Reviewerly/exHarmony huggingface dataset:
Initialization Free Segmentation of Complex Shaped Objects

This paper proposes a new, initialization free segmentation method based on dynamic active agents interacting with the from the image edges. The agents are able to segment complex shaped single and multiple objects characterized by deep and narrow concavities (1 pixel wide) a low contrast and a high level of noise. Numerical tests against conventional methods show an advantage of the proposed algorithm.
Visual Sentiment Analysis with Local Object Regions Attention

Human action recognition has gained popularity because of its worldwide applications such as video surveillance, video retrieval and human–computer interaction. This paper provides a comprehensive overview of notable advances made by deep neural networks in this field. Firstly, the basic conception of action recognition and its common applications were introduced. Secondly, action recognition was categorized as action classification and action detection according to its respective research goals. And various deep learning frameworks for recognition tasks were discussed in detail and the most challenging datasets and taxonomies were briefly reviewed. Finally, the limitations of the state-of-the-art and promising directions of the research were briefly outlined.
The university e-libraries: The foreign experience

The experience of foreign digital libraries of the universities of Marburg (Germany), Chalmers (Sweden) and Graz (Austria) is discussed. The structure of the libraries being reviewed comprises six key elements of DELOS conceptual model, i. e. content, users, functionality, quality, policy, and architecture. The Marburg University Library cooperates with many German libraries and possesses vast collections and resources of various types, both it owns and shares with other libraries. The e-library of Chalmers University of Technology is based on the service-oriented Central Knowledge Database comprising paper and digital collections of full texts and metadata being administered with data import-export applications on the Discovery platform. The Graz Library is the bibliographic and information center; it supports scientific research and learning with literature in printed and digital formats, and preserves the cultural heritage in natural sciences and technology. Based on the review of the mentioned digital libraries, the author concludes that Russian libraries must study the foreign experience to develop the model of an inter-university digital library.
Partially Observed Markov Decision Processes: From Filtering to Controlled Sensing [Bookshelf]

As stated in the book’s preface, the author aimed to write a textbook that gives an accessible description of POMDPs, with emphasis on filtering and controlled sensing. The four parts of this book certainly achieve this objective. A nice feature of the text is that it goes into substantial depth in several areas that have not appeared previously in other works. These include structural results for POMDPs, controlled sensing and weak derivatives for reinforcement learning of MDPs, and quickest detection with social learning that involves the interaction of local and global decision makers. The book also has a wealth of examples that motivate POMDPs, including social networks, behavioral economics (risk averse social learning), adaptive radars, and optimal search. The supplementary material contains numerous problem sets and case studies, making this book an excellent text for a graduate-level course. The book is pitched at a similar mathematical level as the classic [10]. Since the text focuses only on discrete-time filtering and stochastic control (POMDPs) where the state space is the unit simplex (therefore, compact), measure-theoretic aspects [11] are not required. Partially Observed Markov Decision Processes: From Filtering To Controlled Sensing is a valuable contribution to the literature on stochastic decision-making/control and stochastic optimization, and I highly recommend it to a diverse audience, including students and researchers in electrical engineering, applied mathematics, computer science, and operations research.
Concept to code

Deep Learning has shown significant results in Computer Vision, Natural Language Processing, Speech and recommender systems. Promising techniques include Embedding, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and its variant Long Short-Term Memory (LSTM and Bi-directional LSTMs), Attention, Autoencoders, Generative Adversarial Networks (GAN) and Bidirectional Encoder Representations from Transformer (BERT).
Research on robot kinematic calibration based on hand-eye vision and distance error model

Based on the Modified Denavit-Hartenberg (MDH) kinematic model of industrial robot, the homogeneous transformation error model between the coordinates of each joint is established; The distance error model is combined with the established homogeneous transformation error model to obtain the kinematic calibration model based on distance error avoiding the transformation of the coordinate system; Then, the position of the robot's end point is measured by using tahe visual measurement method, and the calibration model is introduced to simplify the calculation and improve the accuracy of the measurement; Finally, the ABB1410 robot is used as the experimental object. Through the compensation and correction of each joint parameter, the experimental results prove that the positioning accuracy and distance accuracy of the robot can be greatly improved, and it can be applied to the experimental industrial field.
Maturity in IT Service Management: A Longitudinal Study

The Information Technology (IT) market represents USD 2,23 trillion worldwide. Of these, about 50% refers to software and IT services. Productivity and quality are fundamental for the growth of this sector. The MR-MPS-SV model represents a way to lead process improvement in the IT services segment, allowing a gradual evolution of the organization through levels of maturity. However, no studies were found that indicate actual improvements with its use. The objective of this study was to understand the benefits achieved by companies using MR-MPS-SV as a reference and to identify the main difficulties faced during the implementation and maintenance of the improvement program. The study was based on a survey conducted on the officially assessed companies. The results point to significant improvements in productivity, quality of service, the organization’s image, and profitability.
EmAP-ML: A Protocol of Emotions and Behaviors Annotation for Machine Learning Labels

The detection of students’ emotions in computer-based learning environments is a complex task. Although emotions can be detected from sensors, a less intrusive method is to train supervised machine learning algorithms for the emotions prediction based on the log of students’ actions on the system. For these algorithms to work as expected, they need to be trained with a large amount of reliable ground truth labels. Generally, labels are generated by students themselves or by coders monitoring students, watching videos from the students, or reviewing logs of students’ actions. Younger learners (i.e., children) are unable to label their emotions properly. Still, it is difficult for a coder to identify students’ emotions only from their face since the emotional facial expression is generally subtle in a learning setting. This article describes EmAP-ML (Emotions Annotation Protocol for Machine Learning), a protocol for coders to annotate students’ learning emotions and behaviors based on video records, which contains facial expressions, ambient audio, and computer screen. The screen and ambient audio records allow coders to infer students’ appraisal (an evaluation that elicits an emotion) to identify emotions even when the facial expression is subtle. This protocol was evaluated by two coders who annotated videos obtained from 55 students while using a tutoring system, having achieved an agreement coefficient of 0.62, measured through Cohen’s Kappa statistics.
Transformer fault diagnosis based on massive vibration data

Transformer is one of the key equipment in the power industry and its safe operation is very important to ensure the reliability of power network. Therefore, transformer fault diagnosis has important engineering value and scientific significance. The fault diagnosis method based on transformer vibration characteristics has the advantages of strong anti-interference and good stability. In order to determine fault types based on the vibration signal on the surface of the transformer tank, this paper combines K-means clustering and BP neural network, and adopts distributed computing framework to train the model by massive data stored in distributed storage system. The results revealed that the model trained by this method can effectively diagnosis transformer faults.
An Approach of Documents Indexing Using Summarization

Document indexing is an active domain, which is interesting a lot of researchers. Generally, it is used in the information retrieval systems. Document indexing encompasses a set of approaches that can be applied to index a document using a corpus. This treatment has several advantages, like accelerating the research process, finding the pertinent contains related to a query, reducing storage space, etc. The use of the entire document in the indexing process affects several parameters, such as indexing time, research time, storage space of treatment, etc. The focus of this chapter is to improve all parameters (cited above) related to the indexing process by proposing a new indexing approach. The goal of proposed approach is to use a summarization to minimize the size of documents without affecting the meaning.
Smart Tourist Guide (Touristo)

Individuals travelling, frequently think that it’s hard to seek places and find nearby amenities, and this issue even looks greater when we cannot talk the neighbourhood dialect. Additionally while travelling in groups, individuals like exploring different places and may get lost, which again wind up troublesome for their companions individuals to find different individuals from the group. Touristo is a project about building up an android-based application in the field of travel and tourism. Android is a Google-developed programming language for mobiles and tablets. Additionally, Firebase is a real-time database which is utilized for information stockpiling and handling. Hence using the features provided, we intend to develop the application. By examining the above issues and different others, we are taking this venture with the goal to develop such application to overcome above issues and serve clients better.
Ethical Implications of Cooperation and Safety vis-à-vis Cyber Security in Africa

Discourses on the threats to cyber security in today's digital society have revealed that cyberspace has become an arena of complex national security concern. This lends credence to the fact that many countries, especially in Africa, need to urgently scale up their efforts to effectively secure the Internet and ICT infrastructures. Drawing upon extensive literature on cyber-security challenges, this chapter examines the phenomenon of cybercrime using Ronald Rogers' “protection motivation theory”. The study employs qualitative analysis of the current cyber-security landscape in Africa. Findings posit that with the risk and vulnerability of the cyberspace, cyber security in Africa poses a number of unique challenges which predicate a coordinated response for security and safety engagement. The study suggests collaborative measures to counter cybercrime through investigation, prosecution, and sharing information.
SpiderHand

Although encounters with spiders are commonplace for people almost everywhere in the world, fear of spiders is one of the most frequently diagnosed phobias and immediate contact is widely perceived as unfavourable. We present a system for indirect, quasi-tangible interaction with spiders, to be applied in an exhibition context - a robotic arm, steered through gestural input, which mimics user's actions and enables indirect physical interaction with the spider. The proof-of-concept prototype has been tested with N=15 users in museum-like environment. The concept of implementing an interactive modality to the exhibition was commented as an asset in terms amusement and education aiding, whilst being also a promising endeavour towards phobia-overcoming exercise.
Motion Recognition Based on Angle-reg Network

Motion recognition of moving target has become one of the hotspots of object detection. Recognition of moving target based on vision is one of the most important methods to solve problems about mention recognition. However, it is complex to achieve real-time recognition for traditional moving target recognition, which needs human assistance, and more external conditions. In order to solve the problems above, we propose a new method for moving object attitude recognition based on CNN. The method has a simple network structure, and can complete the tasks of classification and recognition of attitude angle through one detection. The advantages of our method such as light, high precision and fastness ensure that our method is excellent for intelligent application of mobile terminals.
Research on Chatbots for Open Domain

With the rise and development of natural language processing technology, the question and answer system as a branch of natural language processing has also progressed rapidly [1]. With the emergence of chat robots, it has become a new direction for the research of question and answer system. This paper introduces an chatbot for open domains.
A Fault Monitoring Method Based on Transfer Subspace Learning

Fault monitoring is an important measure to ensure the safety, stability and automatic control of production process. The data-driven methods have been intensively researched and arisen as an active research area in process monitoring. But the current challenge is how to accumulate operational experience in production process and use it effectively. We propose a novel fault monitoring method based on transfer subspace learning, which maps data from both old process (labeled data) and new process (unlabeled data) to a common space where all data is of the same distribution and hence enables the new process to learn knowledge from an old one. Moreover, the proposed method uses a new strategy of low rank and sparse processing to retain the global and local characteristics of data. Finally, we have carried out the simulation analysis of fault monitoring for the Tennessee - Eastman process (Tennessee Eastman TE) and the grinding and grading process respectively, the simulation results verify the effectiveness and practicability of the proposed method.
Tangible urban models

Physical models are a key component in the architectural process and play an important role in understanding material and space relationships. We present Tangible Urban Models, an approach for leveraging the use of conductive material for 3D printed architectural prototypes. This enables non-interactive objects, such as buildings, to become tangible without the need to attach additional components. We combine this capability with an augmented reality (AR) app and explore the use of gestures for interacting with digital and physical content. The multi-material 3D printed buildings consist of thin layers of white plastic filament and a conductive wireframe to enable touch gestures. In this way, we enable a two-way interaction either with the physical model or with the mobile AR interface.
JacquardToolkit

New user interfaces afford innovations in the user experience of computers- the interactive textile approach posed by Jacquard is another step in this evolution[1, 5]. Although it demonstrates four gestures via the Levi's Jacquard Jacket [4], we experiment with potential applications to explore new forms of user inputs. We have built a toolkit that enables developers full access to the Jacquard technology and custom gesture creation [2, 3]. We subsequently used this to develop a custom Force Touch gesture, which is now integrated into the aforementioned iOS toolkit such that developers may use it to implement original and new jacket functionality into their own applications. The playground application helps developers understand how to effectively utilize the library. In parallel, we evaluated the Jacquard hardware by conducting a detailed tear down of the Jacket cuff and tag. We mapped how such sensing technology can be removed from the jacket and applied to other textiles with conductive threads. Moreover, our findings motivate future testing on other garments and accessories as we isolated the Jacquard swatch from the cuff. In order to evaluate the intuitiveness of the jacket gestures and test learnability of the new custom gestures, we designed a user assessment application with the toolkit to conduct a user study with 25 subjects. Results showed that both Brush In and Brush Out had the easiest learning curve and were perceived as most intuitive. Brush In (48%) stood out as the most preferred gesture followed by Double Tap (20%). Although the custom gesture, Force Touch, had the lowest perceived intuitiveness, it serves as the first key step towards a future of rapidly prototyped and consumer-ready custom gestures.
Changes Are Similar: Measuring Similarity of Pull Requests That Change the Same Code in GitHub

Pull-based development is widely used in globally collaborative platforms, such as GitHub and BitBucket. A pull request is a set of changes to existing source code in a project. A developer submits a pull request and tends to update the source code. Due to the parallel mechanism, several developers may submit multiple pull requests to change the same lines of code. This fact results in the conflict between changes, which makes the project manager difficult to decide which pull request should be merged. In this paper, we conducted a preliminary study on measuring the similarity of pull requests that aim to change the same code in GitHub. We proposed two methods, i.e., the cosine and the doc2vec, to quantify the structural similarity and the semantic similarity between pull requests and evaluated the similarity on four widely-studied open source Java projects. Our study shows that there indeed exists high similarity between competing pull requests and the similarity among projects diversifies. This complicates the merging decision by project managers.
Aligning, Interoperating, and Co-executing Air Traffic Control Rules Across PSOA RuleML and IDP

This paper studies Knowledge Bases (KBs) in PSOA RuleML and IDP, aligning, interoperating, and co-executing them for a use case of Air Traffic Control (ATC) regulations. We focus on the common core of facts and rules in both languages, explaining basic language features. The used knowledge sources are regulations specified in (legal) English, and an aircraft data schema. In the modeling process, inconsistencies in both sources were discovered. We present the discovery process utilizing both specification languages, and highlight their unique features. We introduce three extensions to this ATC KB core: (1) While the current PSOA RuleML does not distinguish the ontology separately from the instance level, IDP does. Hence, we specify a vocabulary-enriched version of ATC KB in IDP for knowledge validation. (2) While the current IDP uses relational modeling, PSOA additionally supports graph modeling. Hence, we specify a relationally interoperable graph version of ATC KB in PSOA. (3) The KB is extended to include optimization criteria to allow the determination of an optimal sequence of more than two aircraft.
DIFFERENT TRAILS: ON THE MULTIPLE GENETIC ROOTS DETERMINING A DISTRIBUTED COMPOSITIONAL PROJECT

Abstract Although it is a mistake to believe that the material traces left by a composer's practice can help us to properly reconstruct the creative process, compositional sketches may throw light on it. Nevertheless many such accounts take their respective case studies as isolated objects that are, unfortunately, sometimes decontextualised from the whole record of their authors. Ethnography potentially offers a better methodology and auto-ethnographic accounts, third party observations, and mixtures of these approaches have already been applied to contemporary music. This article aims to combine both these approaches in a discussion of a recent compositional project at IRCAM, carried out by a team of three people. I consider how the previous artistic experiences and achievements of these people, jointly and separately, have had a substantial impact on their shared project. In addition, as the case study had a scientific underpinning, I comment on the cognitive bridge that the composer had to build between scientific and musical conceptions.
Aggregating Rich Deep Semantic Features for Fine-Grained Place Classification

This paper proposes a method that aggregates rich deep semantic features for fine-grained place classification. As is known to all, the category of images depends on the objects and text as well as the various semantic regions, hierarchical structure, and spatial layout. However, most recently designed fine-grained classification systems ignored this, the complex multi-level semantic structure of images associated with fine-grained classes has not yet been well explored. Therefore, in this work, our approach composed of two modules: Content Estimator (CNE) and Context Estimator (CXE). CNE generates deep content features by encoding global visual cues of images. CXE obtains rich context features of images, and it consists of three children Estimator: Text Context Estimator (TCE), Object Context Estimator (OCE), and Scene Context Estimator (SCE). When inputting an image into CXE, TCE encodes text cues to identify word-level semantic information, OCE extracts high-dimensional feature then maps it to object semantic information, SCE gains hierarchical structure and spatial layout information by recognizing scene cues. To aggregate rich deep semantic features, we fuse the information about CNE and CXE for fine-grained classification. To the best of our knowledge, this is the first work to leverage the text information from an arbitrary-oriented scene text detector for extracting context information. Moreover, our method explores the fusion of semantic features and demonstrates scene features to give more complementary information with the other cues. Furthermore, the proposed approach achieves state-of-the-art performance on a fine-grained classification dataset, 84.3% on Con-Text.
Using vibrations from a SmartRing as an out-of-band channel for sharing secret keys

With the rapid growth in the number of Internet of Things (IoT) devices with wireless communication capabilities, and sensitive information collection capabilities, it is becoming increasingly necessary to ensure that these devices communicate securely with only authorized devices. A major requirement of this secure communication is to ensure that both the devices share a secret, which can be used for secure pairing and encrypted communication. Manually imparting this secret to these devices becomes an unnecessary overhead, especially when the device interaction is transient. In this work, we empirically investigate the possibility of using an out-of-band communication channel - vibration, generated by a custom smartRing - to share a secret with a compatible IoT device. Through a user study with 12 participants we show that in the best case we can exchange 85.9% messages successfully. Our technique demonstrates the possibility of sharing messages accurately, quickly and securely as compared to several existing techniques.