Reviewerly/exHarmony huggingface dataset:
Interpretatively automated identification of circulating tumor cells from human peripheral blood with high performance

The detection and analysis of circulating tumor cells (CTCs) would be of aid in a precise cancer diagnosis and an efficient prognosis assessment. However, traditional methods that rely heavily on the isolation of CTCs based on their physical or biological features suffer from intensive labor, thus being unsuitable for rapid detection. Furthermore, currently available intelligent methods are short of interpretability, which creates a lot of uncertainty during diagnosis. Therefore, we propose here an automated method that takes advantage of bright-field microscopic images with high resolution, so as to take an insight into cell patterns. Specifically, the precise identification of CTCs was achieved by using an optimized single-shot multi-box detector (SSD)–based neural network with integrated attention mechanism and feature fusion modules. Compared to the conventional SSD system, our method exhibited a superior detection performance with the recall rate of 92.2%, and the maximum average precision (AP) value of 97.9%. To note, the optimal SSD-based neural network was combined with advanced visualization technology, i.e., the gradient-weighted class activation mapping (Grad-CAM) for model interpretation, and the t-distributed stochastic neighbor embedding (T-SNE) for data visualization. Our work demonstrates for the first time the outstanding performance of SSD-based neural network for CTCs identification in human peripheral blood environment, showing great potential for the early detection and continuous monitoring of cancer progression.
A Reasonable Effectiveness of Features in Modeling Visual Perception of User Interfaces

Training data for user behavior models that predict subjective dimensions of visual perception are often too scarce for deep learning methods to be applicable. With the typical datasets in HCI limited to thousands or even hundreds of records, feature-based approaches are still widely used in visual analysis of graphical user interfaces (UIs). In our paper, we benchmarked the predictive accuracy of the two types of neural network (NN) models, and explored the effects of the number of features, and the dataset volume. To this end, we used two datasets that comprised over 4000 webpage screenshots, assessed by 233 subjects per the subjective dimensions of Complexity, Aesthetics and Orderliness. With the experimental data, we constructed and trained 1908 models. The feature-based NNs demonstrated 16.2%-better mean squared error (MSE) than the convolutional NNs (a modified GoogLeNet architecture); however, the CNNs’ accuracy improved with the larger dataset volume, whereas the ANNs’ did not: therefore, provided that the effect of more data on the models’ error improvement is linear, the CNNs should become superior at dataset sizes over 3000 UIs. Unexpectedly, adding more features to the NN models caused the MSE to somehow increase by 1.23%: although the difference was not significant, this confirmed the importance of careful feature engineering.
MERLIN2: MachinEd Ros 2 pLanINg

Any service robot should be able to make decisions and schedule tasks to reach predefined goals such as opening a door or assisting users at home. However, these processes are not single short-term tasks anymore and it is required to set long-term skills for establishing a control architecture that allows robots to perform daily tasks. This paper presents MERLIN2, a hybrid cognitive architecture based on symbolic planning and state machine decision-making systems that allows performing robot behaviors. The architecture can run in any robot running ROS 2, the latest version of the Robot Operative System. MERLIN2 is available at https://github.com/MERLIN2-ARCH/merlin2.
Dynamically Adjusted and Peripheral Visualization of Reverse Optical Flow for VR Sickness Reduction

Sickness is a major obstacle in the wide adoption of virtual reality (VR). Providing low-resolution peripheral “countervection” visualization could mitigate VR sickness. Herein, we present an extension/improvement to this work, in which the reverse optical flow of the scene features is mixed in, and the extent of the periphery is dynamically adjusted simultaneously. We comparatively evaluated the effects of our extension versus the two notable sickness reduction techniques, (1) the original peripheral countervection flow using the simple stripe pattern (with a fixed field of view and peripheral extent) and (2) the dynamic field of view adjustment (with no added visualization). The experimental results indicated that the proposed extension exhibits competitive or better sickness reduction effects and less user-perceived content intrusion, distraction, and breaks in immersion/presence. Furthermore, we tested the comparative effect of visualizing the reverse optical flow only in the lower visual periphery, which further reduced the content intrusion and lowered the sense of immersion and presence. The test indicated that using just the low visual periphery could achieve a comparable level of sickness reduction with significantly less computational effort, making it suitable for mobile applications.
3D Imaging with Fringe Projection for Food and Agricultural Applications—A Tutorial

The rising global population, in conjunction with the increasing demand, decreasing labor supply, and increasing costs in the agricultural sector, has induced a need for automation in this industry. Many of these tasks are simplified using depth images and are accomplished using the help of 3D sensing technology such as stereo vision and time of flight methods. While there are various merits to these approaches, there is a need for high-speed, high-accuracy 3D profiling approaches in this rapidly advancing industry. Fringe Projection Profilometry is a variation of structured light technology, which has the advantage of having high speed in the kilohertz range, and sub-millimeter accuracy, which could be extremely beneficial for this sector to adopt. In this article, we seek to provide a tutorial on this technology, explain its various principles along with the basic methodology, and expound on its advantages. We demonstrate some example results using soybean roots and spinach leaves to show its utility, discuss potential reasons as to why this has not yet been widely adopted by this industry, review its potential limitations, and examine possible ways those limitations can be addressed so that they do not present a roadblock in its adoption.
EfferDeepNet: An Efficient Semantic Segmentation Method for Outdoor Terrain

The recognition of terrain and outdoor complex environments based on vision sensors is a key technology in practical robotics applications, and forms the basis of autonomous navigation and motion planning. While traditional machine learning methods can be applied to outdoor terrain recognition, their recognition accuracy is low. In order to improve the accuracy of outdoor terrain recognition, methods based on deep learning are widely used. However, the network structure of deep learning methods is very complex, and the number of parameters is large, which cannot meet the actual operating requirements of of unmanned systems. Therefore, in order to solve the problems of poor real-time performance and low accuracy of deep learning algorithms for terrain recognition, this paper proposes the efficient EfferDeepNet network for pixel level terrain recognition in order to realize global perception of outdoor environment. First, this method uses convolution kernels with different sizes in the depthwise separable convolution (DSC) stage to extract more semantic feature information. Then, an attention mechanism is introduced to weight the acquired features, focusing on the key local feature areas. Finally, in order to avoid redundancy due to a large number of features and parameters in the model, this method uses a ghost module to make the network more lightweight. In addition, to solve the problem of pixel level terrain recognition having a negative effect on image boundary segmentation, the proposed method integrates an enhanced feature extraction network. Experimental results show that the proposed EfferDeepNet network can quickly and accurately perform global recognition and semantic segmentation of terrain in complex environments.
Pure-Octet Extraction based Technique for Identifying Malicious URLs based on IP Address Attributes

On the basis of characteristics derived from IPv4 addresses, this paper offers a method for identifying interaction linked with website-based malware and then modelling a machine-learning-based classifier.In this research work, a modified approach is proposed for detecting fraudulent websites and compared with other methods like SVM assessment of IP addresses, octet-based technique, modified extended version of octet-based technique, and bit stringbased characteristics.This modified approach is based on the fact that logical addressing is more reliable and consistent than other measures like URLs and DNS.The characteristic sequence which makes up URLs and domain names are more changeable with respect to IP addresses which are less changeable in comparison to URLs or domain names.The IPv4 address length is encoded into 4-byte space.Here, we have evaluated our modified approach with valid IP addresses from Kaggle [11], published on January 16, 2018, have been used to validate the efficacy of their method.
Developing and creating didactic proposals for Latin and Classical Culture Training course with the educational centre CEFIRE

Abstract The aim of this paper is to set out the results of the Developing and creating didactic proposals for Latin and Classical Culture (now DPLCC) course, which was financed and organised with the Spanish Ministry of Education, CEFIRE, during 2021. The training programme is aimed at teachers and anyone qualified to teach classical languages. It has been divided into two parts: a theory and a practical section. The theory section entailed a review of some fundamental topics in language teaching. The practical section involved the creation of a didactic proposal based on the element of water. The course counted a total of 34 participants; only 22 finished the theory section and 12 the practical section. From a practical perspective for participants, it was observed that various activities were effectively carried out. In particular, the trainees produced quality texts adapted to the required level. In addition, the proposed exercises are diverse and appropriate for the planned objectives. On the other hand, the graphic design, page layout and illustrations proposed did not have a particular focus on quality, indicating a lesser degree of technical and graphic preparation for producing materials that are also visually appealing to a younger audience. The study shows that trainees revisit and create new activities from scratch, always staying aligned with the chosen theme and the proposed educational level.
Parameterization and Design of Telepresence Robot to Avoid Obstacles

Background: The development of telepresence robots is getting much attention in various areas of human–robot interaction, healthcare systems and military applications because of multiple advantages such as safety improvement, lower energy and fuel consumption, exploitation of road networks, reduced traffic congestion and greater mobility. Methods: In the critical decision-making process during the motion of a robot, intelligent motion planning takes an important and challenging role. It includes obstacle avoidance, searching for the safest path to follow, generating appropriate behavior and comfortable trajectory generation by optimization while keeping road boundaries and traffic rules as important concerns. Results: This paper presents a state machine algorithm for avoiding obstacles and speed control design to a cognitive architecture named auto-MERLIN. This research empirically tested the proposed solutions by providing implementation details and diagrams for establishing the path planning and obstacle tests. Conclusions: The results validate the usability of our approach and show auto-MERLIN as a ready robot for short- and long-term tasks, showing better results than using a default system, particularly when deployed in highly interactive scenarios. The stable speed control of the auto-MERLIN in case of detecting any obstacle was shown.
RingFFL: A Ring-Architecture-Based Fair Federated Learning Framework

In the ring-architecture-based federated learning framework, security and fairness are severely compromised when dishonest clients abort the training process after obtaining useful information. To solve the problem, we propose a Ring- architecture-based Fair Federated Learning framework called RingFFL, in which we design a penalty mechanism for FL. Before the training starts in each round, all clients that will participate in the training pay deposits in a set order and record the transactions on the blockchain to ensure that they are not tampered with. Subsequently, the clients perform the FL training process, and the correctness of the models transmitted by the clients is guaranteed by the HASH algorithm during the training process. When all clients perform honestly, each client can obtain the final model, and the number of digital currencies in each client’s wallet is kept constant; otherwise, the deposits of clients who leave halfway will be compensated to the clients who perform honestly during the training process. In this way, through the penalty mechanism, all clients either obtain the final model or are compensated, thus ensuring the fairness of federated learning. The security analysis and experimental results show that RingFFL not only guarantees the accuracy and security of the federated learning model but also guarantees the fairness.
AWI-BS: An adaptive weight incentive for blockchain sharding

The sharding technique enables blockchain to process transactions in parallel by dividing blockchain nodes into small groups, each of which handles a subset of all transactions. One of the issues with blockchain sharding is generating a large number of cross-shard transactions that need to be checked on the output shard as well as the destination shard. Our analysis suggests that the processing efficiency of cross-shard transactions is consistent with the barrel effect, i.e., that efficiency is more dependent on slower processing shard. Most of the existing studies focus on how to deal with cross-shard transactions, but neglecting the fact that the relative independence between sharding results in different incentive costs between sharding. We perform a sharding analysis on 100,000 real transactions data on Ethereum, and the results show that there is a large difference in gas prices between different shards indeed. In this paper, we propose an Adaptive Weight Incentive (AWI) for Blockchain Sharding, which uses adaptive weight in place of traditional incentive, to address the problem of differing incentive costs for each shard. Take Ethereum as an example, AWI-BS computes the weight of a transaction as a function of a combination of the underlying gas price, the latency of the transaction, and the urgency of the transaction. Then the node chooses which transaction to pack based on the AWI-BS. Lastly, we also perform an in-depth analysis of AWI-BS's security and effectiveness. The evaluation indicates that AWI-BS outperforms the other alternatives in terms of transaction confirmation latency, transaction hit rate, and system throughput.
CauRuler: Causal irredundant association rule miner for complex patient trajectory modelling

Discovering causal associations between variables is one of the main goals of clinical trials, with the ultimate aim of identifying the causes of specific health status. Prior knowledge of causal paths could help ensure patients do not develop the resultant conditions. In recent years, thanks to the enormous amount of health data stored with the support of digital tools, attempts have been made to employ Machine Learning to infer causality. Those methodologies suffer from some deficiencies in controlling cofounders when analysing causality, as well as providing causal rules general enough to be useful in healthcare practice. Conversely, this work presents and evaluates CauRuler, a new approach to deal with causality from association rules. The proposed approach uses a pruning strategy to reduce the association rule set, which does not compromise the causality learning capability of the algorithm. This behaviour makes the algorithm suitable for exploiting large health databases with thousands of patients and medical instances. CauRuler can control a larger number of confounders than other proposals, bringing robustness to causal analysis and avoiding the identification of spurious associations. Additionally, the method generalizes causality using anti-monotone properties to obtain complex and general causal paths. The method can target correct causal associations in complex medical databases with retrospective data.CauRuler extends association rule mining with an irredundancy property so that the set of rules learnt is reduced in size and generalized. General association rules, conformed by fewer items, enable controlling more confounding variables to verify, with more statistical evidence on available data, if they represent causal paths in patient disease trajectories.CauRuler has been tested on a complex real medical database (3,5 M visits to the primary care services between 2019 and 2020, and controlling over 15.000 different variables including diagnoses and demographic and other clinical patient data). The reduction of the rule set achieved by the pruning strategy goes from 7.732 to 2.240 rules, from which 46 have been found to have causality relationships in the patient trajectories, and generalized to 14 rules tested as true causal relationships thanks to the confounding analysis. These rules have been validated by clinicians with the support of a graphical map. The obtained causal paths control in average of 906 confounder variables, retrieving robust results.Causal relationships enable predicting causal paths between health conditions according to patient trajectories. Knowing these causal paths is crucial for understanding and preventing the appearance or worsening of diseases in patients. CauRuler, with high demanding thresholds, has proven its efficiency and effectiveness in targeting previously known causal associations between diagnoses, reaching consensus in the medical community. Softening these thresholds should help target interesting general causal paths.
Geochemical assessment of the spring’s waters in the Pushkinsky district, Moscow region

Springs are an essential drinking water resource. Therefore, spring water quality is necessary to control under the increasing anthropogenic pressure on the environment. This study was carried out to evaluate the content of major ions and trace elements in the waters of 28 springs in the Pushkinsky district of the Moscow region. The macro- and micro-component composition of groundwater of various aquifers was characterized according to the sampling data of springs. It is shown that the mean contents of trace elements in the waters of springs are at the level of concentrations characteristic of groundwater in the leaching zone of a temperate climate. Based on the thermodynamic calculation using the Visual-MINTEQ, it was found that the predominant dissolved forms of Ba, Sr, Fe, Mn, Zn, Cd, Ni, Co are free ions in the waters of the surveyed springs, Cu and Pb are carbonate and organic complexes.
Radar emitter signal recognition based on coordinated attention

Aiming at the problem that complex radar emitter signals are difficult to be recognized at low signal-to-noise ratio, a method based on improved coordinate attention network is proposed. Firstly, the radar signal is converted into a two-dimensional time-frequency image to reflect the signal feature information. Then the time-frequency image preprocessing and denoising by convolutional neural network. Finally, the coordinated attention network is used for feature extraction, and then the classification of radar emitter source signals are realized. Experiments results show that the proposed method can validly improve the accuracy of radar signal recognition under the condition of low SNR.
Digital Forensics in IoT Enabled Smart Environment: Need of the Hour

Digital Forensics is a branch of forensic science that performs the task of data investigation, recovery and interpretation. The impact of the Internet of Things (IoT) can be seen everywhere now, but somehow it is quite difficult to identify the challenges posed by IoT criminals. It becomes possible to identify the offender of fraud attacks, cyber threats, security attacks, and computer frauds using digital forensics investigation mechanism. Numerous security challenges are faced in IoT enabled smart infrastructures. Digital forensic experts and law enforcement agencies can efficiently perform investigation processes in the cyber world. This paper provides a brief overview of IoT, its working, and need of digital forensics in an IoT enabled smart environment. In the present paper, vulnerabilities and cyber crimes existing in IoT environment also have been highlighted along with possible solutions. Various phases of digital forensics also have been discussed in the present paper.
A survey on divergent classification of social media networking

Social media networking encircles a group of websites and applications. The main usage of such a networking for the people is to communicate, interact, share and collaborate with others. There are different social media networks such as Twitter, WhatsApp, Facebook, LinkedIn, Instagram, Snapchat, YouTube, Reddit, Quora, Digg of Discussion Forums, Pinterest, Yelp, Zomato, tripAdvisor of Consumer Review Networks, WordPress, Tumblr of Blogging and Publishing Networks. The objective of this research paper is to find the origin, data and features of the social media networks. In recent years, people from various sectors like individuals, students, doctors, lawyers, engineers, and business are using social media widely. This social media network is easy to convey the messages in the form of text, image, audio, and video both locally and internationally. The content of the conversation may be structured or unstructured. But it can be processed and analyzed by the data scientist to predict the interest of social media users by applying machine or deep learning algorithms.
MSA: A Novel App Development Framework for Transparent Multiscreen Support on Android Apps

Multidisplay Android systems are emerging and require new display management. The current Android design exposes and offloads the multiscreen display management to app developers. As a consequence, apps cannot utilize multiscreen without redevelopment. This article proposes MSA, a novel Android app development framework for transparent multiscreen support. MSA cooperates with existing Android system services to map the single-screen views provided by apps to multiple screens and maps input events backward correspondingly. Following the new framework, the app development of single-screen Android systems and multiscreen Android systems are unified. Thus, both existing apps and future app development can directly utilize multiple screens with single-screen-based techniques, such as multiwindow and resizable-activity, instead of redevelopment through multiscreen-dedicated new methods such as presentation classes. We have implemented an MSA prototype with real hardware and released the source code for public access. Experimental results show that using MSA, without any modifications, existing apps can directly run and fully exploit multiple screens with better performance and less overhead compared with the state-of-the-art multiscreen Android system.