Reviewerly/exHarmony huggingface dataset:
An open-source framework for ExpFinder integrating <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e86" altimg="si6.svg"><mml:mi>N</mml:mi></mml:math>-gram vector space model and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e91" altimg="si7.svg"><mml:mi mathvariant="normal">μ</mml:mi></mml:math>CO-HITS

Finding experts drives successful collaborations and high-quality product development in academic and research domains. To contribute to the expert finding research community, we have developed ExpFinder which is a novel ensemble model for expert finding by integrating an N-gram vector space model (nVSM) and a graph-based model (μCO-HITS). This paper provides descriptions of ExpFinder’s architecture, key components, functionalities, and illustrative examples. ExpFinder is an effective and competitive model for expert finding, significantly outperforming a number of expert finding models as presented in Kang et al. (2021).
Toward an Adaptive Software Architecture for Archetype-Based Health-Care Applications

Self-adaptation gives software systems the ability to adjust their behavior or structure in changing environments such as the health software domain. However, adaptability significantly complicates software implementation and brings great challenges to software engineering.
Efficient Scheduling of tasks in Cloud

Wireless Cloud Infrastructure provides a pay-for-use basis data and infrastructure services over the internet. This enables the user to upgrade the software automatically. Only the space required for the server can be used, thus minimizing the carbon footprint. The main problem with cloud computing is task scheduling which reduces system performance. An efficient task-scheduling algorithm is required to improve the performance of the system. Existing task-scheduling algorithms concentrate on memory of the CPU, resource requirements of the task, run time and cost of execution. Bandwidth of the network is not considered in most of the researches. In this paper, an efficient algorithm for task scheduling has been introduced that performs scheduling of the tasks based on network bandwidth. By doing this, the tasks can be allocated based on the availability of the bandwidth of the network. In addition, the algorithm assigns the right number of tasks to each virtual machine. The results shows that the execution time is reduced to a greater extent when the bandwidth factor is included in the algorithm.
Analysis of the modulation depth of some femtosecond laser pulses in holographic interferometry

In this paper, we discuss the quality of holograms generated by two-femtosecond laser pulses of two different colors based on the interference and holography processes by the calculation of the modulation depth. Some numerical simulations for the two Higher-order sh- and ch-Gaussian temporal profiles (shnGTP and chnGTP) are investigated. These simulations show that, when two lasers with a very large frequency detuning are used, the considered profiles exhibit more precise results and give new frequency detuning zones that allow a very significant value of the modulation depth to fringe contrast (MDFC) ratio to obtain the best hologram compared to Gaussian profile. The proposed theory will be a good basis for the development of some new experiments on holographic interferometry and it will certainly be very useful for the specialists using these femtosecond laser pulses.
On the Effects of Skip Connections in Deep Generative Adversarial Models

Deep convolutional neural networks provide significant contribution to GANs in stabilizing the GAN training. However, the convolution operator has a local receptive field and therefore geometric or structural patterns of complex images can only be processed after passing through several convolutional layers. Recent studies suggest that the network depth is indeed of crucial importance for GANs to generate images with consistent objects/scenarios to a certain extent, but it also amplifies the mode collapse problem. As a solution, we investigate a skipping mechanism for going deeper in GANs. By passing the input of layers as a weighted residual, it alleviates the phenomenon of mode collapse. Experimental results show that the proposed method increases the capacity of modeling long range dependencies while retaining the local invariance property obtained by using a relatively small convolution kernel.
A Fast Noniterative Visually Lossless Compression of Dental Images Using AGU-M Coder

This paper deals with lossy compression of medical images in dentistry. For this purpose, we use AGU-M coder based on discrete cosine transform. A peculiarity of the proposed approach is that visually lossless compression is carried out without iterations and, due to this, in a fast way. Invisibility of introduced distortions and/or artifacts is provided by a proper setting of a fixed scaling factor that controls compression. This allows reaching a desired quality of compressed images characterized by PSNR-HVS-M. Experiments are performed for a set of dental images produced by Morita imaging system. Image visual quality has been analyzed by a group of dentistry experts. It is shown that the proposed approach provides several benefits compared to earlier designed counterparts, in particular, a larger compression ratio.
Advanced Fusion ACO Approach for Memory Optimization in Cloud Computing Environment

A wide network of multi-core processors is installed on a scale-out platform such as the Cloud computing. The total device configuration, including the number of processor cores, core frequency, memory hierarchy, capability, the number of memory channels, and the memory data rate, depends on the performance, power usage, and capital costs of such facilities is increasing. This could also pose a much bigger threat to the efficiency of cloud apps and the expense of cloud computing, with the growth of big data and analytics apps. The role of the memory subsystem in the cloud infrastructure, and particularly for this evolving class of applications, is therefore important to understand. Despite the rising interest in recent years, little work has been done to understand patterns in memory specifications and build detailed and accurate models to forecast memory subsystem output and expense. Our proposed algorithm Fusion ACO is very effective for memory optimization in cloud computing environment.
Latent Class Cluster for Clustering Villages Based on Socio-economic Indicators in 2018

Abstract Latent class analysis (LCA) is a statistical method used to classify units into unobserved (latent) variable classes, so-called clusters. In this article, we introduce LCA to demonstrate its use for village groupings. We applied LCA on data processed from the census conducted by BPS-Statistics Indonesia, namely the village potential (Podes) data 2018. The Podes data is used to extract the socio-economic characteristics of a village. The empirical results of the LCA application show that the villages in the Banyumas Regency are grouped into three classes/clusters.
Photometric constraint for absolute phase unwrapping from single-frequency fringe patterns

As a fundamental step in fringe projection profilometry, absolute phase unwrapping via single-frequency fringe patterns is still a challenging ill-posed problem, which attracts lots of interest in the research area. To solve the problem above, additional constraints were constructed, such as spatial smoothness constraint (SSC) in spatial phase unwrapping algorithm and viewpoint consistency constraint (VCC) in multi-view systems (e.g., stereo and light-field cameras). However, there still exists phase ambiguity in the unwrapping result based on SSC. Moreover, VCC-based methods rely on additional cameras or light-field cameras, which makes the system complicated and expensive. In this paper, we propose to construct a novel constraint directly from photometric information in captured image intensity, which has never been fully exploited in phase unwrapping. The proposed constraint, named photometric constraint (PC), provides a prospective constraint for absolute phase unwrapping from single-frequency fringe patterns without any additional cameras. Extensive experiments have been conducted for the validation of the proposed method, which achieved comparable performance with the state-of-the-art method, given a traditional camera-projector setup and single high-frequency fringe patterns.
Daily Health Monitoring Chatbot with Linear Regression

Nowadays, elderly people harm their health from daily routines such as the habits of eating junk food, lack of time for exercising, and so on. When age increases, most of elderly people often have encountered with high blood pressure disease. This disease can lead to many dangerous diseases. Additionally, population of the doctors in hospital is not so high. The doctor usually takes a short note about symptoms for quick services that might not be enough for diagnosis. Therefore, we proposed daily health monitoring chatbot for the elderly people. We need to collect information from the elderly people to create personal health record (PHR). We develop conversational chatbot to interact with the elderly people via LINE application. Outcomes of this research support the doctor's work because after reading daily PHR, the doctor diagnoses the diseases and gives advice for medical treatment more accurately. Furthermore, Linear regression technique was developed to monitor the blood pressure's trend of elderly. Thus, they can prevent or relieve some diseases from chatbot warning and taking care the health.
Driving Behavior-Aware Network for 3D Object Tracking in Complex Traffic Scenes

Recently a large number of 3D object tracking methods have been extensively investigated and applied in a variety of applications using convolutional neural networks. Although most of them have made great progress in partial occlusion, the intricate interweaving of moving agents (e.g. pedestrians and vehicles) may lead to inferior performance of 3D object tracking in complex traffic scenes. To boost the performance of 3D object tracking in cases of severe occlusions, we present an end-to-end deep learning framework with a driving behavior-aware model that takes full advantage of spatial-temporal details in consecutive frames and learns the driving behavior from object variations in 2D center point, depth, rotation and translation in parallel. In contrast to prior work, our novelty formulates driving behavior that reasons about the possible motion trajectories of the investigated target for autonomous systems. We show in experiments that our method outperforms state-of-the-art approaches on 3D object tracking in the challenging nuScenes dataset.
Implementation of Cryptography Merkle-Hellman Knapsack Combination, Discrete Algorithm and ASCII Table Modification for PHP Source Code Security

Abstract The PHP programming language is now widely used by agencies and companies around the world. To distribute applications made using PHP must use the source code so that it is prone to theft of works. If the source code is found by the programmer, it will be a big problem because it can indirectly find a hole in the security of a system from the source code. To solve this problem, the source code was coded so that the source code could not be easily copied by other parties. One of the ways to encode the source uses a combination of the Merklee-Hellman Knapsack algorithm, Discrete Logarithm and character modification to obtain complex encryption
BIOT: A Blockchain-Based IoT Platform for Distributed Energy Resource Management

Smart grids are IoT-enabled grids at which communication devices can be used to interconnect, monitor and manage distributed energy resources (DERs). The emerging efficient and clean energy DERs such as Lithium-ion batteries and Photovoltaic have been widely used in IoT-enabled smart grids that makes their secure operation challenging. In this paper, a blockchain-based IoT platform, namely BIoT, is proposed to maintain security of energy supply network including DERs against IoT platform's security issues, e.g., single point of failure, malicious code and data leak. BIoT verifies resource management requests in a consensus process, provides certificate-based authentication as well as attribute-based access control, and ensures data integrity. BIoT's performance has been evaluated on Ontario Energy Board (OEB) Cybersecurity framework and a performance benchmark.
Failure Analysis of Static Analysis Software Module Based on Big Data Tendency Prediction

With the continuous development of software, it is inevitable that there will be various unpredictable problems in computer software or programs that will damage the normal operation of the software. In the paper, static analysis software is taken as the research object, the errors or failures caused by the potential defects of the software modules are analyzed, and a software analysis method based on big data tendency prediction is proposed to use the software defects of the stacked noise reduction sparse analyzer to predict. This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. Through experimental tests, the performance of the presented method is better than that of the comparison method in correct rate, accuracy rate, recall rate, F1-measurement, AUC value, and running time, which proves that the research results in this paper have more accurate failure prediction effect and can timely eliminate software failures.
Analyzing the Dependency of ConvNets on Spatial Information

Intuitively, image classification should profit from using spatial information. Recent work, however, suggests that this might be overrated in standard CNNs. In this paper, we are pushing the envelope and aim to investigate the reliance on spatial information further. We propose to discard spatial information via shuffling locations or average pooling during both training and testing phases to investigate the impact on individual layers. Interestingly, we observe that spatial information can be deleted from later layers with small accuracy drops, which indicates spatial information at later layers is not necessary for good test accuracy. For example, the test accuracy of VGG-16 only drops by 0.03% and 2.66% with spatial information completely removed from the last 30% and 53% layers on CIFAR-100, respectively. Evaluation on several object recognition datasets with a wide range of CNN architectures shows an overall consistent pattern.
Decoding Linguistic Ambiguity in Times of Emergency based on Twitter Disaster Datasets

As of November 2020, the massively popular social media site Twitter boasts 1.3 billion accounts, 330 million monthly users, and 500 million tweets per day. The sheer volume of data shared on this platform has legitimized Twitter as a real-time information sharing monolith. Millions of people can share information via Twitter making it's power in this space undeniable. In this project, we'd like to use that power for good. Twitter's real-time information sharing prowess has become the platform of interest when attempting to learn information on events in progress. This paper identifies Tweets related to disasters, accidents, or emergencies and determines such statements' sincerity. We introduce a new model that can detect real emergency tweets with a high classification accuracy by using a transfer learning model BERT for emotion classification and behavior modeling approach to detect tweets in Twitter disaster datasets. In doing so, we hope to provide a great service in identifying relevant, real-time information to first responders.
Personalized Dynamic Knowledge-Aware Recommendation with Hybrid Explanations

Explainable recommendation is attracting more and more attention in both industry and research communities. While some existing models utilize reviews for improving the performance of recommender systems, most of them assume that user’s preference is static and each review’s importance is user-independent. However, it is intuitive that user’s preference is always dynamically changing and reviews from similar users should be given more importance as they share similar tastes. Moreover, they achieve model explainability at either feature level that is too concise or review level that is too redundant. To deal with these problems, we propose a Personalized Dynamic Knowledge-aware Recommender (PDKR) for dynamic user modeling and personalized item modeling. In particular, we model user’s preference with defined entities and relations in sequential knowledge graphs and capture its dynamics with a novel interval-aware Gated Recurrent Unit (GRU). Furthermore, by leveraging self-attention mechanism, we can not only learn each review’s user-specific importance, but also provide tailored explanations for each user at both feature level and review level. We conduct extensive experiments on three benchmark datasets from Amazon and Yelp and the results show that PDKR outperforms all the state-of-the-art recommendation approaches in rating prediction task while providing more effective explanations simultaneously.
On the power of spontaneous collisions: preserving culture and effectiveness in the pandemic

In the pivot to virtual interaction imposed by the coronavirus pandemic, some benefits have been realised, for example, time savings from avoiding commuting, the efficiency of back-to-back virtual meetings, needing to dress only from the waist up for work and so on. Yet, we also experience significant loss when our interactions are largely confined to being virtual. Specifically, spontaneous interactions between colleagues, what might be called ‘collisions,’ and their benefits are lost. Such collisions occur, for example, when we bump into a colleague while walking to or from a meeting and so on. These collisions are the nexus for maintaining connectivity between colleagues and can be a forum for creative exchanges that may lead to innovation and enhanced organisational effectiveness. Citing the experience of ‘bootlegging’ of time as a feature of organisations that thrive on innovation, this commentary discusses the value of spontaneous collisions in the healthcare environment. Given the persistence of virtual interaction throughout the pandemic and likely, to at least some extent thereafter, countermeasures to preserve spontaneous interaction in a virtual world and to maintain connectivity among colleagues are proposed. Awareness of the threats posed by a fully virtual experience and of these countermeasures are useful leadership attributes.
Hindsight Goal Ranking on Replay Buffer for Sparse Reward Environment

This paper proposes a method for prioritizing the replay experience referred to as Hindsight Goal Ranking (HGR) in overcoming the limitation of Hindsight Experience Replay (HER) that generates hindsight goals based on uniform sampling. HGR samples with higher probability on the states visited in an episode with larger temporal difference (TD) error, which is considered as a proxy measure of the amount which the RL agent can learn from an experience. The actual sampling for large TD error is performed in two steps: first, an episode is sampled from the relay buffer according to the average TD error of its experiences, and then, for the sampled episode, the hindsight goal leading to larger TD error is sampled with higher probability from future visited states. The proposed method combined with Deep Deterministic Policy Gradient (DDPG), an off-policy model-free actor-critic algorithm, accelerates learning significantly faster than that without any prioritization on four challenging simulated robotic manipulation tasks. The empirical results show that HGR uses samples more efficiently than previous methods across all tasks.
Multi-view Conversational Search Interface Using a Dialogue-Based Agent

We present a demonstration application for dialogue-based search. In this system, a conversational agent engages with the user of an online search tool to support their search activities. Agent-supported conversational search of this type represents a fundamental advance beyond current standard search engines, such as web search tools. Analogous to the role of a human librarian, the agent can direct the user to potentially interesting retrieved information and provide suggestions to help progress the searcher’s activities.
Non-dual modal operators as a basis for 4-valued accessibility relations in Hybrid logic

The modal operators usually associated with the notions of possibility and necessity are classically duals. This paper aims to defy that duality in a paraconsistent environment, namely in a Belnapian Hybrid logic where both propositional variables and accessibility relations are four-valued. Hybrid logic, which is an extension of Modal logic, incorporates extra machinery such as nominals – for uniquely naming states – and a satisfaction operator – so that the formula under its scope is evaluated in the state whose name the satisfaction operator indicates. In classical Hybrid logic the semantics of negation, when it appears before compound formulas, is carried towards subformulas, meaning that eventual inconsistencies can be found at the level of nominals or propositional variables but appear unrelated to the accessibility relations. In this paper we allow inconsistencies in propositional variables and, by breaking the duality between modal operators, inconsistencies at the level of accessibility relations arise. We introduce a sound and complete tableau system and a decision procedure to check if a formula is a consequence of a set of formulas. Tableaux will be used to extract syntactic models for databases, which will then be compared using different inconsistency measures. We conclude with a discussion about bisimulation.
FoV-Net: Field-of-View Extrapolation Using Self-Attention and Uncertainty

The ability to make educated predictions about their surroundings, and associate them with certain confidence, is important for intelligent systems, like autonomous vehicles and robots. It allows them to plan early and decide accordingly. Motivated by this observation, in this letter we utilize information from a video sequence with a narrow field-of-view to infer the scene at a wider field-of-view. To this end, we propose a temporally consistent field-of-view extrapolation framework, namely FoV-Net, that: (1) leverages 3D information to propagate the observed scene parts from past frames; (2) aggregates the propagated multi-frame information using an attention-based feature aggregation module and a gated self-attention module, simultaneously hallucinating any unobserved scene parts; and (3) assigns an interpretable uncertainty value at each pixel. Extensive experiments show that FoV-Net does not only extrapolate the temporally consistent wide field-of-view scene better than existing alternatives, but also provides the associated uncertainty which may benefit critical decision-making downstream applications. Project page is at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://charliememory.github.io/RAL21_FoV</uri> .
Comparison between net metering and battery storage in reference to rooftop solar

With the popularity of solar energy with the increasing awareness among the people, rooftop PVs are a great utilization of the technology as it puts the production directly in the hands of the consumers. With the concept of net and gross metering, the consumers now have the options to be the producers, by selling the extra back to supplying companies. This method along with the existing method of storing extra in the battery for later use has revolutionized the solar industry. In this paper we have tried to compare the benefits associated with both the methods and also estimated the solar panel output using linear regression and available data.