Reviewerly/exHarmony huggingface dataset:
Fully Convolutional One-Stage Circular Object Detector on Medical Images

In an endoscopic thyroidectomy, parathyroid injury can lead to hypocalcemia for life. It is of great help to assist surgeons to complete endoscopic thyroidectomy without damaging the parathyroid. Considering that the shape of the parathyroid is usually round, we replaced the rectangle bounding box with the circular bounding box, and propose a fully convolutional one-stage circular object detector (FCOSC) based on FCOS. The loss and optimization are improved to adapt to the calculation of the circular bounding box. FCOSC achieved 37.9% in AP with single-model testing on the parathyroid dataset, which was 5% higher than FCOS. Considering that our data is strictly confidential, we also evaluated the accuracy of our method in the BCCD dataset and achieved 70.4% in AP, comparing to 68.4% with a rectangle bounding box. Experiments show that the proposed FCOSC framework can obtain expected results on parathyroid detection and be used in another metical circular object detection task.
Transient Virtual Obstacles for Safe Robot Navigation in Indoor Environments

Service robots are expected to work in real world scenarios which are dynamic in nature. The traversable and non-traversable passages of the environment might change at different times. For example, it is common for some of the passages or areas in the map to be inaccessible due to cleaning, repair works, or other reasons. On the other hand, some passages in the environment could have a high congestion of people compared to other passages. This requires a feature to alter the path of the robots to address these dynamic changes, instead of only relying on the shortest paths criterion. To this end, this paper proposes Transient Virtual Obstacles (TVO): a method to actively block desired paths by using virtual obstacles. The virtual obstacles can be placed or removed by a user anywhere in the map for programmable intervals. The robot’s plan their paths considering the virtual obstacles, thereby blocking certain paths, and planning the desired trajectories through specific paths. Another major advantage of the proposed TVO algorithm is that the existing robot path planner does not needs to be modified. The proposed TVO is tested in both simulation and real environments and results are discussed highlighting the merits of safe mobile robot navigation.
Random Projections for Improved Adversarial Robustness

We propose two training techniques for improving the robustness of Neural Networks to adversarial attacks, i.e. manipulations of the inputs that are maliciously crafted to fool networks into incorrect predictions. Both methods are independent of the chosen attack and leverage random projections of the original inputs, with the purpose of exploiting both dimensionality reduction and some characteristic geometrical properties of adversarial perturbations. The first technique is called RP-Ensemble and consists of an ensemble of networks trained on multiple projected versions of the original inputs. The second one, named RP-Regularizer, adds instead a regularization term to the training objective.
Las propuestas de reforma legislativa del libro blanco europeo sobre inteligencia artificial en materia de seguridad y responsabilidad civil

The aim of this paper is focused on the analysis of the main controversies related to civil liability derived from damages caused by intelligent entities such as defective products, which include both the White Paper on Artificial Intelligence and the Report attached to it. Both texts relate the regulations on product safety and civil liability, which this study attempts to clarify in its particular application to damage caused by intelligent machines, analyzing the reform proposals made by the Commission from the framework of the Spanish legal system.
CRediT: Recognising Author Contributions to Publications

The talk presents a mini case study of implementing the CRediT taxonomy (https://www.casrai.org/credit.html), which is a controlled vocabulary to represent the roles that collaborators can have when contributing to research outputs. Examples include supervision, software programming or writing an original draft of a paper. It is one of many tools that help to facilitate open research. Few universities have embedded CRediT so far, but several more are now on the point of adoption. The talk will cover the what and how of the case study, as well as some lessons learned and suggestions for the future.
Inverse Constrained Reinforcement Learning

Standard reinforcement learning (RL) algorithms train agents to maximize given reward functions. However, many real-world applications of RL require agents to also satisfy certain constraints which may, for example, be motivated by safety concerns. Constrained RL algorithms approach this problem by training agents to maximize given reward functions while respecting \textit{explicitly} defined constraints. However, in many cases, manually designing accurate constraints is a challenging task. In this work, given a reward function and a set of demonstrations from an expert that maximizes this reward function while respecting \textit{unknown} constraints, we propose a framework to learn the most likely constraints that the expert respects. We then train agents to maximize the given reward function subject to the learned constraints. Previous works in this regard have either mainly been restricted to tabular settings or specific types of constraints or assume knowledge of transition dynamics of the environment. In contrast, we empirically show that our framework is able to learn arbitrary \textit{Markovian} constraints in high-dimensions in a model-free setting.
An investigation on deep learning approaches for diatoms classification

Diatoms are one of the largest groups of microalgae present in marine, freshwater and transitional environments and their reactivity to environmental changes makes them suitable to be employed as biomarkers for monitoring tasks. Anyway, their presence in a large number of species makes it arduous to perform diatoms taxonomy during monitoring tasks considering that, to date, analysis is conducted by marine biologists on the basis of their own experience and, hence, in a subjective way. Hence, the need for automatic and objective methodologies for the identification and classification of diatoms samples rises. Research efforts in the field of Computer Vision led to a plethora of highly effective deep learning strategies surpassing human capabilities for image classification, as showed in the recent Imagenet challenge editions where they were initially introduced. Despite the very promising results of the proposed solutions, the difficulty arises to determine which technique is most suitable among them for real tasks and in particular for diatoms classification. This work proposes an end-to-end pipeline for automatic recognition of diatoms, acquired by means of holographic microscopy in water samples, employing deep learning techniques. In particular the most recently introduced Convolution Neural Networks (CNNs) architectures have been deeply investigated and compared in order to highlight the pros and cons of each of them. Moreover, in order to feed the CNNs training stages with a suitable amount of labeled data, a strategy to build a synthetic dataset, starting from a single image per class available from commercial glass slides specifically prepared for taxonomy purposes, is introduced. Besides, models ensembling strategies, in order to improve the single model scores, have been exploited. Finally, the proposed approach has been validated employing a dataset built up of holographic images of diatoms sampled in natural water bodies.
On evaluating deep learning-based optical flow methods for gas velocity estimation with optical gas imaging cameras

Besides its importance for greenhouse emission reduction, the remote detection, localization and quantification of gas leaks in industrial facilities remains a challenging problem in industry and research. In that sense, the development of new data processing techniques that allow deriving new and/or more accurate information about the gas leaks from made measurements has gained more attention in the recent years. This becomes apparent from the increased use of optical gas imaging (OGI) cameras (specialised mid-wave infrared cameras e.g. for methane and carbon dioxide) along with image processing and computer vision techniques, to tackle these challenges. In this work, deep-learning-based optical flow methods are evaluated for determining gas velocities from gas images of an OGI camera. For this, a dataset of simulated and real gas images under controlled and real conditions is used for supervised training and validation of two different state of the art CNNs for optical flow computation: FlowNetC, FlowNet2 and PWC-Net. Classical optical flow methods based on variational methods are also considered and the differences in performance and accuracy between classical and deep-learning-based methods are shown. In addition, FlowNet2 is further improved for working with gas images by fine tuning the network weights. This approach has demonstrated to make FlowNet2 more reliable and less sensitive to image noise and jitter in the experiments. For further validation, a set of real gas images acquired in a wind channel and one from a biogas plant with reference mean gas velocities from a 3D anemometer are being used. The results show that the fine-tuned version of FlowNet2 (FNet2-G) allow computing larger optical flow magnitudes than classical optical flow methods while being less sensitive to image noise under field conditions. The obtained results also show the potential of deep-learning-based approaches for image processing tasks such as gas segmentation, disparity computation and scene flow in stereo gas images.
Blockchain-Based Trading and Settlement Framework for Electricity Markets

Due to the complexity of interaction between market players, the diversity of security risks, and the credibility of the market operation effect, the electricity market faces many challenges. In order to build a fair, efficient, and credible electricity market, this paper proposes a trading and settlement framework for electricity markets based on blockchain technologies. In detail, technologies supporting the framework are deeply analyzed including market optimization design, risk comprehensive evaluation, and joint control simulation. Finally, implementation suggestions are proposed to improve the advantages of ensuring transaction security and reducing transaction costs.
MARINA: Faster Non-Convex Distributed Learning with Compression

We develop and analyze MARINA: a new communication efficient method for non-convex distributed learning over heterogeneous datasets. MARINA employs a novel communication compression strategy based on the compression of gradient differences that is reminiscent of but different from the strategy employed in the DIANA method of Mishchenko et al. (2019). Unlike virtually all competing distributed first-order methods, including DIANA, ours is based on a carefully designed biased gradient estimator, which is the key to its superior theoretical and practical performance. The communication complexity bounds we prove for MARINA are evidently better than those of all previous first-order methods. Further, we develop and analyze two variants of MARINA: VR-MARINA and PP-MARINA. The first method is designed for the case when the local loss functions owned by clients are either of a finite sum or of an expectation form, and the second method allows for a partial participation of clients -- a feature important in federated learning. All our methods are superior to previous state-of-the-art methods in terms of oracle/communication complexity. Finally, we provide a convergence analysis of all methods for problems satisfying the Polyak-Lojasiewicz condition.
UNHOLY PRACTICES AMONG YOUTHS IN NIGERIA: VENOMS BREEDING TO A DISJOINTED SOCIETY

Before the year 1990 and up to the late 1990s, Nigeria was ravaged by what was known then as “Advanced Free Fraud” christened (419) and “Trades of Narcotic Drugs”. After more than two decades, a new set of unholy practices have found its way into the fabric of the society and have consistently proven to be worse than the duo of “419” and “trades of narcotic drugs”. This set of practices though not narrowed to the youths but majorly driven and made to flourish by the attendant number of youths who are in haste not only to make a living but flamboyant, thriftless and profligate lives detrimental to norms, values and society itself. Premised on the danger they herald for the society, the paper appraised the numerous unholy practices rampant among youths in Nigeria and their impacts on the society. Exploratory method was made use of; secondary source was employed and inferences were drawn via qualitative analysis. Findings revealed that exponential decadence among youths is directly proportional to the culture of prostitution, regime of internet fraud (yahoo-yahoo), kidnapping for ransom, social revolution of gambling etcetera. The paper concludes that unholy practices among youths have constituted a nuisance in no small measure to the society. The paper however advocates that the government at all levels should up their game in training youths in the vocation that would make them engage and reduce the surge in such practices in the society.
Parametric Analysis of Texture Classification Using Modified Weighted Probabilistic Neural Network (MWPNN)

Texture classification is one of the sort-out methods in pattern recognition. In this research work, a novel proposal called Modified Weighted Probabilistic Neural Network (MWPNN), which can be used to classify the textures, is proposed. It outperforms the previous method by adding inherent capabilities with respect to the weighing characteristic. The weights are modified with help of the Sensitivity Analysis (SA) Method. This MWPNN includes the Self-Organizing Maps of the Neural Network (SOM) and including weighting factors extracted from a supervised labelling process. The proposed approach is tested on sample textures and the results obtained are compared to the Probabilistic Neural Network (PNN) and the Weighted Probabilistic Neural Network (WPNN) with bench mark machine learning algorithms such as Naïve Bayes Classifier and Multi-Layer Perceptron. The efficiency of this method is compared to Mean, Standard Deviation, Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR). The entire simulation is carried out using MATLAB computing tool with the help of Image Processing and ANN toolboxes along with required auxiliary functions and blocksets.
Going Further with WSL 2

Now that we have set up graphical user applications and started our own services at launch, we are ready to go a bit deeper into the things we can do with WSL, specifically WSL 2.
TRIZ Basic Learning Method Through Linkage with Interests

Amid the era of convergence and innovation that requires more creativity than human labor, a growing number of companies and schools are introducing TRIZ, a creativity-based problem-solving tool.However, it is not easy to learn and apply TRIZ theories that are different from existing methods.In particular, if the cases used in education are different from those of students' interests or if the learning content is too difficult, the learning effect will be even worse.In this paper, we conduct research on how to induce immersion of trainees and learn TRIZ theory efficiently.The effectiveness of education can be increased if students combine hobbies that they are interested in or examples of solving real problems at hand.Using DIY (Do It Yourself) production activities, which anyone can easily participate in with interest, the curriculum that solves various problems in the production process directly through TRIZ theory has gained active participation and excellent learning effects.It is a way to effectively understand Learning in conjunction with TRIZ and areas of interest is an effective way to understand TRIZ tools such as invention principles, material-field, and FORS(Function oriented Resource Search).
Challenges of artificial intelligence to patent law and copyright law and countermeasures

This chapter explores the degree of adaptability of truly aspects of the patent and copyright systems such as the objects of protection, qualification of the right holder, ownership of patent or copyright, judgment of infringement, and subjects of liability, in a new AI era when AI algorithms independently make inventions and create literary and artistic works based on their deep learning ability. Then explores the role of the patent and the copyright law system as to the extent that they accord with the principle of fairness, both for the current “Weak AI era” and the future “Strong AI era”. Based on case studies, this chapter analyses a series of challenges brought by the development of AI technology to the current patent law and copyright law, try to suggest some possible solutions including but not limited to changing the primary rights, adding secondary rights, Sui Generis Right and new exceptions and limitations.
Bootstrapping Clustered Data in R using lmeresampler

Linear mixed-effects models are commonly used to analyze clustered data structures. There are numerous packages to fit these models in R and conduct likelihood-based inference. The implementation of resampling-based procedures for inference are more limited. In this paper, we introduce the lmeresampler package for bootstrapping nested linear mixed-effects models fit via lme4 or nlme. Bootstrap estimation allows for bias correction, adjusted standard errors and confidence intervals for small samples sizes and when distributional assumptions break down. We will also illustrate how bootstrap resampling can be used to diagnose this model class. In addition, lmeresampler makes it easy to construct interval estimates of functions of model parameters.
Transition Discourse, Food, and Computing within Limits

The limits workshop series is a transition system, which shapes the work of activists, educators, and researchers.It does so, in part, by the development of language and tools and by catalyzing conversations about reality and possible futures.Given this proposition and drawing on ontologically oriented analysis and design, I explore the transition discourse of eight limits' papers, which consider aspects of food.The papers represent competing views of the future of place and planet.Building on this review, I discuss worldviews and transitions, transitions and time, and I explore how formative and instrumental approaches in computing research can contribute to institutional and cultural adaptation.
PvCT: A Publicly Verifiable Contact Tracing Algorithm in Cloud Computing

Contact tracing is a critical tool in containing epidemics such as COVID-19. Researchers have carried out a lot of work on contact tracing. However, almost all of the existing works assume that their clients and authorities have large storage space and powerful computation capability and clients can implement contact tracing on their own mobile devices such as mobile phones, tablet computers, and wearable computers. With the widespread outbreaks of the epidemics, these approaches are of less robustness to a larger scale of datasets when it comes to resource-constrained clients. To address this limitation, we propose a publicly verifiable contact tracing algorithm in cloud computing (PvCT), which utilizes cloud services to provide storage and computation capability in contact tracing. To guarantee the integrity and accuracy of contact tracing results, PvCT applies a novel set accumulator-based authentication data structure whose computation is outsourced, and the client can check whether returned results are valid. Furthermore, we provide rigorous security proof of our algorithm based on the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>q</mi> </math> -Strong Bilinear Diffie–Hellman assumption. Detailed experimental evaluation is also conducted on three real-world datasets. The results show that our algorithm is feasible within milliseconds of client CPU time and can significantly reduce the storage overhead from the size of datasets to a constant 128 bytes.
Detecting Spam Bots on Social Network

Bots have made an appearance on social media in a variety of ways. Twitter, for instance, has been particularly hard hit, with bots accounting for a shockingly large number of its users. These bots are used for nefarious purposes such as disseminating false information about politicians and inflating celebrity expectations. Furthermore, these bots have the potential to skew the results of conventional social media research. With the multiple increases in the size, speed, and style of user knowledge in online social networks, new methods of grouping and evaluating such massive knowledge are being explored. Getting rid of malicious social bots from a social media site is crucial. The most widely used methods for identifying fraudulent social bots focus on the quantitative measures of their actions. Social bots simply mimic these choices, leading to a low level of study accuracy. Transformation clickstream sequences and semi-supervised clustering were used to develop a new technique for detecting malicious social bots. This method considers not only the probability of user activity clickstreams being moved, but also the behavior's time characteristic. The detection accuracy for various kinds of malware social bots by the detection technique assisted transfer probability of user activity clickstreams will increase by a mean of 12.8 percent, as per results from our research on real online social network sites, compared to the detection method funded estimate of user behaviour.
Complex Mathematical Expressions Recognition using Support Vector Machine as a Classifier

Mathematics is almost unavoidable in daily life as well as in mathematical model with analysis in recent technology. It is today’s need to implement math recognition system to participate in digital world. Each and every element in the equations has been segmented using morphological operations and subsequently the features entropy, mean, variance, standard deviation, skewness, kurtosis, correlation, contrast  are extracted and used appropriate classifier- support vector machine to improve the accuracy .  The more complex handwritten equations have been considered for the recognition. The implemented algorithm can be used in offline recognition of equations, digits and symbols on postal and bank documents, university answer sheets, handwritten notes of mathematics as well as in blind math applications. The efficiency of the system is measured using confusion matrix and ROC (Receiver Operating Characteristics) under AUC (Area under Curve). Basically accuracy is depends on extracted features and classifier used.
PENGARUH GENDER ANAK DAN KEBIASAAN ORANG TUA MEMBACAKAN BUKU TERHADAP KEMAMPUAN LITERASI AWAL ANAK USIA DINI

Literacy is one of the abilities that must be developed from an early age and is referred to as early literacy. There are several factors that can affect a child’s Earl literacy abilities, including the child’s gender and parental involvement, especially the parents’ habit of reading books. The purpose of writing this article is to explain the influence of children’s gender and parents’ habit of reading books with Children to the ability of early childhood literacy. Literature results show that boys need more intense early literacy stimulation than girls. One form of stimulation that can be done to develop children’s early literacy skills is through the involvement of parents by getting used to reading books with children, because children’s literacy experiences first occur in the home environment.