Reviewerly/exHarmony huggingface dataset:
i-Sheet: A Low-Cost Bedsheet Sensor for Remote Diagnosis of Isolated Individuals

In this article, we propose a smart bedsheet-i-Sheet-for remotely monitoring the health of COVID-19 patients. Typically, real-time health monitoring is very crucial for COVID-19 patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-COVID effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of COVID-19 patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient. i-Sheet effectively categorizes the condition of the patient with an accuracy of 99.3% and utilizes 17.5 W of the power. Furthermore, the delay involved in monitoring the health of patients using i-Sheet is 2 s which is very diminutive and is acceptable.
Healthcare Application System with Cyber-Security Using Machine Learning Techniques

Compared to last year Internet of Things Intelligent (IoT), this year’s IoT brings a significant increase in intelligence, or “things,” into the Internet of Things (IoT). Regarding the significance of subjective pain inside such an active communication network, we are not yet beyond the reach of artificial intelligence, but we are close. Pain, as well as organs of emotions and ideas (cell phones and tablets), appear alongside home appliances and mobile devices (such as smartphones and tablets). In addition, several of these devices are accessible in markets all over the globe. When it comes to current Internet problems, the source of the pain is the access to Internet Connectivity that they provide. In order to reap the advantages of research capacity solutions, artificial intelligence methods use intelligence. Globally, health-care services are among the most significant uses that the Internet of Things (IoT) has made possible. In order for patients to monitor their health in real time, advanced sensors may be worn on their bodies or implanted into their organs. Afterwards, the information may be analysed, grouped, and prioritised if necessary. When physicians work with algorithms, they may make adjustments to their treatment plans while simultaneously ensuring that patients get cost-effective health care.
CoopFinder: Finding Collaborators Based on Co–Changed Files

Successful software projects require engaged collaborators interacting with each other across the entire development life-cycle. Unfortunately, for social coding platforms, e.g., GitHub, identifying a suitable collaborator to strengthen their ties and improve their engagement in the project is challenging, given that reliable information for collaborator identification is often not readily available. In this work, we propose and evaluate a collaborator recommendation tool - CoopFinder - to help developers find collaborators in a specific project based on similar interests related to co&#x2013;changed files. We design our Web prototype&#x2013;tool using visualization techniques. As a result of the preliminary user study, we observed that 95% of the participants had positive impressions of the tool. Furthermore, 75% of participants stated that they would use the tool in their daily lives or recommend it to others. Repository: https://github.com/kattiana/CoopFinder
An Effective Framework for Ensuring Data Privacy in Private Cloud

Cloud computing is technological revolution in Information Technology (IT) which allows convenient and on-demand access from anywhere to a pool of computing resources. Though this technology has many significant benefits, it is still facing several research challenges which need to be addressed. The most important issue which needs to be addressed from the cloud service provider’s view is data privacy. Many researchers have proposed various techniques to address the data privacy, wherein one of them is encryption. Though these encryption techniques could preserve the privacy of cloud user’s sensitive data, they are suffering from limitations such as attacks and processing overheads. Hence, our work is aimed to address these limitations through a crypto-scheme that is secured, robust, and efficient in ensuring data privacy. We had proposed an effective scheme for ensuring data privacy in private cloud. Experimental result shows that proposed scheme improves the data privacy in private cloud.
Blockchain-as-a-Service for Business Process Management: Survey and Challenges

Blockchain technology (BCT) has brought a paradigm shift to Business Process Management (BPM). BCT provides a trusted decentralized infrastructure to secure data and process executions using distributed ledgers and smart contract to manage complex business processes. Numerous efforts have been made to exploit BCT in supporting dynamic and trusted collaborations of business processes. This paper aims to understand recent BCT development for its BPM applications and identify the limitations and challenges for further development via a systematic literature review (SLR). It is found that numerous works have reported using BCT as technical solutions to fulfill some traditional BPM functions. This paper is distinguished from existing works, especially several relevant surveys in the sense that (1) the impact of using BCT in BPM is thoroughly explored to identify new constraints and challenges explicitly brought by blockchains; (2) the requirements for Business Process Compliance (BPC) are firstly analyzed in detail. Note that BPC is to assure the adherence of business processes to pre-defined policies, standards, specifications, regulations, and laws when business processes are executed. To fill the gaps of BCT applications in these two aspects, Blockchain-as-a-Service (BCaaS) is adopted in business process architecture, and the trends of BCT developments are identified accordingly.
Feasibility of using YouTube Conversations for Pair Programming Intent Classification

Pair programming conversational agents demand vast amounts of data for training. Recently a benchmark dataset of developer-developer and developer-agent pair programming conversations, from lab studies (8,324 utterances) was released for training natural language unit of a pair programming conversational agent. Unfortunately, this dataset is limited to a single domain and language. To investigate if it was feasible to utilize already available pair programming conversations from online video hosting platforms (i.e. YouTube), we collected five Youtube videos (roughly 350 minutes) with 4,822 utterances. We used transformer-based language model BERT to compare the lab studies with online videos. We found that a transfer learning approach, first training BERT on online videos and then fine-tuning with specific developer-agent data, resulted in the best performance.
Human reaction time in a mixed reality environment

Over the last few years applications based on the use of immersive environments, where physical and digital objects coexist and interact, have gained widespread attention. Thanks to the development of new visualization devices, even at low cost, and increasingly effective rendering and processing techniques, these applications are reaching a growing number of users. While the adoption of digital information makes it possible to provide immersive experiences in a number of different applications, there are still many unexplored aspects. In this work, a preliminary step to understand the impact of the scene content on human perception of the virtual 3D elements in a mixed reality has been performed. To this aim, a subjective test was designed and implemented to collect the reaction time of a set of users in a mixed reality application. In this test each user was asked to wear an augmented reality headset and to catch a virtual objects randomly appearing in the subject's field of view. We first estimated the detection accuracy through omitted, anticipated, and completed responses; then we related stimulus location, scene content and estimated accuracy. For this purpose, the area of stimulus presentation was divided into upper, lower, right, left, inner, and outer, to understand in which area responses were omitted and anticipated with respect to the central point of view. Experimental results show that, in addition to the saliency of the real scene, natural body gesture technology and limited field of view influenced human reaction time.
The One Size Does Not Fit All Approach: Case Studies in Modeling Embedded Librarianship

The pandemic and resulting shift to remote work stressed the importance of distance library services. As librarians explored different modes of engagement, embedded librarianship became increasingly important but also more complicated than before. While there is a wealth of literature on embedded librarianship, few authors agree on how to define it or discuss its specific challenges during the pandemic. This paper will present the experiences of two academic librarians working in different departments at the same institution, informed by the literature; the authors will highlight the complexity of embedded librarianship during this unique time while addressing challenges and offering recommendations.
SSD Optimization Model Based on Shallow Feature Fusion

Object detection has been an important research branch in the field of computer vision. The single-shot-detection (SSD) is an object detection model based on deep learning, which can achieve a good balance between the detection accuracy and the detection speed, but has the problem of poor recognition accuracy for small objects. To address this limitation, this paper improves the structure of the SSD feature pyramid and up-samples the shallow feature map with small object information and fuses it with the upper feature map, thus enhancing the ability of the shallow feature map to represent detailed information. In this way, not only the overall detection accuracy of the SSD is improved, but also a relatively high detection speed is maintained. The proposed model is verified by experiments on two common datasets, the Pascal VOC and MS COCO datasets. On the Pascal VOC07+12, MS COCO14, and VOC07+12+COCO datasets, the improved model achieves the mean average precision values of 80.1% (+3.3% compared with the conventional model), 49.9% (+6.8%), and 82.1% (+3.0%), respectively. Meanwhile, the proposed model can achieve the detection speed of 42.2 frames per second.
(Un)Curating COVID: Lessons from Corona in the City

Shortly after the first lockdown in response to the COVID-19 pandemic in 2020, the Amsterdam Museum launched the online exhibition Corona in the City . The multimedia platform, which invites inhabitants of the city of Amsterdam to contribute their own stories, aims to collect the pandemic as it is experienced. The authors look back on the trajectory of the platform during its first year and observe how, through a combination of curating and ‘uncurating’ the content, the museum demonstrates a broader change in its policy towards collecting and storytelling. We reflect on ways in which the platform has and will change according to how the pandemic unfolds, and propose that it can serve as a pilot for more democratic, co-created institutions in the future.
Full population testing: Applying multidimensional audit data sampling (MADS) to general ledger data auditing

Changes to the General Ledger (GL) represent a link between transactional business events from Journal Entries and prepared financial statements. Errors in these very large datasets can result in material misstatements or account misbalance. Unfortunately, a plethora of conditions renders traditional statistical and non-statistical sampling less effective. As a full-population examination procedure, Multidimensional Audit Data Sampling (MADS) mitigates these issues. In conjunction with top practitioners, we utilize a design science approach in applying the full-population MADS methodology to a real dataset of GL account balance changes. Issues such as the effectiveness of internal controls, detection of low-frequency high-risk errors, and earnings management concerns are addressed. This paper demonstrates how vital insights can be gained using MADS. More importantly, this approach also highlights the exact portion of the population that is error-free with respect to the auditors' tests.
Construction of Innovative Working Mode of College Counselors Based on Data Mining Technology

Data mining technology can analyze and mine university management data, provide more data support for university management, and play an important role in optimizing teaching quality, but these technologies are rarely applied to the work of university counselors. Based on this, this paper studies the construction and research of college counselors’ innovative work mode based on data mining technology. Based on the simple analysis of the impact of big data on the traditional working mode of counselors, this paper introduces the application of data mining commonly used in colleges and universities and puts forward the existing shortcomings. The innovative working mode of college counselors is designed. In the analysis of students’ daily behavior, the advantages of cluster analysis and support vector machine are used to analyze students’ consumption behavior. The Apriori algorithm is applied to the student achievement early warning management to improve the Apriori algorithm. Simulation results show that the proposed algorithm can shorten the running time, reduce the number of frequent item sets, and improve the classification accuracy.
Fast Retrieval Method of Massive Library Literature Resources Based on an Online Hash Algorithm

Abstract Aiming at the relatively large amount of library literature resources, a fast retrieval method for massive library literature resources based on an online hash algorithm is designed. The Pearson correlation coefficient is used to calculate the correlation between library literature resources, the collaborative filtering algorithm is used to realize the library literature resource mining based on the calculation results, and the least squares method is used to filter the nonsignificant data features in the mining results. Construct a Hash-B-tree to search the user’s library literature resources on a certain day, design a hash function lookup table to manage inactive metadata, and use fuzzy theory to simulate data with similar characteristics in the data divergence measurement sample, minimize the divergence of the sample data, and finally visualize the retrieval results based on the subject hierarchical relationship. The experimental results show that this method has the advantages of higher retrieval result accuracy, faster retrieval efficiency, and higher comprehensiveness of retrieval results.
The effects of anthropomorphism and multimodal biometric authentication on the user experience of voice intelligence

Voice intelligence is a revolutionary "zero-touch" type of human-machine interaction based on spoken language. There has been a recent increase in the number and variations of voice assistants and applications that help users to acquire information. The increased popularity of voice intelligence, however, has not been reflected in the customer value chain. Current research on the socio-technological aspects of human-technology interaction has emphasized the importance of anthropomorphism and user identification in the adoption of the technology. Prior research has also pointed out that user perception toward the technology is key to its adoption. Therefore, this research examines how anthropomorphism and multimodal biometric authentication influence the adoption of voice intelligence through user perception in the customer value chain. In this study we conducted a between-subjects online experiment. We designed a 2 × 2 factorial experiment by manipulating anthropomorphism and multimodal biometric authentication into four conditions, namely with and without a combination of these two factors. Subjects were recruited from Amazon MTurk platform and randomly assigned to one of the four conditions. The results drawn from the empirical study showed a significant direct positive effect of anthropomorphism and multimodal biometric authentication on user adoption of voice intelligence in the customer value chain. Moreover, the effect of anthropomorphism is partially mediated by users' perceived ease of use, perceived usefulness, and perceived security risk. This research contributes to the existing literature on human-computer interaction and voice intelligence by empirically testing the simultaneous impact of anthropomorphism and biometric authentication on users' experience of the technology. The study also provides practitioners who wish to adopt voice intelligence in the commercial environment with insights into the user interface design.
Evaluation of the colorimetric cyanmethemoglobin method and the automatical analyser for hemoglobin estimation

ABSTRACT Hemoglobin estimation by cyanmethemoglobin method was carried out in the present study. The intensity of the color was measured by colorimeter and autoanalyser. The samples were collected from St Isabel’s Hospital, Chennai and estimation of hemoglobin was done in autoanalyser. The remaining sample was transported to Loyola Health Centre, Chennai and then estimation of hemoglobin was performed with colorimeter. The readings obtained from colorimeter and autoanalyser processed statistically and was compared. Results indicates the insignificant variation between the autoanalyser and colorimeter for estimation of Hemoglobin respectively. Autoanalyser detects more anemic cases than colorimetric method. However, in resource poor condition where laboratory cannot afford expensive autoanalyser, colorimeter can be used as a suitable alternative. Keywords: Colorimeter, Autoanalyser, Hemoglobin.
Preserving Data Privacy in Mobile Cloud Computing using Enhanced Homomorphic Encryption Scheme

Abstract Mobile cloud-based data collection allows users’ related information to be collected, processed and stored efficiently and instantaneously. Nevertheless, such data collection approach has a drawback of data confidentiality and user’s privacy. Outsourcing users’ related information attracts unforeseen attackers to steal such information for their own benefits. Thus, this paper proposes a mobile cloud-based privacy-preserving data collection approach that allows end-users to provide a response based on information, which related to their personal life information in a privacy-preserving manner. In addition, this approach guarantees user’s information to be stored securely so as to prevent the data from being tempered easily. This approach uses a Homomorphic Cryptosystem protect the data. Such a cryptosystem allows data processing on the collected data to without decrypting. Such a technique is good to protect the privacy of the data owner. Finally, the result of the proposed scheme is evaluated thoroughly to compare the efficiency with the state-of-art scheme. The result illustrates the proposed scheme provides increased delay efficiency up to 170% compare to other method. Such increment has confirmed that the proposed approach is more suited to operate data collection approach in a mobile cloud computing.
Multi‐template temporal information fusion for Siamese object tracking

The object tracking algorithm based on Siamese network often extracts the deep feature of the target to be tracked from the first frame of the video sequence as a template, and uses the template for the whole tracking process. Because the manually annotated target in the first frame of video sequence is more accurate, these algorithms often have stable performance. However, it is difficult to adapt to the changing target features only using the target template extracted from the first frame. Inspired by the feature fusion network based on a transformer, this paper proposes a template update module called multi-template temporary information fusion module (MTFM), which can be trained offline. By fusing multiple target template features on time series, the template can always adapt to the changes of target appearance in the tracking process. In order to train the MTFM, this paper proposes a training method using time series data and Mean Square Error (MSE) as the loss function. This paper uses the MTFM on SiamFC++ tracker, and obtains good experimental results in three challenging datasets, including VOT2016, OTB100 and GOT-10k. The running speed of the algorithm on graphics processing unit (GPU) is maintained at about 200fps, which exhibits good real-time performance.
Hardware-Friendly Delayed-Feedback Reservoir for Multivariate Time-Series Classification

Reservoir computing (RC) is attracting attention as a machine-learning technique for edge computing. In time-series classification tasks, the number of features obtained using a reservoir depends on the length of the input series. Therefore, the features must be converted to a constant-length intermediate representation (IR), such that they can be processed by an output layer. Existing conversion methods involve computationally expensive matrix inversion that significantly increases the circuit size and requires processing power when implemented in hardware. In this article, we propose a simple but effective IR, namely, dot-product-based reservoir representation (DPRR), for RC based on the dot product of data features. Additionally, we propose a hardware-friendly delayed-feedback reservoir (DFR) consisting of a nonlinear element and delayed feedback loop with DPRR. The proposed DFR successfully classified multivariate time series data that has been considered particularly difficult to implement efficiently in hardware. In contrast to conventional DFR models that require analog circuits, the proposed model can be implemented in a fully digital manner suitable for high-level syntheses. A comparison with existing machine-learning methods via field-programmable gate array implementation using 12 multivariate time-series classification tasks confirmed the superior accuracy and small circuit size of the proposed method.
Are NFTs key to accessing the metaverse?

Non-fungible tokens have emerged as something of a foundation for the metaverse&apos;s internal infrastructure and economy, much like many role-playing games. A non-fungible token (NFT) is a financial security consisting of digital data stored in a blockchain, a form of distributed ledger. The ownership of an NFT is recorded in the blockchain, and can be transferred by the owner, allowing NFTs to be sold and traded. The article discusses the way in which NFTs have enabled profiting from gaming as well as the problem of scammers and goes on to look at whether NFTs are a growing trend.