Reviewerly/exHarmony huggingface dataset:
Automatic Bengali Document Categorization Based on Deep Convolution Nets

Automatic document categorization has gained much attention by natural language processing (NLP) researches due to the enormous availability of text resources in digital form in recent years. It is the process of assigning a document into one or more categories that help the document manipulate and sort quickly. An efficient information processing system is required due to the rapid growth of Bengali text contents in digital form for searching, organizing, and retrieving tasks. In this paper, we proposed a framework for classifying Bengali text documents using deep convolution nets. The proposed framework consists of word embedding and document classifier models. Experiments with more than 1 million Bengali text documents reveals that the proposed system worthy of classifying documents with 94.96% accuracy.
Active Learning Through Multi-Standard Optimization

Active learning selects the most critical instances and obtains their labels through interaction with an oracle. Selecting either informative or representative unlabeled instances may result in sampling bias or cluster dependency. In this paper, we propose a multi-standard optimization active learning (MSAL) algorithm that considers the informativeness, representativeness, and diversity of instances. Informativeness is measured by the soft-max predicted entropy, whereas representativeness is measured by the probability density function obtained by a non-parametric estimation. The multiplex of the two is used as an optimization objective to reduce model uncertainty and explore the distribution of unlabeled data. Diversity is measured by the difference between the selected critical instances. This is used as a constraint to prevent the selection of instances that are too similar. Learning experiments were performed with 12 datasets from various domains. The results of significance tests verify the effectiveness of MSAL and its superiority over state-of-the-art active learning algorithms.
Model Predictive Control and Trajectory Optimization of Large Vehicle-Manipulators

In this paper, a model predictive control (MPC) is developed for on- and off-road mid-sized heavy duty vehicle-manipulator systems with a hydraulic working arm. The proposed concept for the control model is also new in the sense of working only within a local reference coordinate-system relative to the reference trajectory (so-called Frenet-System), The control model only needs the errors to the reference trajectory. In contrast to other state-of-the-art approaches, there is no global localization method necessary. The control model is kept as simple as possible, to allow real-time motion prediction of the real system. For this reason, a kinematic model is used in the MPC which consists of a bicycle model and a planar robotic arm with two control variables. The dynamics of the overall system are considered as optimization constraints, assuming that the optimized system inputs and states are kinetically and dynamically feasible. Through this control method, the dual-trajectories are also optimized and they provide smooth motions for the overall system. The underlying control of the robotic arm is realized with a proportional-integral-derivative (PID) controller with feedback linearisation and gravity compensation. The control algorithm is tested and validated in a MATLAB/Simulink simulation environment.
Analysis of Women Safety in Indian Cities Using Machine Learning on Tweets

Women and girls have been experiencing a lot of violence and harassment in public places in various cities starting from stalking and leading to sexual harassment or sexual assault. This research paper basically focuses on the role of social media in promoting the safety of women in Indian cities with special reference to the role of social media websites and applications including Twitter platform Facebook and Instagram. This paper also focuses on how a sense of responsibility on part of Indian society can be developed the common Indian people so that we should focus on the safety of women surrounding them. Tweets on Twitter which usually contains images and text and also written messages and quotes which focus on the safety of women in Indian cities can be used to read a message amongst the Indian Youth Culture and educate people to take strict action and punish those who harass the women. Twitter and other Twitter handles which include hash tag messages that are widely spread across the whole globe sir as a platform for women to express their views about how they feel while we go out for work or travel in a public transport and what is the state of their mind when they are surrounded by unknown men and whether these women feel safe or not?
Modern Architecture Style Transfer for Ruin or Old Buildings

In this work, we focus on building style transfer, which transforms ruin or old buildings to modern architecture. Inspired by Gaty's and Goodfellow's style transfer and generative adversarial network (GAN), we use CycleGAN to conquer this type of problem. As we know, image style transfer usually generated unexpected artifacts. To avoid the artifacts and generate better images, we add so called “perception loss” into the network, which is the feature loss extracted by VGG pre-trained model. In the part of “cycle” structure, we adjust cycle loss by changing the ratio of weighting parameters. Finally, we collect images of both ruin (or old) and modern architecture from websites and use unsupervised learning to train the model. The experimental results show our proposed method indeed realize the modern architecture style transfer for ruin or old buildings.
Picture Passwords in Mixed Reality

We present HoloPass, a mixed reality application for the HoloLens wearable device, which allows users to perform user authentication tasks through gesture-based interaction. In particular, this paper reports the implementation of picture passwords for mixed reality environments, and highlights the development procedure, lessons learned from common design and development issues, and how they were addressed. It further reports a between-subjects study (N=30) which compared usability, security, and likeability aspects of picture passwords in mixed reality vs. traditional desktop contexts aiming to investigate and reason on the viability of picture passwords as an alternative user authentication approach for mixed reality. This work can be of value for enhancing and driving future implementations of picture passwords in mixed reality since initial results are promising towards following such a research line.
Minimizing cost by reducing scaling operations in distributed stream processing

Elastic distributed stream processing systems are able to dynamically adapt to changes in the workload. Often, these systems react to the rate of incoming data, or to the level of resource utilization, by scaling up or down. The goal is to optimize the system's resource usage, thereby reducing its operational cost. However, such scaling operations consume resources on their own, introducing a certain overhead of resource usage, and therefore cost, for every scaling operation. In addition, migrations caused by scaling operations inevitably lead to brief processing gaps. Therefore, an excessive number of scaling operations should be avoided. We approach this problem by preventing unnecessary scaling operations and over-compensating reactions to short-term changes in the workload. This allows to maintain elasticity, while also minimizing the incurred overhead cost of scaling operations. To achieve this, we use advanced filtering techniques from the field of signal processing to pre-process raw system measurements, thus mitigating superfluous scaling operations. We perform a real-world testbed evaluation verifying the effects, and provide a break-even cost analysis to show the economic feasibility of our approach.
Transferring learning from multi-person tracking to person re-identification

Learning to discriminate, whether two person-images correspond to the same person or not, is a daunting challenge when only two images per person are available. This task is called single-shot person re-identification (re-id) and it assumes that each one of the two available images was captured fro m a different camera view entailing variations in pose, resolution, scale, illumination and background. Addressing this task through supervised training of a deep convolutional neural network is susceptible to model overfitting due to the critical lack of enough labelled data. This paper proposes to exploit the transference of learning previously acquired from a multi-object-tracking (MOT) domain. In this context, a unique deep triplet architecture has been trained on both domains. Six different levels of transfer learning have been implemented and evaluated, proving that the transference of leaning from a different domain remarkably increases the re-id performance. Experimental results validate accuracy and robustness of the proposed method as comparable to other state-of-the-art techniques. These results also confirm that, despite the data problem, deep learning is also applicable to the single-shot re-id task.
Face Aging on Realistic Photos by Generative Adversarial Networks

Inspired by Gatys and Goodfellow's style transfer and generative adversarial network (GAN), we use CycleGAN to achieve age progression. CycleGAN is good at generating fake images and also competitive with other GANs. It not only generates fake images but also increases the number of images in our database. We know the better database, the better performance of the model. We also try a deeper generator to transform youth photos to elder photos. To avoid the artifacts, we not only adopt the idea of “cycle” but also add a new loss which can tell the discriminator not too strict to generated images. Finally, we collect images of young and old people from the Internet and use unsupervised learning to train our model. The experimental results show our proposed method is indeed improved and better than before.
Disruptive Approaches for Subtitling in Immersive Environments

The Immersive Accessibility Project (ImAc) explores how accessibility services can be integrated with 360o video as well as new methods for enabling universal access to immersive content. ImAc is focused on inclusivity and addresses the needs of all users, including those with sensory or learning disabilities, of all ages and considers language and user preferences. The project focuses on moving away from the constraints of existing technologies and explores new methods for creating a personal experience for each consumer. It is not good enough to simply retrofit subtitles into immersive content: this paper attempts to disrupt the industry with new and often controversial methods.
MMSIA

Cloud computing is one of the most advanced technologies in information technology, a convergence of many achievements in research and development and application of new technologies. Cloud computing has also helped to reduce the cost of small and medium enterprises based on cloud provider services. As cloud computing evolves rapidly, researching optimizations such as task execution time, completion time, responce time, and virtual machine resources (VMs) are tremendous challenges. This article proposes an MMSIA algorithm to improve the Max-Min scheduling algorithm, which improves the completion time of the requests by using the "learned learning" machine learning, by clustering size of requests and clustering utilization percent of VMs. The algorithm then assigns the largest cluster requests to the VM with the least utilization percent, which is repeated when the request list is empty. In particular, the MMSIA algorithm has improved the completion time. The simulation results show that the proposed MMSIA algorithm has improved the completion time compared to the three algorithms: Max-Min, Min-Min and Roud Robin.
Let Us Say What We Mean

Recent years have seen a dramatic increase in HCI research on the use of technology in spiritual practices and environments. Some of these works cover spiritual/transcendent experiences associated with these contexts, but strikingly few of them describe in any way the experiences they studied or aimed to support, let alone give definitions of the terms they use for those experiences. Even fewer papers cite any literature on the relevant experiences. We have to ask: How do the authors understand the experiences their work is aiming to observe, invite, or support? How do they know when and whether they have observed, invited, or supported the kinds of experiences they target? How do they know what they are studying? This paper discusses the presence and absence of definitions of terms for spiritual/transcendent experiences in HCI research, and of citations of relevant literature. It speculates about possible reasons for the oversight, proposes some definitions aimed at filling the gap, and suggests an approach to operationalizing some of the proposed definitions.
Designing Performative, Gamified Cultural Experiences for Groups

Envisioning cultural institutions as "social places", where the visitors can "create, share, and connect to each other around the cultural heritage content" (as defined by Nina Simon), we explore how to design cultural group experiences that combine personal moments of reflection to social encounter. In previous work we proposed a storytelling game where visitors conceive and narrate stories about the artworks, orchestrating group interactions according to the game phases. Playtesting with physical materials revealed promising potential, cultivating theatrical narrations, lively discussions and fruitful social interactions. Here we present a mobile-based, group experience design for gamified cultural visits with performative elements, leveraging the trajectories HCI framework. We highlight the key role, interface and space transitions encountered in the experience and we elaborate on the adopted design choices, while we reflect on main challenges and future directions.
Multilingual Open Information Extraction: Challenges and Opportunities

The number of documents published on the Web in languages other than English grows every year. As a consequence, the need to extract useful information from different languages increases, highlighting the importance of research into Open Information Extraction (OIE) techniques. Different OIE methods have dealt with features from a unique language; however, few approaches tackle multilingual aspects. In those approaches, multilingualism is restricted to processing text in different languages, rather than exploring cross-linguistic resources, which results in low precision due to the use of general rules. Multilingual methods have been applied to numerous problems in Natural Language Processing, achieving satisfactory results and demonstrating that knowledge acquisition for a language can be transferred to other languages to improve the quality of the facts extracted. We argue that a multilingual approach can enhance OIE methods as it is ideal to evaluate and compare OIE systems, and therefore can be applied to the collected facts. In this work, we discuss how the transfer knowledge between languages can increase acquisition from multilingual approaches. We provide a roadmap of the Multilingual Open IE area concerning state of the art studies. Additionally, we evaluate the transfer of knowledge to improve the quality of the facts extracted in each language. Moreover, we discuss the importance of a parallel corpus to evaluate and compare multilingual systems.
High Precision Angular Measurement via Dual Imaging Detectors

This paper proposes a novel error compensation arithmetic based on dual imaging detectors which can eliminate errors caused by grating eccentricity. First, we built an error model and analyzed the error change current. Furthermore, we deduced the error compensation arithmetic and established the dual-image-detector-based method. Finally, we conducted a series of experiments to test the performance of this arithmetic. The test results showed that we achieved a standard error deviation of 6.33" and grating diameter of only 38 mm. This arithmetic outperforms the single detector and is far superior to the traditional method. The results presented here may provide a theoretical foundation for further research on angle-displacement measurement.
Integrated and secure web‐based examination management system

Abstract A web‐based examination management system that is implemented in‐house is discussed in this paper. It is designed based on the Java enterprise edition three‐tier architecture. It also allows defining and setting up exams according to a flexible tree‐based exams structure. Moreover, it integrates a rich text editor for composing exams suitable for different engineering and language disciplines. In addition, it automates the scheduling, grading, and reporting processes to relieve instructors from such cumbersome tasks. Furthermore, its capabilities and integration with different databases enable it to offer several security schemes that support strong multifactor authentication and authorization, detect impersonation, and prevent cheating. Besides, it provides an easy to use and informative wizard that enables students to take exams. Not to mention, the deployment results illustrate that the system has been successfully used to organize online exams in several semesters over the past 3 academic years. Finally, the conducted user surveys responses assert that the system is also user‐friendly, cutting edge, capable, reliable, fast, and highly available.
An Integrated Adaptive Kalman Filter for High-Speed UAVs

In order to solve the problems of filtering divergence and low accuracy in Kalman filter (KF) applications in a high-speed unmanned aerial vehicle (UAV), this paper proposed a new method of integrated robust adaptive Kalman filter: strong adaptive Kalman filter (SAKF). The simulation of two high-dynamic conditions and a practical experiment were designed to verify the new multi-sensor data fusion algorithm. Then the performance of the Sage–Husa adaptive Kalman filter (SHAKF), strong tracking filter (STF), H∞ filter and SAKF were compared. The results of the simulation and practical experiments show that the SAKF can automatically select its filtering process under different conditions, according to an anomaly criterion. SAKF combines the advantages of SHAKF, H∞ filter and STF, and has the characteristics of high accuracy, robustness and good tracking skill. The research has proved that SAKF is more appropriate in high-speed UAV navigation than single filter algorithms.
Design-Level and Code-Level Security Analysis of IoT Devices

The Internet of Things (IoT) is playing an important role in different aspects of our lives. Smart grids, smart cars, and medical devices all incorporate IoT devices as key components. The ubiquity and criticality of these devices make them an attractive target for attackers. Therefore, we need techniques to analyze their security so that we can address their potential vulnerabilities. IoT devices, unlike remote servers, are user-facing and, therefore, an attacker may interact with them more extensively, e.g., via physical access. Existing techniques for analyzing security of IoT devices either rely on a pre-defined set of attacks and, therefore, have limited effect or do not consider the specific capabilities the attackers have against IoT devices. Security analysis techniques may operate at the design-level, leveraging abstraction to avoid state-space explosion, or at the code-level for ensuring accuracy. In this article, we introduce two techniques, one at the design-level, and the other at the code-level, to analyze security of IoT devices, and compare their effectiveness. The former technique uses model checking, while the latter uses symbolic execution, to find attacks based on the attacker’s capabilities. We evaluate our techniques on an open source smart meter. We find that our code-level analysis technique is able to find three times more attacks and complete the analysis in half the time, compared to the design-level analysis technique, with no false positives.
Simulated Annealing-based Ontology Matching

Ontology alignment is a fundamental task to reconcile the heterogeneity among various information systems using distinct information sources. The evolutionary algorithms (EAs) have been already considered as the primary strategy to develop an ontology alignment system. However, such systems have two significant drawbacks: they either need a ground truth that is often unavailable, or they utilize the population-based EAs in a way that they require massive computation and memory. This article presents a new ontology alignment system, called SANOM, which uses the well-known simulated annealing as the principal technique to find the mappings between two given ontologies while no ground truth is available. In contrast to population-based EAs, the simulated annealing need not generate populations, which makes it significantly swift and memory-efficient for the ontology alignment problem. This article models the ontology alignment problem as optimizing the fitness of a state whose optimum is obtained by using the simulated annealing. A complex fitness function is developed that takes advantage of various similarity metrics including string, linguistic, and structural similarities. A randomized warm initialization is specially tailored for the simulated annealing to expedite its convergence. The experiments illustrate that SANOM is competitive with the state-of-the-art and is significantly superior to other EA-based systems.
An efficient blind filter: Location privacy protection and the access control in FinTech

Financial technology(FinTech) is a new item in the financial industry, which has become a popular item that describes novel technologies adopted by the financial service institutions. This term covers a large range of techniques, from data security to financial service. Specially, user privacy protection is generally considered one of the most significant aspects in the financial security domain and preserving data carrying privacy is a critical task in producing a privacy protection strategy, e.g., one of the crucial issues in mobile finance is to ensure the legitimate mobile device users can efficiently search inclusive information from servers without leaking the user privacy. More precisely, more and more mobile finance APP(e.g., AliPay, China Unionpay Quick Pass) has the auxiliary tool or third-party services function that enable users make a location-based services(LBS) query, while the LBS usually carry users' location privacy and that data of service providers should be accessed by legitimate users. In order to address this problem, in this paper, we propose a privacy-preserving LBS framework which supports the query area is a square area based on the user's location, and achieves fine-grained access control on the financial service provider data, user's privacy(especially location privacy), confidentiality of the service provider data, and accurate query result. More precisely, our framework also uses redundant point-of-interesting(POI) records to protect privacy against LBS provider(LBSP), but employs a semi-trusted third party(called proxy) to filter out redundant POI records. We propose a novel blind filter protocol based on comparable attribute-based encryption(CABE) and "transformation" technique, which can filter out the encrypted POI records under the condition that both LBSP and proxy without knowing the user's location information. In comparison with existing solutions, our framework not only realize access control on service provider data innately, but also incurs lower communication and computation overhead on the user side. The analysis and the experiments indicate that our framework is secure and efficient for mobile devices in terms of computation and the communication overhead.