Reviewerly/exHarmony huggingface dataset:
Cyclic and cyclic controlled quantum teleportation in a high-dimension system

In this paper, two schemes for cyclic quantum teleportation (CQT) and cyclic controlled quantum teleportation (CCQT) in a high-dimension system are proposed. In the first scheme, three distant agents, Alice, Bob and Charlie, are not only the senders but also the receivers, and via the six-qutrit entangled state as the quantum channel, Alice can transfer her quantum information to Bob, at the same time, Bob can transfer his quantum information to Charlie and Charlie can transfer his quantum information to Alice. While for the second scheme, there is one controller David, and only with the help of him can the teleportation be accomplished via the seven-qutrit entangled state as the quantum channel. These schemes can be generalized to realize the teleportation with [Formula: see text] agents. Furthermore, the resource consumption and feasibility of the schemes are investigated at the end of the paper.
Analytical study on use of AI techniques in tourism sector for smarter customer experience management

Artificial Intelligence is the new prime factor for paradigm shift of the new age technologies. It has created a new realm in every field- from education to entertainment or from biotechnology to manufacturing industry. Though tourism is a late runner in this race, but this sector has also witnessed a huge change with the magical touch of AI. This sector being one of the highly emerging sectors, contributing very high GDP , has adapted several machine learning techniques or data analytics, which has made tourism model smarter and dynamic. In India , tourism has an ample scope to grow and Indian tourism sectors are also adapting several popular AI techniques like deep learning, Artificial neural network, predictive analytics, robotics or new technologies like virtual reality or augmented reality. This technological adaptation has made their services much better, heled in dynamic pricing, or for smart customer experience management. This paper has conducted a study on Indian tourism sectors providing online services and discusses about the current AI technologies used by them while exploring the pros and cons faced by them . The paper is alienated in three different segments- section 1 contains introduction part, section 2 discusses about related works in similar area, third section deliberates about different AI techniques adapted by Indian tourism sectors along with their pro and cons.
A novel strategy to balance the results of cross-modal hashing

Hashing methods for cross-modal retrieval has drawn increasing research interests and has been widely studied in recent years due to the explosive growth of multimedia big data. However, a significant phenomenon which has been ignored is that there is a large gap between the results of cross-modal hashing in most cases. For example, the results of Text-to-Image frequently outperform that of Image-to-Text with a large margin. In this paper, we propose a strategy named semantic augmentation to improve and balance the results of cross-modal hashing. An intermediate semantic space is constructed to re-align the feature representations that embedded with weak semantic information. By using the intermediate semantic space, the semantic information of visual features can be further augmented before being sent to cross-modal hashing algorithms. Extensive experiments are carried out on four datasets via seven state-of-the-art cross-modal hashing methods. Compared against the results without semantic augmentation, the Image-to-Text results of these methods with semantic augmentation are improved considerably, which demonstrates the effectiveness of the proposed semantic augmentation strategy in bridging the gap between the results of cross-modal retrieval. Additional experiments are conducted on the real-valued, semi-supervised, semi-paired, partial-paired, and unpaired cross-modal retrieval methods, the results further indicates the effectiveness of our strategy in improving performance of cross-modal retrieval.
Crafting an Embodied Speculation

In this pictorial, we show and discuss the prototyping process we undertook to craft the High Water Pants, a mechatronic pair of pants that make climate change tangible for everyday cyclists. We position the pants as an example of embodied speculation and discuss how our prototyping methods helped us craft a speculative artifact that bridged the gap between the embodied experience of cycling and speculative futures with climate change. The pictorial contributes an accounting of our prototyping methods and how they strove to connect the dual contexts of (1) how the pants felt in the present, and (2) how that in-situ feeling reflected climate change data in order to create space for cyclists' embodied speculation about possible futures with climate change.
Limiting the Power of RPKI Authorities

Although Resource Public Key Infrastructure (RPKI) is critical for securing the inter-domain routing, one of the arguments hindering its adoption is the significant power that it provides to the Regional Internet Registries (RIRs), allowing prefix takedowns. In this work, we propose a small change to RPKI to distribute the power of RIRs preventing any single one of them from taking down a prefix. We design and implement a distributed RPKI system that relies on threshold signatures. This ensures that any change to the RPKI certificates requires a joint action by a number of RIRs, avoiding unilateral IP address takedowns. We evaluate the performance of our design and use historic RPKI data to analyse its performance and efficiency.
Practitioners and ICTD

ICTD is a field with a long history of interventionist research in a broad set of domains, including health, agriculture, education, and civics. A common thread between many of these interventions is that they addressed the knowledge and actions of practitioners who were engaged in development activities in their contexts. In this paper, I survey the past literature of ICTD interventions targeting practitioners to identify a common typology that spans domain and context. I use Lave and Wenger's Communities of Practice (CoP) theory as a way to understand the situated and social aspects of practice and describe how ICTD interventions have often engaged with such communities. I discuss how a CoP lens may intersect with other theoretical lenses in ICTD and related fields, specifically around concepts of agency, intrinsic motivation, amplification, and sustainability. I describe how such intersections may inform future interventionist research in the Global South.
An Indonesian Adaptation of the E-Learning Usability Scale

Abstract This paper investigates how the instruments of E-Learning Usability Scale adapted in the Indonesian context. The need for adapting the original version arises while assessing the usability of e-learning in a higher degree in Indonesia, namely Student-Centered e-Learning Environment or (SCELE). In the process of usability evaluation, a questionnaire is needed. E-learning Usability Scale (EUS), which is created by Sandoval (2016) will be used as the usability scale. However, the EUS was originally built in the English version and it has not been developed in the Indonesian version. Overall, there are five steps to adapt the usability scale to Indonesian culture which are forward translation, synthesis of the translation from the first step, back translation, consulting to the experts, and reliability testing. The results show that Croncbach’s Alpha of the adaptation Indonesian of EUS was 0.922. It can be concluded that the proposed version is reliable to be used by usability researchers. In the future, the adapted version that is proposed in this study is beneficial to help usability researchers, academicians and practitioners in Indonesia while conducting usability testing for e-Learning applications.
An approach to assess swarm intelligence algorithms based on complex networks

The growing number of novel swarm-based meta-heuristics has been raising debates regarding their novelty. These algorithms often claim to be inspired by different concepts from nature but the proponents of these seldom demonstrate whether the novelty goes beyond the nature inspiration. In this work, we employed the concept of interaction networks to capture the interaction patterns that take place in algorithms during the optimisation process. The analyses of these networks reveal aspects of the algorithm such as the tendency to achieve premature convergence, population diversity, and stability. Furthermore, we make use of portrait divergence, a newly-proposed state-of-the-art metric, to assess structural similarities between our interaction networks. Using this approach to analyse the cat swarm optimization (CSO) algorithm, we were able to identify some of the algorithm's characteristics, assess the impact of one of the CSO's parameters, and compare this algorithm to two other well-known methods (particle swarm optimization and artificial bee colony). Lastly, we discuss the relationship between the interaction network and the performance of the algorithms assessed.
Careful Design

Data-driven technologies increasingly participate in everyday experiences as implicit interactions that are unseen and dynamically configured. My research explores the design and implications of implicit interactions by designing within social relations of care that are often considered taboo. These include caring for loved ones and technologies to manage human excretion: situations that are difficult to quantify and where an unintended consequence of implicit interactions can be devastating. To carefully challenge definitions of implicit interactions, I draw upon autobiographic and speculative design methods, as well as humor to unsettle others and implicate myself in care.
Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel coded systems with convolutional coding (CC), in order to understand the potential of deep learning-based systems as alternatives to conventional systems. From the simulations, autoencoder implementation was observed to have a better BER in 0-5 dB E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> range than its equivalent half-rate convolutional coded BPSK with hard decision decoding, and to have only less than 1 dB gap at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> . Furthermore, we have also proposed a novel low complexity autoencoder architecture to implement end-to-end learning of coded systems in which we have shown better BER performance than the baseline implementation. The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> range and a better BER performance than the soft decision decoding in 0-4 dB E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> range.
Effect of Activation Functions on Deep Learning Algorithms Performance for IMDB Movie Review Analysis

Huge amount of data is generated every moment over the Internet on various platforms such as social networking sites, blogs, customer reviews on various sites where individuals express their views or thoughts about different subjects. Users’ sentiments expressed over the Web influence the readers, product vendors, and politicians greatly. This unstructured form of data needs to be analyzed and converted into a well-structured form and for this purpose, we require Sentiment Analysis. Sentiment Analysis is the process of contextual mining of text that is used to identify and extract the expressed mindset or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. In this paper, we have used Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and a hybrid approach of CNN and LSTM to perform sentiment classification of IMDB Movie Review dataset. We have applied the trained model on the dataset using various activation functions and compared the accuracy achieved. Maximum accuracy (88.35%) is achieved with CNN with ReLU Activation Function whereas minimum accuracy (48.19%) is achieved with LSTM when used with Linear Activation Function.
Digital Innovation and Digital Business Transformation in the Age of Digital Change

This chapter introduces the central theme of the book and the following chapters. In our book, we present 20 selected examples of digitalization in the age of digital change. The chapters are divided into two parts, called "Digital Innovation" and "Digital Business Transformation". On the one hand, digital innovation includes cases where a new technology stimulates or enables new business opportunities. On the other hand, digital business transformation collects examples of digitalization in which business or management concepts exploit certain technological solutions for their practical implementation. To share this mind-set with the reader, quantum-computing technology will be taken as an example of digital innovation. This will be followed by reflections on digital business transformation, which seeks and uses digital technologies. Finally, the next chapters will be presented accordingly.
Dash Lane

Access to engaging physical activity is difficult for many wheelchair users; technology offers the potential to support activity in place, cutting out travel to sports facilities as a major access barrier. Here, we present Dash Lane, an inclusive music-based exergame tailored toward wheelchair users. The game adapts to player abilities and preferences in playing style, while drawing from official exercise recommendations for people using wheelchairs. In this paper, we reflect on our player-centric design and development process, and provide an overview of the final implementation and player experience provided by the game. Through Dash Lane, we aim to explore the value of game-based physical activity for wheelchair users.
Verification of Cryptocurrency Mining Using Ethereum

With advancement in computer technology, financial technology has also evolved.Blockchain technology has evolved over the past decade; however, this has also resulted in some malicious attacks.To earn mining rewards of a blockchain, a new type of attack, called cryptojacking, has emerged in the online world.This attack uses the computer resources of a victim to obtain mining rewards without user confirmation.However, this monetization model was originally intended to replace advertising as a source of revenue for a website.To resolve such problems, a storage verification architecture based on smart contracts was proposed in the study.The decentralized system of blockchain enables users to identify and participate in verification of mining sites through Ethereum smart contracts.
Microbial community-level physiological profiles: Considering whole data set and integrating dynamics of colour development

Community Level Physiological Profiles (CLPPs) have been used as ecological indicators from long ago as they determine physiologic and metabolic potential of microbial communities and indicate the capability to adapt their metabolism to anthropogenic stressors or to changes in environmental conditions. Also, these profiles are proxies for functional diversity or functional traits of a microbial community, usually linked to the ecosystem functioning. However, statistical data analysis of CLPPs has turned out to be the main handicap in these studies. Specifically, the interference of inoculum density, the need to choose an optimal incubation time and the different profiles obtained depending on the incubation time-point limits its usefulness. To this aim, we have developed a new approach for CLPP data analysis that considers the whole data set and dynamics of colour development along the incubation period. This approach is based on compositional data (CoDa) transformation, which reflects relative rather than absolute information, followed by repeated measures multivariate analysis of variance and data representation in a canonical variates plot. The approach has been tested by using CLPPs from real fluvial biofilm samples affected by seasonality, flow stress and different sediment depth conditions, and it has been compared to the most classic applied approaches until today (incubation fixed point or curve integration, followed by a Principal Components Analysis). Our results highlight that the proposed approach (i) makes ecological interpretation of CLPPs more sensitive, (ii) clarifies differences between CLPPs even in situations of marked seasonality while classical approaches provided confounding results, (iii) produces ecologically meaningful results (i.e. the proposed approach showed significant differences in organic matter use capabilities among flow conditions and depth), and (iv) unifies the analysis methodology for CLPPs data. With this work we provide an R-script to be used in future studies.
Developing a Twitter bot that can join a discussion using state-of-the-art architectures

Today, microblogging platforms like Twitter have become popular by spreading news and opinions that gather attention. Engaging interactions, such as likes, shares, and replies, between users are the key determinants of these platforms’ news feed prioritization algorithms. These interactions attract people to ongoing debates and help inform and shape their opinions. Since being influential and attracting followers in these debates are considered as important, understanding the automation of these processes becomes critical in order to contribute positively. In this work, we aim to train a chatbot system that classifies tweets according to their positions, and it can also generate tweets related to a conversation. In this study, we test our system on a recently popular topic, namely the gun control debate in the U.S. Chatbots, are trained to tweet independently for their side and also reply meaningfully to a tweet from the opposite side. State-of-the-art architectures are tested to obtain a more accurate classification. We applied GloVe embedding model for representing tweets. Instead of using handcrafted features, long short-term memory (LSTM) neural network is applied to these embeddings to get more informative and equal-sized feature vectors. This model is trained to encode a tweet as a sequence of embeddings. Encoding is used for both message classification and generation tasks. LSTM sequence-to-sequence model is used to generate topical tweets and replies to tweets. We develop a new salience metric for measuring the relatedness of a generated message to a target tweet. Additionally, human evaluations are performed to measure the quality of the chatbot generated tweets according to their topic relevance and bias, and the quality of its replies to target tweets.
Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination

Visual can be easily disturbed by similar surrounding objects. Such objects as hard distractors, even though being the minority among negative samples, increase the risk of target drift and model corruption, which deserve additional attention in online tracking and model update. To enhance the tracking robustness, in this paper, we propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates via an efficient convolutional regression. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples, which serves as an alternative of fully-connected layers and benefits from the closed-form solver for efficient learning. During the model update, we utilize the hard negative mining technique and an adaptive ridge regression scheme to improve the discrimination capability of the second-stage regressor. Extensive experiments are conducted on 11 challenging tracking benchmarks including OTB-2013, OTB-2015, VOT2018, VOT2019, UAV123, Temple-Color, NfS, TrackingNet, LaSOT, UAV20L, and OxUvA. The proposed method achieves state-of-the-art performance on prevalent benchmarks, while running in a real-time speed.
Comparative Analysis of Bagging and Boosting Algorithms for Sentiment Analysis

Sentiment analysis has become a state-of-the-art to make products market adaptive and understand the market gaps to launch updates and variants of a product entity. The contributions of this paper are providing prior point of sale based key features to generate rule based algorithms to predict the attitude and opinion implied. Amazon food reviews data has been used in the paper. We have done a comparative analysis of Random Forest and Extreme Gradient Boosting model using Bag of Words(BoW) and Term Frequency-Inverse Document Frequency(TFIDF) featurizations.
Post-turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence

This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” reflected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” that excludes any possibility of transition from a complex observable phenomenon to an abstract image or concept. It is, therefore, sensible to factor in new requirements for AI (artificial intelligence) maturity assessment when approaching the Turing test. Such AI must support all forms of communication with a human being, and it should be able to comprehend abstract images and specify concepts as well as participate in social practices.