Reviewerly/exHarmony huggingface dataset:
Entry Market Strategy for Weaver Chatbot using the Digital B2B Model

This case study examines the international positioning and go-to market strategy of Weaver Chatbot solution. Weaver is AI-based enterprise platform, with the ability of chatting forms of communication, while exchanging rich content and talking to the end-users. The Chatbot Market size was estimated to be over USD 250 million in 2017 and is anticipated to grow at a CAGR of over 31% from 2018 to 2024. Chatbot market can be divided into five regions: North America, Europe, Asia Pacific, Latin America and MEA. Raiffeisen bank implemented the first enterprise chatbot solution in Serbia. REA (Raiffeisen Electronic Assistant) is a chatbot built on Weaver platform. We also present customer acquisition and sales process using chatbot in marketing & sales funnel. At the end, digitizing business models represents a tough challenge for the companies since many of them miss a common language or framework to guide the digital transformation. This study in interesting both for academia and practitioners.
NAO Humanoid Robot Obstacle Avoidance Using Monocular Camera

In this paper, we present an experimental approach that allows a humanoid robot to effectively plan and execute whole body motions, like climbing obstacles and straight stairs up or down, besides jumping over obstacles using only on-board sensing.Reliable and accurate motion sequence for humanoid employed in complex indoor environments is a necessity for high-level robotics tasks.Using the robot's own kinematics will construct complex dynamic motions.A series of actions to prevent the object from being performed on the basis of the identified object from the database of the robot, obtained using the robot's own monocular camera.As shown in real world experiments beside simulation using NAO H25 humanoid, the robot can effectively perform whole body movements in cluttered, multilevel environments containing items of various shapes and dimensions.
Adversarial examples detection through the sensitivity in space mappings

Adversarial examples (AEs) against deep neural networks (DNNs) raise wide concerns about the robustness of DNNs. Existing detection mechanisms are often limited to a given attack algorithm. Therefore, it is highly desirable to develop a robust detection approach that remains effective for a large group of attack algorithms. In addition, most of the existing defences only perform well for small images (e.g. MNIST and Canadian institute for advanced research (CIFAR)) rather than large images (e.g. ImageNet). In this paper, the authors propose a robust and effective defence method for analysing the sensitivity of various AEs, especially in a much harder case (large images). Their method first creates a feature map from the input space to the new feature space, by utilising 19 different feature mapping methods. Then, a detector is learned with the machine‐learning algorithm to recognise the unique distribution of AEs. Their extensive evaluations on their proposed detector show that their detector can achieve: (i) low false‐positive rate (&lt;1%), (ii) high true‐positive rate (higher than 98%), (iii) low overhead (&lt;0.1 s per input), and (iv) good robustness (work well across different learning models, attack algorithms, and parameters), which demonstrate the efficacy of the proposed detector in practise.
A Comparative Performance Analysis of Extreme Learning Machine and Echo State Network for Wireless Channel Prediction

In this work, a comparative performance analysis of an extreme learning machine (ELM) and an echo state network (ESN) for forecasting of wireless channel conditions is carried out. These two algorithms are applied to predict signal-to-noise ratio (SNR) for single-input single-output (SISO) system in both picocellular and microcellular environments. Performance indicators used to gain insight into accuracy and effectiveness of ELM and ESN techniques are normalized mean squared error (NMSE) and time consumption. The experimental results performed on measured SNR values show that the ESN algorithm is characterized by shorter test time and higher prediction accuracy in picocellular environment, while the ELM model is recommended for channel prediction in environment which is less frequency selective.
HackerNets: Visualizing Media Conversations on Internet of Things, Big Data, and Cybersecurity

The giant network of Internet of Things establishes connections between smart devices and people, with protocols to collect and share data. While the data is expanding at a fast pace in this era of Big Data, there are growing concerns about security and privacy policies. In the current Internet of Things ecosystems, at the intersection of the Internet of Things, Big Data, and Cybersecurity lies the subject that attracts the most attention. In aiding users in getting an adequate understanding, this paper introduces HackerNets, an interactive visualization for emerging topics in the crossing of IoT, Big Data, and Cybersecurity over time. To demonstrate the effectiveness and usefulness of HackerNets, we apply and evaluate the technique on the dataset from the social media platform.
Skeleton-Based Labanotation Generation Using Multi-model Aggregation

Labanotation is a well-known notation system for effective dance recording and archiving. Using computer technology to generate Labanotation automatically is a challenging but meaningful task, while existing methods cannot fully utilize spatial characteristics of human motion and distinguish subtle differences between similar human movements. In this paper, we propose a method based on multi-model aggregation for Labanotation generation. Firstly, two types of feature are extracted, the joint feature and the Lie group feature, which reinforce the representation of human motion data. Secondly, a two-branch network architecture based on Long Short-Term Memory (LSTM) network and LieNet is introduced to conduct effective human movement recognition. LSTM is capable to model long-term dependencies in temporal domain, and LieNet is a powerful network for spatial analysis based on Lie group structure. Within the architecture, the joint feature and the Lie group feature are fed into LSTM model and LieNet model respectively for training. Furthermore, we utilize score fusion methods to fuse the output class scores of the two branches, which performs better than any of the single models, due to complementarity between LSTM and LieNet. In addition, skip connection is applied in the structure of LieNet, which simplifies the training procedure and improves the convergence behavior. Evaluations on standard motion capture dataset demonstrate the effectiveness of proposed method and its superiority compared with previous works.
Virtual Teleportation of a Theatre Audience Onto the Stage: VR as an Assistive Technology

For more than a decade, virtual reality (VR) has been employed to enrich and heighten media experiences. Despite the recognized potential and promise of VR, and ample investment, it has yet to fully transform or replace existing screen-based experiences (e.g. film or gaming). This research forms a part of a larger project to shift VR applications beyond otherwise apparent areas of screen-based media, in order to enhance audience access and propinquity to a live performance. This study is being conducted in the field of theatre, a dramatic medium in which audience are traditionally static and where an individual’s seating position determines their perceptual experience (distance, angle, lights, obstructed vs clear view). This paper introduces the broader project and its experimentation with VR as an assistive technology. The project seeks to utilize VR as a means of converting an otherwise static experience to provide collective moments of visual teleportation, onto stage, into props and on actors. VR offers a non-invasive means of introducing variance in viewer proximity or position relative to performance. This paper reports on the early development and use of a three-dimensional theatre prototyping in order to explore the technical requirements for application to a VR theatre experience.
A Fine-grained Privacy-preserving k-means Clustering Algorithm Upon Negative Databases

Nowadays, privacy protection has become an important issue in data mining. k-means algorithm is one of the most classical data mining algorithms, and it has been widely studied in the past decade. Negative database (NDB) is a new type of data representation which can protect privacy while supporting distance estimation, so it is promising to apply NDBs to privacy-preserving k-means clustering. Existing privacy-preserving k-means clustering algorithms based on NDBs could effectively protect data privacy, but their clustering performance has a non-negligible degradation. In this paper, we propose a new NDB generation algorithm (named QK-hidden algorithm), and based on this algorithm, we propose a privacy-preserving k-means algorithm. The proposed algorithm can control the accuracy of distance estimation in a fine-grained manner, and thus it can control the clustering results granularly. Experimental results demonstrate the proposed algorithm has better clustering performance than existing privacy-preserving k-means algorithms based on NDBs.
CoSafe: Securing Mobile Devices through Mutual Mobility Consistency Verification

As mobile devices play increasingly important roles in our daily lives, it is of great significance to protect personal mobile devices from being lost. Noticing the trend that one person normally carries more than one mobile device, we propose an innovative scheme, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CoSafe</i> , to detect device loss by verifying the motion consistency between a pair of devices. The rationale is that the vibrations perceived on devices carried by the same person should be tightly coupled whereas a lost device would show distinct mobility characteristics from others. Specifically, CoSafe compares the mobility consistency between a pair of devices on three levels, where coarse features (i.e., the mobility state and motion periodicity) are first compared to give fast response and more complex comparison on subtle feature (i.e., the relative phase) is conducted only when needed. In this way, CoSafe can instantly respond and introduce very low computation and communication costs. We implement CoSafe on a Commercial-Off-The-Shelf Android smartphone and a smartwatch, and conduct both trace-driven simulations and real-world experiments to evaluate the performance of CoSafe. The results show that CoSafe achieves a mean false negative ratio and false positive ratio of 1.46 and 3.12 percent, respectively, even under sophisticated stealing attacks.
A Personal Data Determination Method Based On Blockchain Technology and Smart Contract

Data determination is the primary issue to be addressed in open data sharing. This paper proposes a personal data determination method based on blockchain and smart contract, which are used to solve the problem that the current data transaction market relies heavily on system-center entities. it is difficult for centralized method to confirm the data is true and reliable and cause the owner lose the control over data access. The method implements identity authentication by means of certificate authority, and uses digital signature and hash function to store a digest of the original data into the blockchain.By doing this can determine ownership of the data, and prevent it from being tampered with. Constructing a blockchain serving at data interaction platform to realize access control and transfer rights about personal data through the smart contract installed on it,because the operation record of data will be kept on the blockchain one by one.Then using resource state view to speed up the query process. According to experimental simulation concerning about throughput and latency, blockchain and smart contracts can guarantee the authenticity of the data and achieve the right to confirm the data and promote the open sharing of data.
Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects

Our aging population increasingly suffers from multiple chronic diseases simultaneously, necessitating the comprehensive treatment of these conditions. Finding the optimal set of drugs for a combinatorial set of diseases is a combinatorial pattern exploration problem. Association rule mining is a popular tool for such problems, but the requirement of health care for finding causal, rather than associative, patterns renders association rule mining unsuitable. To address this issue, we propose a novel framework based on the Rubin-Neyman causal model for extracting causal rules from observational data, correcting for a number of common biases. Specifically, given a set of interventions and a set of items that define subpopulations (e.g., diseases), we wish to find all subpopulations in which effective intervention combinations exist and in each such subpopulation, we wish to find all intervention combinations such that dropping any intervention from this combination will reduce the efficacy of the treatment. A key aspect of our framework is the concept of closed intervention sets which extend the concept of quantifying the effect of a single intervention to a set of concurrent interventions. Closed intervention sets also allow for a pruning strategy that is strictly more efficient than the traditional pruning strategy used by the Apriori algorithm. To implement our ideas, we introduce and compare five methods of estimating causal effect from observational data and rigorously evaluate them on synthetic data to mathematically prove (when possible) why they work. We also evaluated our causal rule mining framework on the Electronic Health Records (EHR) data of a large cohort of 152000 patients from Mayo Clinic and showed that the patterns we extracted are sufficiently rich to explain the controversial findings in the medical literature regarding the effect of a class of cholesterol drugs on Type-II Diabetes Mellitus (T2DM).
Facilitation of Domain-Specific Data Models Design using Semantic Web Technologies for Manufacturing

Modern manufacturing faces a challenge of integrating data models from various sources/domains which may differ both semantically and technically when particular domain specific data models are designed by different users and stored in different formats. This paper introduces an approach for facilitating the design of domain-specific data models using semantic web technologies. In this approach, all the information required for managing the production (including a description of a product, processes involved in the production, and existing resources and their specifications) is captured in an ontology. The proposed Product, Process, and Resource (PPR) ontology defines fundamental conceptualization of the production that can be easily applied to the arbitrary domain. Application of the PPR ontology is demonstrated in the case of simple truck assembling by means of robots. Capturing the knowledge in the form of ontology provides the advantage of employing supporting tools such as reasoners for consistency checking or query languages for information extraction. The paper demonstrates the utilization of SQWRL for searching resources suitable to manipulate given truck parts on the basis of semantic matching between properties of particular elements.
Towards Knowledge Graph Construction using Semantic Data Mining

Over the last few years, constructing knowledge graphs for new domains and linking them to existing ones has gained significant attention, especially in domains which have experienced a tremendous increase in available data such as biodiversity research. To this end, in this paper, we introduce a new semantic data mining-based approach to support the (semi-)automatic generation of a biodiversity knowledge graph. The proposed approach exploits and links information from several biodiversity-related resources, including the Encyclopedia of Life (EOL), the Global Biodiversity Information Facility (GBIF), and the Global Biotic Interactions (GLOBI). In particular, we adopt a data mining technique to extract association rules that support the construction of an initial species interactions knowledge graph. We then make use of available biodiversity resources to enrich the knowledge graph. We believe that this graph will support scientists from the biodiversity domain to gain new insights and enrich the data interoperability.
Measuring, Quantifying, and Predicting the Cost-Accuracy Tradeoff

Exponentially increasing data volumes, coupled with new modes of analysis have created significant new opportunities for data scientists. However, the stochastic nature of many data science techniques results in tradeoffs between costs and accuracy. For example, machine learning algorithms can be trained iteratively and indefinitely with diminishing returns in terms of accuracy. In this paper we explore the cost-accuracy tradeoff through three representative examples: we vary the number of models in an ensemble, the number of epochs used to train a machine learning model, and the amount of data used to train a machine learning model. We highlight the feasibility and benefits of being able to measure, quantify, and predict cost accuracy tradeoffs by demonstrating the presence and usability of these tradeoffs in two different case studies.
Proposal for User Authentication Method Combining Random Number and Password

When performing user authentication, security is often enhanced by combining another authentication factor with the password, however, usability becomes worse. In this paper, we propose a multi-factor authentication method using a random number stored in the user device. The hash value is calculated using the password and the random number, and is treated as the password to be registered in the server. It is shown that both security and usability can be achieved.
Learning and Modeling Unit Embeddings Using Deep Neural Networks for Unit-Selection-Based Mandarin Speech Synthesis

A method of learning and modeling unit embeddings using deep neutral networks (DNNs) is presented in this article for unit-selection-based Mandarin speech synthesis. Here, a unit embedding is defined as a fixed-length embedding vector for a phone-sized unit candidate in a corpus. Modeling phone-sized embedding vectors instead of frame-sized acoustic features can better measure the long-term dependencies among consecutive units in an utterance. First, a DNN with an embedding layer is built to learn the embedding vectors of all unit candidates in the corpus from scratch. In order to enable the extracted embedding vectors to carry both acoustic and linguistic information of unit candidates, a multitarget learning strategy is designed for the DNN. Its optional prediction targets include frame-level acoustic features, unit durations, monophone and tone identifiers, and context classes. Then, another two DNNs are constructed to map linguistic features toward the extracted embedding vectors. One of them employs the unit vectors of preceding phones besides the linguistic features of current phone as its input. At synthesis time, the distances between the unit vectors predicted by these two DNNs and the ones derived from unit candidates are used as a part of the target cost and a part of the concatenation cost, respectively. Our experiments on a Mandarin speech synthesis corpus demonstrate that learning and modeling unit embeddings improve the naturalness of hidden Markov model (HMM)-based unit selection speech synthesis. Furthermore, integrating multiple targets for learning unit embeddings achieves better performance than using only acoustic targets according to our subjective evaluation results.
Quantum Relay Blockchain and Its Applications in Key Service

As a promising direction of cryptography, the feasibility of quantum key distribution has been widely verified. However, the bottle-neck problems encountered by quantum key distribution network, such as concurrent conflict of large-scale quantum relay links, relay delay and inconvenient access to applications, are still not solved completely. In order to solve these problems, a quantum relay blockchain scheme is proposed in this paper. In the scheme, each relay node in a target network negotiates a quantum key with everyone of its adjacent nodes respectively, and calculates the XOR values of any two quantum keys from these quantum keys, then encapsulates these XOR values into a transaction. A server encapsulates all the current transactions into a block based on which real-time quantum key services can be realized. Furthermore, the scheme can be easily employed to realize quantum key service in cloud platforms with unconditional security and high efficiency. The scheme, which can be used to overcome the bottle-neck of quantum key distribution network, will play a critical role in future world-wide quantum key service.
Cybercrime, ransomware, and the role of the informatics nurse

Lee Kim is the director of privacy and security at HIMSS in Arlington, Va. The author has disclosed no financial relationships related to this article.
Applying SDN based data network on HPC Big Data Computing – Design, Implementation, and Evaluation

Large scale storage data networks are difficult to conFigure and tricky to maintain [1][2][3]. As storage data volumes grow and the pace of change accelerates, it can be a struggle to keep up the integrity of a large scale storage network. Software Defined Networking (SDN) [5][6][7][8][10][11] provides a method to centrally conFigure and manage physical and virtual network devices such as routers, switches, and gateways in HPC datacenter. Current HPC computer cluster and data centers are using homogeneous data network technologies such as the Infiniband network (QDR, FDR, EDR, and HDR). Due to the connector, cable, and bandwidth backward compatibility issues, once an aged HPC computing system was retired, we have to demolish all established computer clusters, data network, and data storage. Eventually we lost all costly investment on the data network and storage. Those current deployment approaches do not provide a feasible and compatible growing path and cannot meet the need for future extreme scale HPC computing systems [4][12][13].
Finite-Precision Implementation of Arithmetic Coding Based Distribution Matchers

A distribution matcher (DM) encodes a binary input data sequence into a sequence of symbols with a desired target probability distribution. Several DMs, including shell mapping and constant- composition distribution matcher (CCDM), have been successfully employed for signal shaping, e.g., in optical-fiber or 5G. The CCDM, like many other DMs, is typically implemented by arithmetic coding (AC). In this work we implement AC based DMs using finite-precision arithmetic (FPA). An analysis of the implementation shows that FPA results in a rate-loss that shrinks exponentially with the number of precision bits. Moreover, a relationship between the CCDM rate and the number of precision bits is derived.