Reviewerly/exHarmony huggingface dataset:
EllSeg-Gen, towards Domain Generalization for Head-Mounted Eyetracking

The study of human gaze behavior in natural contexts requires algorithms for gaze estimation that are robust to a wide range of imaging conditions. However, algorithms often fail to identify features such as the iris and pupil centroid in the presence of reflective artifacts and occlusions. Previous work has shown that convolutional networks excel at extracting gaze features despite the presence of such artifacts. However, these networks often perform poorly on data unseen during training. This work follows the intuition that jointly training a convolutional network with multiple datasets learns a generalized representation of eye parts. We compare the performance of a single model trained with multiple datasets against a pool of models trained on individual datasets. Results indicate that models tested on datasets in which eye images exhibit higher appearance variability benefit from multiset training. In contrast, dataset-specific models generalize better onto eye images with lower appearance variability.
Decentralized Private Freight Declaration &amp; Tracking with Data Validation

In January 2017, a truck crossed the border between Spain and France for the first time using an e-CMR: An electronic version of the primary transport document required for inter-European logistics.Since that crossing, researchers and logistic organizations have proposed a large number of ideas to further digitize Europe's supply chain.Many of these ideas involve blockchains, but not all of them validate the data that is posted to them.As a result, participants can make illegitimate claims: Even though the blockchain enables transparency and immutability of the data stores, it does not ensure veracity.We provide several examples of works about information sharing in the supply chain that do not perform such validation.One work that does use the blockchain's validation functionality is DEFEND.DEFEND addresses customs agencies' lack of information for international freight inspection by tracking shipping containers throughout their journey.As containers pass from one operator to another, the blockchain participants ensure that containers are not doubly spent.In this work, we propose an extension of DEFEND, in which we further extend the capabilities for validation.Moreover, we provide actual cryptographic protocols to preserve participants' privacy while DEFEND only described privacy on a high level.Finally, by making a more fine-grained distinction between different actors in the chain, we model the entire supply chain from buyer to seller.As a result, the buyer and seller can now track the respective package's whereabouts through each leg of its journey.
LC-Net: Localized Counting Network for Extremely Dense Crowds

Large mass gatherings such as pilgrimages, protests, etc., often pose serious challenges for the crowd management personnel to maintain public safety and security especially in dense crowds. These challenges can be mitigated through estimating the number of attendees as well as localizing them in a particular crowded event, where existing research studies are yet to provide accurate information in an efficient manner. Therefore, in this paper, we propose a novel deep learning architecture namely LC-Net to precisely and efficiently locate as well as count the attendees in dense crowds using a crowd localization map. Here, we exploit the notions of residual layers and dilated convolution to improve both the accuracy and efficiency of our architecture. Besides, we propose a new data augmentation technique to resize the high-resolution training images based on crowd density that substantially boosts our localization accuracy. Rigorous experimental evaluation of our proposed LC-Net over four different public crowd datasets such as NWPU-Crowd, UCF-QNRF, ShanghaiTech-A, and ShanghaiTech-B shows a substantial performance improvement while using LC-Net in terms of precision and recall in most of the cases. The improvement eventually results in an improved F1 score in all cases compared to the state-of-the-art approaches. Further, we present a real implementation of our proposed approach using a client–server application. In the server, we execute the LC-Net model over the images captured in real-time using an IP Camera and then visualize the results in a graphical manner. This implementation demonstrates the applicability of our proposed approach in real cases.
HDPP: High-Dimensional Dynamic Path Planning Based on Multi-Scale Positioning and Waypoint Refinement

Algorithms such as RRT (Rapidly exploring random tree), A* and their variants have been widely used in the field of robot path planning. A lot of work has shown that these detectors are unable to carry out effective and stable results for moving objects in high-dimensional space, which generate a large number of multi-dimensional corner points. Although some filtering mechanisms (such as splines and valuation functions) reduce the calculation scale, the chance of collision is increased, which is fatal to robots. In order to generate fewer but more effective and stable feature points, we propose a novel multi-scale positioning method to plan the motion of the high-dimensional target. First, a multi-scale feature extraction and refinement scheme for waypoint navigation and positioning is proposed to find the corner points that are more important to the planning, and gradually eliminate the unnecessary redundant points. Then, in order to obtain a stable planning effect, we balance the gradient of corner point classification detection to avoid over-optimizing some of them during the training phase. In addition, considering the maintenance cost of the robot in actual operation, we pay attention to the mechanism of anti-collision in the model design. Our approach can achieve a complete obstacle avoidance rate for high-dimensional space simulation and physical manipulators, and also work well in low-dimensional space for path planning. The experimental results demonstrate the superiority of our approach through a comparison with state-of-the-art models.
ThCoRe: Things of interest recommendation based on novel things correlations

Despite that the emerging internet of things (IoT) increases the chance for users to interact with things, generating massive context data, the ground-truth user-thing interaction data is still quite sparse compared with the enormous numbers of users and things. Current existing things recommendation methods mostly adopt machine learning or deep learning (e.g., matrix factorization or neural network) as their basic models, which continuously results in that the data sparsity has a great impact on their performance. Although they integrate different contexts to alleviate the problem, the real effect is rather limited. To the end, in the paper, we study how to achieve better recommendation results in the case of the data sparsity. Along this line, the very critical challenge is how to learn user preferences without relying too much on big data. As a solution, we first build an interaction event experience graph (IEEG) from the user interaction data. Secondly, we develop a Non-Axiomatic Logic-based learning framework for novel things correlations, named NALC, running over IEEG, to mine the user-thing relationship. We measure the user-user relationship and the thing-thing relationship from real-time contexts. Furthermore, we fuse the user-user, the thing-thing and the user-thing relationships based on a product rule, to model the recommender. Finally, we evaluate the performance of our proposed model on the real-world dataset and demonstrate that the proposed model outperforms existing state-of-the-art models.
Rakshak

Almost every country in the world is facing the issue of child trafficking. Besides abduction, children below 10 years sometimes get missed from their homes or other locations due to many reasons. following the criminal record, many of the abducted or missed children got received by police officials where majorly officials face difficulty to get correct information as the founded children are not in their normal state in general due to fear factor or less trust. It is also observed that such children also usually start stammering due to uncontrolled emotions. In this kind of situation, Police officials would not get the exact information of founded children and unable to contact their respective guardians. This paper proposes a possible solution “Rakshak” for this kind of difficult situation. The Rakshak is Machine Learning based software which inputs the recorded voice of such children by police officials and returns the voice as well as text output after being corrected by various techniques of the Rakshak.
Construction of a Virtual Reality Model of Smart Tourism Scenic Spot Based on Cloud Computing

This paper studies the technology and cloud platform of “cloud computing” in detail. Technically, this paper studies Map-Reduce, HDFS, and BigTable in cloud platform technology. Using the theoretical thinking of comprehensive advantages, the design ideas and principles of the smart tourism service model based on the network platform are proposed, and the overall architecture of the smart tourism service model based on the network platform is constructed according to the three dimensions of the tourist demand level, network platform development stage, and service resource aggregation and integration. The virtual network interaction platform is realized through Unity3D combined with Web3D technology, and the system interaction function is realized according to the communication principle of the front and back of the web page, including the fast switching of scenic spots, the selection of roaming methods, and the realization of navigation maps. In the user evaluation stage, usability and user satisfaction of the system were effectively measured by the SUS system usability scale and the heuristic experiment combined with Likert scale. The virtual reality system of smart tourist attractions based on somatosensory interaction integrates immersion, interactivity, gameplay, education, and dissemination. It can break the time and space barriers for the creation and experience of smart tourist attractions, improve the extremely cold experience environment of smart tourist attractions, increase the display and publicity dimensions of smart tourist attractions, change the situation of one-way passive reception of traditional smart tourist attractions, and improve the interaction and interaction of tourists. It also provides new teaching and design methods for creators of smart tourism scenic spots, which is conducive to the promotion of teaching in smart tourism scenic spots. It enhances the importance of ice and snow culture in the ice and snow industry, changes the traditional business model of ice and snow scenic spots, and uses modern technology to make up for the cultural development defects of smart tourism scenic spots. The digital sustainable development construction of smart tourism scenic spots provides new ideas.
CHORS

High-assurance security systems require strong isolation from the untrusted world to protect the security-sensitive or privacy-sensitive data they process. Existing regulations impose that such systems must execute in a trustworthy operating system (OS) to ensure they are not collocated with untrusted software that might negatively impact their availability or security. However, the existing techniques to attest to the OS integrity fall short due to the cuckoo attack. We present and formally prove a novel defense against this attack. We implement it as part of an integrity monitoring and enforcement system that attests to the remote OS integrity 3.7× -- 8.5× faster than the existing integrity monitoring systems. We demonstrate its practicality by protecting a real-world eHealth application, and performing micro and macro-benchmarks.
Long Hands gesture recognition system: 2 step gesture recognition with machine learning and geometric shape analysis

Abstract A two stage real-time hand gesture recognition system is presented. It combines a machine learning trained detection step with a colour processing contour shape validation step. The detection step is done with either Adaboost Cascades or Support Vector Machines using HOG features. The system achieves a low false positive rate and a sufficient true positive rate necessary for robust real-time performance. It performs well compared to MobileNets a state of the art Neural Network for mobile real-time applications.
Synthesis of new, more powerful statistical tests through multiplicative clustering of classical Frozini and Murota-Takeuchi tests with the Hurst test for the purpose of testing small samples for normality

Aim. The paper examines the problem of small sample analysis by means of synthesizing new statistical tests generated by the clustering of the Hurst statistical test with the Frozini test, as well as with the Murota-Takeuchi test. The problem of normal distribution hypothesis testing on samples of 16 to 25 experiments is solved. Such significant limitations of the sample size arise in subject areas that include biometrics, biology, medical science and economics. In this case, the problem can be solved by applying not one, but a number of statistical tests to the analysis of the same small sample. Methods. It is suggested multiplying the Hurst test outputs by the Frozini test and/or the Murota-Takeuchi test outputs. A multiplicative clustering was performed for pairs of examined tests and their combination. It was shown that for each known statistical test, an equivalent artificial neuron can be constructed. A neural network integration of about 21 classical statistical tests constructed in the last century becomes possible. It is expected that the addition of new statistical tests in the form of artificial neurons will improve the quality of multi-criteria analysis solutions. Formally, the products of non-recurrent pairs of 21 original classical statistical tests should produce 210 new statistical tests. That is significantly more than the total number of statistical tests developed in the last century for the purpose of normality testing. Results. Pairwise product of the examined tests allows reducing the probability of errors of the first and second kind by more than 1.55 times as compared to the basic Hurst test. In case of triple product of the tests, the probabilities of error decrease relative to the basic Hurst test and to the associated second test. It is noted that there is no steady improvement in the quality of the decisions made by multiplicative mathematical constructions. The probabilities of error of the new test obtained by multiplying three of the examined tests are approximately 1.5% worse as compared to those of the tests obtained by multiplying pairs of the original tests. Conclusions. By analogy with the examined tests, the proposed data processing methods can also be applied to other known statistical tests. In theory, it becomes possible to significantly increase the number of new statistical tests by multiplying their final values. Unfortunately, as the number of clustered statistical tests grows, mutual correlations between the newly synthesized tests grow as well. The latter fact limits the capabilities of the method proposed in the paper. Further research is required in order to identify the most efficient combinations of pairs, triples or large groups for known statistical tests.
Teaching Innovation and Development of Ideological and Political Courses in Colleges and Universities: Based on the Background of Wireless Communication and Artificial Intelligence Decision Making

After entering the first decade of the new century, information technology has brought some profound changes in all aspects of social life. In the field of education, teaching mode to teaching concept has been severely impacted. Especially in the more open university classroom, the changes brought about by this shock are more pronounced. Classrooms of all majors have ushered in a wave of innovation. The use of cutting-edge information technology to build efficient classrooms has become the direction of multidisciplinary efforts. While promoting the upgrading of ideological and political classroom teaching in colleges and universities, it has become a common topic for ideological and political teachers in colleges and universities to effectively promote the effectiveness of ideological and political classroom teaching. In order to investigate the current teaching recognition of ideological and political courses in colleges and universities and the need for courses to integrate new technologies, this study used questionnaire survey and group comparison analysis as the main methods to distribute online questionnaires to 621 students from 6 colleges and universities in Nanjing, Jiangsu Province. It is divided into students’ interest in ideological and political courses and the interest of teaching content. According to the analysis of previous literature, a control group experiment of ideological and political courses is designed. The experimental results show that after wireless communication and artificial intelligence technology are combined with ideological and political courses, students’ learning is more efficient. Finally, according to the results of experiments and questionnaires, the problems existing in the current teaching of ideological and political courses in colleges and universities are summarized, and then, innovative reform suggestions for the teaching methods of ideological and political courses are put forward.
Calibrating the performance and security of blockchains via information propagation delays

Miners of a blockchain exchange information about blocks and transactions with one another via a peer-to-peer (P2P) network. The speed at which they learn of new blocks and transactions in the network determines the likelihood of forks in the chain, which in turn has implications for the efficiency as well as security of proof-of-work (PoW) blockchains. Despite the importance of information propagation delays in a blockchain's peer-to-peer network, little is known about them. The last known empirical study was conducted, for instance, by Decker and Wattenhofer in 2013 [11].
HYGAR

Genetic algorithms have been used for clustering modules of a software system in line with the modularity principle. The goal of these algorithms is to recover an architectural view in the form of a modular structural decomposition of the system. We discuss design decisions and variations in existing genetic algorithms devised for this purpose. We introduce HYGAR, a novel hybrid variant of existing algorithms. We apply HYGAR for software architecture recovery of 5 real systems and compare its effectiveness with respect to a baseline and a state-of-the-art hybrid algorithm. Results show that HYGAR outperforms these algorithms in maximizing the modularity of the obtained clustering.
Explainable spatiotemporal reasoning for geospatial intelligence applications

Abstract This article explores the design, implementation, and querying of a prototype system for automated spatial reasoning for geospatial intelligence applications, called NEXUS. The system combines multiple different reasoning components that can support a wide range of spatiotemporal queries. Fundamental to the requirements of intelligence analysts is the need to provide explanations of system outputs that help better inform users and engender trust in the system. The NEXUS architecture leverages semantic web technologies, and in particular the Simple Event Model and PROV‐O ontologies, to support queries not only about reasoner inferences, but also explanations as to why the system arrived at a particular conclusion. The manipulation of location reports in marine automatic identification system (AIS) data is used as a running example to demonstrate the approach. The range of queries developed illustrates both the detection of suspicious activities and explanations of the inference processes used to identify those activities.
Visualising the effects of ontology changes and studying their understanding with ChImp

Due to the Semantic Web’s decentralised nature, ontology engineers rarely know all applications that leverage their ontology. Consequently, they are unaware of the full extent of possible consequences that changes might cause to the ontology. Our goal is to lessen the gap between ontology engineers and users by investigating ontology engineers’ understanding of ontology changes’ impact at editing time. Hence, this paper introduces the Protégé plugin ChImp which we use to reach our goal. We elicited requirements for ChImp through a questionnaire with ontology engineers. We then developed ChImp according to these requirements and it displays all changes of a given session and provides selected information on said changes and their effects. For each change, it computes a number of metrics on both the ontology and its materialisation. It displays those metrics on both the originally loaded ontology at the beginning of the editing session and the current state to help ontology engineers understand the impact of their changes. We investigated the informativeness of materialisation impact measures, the meaning of severe impact, and also the usefulness of ChImp in an online user study with 36 ontology engineers. We asked the participants to solve two ontology engineering tasks – with and without ChImp (assigned in random order) – and answer in-depth questions about the applied changes as well as the materialisation impact measures. We found that ChImp increased the participants’ understanding of change effects and that they felt better informed. Answers also suggest that the proposed measures were useful and informative. We also learned that the participants consider different outcomes of changes severe, but most would define severity based on the amount of changes to the materialisation compared to its size. The participants also acknowledged the importance of quantifying the impact of changes and that the study will affect their approach of editing ontologies.
Security Enhancement Algorithm of SM-254 Cloud System and Automated Personnel Identification of the Management in the Supply Chain Enterprise

To resolve the problems pertinent to data loss and privacy leakage caused by hackers in the cloud system during the computational process, the symmetric encryption (SM)-254 algorithm is employed to optimize the security function based on the original algorithm of the cloud system. The personnel data of the supply chain enterprise are loaded through the backpropagation neural network (BPNN) algorithm, and an automated management model of the cloud system is constructed according to the management system and the personnel organization structure of each department. Thus, the automated identification system of the personnel management of the supply chain enterprise is realized. Through the establishment of an automated management system by the cloud system, the accuracy of a cloud system to capture data pertinent to the personnel management of supply chain enterprises has increased by about 90% and the accuracies of both text recognition and image recognition for personnel management systems of supply chain enterprises reach about 92%. Hence, it enables supply chain enterprises to save time, money, and personnel and increases flexibility and brings convenience to personnel management, which is conducive to the personnel training, evaluation, and subsequent development of enterprises. Consequently, automated management gives enterprises an upper hand to use talents flexibly and comprehensively and establish an intact database of human resources (HR). It is found that the security enhancement algorithm of the cloud system can greatly reduce the risk of information leakage in the HR database and greatly improve the operational efficiency of the supply chain enterprise.