Reviewerly/exHarmony huggingface dataset:
Towards Agent Design for Forming a Consensus Remotely Through an Analysis of Declaration of Intent in Barnga Game

This paper explores the key-factors that can promote people to form a consensus remotely in such an Internet environment, and designs the agent according to the found key-factor for a better online communication. To address this issue, this paper focuses on “declaration of intent” and regards that people form a consensus when sharing one thought with declaring their intents. To investigate an influence of declaration of intent in a group, this paper employs cross-cultural game, Barnga, in which the players are forced to form a consensus for progressing the game. Through the human subject experiments of Barnga, the following insights are revealed: (1) the players are promoted to form a consensus when their rate of declaration of intent is high in the early stage of the game; and (2) the relationship between the rate of declaration of intent and the winning rate of the players shows the weak positive correlation.
WITHDRAWN: Intelligent secure phrase search of encrypted data in cloud based IoT

Phrase search enables relevant documents retrieval of an exact phrase in many cloud-based IoT machine learning applications to safeguard information from leakage. Here the documents are checked by the data proprietors before being exported to the cloud, rendering the search process a difficult job. Multi-keyword search evolving searchable encryption algorithms unable to establish the position association of phrases on the remote server in a parsed phrase over encrypted data. In this paper, an efficient privacy-preserving phrase search is introduced to protect users’ search patterns; a probabilistic phrase recognition algorithm is also used. The proposed work demonstrates the security of real-world datasets and compared with existing multi-keyword search schemes to show the better matched documents with moderate overheads.
Standards: A Scientific Journal of Standardization

We first planned to launch the journal Standards in 2012 and obtained ISSN 2305-6703 for Standards (Basel) [...]
Research on Target Tracking Algorithm in occlusion scene

Abstract Target tracking is widely used in video image, intelligent transportation, behavior analysis and other fields. At the same time, target tracking technology faces more and more challenges. In this paper, the target tracking technology in occlusion scene is studied, and the target tracking method based on spatiotemporal context and the target tracking method combined with depth network model are introduced respectively. The main ideas and principles of the two methods are analyzed, and the improvements based on these two methods are summarized. Finally, the problems existing in the future development of occluded target tracking algorithm are prospected.
Cloud's Transformative Involvement in Managing BIG-DATA ANALYTICS For Securing Data in Transit, Storage And Use: A Study

with the advent of Cloud Computing a new era of computing has come into existence. No doubt, there are numerous advantages associated with the Cloud Computing but, there is other side of the picture too. The challenges associated with it need a more promising reply as far as the security of data that is stored, in process and in transit is concerned. This paper put forth a cloud computing model that tries to answer the data security queries; we are talking about, in terms of the four cryptographic techniques namely Homomorphic Encryption (HE), Verifiable Computation (VC), Secure Multi-Party Computation (SMPC), Functional Encryption (FE). This paper takes into account the various cryptographic techniques to undertake cloud computing security issues. It also surveys these important (existing) cryptographic tools/techniques through a proposed Cloud computation model that can be used for Big Data applications. Further, these cryptographic tools are also taken into account in terms of CIA triad. Then, these tools/techniques are analyzed by comparing them on the basis of certain parameters of concern.
An Isabelle/HOL formalization of AProVE’s termination method for LLVM IR

AProVE is a powerful termination prover for various programming languages, including a termination analysis method for imperative programs specified in the LLVM intermediate representation (IR). The method internally works in three steps: first, it transforms LLVM IR code into a symbolic execution graph; second, the graph is translated into an integer transition system; finally, termination of the transition sys- tem is proved by the back end of AProVE. Since AProVE is unverified software, our aim is to increase its reliability by certifying the generated proofs. To this end, we require formal semantics of all program representations, i.e., for LLVM IR, for symbolic execution graphs and for inte- ger transition systems. As the latter is already available, we define the former ones. We note that our semantics for LLVM IR use arithmetic with unbounded integers. We further verify the first and the second step of AProVE’s termination method, including verified algorithms to check concrete proofs. Since the third step can already be certified, we obtain a complete formally verified method for certifying AProVE’s termination proofs of LLVM IR programs. The whole formalization has been done in Isabelle/HOL and our certifier is available as a Haskell program via code generation.
Bayesian Nonparametric Forecast Pooling

This paper introduces new approaches to forecast pooling methods based on a nonparametricprior for a weight vector combining predictive densities. The first approach places a Dirichletprocess prior on the weight vector and generalizes the static linear pool. The second approachuses a hierarchical Dirichlet process prior to allow the weight vector to follow an infinite hiddenMarkov chain. This generalizes dynamic prediction pools to the nonparametric setting.We discuss efficient posterior simulation based on MCMC methods. Detailed applications toshort-term interest rates, realized covariance matrices and asset pricing models show the nonparametricpool forecasts well. The nonparametric dynamic weight vector can be applied toother forecast pooling methods and the paper concludes with extensions and an application tocalibration and combination of predictive densities.
Construction and Effectiveness Analysis of the Effectiveness Evaluation Model of Formation Cooperative Detection

Cooperative formation detection is an important means of future maritime ship detection. Starting from the needs of future maritime formation coordinated operations, the concept and advantages of coordinated detection are analyzed, and the levels of different coordination modes are clarified. Based on the coordination strategy, a target threat degree model and an effectiveness evaluation model are established to solve the problem of choosing the better coordination strategy in the battlefield environment. The example simulation shows that the model and method can better realize the effectiveness evaluation of the collaborative strategy and meet the needs of strategy selection.
PARTICIPATION OF ACADEMIC LIBRARIES IN PREPARATION FOR CELEBRATING THE 220th ANNIVERSARY OF THE RUSSIAN ACADEMY OF SCIENCES IN 1945

Here the authors present another article from the series devoted to the work of libraries of academic institutions and their staff during the Great Patriotic War. The article bases on the documents found in the archive of the Library for Natural Sciences of the Russian Academy of Sciences, Moscow and St. Petersburg Archives of the Academy of Sciences. The authors used the general scientific methodology, applying source study, archaeographic and analytical-synthetic methods of work with archival documents. This work is devoted to the grandiose event, preparation to which took place in in the last war months – the 220th anniversary of the USSR Academy of Sciences celebration. Preparation for this jubilee celebration was complex one, engaging various specialists and reflecting different directions of scientific development in the country. Large-scale book and rare archive documents exhibitions in which the best library specialists of the USSR Academy of Sciences took part displayed successes of the Russian science. These jubilee arrangements were to demonstrate the history of the development of science in Russia and the Soviet state power having won the severe war. When arranging jubilee celebrations the USSR Academy of Science librarians used the experience acquired while organizing famous scientists books’ exhibitions in the most difficult period of the Great Patriotic War. There were exhibited book treasures from the collections of academic libraries and archives as well as the results of studies of soviet scientists, foreign scientific publications that included native scientists’ articles. These exhibitions took place in Moscow and Leningrad – the cradle of the Russian Academy of Sciences. During this hard war period, the State allotted considerable resources for the jubilee arrangements, attaching great importance to international contacts of native scientists, showing their impact into the global science. The jubilee exhibitions revealed the big potential of the USSR Academy of Science libraries in spite of the enormous stuff and resources loss during the war. The experience of holding events on celebrating the anniversary of the Academy of Sciences of the USSR in 1945 may be useful in organizing work on the upcoming 300-th anniversary of the Russian Academy of Sciences in 2024.
Analysis of problems in building ventilation energy-saving design

Abstract With the development of modern science and technology, the work of all walks of life has been further improved, and related content has become more abundant. Building construction is a problem that people attach great importance to now. Only by better grasping the relevant content, developing the core strength, and improving the strength of the enterprise can practical problems be effectively solved. Building ventilation and energy-saving design is one of the important aspects. The problems in the ventilation design are handled well, the analysis is carried out accurately, and effective solutions are grasped to ensure the quality of the design, so as to play an active role in the development.
FACE DETECTION IN VR GAMES

Face detection has been made insufficient progress in the VR games field, due to the lack of database of VR games. In available technology of face detection, specifically for the uncovered facial occlusion, mainly relies on the eyes features to detect faces. However, it does not work to detect a face when the face has facial occlusion. Therefore, it needs to train a model with a neural network to detect faces. In this paper, we released a face detection dataset for VR games. At the same time, a training model of face detection in the VR game will also be released. It can be checked on the GitHub: https://github.com/whitstar007/YOLO-v5-Face-Detection-in-VR-Game.
Robotic Waiters: Detecting Table to Serve Using RFID Tags

Robots are now providing services in the field of hospitality in China and Japan. They are using a guided path to navigate and serve the food to the table, from where the order comes. Detecting and stop toward correct table is one of the most important factors during serving by robots. Some time it happens when robots do stop at a little distance from the table to be serve or it can also possible that robot detects wrong table. In this research paper, researchers are discussing the present issues exist in current table detecting technique in existing robots and also proposing the solution to detect the correct table using RFID tags.
Risk Analysis in Software Process Models

In this research, development of software is one of the most predictable development methodologies in the world of Computer science. When the software is risk free. This not only brings down the failure possibilities in the project, also ensures the client fulfillment. Hence, it is compulsory to analysis the possibility of disasters in a project from different aspect with improvement ready in hand so that the impact of the difficulty can be brought down. This paper concentrate on one such part of investigate risks where the analysis of risk in the following software development process methodologies like Waterfall Model, Incremental Model, and Spiral Model. Risk analysis is definitely bring up the success rate as the parameters which affect the project success such as Time, Cost, People, and Process may be thoroughly looked into. Hence, project managers might build considered judgment concerning risk management ways and guarantee action of project success.
Vid2Pix - A Framework for Generating High-Quality Synthetic Videos

Data is arguably the most important resource today as it fuels the algorithms powering services we use every day. However, in fields like medicine, publicly available datasets are few, and labeling medical datasets require tedious efforts from trained specialists. Generated synthetic data can be to future successful healthcare clinical intelligence. Here, we present a GAN-based video generator demonstrating promising results.
The Contribution of Internet Utilization towards the Student Learning Outcomes

The emergence of the internet has a major effect on human life, including in education. The internet contributes to academics so that it has a very important role for student learning success. This research aims to describe the contribution of internet utilization as a learning resource towards the student learning outcomes at SMA Negeri 2 Padang. This study used a descriptive quantitative research method, and the data were analyzed by using the simple linear regression. The population of this research was 920 students of SMA Negeri 2 Padang, and the sample chosen was 279 students selected by using the proportional random sampling technique. The research instrument used was a scale of internet utilization with a Likert model. The findings of this research showed that the use of the internet as a learning resource contributes towards the student learning outcomes at SMA Negeri 2 Padang with an effective contribution 40.4%.
Real-time tracking of visual objects based on deep feature fusion

In order to improve SiamFC's recognition and localization capabilities in complex scenes, this paper proposes a visual object tracking network structure based on deep feature fusion. Aiming at the recognition ability, the feature extraction network IVBRNet with deep structure is designed based on VGGNet and ResNet, and the deep features with rich semantic information extracted by IVBRNet are used to assist object recognition. For the positioning ability, the ECA attention module is added at the end of the original AlexNet object feature extraction branch and the original Relu activation function is replaced with the ELU activation function to improve the expression ability of the object feature. Finally, the response graphs obtained by AlexNet and IVBRNet are calculated and fused to perform object tracking together. The results on the OTB2015 and VOT2016 data sets show that the algorithm has a more stable tracking effect than other algorithms, and can better cope with scenes such as fast moving and complex backgrounds. At the same time, the algorithm reaches 32 frames per second, which meets real-time requirements.
Malarial Parasite Detection by Leveraging Cognitive Algorithms: A Comparative Study

Malaria alludes to an infectious mosquito-borne malady brought about by parasite sort plasmodium transferred by female anopheles mosquito. In the host liver, the parasite duplicates itself and starts annihilating the red blood cells. The visual examination of disease is inspected by a magnifying instrument for diseased red cells. Doing diagnosis manually majorly relies on the pathologists with good skills and expertise, and reports may change in various laboratories. Another route around, many ML algorithms have been applied for the unconstrained location of blood spread. This paper features the headway in the field of ML and its useful applications in the discovery, distinguishing proof and finding of the malarial disease. This paper gives an insight of how transfer learning and deep learning is considered a prominent technique in computer vision, it is entirely possible to achieve certain rather good performance contrasted with other conventional ML algorithms that need a thorough feature engineering and complex information pipelines.
Sustainability-aware Resource Provisioning in Data Centers

In the big data era, cloud computing provides an effective usage model for providing computing services to handle diverse data-intensive workloads. Data center capacity planning and resource provisioning policies play a vital role in longterm life cycle management of datacenters. Effective design and management of data center infrastructures while ensuring good performance is critical to minimizing the carbon footprint of the datacenter. Traditional solutions have primarily focused on optimizing data center operational phase impacts including reducing energy cost during the resource management phase. In this paper, we propose a two-phase sustainability-aware resource allocation and management framework for data center life-cycle management that jointly optimizes the data center manufacturing phase and operational phase impact without impacting the performance and service quality for the jobs. Phase 1 of the proposed approach minimizes data center building phase carbon footprint through a novel manufacturing cost-aware server provisioning plan. In phase 2, the approach minimizes the operational phase carbon footprint using a server lifetime-aware resource allocation scheme and a manufacturing cost-aware replacement plan. The proposed techniques are evaluated through extensive experiments using realistic workloads generated in a data center. The evaluation results show that the proposed framework significantly reduces the carbon footprint in the data center without impacting the performance of the jobs in the workload.
&gt;Towards Distributed Systems Testing in Cloud Environment

There exists a close relationship between cloud computing and distributed systems. In fact, Cloud computing technology brings new organizational and technological capabilities to build an underlying infrastructure for distributed systems. Therefore, even if cloud computing offers opportunities to improve productivity and reduce costs, it also introduces a number of technical challenges, especially in testing area. In this paper, we introduce concepts such as cloud computing, cloud testing and Infrastructure as a Service (IaaS). Then, we present our distributed testing architecture for applications running in an elastic cloud computing environment using these concepts.
The new baseline for high dimensional dataset by ranked mutual information features

Feature selection is a process of selecting a group of relevant features by removing unnecessary features for use in constructing the predictive model. However, high dimensional data increases the difficulty of feature selection due to the curse of dimensionality. From the past research, the performance of the predictive model is always compared with the existing results. When attempting to model a new dataset, the current practice is to benchmark for the dataset obtained by including all the features, including redundant features and noise. Here we propose a new optimal baseline for the dataset by mean of ranked features using a mutual information score. The quality of a dataset depends on the information contained in the dataset, and the more information contains in the dataset, the better the performance of the predictive model. The number of features to achieve this new optimal baseline will be obtained at the same time, and serve as the guideline on the number of features needed in a feature selection method. We will also show some experimental results that the proposed method provides a better baseline with fewer features compared to the existing benchmark using all the features.
Grid Text Retrieval based on Deep Learning

The Internet of Things (IOT) have developed rapidly in recent years, which produces a vast amount of data including the electronic text data and the distributed data etc. These massive data hide a wealth of useful information and knowledge, and analyze them on the needs of users and assess the effect of products has a great significance. Therefore, it is an urgent problem to propose an efficient method of grid text retrieval. As is known to all, deep learning models are the most effective way to deal with massive data. In our paper, we will use an effective deep learning model to retrieve the grid text. i.e., the Deep Boltzmann Machines model (DBMs). The DBMs model is an effective text clustering algorithm. In particular, DBMs integrate a special energy function and use a layer-by-layer pre-training phase, which can make model more effective. The design of deep structure also enables the DBMs to extract the good text representations. The experimental results show that the DBMs model gets the desirable effect in handling with an extract abstract concept and has a good performance in large scale grid text clustering.
Crowd counting by feature-level fusion of appearance and fluid force

Crowd counting is a research hotspot for video surveillance due to its great significance to public safety. The accuracy of crowd counting depends on whether the extracted features can effectively map the number of pedestrians. This paper focuses on this problem by proposing a crowd counting method based on the expression of image appearance and fluid forces. Firstly, Horn-Schunck optical flow method is used to extract the motion crowd. Secondly, based on the motion information of crowd, pedestrians in different directions are distinguished by the k-means clustering algorithm. Then, image appearance features and fluid features are extracted to describe different motion crowd. The image appearance features are gained by calculating the foreground area, foreground perimeter and edge length. The gravity, inertia force, pressure and viscous force are taken as the fluid features. Finally, two kinds of features are combined as the final descriptor and then least squares regression is used to fit features and the number of pedestrians. The experimental results demonstrate that the proposed crowd counting method acquires satisfied performance and outperforms other methods in terms of the mean absolute error and mean square error.
Handling Unknown Words in Neural Machine Translation System

The corpus-based approach is an emerging approach to develop the machine translation system nowadays. Statistical Machine Translation(SMT) and Neural Machine Translation(NMT) are two corpus-based systems. NMT yields better results as compared to the traditional rule-based approach as well as a statistical-based approach. The computation complexity of the NMT system is more as compared to the SMT system due to the use of softmax function at the output layer of NMT. Due to the constraint of complexity, NMT uses fixed vocabulary, but Machine Translation (MT) is an open problem. This causes the out-of-vocabulary (OOV) in the predictions of the NMT system. To overcome these OOV words in NMT, Word Embedding (WE) has been used in Our NMT model for Punjabi to English. With WE, Byte-Pair-Encoding (BPE) has also been used to increase the effectiveness of the overall system. The system has been evaluated by using the automated evaluation tools BLEU score and Translation Error Rate (TER) score.