Reviewerly/exHarmony huggingface dataset:
Ascertain Quality Attributes for Design and Development of New Improved Chatbots to Assess Customer Satisfaction Index (CSI): A Preliminary Study

Chatbots are artificial intelligence applications that are used as tools to communicate and assist humans in any task designed. It uses knowledge that has been provided by the developer and continue to learn on its own through a Natural Language Processing (NLP) approach. This paper highlights a study that aims to investigate quality attributes for a new improved Chatbots to assess customer satisfaction. The preliminary study was conducted to acquire prior understanding on the characteristics and functionalities capable of Chatbots to capture potential customer satisfaction in the tourism domain before a prototype is developed. The findings from this study reveal seven (7) plausible dimensions with several sub-factors of quality attributes that can be applied to new improved Chatbots. These dimensions and sub-factors are useful inputs to the Systems Requirement Specifications (SRS) for the design and development of new improved Chatbots to assess Customer Satisfaction Index (CSI).
Can Container Fusion Be Securely Achieved?

Linux containers are key enablers for building microservices. The application's microservices fall broadly under two categories, the core-microservices implementing the business logic and the utility-microservices implementing middleware functionalities. Such functionalities include vulnerability scanning, monitoring, telemetry, etc. Segregating the utility-microservices in separate containers from the core-microservice containers may prevent them from achieving their functionality. This is due to the strong isolation between containers. By diffusing the boundaries between containers we can fuse them together and enable close collaboration. However, this raises several security concerns, especially that the utility-microservices may include vulnerabilities that threaten the entire application. In this paper, we analyze the different techniques to enhance the security of container fusion and present an automated solution based on Kubernetes to configure utility-microservices containers to fuse with core-microservices containers.
Multi-Hot Compact Network Embedding

Network embedding, as a promising way of the network representation learning, is capable of supporting various subsequent network mining and analysis tasks, and has attracted growing research interests recently. Traditional approaches assign each node with an independent continuous vector, which will cause memory overhead for large networks. In this paper we propose a novel multi-hot compact network embedding framework to effectively reduce memory cost by learning partially shared embeddings. The insight is that a node embedding vector is composed of several basis vectors according to a multi-hot index vector. The basis vectors are shared by different nodes, which can significantly reduce the number of continuous vectors while maintain similar data representation ability. Specifically, we propose a MCNE$_p $ model to learn compact embeddings from pre-learned node features. A novel component named compressor is integrated into MCNE$_p $ to tackle the challenge that popular back-propagation optimization cannot propagate loss through discrete samples. We further propose an end-to-end model MCNE$_t $ to learn compact embeddings from the input network directly. Empirically, we evaluate the proposed models over four real network datasets, and the results demonstrate that our proposals can save about 90% of memory cost of network embeddings without significantly performance decline.
Allocating resources via price management systems: a dynamic programming-based approach

In this paper, a novel model for price management systems in resource allocation problems is proposed. Stochastic customer requests for resource allocations and releases are modelled as constrained parallel Birth–Death Processes (BDP). We address both instant (i.e. the customer requires a resource to be allocated immediately) and advance (i.e. the customer books a resource for future use) reservation requests, the latter with both bounded and unbounded time interval options. Algorithms based on Dynamic Programming (DP) principles are proposed for the calculation of suitable price profiles. At the core of such algorithms, there is the resolution of stochastic optimisation problems. In particular, the maximisation of the expected total revenue is formulated via a constrained Stochastic Dynamic Programming (SDP) approach, which becomes time-variant in case of advance reservation requests. Approximate Dynamic Programming (ADP) techniques are adopted in case of large state spaces. Simulations are performed to show the effectiveness of the proposed models and the related algorithms.
Visualization, Discriminability and Applications of Interpretable Saak Features

In this work, we study the power of Saak features as an effort towards interpretable deep learning. Being inspired by the operations of convolutional layers of convolutional neural networks, multi-stage Saak transform was proposed. Based on this foundation, we provide an in-depth examination on Saak features, which are coefficients of the Saak transform, by analyzing their properties through visualization and demonstrating their applications in image classification. Being similar to CNN features, Saak features at later stages have larger receptive fields, yet they are obtained in a one-pass feedforward manner without backpropagation. The whole feature extraction process is transparent and is of extremely low complexity. The discriminant power of Saak features is demonstrated, and their classification performance in three well-known datasets (namely, MNIST, CIFAR-10 and STL-10) is shown by experimental results.
Colorblind-shareable videos by synthesizing temporal-coherent polynomial coefficients

To share the same visual content between color vision deficiencies (CVD) and normal-vision people, attempts have been made to allocate the two visual experiences of a binocular display (wearing and not wearing glasses) to CVD and normal-vision audiences. However, existing approaches only work for still images. Although state-of-the-art temporal filtering techniques can be applied to smooth the per-frame generated content, they may fail to maintain the multiple binocular constraints needed in our applications, and even worse, sometimes introduce color inconsistency (same color regions map to different colors). In this paper, we propose to train a neural network to predict the temporal coherent polynomial coefficients in the domain of global color decomposition. This indirect formulation solves the color inconsistency problem. Our key challenge is to design a neural network to predict the temporal coherent coefficients, while maintaining all required binocular constraints. Our method is evaluated on various videos and all metrics confirm that it outperforms all existing solutions.
New Quaternary Codes Derived from Posets of the Disjoint Union of Two Chains

Based on the generic construction of linear codes, we construct linear codes over the ring Z4 via posets of the disjoint union of two chains. We determine the Lee weight distributions of the quaternary codes. Moreover, we obtain some new linear quaternary codes and good binary codes by using the Gray map.
Reproduction, replication, analysis and adaptation of a term alignment approach

Abstract In this paper, we look at the issue of reproducibility and replicability in bilingual terminology alignment (BTA). We propose a set of best practices for reproducibility and replicability of NLP papers and analyze several influential BTA papers from this perspective. Next, we present our attempts at replication and reproduction, where we focus on a bilingual terminology alignment approach described by Aker et al. (Extracting bilingual terminologies from comparable corpora. In: Proceedings of the 51st annual meeting of the association for computational linguistics, vol. 1 402–411, 2013) who treat bilingual term alignment as a binary classification problem and train an SVM classifier on various dictionary and cognate-based features. Despite closely following the original paper with only minor deviations—in areas where the original description is not clear enough—we obtained significantly worse results than the authors of the original paper. We then analyze the reasons for the discrepancy and describe our attempts at adaptation of the approach to improve the results. Only after several adaptations, we achieve results which are close to the results published in the original paper. Finally, we perform the experiments to verify the replicability and reproducibility of our own code. We publish our code and datasets online to assure the reproducibility of the results of our experiments and implement the selected BTA models in an online platform making them easily reusable even by the technically less-skilled researchers.
Ambient Explanations: Ambient Intelligence and Explainable AI

With renewed prominence of Explainable AI (XAI), many areas are revisiting early work on Explainability. Within the broader field of Artificial Intelligence (AI), Ambient Intelligence (AmI) has an advantage in the development of transparent and ethical systems because such work has long been an integral part of research, development and operations in AmI. In this paper we argue that, because of the paradigm requirements of system intelligence, social intelligence and embeddedness, AmI is uniquely prepared to support the push for ethical and transparent technology development. We argue that Ambient Intelligent Systems are well suited to infer when an explanation might be needed (and of what kind), and the form that it should take. We further propose AmI devices as mediators between humans and machines because they can combine social and technical systems in a fully embedded way.
Clustering by Directly Disentangling Latent Space

To overcome the high dimensionality problem of data, learning feature representations for clustering has been widely studied. In this work, we propose Disentangling Latent Space Clustering (DLS-Clustering), a new clustering framework that directly learns cluster assignments from disentangled latent spacing without additional clustering methods. We enforce the encoder and the generator of GAN to form an encoder-generator pair in addition to the generator-encoder pair. We train the encoder-generator pair using real data, which can implicitly estimate the real conditional distribution. Meanwhile, this framework enforces the outputs of the encoder to match the inputs of GAN and the prior noise distribution, which disentangles latent space into two parts: one-hot discrete and continuous latent variables. The former can be directly expressed as clusters and the latter represents remaining unspecified factors. Our experiments show that the proposed method achieves the optimal disentanglement performance and outperforms existing generative model-based clustering methods.
CamDrop

To force convolutional networks to explore more discriminative evidence throughout spatial regions, this paper presents a novel CamDrop to improve the conventional dropout in two aspects. First, by considering the intensity of class activation mapping (CAM) all around, CamDrop selectively abandons some specific spatial regions in predominating visual patterns at each iteration. In many classification tasks, CamDrop demonstrates its effectiveness and achieves considerable improvements on robust predictions for adversarial examples. Second, although dropout is a widely adopted technique that has been applied to regularize large models, the improvement in performance always attributes to better preventing DNN from overfitting. Here we give a new explanation of dropout from the perspective of optimization that it makes the upper bound of the magnitude of gradients much tighter, which leads to a more stable behavior of the gradients and effectively avoids neurons falling into the saturation region of the nonlinear activation, even when using high learning rates. Extensive experiments have been performed to prove the above two strengths of CamDrop.
Minimal binary 2-neighbour-transitive codes

The main result here is a characterisation of binary 2-neighbour-transitive codes with minimum distance at least 5 via their minimal subcodes, which are found to be generated by certain designs. The motivation for studying this class of codes comes primarily from their relationship to the class of completely regular codes. The results contained here yield many more examples of 2-neighbour-transitive codes than previous classification results of families of 2-neighbour-transitive codes. In the process, new lower bounds on the minimum distance of particular sub-families are produced. Several results on the structure of 2-neighbour-transitive codes with arbitrary alphabet size are also proved. The proofs of the main results apply the classification of minimal and pre-minimal submodules of the permutation modules over F2 for finite 2-transitive permutation groups.
Human Activity Recognition for Ambient Assisted Living

The considerable rise in the average age of the population leads to the exceeding number of seniority people. This has made a strong impact in demanding community requirement such as rehabilitation, physical support, home assistance etc. For this reason, Ambient Assisted Living (AAL) plays an exceptional role in supervising the Activities of Daily Living (ADL) of the target users. AAL would be an encouraging technology for the current care models by acting as a companion. This becomes a thought-provoking research area in the fast-developing world, but the condition for examining various ADL and the self-classification becomes a big challenge. There are numerous ways to accumulate data from camera, microphone and other sensors. Datasets that are used in this paper have been collected from publicly available resources for Human Activity Recognition. In this work, we have discussed about different classification algorithm used for grouping of features into their corresponding activity and by then their performance is evaluated. However, many classifiers face the limitations of enormous number of datasets and immense training time of the feature vector. Hence to overcome these problems, we have used RF and K-NN classifiers. Both are tremendously helpful in dealing with larger datasets and provides an accuracy level of about 83.05%.
The Strategy of Constructing an Interdisciplinary Knowledge Center

This paper aims to explain the construction strategy of interdisciplinary knowledge centers from the perspective of expert systems, knowledge management and educational information technology. The method is: Firstly, it explores how to construct the strategy of interdisciplinary knowledge centers sharing, and then makes necessary explorations from computer assisted knowledge management, expert knowledge acquisition and educational autonomous learning. Finally, establish the foundation of natural language understanding in the domain knowledge base which is the basis of the interdisciplinary knowledge center. It is characterized by the application of the wisdom system studied strategy, with machine translation, machine learning and human-computer interaction, starting from the excellent courses in research universities, with the combination of intelligent text analysis and knowledge module finishing, and with the application of “seven-stapes pass” and “eight-persons group” as the education management innovation paradigm. The result is the new paradigm with both standardization and individuality, namely the construction of an interdisciplinary knowledge center that combines large production with small production. The significance lies in the combination of “language, knowledge, software, hardware formal system engineering” and “education, management, learning, application social system engineering”, that help to promote the interdisciplinary knowledge center and its systems engineering talents training. It is more efficient to build the smart systems studied for serving the society.
MiCRon

Knowledge graphs (KGs) have been extensively used to annotate text, e.g., news articles, in order to enhance its comprehension by readers. This requires to map entities occurring in the news to the target entities of the KG and to extract a so-called relationship sub-graph (RSG) that spans these entities. RSG extraction is computationally demanding and cannot scale to large KGs. Existing approximation algorithms that focus on structurally compact RSGs are not satisfactory since they often return no answers. We address this problem and develop an efficient algorithm to find approximations that connect the most salient subset of the target entities. Moreover, we propose a context-aware method to rank RSGs by their relevance to the news and their semantic cohesion. In the demo we will present our approach and the attendees will be able to experience how our system MiCRon helps to make sense of news article by computing and presenting RSGs relevant to these articles.
Lightweight merging of compressed indices based on BWT variants

In this paper we propose a flexible and lightweight technique for merging compressed indices based on variants of Burrows-Wheeler transform (BWT), thus addressing the need for algorithms that compute compressed indices over large collections using a limited amount of working memory. Merge procedures make it possible to use an incremental strategy for building large indices based on merging indices for progressively larger subcollections. Starting with a known lightweight algorithm for merging BWTs Holt and McMillan (2014) [22], we show how to modify it in order to merge, or compute from scratch, also the Longest Common Prefix (LCP) array. We then expand our technique for merging compressed tries and circular/permuterm compressed indices, two compressed data structures for which there were hitherto no known merging algorithms.
A Monocular SLAM-based Controller for Multirotors with Sensor Faults under Ground Effect

Multirotor micro air vehicles can operate in complex and confined environments that are otherwise inaccessible to larger drones. Operation in such environments results in airflow interactions between the propellers and proximate surfaces. The most common of these interactions is the ground effect. In addition to the increment in thrust efficiency, this effect disturbs the onboard sensors of the drone. In this paper, we present a fault-tolerant scheme for a multirotor with altitude sensor faults caused by the ground effect. We assume a hierarchical control structure for trajectory tracking. The structure consists of an external Proportional-Derivative controller and an internal Proportional-Integral controller. We consider that the sensor faults occur on the inner loop and counteract them in the outer loop. In a novel approach, we use a metric monocular Simultaneous Localization and Mapping algorithm for detecting internal faults. We design the fault diagnosis scheme as a logical process which depends on the weighted residual. Furthermore, we propose two control strategies for fault mitigation. The first combines the external PD controller and a function of the residual. The second treats the sensor fault as an actuator fault and compensates with a sliding mode action. In either case, we utilize onboard sensors only. Finally, we evaluate the effectiveness of the strategies in simulations and experiments.
A Survey on Securing Smart Gadgets Using Lightweight Cryptography

Current security world having big security challenges of IoT devices such as baby monitoring cameras. These devices are susceptible to any cyberattacks. Manufactures also not bother about security issues but it makes lightweight cryptography and wireless network security highly vulnerable. Rather to control over any computer, it is easy to attack such lightweight devices and use it for monitoring the personal activity or conduct DoS attack. Now different malwares are used to infect baby monitoring camera and exploit vulnerabilities. Through this study, we are going to focus on baby monitoring camera which is more vulnerable with respect to personal and commercial security breaching. We are proposing solution for lightweight cryptographic algorithm to secure default password and data storage in baby monitoring camera. DDoS attack happened because of looping in network so we are proposing solution for avoiding looping in network traffic that is implementation of spanning tree algorithm.
Bi-LSTM Model for Morpheme Segmentation of Russian Words

The paper addresses the task of automatic morpheme segmentation involving both splitting words into morphs and classification of resulted morphs. For segmentation of Russian words, a new model based on Bi-LSTM neural network is proposed and experimentally evaluated on several training data sets differing in labeling. The proposed model has comparable quality with the best supervised machine learning models for morpheme segmentation with classification, slightly outperforming them in word-level classification accuracy with score 89%.
HMM-based Convolutional LSTM for Visual Scanpath Prediction

The human visual system performs a dynamic process of scanning the scene by rapid eye movements and fixations, yielding a visual scanpath. We propose an approach to generate artificial visual scanpaths for natural images. A convolutional long short term memory (LSTM) neural network is employed, which learns the mapping of image features to eye fixations by modeling the sequential dependencies of the fixations in a scanpath. A novel approach of hidden Markov model (HMM) based data augmentation is presented that increases the number of available image-specific input-output pairs to train the LSTM appropriately. Both the HMM and the LSTM are designed to be consistent with existing knowledge on saccadic eye movements. Experimental results on a standard eye-tracking dataset demonstrate that the proposed approach does better than the state-of-the-art and generates realistic visual scanpath data.
Evaluating the numerical instability in fuzzy clustering validation of high-dimensional data

Fuzzy clustering validation of high-dimensional datasets is only possible using a reliable cluster validity index (CVI). A good CVI must correctly recognize a data structure and its validations must be independently of any parameter of a clustering algorithm or data property. However, some classical fuzzy CVIs as Partition Coefficient (PC), Partition Entropy (PE) and Fukuyama-Sugeno (FS) have the monotonic tendency in function of the number of clusters. Although the literature presents extensive investigations about such tendency, they were conducted for low-dimensional data, in which such data property does not affect the clustering behavior. In order to investigate how such aspects affect the fuzzy clustering results of high-dimensional data, in this work we have clustered objects of thirteen real datasets, using the Fuzzy c-Means algorithm. The fuzzy partitions were validated by PC, PE, FS and some proposed improvements of them to lead with the monotonic tendency, totaling eight fuzzy CVIs analyzed. Besides the analysis made about the number of clusters selected by the CVIs, the Mann-Kendall test was performed to verify statistically the monotonic trend of the CVIs results. From the two analysis made, the Modified Partition Coefficient and Scaled Partition Entropy indices were successful in respectively improving the PC and PE indices.