Reviewerly/exHarmony huggingface dataset:
Identifying Variables that Improve Communication with Bots

Some people find communicating with bots so difficult and daunting that they avoid interaction, as they do with the obnoxious friend or colleague who has the answer to their question. Eventually, they communicate with the colleague to obtain the answer that will benefit them and the success of their endeavor. As technology advances, more businesses and households are computerized with the Internet of Things, robots, chatbots, virtual assistants and other forms of artificial intelligence (AI). Businesses are spending billions of dollars on AI, so it is imperative that today's workforce learns to communicate with bots despite the difficulties faced. Shankar states that by 2021 retailers will spend 6 billion dollars on AI [1]. Retail is one of many sectors investing in AI systems, so the 6 billion dollars is only a snippet of the total investment in AI. The purpose of this study is to assess human communication with bots and identify variables that improve communication between humans and bots by analyzing data from chatbots. Variables were identified through a review of the literature, data visualization and text mining. In reviewing the literature, there were obvious elements that engineers could implement to improve communication between humans and bots from enhance natural language processing to incorporating more data. Also, the analysis of the text data identified variables humans can implement to improve communication with bots. The significance of this study is to improve human and bot communication, especially as more and more careers will require human and machine collaboration. This study will benefit individuals who have a difficult time communicating with bots, so they can excel and be prepared for the future.
Filtering Mechanisms of Shared Social Surrounding Environments in AR

This work describes a system and a user study for hiding and showing parts of the shared surrounding spaces in social AR applications for wearable devices. It extends previous work on the Social AR Continuum by exploring how sharing the surrounding environment can vary based on the social proximity between contacts. We built a prototype system for sharing the surrounding environment between two HoloLens devices. We found that Remove is the preferred hiding mechanism for the sharer in terms of social presence. We discuss the research findings and outline future directions for research in sharing surrounding spaces on social AR applications for wearable devices.
Ships' Periodic Pattern Mining Based on Multi-Constraint Closure Condition Discrimination Constraint

To solve the problem that the periodic of pattern is difficult to predict, the rare patterns is easy to miss and the discrimination constraints are not anti-monotonicity during ships' periodic pattern mining, the multi-support and multi-periodic oriented algorithm based on Multi-Constraint Closure Conditional Discrimination Constraint (MCCCDC) is proposed. On the one hand, setting a special period calculation method to avoid the uncertainty of the artificially estimated period range. On the other hand, a multi-support and multi-periodic oriented ships' periodic pattern discrimination method is proposed, so that the minimum support threshold and the maximum period threshold can be adjusted dynamically according to the characteristics of different patterns and the omission of rare patterns can be solved to a certain extent. In addition, the mathematical proof of the anti-monotonicity of the MCCCDC method is also given. Finally, the real AIS data is used to test the algorithm, the results show that compared to the traditional periodic pattern mining algorithm, the MCCCDC method can find more rare periodic patterns and its results is more accurate and credible.
Modelling of Number to Javanese Voice Recursion using Sliding through Audio window Location

Abstract This study tries to display a number to Javanese voice using recursion through sliding audio location. The modelling process is done by making the conversion of number to text and number to index, which is then used to create a voice index. Furthermore, the audio recording is adjusted to the voice index created. This audio recording is then carried out the process of determining the location of the voice index. When the numbers are entered, this number will change to the index form, from this index the next will be the location of the voice index, and from this voice index then play according to the location of the voice index. From the test results, 999 conversions can go as planned, and can change from number to Javanese voice.
Torwards Flexible Multi-factor Combination for Authentication Based on Smart-Devices

The number of transactions that are performed electronically between coupled smart-devices increases rapidly. These devices are not only sensors nodes that collect the non-private data, but also are devices that process sensitive information that has higher requirements into security and privacy. Unique and qualified identification and high-secure authentication are essential basics to facilitate these requirements in security and privacy. While security and privacy are widely described and examined for applications used on personal computers, the situation is more demanding for smart-devices. Due to the steadily increasing number and the continuous enhancement of smart-devices, there will be no stable technology over the years. In consequence, new agile and secure methods become necessary to bring identification and high-secure authentication on smart platforms in a proper way. We propose a model for agile smart-device based multi-factor authentication combination to close this open gap and to provide secure authentication on mobile devices only. By using our proposed model, a user can combine multiple authenticators by using a cryptographic protocol on client-side only to increase the assurance into authentication. One significant advantage of our model is that it is transparent to existing eID validation infrastructure and can be used without modifications on the verification side. We proof the practical applicability of our model by implementing all components in combination with Austrian eGovernment infrastructure components. A first evaluation was done by a small group of users in conjunction with real eGovernment components on the testing stage.
A Rapid Road Image Mosaic Method Based on Monocular Camera

Abstract As an important infrastructure, road has attracted a lot of attentions in recent years. Most of current road information extraction methods rely on either elaborate algorithms or complex equipment. This manuscript proposes a simple and rapid image mosaic method to extract road surface based on a monocular camera. First, key frames are extracted to remove redundant information from image sequences. To eliminate perspective effect, extracted key frames are transformed into top view of road images based on inverse perspective mapping algorithm with the help of an attitude tensor. Then, a coarse-to-fine registration strategy is proposed to align all transformed key frames into a unified coordinate system. Finally, considering the specificity of transformed images, a superposing and overlapping image fusion strategy is proposed to alleviate the effect of stitching seam. The experiments are conducted on a road with the size of 128×17 meters, and the experimental results demonstrate the effectiveness and efficiency of proposed method. In conclusion, the proposed method is simple, effective and efficient and can be applied in a wide range of applications such as large scale road surface diagnose.
Smart Connection of User Profiles in a Big Data Context

The idea we propose in this article is a follow-up to our research series in the ontology-based profiling framework. The approach relies on tracking user profile changes for user connection within a Big Data context. We have worked in our series of research on the identification and qualification of profiles in web 2.0 context based on the ontological approach and multi agent system. Among the limitations of our research is the fact that changing interests over time does not affect the relationships between profiles. The goal of our approach is to follow the change of the interests of internet users and to propose afterwards new relations having changed activities in the same direction. In order to implement this approach, we will first use the ontology approach. The ontology approach will allows describing semantic models and determine the properties, restrictions and axioms of our application domain. The ontology we propose will generate a set of domains and sub domains of activities used to identify user profiles. On the other hand, we will use the Multi Agents approach to process users' activities before classifying them in their profiles.
A hybrid KRR-ML approach to predict malicious email campaigns

Great success has been witnessed in the last few years for approaches combining Machine Learning (ML) with Knowledge Representation and Reasoning (KRR) to predict cybersecurity events. These approaches benefited from the high accuracy of ML, and the inherent transparency of KRR. In this paper, we develop a multi-layered, hybrid system that benefits from both approaches. When the developed system is fused with an existing statistical forecasting model, it demonstrates an average recall improvement of more than 14% while maintaining precision.
On the Construction of Multi-valued Concurrent Dynamic Logics

Dynamic logic is a powerful framework for reasoning about imperative programs. An extension with a concurrent operator, called concurrent propositional dynamic logic (CPDL) [20], was introduced to formalise programs running in parallel. In a different direction, other authors proposed a systematic method for generating multi-valued propositional dynamic logics to reason about weighted programs [15]. This paper presents the first step of combining these two frameworks to introduce uncertainty in concurrent computations. In the proposed framework, a weight is assigned to each branch of the parallel execution, resulting in a (possible) asymmetric parallelism, inherent to the fuzzy programming paradigm [2, 23]. By adopting such an approach, a family of logics is obtained, called multi-valued concurrent propositional dynamic logics ($$\mathcal {GCDL}(\mathbf {A})$$), parametric on an action lattice $$\mathbf {A}$$ specifying a notion of “weight” assigned to program execution. Additionally, the validity of some axioms of CPDL is discussed in the new family of generated logics.
Evolutionary Architecture and Engineering Concepts for Very Large-scale Sensor-based Solutions : (Invited Paper)

The design and engineering of solutions based on sensor data is currently based on legacy low-level sensor communication. The transition to IoT-based sensors has started to provide IP-based sensor communication based on service concepts. This requires radically different solution architectures and the associated engineering process. Here, IoT sensors will provide services instead of a communication interface, as a means of integrating into application solutions. It is further anticipated that the size of the solution will reach well beyond that of the current legacy automation implementations.This paper addresses architectural concepts for very large-scale SoS- and IoT-based solutions. Architecture concepts and their associated engineering concepts are considered. IoT and SoS evolvability, run-time dynamics, scalability and segmentation, run-time engineering, self-engineering, self-mitigation, and machine-to-machine business are some examples of such concepts. These concepts must be coupled with the requirements such as the management of IoT/SoS solution functionality, security, safety, maintenance, and evolution.
Representation Learning for Early Sepsis Prediction

As part of the PhysioNet/Computing in Cardiology Challenge 2019, we propose a neural network called AEC-Net to early detect sepsis based on physiological data. AEC-Net consists of two main components: 1) an Auto Encoder for dimension reduction and feature extraction, and 2) a Fully Connected Neural Network (FCNN) taking the extracted features by the Auto Encoder as the input and generating prediction of sepsis as output. The losses of both the Auto Encoder and FCNN are minimized concurrently. This concurrent optimization helps AEC-Net to have a better generalization and the extracted features by Auto Encoder to be more relevant to the classification problem. Finally, we propose an ensemble method of AEC-Net, Random Forest and Gradient Boosting Decision Trees to achieve a better prediction.We train our proposed models using data from 40336 patients with 40 physiological features ranging from 8 to 336 hours. Our team Infolab USC evaluated Ensemble with the hidden full test set of the Physionet Challenge 2019, and achieved a Utility score of 0.284 and 24th place in the challenge.
Redefining Idiomatic Writing for the Pedal Harp

Sounds of shimmering glissandi and resonant arpeggios combine with romantic and angelic imagery to form a stereotype of the modern pedal harp as an instrument capable of a relatively narrow band of expression. Similarly, its fundamentally diatonic design gives rise to issues with chromatic music that its pedal system addresses without fully negating. Taking pitch and timbre as means through which to reconsider the instrument, this paper presents an approach that reflects and celebrates the inherent properties of the harp whilst redefining what idiomatic writing might be. A reappraisal of the instrument’s sonic qualities contrasts typical sounds with alternative approaches to timbre. The pitch limitations of the harp inform a new method for localised and structural pitch organisation that is rooted in the pedal mechanism of the instrument. These new modalities and alternative timbres are illustrated with a range of compositional examples throughout.
Optimization of Kernel Estimators of Probability Densities

The constructive kernel algorithm for approximation of probability densities using the given sample values is proposed. This algorithm is based on the approaches of the theory of the numerical functional approximation. The critical analysis of the optimization criterion for the kernel density estimators (based on decrease of upper boundary of mean square error) is conducted. It is shown that the constructive kernel algorithm is nearly equal to the randomized projection-mesh functional numerical algorithm for approximation of the solution of the Fredholm integral equation of the second kind. In connection with this it is proposed to use the criterion of conditional optimization of functional algorithms for the kernel algorithm for approximation of probability densities. This criterion is based on minimization of the algorithm’s cost for the fixed level of error. The corresponding formulae for the conditionally optimal parameters of the kernel algorithm are derived.
Capped l_2,1-Norm Regularized Dictionary Coding for Scalable Semi-Supervised Learning

During the past decade, graph-based semi-supervised learning has become one of the most important research areas in machine learning and artificial intelligence community. In this paper, we propose a Capped l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -Norm Regularized Dictionary Learning to construct the graph for semi-supervised learning (SSL). The new sparse coding is robust to the outliers and insensitive to noise. Then, the developed graph construction can better characterize the geometrical structure of data mainfold and be utilized for efficient graph based SSL. the proposed SSL methods can also be easily extended to deal with out-of-sample data. Simulation results show that the proposed method can achieve better performance compared with other state-of-the-art graph based SSL methods.
ALGORITHMS FOR ESTIMATING THE NUMBER OF NOISE-IMMUNE CODES OF GENERAL AND SPECIAL TYPES

Cyclic codes are used when transmitting information over communication channels, especially in the absence of the possibility of retransmission of data. They are one of the options to reduce the likelihood of communication errors. The article describes the various applications of cyclic codes, and in connection with the multiplicity of these areas, a natural question arises about the number of such codes-for some previously fixed values of the corresponding parameters. In this paper, we consider algorithms that allow us to solve the problem of estimating the number of different noise-resistant codes with given parameters in a general form. Some results have also been obtained for the particular case of cyclic codes, the socalled Bose – Chaudhuri – Hocquenghem codes (BCH-codes). The algorithms considered in the article have a polynomial complexity. All of them were software implemented, and the authors have not only program confirming the theoretical obtained estimating, but also have obtained lists of all possible generating polynomials corresponding to the cyclic codes under consideration with given parameters.
Towards Large-Scale Object Instance Search: A Multi-Block N-Ary Trie

Object instance search is a challenging task with a wide range of applications, but the fast search with high accuracy has not been well solved yet. In this paper, we investigate the object instance search from a new perspective in terms of joint precision and computational cost optimization, and propose a novel index structure i.e., Multi-Block N-ary Trie (MBNT) to accelerate the exact r-neighbor search in the Hamming space. Comprehensive studies are first carried out to reveal the performance of exact and approximate nearest neighbor (NN) algorithms for object instance search. An interesting finding that the exact search is more promising for very compact binary codes (e.g., 64-bit and 128-bit) is analyzed. Along this vein, we introduce a Trie structure, i.e., MBNT, which is specifically designed for improving the exact NN search performance in the context of large-scale object instance search. To index the binary codes, a subset of continuous bits of a binary string, denoted as a block, is regarded as an atomic indexing element. As such, the problem of lookup misses can be addressed. Theoretical analyses are also provided to show that our MBNT scheme can incur less computational cost than other hash table-based methods. Extensive experimental results on the 100M dataset have demonstrated that our method achieves faster search speed while maintaining the promising search precision towards large-scale object instance search.
Decomposing the Inverse of a Masked Vector in an Isomorphic Galois Field for Perfectly Masked S-Box

The increment of opportunities for using machine learning (ML) technologies has brought a new threat to cryptosystems. As a remarkable example, the ML technologies have gradually been employed in the side-channel attack (SCA) to obtain sensitive information. In this paper, the authors focus on the structure of a masked S-Box in AES, which aims to equip the SCA resistance even for the attacks using the ML technologies. More precisely, this paper analyzes the mathematical structure of the inverse operation over F_(2^4)^2 which is an isomorphic field for obtaining efficient arithmetic for the AES, so that all functions in the encryption scheme can handle masked data as it is. The mathematical structure is realized by introducing several mathematical tools such as the Gauss periods and the Itoh-Tsujii inversion algorithm, and as a result, we clarified the factors of the coefficients of A^-1 for an element A ϵ F_(2^4)^2. It enables us to generate the corresponding element directly, which allows canceling the mask even after processing the SubBytes.
A Saliency Detection Method via Color Contradistinction and Background Similarity

Image saliency detection helps the computer quickly analyze the surrounding environment, locate the interested objects and extract the salient regions from the background. Conventional image saliency detection algorithms usually have high computational complexity, and the detection results seems to be less than satisfactory under complex application circumstances. In this paper, a novel image saliency detection method via color contradistinction and background similarity is proposed, which is effective. In our method, the input image is reconstructed according to block-based compressed sensing for reducing the computational complexity. Then, a weighted local contrast principle and a background similarity calculation framework are designed to obtain two different primary saliency maps. Finally, a weighted fusion strategy is used to combine the two saliency maps to get the final result which has the best detection performance. The experimental results show that the proposed method has good detection performance in terms of accuracy and running time.
Software As A Service (Saas) As A Handicraft Portal Bag

Abstract Handicrafts is one of the proud industrial products produced by the Kudus community, one of the superior products that has become a distinctive feature, craft bags are very helpful in developing the economy of rural communities in Kudus district. Kudus prestige as a center for craft bags is only limited to the merchant’s environment. Not many people know that in Kudus Regency as a center for craft bags, there are more than 50 artisans who are members of a business group. Therefore, Trading Businesses engaged in bag crafting will be better if they use an information system to facilitate the marketing process so that the public can get to know their products, in this case it is focused on the sales system The method that will be used in this study is to use object oriented design Analysis and design of web-based bag craft portal system in Kudus Regency is a form of information technologybased information system development using the Object oriented design modeling method. This study aims to analyze and design a web-based bag craft portal system with software as a service (SAAS) so as to produce a web-based application that can help create a forum for Handicraft entrepreneurs in a site so as to facilitate marketing their products using information.
A Hybrid Approach for Video Watermarking Using DWT and SVD

Digital data is being created every day and spread across the internet. Since the data is on the internet, it can be accessed and misused by anyone who has access to the internet. Even with encryption technique, when distributed it can be easily decrypted and modified to the persons needs. The problem can be addressed by embedding the information into image, audio, and video using digital watermarking technique. The embedded information can be later retrieved and used for identification and authentication purposes. The challenging task in watermarking is illegal distribution and handling of digital video is turning out. It can be solved by embedding copyright information into bit streams of any video. In recent research, image watermarking technique in which digital images can be embedded with a code. It is done is such a way to ensure non-erasability of the image watermarking. While it ensures non-erasability, it does introduce Gaussian noise during the process of watermarking, the contrast and brightness of the hidden image will be affected due to the watermarking of information and also the entire image consumes a lot of memory after the entire watermarking procedure is done. In this paper, a hybrid approach for watermarking is proposed and tested which incorporates FT (Framelet Transform), PCA (Principal Component Analysis) and SVD (Singular Value Decomposition). The model is robustness against watermarking attacks, imperceptibility, capacity and security.