Reviewerly/exHarmony huggingface dataset:
Interactions Environmental Processes and Development of Human Organisms Activities Via Mechanisms of Mental Activity from the Point of Views of Biophysics, Biochemistry and Thermodynamics

The article studies development of human organism in condition interactions with environment activity. There were described the role of solar processes in forming both non-living substances and living organisms through, from the one hand, germination living substances and also processes germination prokaryotic organisms and, from the second hand, germination living substances and also processes germination prokaryotic organisms which peculiarities were presented. Further there were elucidated dependence eukaryotic lives on Environmental influences of Solar system via creating mechanism maintenance stability an open thermodynamic system of an eukaryotic organisms according to famous Prigogine theorem. Thus there were described in details mechanisms maintenance stability an open non equilibrium non linear thermodynamic system of human organism in which Basic Internal Energy (Ebas) as builded bonds into molecule and atoms, bonds among compound processes of open thermodynamic biologic system, as inherited energy of genomic processes, bonds processes development of open thermodynamic system of an organism form Basic Internal Energy of Molecular Bonds (EbasMolBonds), Basic Internal Energy of Trophic Processes (EbasTroPr) and Basic Internal Energy of Mental-Soul Processes (EbasMenPr). At last it was described mechanisms operations of different receptors between an organism and Environment as well as neurons cellular mechanisms with environmental interactions causing development and maintenance stability Basic Internal Energy of Mental-Soul Processes (EbasMenPr), of Trophic Processes (EbasTroPr) of Molecular Bonds (EbasMolBonds) that form mental peculiarity of an organism
Exploring Time-aware Multi-pattern Group Venue Recommendation in LBSNs

Location-based social networks (LBSNs) have become a popular platform for users to share their activities with friends and families, which provide abundant information for us to study issues of group venue recommendation by utilizing the characteristics of check-in data. Although there are some studies on group recommendation for venues, few studies consider the group’s venue preference in different temporal patterns. In this article, we discover that the group’s activity venue has a temporal effect, that is, the group’s preference for the activity venue is different at different times. For example, a couple of lovers prefer to travel to tropical regions in winter and relax in bars in the evening. Based on this discovery, we present a Time-aware Multi-pattern (TaMp) topic model to capture the group’s interest in the activity venue in multiple temporal patterns (including the daily pattern, the weekly pattern, the monthly pattern, and the quarterly pattern). The TaMp model takes into account the topic, members, temporality, and venue information of group activities and the latent relations among them, especially the strong correlation between the activity time and the corresponding activity venue. Then, we propose a group venue recommendation method based on the TaMp model. In addition, an improved grouping algorithm (iGA) in LBSNs is put forward to enhance the rationality of grouping and the accuracy of group venue recommendation. We conduct comprehensive experiments to evaluate the performance of TaMp on two real-world datasets. The results show that our proposed method outperforms the state-of-the-art group venue recommendation and demonstrates the significance of temporal effects in explaining group activities.
AI-Enabled Augmented Reality-Based Shared Collaborative Experience

The unpopular reality of taxpayers we see in the placement of 3D animations in the real world. The objects used can be viewed and communicated by both individual and multiple users. For example, two users, if they are in the same location, can create a shared experience where both can interact with real-world objects. The real-life experience of unpopular taxpayers we see could improve the efficiency of the education sector as well, as students can have practical experience and visual cues that all students can access using their own learning-enhancing devices.
‘What do you see now?’: lessons learned while integrating a virtual reality application in ambulatory care of a pediatric surgery

In many medical contexts, virtual reality (VR) applications are associated with high hopes for improved forms of care. In order for these hopes to be fulfilled not only in controlled (laboratory) studies, possibly accompanied by technicians, but also in everyday clinical practice, applications must be suitable for the users on the one hand and efficiently integrated into the organisational processes on the other. While the first aspect has already been widely studied in terms of VR-related user interface and interaction design, there are fewer references and recommendations on the second one. These are given in this contribution. They are based on findings of applying human-centred design methods (contextual inquiry, work models, focus group) during the integration of a VR application in ambulatory care of children and adolescents after bone fractures. Finally, transferability of the recommendations to other contexts, including beyond clinical scenarios, is discussed.
Comprehensive Approach to Assessing the Reliability of Information in Online Media

The article proposes a comprehensive approach to assessing the reliability of information in online media. The approach was tested on twenty Internet media texts published on the RBC and RIA Novosti news portals. The approach is based on separate ideas of media researchers about the qualitative content of media publications. It includes the following criteria: 1) explicit (external) position of the publication in the media market, e.g., professionalism of the editorial staff, discourse that surrounds the media, etc.; 2) latent (hidden) characteristics of the publication, i.e., its value system, ideology, media message, style, etc.; 3) goals and interests of the media vs. those of the audience, e.g., type of information, communicative goals, conflict between verbal and non-verbal content, accuracy, completeness, sufficiency, references, links to reliable sources, logical nomination and hypotheses development, etc. This approach proved to be objective as it is able to assess both individual texts and the whole media source.
Vkontakte Social Network as a Tool of Professional Skills Development in University Students (the example of the educational standard Journalism)

The article considers social networks as a platform for online learning as in case of experimental television programs made by Kemerovo State University students of Journalism. Social networks provide a gaming environment for the academic process. Pedagogical theory and practice lack tools for assessing the impact of social networks on the formation of professional competencies in university students. They create content for online programs in professional television editorial offices under realistic conditions. This experience motivates them and develops their professional skills. Social networks increase motivation for learning, reveal creative abilities, and organize the academic process.
An outbreak of malaria caused by increase in malaria breeding sites in swamps.

On 10th June 2019, routine analysis of malaria surveillance data at the National Malaria Control Division, Ministry of Health in Uganda revealed that there was an unusual increase in the number of malaria cases reported in the Oyam District. On 11th June 2019, the District Health Officer in Oyam District convened a meeting with the District Health Team (DHT) in which the District Biostatistician confirmed that the number of malaria cases had indeed exceeded the upper limit, starting in epidemic week 24 (approximately the week of June 10). The District Health Officer issued a formal request to the Ministry of Health for assistance in dealing with the malaria outbreak in Oyam. Two field epidemiology residents were assigned to work with the District Health Team to investigate the outbreak. The residents followed the steps in conducting vector borne disease outbreak investigations including preparation for field work, establishment of the existence of an outbreak by analyzing surveillance data, descriptive data analysis, hypothesis generation, conducting environmental and entomological assessments, conducting analytic studies with a focus on the utility of retrospective cohort studies as well as reporting findings. This case study teaches trainees in Field Epidemiology and Laboratory Training Programs, public health students, public health workers who are interested or who may participate in vector borne disease outbreak investigation and response.
Developing research questions in conversation with the literature: operationalization &amp; tool support

Empirical Software Engineering rests on the understanding of practical problems and their solution counterparts. Frequently, solutions are not absolute but relative to the context where the problem is observed. This tends to imply that the solution and the problem unveil gradually together, and hence, researchers are not always in the position to state the research question (RQ) at the onset. Like software engineers when facing blurred requirements, researchers might not be familiar enough with the problem in the early phases of a research to properly scope their RQs (hereafter referred to as RQ Scoping). Here, the literature may play the role of the stakeholders in Agile methods: keeping the focus on the aspects that are essential (vs. accidental) of the RQ. Informed by Inductive Top-Down Theorizing, this article acknowledges RQ Scoping as iterative and incremental, entailing a conversation between the experimental work and literature reviewing. Yet, for literature reviewing to become "Agile" it is not only required to be driven by the RQ but also to have tool support. Tools might bring transparency and traceability, both factors especially welcome in a scenario characterized by testing (is my RQ relevant?) and adjustment (how can I make my RQ relevant?). Specifically, the advent of the RQ in close relationship with the literature advises for "Agile" literature reviewing to be conducted at the place where the literature is naturally kept: the Reference Management System (e.g., Mendeley). This article introduces the theoretical underpinnings, design principles, proof of concept and evaluation for FRAMEndeley, a Mendeley-integrated utility for RQ Scoping.
Unsupervised Domain Adaptation through high-order tensor matching with discriminative manifold learning

Unsupervised Domain Adaptation (UDA) seeks to exploit the source domain knowledge to address similar but unsupervised target domain tasks. To perform UDA, most of the existing works typically learn domain-invariant representations or align the distributions across source and target domains through low-order statistical matching, which fail to explore more discriminative and high-order adaptation information. In this work, we proposed a kind of UDA, namely UDA through high-order tensor matching with discriminative manifold learning (UDA-HOTDML), by exploring the discriminative manifold structure jointly with the high-order tensor to more desirably match cross-domain alignment. In UDA-HOTDML, multiple aspects of domain knowledge are exploited to benefit more generalizable UDA by jointly modeling the discriminative structures of target domain, consistence as well as high-order tensor statistical characteristics. Finally, our evaluation results show that the proposed UDA-HOTDML method outperforms many related UDA methods.
Complementary in Time and Space: Optimization on Cost and Performance with Multiple Resources Usage by Server Consolidation in Cloud Data Center

The recent COVID-19 pandemic has accelerated the use of cloud computing. The surge in the number of users presents cloud service providers with severe challenges in managing computing resources. Guaranteeing the QoS of multiple users while reducing the operating cost of the cloud data center (CDC) is a major problem that needs to be solved urgently. To solve this problem, this paper establishes a cost model based on multiple computing resources in CDC, which comprehensively considers the hosts’ energy cost, virtual machine (VM) migration cost, and SLAV penalty cost. To minimize this cost, we design the following solution. We employ a convolutional autoencoder-based filter to preprocess the VM historical workload and use an attention-based RNN method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migration before the host enters an overloaded state to reduce the occurrence of SLAV. A heuristic algorithm based on the complementary use of multiple resources in space and time is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing methods, our proposed method reduces the energy consumption of the hosts and SLAV and reduces the total cost by 26.1~39.3%.
Evolutionary Approaches to the Identification of Dynamic Processes in the Form of Differential Equations and Their Systems

Evolutionary approaches are widely applied in solving various types of problems. The paper considers the application of EvolODE and EvolODES approaches to the identification of dynamic systems. EvolODE helps to obtain a model in the form of an ordinary differential equation without restrictions on the type of the equation. EvolODES searches for a model in the form of an ordinary differential equation system. The algorithmic basis of these approaches is a modified genetic programming algorithm for finding the structure of ordinary differential equations and differential evolution to optimize the values of numerical constants used in the equation. Testing for these approaches on problems in the form of ordinary differential equations and their systems was conducted. The influence of noise present in the data and the sample size on the model error was considered for each of the approaches. The symbolic accuracy of the resulting equations was studied. The proposed approaches make it possible to obtain models in symbolic form. They will provide opportunities for further interpretation and application.
K-MNSOA: K-Anonymity Model for Privacy in the Presence of Multiple Numerical Sensitive Overlapped Attributes

Knowledge is the main discussing and explored topic of today’s era. Everyone is working toward improving information and tries to consider it as a ladder to move forward. Data is the main object to get information, and data is considered as a big data nowadays as it contains numerous information in all directions. As knowledge is bliss, it is also possible that an adversary can use this information to harm an individual. To protect data from an adversary privacy preserving data publishing techniques is used. But when multiple sensitive data present in a data set which is correlated to each other’s several model are unable to protect data in an efficient way. In this paper, a novel model K-MNSOA is proposed for privacy preserving data publishing, which protect sensitive data privacy breach, even if the data set contains multiple sensitive numerical overlapped attributes. A proposed model assumes that all sensitive attributes are not actually sensitive, so when data is protected, information loss will increase. To overcome this issue, new model suggests to divide sensitive data into levels of sensitivity and apply generalization only for the privacy of high sensitive attribute.
Mobile Device Camera Calibration Using Building Images and Onboard Accelerometer

Recent mobile devices are mostly integrated with camera and accelerometer sensors. As long as the device is immobile, the accelerometer is affected just by gravity, hence the measured acceleration refers to gravity. On the other hand, synchronized captured images can also carry the direction of gravity information depending on the content of the scene. For instance, structures as buildings, lighting poles, furniture, walls, etc. can show the direction of gravity on the images. These vertical structures in the image can be used to detect the vanishing point indicating zenith. Hence, estimation of the camera internal parameters which map gravity vector into the vanishing point is possible. Based on this theory, in this work, we propose a novel camera calibration method which only requires taking photos of an arbitrary building and recording the synchronous acceleration vectors from an onboard accelerometer. Then, the vanishing points detected from the images and the acceleration vectors replace the 3D calibrator. The resulting camera calibration method has competitive results compared to the popular calibrator-based methods despite it does not need any external calibrator object. The proposed camera calibration method both has the convenience of self-calibration approaches, and gives highly competitive accuracy within calibrator-based approaches. The dataset and the code are available at https:// github.com/okayarik/Camera-Calibration-Using-Accelerometer.
Towards Runtime Monitoring of Complex System Requirements for Autonomous Driving Functions

Autonomous driving functions (ADFs) in public traffic have to comply with complex system requirements that are based on knowledge of experts from different disciplines, e.g., lawyers, safety experts, psychologists. In this paper, we present a research preview regarding the validation of ADFs with respect to such requirements. We investigate the suitability of Traffic Sequence Charts (TSCs) for the formalization of such requirements and present a concept for monitoring system compliance during validation runs. We find TSCs, with their intuitive visual syntax over symbols from the traffic domain, to be a promising choice for the collaborative formalization of such requirements. For an example TSC, we describe the construction of a runtime monitor according to our novel concept that exploits the separation of spatial and temporal aspects in TSCs, and successfully apply the monitor on exemplary runs. The monitor continuously provides verdicts at runtime, which is particularly beneficial in ADF validation, where validation runs are expensive. The next open research questions concern the generalization of our monitor construction, the identification of the limits of TSC monitorability, and the investigation of the monitor's performance in practical applications. Perspectively, TSC runtime monitoring could provide a useful technique in other emerging application areas such as AI training, safeguarding ADFs during operation, and gathering meaningful traffic data in the field.
Supervised Automatic Text Summarization of Konkani Texts Using Linear Regression-Based Feature Weighing and Language-Independent Features

Automatic summarization of text documents is a widely researched domain in natural language processing. A lot of research is carried out on the most commonly spoken languages in the world. Automatic text summarization needs to be explored to include some of the less popular languages in the world to help sustain such languages and promote their use. A language-independent summarization system that can be effortlessly extended to other such languages, which could have a limited number of resources to carry out such research is required. In this paper, we examine the efficiency of supervised linear regression models for the performing single document extractive automatic text summarization on Konkani language folktales dataset. We use 13 language-independent features and linear regression models to learn feature weights. These weights are then used to calculate a sentence’s score; top ranking sentences are then chosen for summary generation. We employ a k-fold evaluation strategy to evaluate the system-generated summary against a human-generated summary using ROUGE evaluation toolkit. Additionally, we also evaluate the use of L1 and L2 regularization on the summarization task. The work represents early attempts in automatic text summarization pertaining to Konkani language, and the dataset employed in these experiments is unique and devised particularly to facilitate research in this domain. The language-independent features used can be readily extended to other low-resource languages. The systems implemented in this work performed better as compared to an unsupervised system based on k-means approach and also beat the baseline systems.
Analysis of Browsing Activity of Portable Opera Browser in Windows 10 Pro System in VMware Workstation Using Digital Forensics Software

For human beings, irrespective of their profession, Internet connectivity became a necessity for survival in this digital age, but at the same time, privacy is becoming a growing concern for every Internet user. With rapid growing trends of personalized Internet activity, digital profiling is becoming a new normal, and all Internet servers are compiling users’ activities to provide better suggestions or recommendations. So, to avoid compilation of such activities, users are using facilities like portable browsers, private browsers, and tor browsers to remain anonymous. When similar facilities are used by criminals, then it is becoming a challenge for law enforcement agencies to investigate cases. Therefore, in this paper, we intend to analyze the portable browser in windows 10 pro in virtual machine for exploring the possible artifacts retrieval using digital forensics software. During analysis, it was found that various types of artifacts were available about the browsing activity from virtual machine hard drive and from RAM dump.
Copy-Move Forgery Detection Using K-Means and Hu’s Invariant Moments

Image forensics is one of the most active research domains. As technology is advancing, we can add or take out crucial features from a picture without any trail of tampering. Therefore, its authenticity is called into question especially when images have impressive power. Copy-move is a kind of forgery, where portions of a picture are transformed and inserted into the same picture. Copy-move forgery detection is one such research domain that has put forward various methods to find out copy-move forgery. Many techniques based on image processing and machine learning have been put forward to detect the forgery. Since the duplicated parts are from the same image, many of the features will be similar to the rest of the image making it difficult to detect forgery using the latest methods. In this work, we propose to use SIFT keypoint-based forgery detection with clustering for quickly identifying copy-move forgeries in highly textured regions. As the SIFT keypoints are difficult to detect in smooth regions, we propose to use Hu’s invariant-based block-based forgery detection strategy to detect the missing cases. We show that the joint approach outperforms the method reported by Li et al. (IEEE Trans Inf Forensics Secur 10:507–518, 2015) on the popular copy-move forgery detection dataset MICC-600.