify and compare values; (2) the characteristics of data most suitable for wrapped bars. In the first study (n=98) using real-world datasets, we find that wrapped bar charts lead to higher accuracy in identifying and estimating ratios between bars. In a follow-up study (n=190) with 13 simulated datasets, we find participants were consistently more accurate with wrapped bar charts when certain category values are disproportionate as measured by entropy and H-spread. Finally, in an in-lab study, we investigate participants' experience and strategies, leading to guidelines for when and how to use wrapped bar charts.
Towards Real-time CNN Inference from a Video Stream on a Mobile GPU (WiP Paper)

While there are several frameworks for CNN inference on mobile GPUs, they do not achieve real-time processing for the most of the CNNs that aim at reasonable accuracy since they all employ kernel-by-kernel execution model and do not effectively support INT8 quantization yet. In this paper, we reveal that mobile GPUs suffer from large kernel launch overhead unlike server GPUs, and then propose an on-device deep learning inference framework that can achieve real-time inference of CNNs on mobile GPUs by removing kernel launch overhead and by effectively exploiting INT8 quantization. We have evaluated the proposed framework with a state-of-the-art CNN based face detector (RetinaFace), and observed up to 2.01X of speedup compared to ARM Compute Library (ACL) on a commodity smartphone.
Enforcing a Cross-Organizational Workflow: An Experience Report

Today business processes often exceed organizational boundaries and the participants may not fully trust each other. In the past, this trust was guaranteed solely through legal contracts. To digitally establish trust without requiring a trusted third party, Blockchain technology together with Smart Contracts is used to ensure that involved organizations can not break their agreements. This experience report introduces a workflow enforcement method for a particular use case, the certification of the construction of industrial plants. The method comprises the modelling of the workflow, the verification of the models and the translation into Smart Contracts.
SARGoT:Smart Autonomous Robotic Goods Transporter

In the present condition industrial loading and unloading of heavy loads manually is one of the most important objectives which turn out to be quite difficult, time-consuming and risky for humans. In this modern era robots are being developed for various purposes to accomplish many tasks which seem to be too complex for humans. The advantage of using robots, for industrial purpose have been excellent in terms of speed and efficiency of doing required tasks compared to that of humans. To solve this problem we have designed a robot named SARGoT. This robot has got mainly three features and they are: Path-Tracking. Avoiding Collision, Loading and Unloading heavy industrial goods. The autonomous robot is designed to start its movement from a starting position where goods are loaded on it, then follow a black track and unload goods by itself after reaching a destination place. Infrared sensor has been mounted in the front of the robot so that the sensor can detect path by emitting and receiving signals allowing it to move in the predefined path having left and right turns to carry goods from starting position to the destination position. Arduino Uno has been used for the coding section and the control circuit of the robot has been implemented for controlling the robot's movement within the defined track. The robot has been designed in such a way that it has the ability to avoid collision with any obstacles that come in its way. Ultrasonic sensor has been used in the robot to send the signal and detect the presence of any obstacle that may appear in the robot's track and stop momentarily for certain time until the obstacle move away. Building an industrial robot with moderate Speed, good efficiency in loading and unloading purpose within a short time to ease human suffering has been the main objective of this paper.
Using Smart Technologies at the Classes of Foreign Languages at a Non-linguistic University

The article considers different approaches towards the using of smart technologies in education in general, and at the classes of foreign languages in detail. It also mentions the attitude and support given to smart technologies in education by Russian competent bodies. The elements of smart education as well as the methods appropriate to apply when learning are mentioned. The article also shows the practical experience of using information technologies in the learning process at Nizhny Novgorod State Engineering and Economic University. The opportunities of smart education for students learning foreign languages as well as the ways to use different resources are highlighted. The positive and negative aspects of using smart technologies in education are given.
Magnitude Bounded Matrix Factorisation for Recommender Systems

Low rank matrix factorisation is often used in recommender systems as a way of extracting latent features. When dealing with large and sparse datasets, traditional recommendation algorithms face the problem of acquiring large, unrestrained, fluctuating values over predictions. Imposing bounding constraints has been proven an effective solution. However, existing bounding algorithms can only deal with one pair of fixed bounds, and are very time-consuming when applied on large-scale datasets. In this paper, we propose a novel algorithm named Magnitude Bounded Matrix Factorisation (MBMF), which allows different bounds for individual users/items and performs very quickly on large scale datasets. The key idea of our algorithm is to construct a model by constraining the magnitudes of each individual user/item feature vector. By converting coordinate system with radii set as the corresponding magnitudes, MBMF allows the above constrained optimisation problem to become an unconstrained one, which can be solved by unconstrained optimisation algorithms such as the stochastic gradient descent. We also explore an acceleration approach and the choice of magnitudes are given in detail as well. Experiments on synthetic and real datasets demonstrate that in most cases the proposed MBMF is superior over all existing algorithms in terms of accuracy and time complexity.
Seam carving-based Arabic handwritten sub-word segmentation

Collecting, archiving, and analyzing handwritten documents are still indispensable acts as people continue to use paper documents. In addition, these archives are an endless source of information. Retrieving information from a handwritten document using automatic text queries as an alternative to manual search in a scanned image requires precise optical character recognition systems. However, properties of the Arabic handwriting such as cursive writing with characters overlapping and touching limit the recognition stages. Inspired by the use of the seam carving algorithm for image resizing and line segmentation, we propose a seam carving algorithm that aims to solve character overlapping and touching. We propose a new energy function for the seam carving algorithm to solve the overlapping problem. Furthermore, we designed a pre-processing stage that avoids generating seams that pass through inappropriate regions. Our proposed segmentation technique reached a 95.66% correct segmentation on IESK-ArDB and IFN/ENIT datasets. Furthermore, our method outperforms state-of-the-art techniques in resolving the overlapping issue.
Combining User-Centered Design and Lean Startup with Agile Software Development: A Case Study of Two Agile Teams

The combined use of User-Centered Design and Lean Startup with Agile Development has been pointed out by the literature as a manner to boost software development. User-Centered Design principles focus on providing tools for developers to better explore user needs and seek for a fitter solution. Lean Startup, on the other hand, supplements the triad combination by bringing the Build-Measure-Learn cycle and the concept of pivoting, either the problem understanding or the proposed solution. This paper reports on a case study of two software teams that have been undergoing the changes and impacts of such combined adoption. We investigated these teams for six months, from the moment that team members were trained on the job to grasp the essence of using the integrated approach inspired on Pivotal Labs proposal to the time they were considered mature enough to share their experiences with others within the organization. Through our in-depth study, we illustrate how this adoption promotes changes regarding to mindset, activities, practices, and techniques. We also report on the ‘team rhythm’ (or work flow) as experienced by the two teams. The paper contributes to current knowledge on the topic reporting on the changes and impacts that teams observed during the combined approach adoption.
Should my Chatbot be Register-Specific? Designing Appropriate Utterances for Tourism

Chatbots promote new forms of human-computer interaction by using natural language to achieve communicative goals. However, little is known about the impact of chatbots' linguistic choices on users' experiences. My research draws on sociolinguistic theory to investigate how a chatbot's language choices can adhere to the expected social role the agent performs within a given context; i.e., understand whether chatbots design should account for linguistic register . This research involves adapting chatbot utterances to the register of tourist assistants and evaluating to what extent this strategy meets users' expectations. Ultimately, I want to determine whether register-specific language influences users' perceptions and experiences with chatbots.
Influence of leakage on characteristics of the vacuum transport unit based on the water-ring pump

Abstract The mathematical model of the liquid pumping process using the autonomous vacuum transport unit based on the water-ring pump is proposed. Such units are used, in particular, for pumping aggressive liquids. The performance characteristics of ERSTVAK’s ELRS-2 water-ring vacuum pump were used in the calculation. The system of differential equations was solved numerically. An increase in the leakage coefficient leads to a raising the absolute pressure in the tank at the end of the first stage. As a result, the volume of liquid pumped in one cycle is noticeably reduced. Therefore, the average performance and efficiency of the unit is significantly reduced. The duration of one cycle changes slightly
Large-Scale Agile Transformation: A Case Study of Transforming Business, Development and Operations

Today, product development organizations are adopting agile methods in units outside the software development unit, such as in sales, market, legal, operations working with the customer. This broader adoption of agile methods has been labeled large-scale agile transformation and is considered a particular type of organizational change, originating in the software development units. So far, there is little research-based advice on conducting such transformations. Aiming to contribute towards providing relevant research advice on large-scale agile transformation, we apply a research-based framework for evaluating organizational agility on a product development program in a maritime service provider organization. We found that doing a large-scale agile transformation involves many significant challenges, such as having a shared understanding of the problem, getting access to users, and getting commitment to change that needs to be done. In order to overcome such challenges, we discuss the need for a holistic and integrated approach to agile transformation involving all the units linked to software development.
BaPa: A Novel Approach of Improving Load Balance in Parallel Matrix Factorization for Recommender Systems

A simplified approach to accelerate matrix factorization of big data is to parallelize it. A commonly used method is to divide the matrix into multiple non-intersecting blocks and concurrently calculate them. This operation causes the Load balance problem, which significantly impacts parallel performance and is a big concern. A general belief is that the load balance across blocks is impossible by balancing rows and columns separately. We challenge the belief by proposing an approach of “Balanced Partitioning (BaPa)”. We demonstrate under what circumstance independently balancing rows and columns can lead to the balanced intersection of rows and columns, why, and how. We formally prove the feasibility of BaPa by observing the variance of rating numbers across blocks, and empirically validate its soundness by applying it to two standard parallel matrix factorization algorithms, DSGD and CCD++. Besides, we establish a mathematical model of “Imbalance Degree” to explain further why BaPa works well. BaPa is applied to synchronous parallel matrix factorization, but as a general load balance solution, it has significant application potential.
Enhanced Performance and Privacy for TLS over TCP Fast Open

Abstract Small TCP flows make up the majority of web flows. For them, the TCP three-way handshake induces significant delay overhead. The TCP Fast Open (TFO) protocol can significantly decrease this delay via zero round-trip time (0-RTT) handshakes for all TCP handshakes that follow a full initial handshake to the same host. However, this comes at the cost of privacy limitations and also has some performance limitations. In this paper, we investigate the TFP deployment on popular websites and browsers. We found that a client revisiting a web site for the first time fails to use an abbreviated TFO handshake in 40% of all cases due to web server load-balancing using multiple IP addresses. Our analysis further reveals significant privacy problems of the protocol design and implementation. Network-based attackers and online trackers can exploit TFO to track the online activities of users. As a countermeasure, we introduce a novel protocol called TCP Fast Open Privacy (FOP). TCP FOP prevents tracking by network attackers and impedes third-party tracking, while still allowing 0-RTT handshakes as in TFO. As a proof-of-concept, we have implemented the proposed protocol for the Linux kernel and a TLS library. Our measurements indicate that TCP FOP outperforms TLS over TFO when websites are served from multiple IP addresses.
“The Ecological Person” in the Digital Era

The author of the article raises a topical question: “How will digitalization turn the humanity – towards civilization or collapse?” With a reliance on the “The Human quality” by Aurelio Peccei, a new project was justified aimed at preserving a person and educating a healthy personality. The project implementation requires at least 16–18 years. The successive system for the development of an “ecological person” focuses on the prevention of hereditary impairement and consists of six stages and is reinforced by already existing experience of several centers and institutions where innovative approaches to the development of a healthy personality are tested, starting from the embryonic period until adulthood.
Attention Based Multi-Modal Fusion Architecture for Open-Ended Video Question Answering Systems

Open-ended Video Question Answering systems is a very challenging problem with widespread applications in real life. Existing systems tend to focus on single word video question answering system, which cannot be easily extended to develop. In this paper, we propose using an architecture, popularly used for video captioning systems to solve the problem of open-ended video based question answering systems. For generating good answers, the model is required to focus on each frame separately as well as understand how to link information from different frames to generate the answer. The model also needs to keep in mind the different modalities and adapt itself accordingly while processing the videos as well as the questions. We propose an attention based multimodal fusion architecture for Video Question Answering (AMF-VQA) that uses attention mechanism at every time to output a word. Such kind of mechanism allows the model to focus on different frames as well as focus on different modalities while outputting every single word. The proposed model is very flexible were we can just add other modalities such as audio features, captions, etc. to the existing model and fine-tune the model to get improve results if these new features are available.