Reviewerly/exHarmony huggingface dataset:
PolarNet: Accelerated Deep Open Space Segmentation using Automotive Radar in Polar Domain

Camera and Lidar processing have been revolutionized with the rapid development of deep learning model architectures. Automotive radar is one of the crucial elements of automated driver assistance and autonomous driving systems. Radar still relies on traditional signal processing techniques, unlike camera and Lidar based methods. We believe this is the missing link to achieve the most robust perception system. Identifying drivable space and occupied space is the first step in any autonomous decision making task. Occupancy grid map representation of the environment is often used for this purpose. In this paper, we propose PolarNet, a deep neural model to process radar information in polar domain for open space segmentation. We explore various input-output representations. Our experiments show that PolarNet is a effective way to process radar data that achieves state-of-the-art performance and processing speeds while maintaining a compact size.
Automatic Persian Text Emotion Detection using Cognitive Linguistic and Deep Learning

In the modern age, written sources are rapidly increasing. A growing number of these data are related to the texts containing the feelings and opinions of the users. Thus, reviewing and analyzing of emotional texts have received a particular attention in recent years. A System which is based on combination of cognitive features and deep neural network, Gated Recurrent Unit has been proposed in this paper. Five basic emotions used in this approach are: anger, happiness, sadness, surprise and fear. A total of 23,000 Persian documents by the average length of 24 have been labeled for this research. Emotional constructions, emotional keywords, and emotional POS are the basic cognitive features used in this approach. On the other hand, after preprocessing the texts, words of normalized text have been embedded by Word2Vec technique. Then, a deep learning approach has been done based on this embedded data. Finally, classification algorithms such as Naive Bayes, decision tree, and support vector machines were used to classify emotions based on concatenation of defined cognitive features, and deep learning features. 10-fold cross validation has been used to evaluate the performance of the proposed system. Experimental results show the proposed system achieved the accuracy of 97%. Result of proposed system shows the improvement of several percent’s in comparison by other results achieved GRU and cognitive features in isolation. At the end, studying other statistical features and improving these cognitive features in more details can affect the results.
Adaptive Deep Learning for Soft Real-Time Image Classification

CNNs (Convolutional Neural Networks) are becoming increasingly important for real-time applications, such as image classification in traffic control, visual surveillance, and smart manufacturing. It is challenging, however, to meet timing constraints of image processing tasks using CNNs due to their complexity. Performing dynamic trade-offs between the inference accuracy and time for image data analysis in CNNs is challenging too, since we observe that more complex CNNs that take longer to run even lead to lower accuracy in many cases by evaluating hundreds of CNN models in terms of time and accuracy using two popular data sets, MNIST and CIFAR-10. To address these challenges, we propose a new approach that (1) generates CNN models and analyzes their average inference time and accuracy for image classification, (2) stores a small subset of the CNNs with monotonic time and accuracy relationships offline, and (3) efficiently selects an effective CNN expected to support the highest possible accuracy among the stored CNNs subject to the remaining time to the deadline at run time. In our extensive evaluation, we verify that the CNNs derived by our approach are more flexible and cost-efficient than two baseline approaches. We verify that our approach can effectively build a compact set of CNNs and efficiently support systematic time vs. accuracy trade-offs, if necessary, to meet the user-specified timing and accuracy requirements. Moreover, the overhead of our approach is little/acceptable in terms of latency and memory consumption.
On the Privacy–Utility Trade-Off in Differentially Private Hierarchical Text Classification

Hierarchical text classification consists of classifying text documents into a hierarchy of classes and sub-classes. Although Artificial Neural Networks have proved useful to perform this task, unfortunately, they can leak training data information to adversaries due to training data memorization. Using differential privacy during model training can mitigate leakage attacks against trained models, enabling the models to be shared safely at the cost of reduced model accuracy. This work investigates the privacy–utility trade-off in hierarchical text classification with differential privacy guarantees, and it identifies neural network architectures that offer superior trade-offs. To this end, we use a white-box membership inference attack to empirically assess the information leakage of three widely used neural network architectures. We show that large differential privacy parameters already suffice to completely mitigate membership inference attacks, thus resulting only in a moderate decrease in model utility. More specifically, for large datasets with long texts, we observed Transformer-based models to achieve an overall favorable privacy–utility trade-off, while for smaller datasets with shorter texts, convolutional neural networks are preferable.
ByzShield: An Efficient and Robust System for Distributed Training

Training of large scale models on distributed clusters is a critical component of the machine learning pipeline. However, this training can easily be made to fail if some workers behave in an adversarial (Byzantine) fashion whereby they return arbitrary results to the parameter server (PS). A plethora of existing papers consider a variety of attack models and propose robust aggregation and/or computational redundancy to alleviate the effects of these attacks. In this work we consider an omniscient attack model where the adversary has full knowledge about the gradient computation assignments of the workers and can choose to attack (up to) any q out of K worker nodes to induce maximal damage. Our redundancy-based method ByzShield leverages the properties of bipartite expander graphs for the assignment of tasks to workers; this helps to effectively mitigate the effect of the Byzantine behavior. Specifically, we demonstrate an upper bound on the worst case fraction of corrupted gradients based on the eigenvalues of our constructions which are based on mutually orthogonal Latin squares and Ramanujan graphs. Our numerical experiments indicate over a 36% reduction on average in the fraction of corrupted gradients compared to the state of the art. Likewise, our experiments on training followed by image classification on the CIFAR-10 dataset show that ByzShield has on average a 20% advantage in accuracy under the most sophisticated attacks. ByzShield also tolerates a much larger fraction of adversarial nodes compared to prior work.
Robust Digital Image Watermarking in YCbCr Color Space using Hybrid Method

The image watermarking is widely used to provide the security. This paper provides the conceptual framework on image watermarking which is widely used for security purpose within the epoch of data and communication technology. Detailed discuss has been provided on the basis of the three phases. In the first phase of present paper focus has been made on image watermarking and data set will be prepared on which watermarking technique will be executed. Second phase need to locate a specific watermarking technique [LWT- WALSH HADMARD-SVD] which will provide appropriate results in term of PSNR and processing time. In second phase various attacks will be tested on images so that implemented method must stand against various attacks. Last phase emphasise on reverse process will be executed to extract cover and watermark image. Proposed technique is based on the secure encryption watermark .It is associated with image encryption. The encryption algorithm had been introduced in which watermarking information was based on the size of an image. It is clear from the outcomes of the hybrid proposed technique shows that it provides more security than the existing technique. Proposed techniques provide high level of security as compared to existed technology.&#x0D;
Ad Recommendation utilizing user behavior in the physical space to represent their latent interest

Advertisement (ad) recommendation services for mobile users are rapidly increasing. The conventional ways of recommending ads are based on the analysis of user's explicit behavior such as search keywords and keyword matching based on browsing history. However, it might not be effective enough for latent buyers. We have been working on a method to analyze the user's latent interest in web browsing history which categorized positive and negative behaviors. However, we think that the latent interest of users appears not only in the web space but also in the physical space. In this paper, we adapt the method of the linked pages to physical space locations using geo-tagged tweets. Based on several evaluations, we discuss the possibility to recommend ads according to the user's current location.
PENDEFINISIAN OBJEK DAN PELENGKAP DALAM KAMUS BESAR BAHASA INDONESIA SEBAGAI PENUNJANG LITERASI

Literacy cannot be separated from language skills. The most basic language skills are the ability to compose meaningful utterances. For that purpose, a proper understanding of the sentence-forming elements and their functions is needed. Unfortunately, the definitions of the two sentence element functions, namely objects and complements, in KBBI still overlap. The overlapping definition can confuse language users. This paper aims to clarify the differences between object and complement and to correct the inaccuracies in the definition of these two words. Thus, the definition of each of the two functions becomes more appropriate and easily understood by Indonesian language learners. This research was conducted with a functionalism approach through the analysis of syntactic and semantic functions, by paying attention to lexicographic principles. The analysis was carried out on six naturally occurring sentences. The sentences are taken from the Indonesian web corpus. The results of the data analysis in this study indicate that objects and complements are indeed different. From these differences clear and non-overlapping boundaries are obtained.
Neuromanagement: Convergent Integration of the Human Brain and Artificial Intelligence

World achievements in the field of neuroscience have provided previously inaccessible opportunities for creating fundamentally new control systems based on neurointerfaces (brain — computer — brain). Hybridization of environments — gradual blurring of the boundaries between physical, cognitive and digital realities — is taking place. Descriptions of social and cognitive practices of real people are transformed into forming an artificial electronic subject, which becomes more real, replacing a biological object in society (a person is how he is represented in the electronic information environment). At the same time, development of the neurointerface perspectively leads to conversion of nervous tissue and changes biological substrate of the human brain and body in the vector of convergent collaboration of living and artificial nervous systems. Our American competing partners (the US Department of Defence represented by DARPA) carry out multidisciplinary comprehensive research in this area, leading in real results, the US leadership is increasing government funding. A qualitative change in technologies for managing people, society and the state is taking place. Russia’s objective in these conditions is to form its own segment of Neuronet, relying on domestic neurotechnologies, by analogy with the policy of import substitution in Russian nuclear energy.
Looking Deeper: Using the Mobile Microscope to Support Young Children’s Scientific Inquiries

This study adopted a quasi-experimental design with the support of qualitative information to investigate the impact of the mobile microscope on students’ science learning outcomes in a laboratory setting. The duration of the educational experiment was 5 weeks. Research participants comprised 56 third graders from two different classes at a public elementary school in Taiwan. Two classes with an equal number of students formed the experimental and control groups. Students in the experimental group employed mobile microscopes to support their scientific inquiries, whereas students in the control group used only tablet computers to facilitate their science learning. A standardized test was developed to measure students’ basic understanding of botany. Additionally, semi-structured concept mapping was employed as an alternative non-standardized test to evaluate students’ natural science learning. The quantitative findings indicated a significant instructional effect in the students’ concept mapping but not in the standardized test. The qualitative results revealed that mobile microscopes might motivate students to actively engage in knowledge discussion and sharing during plant observation.
Threats and Major Data Breaches: Securing Third-Party Vendors

The emerging trend of increasing numbers of third-party data breaches and publicly exposed data has deep impact on consumer trust. These breaches affect multiple industries and government agencies including online retailer Target Corporation, Marriott Corporation hotel chain, and the United States Office of Personnel Management which have all been breached by cybercriminals due to inadequate security or the lack of secure connections with their third-party vendors. This paper provides an overview of three major third-party breaches resulting in millions of exposed records and the impact of those breaches, followed by recommendations to reduce the risk of third-party data breaches.
Non-perfect maze generation using Kruskal algorithm

A non-perfect maze is a maze that contains loop or cycle and has no isolated cell. A non-perfect maze is an alternative to obtain a maze that cannot be satisfied by perfect maze. This paper discusses non-perfect maze generation with two kind of biases, that is, horizontal and vertical wall bias and cycle bias. In this research, a maze is modeled as a graph in order to generate non-perfect maze using Kruskal algorithm modifications. The modified Kruskal algorithm used Fisher Yates algorithm to obtain a random edge sequence and disjoint set data structure to reduce process time of the algorithm. The modification mentioned above are adding edges randomly while taking account of the edge’s orientation, and by adding additional edges after spanning tree is formed. The algorithm designed in this research constructs an non-perfect maze with complexity of where and denote vertex and edge set of an grid graph, respectively. Several biased non-perfect mazes were shown in this research by varying its dimension, wall bias and cycle bias.
A Cost-Efficient Proof-of-Stake-Voting Based Auditable Blockchain e-Voting System

Abstract In recent years, blockchain-based systems have emerged powerfully and led to the development of several new and improved applications in terms of increased security, trust, accountability and reliability. One important trust-based application is electronic voting system which still lacks in providing good throughput while also reducing the costs in voting given the deployment of state-of-the-art technologies. So, there is a need to imply an efficient consensus protocol to impart cost-efficiency as well as better performance of a blockchain-based electronic voting system. This paper focuses on boosting up the cost-efficiency in the voting process, while also being auditable, thus, strengthening the trust of voters on the government and lessening the overall costs incurred in voting. In this paper, Proof-of-Stake-Voting is used as consensus protocol which is observed to give a throughput of 4-5K transactions per second. Then its performance is compared with the other existing consensus protocols such as Proof-of-Work and Proof-of-Authority in terms of gas consumption, gas cost, and throughput. It has been shown through the results that there is a significant difference in the total gas cost, with Proof-of-Stake-Voting protocol consuming 3.1 x 10 −7 Ethers (ETH), as compared to the Proof-of-Work, taking 5.42536 x 10 −4 ETH and Proof-of-Authority protocols taking 2.49759 x 10 −4 ETH.
Studying Genetic Algorithm and Particle Swarm Optimization In Machine Learning

Each streamlining issue can be disentangled into the basic minimum-seeking dilemma.Which way is the most limited in separation?Which choice is the least expensive?Such inquiries are surely key to the investigation of optimisation.The accomplishment of genetic algorithm and swarm intelligence in managing enhancement issues is their common capacity to progressively find minima through straightforward, nearby associations of potential arrangements.This paper learns about streamlining methods like Genetic Algorithm utilizing Swarm Intelligence.
Visual Perception in AR/VR

In an online OSA Incubator Meeting, top industry and academic researchers explored the importance of accounting for aspects of the human visual system to take augmented and virtual reality to the next step.
Camera calibration based on color-coded phase-shifted fringe

Aiming at the low adaptability of blurring noise of target feature points in traditional calibration methods, a calibration method based on the color-coded phase-shifted fringe is proposed. Using a liquid crystal display panel as the calibration target, horizontal and vertical color-coded phase-shifted stripes are displayed in sequence; the orthogonal phase-shifted stripes are obtained by separating color channels; based on the phase-shifteg theory, the intersections of the orthogonal phase truncation lines are calculated as the feature points. After changing the target position multiple times and extracting feature points, the plane-based camera calibration technique is applied to realize the calibration of both the single camera and the binocular system. Furthermore, color-coded phase-shift circles are added to four corners of the target pattern to automatically extract and sort feature points. Accordingly, the efficiency of calibration is promoted. The experimental results indicate that when the target image is blurred, the reprojection error of the single-camera calibration is 0.15 pixels, and the standard deviation of the binocular system measurement after calibration is 0.1 mm.
Geospatial Mapping, Environmetrics and Indexing Approach for a Tropical River Sediment in Southern Nigeria

The objectives of this study are to assess the trace and heavy metals pollution in the sediments of Ossiomo river, using geospatial mapping, environmetrics and ecological risk indices. The results from the descriptive statistics showed that there was significant difference (P&lt;0.05) of the mean values of Fe, Mn, Cu, Cr, Cd, Pb, Ni and V. A posterior analysis using Duncan multiple regression analysis showed that stations 2 and 3 were significantly different from stations 1 and 4. While, there was no significant difference (P&gt;0.05) in the mean values of Zn across the stations. The results of the relationship of the metals revealed a negative correlation between Fe and Mn with the other metals correspondingly. The results of the Kriging interpolation indicated a strong bull eye colour for stations 2 and 3 (6.42), while stations 1 and 4 were minimal (1.4). The results of the geospatial mapping indicated Fe, Zn and Mn to be the most dominant metals across the stations. The results of the PCA (principal component analysis) yielded 16 variables under 9 components with Eigenvalues &gt;1 in components 1- 6 and these variables explained 99.99 % of the total variance in the sediment. The results of the degree of suitability and sphericity of the PCA revealed a high significant difference at P&lt;0.001. The results of the potential ecological risk index values were very high in station 2 (824.30) and 3 (802.11) correspondingly. That of index of geo-accumulation was generally low (&lt; 2). The findings from this study generally revealed the source apportionment of the trace and heavy metals to come from anthropogenic influences such as farming; fertilizers. Sustainable agriculture is highly recommended in order to reduce the impacts of anthropogenic activities, deterioration of the ecosystem and possible death of the life forms in this watercourse.&#x0D;
WHAT DO UNIVERSITY STUDENTS DO IN ONLINE TEACHING? REFLECTIONS ON CURRENT FORMS OF ACADEMIC PROCRASTINATION, EXPERIENCE FROM THE CZECH REPUBLIC

Information about the paper titled "WHAT DO UNIVERSITY STUDENTS DO IN ONLINE TEACHING? REFLECTIONS ON CURRENT FORMS OF ACADEMIC PROCRASTINATION, EXPERIENCE FROM THE CZECH REPUBLIC" at IATED Digital Library
Fake Product Identification using Blockchain

Fake product identification using blockchain will help the customer to identify the genuine product and will also help the manufacturer to build confidence in the customer.In recent years, block chain has received increasing attention and even numerous applications are being emerged in this technology.Our Project uses the Decentralized Block chain technology approach to ensure that the consumers do not fully rely on the merchants to determine if the products are genuine, in that way the manufacturers can use this system to provide genuine products without having to manage direct-operated stores, which can significantly reduce the cost of product quality assurance.This system will also help the manufacturers to deliver the complete information of the product to the customer through scanning the QR code.The information shared is scripted using Blockchain to make it more secure and trusted.This will also enable the customers to determine whether the products aren't fake.
Forecasting Tourist Visit Using the Vector Autoregressive Exogenous Method (VARX)

Forecasting is an activity to predict what will happen in the future by paying attention to information from the past and the present. A regression model that explains the past movement of the variable itself and also all other variables without distinguishing which endogenous and exogenous variables are called Vector Autoregressive (VAR). But in practice, endogenous variables are supported by exogenous variables. The Vector Autoregressive Exogenous (VARX) model is a development of the VAR with the addition of exogenous variables. The purpose of this study is to form the best model in the VAR method with the addition of an exogenous variable in the form of an effect calendar for forecasting the number of tourists coming to the Special Region of Yogyakarta (DIY). The data used in this study are time series data for 10 years from January 2009 to December 2018 in the form of tourist visit data in the Special Region of Yogyakarta (DIY). The results obtained indicate that the effect calendar variable that affects tourist visitor data in DIY is at Christmas. After being analyzed using MAPE, the best model is the VARX (1.0) model which produces a smaller. So, it can be concluded that the VARX model with the addition of an effects calendar is suitable for predicting tourist visits