Reviewerly/exHarmony huggingface dataset:
ComfOnt: A Semantic Framework for Indoor Comfort and Energy Saving In Smart Homes

This work introduces ComfOnt, a semantic framework developed within the context of ambient assisted living, context awareness, and ambient intelligence Italian research projects. ComfOnt leverages knowledge regarding Smart Home inhabitants and their particular needs, the devices deployed inside the domestic environment (appliances, sensors, and actuators), the amount of their energy consumption, and indoor comfort metrics to provide dwellers with customized services. Developed reusing widely adopted ontologies, ComfOnt aims at providing inhabitants with the possibility of having personalized indoor comfort in their living environments and at helping them in scheduling their daily activities requiring appliances; in fact, the proposed semantic framework enables the representation of appliances’ energy consumption and the energy profile of the Smart Home, thus assisting the dwellers in avoiding power cuts and fostering energy savings. ComfOnt serves as a knowledge base for a prototypical application (DECAM) dedicated to Smart Home inhabitants; the architecture and the functionalities of DECAM are here presented.
Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

This article proposes an utterance-to-utterance interactive matching network (U2U-IMN) for multi-turn response selection in retrieval-based chatbots. Different from previous methods following context-to-response matching or utterance-to-response matching frameworks, this model treats both contexts and responses as sequences of utterances when calculating the matching degrees between them. For a context-response pair, the U2U-IMN model first encodes each utterance separately using recurrent and self-attention layers. Then, a global and bidirectional interaction between the context and the response is conducted using the attention mechanism to collect the matching information between them. The distances between context and response utterances are employed as a prior component when calculating the attention weights. Finally, sentence-level aggregation and context-response-level aggregation are executed in turn to obtain the feature vector for matching degree prediction. Experiments on four public datasets showed that our proposed method outperformed baseline methods on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.
Enhanced Echo-State Restricted Boltzmann Machines for Network Traffic Prediction

Network traffic prediction is a great challenge due to complex statistical properties, generally covering the long-range correlations and self-similarity. To address this issue, this article applies an integrated neural computing model to predict network traffic, namely, enhanced echo-state restricted Boltzmann machine (eERBM). In structure, this model possesses the following functional components of feature learning, information compensation, input superposition, and supervised nonlinear approximation. It is motivated by the introduction of information theory in modeling the hybrid architecture of the echo state network and the restricted Boltzmann machine. This is the first attempt that eERBM is applied in network traffic prediction tasks of different origin and characteristics, considering TCP/IP packet and variable-bit-rate video. By performing a theoretical analysis, we show that eERBM achieves superior nonlinear approximation and robustness in comparison to the baseline methods, and effectively preserves the self-similarity of network traffic traces.
Blockchain security risk assessment and the auditor

Abstract A blockchain is an Internet‐based peer‐to‐peer system that forms a network of independent and connected computers that simultaneously record and verify transactions. This peer‐to‐peer system focuses on who owns the information and how that information is transferred. Blockchains offer significant advantages over traditional databases where users can delete, modify, and change records. The advantages include improved efficiencies, lower costs, enhanced transparency, and an immutable audit history of all transactions. The advantages, however, are not without significant risks. The risks include technological risks, data security risks, interoperability risks, and third‐party vendor risks. Because of the inherent advantages in blockchains, auditors are being called upon to provide assurance services to clients who use blockchains and to advise clients on blockchain technology. Therefore, auditors must be equipped with the knowledge and expertise of not only blockchain technology, but also the assessment of risks inherent in blockchain technology.
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In this paper, we study quantum error-correcting codes from skew constacyclic codes over the ring R=Fq[u,v]〈u2−1,v2−1,uv−vu〉, where q=pm for any odd prime p and positive integer m. We decompose skew constacyclic codes over the ring R as a direct sum of skew constacyclic codes over Fq. Self-dual skew constacyclic codes over the ring R are characterized. Necessary and sufficient conditions for skew negacyclic and skew constacyclic codes to be dual-containing are obtained. As an application, we construct new quantum error-correcting codes from skew constacyclic codes over Fq.
Success Factor of Implementation Blockchain Technology in Pharmaceutical Industry: A Literature Review

In the pharmaceutical industry, the implementation of blockchain technology in its supply chain has begun to achieve commercial objectives. However, there are still many who do not yet know the benefits or are not sure of the implementation. This study aims to find success factors in the application of blockchain technology in the pharmaceutical industry. Therefore the process used in the literature review by the meta-analysis method. The results of this study found 21 factors, and five factors found to be the most dominant in the application of blockchain technology are Track, Trust, Traceability, Transparency, And Real-Time. By knowing this success factor, other pharmaceutical industries are encouraged to implement blockchain technology as a solution to the problems that they have.
A CNN Based Automated Activity and Food Recognition Using Wearable Sensor for Preventive Healthcare

Recent developments in the field of preventive healthcare have received considerable attention due to the effective management of various chronic diseases including diabetes, heart stroke, obesity, and cancer. Various automated systems are being used for activity and food recognition in preventive healthcare. The automated systems lack sophisticated segmentation techniques and contain multiple sensors, which are inconvenient to be worn in real-life settings. To monitor activity and food together, our work presents a novel wearable system that employs the motion sensors in a smartwatch together with a piezoelectric sensor embedded in a necklace. The motion sensor generates distinct patterns for eight different physical activities including eating activity. The piezoelectric sensor generates different signal patterns for six different food types as the ingestion of each food is different from the others owing to their different characteristics: hardness, crunchiness, and tackiness. For effective representation of the signal patterns of the activities and foods, we employ dynamic segmentation. A novel algorithm called event similarity search (ESS) is developed to choose a segment with dynamic length, which represents signal patterns with different complexities equally well. Amplitude-based features and spectrogram-generated images from the segments of activity and food are fed to convolutional neural network (CNN)-based activity and food recognition networks, respectively. Extensive experimentation showed that the proposed system performs better than the state of the art methods for recognizing eight activity types and six food categories with an accuracy of 94.3% and 91.9% using support vector machine (SVM) and CNN, respectively.
No Fear of the Dark: Image Retrieval Under Varying Illumination Conditions

Image retrieval under varying illumination conditions, such as day and night images, is addressed by image preprocessing, both hand-crafted and learned. Prior to extracting image descriptors by a convolutional neural network, images are photometrically normalised in order to reduce the descriptor sensitivity to illumination changes. We propose a learnable normalisation based on the U-Net architecture, which is trained on a combination of single-camera multi-exposure images and a newly constructed collection of similar views of landmarks during day and night. We experimentally show that both hand-crafted normalisation based on local histogram equalisation and the learnable normalisation outperform standard approaches in varying illumination conditions, while staying on par with the state-of-the-art methods on daylight illumination benchmarks, such as Oxford or Paris datasets.
Bot detection using a single post on social media

Recent studies of social media have made a unanimous conclusion that public opinions can be altered through systematic exploitation of social media using bot accounts. The existing bot detection methodologies utilize features of the accounts to label them as either bot or human. However, in this work, we propose a convolutional neural network (CNN) to identify the bot accounts using a single post on the social media. We have compared our results with an artificial neural network (ANN) trained on the features extracted from the accounts' profiles. Results have shown that bot accounts can be detected with 98.71% accuracy using CNN as compared to the 97.6% of ANN. Moreover, we have also proposed a model that combine both the techniques and have achieved 99.43% accuracy.
Kernel extreme learning machine with iterative picking scheme for failure diagnosis of a turbofan engine

The kernel extreme learning machine (KELM) has attracted attention for failure diagnosis of turbofan engines, but its application for time-sensitive scenarios is inherently limited by its lack of sparseness. The original KELM constructs the hidden layer using all the training samples; thus, the real-time performance may be seriously degraded for large datasets. To address this limitation, a novel iterative picking scheme for improving the sparseness of KELM is proposed in this study. It has two noteworthy features, a compact structure and a sparse solution, which gives better real-time performance. The proposed scheme improves the sparseness of the KELM algorithm by two alternating components, an insertion strategy to expand the network and an elimination strategy to reduce the scale of the network. Validation on regression and classification benchmark datasets demonstrates that the proposed algorithm can produce a sparse network structure with fewer hidden nodes without sacrificing the model's accuracy. Finally, simulations of failure diagnosis of a turbofan engine show that the proposed algorithm can perform at an accuracy comparable to that of KELM with a much faster testing speed, which is crucial in real-time and onboard applications.
CNN based spectral super-resolution of remote sensing images

The spectral super-resolution techniques attempt to re-project spectrally coarse images to a set of finer wavelength bands. However, complexity of the mapping between coarser and finer scale spectra, large variability of spectral signatures, and the difficulty in simultaneously modeling spatial and spectral contexts make the problem highly ill-posed. Our main hypothesis is that the consideration of spatial as well as spectral aspects is essential for spectral enhancement of remote sensing images. In this regard, this paper proposes a framework consisting of sparse-coding-based pixel-spectra enhancement, collaborative unmixing, and spatial-spectral prior based transformation. Two sparse-coding-based architectures are proposed to project the coarser scale pixel-spectra to the target scale. These models facilitate simultaneous optimization of sparse codes and dictionaries with regard to the spectral super-resolution objective. A CNN based encoding-decoding architecture is explored to model the spatial-spectral prior for improving fidelity of the reconstructions. The endmember similarities and spectral image prior are considered while designing the proposed loss functions. The experiments, over standard as well as AVIRIS-NG and drone-derived datasets, confirm better accuracy of the proposed frameworks as compared to the prominent approaches. In addition, the proposed CNN models for spectral upscaling and spatial-spectral transformation are found to be less sensitive to the variation in network parameter values.
Secure smart contract-enabled control of battery energy storage systems against cyber-attacks

Battery Energy Storage Systems (BESSs) are an integral part of a sustainable and resilient smart grid. The security of such critical cyber-physical infrastructure is considered as a major priority for both industry and academia. In this paper, we propose a new distributed smart-contract based control approach of BESSs to enable collaborative and secure operations among them. We present a comprehensive discussion on how control strategies can be implemented as smart contracts and deployed on a distributed network of BESSs nodes in order to operate these storage systems according to secure consensus. To verify the effectiveness of the proposed method, we analyze the vulnerabilities of BESSs when controlled according to traditional schemes vs. smart-contract enabled control. Simulation results show that if individual BESSs achieve a certain maximum threshold of exploitability, then the network of distributed BESSs is more robust to cyber-attacks in smart contract-defined control.
Monitorless

Today, software operation engineers rely on application key performance indicators (KPIs) for sizing and orchestrating cloud resources dynamically. KPIs are monitored to assess the achievable performance and to configure various cloud-specific parameters such as flavors of instances and autoscaling rules, among others. Usually, keeping KPIs within acceptable levels requires application expertise which is expensive and can slow down the continuous delivery of software. Expertise is required because KPIs are normally based on application-specific quality-of-service metrics, like service response time and processing rate, instead of generic platform metrics, like those typical across various environments (e.g., CPU and memory utilization, I/O rate, etc.)
Internet of Things technologies for elderly health-care applications

The Internet of Things (IoT) is the trend in the next-generation technologies, which interconnects the smart devices in today's Internet structure with more associated benefits. The major advantage of the system includes the connectivity of these smart devices with the services and system that exceed the machine-to-machine structure. IoT plays a major role in building smart pervasive blueprints. A variety of application domains have the usage of IoT, mainly health care. The IoT revolution is reframing modern health care with assuring technological, social, and economic prospects. The count of elderly people who stay alone is increasing exponentially. Advancements in terms of monitoring them remotely are the need of the time, and IoT growth has leveraged the same. Elderly persons face a lot of challenges in conducting their day-to-day life, the challenges range from memory loss, susceptibility to diseases, and impairments making them live in a survival mode. IoT assistance can be deployed for elderly monitoring with the aid of smartphones, vital signal sign monitors, smart wearables, audio–video sensors, smart devices, and smart TV. IoT techniques combined with Big Data and Cloud can be used effectively to give numerous solutions to the problem of remote elder monitoring. There are several manual aids that already exist, which are either slow or inefficient to be used with elders. The IoT-based remote monitoring can be used to monitor elders without any safety concerns and in an effective and uninterrupted way by deploying all the implementations in a typical living scenario. Currently, there are systems that facilitate self-care and extend the independence of aging population. Such systems are often known as assisted living systems. The main aims of the systems are to enable elderly people to independently live longer in their own homes, to enhance living qualities, and to reduce costs for society and public health systems. Assisted living systems can help support elderly persons with their daily activities in order to help them maintain healthy and safety while living independently. In this chapter, IoT technologies for elderly health care will be detailed.
Infinite families of 2‐designs from a class of cyclic codes

Abstract Combinatorial ‐designs have wide applications in coding theory, cryptography, communications, and statistics. It is well known that the supports of all codewords with a fixed weight in a code may give a ‐design. In this paper, we first determine the weight distributions of a class of linear codes derived from the dual of some extended cyclic codes. We then obtain infinite families of 2‐designs and explicitly compute their parameters from the supports of all the codewords with a fixed weight in the codes. By a simple counting argument, we obtain exponentially many 2‐designs.
Vessel Detection and Tracking Method Based on Video Surveillance

Ship detection and tracking is a basic task in any vessel traffic monitored area, whether marine or inland. It has a major impact on navigational safety and thus different systems and technologies are used to determine the best possible methods of detecting and identifying sailing units. Video monitoring is present in almost all of them, but it is usually operated manually and is used as a backup system. This is because of the difficulties in implementing an efficient and universal automatic detection method that would work in quickly alternating environmental conditions for all kind of sailing units-from kayaks to seagoing merchant vessels. This paper presents a method that allows the detection and tracking of ships using the video streams of existing monitoring systems for ports and rivers. The method and the results of experiments on three sets of data using cameras with different characteristics, settings, and scene locations are presented. The experiments were carried out in variable light and weather conditions, and a wide range of unit types were used as detection objectives. The results confirm the usability of the proposed solution; however, some minor issues were encountered in the presence of ships wakes or highly unfavourable weather conditions.
A Multi-Task Neural Network Architecture for Renal Dysfunction Prediction in Heart Failure Patients With Electronic Health Records

Renal dysfunction, which is associated with bad clinical outcomes, is one of the most common complications of heart failure (HF). Timely prediction of renal dysfunction can help medical staffs intervene early to avoid catastrophic consequences. In this paper, we proposed a multi-task deep and wide neural network (MT-DWNN) for predicting fatal complications during hospitalization. The algorithm was tested on a dataset collected from Chinese PLA General Hospital, which contains 35,101 hospitalizations with HF diagnosis during the last 18 years, and 2,478 hospitalizations with a diagnosis of renal dysfunction. For the renal dysfunction task, the AUC of the proposed method is 0.9393, which is a significant improvement ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.01$ </tex-math></inline-formula> ) compared to that of conventional methods, while that of single task deep neural networks is 0.9370, that of random forest is 0.9360, and that of logistic regression is 0.9233. The experimental results show that the proposed MT-DWNN model achieves better prediction performance on renal dysfunction in HF patients than conventional models.
Superpixel Soup: Monocular Dense 3D Reconstruction of a Complex Dynamic Scene

This work addresses the task of dense 3D reconstruction of a complex dynamic scene from images. The prevailing idea to solve this task is composed of a sequence of steps and is dependent on the success of several pipelines in its execution. To overcome such limitations with the existing algorithm, we propose a unified approach to solve this problem. We assume that a dynamic scene can be approximated by numerous piecewise planar surfaces, where each planar surface enjoys its own rigid motion, and the global change in the scene between two frames is as-rigid-as-possible (ARAP). Consequently, our model of a dynamic scene reduces to a soup of planar structures and rigid motion of these local planar structures. Using planar over-segmentation of the scene, we reduce this task to solving a “3D jigsaw puzzle” problem. Hence, the task boils down to correctly assemble each rigid piece to construct a 3D shape that complies with the geometry of the scene under the ARAP assumption. Further, we show that our approach provides an effective solution to the inherent scale-ambiguity in structure-from-motion under perspective projection. We provide extensive experimental results and evaluation on several benchmark datasets. Quantitative comparison with competing approaches shows state-of-the-art performance.
Deep Entity Linking via Eliminating Semantic Ambiguity With BERT

Entity linking refers to the task of aligning mentions of entities in the text to their corresponding entries in a specific knowledge base, which is of great significance for many natural language process applications such as semantic text understanding and knowledge fusion. The pivotal of this problem is how to make effective use of contextual information to disambiguate mentions. Moreover, it has been observed that, in most cases, mention has similar or even identical strings to the entity it refers to. To prevent the model from linking mentions to entities with similar strings rather than the semantically similar ones, in this paper, we introduce the advanced language representation model called BERT (Bidirectional Encoder Representations from Transformers) and design a hard negative samples mining strategy to fine-tune it accordingly. Based on the learned features, we obtain the valid entity through computing the similarity between the textual clues of mentions and the entity candidates in the knowledge base. The proposed hard negative samples mining strategy benefits entity linking from the larger, more expressive pre-trained representations of BERT with limited training time and computing sources. To the best of our knowledge, we are the first to equip entity linking task with the powerful pre-trained general language model by deliberately tackling its potential shortcoming of learning literally, and the experiments on the standard benchmark datasets show that the proposed model yields state-of-the-art results.