Reviewerly/exHarmony huggingface dataset:
LB955 Predicting metastatic melanoma from melanoma pathological images using a Convolutional Neural Network: A Multicenter Study

Pathological images are the gold standard for the diagnosis of melanoma.Recent studies have shown that convolutional neural networks(CNNs)' ability to diagnose melanoma from pathological images is as good as or slightly better than that of pathologists.However, few studies have verified whether CNN can directly predict the metastasis of melanoma from pathological images, which is an experiment of great medical significance. In the present study, We investigated whether CNNs can predict the metastasis of melanoma from pathological images.First, We collected 472 digital whole-slide images (metastatic melanoma 143, primary melanoma 329) to develop the model. Of these, 85 whole-slide images were from xiangya hospital and 387 whole-slide images were from Yale medical center.Furthermore, we trained a classical convolutional neural network model VGG19, and evaluated its performance. Our model showed 93.5% of accuracy, 98.6% of specialty, 89.3% of sensitivity. The results showed that our model forecast precisely. These findings suggest that CNN is able to predict whether melanoma is metastatic directly from pathological images.Thus will accelerate clinical trial design and reduce patient pain.
Single Functional Index Model under Responses MAR and Dependent Observations

This contribution deals with the estimation of the functional single index regression model (FSIRM) with responses missing at random (MAR) for strong mixing time series data. Some asymptotic properties such as the uniform almost complete convergence rate and asymptotic normality of the estimator are obtained respectively under some general conditions.
Mathematics Teaching Technology for Humanitarian Students in Pedagogical Heu

The article discusses the problems of teaching mathematics in higher education for humanitarian students in the context of new educational standards and the reduction of hours allocated to the study of the discipline. The results of the introduction of distance learning educational complex based on a combination of contact work with students and distance learning methods are presented. The purpose of the study is empirical confirmation of the hypothesis about improving the quality of students' learning of material with the systematic use of innovative forms of training based on the use of distance learning technologies and on an individual approach to each student.
Approximate multi-object filter with known SNR information for an optical sensor system

In the image plane of an optical sensor, the amplitude information (AI) is very reliable for distinguishing returns generated by actual targets or coming from clutter generators. However, the majority of recently derived multi-object filters based on Mahler’s finite set statistics (FISST) theory have ignored utilizing this information. This paper proposes an approximate multi-object filter with additive AI applied for optical sensor systems. The algorithm is operated on an image plane generated by the optical sensor, which has been pre-processed. After each prediction step, we sample multiple particles to approximate the prior multi-object density. Moreover, at the update step, we employ the amplitude feature likelihood for situations where the signal-to-noise ratio (SNR) information of targets is known. The loopy belief propagation (LBP) method with sequentially updated initialization messages is designed to solve the data association problem involved in the update step of the multi-object particle filter. We analyze the convergence performance of the LBP algorithm with additive AI and sequentially updated initialization messages; an ad hoc method for improving the performance of the AI-aided LBP is designed.
A Novel Key Generating Scheme of Automatic Variable Key

The major objectives of the computer networks are, sharing information and providing uninterrupted services over a distance. Because of the critical nature of computer networks and continuous development in information technology, the probability of getting hacked and leaking of the shared message becomes a bigger threat. Therefore, achieving perfect security in such shared data is an incredible research challenge. The pioneering work of Shannon on perfect security says that shared data can only be kept confidential if the length of the key is higher or equal to the size of the message. Therefore, in this paper, we have proposed a pseudorandom function which will take negotiated session keys and data block as a seed value and produce the required number of distinct keys i.e. Automatic Variable key (AVK) for each block of data transmission instead using the same key every time.
Withdrawn: A New Secret Sharing Scheme of Large-scale Electronic Voting

The article for the journal Recent Advances in Electrical and Electronic Engineering has been withdrawn on the request of the authors due to some technical errors in the article. Bentham Science apologizes to the readers of the journal for any inconvenience this may cause. BENTHAM SCIENCE DISCLAIMER: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication the authors agree that the publishers have the legal right to take appropriate action against the authors, if plagiarism or fabricated information is discovered. By submitting a manuscript the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.
Quality of Musicians’ Experience in Network Music Performance: A Subjective Evaluation

In Network Music Performance (NMP), audio quality and audio delay are considered to be the most critical variables affecting the Quality of Musicians’ Experience (QoME). In order to quantify the extent to which these parameters affect QoME, we executed a pilot study where eight musicians performed music in pairs in a controlled NMP setting and were asked to evaluate eight variables related to perception, while the end-to-end delay and the quality of the exchanged audio were varied. We present the design and execution of this experiment and discuss its results and their implications for the tolerance of musicians to increased delay and degraded audio quality.
Development Of Teachers ' Teaching Skills Perceptions On The Contexts Of Achievement

The quality of teaching is a decisive factor in facilitating successful learning and academic outcomes in students. Teaching is also a complex task implying a wide range of comprehensive knowledge and skills teachers must ideally master to manage successfully the specific requirements of teaching in class. The professional development of teachers can be achieved by enrolling them in activities that are regularly and systematically organized by competent bodies in the field of education, such as those included in the continuous vocational training programmes. Teachers’ professional development can also be enhanced through constant self-study efforts, according to the needs of everyone, or by practising daily teaching activities. Many teaching skills seem to develop much better once teachers start conducting classes, rather than during the initial training or by participating in activities that are designed specifically for this purpose, such as training courses or tutoring. This article aims at presenting the perception of teachers who conduct primary school classes, on the context in which they developed the specific skills and expertise required for the successful unfolding of their teaching activities in the classroom, and optimal interaction with students.
The 5 Best Canon EF Camera Lenses

The Canon EF is an incredible film camera. The top 5 lenses to use with the Canon EF are going to be covered in this post. Additional details are ...
Environment ISO14001 Activities and Environmental Education and Research in University of Fukui

University of Fukui has been accredited for ISO14001, so called as environment ISO, since 2003. Major targets of the ISO activities of University of Fukui are as follows; i) saving energy and material, ii) prevention of environmental pollution, iii) environmental education and research, iv) keeping physical and mental health, v) maintaining clean campus, and vi) observance of law and regulation. In this report, outline of the ISO activities of University of Fukui is shown, especially focusing on environmental education and research, and significance of environment ISO in university is discussed. As one of outcomes of this ISO activities, it is demonstrated that energy consumption has been reduced year by year.
EARTHQUAKE IMPACT ASSESSMENT USING NAÏVE BAYESIAN AND LONG SHORT TERM MEMORY MODELS

This research work highlights the use of Deep learning Techniques to assess the impact of earthquakes in a place. This research concentrates on studying the impacts of earthquakes on various situations using the Deep Neural Network and the output of the prediction in terms of the impact will be used to appropriately alert the people in earthquake prone areas, so that the damage or loss of resources could be reduced. We used the historical information from various sources with data on earthquakes which have happened as well as the satellite remote sensing data. The data is used to train a deep neural network, after preprocessing of data, and adjusting the weight connections. The model was tested and the desired classes in terms of damage level, whether it is high, moderate or low were predicted. Using the Naive Bayesian model we achieved an accuracy of 97% and using LSTM we got 98% accuracy.
A Deep Learning Approach to Locate Buggy Files

Bug localization, using a bug description to locate potential buggy source code files automatically, can assist developers to quick fix bugs and relieve maintaining costs for industry. Traditional models are mainly based on information retrieval (IR) techniques, employing the lexical similarity between code and text to locate potential buggy files. Recent researches involve deep learning techniques, utilizing a neural network based encoder to extract semantics from both code and text. However, rather than natural language, code contains not only semantic information, but also important relations among classes, functions, and variables. For this purpose, we introduce a deep learning approach with various embedding methods for bug localization, and it achieves competitive performances on two public datasets.
Lightweight Stream Authentication for Mobile Objects

Conventional authentication is a temporal action that takes place at a specific point in time. During the period between this action and when the associated task(s) is (are) executed several events may occur that impact on the task(s), e.g., an authenticated user may take a short break without logging out. This is a vulnerability that may lead to exploits. For applications where such exploits are a concern, authentication should be dynamic with a continuous monitoring loop, where trust is updated while the tasks associated with the authentication are executed. Continuous user authentication addresses this issue by using biometric user traits to monitor user behavior. In this paper we extend this notion for applications where monitoring mobile objects has to be a continuous process, e.g., for liveness probing of unmanned aerial vehicles (UAVs), or to protect UAVs (with WiFi based UAVs an attacker may use a WiFi de-authentication attack to disconnect an authorized operator and then take control of the vehicle while the operator is trying to re-establish connectivity). We propose a lightweight stream authentication scheme for mobile objects that approximates continuous authentication. This only requires the user and object to share a loosely synchronized pseudo-random number generator, and is provably secure.
An Algorithm for the Exact Treedepth Problem.

We present a novel algorithm for the minimum-depth elimination tree problem, which is equivalent to the optimal treedepth decomposition problem. Our algorithm makes use of two cheaply-computed lower bound functions to prune the search tree, along with symmetry-breaking and domination rules. We present an empirical study showing that the algorithm outperforms the current state-of-the-art solver (which is based on a SAT encoding) by orders of magnitude on a range of graph classes.
Существование человека в обществе интернета без доступа к нему: опыт самонаблюдения

The article examines the situation of the modern world, associated with the problem of complete immersion of humanity in the Internet and its - the loss of life, connections, completely based on the Internet. The key point of the report is the experience of self-observation, which includes the complete absence of means of communication and access to the world wide web for a certain number of days.
Stochastic Subset Selection.

Current machine learning algorithms are designed to work with huge volumes of high dimensional data such as images. However, these algorithms are being increasingly deployed to resource constrained systems such as mobile devices and embedded systems. Even in cases where large computing infrastructure is available, the size of each data instance, as well as datasets, can provide a huge bottleneck in data transfer across communication channels. Also, there is a huge incentive both in energy and monetary terms in reducing both the computational and memory requirements of these algorithms. For non-parametric models that require to leverage the stored training data at the inference time, the increased cost in memory and computation could be even more problematic. In this work, we aim to reduce the volume of data these algorithms must process through an end-to-end two-stage neural subset selection model, where the first stage selects a set of candidate points using a conditionally independent Bernoulli mask followed by an iterative coreset selection via a conditional Categorical distribution. The subset selection model is trained by meta-learning with a distribution of sets. We validate our method on set reconstruction and classification tasks with feature selection as well as the selection of representative samples from a given dataset, on which our method outperforms relevant baselines. We also show in our experiments that our method enhances scalability of non-parametric models such as Neural Processes.
Multi-agent Path Planning based on MA-RRT* Fixed Nodes

In cooperative pathfinding problems, no-conflicts paths that bring several agents from their start location to their destination need to be planned. This problem can be efficiently solved by Multi-agent RRT*(MA-RRT*) algorithm. However, the implementation of this algorithm is hindered in systems with limited memory because the number of nodes in the tree grows indefinitely as the paths get optimized. This paper proposes an improved version of MA-RRT*, called Multi-agent RRT* Fixed Nodes(MA-RRT*FN), which limits the number of nodes stored in the tree by removing the weak nodes which are not likely to reach the goal. The results show that MA-RRT*FN performs close to MA-RRT* in terms of scalability and solution quality while the memory required is much lower and fixed.
Towards a typology for playable digital interventions in urban public space

This paper explores the junctional juncture between playable digital interventions and urban public spatial design. Ubiquitous mobile digital devices and their pervasiveness in daily life are creating a shift in the traditional uses of urban public spaces and redefining how play occurs in urban public spaces. Activating possibilities of play in public space encourages people to go on a spontaneous and amorphous way of discovering new things in daily life relying on awareness, taking one’s time, reflecting, experimenting, exploring, etc. Utilising the framework of playable digital interaction and exploring urban spatial environment narrative literature, twenty five existing and proposed playable digital interventions in urban public space are studied to develop an initial typology. This typology includes characteristics such as diverse interactive mechanism, experience, and spatial features. This classification of playful digital interventions in urban public spaces will enable urban planners, designers, decision-makers, game designers, and researchers to realise the great potential and conceptualize the design of future digital playable installations for public spaces.
An Efficient Secure and Time-Based Integrated Encryption System

As significant electronic communications emerge, there is also an expanding necessity for data protection. Every encryption techniquehas its own weakand strong points. The application of cryptographic algorithms needs knowledge regarding strength, weakness, and performance. As security plays acrucial role in military applications, so that the data sent is not understandable by third party.In cryptography, data is co mmunicated in the form ofciphertext. There are various algorithms available for encryption and decryption. Out of all algorithms, AES (Advanced Encryption Standard) algorithmhas better security, but it requires many rounds for a fixed data block size 128,192,256, etc., which requires longer execution time.In this paper, a novelalgorithm that integrates coding techniques with AES to reduce the execution time and storage space required to store the encrypted data is proposed.MATLAB is used for simulation of the proposed algorithm, and the results are compared withstandalone AES algorithm by considering differentfiles ofdifferent sizes
Arduino Based Solar Tracking System.

Solar energy is a clean, easily accessible and abundantly available alternative energy source in nature. Getting solar energy from nature is very beneficial for power generation. Using a fixed Photovoltaic panels extract maximum energy only during 12 noon to 2 PM in Nigeria which results in less energy efficiency. Therefore, the need to improve the energy efficiency of PV solar panel through building a solar tracking system cannot be over-emphasized. Photovoltaic panels must be perpendicular with the sun in order to get maximum energy. The methodology employed in this work includes the implementation of an Arduino based solar tracking system. Light Dependent Resistors (LDRs) are used to sense the intensity of sunlight and hence the PV solar panel is adjusted accordingly to track maximum energy. The mechanism uses servo motor to control the movement of the solar panel. The microcontroller is used to control the servo motor based on signals received from the LDRs. The result of this work has clearly shown that the tracking solar panel produces more energy compared to a fixed panel.
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis.

In this paper, we study random subsampling of Gaussian process regression, one of the simplest approximation baselines, from a theoretical perspective. Although subsampling discards a large part of training data, we show provable guarantees on the accuracy of the predictive mean/variance and its generalization ability. For analysis, we consider embedding kernel matrices into graphons, which encapsulate the difference of the sample size and enables us to evaluate the approximation and generalization errors in a unified manner. The experimental results show that the subsampling approximation achieves a better trade-off regarding accuracy and runtime than the Nystrom and random Fourier expansion methods.
E-healthcare monitoring using internet of things

Abstract Internet of Things has given a lot simpler answer for remote continuous wellbeing checking of patients from the clinic just as home. Sensors secure the information of different parameters in regards to patients’ wellbeing, and the Internet of Things stores that information and shows through the site, which gives access to remote observing. Utilization of Sensor decreases the human mistake, and the span of the framework lessens the involved space of the room. The extraordinary piece of this proposed arrangement is the alert age to give the recommended prescription to the patient in time. The other gainful zone of the framework is the plan of sending the warning through email and SMS alert if any of the wellbeing parameters crosses the limit esteem. Notice plan will keep the separate expert aware of the circumstance. Another huge region of the proposed arrangement is to make the ideal surroundings according to the necessity of patient’s wellbeing condition. In this paper, we have talked about the checking of pulse, circulatory strain, breath rate, body temperature, body development and saline dimensions.
LdSM: Logarithm-depth Streaming Multi-label Decision Trees

We consider multi-label classification where the goal is to annotate each data point with the most relevant $\textit{subset}$ of labels from an extremely large label set. Efficient annotation can be achieved with balanced tree predictors, i.e. trees with logarithmic-depth in the label complexity, whose leaves correspond to labels. Designing prediction mechanism with such trees for real data applications is non-trivial as it needs to accommodate sending examples to multiple leaves while at the same time sustain high prediction accuracy. In this paper we develop the LdSM algorithm for the construction and training of multi-label decision trees, where in every node of the tree we optimize a novel objective function that favors balanced splits, maintains high class purity of children nodes, and allows sending examples to multiple directions but with a penalty that prevents tree over-growth. Each node of the tree is trained once the previous node is completed leading to a streaming approach for training. We analyze the proposed objective theoretically and show that minimizing it leads to pure and balanced data splits. Furthermore, we show a boosting theorem that captures its connection to the multi-label classification error. Experimental results on benchmark data sets demonstrate that our approach achieves high prediction accuracy and low prediction time and position LdSM as a competitive tool among existing state-of-the-art approaches.
Infinite families of five-valued Walsh spectrum Boolean functions

In this paper, we provide some sufficient conditions for a function of the form to be five-valued with the Walsh spectrum {0, ±2n/2, ±2n/2+1}. In particular, several classes of five-valued Walsh spectrum functions by adding the arbitrary product of linear functions to some known quadratic bent function are obtained.
A Walsh Basis Selection Method for Visual Feedback Control without Geometric Features

For visual feedback without geometric features, the Walsh basis is superior to other basis with respect that we can design the spatial resolution. However, the standard Walsh basis can not achieve high input output linearity in general. In this paper we propose a new basis selection method that achieve high input output linearity. By using a sloshing experimental setup, the validity of the proposed method is confirmed via comparisons between the standard Walsh basis and the new Walsh basis by the new basis selection method.