Reviewerly/exHarmony huggingface dataset:
Bank Customer Green Banking Technology Adoption

Sustainability has become the global need for survival in all scopes due to financial development's side effects that have resulted in environmental destruction. The world leaders have proposed green banking (GB) to reduce carbon footprints from banking operations by promoting paperless financial services based on technology. However, the adoption of GB remains unsatisfactory in the UAE. The study attempts to investigate the determinants of consumers' adoption of GB technology. An exploratory sequential mixed-method approach is employed. The qualitative analysis identified six new challenges facing customers' intention adoption of GB technology: customer awareness, personal innovativeness, bank reputation, security and privacy, system quality, and government support. The preliminary qualitative findings are mostly confirmed by quantitative study whereby customer awareness, personal innovativeness, system quality, and bank reputation significantly impact customers' intention to adopt GB technology. The discussions and implications of these findings are further elaborated.
Satellite IoT for Monitoring and Tracking of Athletes in Extreme Environments

This paper aims at demonstrating the feasibility of a satellite Internet of Things (IoT)-based solution for the monitoring of vital parameters of athletes performing sport activities in extreme environments, as well as for the tracking of their position to carry out rescue actions in case of accidents. In order to overcome the limitations that come from the lack of connectivity in the areas where these sports are in general performed, the solution presented in this paper is based on the so-called satellite IoT technologies. This term is used to indicate all those facilities that exploit satellite communication to transmit data related to IoT services and applications. In this context, data are generally transmitted at a relatively low rate and in a small number of bytes containing general information acquired by sensors. The system presented in this paper is based on the Swarm technology which allows the transmission of a predefined amount of monthly packets in accordance to the subscription of a low price data plan. The system is exploited to periodically send a set of parameters (i.e., heart rate and body temperature) related to the activities of the athlete, as well as his/her position. The final purpose of this system is to keep on monitoring the athlete regardless of his/her position in order to prevent possible accidents and to intervene in case of casualties.
Decentralized electronic voting system using Hyperledger Fabric

Voting is an indispensable part of life in the current society. Everyone is part of social groups of different size and in most of them can influence important decisions by casting a vote. The development of technology makes it possible to support the voting process. The technology can be used during the process of checking and counting votes, as well as during voting.The paper presents the development process of e-voting system named BlockVote. System contains three components: Hyperledger Fabric network, middleware application and user interface. Hyperledger Fabric is blockchain network, in which two smart contracts EVotingContract and CodesContract were installed. Those smart contracts provide logic for voting. The middleware application enables communication between user interface and blockchain network. BlockVote allows to conduct voting that may consist of multiple closed questions. Users can cast a vote by entering the voting code and voting ID number. User receives confirmation of a vote in the form of transaction number, by means of which he can verify that his vote has been saved in the blockchain network. The voting history along with each vote cast is saved in the blockchain network.
Caching Hierarchical Skylines for Efficient Service Composition on Service Graphs

Service-oriented computing (SOC) is a paradigm for developing applications by reusing existing services. Through a standardized publishing, discovery, and composition process, SOC enables the orchestration of multiple (including third-party) services to constitute new applications. Hereby the quality of a composite service is fundamentally determined by its constituent services. To satisfy users’ non-functional requirements, it is important to identify the optimal set of constituent services to participate in the composition. Practical applications usually require the optimal set to be identified with high efficiency and accuracy. This poses challenges to existing service composition methods as they either provide no accuracy guarantee or are inapplicable to large-scale problems. The challenges become more evident when considering service graphs, which contain multiple execution paths that could multiply the computational overhead. In this paper, we propose a hierarchical skyline-based approach for highly efficient service composition, which maintains and reuses varying levels of service skylines to accelerate service composition. We discuss how the skylines can be selectively computed, lazily updated, and efficiently retrieved for reuse. Experiments demonstrate the effectiveness of our approach.
An Experimental Analysis of Solar PV on Higher Concentration of Methane

Solar Photovoltaic output power depends on various environmental conditions as it is intermediate in nature. And using a various methods, the output of the solar system can be predicted by using a different algorithm in advance. For the sustainable environmental purpose and coming decay, it is essential to produce more energy by solar system as it is cost-effective and clean energy among all non-conventional sources. The output power system depends on environmental factors. Out of all environmental factor irradiation and temperature is the most important factor affecting the solar PV panel. These two factors directly or indirectly depend on the Greenhouse gas concentration present in the atmosphere. Nowadays GHG is the most affected factor inefficiency of Solar energy. To know the actual effect of GHG on the solar system an experimental analysis has been done. a higher concentration of methane has been taken to have the experimental analysis.
Resource Scaling Strategies for Open-Source FaaS Platforms compared to Commercial Cloud Offerings

Open-source offerings are often investigated when comparing their features to commercial cloud offerings. However, performance benchmarking is rarely executed for open-source tools hosted on-premise nor is it possible to conduct a fair cost comparison due to a lack of resource settings equivalent to cloud scaling strategies.Therefore, we firstly list implemented resource scaling strategies for public and open-source FaaS platforms. Based on this we propose a methodology to calculate an abstract performance measure to compare two platforms with each other. Since all open-source platforms suggest a Kubernetes deployment, we use this measure for a configuration of open-source FaaS platforms based on Kubernetes limits. We tested our approach with CPU intensive functions, considering the difference between single-threaded and multi-threaded functions to avoid wasting resources. With regard to this, we also address the noisy neighbor problem for open-source FaaS platforms by conducting an instance parallelization experiment. Our approach to limit resources leads to consistent results while avoiding an overbooking of resources.
Design of University Financial Information System Based on Data Mining Technology and IoT

As China’s information construction level keeps getting better and higher education keeps getting better, new rules and regulations are being made and put into place. These new rules and regulations require colleges and universities to have comprehensive budget management, dynamic fund management, scientific forecast analysis and performance assessment, and the integration of campus cards and digital campuses. At present, the financial management concept and financial management software used by colleges and universities have been relatively lagging behind, which cannot adapt to the needs of current information and career development. Therefore, the financial information system of colleges and universities based on data mining technology is designed. The hardware part is designed with CORBA interceptor, B/S structure, and SQL converter. The software part first figures out what kind of financial information platform is needed. Then, using data mining technology, it builds a financial information model. Finally, it designs the functional module of financial information technology and builds the financial index system for colleges and universities. The results of the system tests show that the designed financial information system works well and has some use value.
Video Content Placement At the Network Edge: Centralized and Distributed Algorithms

In the traditional video streaming service provisioning paradigm, viewers typically request video content through a central Content Delivery Network (CDN) server. However, because of the uncertain wide area network delays, the (remote) viewers usually suffer from long video streaming delay, which affects the quality of experience. Multi-Access Edge Computing (MEC) offers a way to shorten the video streaming delay by building small-scale cloud infrastructures at the network edge, which are in close proximity to the viewers. In this paper, we present novel centralized and distributed algorithms for the video content placement problem in MEC. In the proposed centralized video content placement algorithm, we leverage the Lyapunov optimization technique to formulate the video content placement problem as a series of one-time-slot optimization problems and apply an Alternating Direction Method of Multipliers (ADMM)-based method to solve each of them. We further devise a distributed Multi-Agent Reinforcement Learning (MARL)-based method with value decomposition mechanism and parallelization policy update method to solve the video content placement problem. The value Decomposition mechanism deals with the credit assignment among multiple agents, which promotes the cooperative optimization of the global target and reduces the frequency of information exchange. The parallelization of policy network can speed up the convergence process. Simulation results verify the effectiveness and superiority of our proposed centralized and distributed algorithms in terms of performance.
LsHASHq: A string matching algorithm exploiting longer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si221.svg" display="inline" id="d1e3534"><mml:mi>q</mml:mi></mml:math>-gram shifting

String matching is a classical computer science problem where we search for all the occurrences of a text string of size m, typically called pattern, in a string of size n, where both strings are drawn from the same alphabet. It is an essential task for many applications such as data mining, web search engines, bioinformatics, and natural language processing. Fast hash algorithms were developed to speed up the searching process. Here, we compare the hash value of strings (signature) instead of the letters. The hash function allows exploiting bitwise operations while considering the alphabet’s and pattern’s sizes. However, the efficiency of the hash algorithms calls for further improvements. The problem with q-gram hash algorithms is that the shift skips at most m−q+1 positions, where m is the same as before, and q is the length of hashed q-gram. For a fixed m, the number of skipped positions decreases as q increases. This paper presents a new variation of the q-gram hash algorithm, which elongates the shift by skipping at most m positions over text. Theoretically, the proposed hash algorithm, namely, Longer shift HASHq (LsHASHq), has a longer shift than the state-of-the-art hash algorithms. Experimentally, the new algorithm is the fastest among the following algorithms: BNDMq, BXSq, EPSM, FHASHq, FSBNDMq, HASHq, LWFRq, QLQS, SBNDMq, TWFRq, and WFRq on different natural language texts for m>10. For human genome sequence the new algorithm was second fastest for short patterns of length 10.
Model Checking Fuzzy Computation Tree Logic Based on Fuzzy Decision Processes with Cost

In order to solve the problems in fuzzy computation tree logic model checking with cost operator, we propose a fuzzy decision process computation tree logic model checking method with cost. Firstly, we introduce a fuzzy decision process model with cost, which can not only describe the uncertain choice and transition possibility of systems, but also quantitatively describe the cost of the systems. Secondly, under the model of the fuzzy decision process with cost, we give the syntax and semantics of the fuzzy computation tree logic with cost operators. Thirdly, we study the problem of computation tree logic model checking for fuzzy decision process with cost, and give its matrix calculation method and algorithm. We use the example of medical expert systems to illustrate the method and model checking algorithm.
CGNN: Caption-assisted graph neural network for image-text retrieval

Image-text retrieval has drawn much attention in recent years, where similarity measure between image and text plays an important role. Most existing works focus on learning global coarse-grained or local fine-grained features for similarity computation. However, the large domain gap between different modalities is often neglected, which makes it difficult to match the images and texts effectively. In order to deal with this problem, we propose to use auxiliary information to release the domain gap, where the image captions are generated. Then, a Caption-Assisted Graph Neural Network(CGNN) is designed to learn the structured relationships among images, captions, and texts. Since the captions and the texts are from the same domain, the domain gap between images and texts can be effectively released. With the help of caption information, our model achieves excellent performance on two cross-modal retrieval datasets, Flickr30K and MS-COCO, which shows the effectiveness of our framework.
Global Local Fusion Neural Network for Multimodal Sentiment Analysis

With the popularity of social networking services, people are increasingly inclined to share their opinions and feelings on social networks, leading to the rapid increase in multimodal posts on various platforms. Therefore, multimodal sentiment analysis has become a crucial research field for exploring users’ emotions. The complex and complementary interactions between images and text greatly heighten the difficulty of sentiment analysis. Previous works conducted rough fusion operations and ignored the study for fine fusion features for the sentiment task, which did not obtain sufficient interactive information for sentiment analysis. This paper proposes a global local fusion neural network (GLFN), which comprehensively considers global and local fusion features, aggregating these features to analyze user sentiment. The model first extracts overall fusion features by attention modules as modality-based global features. Then, coarse-to-fine fusion learning is applied to build local fusion features effectively. Specifically, the cross-modal module is used for rough fusion, and fine-grained fusion is applied to capture the interaction information between objects and words. Finally, we integrate all features to achieve a more reliable prediction. Extensive experimental results, comparisons, and visualization of public datasets demonstrate the effectiveness of the proposed model for multimodal sentiment classification.
Visible–Infrared Person Re-Identification via Global Feature Constraints Led by Local Features

Smart security is needed for complex scenarios such as all-weather and multi-scene environments, and visible–infrared person re-identification (VI Re-ID) has become a key technique in this field. VI Re-ID is usually modeled as a pattern recognition issue, which faces the problems of inter-modality and intra-modality discrepancies. To alleviate these problems, we designed the Local Features Leading Global Features Network (LoLeG-Net), a representation learning network. Specifically, for cross-modality discrepancies, we employed a combination of ResNet50 and non-local attention blocks to obtain the modality-shareable features and convert the problem to a single-modality person re-identification (Re-ID) problem. For intra-modality variations, we designed global feature constraints led by local features. In this method, the identity loss and hetero-center loss were employed to alleviate intra-modality variations of local features. Additionally, hard sample mining triplet loss combined with identity loss was used, ensuring the effectiveness of global features. With this method, the final extracted global features were much more robust against the background environment, pose differences, occlusion and other noise. The experiments demonstrate that LoLeG-Net is superior to existing works. The result for SYSU-MM01 was Rank-1/mAP 51.40%/51.41%, and the result for RegDB was Rank-1/mAP 76.58%/73.36%.
Using developmental bibliotherapy design to improve self-management skills of post-graduate students

Students come to schools and college to not only learn academic subjects but also gain personal effectiveness skills for life. During the developmental and transition phase of growing up, young students can face a lot of issues and problems related to the self and their surroundings. The participants of this research were post-graduate management students who suffered from a lack of self-management skills. A reading course/intervention based on Shrodes (1950 Shrodes, C. (1950). Bibliotherapy: A theoretical and clinical-experimental study [Doctoral dissertation]. University of California at Berkeley. Dissertation Abstracts Online. [Google Scholar]) model of bibliotherapy was developed. Using developmental and didactic bibliotherapy based on CBT a short reading course was conducted with 138 students. Reflective writing and discussion activities were conducted along with group reading. The impact of the reading course/intervention was analyzed using descriptive research methods (paired sample t-test) and qualitative reading of the reflective writing. The intervention has been successful in bringing positive habit formation and attitude change in the students.
CarM

Continual Learning (CL) is an emerging machine learning paradigm in mobile or IoT devices that learns from a continuous stream of tasks. To avoid forgetting of knowledge of the previous tasks, episodic memory (EM) methods exploit a subset of the past samples while learning from new data. Despite the promising results, prior studies are mostly simulation-based and unfortunately do not promise to meet an insatiable demand for both EM capacity and system efficiency in practical system setups. We propose CarM, the first CL framework that meets the demand by a novel hierarchical EM management strategy. CarM has EM on high-speed RAMs for system efficiency and exploits the abundant storage to preserve past experiences and alleviate the forgetting by allowing CL to efficiently migrate samples between memory and storage. Extensive evaluations show that our method significantly outperforms popular CL methods while providing high training efficiency.
E <sup>2</sup> SR

Nowadays high-resolution (HR) videos have been a popular choice for a better viewing experience. Recent works have shown that super-resolution (SR) algorithms can provide superior quality HR video by applying the deep neural network (DNN) to each low-resolution (LR) frame. Obviously, such per-frame DNN processing is compute-intensive and hampers the deployment of SR algorithms on mobile devices. Although many accelerators have proposed solutions, they only focus on mobile devices. Differently, we notice that the HR video is originally stored in the cloud server and should be well exploited to gain high accuracy and performance improvement. Based on this observation, this paper proposes an end-to-end video CODEC assisted system (E2SR), which tightly couples the cloud server with the device to deliver a smooth and real-time video viewing experience. We propose the motion vector search algorithm executed in the cloud server, which can search the motion vectors and residuals for part of HR video frames and then pack them as addons. We further propose the reconstruction algorithm executed in the device to fast reconstruct the corresponding HR frames using the addons to skip part of DNN computations. We design the corresponding E2SR architecture to enable the reconstruction algorithm in the device, which achieves significant speedup with minimal hardware overhead. Our experimental results show that the E2SR system achieves 3.4x performance improvement with less than 0.56 PSNR loss compared with the state-of-the-art "EDVR" scheme.
QuiltNet

We have seen many successful deployments of deep learning accelerator designs on different platforms and technologies, e.g., FPGA, ASIC, and Processing In-Memory platforms. However, the size of the deep learning models keeps increasing, making computations a burden on the accelerators. A naive approach to resolve this issue is to design larger accelerators; however, it is not scalable due to high resource requirements, e.g., power consumption and off-chip memory sizes. A promising solution is to utilize multiple accelerators and use them as needed, similar to conventional multiprocessing. For example, for smaller networks, we may use a single accelerator, while we may use multiple accelerators with proper network partitioning for larger networks. However, partitioning DNN models into multiple parts leads to large communication overheads due to inter-layer communications. In this paper, we propose a scalable solution to accelerate DNN models on multiple devices by devising a new model partitioning technique. Our technique transforms a DNN model into layer-wise partitioned models using an autoencoder. Since the autoencoder encodes a tensor output into a smaller dimension, we can split the neural network model into multiple pieces while significantly reducing the communication overhead to pipeline them. Our evaluation results conducted on state-of-the-art deep learning models show that the proposed technique significantly improves performance and energy efficiency. Our solution increases performance and energy efficiency by up to 30.5% and 28.4% with minimal accuracy loss as compared to running the same model on pipelined multi-block accelerators without the autoencoder.
Multi-Account Dashboard for Authentication Dependency Analysis

User authentication is necessary for the majority of online services. If users fail to authenticate due to the loss of an authentication factor, fallback processes allow users to regain access to their accounts. However, most of the proposed and deployed fallback methods have substantial weaknesses that degrade security, e.g., guessable security questions. This is even more serious since through account dependencies (e.g., password reset via email), additional accounts can be compromised. On the other hand, misconfiguration of (fallback) authentication might result in locking a user out of an account.
Concept Annotation from Users Perspective: A New Challenge

Text data is highly unstructured and can often be viewed as a complex representation of different concepts, entities, events, sentiments etc. For a wide variety of computational tasks, it is thus very important to annotate text data with the associated concepts / entities, which can put some initial structure / index on raw text data. However, It is not feasible to manually annotate a large amount of text, raising the need for automatic text annotation.