Reviewerly/exHarmony huggingface dataset:
Continual General Chunking Problem and SyncMap

Humans possess an inherent ability to chunk sequences into their constituent parts. In fact, this ability is thought to bootstrap language skills and learning of image patterns which might be a key to a more animal-like type of intelligence. Here, we propose a continual generalization of the chunking problem (an unsupervised problem), encompassing fixed and probabilistic chunks, discovery of temporal and causal structures and their continual variations. Additionally, we propose an algorithm called SyncMap that can learn and adapt to changes in the problem by creating a dynamic map which preserves the correlation between variables. Results of SyncMap suggest that the proposed algorithm learn near optimal solutions, despite the presence of many types of structures and their continual variation. When compared to Word2vec, PARSER and MRIL, SyncMap surpasses or ties with the best algorithm on 66% of the scenarios while being the second best in the remaining 34%. SyncMap's model-free simple dynamics and the absence of loss functions reveal that, perhaps surprisingly, much can be done with self-organization alone.
A Wireless Hand Grip Device for Motion and Force Analysis

A prototype portable device that allows for simultaneous hand and fingers motion and precise force measurements has been. Wireless microelectromechanical systems based on inertial and force sensors are suitable for tracking bodily measurements. In particular, they can be used for hand interaction with computer applications. Our interest is to design a multimodal wireless hand grip device that measures and evaluates this activity for ludic or medical rehabilitation purposes. The accuracy and reliability of the proposed device has been evaluated against two different commercial dynamometers (Takei model 5101 TKK, Constant 14192-709E). We introduce a testing application to provide visual feedback of all device signals. The combination of interaction forces and movements makes it possible to simulate the dynamic characteristics of the handling of a virtual object by fingers and palm in rehabilitation applications or some serious games. The combination of these above mentioned technologies and open and portable software are very useful in the design of applications for assistance and rehabilitation purposes that is the main objective of the device.
Communities of Practice for Functional Learning in Agile Contexts: Definition Approach and Call for Research

With the increasing relevance of information technology and software development in particular, the popularity of agile working methods like Scrum and Kanban has grown significantly in recent years. Characteristic for many agile frameworks like Scrum is the work in cross-functional teams. While this has many advantages in development, cross-functional teams make functional learning very challenging. Therefore, so-called Communities of Practice (CoPs) have been established in practice. This paper defines CoPs in the agile context and reviews existing literature on CoPs in agile context. There is very little literature how CoPs in the agile context are employed to enhance functional learning. The author calls for more scientific research for example on CoP’s success factors and contribution to functional learning outcomes in agile environments.
Classification of Finger Spelling American Sign Language Using Convolutional Neural Network

Sign language is a combination of complex hand movements, body postures, and facial expressions. However, only a limited number of people can understand and use it. A computer aid sign language recognition with finger spelling style utilizing a convolutional neural network (CNN) is proposed to reduce the burden. We compared two CNN architectures such as Resnet 50, and DenseNet 121 to classify the American sign language dataset. Several data splitting proportions were also tested. From the experimental result, it is shown that the Resnet 50 architecture with 80:20 data splitting for training and testing indicates the best performance with an accuracy of 0.999913, sensitivity 0.998966, precision 0.998958, specificity 0.999955, F1-score 0.999913, and error 0.0000898.
PENERAPAN MODEL WATERFALL PADA APLIKASI BANK SAMPAH

The Waterfall Model is also very widely used by organizations and is developed with certain software whose results can overcome changes and weaknesses. This research is designed to manage the data of 'Jawara' customers because currently, they are still using a semi-computerized method that only writes them down in a book list. This study uses a qualitative approach with descriptive analysis with stages, namely system engineering, analysis, design, code generation, and the Waste Bank application testing. The application of the waterfall model in the Waste Bank information system application is a solution in the use of previously still recording customer waste deposits in Excel files, making quick and detailed waste price list reports, simplifying customer data processing, accelerating the search for required information and making it easier for customers to view a history of withdrawal and deposit transactions that previously still used semi-computerized methods. The result of this research is an application that has been made into a solution in making quick and detailed waste price list reports, simplifying customer data processing and speeding up information search, viewing withdrawal and deposit transaction history.
The paradigm shift in intercultural communication in digital space

The paradigm of intercultural communication has changed its vector orientation in a period of digital space expansion. Previously existing behavioral patterns, which included basic components, have led to the emergence of new behavioral patterns through the development of high-tech production, the media industry and media space. The functional approach made it possible to identify the strengths and weaknesses of digitization, the specifics of communication space in the spheres of science, technology and education and to predict possible changes in intercultural communication on the basis of the developing digital platform. The standardization and unification of digital space, which have penetrated all spheres of human activity, have revealed opportunities for improving digital content and its knowledge component. The study reflects the features of implementation and functioning of high-tech digital equipment in the education system, which is a basic component for the formation of the man of a new generation, capable of living in digital space, managing this space, finding creative solutions and creating opportunities for further development of society. Digitization changes social consciousness completely, intensifies the gap between the older and the younger generation, but this process is inevitable, as it opens up prospects for the development of the world community and forms conditions for its unification and improvement.
Off-campus Access to Licensed Online Resources through Shibboleth

Institutions of advanced education and research, through their libraries, invest substantially in licensed online resources. Only authorized users of an institution are entitled to access licensed online resources. Seamless on-campus access to licensed resources happens mostly through Internet Protocol (IP) address authentication. Increasingly, licensed online resources are accessed by authorized users from off-campus locations as well. Libraries will, therefore, need to ensure seamless off-campus access to authorized users. Libraries have been using various technologies, including proxy server or virtual private network (VPN) server or single sign-on, to facilitate seamless off-campus access to licensed resources. In this paper, authors share their experience in setting up a Shibboleth-based single sign-on (SSO) access management system at the JRD Tata Memorial Library, Indian Institute of Science, to enable authorized users of the institute to seamlessly access licensed online resources from off-campus locations.
Testing Transferability of Deep-Learning-Based Dwelling Extraction in Refugee Camps

For effective humanitarian response in refugee camps, reliable information concerning dwelling type, extent, surrounding infrastructure, and respective population size is essential.As refugee camps are inherently dynamic in nature, continuous updating and frequent monitoring is time and resource-demanding, so that automatic information extraction strategies are very useful.In this ongoing research, we used labelled data and highresolution Worldview imagery and first trained a Convolutional Neural Network-based U-net model architecture.We first trained and tested the model from scratch for Al Hol camp in Syria.We then tested the transferability of the model by testing its performance in an image of a refugee camp situated in Cameroon.We were using patch size 32, at the Syrian test site, a Mean Area Intersection Over Union (MIoU) of 0.78 and F-1 score of 0.96, while in the transfer site, MIoU of 0.69 and an F-1 score of 0.98 were achieved.Furthermore, the effect of patch size and the combination of samples from test and transfer sites are investigated.
Perencanaan Strategi Sistem Informasi Dengan Metode Ward And Peppard di Perusahaan Toko Surabaya cabang Surakarta

The role of information technology is very important for the progress of a company, by using information technology, strategic planning for the company's development will be better, some companies will definitely take advantage of information technology for the progress of their companies, this is also used for Toko baju Surabaya cabang Surakarta. This shop is a company engaged in the sale of clothing, this company uses the SmartConsole application to support sales. However, there are still sectors in this company that have not implemented an information system, one of which is in the section on stock data collection and payroll. Therefore, an appropriate IS / IT strategic planning is needed. This research was conducted using the Ward and Peppard method with SWOT analysis, Five Forces Porter, and McFarlan Strategic Grid. The output produced from this study is in the form of strategic recommendations from the results of the internal and external environmental analysis of the company's information system, as well as a proposal for an Information System to be mapped using the McFarlan Strategic Grid to be applied to companies in the next two years.
Distributed Ledger Technologies and Social Machines: How to “Smartify” the Economy with Blockchain-based Digital Extension Services?

IntroductionWhat are now called “social machines” have been around for decades as part of a computer-driven wave of digitalization that has taken over developed societies around the world, including but not limited to the invention of the Internet. People and machines are becoming increasingly integrated through computing power, data processing and storage, information
Empowering Conversational AI is a Trip to Mars: Progress and Future of Open Domain Human-Computer Dialogues

Dialogue systems powered by conversational artificial intelligence (AI) have never been so popular. Interacting with computer through languages reveals a more natural interface to give orders and acquire information---just like human communication. Due to promising potential as virtual assistants and/or social bots, major NLP, AI and even Search &amp; Mining communities are explicitly calling-out for contributions of conversational studies. Learning towards real conversational intelligence is a trip to Mars; perhaps we are yet on Earth. We have achieved substantial progress from recent research outputs. Still we have major obstacles to overcome. In this paper, we present an overview of progress and look forward to future trends so as to shed light on possible directions towards success.
Towards Comprehensive Definitions of Data Quality for Audiovisual Annotated Language Resources

Though digital infrastructures such as CLARIN have been successfully established and now provide large collections of digital resources, the lack of widely accepted standards for data quality and documentation still makes re-use of research data a difficult endeavour, especially for more complex resource types.The article gives a detailed overview over relevant characteristics of audiovisual annotated language resources and reviews possible approaches to data quality in terms of their suitability for the current context.Conclusively, various strategies are suggested in order to arrive at comprehensive and adequate definitions of data quality for this specific resource type and possibly for digital language resources in general.
Predicting Teamwork Performance in Collaborative Project-Based Learning

Pulse of the Profession, published by Project Management Institutes (2017), reported that failed projects always lacked (a) clearly defined objectives to measure progress and (b) poor communication between team members. Minimizing communication costs and maximizing trust levels are essential to improve the efficiency of team performance. This study&amp;rsquo;s objectives required including how to formulate the problem and design the theoretical framework. The approach used involved a five-step team formation model with related definitions, including initial team forming, depending on group size, team agreement, role assignment, and team performance. The Predicting Teamwork Performance (PTPA) system was developed to help identify the functional roles of each member automatically. Role assignment provided a strong positive effect on team projects, while the role identification mechanism can assign team members responsibilities for some role(s) to enable learning. Self-assessment was used to identify team members&amp;rsquo; strengths and weaknesses so that team leaders could easily recognize suitable types of roles for each member. Three primary team performance indicators&amp;mdash;&amp;rdquo;Good&amp;rdquo;, &amp;ldquo;Pass&amp;rdquo; and &amp;ldquo;Marginal&amp;rdquo;&amp;mdash;were reflected in the teamwork collaboration outcomes. The Predicting Teamwork Performance system reveals information about those outcomes through 1) individual performance indicator; 2) teamwork performance indicator; 3) personal skill sets results; 4) recommended skill sets improvements. The relationship between those indicators and practical roles was examined as analytical information for further project team formation.
The Communicative Function of Non-Face Emoji "Folded Hands" in WhatsApp Interaction among Indonesians

This study aimed to examine the communicative functions of the non-face “folded hands” emoji that Indonesians use in WhatsApp interactions. Using descriptive qualitative, the data were analysed through the social semiotic approach proposed by Lemke (1998), the interactional sociolinguistic theory (Gumperz, 1982), and speech act theory. The data were taken from the messages that occurred naturally in several WhatsApp groups. It was re-transcribed and translated in English based on the theory by Dresner and Herring (2010). The findings revealed some functions of the non-face “folded hands” emoji in online communication. At the end of an utterance, it emphasizes message tone, politeness, and soft interaction. Furthermore, rather than this emoji expresses an emotion such as face emoji does, it performs illocutionary forces in communication such as thanking, apologizing, and asking. It is also performed in situational interaction in which the relation of speaker and interlocutor has a formal relation such as teacher-students and leader-staffs. This empirical research added to computer-mediated discourse literature by analyzing the communicative function of folded hands emoji in written discourse. Practically, it might help users interpret the emoji as an abstract concept in the messages.
Early warning flood detector adopting camera by Sobel Canny edge detection algorithm method

Early warning of floods is an essential part of disaster management. Various automatic detectors have been developed in flood mitigation, including cameras. But reliability and accuracy have not been improved. Besides, the use of monitoring devices has been employed to monitor water levels in various water building facilities. The early warning flood detector was carried out with a sensor camera using an orange ball that floats near the water level gauge in a bounding box. This approach uses the integration of computer vision and image processing, namely digital image processing techniques, with Sobel Canny edge detection (SCED) algorithms to detect quickly and accurately water levels in real-time. After the water level is measured, a flood detection process is carried out based on the specified water level. According to the results of experiments in the laboratory, it has been shown that the proposed approach can detect objects accurately and fast in real-time. Besides, from the water level detection experiment, good results were obtained. Therefore, the object detection system and water level can be used as an efficient and accurate early detection system for flood disasters.
Ternary Toffoli-type Reversible Gates: Control Alternatives and Quantum Models

Toffoli-type reversible gates are analyzed. Peres-type ternary gates are introduced for the first time. Conjunctive, disjunctive, majority, XOR types and hybrid control strategies are disclosed. Barenco-type of quantum models for these cases are developed.
Fingerprinting Fine-tuned Language Models in the Wild

There are concerns that the ability of language models (LMs) to generate high quality synthetic text can be misused to launch spam, disinformation, or propaganda.Therefore, the research community is actively working on developing approaches to detect whether a given text is organic or synthetic.While this is a useful first step, it is important to be able to further fingerprint the author LM to attribute its origin.Prior work on fingerprinting LMs is limited to attributing synthetic text generated by a handful (usually < 10) of pre-trained LMs.However, LMs such as GPT2 are commonly fine-tuned in a myriad of ways (e.g., on a domain-specific text corpus) before being used to generate synthetic text.It is challenging to fingerprinting fine-tuned LMs because the universe of fine-tuned LMs is much larger in realistic scenarios.To address this challenge, we study the problem of large-scale fingerprinting of fine-tuned LMs in the wild.Using a real-world dataset of synthetic text generated by 108 different fine-tuned LMs, we conduct comprehensive experiments to demonstrate the limitations of existing fingerprinting approaches.Our results show that fine-tuning itself is the most effective in attributing the synthetic text generated by fine-tuned LMs.
Face Recognition Framework based on Convolution Neural Network with modified Long Short Term memory Method

For real-world applications, such as video monitoring, interaction between human machines and safety systems, face recognition is very critical. Deep learning approaches have demonstrated better results in terms of precision and processing speed in image recognition compared to conventional methods. In comparison to traditional methods. While facial detection problems with different commercial applications have been extensively studied for several decades, they still face problems with many specific scenarios, due to various problems such as severe facial occlusions, very low resolutions, intense lighting and exceptional changes in image or video compression artifacts, etc. The aim of this work is to robustly solve the issues listed above with a facial detection approach called Convolution Neural Network with Long short-term Model (CNN-mLSTM). This method first flattened the original frame, calculating the gradient image with Gaussian filter. The edge detection algorithm Canny-Kirsch Method will then be used to identify edge of the human face. The experimental findings suggest that the technique proposed exceeds the current modern methods of face detection.
From What to Why: Improving Relation Extraction with Rationale Graph

Which type of information affects the existing neural relation extraction (RE) models to make correct decisions is an important question.In this paper, we observe that entity type and trigger are the most indicative information for RE in each instance.Moreover, these indicative clues are always constrained to co-occur with specific relations at the corpus level.Motivated by this, we propose a novel RAtionale Graph (RAG) to organize such co-occurrence constraints among entity types, triggers and relations in a holistic graph view.By introducing two subtasks of entity type prediction and trigger labeling, we build the connection between each instance and RAG, and then leverage relevant global co-occurrence knowledge stored in the graph to improve the performance of neural RE models.Extensive experimental results indicate that our method outperforms strong baselines significantly and achieves state-ofthe-art performance on the document-level and sentence-level RE benchmarks.
Discovering Fully Oriented Causal Networks

We study the problem of inferring causal graphs from observational data. We are particularly interested in discovering graphs where all edges are oriented, as opposed to the partially directed graph that the state of the art discover. To this end, we base our approach on the algorithmic Markov condition. Unlike the statistical Markov condition, it uniquely identifies the true causal network as the one that provides the simplest— as measured in Kolmogorov complexity—factorization of the joint distribution. Although Kolmogorov complexity is not computable, we can approximate it from above via the Minimum Description Length principle, which allows us to define a consistent and computable score based on non-parametric multivariate regression. To efficiently discover causal networks in practice, we introduce the GLOBE algorithm, which greedily adds, removes, and orients edges such that it minimizes the overall cost. Through an extensive set of experiments, we show GLOBE performs very well in practice, beating the state of the art by a margin.
Static and Dynamic Hand Gesture Recognition Using GIST and Linear Discriminant Analysis

Hand gesture detection and recognition is a way to communicate between deaf-mute person and rest of society.In this study, the static sign and the dynamic sign recognition system based on hand gesture is presented.The dynamic sign gesture is more challenging than static sign gesture which contains the movement.The vision based sign language recognition using hand gesture consists of the following main steps: acquisition the sign data, detection the hand region, extraction the features and recognition.The data acquisition stage, the data acquired by using web camera without using any data gloves and special markers.In hand region detection process, this study proposed hybrid hand region detection process by combining CbCr channel from YCbCr colour space and motion detection by using mean filter background subtraction to definitely segment the hand region from the background.In hand feature extraction, the scale, shape, texture and orientation features extracted by using GIST (Generalized Search Tree), HOG (histogram of gradient) and HOG-LBP (Local Binary Pattern) methods.Finally, Quadratic Support Vector Machine (QSVM), Cubic Support Vector Machine (CSVM) and Linear Discriminant recognized the static and dynamic hand gesture.The recognition accuracy achieved 86.7% and 99.22 % acceptable accuracy on self-construct dynamic Myanmar sign word dataset and MUDB dataset.