Reviewerly/exHarmony huggingface dataset:
Energy Trading Blockchain Framework Agents Modeling Approach

The growing interest in decentralized production of renewables calls for new energy trade approaches. An energy blockchain platform enables a decentralized energy market, to eliminate the need of an intermediary trust entity. We proposed to explore the design of an energy blockchain platform in the Qatari context using an ABM simulation framework with real-world data from households in Doha. The aim of this paper was to illustrate the essential components and functioning of a blockchain system and implement a generic ABM simulation framework for energy exchange. A major novel contribution of this work was the development of a virtual environment to test and validate the secure and equitable energy trading among participants using a simplified agent modeling approach to analyze the impacts of peer-to-peer energy trading on market operational performance.
Reframing explanation as an interactive medium: The <scp>EQUAS</scp> (Explainable <scp>QUestion</scp> Answering System) project

Abstract This letter is a retrospective analysis of our team's research for the Defense Advanced Research Projects Agency Explainable Artificial Intelligence project. Our initial approach was to use salience maps, English sentences, and lists of feature names to explain the behavior of deep‐learning‐based discriminative systems, with particular focus on visual question answering systems. We found that presenting static explanations along with answers led to limited positive effects. By exploring various combinations of machine and human explanation production and consumption, we evolved a notion of explanation as an interactive process that takes place usually between humans and artificial intelligence systems but sometimes within the software system. We realized that by interacting via explanations people could task and adapt machine learning (ML) agents. We added affordances for editing explanations and modified the ML system to act in accordance with the edits to produce an interpretable interface to the agent. Through this interface, editing an explanation can adapt a system's performance to new, modified purposes. This deep tasking, wherein the agent knows its objective and the explanation for that objective, will be critical to enable higher levels of autonomy.
On $ \mathbb{Z}_4\mathbb{Z}_4[u^3] $-additive constacyclic codes

Let $ \mathbb{Z}_4 $ be the ring of integers modulo $ 4 $. This paper studies mixed alphabets $ \mathbb{Z}_4\mathbb{Z}_4[u^3] $-additive cyclic and $ \lambda $-constacyclic codes for units $ \lambda = 1+2u^2,3+2u^2 $. First, we obtain the generator polynomials and minimal generating set of additive cyclic codes. Then we extend our study to determine the structure of additive constacyclic codes. Further, we define some Gray maps and obtain $ \mathbb{Z}_4 $-images of such codes. Finally, we present numerical examples that include six new and two best-known quaternary linear codes.
Empathy-Centric Design At Scale

EmpathiCH aims at bringing together and blend different expertise to develop new research agenda in the context of “Empathy-Centric Design at Scale”. The main research question is to investigate how new technologies can contribute to the elicitation of empathy across and within multiple stakeholders at scale; and how empathy can be used to design solutions to societal problems that are not only effective but also balanced, inclusive, and aware of their effect on society. Through presentations, participatory sessions, and a living experiment—where data about the peoples’ interactions is collected throughout the event—we aim to make this workshop the ideal venue to foster collaboration, build networks, and shape the future direction of “Empathy-Centric Design at Scale”.
ALO-SBD: A Hybrid Shot Boundary Detection Technique for Video Surveillance System

A novel shot boundary detection (SBD) method is proposed using nature-inspired algorithm called ant lion optimizer (ALO). Here, ALO is used to optimize the weights of the feed-forward neural network (FNN) which in turn enhances the performance of the system. A hybrid technique is formulated using the aid of continuity matrix $$(\phi )$$ and an outlier to fetch possible set of transition frames. Using a threshold $$\delta _1$$ , the actual transition frames are drawn out from all the possible transition frames. For the experimentation, some challenging videos from TRECVid 2001 and 2007 datasets are selected. ALO-SBD generates better performance in terms of F1 score and outperforms recent state-of-the-art techniques in the field of SBD.
Solar Radiation Forecasting for Smart Building Applications

The development of solar energy and the concept of net zero or positive energy buildings are being increased in a sort of symbiotic relationship. To make buildings more energy efficient, smart and optimal energy management systems called predictive building control were developed both for heat and electricity utilization. In these energy management systems (EMSs), a weather forecasting platform is often incorporated and allows to anticipate the meteorological events influencing the electrical and thermal energy consumption and to react accordingly. The objectives of this chapter consist in showing how the introduction of a solar radiation forecasting tool into the EMS improves its performances and saves energy and money. A brief overview of solar radiation forecasting methods is shown and focuses on solar prediction at short time horizon using statistical and artificial intelligences technics. These solar radiation prediction models are applied, validated, and compared on the Mediterranean site of Ajaccio, France. The most reliable forecasting tool, ARMA model, is then incorporated into the EMS which manages electricity into a microgrid composed of a photovoltaic/battery energy system and supplying a building and an electrical vehicle. The electricity cost benefit is then estimated and discussed. It appeared that the addition of PV forecasting based on an ARMA model into the EMS increases the gain of about 7% compared to a EMS without forecasting; this gain could reach 10% with a perfect forecasting reaches.
Improved Ant Colony Optimization for Optimal Resource Utilization in Cloud Computing

Recently, cloud computing is an evolving research field deployed for computing by many researchers. The computing is offered as a service in cloud which is regarded as novel technology. In order to meet customer necessitates, various services are offered on the basis of customer dynamic request continuously in cloud computing, and it is the foremost task of cloud computing for providing the desired services to every consumer. The challenge lies in servicing all the customers with the limited existing resource, and it has been tricky to meet all the demanded services by the cloud providers. The allotment of perspective cloud resources through the cloud providers is yet another endeavor which should be done in reasonable way. Hence, cloud consumers’ quality of service and fulfillment are the most noteworthy factors to be considered. The prevailing research discussed about the challenges, techniques, key performance issues etc., encompassed in cloud computing resource sharing. Ant colony optimization algorithm is greatly utilized for optimizer analysis of load on physical machine on the basis of local migration agent which aids in migrated VMs load computation and for choosing proper physical server. Conversely, trapping of local optima may happen at certain time which in turn impacts on performance degradation pertaining to global search. The search diversity enhancing is one among the possible solutions for evading the trapping into local optima in ACO. Mutation-based improved ant colony optimization (IACO) is greatly deployed in this research work for analysis of physical machine load VM migration besides effectual resource exploitation.
Immediate Text Search on Streams Using Apoptosic Indexes

Applications that involve streams of documents require a mechanism for search over the newest arrivals. In this paper we explore provision of immediate indexing and fast search of recent documents only, in contrast to focus on dynamic construction of an index of all observed material. Our contribution is a new structure, an apoptosic index, that operates in a fixed volume of memory and in which expired index entries vanish without significant overhead; there is neither explicit removal of old data nor explicit memory management. We demonstrate the practicality of apoptosic indexes with a straightforward implementation and experiments on microblog and newswire data, showing dramatically faster performance than observed with alternatives.
FairShades: Fairness Auditing via Explainability in Abusive Language Detection Systems

At every stage of a supervised learning process, harmful biases can arise and be inadvertently introduced, ul-timately leading to marginalization, discrimination, and abuse towards minorities. This phenomenon becomes particularly im-pactful in the sensitive real-world context of abusive language detection systems, where non-discrimination is difficult to assess. In addition, given the opaqueness of their internal behavior, the dynamics leading a model to a certain decision are often not clear nor accountable, and significant problems of trust could emerge. A robust value-oriented evaluation of models' fairness is therefore necessary. In this paper, we present FairShades, a model-agnostic approach for auditing the outcomes of abusive language detection systems. Combining explainability and fairness evaluation, Fair-Shades can identify unintended biases and sensitive categories towards which models are most discriminative. This objective is pursued through the auditing of meaningful counterfactuals generated within CheckList framework. We conduct several ex-periments on BERT-based models to demonstrate our proposal's novelty and effectiveness for unmasking biases.
Effective Detection for Invasive Ductal Carcinoma Histopathology Images Based on ResNet

Breast cancer is the second biggest cause of cancer death among women in the United States. The most prevalent subtype of all breast cancers is invasive ductal carcinoma which is invasive ductal carcinoma. Traditional methods are hard to predict this cancer well. In this paper, we proposed a self-defined convolutional neural network (CNN) and employed a typical structure named ResNet for classification. Moreover, we compared the CNN model and the ResNet-50 model's ability in detecting breast cancer. Different parameters e.g. batch size were also changed in terms of different situations to find the difference of the performance. We built 4 sets of experiments for testing the collected datasets by testing the training accuracy and validation accuracy of the two models. The result showed that the accuracy of the ResNet-50 is higher than that of a self-defined CNN. Therefore, the new method should be applied in the process of the detection of breast cancer.
A comparative analysis of the impact of data distribution on distributed learning with a traveling model for brain age prediction

Limited access to medical datasets, due to regulations that protect patient data, is a major hinderance to the development of machine learning models for computer-aided diagnosis tools using medical images. Distributed learning is an alternative to training machine learning models on centrally collected data that solves data sharing issues. The main idea of distributed learning is to train models remotely at each medical center rather than collecting the data in a central database, thereby avoiding sharing data between centers and model developers. In this work, we propose a travelling model that performs distributed learning for biological brain age prediction using morphological measurements of different brain structures. We specifically investigate the impact of nonidentically distributed data between collaborators on the performance of the travelling model. Our results, based on a large dataset of 2058 magnetic resonance imaging scans, demonstrate that transferring the model weights between the centers more frequently achieves results (mean age prediction error = 5.89 years) comparable to central learning implementations (mean age prediction error = 5.93 years), which were trained using the data from all sites hosted together at a central location. Moreover, we show that our model does not suffer from catastrophic forgetting, and that data distribution is less important than the number of times that the model travels between collaborators.
Feature reduction of rich features for universal steganalysis using a metaheuristic approach

The development of content adaptive steganographies has become a challenge for steganalysis. This led researchers towards extraction of a rich space of features. The detection of stego images based on spatial rich model (SRM) features and its variants is a promising research area in the field of universal steganalysis. SRM features are extracted as 106 sub-models which collectively provide 34,671 features. So, one of the most significant challenges in universal steganalysis is feature selection. In this paper an improved binary particle swarm optimisation, global and local best particle swarm optimisation (GLBPSO) with Fisher linear discriminant classifier is used to identify relevant feature sub-models which improve the efficiency of a steganalyser. The significant reduction rate of more than 70% is achieved by the proposed approach. This further helps in reducing computational complexity without much affecting the detection capability. The proposed methodology gives superior results when compared with state-of-the-art algorithms.
PAE: Portable Appearance Extension for Multiple Object Detection and Tracking in Traffic Scenes

Multi-object tracking (MOT) is an important field in computer vision that provides a critical understanding of video analysis in various applications, such as vehicle tracking in intelligent transportation systems (ITS). Several deep learning-based approaches have been introduced to basic motion and IoU trackers by extracting appearance features to assist in challenging situations such as lossy detection and occlusion. This study proposes a portable appearance extension (PAE) for single-stage object detection to jointly detect and extract appearance embeddings using a shared model. Furthermore, a novel training framework with a single image and without re-identification annotations is presented using an augmentation module, saving a tremendous amount of human labeling effort and increasing the real-world application adoption rate. Using UA-DETRAC dataset, RetinaNet-PAE and SSD-PAE achieve comparable results with current state-of-the-art models, where RetinaNet-PAE prioritizes detection and tracking performance with a 58.0&#x0025; HOTA score and 4 FPS. In contrast, SSD-PAE prioritizes latency performance with a 47.3&#x0025; HOTA score and 40 FPS.
Design and Implementation of Marine Datasets Based on DCGAN Network

In this paper, the deep learning method is applied to the ship detection SAR datasets to enlarge the existing SAR images of ships.This paper first introduces the basic knowledge of convolutional neural networks, and also introduces the common convolutional neural network structure.Secondly, this paper introduces the generative adversarial network GAN,, analyzes the GAN principle and network structure according to the objective function, introduces the training method of the GAN model, and analyzes the advantages and disadvantages of GAN.This introduces the DCGAN network model that combines the above two, analyzes its basic knowledge and network structure, and the improvement of GAN model, and introduces the training process in detail.Finally, we compare the loss function images generated by DCGAN and GAN and summarize the lessons.
Evaluating Code Summarization with Improved Correlation with Human Assessment

Code summarization aims to automatically generate functionality descriptions of code snippets. Faithful metrics are needed to measure to which degree the machine generated summaries capture the semantics of the code snippets. Most commonly used metrics in code summarization, such as BLEU -4, METEOR, and ROUGE-L, originate from machine translation and text summarization, and have constantly been found to be inconsistent with human assessment. In this paper, we propose a novel evaluation metric, Consensus-based Code Summarization Evaluation (CCSE), which assigns different semantic weights to the n-grams of the summary. We also provide an algorithm to match the n-gram pairs from the reference and candidate based on the similarities. To validate the effectiveness of our proposed metric, we collect summary pairs from two public Java datasets and calculate the correlation coefficients between CCSE and the human evaluations. The experiment results show that, compared with BLEU-4, METEOR, and ROUGE-L, CCSE is more consistent with the scores assessed by human developers.
An Inquisitive Prospect on the Shift Toward Online Media, Before, During, and After the COVID-19 Pandemic: A Technological Analysis

Online media plays a vital role in defining the future of tomorrow. Almost every field in the present-day is dependent on technology and online media either for procuring better outputs or for the satisfaction of end clients. In this paper, the authors have tried to bring out the various factors that led to the shift of the majority of individuals to online media, briefly discussing its impact on the economy and social factors. It leverages the facts of how the offline media got impacted not only during the days of the novel coronavirus but before as well. It has been plausibly displayed that fake word gets out quicker and more considerably than authentic news utilizing online media. A boost toward the use of E-platforms has predominantly been taken a closer look at in the middle sections of the paper. Further, the exploratory analysis fore- casts the number of online media users by 2031 and presents inquisitive visualizations on the study of various websites during, before, and after the pandemic in the later sections of the paper.
SpinQ Triangulum: A commercial three-qubit desktop quantum computer

SpinQ Triangulum is the second generation of the desktop quantum computers designed and manufactured by SpinQ Technology. SpinQ’s desktop quantum computer series, based on a room-temperature nuclear magnetic resonance (NMR) spectrometer, provides lightweight, cost-effective, and maintenance-free quantum computing platforms that aim to provide real-device experience for quantum computing education for kindergarten through 12th grade (K–12) and the college level. These platforms also feature quantum control design capabilities for studying quantum control and quantum noise.
A Two-Step Calibration Method for Vision Measurement With Large Field of View

Nowadays, the commonly used large view calibration methods usually have the problems of low measurement accuracy, big on-site operation limitation, or high tolerance requirement for target preparation. In order to resolve the previously mentioned issues, a two-step calibration method for the interior and exterior parameters of multicameras is proposed based on the principle of industrial close-range photogrammetry. In the first step, by using a small-scale cross target at the closer range, the interior parameter of the front section of the camera is calibrated based on the pyramid method. In the second step, by setting a proper number of serialized targets at the further range of the measured area, the exterior parameter is calibrated based on space resection with multi-image. Finally, the interior and exterior parameters are all optimized by using the bundle adjustment method to get accurate measurement results. A number of experiments are performed to verify the feasibility and accuracy of this proposed method. As per the experiment results, with a camera resolution of <inline-formula> <tex-math notation="LaTeX">$2448 \times 2048$ </tex-math></inline-formula> pixels, the reprojection error is less than 0.11 pixels. For the measuring volume of <inline-formula> <tex-math notation="LaTeX">$10.0 \times 8.4 \times 3.6$ </tex-math></inline-formula> m, the maximum absolute error of 3-D measurement is 0.42 mm. The measurement error for the flapping angle of rotor whose diameter is 10 m is less than 4.5&#x0025;. By using this method, the interior parameter can be collected in the laboratory, while the exterior parameter can be collected in the open field separately. This method has high practicality under the actual engineering working environment.
Intelligent Value Added Service Platform of Smart Library Based on Blockchain Technology

In order to overcome the problems of smart Library in collecting resources, storing data and disseminating information, this paper proposes an intelligent value-added service platform of smart Library Based on blockchain technology. The smart value-added service platform makes full use of the advantages of blockchain technology, such as decentralization, non tampering, traceability and consensus mechanism, which makes the traditional way of resource acquisition and user communication fundamentally changed in time and space. The research results show that the smart value-added service platform can overcome the security problems of smart library in the aspects of resource collection, data storage and information dissemination, and improve the user experience.
Virtual Reality: A Survey of Enabling Technologies and its Applications in IoT

To further develop the client experience and proficiency of human–PC association in augmented simulation innovation, specialists are researching an extensive innovation that consolidates multi-field super advanced accomplishments against the scenery of the Internet of Things to acknowledge regular and astute human–PC connection. The outcomes show that by means of the handling and activity of information by a PC program, an intelligent, mimicked normal state, and three-layered climate can be made at the showcase terminal, empowering clients to feel locked in.It is obvious that computer generated experience examination ought not just spread out high level plan and increment security and transmission effectiveness with regards to the Internet of Things, yet additionally raise modern pertinence and client tenacity. It would be useful in uncovering the innovative progression pattern and the modern format, just as prompting significant subjects in further developing their innovation R&D (innovative work) quality and raising cutthroat hostile and guarded techniques.
On Post-Quantum Identification in 5G

We introduce PQ 5G AKA, a prototype for possible extensions of the current 5G authentication and key agreement protocol to the post-quantum setting, we further analyse the computational and communication complexities of our prototype using potential post-quantum KEMs