Reviewerly/exHarmony huggingface dataset:
Finding Geometric Medians with Location Privacy

We examine the problem of discovering the set P of points in a given topology which constitutes a k-median set for that topology, while maintaining location privacy. That is, there exists a set U of points in a d-dimensional topology for which a k-median set must be found by some algorithm A, without disclosing the location of points in U to the executor of A. We define a privacy preserving data model for a coordinate system we call a “Topology Descriptor Grid”, and show how it can be used to find the rectilinear 1-median of the system and a constant factor approximation for the Euclidean 1-median. Additionally, we achieve a constant factor approximation for the rectilinear 2-median of a grid topology.
Modern models of engineers’ training in Russia

The article considers the models of engineering personnel training for modern economic factors and employers requirements in innovative and high-tech economy. A detailed description and comparative analysis of two effectively implemented models in the technical education system of university are given.
Blockchain and Data Protection: An Evaluation of the Challenges and Solutions Mentioned by German Stakeholders

This paper analyzes data protection challenges and possible solutions associated with the usage of the blockchain (BC) technology from the perspective of 94 German companies and organizations. This paper clusters 537 data protection-relevant statements into three subject areas: (1) relevance of data protection in BC, (2) articulated challenges and (3) proposed solutions. Each group is then collated with insights from computer science. The results show that a majority of the respondents do see data protection issues with using BC, which mainly relate to data erasure and identifying the data controller. However, the majority also consider these problems to be solvable utilizing already available technologies, e.g. off-chain storage, encryption, pseudonymization or usage of private BCs. Comparing these proposals with the findings in computer science literature shows that especially off-chain storage, encryption and redactable blockchains can be regarded as adequate solutions.
Empirically Evaluating the Effect of the Physics of Notations on Model Construction

In 2009, Moody introduced nine principles for evaluating, improving and designing cognitively effective notations called the “Physics of Notations” [49] motivating many research works ever since, being cited more than 1250 times at the time of writing this paper. Many research works have adopted the nine principles of the Physics of Notations to improve existing notations or devise new notations. Modeling is a two-step process that has the goal of communicating a mental concept by a model constructor (step one) to a model reader (step two). A subset of the research works utilizing the Physics of Notations have empirically validated the cognitive effectiveness of the new notations by their readers. However, there lacks any empirical evidence that investigates the effect of using Physics of Notations-enabled notations in model construction. This is a serious matter to be investigated as naturally model construction preludes model comprehension. Poorly constructed models can at best be poorly comprehended by its readers having dire consequences in downstream development activities. This paper reports on three different experiments that use software engineering professionals as subjects. The experiments investigate the effect of using notations that adhere to the Physics if Notations principles on model construction efforts. The results do not indicate an outright advantage for model constructors who utilize Physics of Notations-enabled notations in comparison to using their original versions of these notations.
Variational HyperAdam: A Meta-learning Approach to Network Training

Stochastic optimization algorithms have been popular for training deep neural networks. Recently, there emerges a new approach of learning-based optimizer, which has achieved promising performance for training neural networks. However, these black-box learning-based optimizers do not fully take advantage of the experience in human-designed optimizers and heavily rely on learning from meta-training tasks, therefore have limited generalization ability. In this paper, we propose a novel optimizer, dubbed as Variational HyperAdam, which is based on a parametric generalized Adam algorithm, i.e., HyperAdam, in a variational framework. With Variational HyperAdam as optimizer for training neural network, the parameter update vector of the neural network at each training step is considered as random variable, whose approximate posterior distribution given the training data and current network parameter vector is predicted by Variational HyperAdam. The parameter update vector for network training is sampled from this approximate posterior distribution. Specifically, in Variational HyperAdam, we design a learnable generalized Adam algorithm for estimating expectation, paired with a VarBlock for estimating the variance of the approximate posterior distribution of parameter update vector. The Variational HyperAdam is learned in a meta-learning approach with meta-training loss derived by variational inference. Experiments verify that the learned Variational HyperAdam achieved state-of-the-art network training performance for various types of networks on different datasets, such as multilayer perceptron, CNN, LSTM and ResNet.
PLB: Adaptive Partial Congestion-aware Load Balancing for Datacenter Networks

In order to accommodate ever-increasing new tenants and applications, datacenter networks (DCNs) require an efficient load balancing scheme to fully utilize their bisection bandwidth. Equal-cost MultiPath routing (ECMP) is a widely used load-balancing mechanism in the DCN. However, ECMP blindly hashes traffic to parallel paths and results in imbalance and collisions. Motivated by ECMP's shortcomings, some recent schemes provide more visibility into networks via active probing. They could be broadly classified as probing all the paths or a fixed number of paths (e.g., 3 paths) each probe interval. However, they all suffer from some limitations. Probing all paths introduces high probing overhead while probing a fixed number of paths is suboptimal when the network topology and traffic load change. To our best knowledge, none of the existing schemes adapt the number of paths being probed to the network conditions. Enlightened by the defects of previous work, we introduce PLB, an adaptive partial congestion-aware load-balancing mechanism. At its heart, PLB randomly probes partial paths each probe interval and the number of them changes according to the network topology and the traffic load. Besides, PLB splits flow into flowlets and makes careful routing/rerouting decisions for them. Through analysis, we formulate the correlations between the number of paths being probed and the network conditions. Furthermore, simulations with realistic workloads validate our conclusions and show that PLB reduces overall flow completion times compared to the state-of-the-art load balancing schemes both in symmetric and asymmetric topologies.
A Novel One-step Method Based on YOLOv3-tiny for Fatigue Driving Detection

Recently, fatigue driving as the most significant factor for traffic accidents has increasingly attracted wide-range concerns. Although diverse fatigue driving detection pipelines based on multi-step eye feature extraction work to some degree, they suffer from lots of computation load resulting in hard deployment for real-time scenes. Additionally, current research lack of real-time scenes dataset. In this paper, we employ a well-designed YOLOv3-tiny model to overcome the above drawbacks. Furtherly, we contribute a novel real-driving scenes dataset named as REAL-Driving dataset which contains 28000 images and 40 real-driving videos in total. Experiments on both the CEW dataset and the self-made dataset are conducted to demonstrate the effectiveness of our approach. The mAP in CEW dataset is 0.999 and the one received in REAL-Driving dataset is higher than other well-known deep learning based object detection algorithms. In the detection of real-driving scenes, we achieve 97.1% accuracy with speed of 5.365ms per frame. The results obtained from sufficient experiments demonstrate the effectiveness and advancement of our method.
Application of Improved Coyote optimization Algorithm in Optimal Configuration of Photovoltaic Intelligent Edge Terminal

Photovoltaic intelligent edge terminal(PV IET) is one of the important devices for the safe and stable operation and smart operation of PV power stations. It is powerful but expensive. Large PV power stations can conFigure this device to improve operation and maintenance level, while smaller distributed PV power stations cannot afford its high price. In order to achieve the goal of low cost of distributed PV operation and maintenance, this paper proposes an optimal configuration method of PV intelligent edge terminals based on coyote optimization algorithm, which optimizes the layout of PV intelligent edge terminals from two aspects of quantity and location. At the same time, aiming at the weak optimization performance of coyote optimization algorithm (COA), a single dimension growth strategy of coyote individuals is introduced to prevent premature algorithm. The test results of typical cases show that the proposed method can effectively reduce the cost, and the proposed ICOA performs better than COA in accuracy and stability in solving the optimal layout problem, which verifies the effectiveness and feasibility of the proposed method.
Modern day digital pen pals: A semester-long Collaborative Online International Learning (COIL) project

This paper outlines a unit-long or semester-long project that aims to build students competencies in interpersonal, international, intercultural, and language-learning proficiencies, mediated through the use of technology. The project is considered a Collaborative Online International Learning or COIL project, and offers detailed instructions on how to pair and run a meaningful cross-border partner-based educational collaboration through a series of three preparation assignments and three conversation reflections. This activity ends with both discussion questions for the synchronous in-class debrief, as well as suggestions for modifications or alterations based on commonly found challenges.Courses Interpersonal/Intercultural/Group Communication, Language Exchange Program.Objectives This original teaching idea can be a semester-long or extended unit-long project. By the end of this unit, students will be able to: Identify both theoretical and practical applications of self-disclosure and employ more informed choices concerning the role of self-disclosure in the development of friendship.Identify the intercultural concepts of expressions of personal, social, and cultural identities, differences in cultural dimensions, and language-learning practice.Navigate distance learning through the use of technology and employ logistics skills required to establish shared expectations concerning logistics during collaboration.Employ greater capabilities in interpersonal and intercultural competence in cross-cultural friendships and greater competencies in the technologies of Zoom, Skype, and WhatsApp.
Research on improved hough algorithm and its application in lunar crater

Traditional Hough circle detection algorithm usually determines the center and radius of a circle by mapping points in cartesian coordinate system to polar coordinate system. Since it accumulates in the three-dimensional space, it requires more calculation consumption. In this paper, we solve the problem of high time complexity of Hough algorithm in judging circle radius and circle center from two aspects of circle angle and circle radius according to the geometric features of quasi-circles. A large number of experiments show that, compared with the traditional algorithm, this algorithm can not only identify quasi-circles, but also improve the detection success rate of circles by about 10%, with efficient running speed, and obtain good experimental results in the detection of craters.
A blockchain-enabled 5G authentication scheme against DoS attacks

Abstract The 5G network is designed to accommodate the increasing number of connected devices with higher speed, lower latency, and better security. To improve the network security and robustness, the Third Generation Partnership Project (3GPP) has standardized the 5G-AKA protocol for mutually authenticating user equipment (UE), base stations, and the core network. However, since there will be more devices in the 5G network, the denial-of-service (DoS) attacks are much easier to be carried out than ever before. In this paper, we address the security issues in the 5G network by introducing the blockchain-enabled authentication and key agreement scheme for the 5G Networks. Our proposed scheme uses private blockchain to provide a distributed database to store all authentication records. Also, we exploit the trapdoor collision property of the chameleon hash function, such that the entries in the blockchain can verify the incoming authentication requests. Furthermore, a subscription concealed identifier (SUCI) is mandatory in our protocol to protect the anonymity of the devices, and ECDH is employed to generate a session key. We use Scyther Tool to verify our protocol formally, and the security analysis shows that our protocol can achieve perfect forward secrecy, device anonymity, and mutual authentication and key agreement. Our scheme is also resistant to replay attacks, and most importantly, the DoS attacks. Finally, performance evaluation shows that our scheme is efficient for both UEs and base stations with rational computational costs.
Fine Acquisition of Vessel Training Data for Machine Learning from Sentinel-1 SAR Images Accompanied by AIS Imformation

Ship detection in coastal regions accompanied by machine learning could be effective in economic and martial issues. However, conventional researches on ship detection mainly focused on modifying the training model itself instead of obtaining qualified training data. In order to ameliorate the training data in the aspect of quality and quantity, this research aims to directly obtain training data from AIS information. From discrete AIS information, interpolation on SAR acquisition time was conducted, followed by adjusting the Doppler frequency shift caused by each vessel's velocity. Training data was constructed from the adjusted position using internal location of AIS sensor on each type of vessel. Extracted training data by the proposed algorithm from Sentinel-1 images was tested with CNN model. As the detection performance of the extracted training data exceeded that from visual interpretation, this study concluded that qualified training data could be extracted from the proposed algorithm.
Feature concatenation for scene classification of VHR images based on canonical correlation analysis.

Abstract The recent advancements of technology in remote sensing enable us to get very high-resolution images (VHR). To do scene classification in these images turned significant and become a challenging problem due to the lack of availability of adequate labelled data. We get over fitting problems by training a limited amount of labelled data. Considering the features obtained by deep learning convolutional nets as inputs we address this issue. Here we utilize the existing VGG16, Alex Net frameworks as a feature extractor to extricate informative features from the authentic VHR Images. Succeeding, discuss feature concatenation and classification framework based on Canonical Correlation Analysis (CCA). This strategy uses the correspondence of two feature vectors of discriminant information and eradicates redundant information within the features. This permits a more effective approach than conventional extraction techniques exclusively. The experimental results demonstrate that the feature concatenation strategy based on the CCA technique produces good informative features and accomplishes a higher accuracy with much dimension reduction than exclusively using the raw deep features. We use a 0.3 sub-meter resolution UC MERCED data set to explore our approach.
Rational Against Irrational Causes of Symptom Recognition Using Data Taxonomy

The rational factor generally discharges the exact requirements from the mindset to highlight the questions as it is with the actual cause of nature. Irrespectively, the irrational factors will flow from the mindset based on their individual scares and fearfulness of their subconscious thoughts of the mind. In general sequence, symptoms are the real facts to find out the cause for their solution to detect from the known values of attributes. Opinion mining is essential to find the data values and taxonomy to roll out the irrational factor on symptoms recognition. The research methodology is applied to find the new insight, which identifies the actual symptoms towards the disease belongings in a quick manner. The expected working hypothesis of outcomes may vary from the rational as dynamic issues against irrational, as unfounded factors, because the examination category depends on an expressive manner. The research work planned to expose and estimate roughly the rational and irrational factors of symptoms for the diseases. The paper presents features to detect the symptoms for the specific two diseases with positive or negative identification to correlate them in future survival.
A Computability Perspective on (Verified) Machine Learning

In Computer Science there is a strong consensus that it is highly desirable to combine the versatility of Machine Learning (ML) with the assurances formal verification can provide. However, it is unclear what such ‘verified ML’ should look like. This paper is the first to formalise the concepts of classifiers and learners in ML in terms of computable analysis. It provides results about which properties of classifiers and learners are computable. By doing this we establish a bridge between the continuous mathematics underpinning ML and the discrete setting of most of computer science. We define the computational tasks underlying the newly suggested verified ML in a model-agnostic way, i.e., they work for all machine learning approaches including, e.g., random forests, support vector machines, and Neural Networks. We show that they are in principle computable.
Flexible Recurrent Neural Networks

We introduce two methods enabling recurrent neural networks (RNNs) to trade off accuracy for computational cost during the analysis of a sequence. This opens up the possibility to adapt RNNs in real time to changing computational constraints, such as when running on shared hardware with other processes or in mobile edge computing nodes. The first approach makes minimal changes to the model. Therefore, it avoids loading new parameters from slow memory. In the second approach, different models can replace one another within a sequence analysis. The latter works on more data sets. We evaluate these two approaches on permuted MNIST, adding task and a human activity recognition task. We demonstrate that changing the computational cost of a RNN with our approaches leads to sensible results. Indeed, the resulting accuracy and computational cost is typically a weighted average of the corresponding metrics of the models used. The weight of each model also increases with the number of time steps a model is used.
Refined Imbricate Cryptography with addition of Polygram Substitution Cipher Method: an enhanced tool for security

Abstract The booming of technologies these days make us more comfortable and also take us into the complex world. As information increases rapidly, the level of security also matters. As a technological paradigm, the security can be done using many cryptography techniques. Cryptography is a process of converting a plain text into a cipher text so that the data can be viewed or extracted only by an intended user. In this paper, we describe a methodology inorder to enhance the speed of encryption and decryption process. This process uses ciphertext formation via Imbricate Cryptography and Polygram Substitution Cipher Technique. On performing cryptography, there are a lot of improvements in the actual implementation. Most importantly, the advantage of combining these two algorithms is to make data more secure from the exploiters and also making the computer to process faster.
An Effective Method of Feature Selection in Persian Text for Improving the Accuracy of Detecting Request in Persian Messages on Telegram

In recent years, data received from social media has increased exponentially. They have become valuable sources of information for many analysts and businesses to expand their business. Automatic document classification is an essential step in extracting knowledge from these sources of information. In automatic text classification, words are assessed as a set of features. Selecting useful features from each text reduces the size of the feature vector and improves classification performance. Many algorithms have been applied for the automatic classification of text. Although all the methods proposed for other languages are applicable and comparable, studies on classification and feature selection in the Persian text have not been sufficiently carried out. The present research is conducted in Persian, and the introduction of a Persian dataset is a part of its innovation. In the present article, an innovative approach is presented to improve the performance of Persian text classification. The authors extracted 85,000 Persian messages from the Idekav-system, which is a Telegram search engine. The new idea presented in this paper to process and classify this textual data is on the basis of the feature vector expansion by adding some selective features using the most extensively used feature selection methods based on Local and Global filters. The new feature vector is then filtered by applying the secondary feature selection. The secondary feature selection phase selects more appropriate features among those added from the first step to enhance the effect of applying wrapper methods on classification performance. In the third step, the combined filter-based methods and the combination of the results of different learning algorithms have been used to achieve higher accuracy. At the end of the three selection stages, a method was proposed that increased accuracy up to 0.945 and reduced training time and calculations in the Persian dataset.