Reviewerly/exHarmony huggingface dataset:
Trust and Reputation Mechanisms in Vehicular Ad-Hoc Networks: A Systematic Review

An emerging trend has been observed in the Trust and Reputation (T & R) systems in field of decision-making support for a majority of the provisions propagated by the Internet.It is the extreme importance that peers (users) are able to trust each other and rely on them for file sharing and for services.This paper provides the reader an apprehensive and completely true information and details on a large number of the present conceptions, proposals, issues, and the key to those problems in VANETs and other fields to enhance the eminence of the data in transportation through a systematized literature review.Trust and reputation have also been discussed gravely in this paper.After the scrutinized analysis of more than 90 articles related to trust in a plethora of fields, extracted from few of the apt scientific sources ((i.e., SIEEE Computer Society, ACM Digital Library, Springer Link, Science Direct, and Wiley Online Library), and hence, succeeds to bring about the major hurdles and necessities for trust in real world and future research.
Linking of knowledge into a devised entirety

Point of learning in school is acquiring systematic knowlege and formation of a complete, creative personality. Prerequisite for this is that the teaching, which is by the nature an artificial way of learning, strives towards entirety too, as one of the equal poles with analysis. Disassembling of content complex structures is necessary because of the need to respect psychophysical abilities of the pupils and when that is achieved there follows the next phase - linking of the new contents with the previously acquired ones. Functional entity presents a value from which, one can move on, in a spiral form, towards more complex foundations. In the times of rapid expansion of scientific knowledge, a need to be acquainted with it and realization that it is not possible to be achieved fully, especially not when done in the old fashioned way, memorizing by heart and reproducing (without understanding), the only logical approach left is devising and linking the strong points of new teaching contents with those previously comprehended and learnt.
An Efficient Approach for Job Recommendation System Based on Collaborative Filtering

Managing huge measure of enlisting data on the web, a job seekers dependably invests hours to find helpful ones. To decrease this relentless work, we structure and actualize a recommendation system for online job-seeking job recommender systems are wanted to achieve an uncommon state of precision while making the rating predicts which are significant to the client, as it turns into a repetitive assignment to review a huge number of jobs, posted on the web for instance LinkedIn, fresherworld.com , naukri.com and so on intermittently. In spite of the fact that a great deal of job recommender systems exist that utilization various techniques, here undertaking have been put to make the job recommendations based on applicants profile coordinating just as safeguarding applicants job conduct or inclinations. The collaborating filtering contains a list of rating that the previous user has already given for an item. This paper shows a concise review of collaborative filtering rating prediction based job recommender system and their execution utilizing RapidMiner.
Playing with Virtual Realities: Navigating Immersion within Diverse Environments (Artist-Led Perspective)

Dealing with the intermedial performative practice of dancing with VR technology, in this account I frame the creative media of VR technology and bodily performance as epistemological media, in which we can be immersed but also develop reflective awareness to the processes of leading visions and movements. The account follows the interdisciplinary and intermedial practice-based research experiment ‘Playing with Virtual Realties’ (<a href="http://www.gamelab.berlin/en/home__en/" target="_blank">gamelab.berlin</a>, Humboldt University of Berlin). The project brought together two dancers, a philosopher/choreographer, two theatre scholars/dramaturges, two computer scientists/gamers, and two media experience designers to co-explore how the embodied practice of dancing can interact with HTC Vive, a virtual reality headset developed by HTC and Valve Corporation. At the outset, the experience of moving within VR technology interrupts the perceptual processes in dance, and throws embodied thinkers into a dualist – Cartesian – state of mind, in which visions, actions, and thoughts are disconnected from the sensual body. This account reports how we faced the challenges of navigating immersive experience between diverse environments, and our methods for developing a movement practice in mixed realities. I demonstrate how, by using cross over methods from media experience design and choreography, we composed a physical performance in which the dancers learned to assimilate awareness to the actual space, to their dancing partner, and to the audience, while negotiating their immersive experience within the virtual environment. For performance trailer: <a href="https://www.youtube.com/watch?v=g9Un6TDYyE4" target="_blank">https://www.youtube.com/watch?v=g9Un6TDYyE4</a>. For research trailer: <a href="https://www.youtube.com/watch?v=YcGdKuUhMf4" target="_blank">https://www.youtube.com/watch?v=YcGdKuUhMf4</a>.
Preliminary analyses of volcanic hazards at Kīlauea Volcano, Hawai‘i, 2017–2018

First posted February 24, 2020 For additional information, contact: Hawaiian Volcano ObservatoryU.S. Geological Survey1266 Kamehameha Avenue, Suite A-8Hilo, HI 96720 From 2017 to 2018, the U.S. Geological Survey (USGS) Hawaiian Volcano Observatory (HVO) responded to ongoing and changing eruptions at Kīlauea Volcano as part of its mission to monitor volcanic processes, issue warnings of dangerous activity, and assess volcanic hazards. To formalize short-term hazards assessments—and, in some cases, issue prognoses for future activity—and make results discoverable to both the public and the authorities, HVO released reports online. These reports were published rapidly, received peer review under the USGS’s Fundamental Science Practice guidelines, and were intended to address a focused question posed by one or more cooperating agencies—for this reason, they were called “cooperator reports.” This Open-File Report concatenates four such products issued in 2017 and 2018 into a single publication. These reports have been reformatted and lightly edited for clarity, but the content has not otherwise been changed from the versions first publicly released.
Monotonicity analysis of absolute phase unwrapping by geometric constraint in a structured light system

The monotonicity of depth in a geometric constraint based absolute phase unwrapping is analyzed and a monotonic discriminant of Δ( u c , v c ) is presented in this paper. The sign of the discriminant determines the distance selection for the virtual plane to create the artificial absolute phase map for a given structured light system. As Δ( u c , v c ) ≥ 0 at an arbitrary point on the CCD pixel coordinates the minimum depth distance is selected for the virtual plane, and the maximum depth distance is selected as Δ( u c , v c ) ≤ 0. Two structured light systems with different signs of the monotonic discriminant are developed and the validity of the theoretical analysis is experimentally demonstrated.
Clustering with Fuzzy C-means and Common Challenges

Abstract Clustering algorithm is massively used in various fields such as computer vision and so do FCM. One of the purpose of this paper is to generate new insight into improvement on general clustering algorithms through this inspection of one specific clustering algorithm (FCM) help. Three common challenges of clustering (Noise problem, long operational time, and initial bias) in Fuzzy C-Means algorithm and corresponding solutions to each of these problems are introduced. Further potential of meta learning and adversarial learning to solve the clustering problems are recognized in the end.
Lyndon Words versus Inverse Lyndon Words: Queries on Suffixes and Bordered Words

The Lyndon factorization of a word has been extensively studied in different contexts and several variants of it have been proposed. In particular, the canonical inverse Lyndon factorization $$\mathrm {ICFL}$$ , introduced in [5], maintains the main properties of the Lyndon factorization since it can be computed in linear time and it is uniquely determined. In this paper we investigate new properties of this factorization with the purpose of exploring its use in string queries. As a main result, we prove an upper bound on the length of the longest common extension (or longest common prefix) for two factors of a word w. This bound is at most the maximum length of two consecutive factors of $$\mathrm {ICFL}(w)$$ . A tool used in the proof is a property that we state for factors with nonempty borders in $$\mathrm {ICFL}(w)$$ : a nonempty border of a factor $$m_i$$ cannot be a prefix of the next factor $$m_{i+1}$$ . Another interesting result relates sorting of global suffixes, i.e., suffixes of a word w, and sorting of local suffixes, i.e., suffixes of the factors in $$\mathrm {ICFL}(w)$$ . Finally, given a word w and a factor x of w, we prove that their Lyndon factorizations share factors, except for the first and last term of the Lyndon factorization of x. This property suggests that, given two words sharing a common overlap, their Lyndon factorizations could be used to capture the common overlap of these two words.
Correlated Randomized Benchmarking

To improve the performance of multi-qubit algorithms on quantum devices it is critical to have methods for characterizing non-local quantum errors such as crosstalk. To address this issue, we propose and test an extension to the analysis of simultaneous randomized benchmarking data -- correlated randomized benchmarking. We fit the decay of correlated polarizations to a composition of fixed-weight depolarizing maps to characterize the locality and weight of crosstalk errors. From these errors we introduce a crosstalk metric which indicates the distance to the closest map with only local errors. We demonstrate this technique experimentally with a four-qubit superconducting device and utilize correlated RB to validate crosstalk reduction when we implement an echo sequence.
Augmented reality technology as a new source of competitive strengths in engineering products

The article analyzes opportunities for obtaining additional competitive strengths in engineering products due to the use of augmented reality technology for interactive information provision and support in terms of assembly, operation and maintenance for the products themselves as well as machinery units and production system components employed in the processes listed. The article also provides a short overview of equipment available on the market that can be viewed as a prototype for hardware and software complexes that provide informational support for technical personnel while the latter performs its job functions. It covers prospects of implementing informational systems that provide the operator with visualized supporting content online through the use of AR technology into the life cycle stages, processes of production, maintenance and operation of products and machinery equipment, as well as specialized applications. It also analyzes the necessary functional options and scenarios of using these applications, thus explaining the point of implementing them not as separate software products oriented on separate product units and equipment, but in the context of a unified information service of support for the production process, basically allowing the formation of a new production engineering instrument that goes along with the concept of the fourth technological revolution - reference-informational AR systems, the concept of use for which is universal to all industries and the consumer sector of the national economy. It analyzes the main limitations of the wide use of informational systems based on AR technology and gives grounds for its lessening midterm influence, based on, including but not limited to, the options for implementing new architectural solutions that are supported by the developing wireless data transfer technology (5G standard) as well as video compressing and transfer standards (MPEG-I). Based on the analysis of materials provided, rationale for implementing AR technology into the stages of machinery production and equipment life cycle is given, as well as new opportunities for obtaining their additional competitive strengths and relevance of information security of the corresponding reference-informational systems based on AR technology.
On the Weights of General MDS Codes

The weight spectra of MDS codes of length $ n $ and dimension $ k $ over the arbitrary alphabets are studied. For all $ q $-ary MDS codes of dimension $ k $ containing the zero codeword, it is shown that all $ k $ weights from $ n $ to $ n-k+1 $ are realized. The remaining case $ n=q+k-1 $ is also determined. Additionally, we prove that all binary MDS codes are equivalent to linear MDS codes. The proofs are combinatorial, and self contained.
Identification of Bacterial Leaf Blight and Brown Spot Disease In Rice Plants With Image Processing Approach

In agriculture, technology can provide benefits to farmers.However, at present there are still very few farmers who use technology, especially computerization in their agricultural processes, such as the identification of diseases in rice plants, there are still many rice farmers who cannot recognize and distinguish the types of diseases in rice plants.Research on the identification of bacterial leaf blight and brown spots on rice plants has carried out before, but the accuracy rate is only 70%.This research developed a system to identify bacterial leaf blight and brown spot in rice plants through leaf images with an image processing approach.Image of affected rice leaves is segmented first using K-Means Clustering, then the texture features are extracted using the Gray Level Co-Occurrence Matrix (GLCM) with features extracted in the form of energy, contrast, correlation, homogeneity and shape pattern characteristics using metric and eccentricity features, then identified using Euclidean Distance.The training data used 40 images for each disease and 12 images for each disease.The test results show that the system has a better level of accuracy than previous studies that reached 100% with a Mean Squared Error (MSE) value of 0.007282214.
An improved gravitational search algorithm for solving an electromagnetic design problem

The gravitational search algorithm (GSA) is a novel optimization technique that relies upon the law of motion and law of gravity of masses to describe the interaction between the agents. The GSA has shown outstanding performance but suffers from the drawback of a slow process due to the dependence of the fitness function on the masses of the agents. As a result, after each iteration, the masses get heavier, restricting their movement. Due to this effect, the masses cancel out the gravitational forces on each other, preventing them from finding the optimum quickly. To overcome this limitation, an improved GSA based on a modified exploitation strategy is proposed herein. The primary aim of this modification is to enhance the performance of the algorithm in terms of faster convergence and avoidance of premature convergence. An electromagnetic optimization problem is used to validate the performance of the presented method. The simulation results confirm that the proposed method provides outstanding results in solving Loney’s solenoid design problem and that the stability of the solution is much better compared with those obtained using the standard gravitational search algorithm or various other state-of-the-art techniques that have previously been applied to solve this problem.
Review and Systematization of Solutions for 3D Object Detection

Since 2017 there has been an exponential growth in scientific publications regarding the field of 3D object detection (3DOD). On the one hand, this growth can be explained by the strong demand for autonomous vehicles, and on the other hand, by the wide availability of 3D sensors. Due to the strong heterogeneity of developed approaches, this paper aims to identify, analyze and systematize publications that propose end-to-end solutions for 3DOD towards the goal to provide a structured framework which can guide future development, evaluation and application activities. To carry out the research, a systematic literature review is combined with a taxonomy development approach. The resulting framework consists of six dimensions, covering the addressed domains, applied datasets, sensor properties, data representation formats, modeling techniques, and evaluation criteria. The taxonomy can help researchers and practitioners to get a quick overview about the field by decomposing 3DOD solutions into more manageable pieces.
AGE ESTIMATION USING SPECIFIC DOMAIN TRANSFER LEARNING

Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many learning tasks such as face recognition and detection. However, the automatic estimation of human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. In this paper, we attempt to obtain a satisfied model that overcomes the overfitting problem, by fine-tuning CNNs model which was pretrained on face recognition task to estimate the real age. To make the model more robust, we evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset we achieved 3.446 of MAE, on the unconstrained UTKFace dataset we achieved 4.867 MAE. The experimental results of our approach outperform other state-of-the-art age estimation models on the benchmark datasets. We also fine-tuned the model for age groups classification task on Adience dataset, and our model achieved an accuracy of 61.4.
RIF: Reactive information flow labels

Restrictions that a reactive information flow (RIF) label imposes on a value are determined by the sequence of operations used to derive that value. This allows declassification, endorsement, and other forms of reclassification to be supported in a uniform way. Piecewise noninterference (PWNI) is i ntroduced as a fitting security policy, because noninterference is not suitable. A type system is given for static enforcement of PWNI in programs that associate checkable classes of RIF labels with variables. Two checkable classes of RIF labels are described: RIF automata are general-purpose and based on finite-state automata; κ-labels concern confidentiality in programs that use cryptographic operations.
Toward Cross-Domain Speech Recognition with End-to-End Models

In the area of multi-domain speech recognition, research in the past focused on hybrid acoustic models to build cross-domain and domain-invariant speech recognition systems. In this paper, we empirically examine the difference in behavior between hybrid acoustic models and neural end-to-end systems when mixing acoustic training data from several domains. For these experiments we composed a multi-domain dataset from public sources, with the different domains in the corpus covering a wide variety of topics and acoustic conditions such as telephone conversations, lectures, read speech and broadcast news. We show that for the hybrid models, supplying additional training data from other domains with mismatched acoustic conditions does not increase the performance on specific domains. However, our end-to-end models optimized with sequence-based criterion generalize better than the hybrid models on diverse domains. In term of word-error-rate performance, our experimental acoustic-to-word and attention-based models trained on multi-domain dataset reach the performance of domain-specific long short-term memory (LSTM) hybrid models, thus resulting in multi-domain speech recognition systems that do not suffer in performance over domain specific ones. Moreover, the use of neural end-to-end models eliminates the need of domain-adapted language models during recognition, which is a great advantage when the input domain is unknown.
Modulation of oculomotor control during reading of mirrored and inverted texts

Abstract The interplay between cognitive and oculomotor processes during reading can be explored when the spatial layout of text deviates from the typical display. In this study, we investigate various eye-movement measures during reading of text with experimentally manipulated layout (word-wise and letter-wise mirrored-reversed text as well as inverted and scrambled text). While typical findings (e.g., longer mean fixation times, shorter mean saccades lengths) in reading manipulated texts compared to normal texts were reported in earlier work, little is known about changes of oculomotor targeting observed in within-word landing positions under the above text layouts. Here we carry out precise analyses of landing positions and find substantial changes in the so-called launch-site effect in addition to the expected overall slow-down of reading performance. Specifically, during reading of our manipulated text conditions with reversed letter order (against overall reading direction), we find a reduced launch-site effect, while in all other manipulated text conditions, we observe an increased launch-site effect. Our results clearly indicate that the oculomotor system is highly adaptive when confronted with unusual reading conditions.
PushNet: Efficient and Adaptive Neural Message Passing

Message passing neural networks have recently evolved into a state-of-the-art approach to representation learning on graphs. Existing methods perform synchronous message passing along all edges in multiple subsequent rounds and consequently suffer from various shortcomings: Propagation schemes are inflexible since they are restricted to $k$-hop neighborhoods and insensitive to actual demands of information propagation. Further, long-range dependencies cannot be modeled adequately and learned representations are based on correlations of fixed locality. These issues prevent existing methods from reaching their full potential in terms of prediction performance. Instead, we consider a novel asynchronous message passing approach where information is pushed only along the most relevant edges until convergence. Our proposed algorithm can equivalently be formulated as a single synchronous message passing iteration using a suitable neighborhood function, thus sharing the advantages of existing methods while addressing their central issues. The resulting neural network utilizes a node-adaptive receptive field derived from meaningful sparse node neighborhoods. In addition, by learning and combining node representations over differently sized neighborhoods, our model is able to capture correlations on multiple scales. We further propose variants of our base model with different inductive bias. Empirical results are provided for semi-supervised node classification on five real-world datasets following a rigorous evaluation protocol. We find that our models outperform competitors on all datasets in terms of accuracy with statistical significance. In some cases, our models additionally provide faster runtime.