Reviewerly/exHarmony huggingface dataset:
A Path Planning Learning Strategy Design for a Wheel-Legged Vehicle Considering Both Distance and Energy Consumption

To obtain an optimal feasible path, this paper presents a path planning learning strategy for a wheel-legged vehicle considering both distance and energy consumption. Firstly, a new reward function and update rule of the Q-Learning algorithm, considering the influence of obstacle crossing parameters and modification of path energy consumption, is presented to ensure the path shortening and energy consumption reduction. Secondly, the future energy consumption is introduced into the modification of path energy consumption. It evaluates the potential energy consumption between each state reached by the vehicle and the target state. The priority sequence of Q table update is provided, which greatly speeds up the convergence speed of the algorithm. Finally, the proposed strategy is verified on different size maps with 0-1m obstacle height. Results show that, in the complex map, the proposed strategy is effective to shorten 7.6m distance compared with the wheeled driving strategy and to reduce energy consumption by 31% compared with the wheel-legged obstacle crossing strategy. It has a faster convergence speed. Compared with the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> -based and Dijkstra-based strategies, their planning effect is approximately the same, but the energy consumption using the proposed strategy can be reduced by 3.5%.
Validitas Media Pembelajaran E-learning Berbasis Edmodo tentang Materi Genetika

The rapid development of technology and information affect many fields, including education. In field of education, the development of technology and information can be observed in the use of e-learning in learning activities. E-learning is a systems that allows learning activities to occur without being limited by space and time so tha learning activities can be carried out even though teachers and students do not meet face to face. One of the easiest to use e-learning is edmodo. Edmodo is an e-learning platform that has easy acces like the facebook social media platform. Research on edmodo was carried out with the aim of producing valid edmodo learning media. The research was carried out using the Research and Development (R&amp;D) method with 4-D model. Validation is carried out on four main aspect, namely the feasibility of content, language, presentation, and graphics. From the validation process on these four aspects, the validity value is 89,51% with valid category. Therefore, the edmodo learning media that was developed was valid.
Database Forensics

This chapter provides a high-level overview on databases including relational databases, database management systems and the structure query language (SQL). A database forensic process is also described, following by some examples of the database investigation. Concepts presented in this chapter are necessary for understanding the later chapters in this book.
Chaotic Maps Based Video Encryption: A New Approach

This study proposes a new encryption method for safe video transmission. The approach introduced in this paper is based on MPEG-2 compression and several chaotic maps. The Arnold map is used to encrypt an explicit chosen frame, which is then XORed with the encrypted video frames produced by the Skew Tent map. This map achieves high performance and low time consuming. Furthermore, bit shifts of pixel values are utilized to produce a more uniform histogram for the encrypted video, improve the encryption scheme’s speed, and boost security. To reduce processing time before the encryption process begins, the row vector method is applied. According to the experimental results, the encrypted video exhibits low correlation coefficients between adjacent pixels, excellent entropy, a decent histogram, a low time consumption, and resistance to differential assaults, additive noise, and cropping attacks. The average correlation coefficients between pixels are obtained as -0.0133, -0.0155 and 0.0037 for horizontal, vertical and diagonal components for foreman frame. Moreover, the entropy for the encrypted foremen frame is 7.3348. Furthermore, the processing time 0.7015 s and 1.81 s, respectively, for encryption and decryption.
Spatio-Temporal Graph-based Semantic Compositional Network for Video Captioning

Video Captioning aims to generate natural language descriptions for given videos and is one of the challenging problems in computer vision's high-level understanding tasks. Existing methods are relatively lacking in the mining of object-level spatio-temporal relationships, which is important for generating captions with accurate object information. In this paper, we improve the existing SCN-LSTM method from the perspective of modeling spatio-temporal relationships and propose the Spatio-Temporal Graph-based Semantic Compositional Network for Video Captioning (STG-SCN). In terms of spatial-temporal relationships modeling, we propose the Spatial Relation Graph (SRG) and the Temporal Relation Graph (TRG) based on the Graph Attention Network, respectively. SRG is employed to establish the spatial relationships between spatially Neighboring objects within each keyframe conditioned on their correlation with the current keyframe. TRG is used to model the temporal relationship between all the objects at different time steps and incorporates the object-level information into frame-level features. Based on the proposed Semantics Guided Decoder, visual representations enhanced by object-level information are dynamically fused with high-level semantic concepts to generate captions that not only consider the global visual content but also have stronger language expressiveness. Extended experiments show that our proposed method achieves significant performance gains on Microsoft Video Description (MSVD) and Microsoft Research Video-to-Text (MSR-VTT) datasets, outperforming existing methods.
Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation

As human-robot collaboration increases in the workforce, it becomes essential for human-robot teams to coordinate efficiently and intuitively. Traditional approaches for human-robot scheduling either utilize exact methods that are intractable for large-scale problems and struggle to account for stochastic, time varying human task performance, or application-specific heuristics that require expert domain knowledge to develop. We propose a deep learning-based framework, called HybridNet, combining a heterogeneous graph-based encoder with a recurrent schedule propagator for scheduling stochastic human-robot teams under upper- and lower-bound temporal constraints. The HybridNet's encoder leverages Heterogeneous Graph Attention Networks to model the initial environment and team dynamics while accounting for the constraints. By formulating task scheduling as a sequential decision-making process, the HybridNet's recurrent neural schedule propagator leverages Long Short-Term Memory (LSTM) models to propagate forward consequences of actions to carry out fast schedule generation, removing the need to interact with the environment between every taskagent pair selection. The resulting scheduling policy network provides a computationally lightweight yet highly expressive model that is end-to-end trainable via Reinforcement Learning algorithms. We develop a virtual task scheduling environment for mixed human-robot teams in a multi-round setting, capable of modeling the stochastic learning behaviors of human workers. Experimental results showed that HybridNet outperformed other human-robot scheduling solutions across problem sizes for both deterministic and stochastic human performance, with faster runtime compared to pure-GNN-based schedulers.
A First Look at Information Highlighting in Stack Overflow Answers

Technical question and answer (Q&A) sites such as Stack Overflow (SO) have become increasingly important for software developers to share knowledge and contribute to communities. Despite their success and prevalence, navigating the knowledge on these sites remains challenging. To make the posts vivid to users, the Stack Overflow platform allows users to write and edit posts with Markdown or HTML, so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information.In this study, we carry out the first large-scale exploratory study on the information highlighting in SO answers. We observe that overall, information highlighting is prevalent on SO, i.e., 47.6% of the answers have information highlighted. More specifically, 38.5%, 11.3%, 7.2% of the answers use Code, Bold, and Italic, respectively. Besides source code related content (e.g., identifiers, and programming keywords), users also frequently highlight updates (e.g., updates of answers), caveats (i.e., a reminder or warn of in which context or condition the provided solution works or does not work), and reference. Our findings provide insights for future research, e.g., future research should consider the highlighted content for the downstream tasks that leverage information from the SO answers.
Properties of SAT Formulas Characterizing Convex Sets with Given Projections

One of the most interesting and challenging problems in Discrete Tomography concerns the faithful reconstruction of an unknown finite discrete set from its horizontal and vertical projections. The computational complexity of this problem has been considered and solved in case of horizontal and vertical convex polyominoes, by coding the possible solutions through a 2-SAT formula. On the other hand, the problem is still open in case of (full) convex polyominoes. As a matter of fact, the previous polynomial-time reconstruction strategy does not naturally generalize to them. In particular, it has been observed that the convexity constraint on polyominoes involves, in general, a k-SAT formula $$\varphi $$ , preventing, up to now, the polynomiality of the entire process, assuming that $$P \ne NP$$ . Our studies focus on the clauses of $$\varphi $$ . We show that they can be reduced to 2-SAT or 3-SAT only and that a subset of the variables involved in the reconstruction may appear in the 3-SAT clauses of $$\varphi $$ , thus detecting some situations that lead to a polynomial time reconstruction. Some examples of situations where 3-SAT formulas arise are also provided.
KEMTLS vs. Post-quantum TLS: Performance on Embedded Systems

TLS is ubiquitous in modern computer networks. It secures transport for high-end desktops and low-end embedded devices alike. However, the public key cryptosystems currently used within TLS may soon be obsolete as large-scale quantum computers, once realized, would be able to break them. This threat has led to the development of post-quantum cryptography (PQC). The U.S. standardization body NIST is currently in the process of concluding a multi-year search for promising post-quantum signature schemes and key encapsulation mechanisms (KEMs). With the first PQC standards around the corner, TLS will have to be updated soon. However, especially for small microcontrollers, it appears the current NIST post-quantum signature finalists pose a challenge. Dilithium suffers from very large public keys and signatures; while Falcon has significant hardware requirements for efficient implementations. KEMTLS is a proposal for an alternative TLS handshake protocol that avoids authentication through signatures in the TLS handshake. Instead, it authenticates the peers through long-term KEM keys held in the certificates. The KEMs considered for standardization are more efficient in terms of computation and/or bandwidth than the post-quantum signature schemes. In this work, we compare KEMTLS to TLS 1.3 in an embedded setting. To gain meaningful results, we present implementations of KEMTLS and TLS 1.3 on a Cortex-M4-based platform. These implementations are based on the popular WolfSSL embedded TLS library and hence share a majority of their code. In our experiments, we consider both protocols with the remaining NIST finalist signature schemes and KEMs, except for Classic McEliece which has too large public keys. Both protocols are benchmarked and compared in terms of run-time, memory usage, traffic volume and code size. The benchmarks are performed in network settings relevant to the Internet of Things, namely low-latency broadband, LTE-M and Narrowband IoT. Our results show that KEMTLS can reduce handshake time by up to 38%, can lower peak memory consumption and can save traffic volume compared to TLS 1.3.
Trade and Foreign Direct Investment in Tanzania: Do they Matter for Economic Growth?

This paper analyses the effect of trade and foreign direct investments (FDI) on Tanzania’s economic growth using time-series data from 1970 to 2019. All the variables are found to be integrated of order one, I (1). The study applies the cointegration test and a VECM accordingly. The Johansen test underscores the presence of two co-integrating equations, which confirms the long-run associations between variables. The VECM demonstrates the presence of a long-run relationship running from FDI, TRD, and EXR to GDP growth. While the Wald test reveals the presence of short-run causality running from FDI and TRD to GPD; however, there is no short-run causality from EXR to GDP. The study concludes that there is a positive relationship between the explanatory variables and economic growth. Therefore, the Tanzanian government should encourage exports to realize the potential effects of trade and FDI on economic growth.&#x0D; JEL Classification: O4, F1, FE
The Implementation of Artificial Neural Network (ANN) on Offline Cursive Handwriting Image Recognition

Identifying a writing is an easy thing to do for human, but this does not apply to computers, in particular if it is handwriting. Handwriting recognition, especially cursive handwriting is a research in the area of image processing and pattern matching that is challenging to complete, following the different characteristics of each person's cursive handwriting style. In this study, the use of the ANN model will be implemented in performing offline handwriting image recognition. The cursive handwriting image that has been obtained is then preprocessed and segmented using bounding box rectangle and contour techniques. Evaluation of system performance using global performance metrics in this study resulted in a percentage of 93% where the bounding box and contour succeeded in determining the segmentation point correctly, so that the ANN model worked optimally.
Conscious choice of profession as the dominant motive of studying at the university

Introduction. Highly qualified specialists with higher education are the basis of the economy of any modern state. The choice of a future profession by an individual is an important process for the individual and society. Regardless of the type of competition, resources (time, money) for the training of an expelled student or graduate who does not work by profession are spent inefficiently. Reducing negative consequences is impossible without identifying the reasons for expulsion or employment not in the specialty. Therefore, issues related to the choice of profession, motivation for training and professional activity are relevant. The purpose of the study: to identify the types of students with a high risk of expulsion based on several criteria, including the quality of career guidance. Methodology and methods of research: Theoretical and methodological foundations of research are systemic and institutional approaches. 551 students studying in various fields of study at Perm National Research Polytechnic University (277 girls and 274 boys) took part in the survey. Methods of mathematical statistics: correlation analysis, analysis of conjugacy tables, typological analysis, criterion χ2. The results of the study confirm the relationship between the passage of career guidance measures and the independence of choosing a future profession (r = 0.709). The study allowed us to construct a typology of students according to the criteria of work plans and the availability of career guidance. There are 4 types of respondents: "professionally oriented", "self-determined", "disappointed" and "mistaken". The scientific novelty lies in the fact that the criteria for identifying groups with a high risk of leaving the university, specific to the Russian sample of students, have been identified. The practical significance lies in the possibility of using the data obtained for timely identification of risk groups among students of different courses and carrying out comprehensive measures to reduce the risk of expulsion.
CRYTICAL ANALYSIS OF MORPHEMES IN WILLIAM SHAKESPEARE POEMS

The main thing that must be learned in mastering a language is words. Words are related to morphology because morphology affects spelling, reading comprehension, and vocabulary. In understanding the meaning of vocabulary, language users need to study morphology, especially derivational and inflectional morphemes. This research is designed to identify and explain the process of derivational and inflectional morphemes in the poems. This study used the descriptive qualitative method. The data of this research is the poetries of William Shakespeare entitled All the World’s A Stage and Fidele ((Fear no more the heat o’ the sun). The results showed that there were 61 morphemes consisting of 18 derivational morphemes and 43 inflectional morphemes. The meaning of derivational and inflectional are derivational prefix: un-, derivational suffix: -ly, -er, -ful, -ish, -ness, -ous, -ing, -ed, and –tion; and inflectional suffix: -s, -ed, irregular verb, -‘s, -ing.
Delay-Aware and Profit-Maximizing Task Migration for the Cloudlet Federation

As the result of Open Edge Computing (OEC) project, cloudlet embodies the middle layer of edge computing architecture.Due to the close deployment to the user side, responding to user requests through cloudlet can reduce delay, improve security, and reduce bandwidth occupancy.In order to improve the quality of user experience, more and more cloudlets need to be deployed, which makes the deployment and management costs of Clouldlet service Providers (CLP) significantly increased.Therefore, the cloudlet federation appears as a new paradigm that can reduce the cost of cloudlet deployment and management by sharing cloudlet resources among CLPs.Facing the cloudlet federation scenario, more attention still needs to be paid to the heterogeneity of resource prices, the balance of benefits among CLPs, and the more complex delay computation when exploring task migration strategies.For delay-sensitive and delay-tolerance tasks, a delay-aware and profit-maximizing task migration strategy is proposed considering the relationship between the delay and the quotation of different tasks, as well as the dynamic pricing mechanism when resources are shared among CLPs.Experimental results show that the proposed algorithm outperforms the baseline algorithm in terms of revenue and response delay.
A Convergence Study of Firefly Algorithm

In Recent era various optimization dilemmas are usually resolved by applying nature stimulated and meta-heuristic method. Firefly algorithm is one of them wherein male fireflies are supervised by two randomly selected female fireflies to give attention on global search, which provides repaid exploration and exploitation. Firefly algorithm is a population based algorithm it is widely used due to its simple search process and easy way to implementation. In this paper, Lightning bug (Firefly) is applied over fourteen bench mark function including multi-model and uni-model for examine the global optimal solution. Convergence analysis has been carried out effectively after various simulation experiments.
A Critical Look into Quantization Table Generalization Capabilities of CNN-based Double JPEG Compression Detection

Double JPEG compression detection has become a core issue in image forensics, as it provides information about the processing history of the image and its authenticity. Several recent works address this problem by exploiting the potential of CNNs to achieve state-of-the-art performance on test datasets. Unfortunately, those schemes are typically tailored to their specific training conditions and suffer a significant drop of performance in real-life scenarios. This paper aims at assessing the influence of quantization table mismatch (with regards to those seen in training) in the detection of double JPEG compression. Experimental results show inconsistency between different sets of quantization tables, with trained models yielding significantly worse results on unknown sets. This effect is also evident in a more realistic setting, where it appears to be more noticeable for sources falling in operating regions with greater inconsistency.
Systematic Literature Review on Application of Artificial Intelligence in Cancer Detection Using Image Processing

Having a low survival rate, cancer has become one of the deadliest diseases with complicated prognosis. In this matter, Artificial Intelligence (AI) is frequently used to aid humans due to the difficulties of cancer identification. Specifically, the use of AI in cancer detection and treatment uses two of its subclasses, machine learning and deep learning. Because those two subclasses can handle large amounts of data, their algorithms can support humans in making decisions that yield the best possible results with the least amount of risk. The purpose of this research is to find out the effectiveness of the Artificial Intelligence algorithm that is used in image processing to diagnose cancer and what is the most effective image processing algorithm for cancer diagnosis. The methods that are used in this research is Systematic Literature Review (SLR). As a result, Artificial Intelligence algorithms are sufficiently effective to be used in diagnosing cancer with image processing. Meanwhile, the most effective algorithm is CatBoost with AUC of 0.939 and Support Vector Machine (SVM) with AUC of 0.934.
Distributed storage and retrieval of massive remote sensing images

With the continuous development of remote sensing technology, remote sensing images show multi-source, massive and high resolution, resulting in exponential growth of remote sensing data, which brings great challenges to image pyramid storage and retrieval. In order to solve the problem of low storage and query efficiency of tile data, the Z-Curve index is proposed in this paper by improving the Z curve. This paper uses the distributed Accumulo database for storage. The experiments show that this method can effectively improve the efficiency of tile data query.
Application of Computer Assisted Instruction to the Development of Hypermedia Learning

Hypermedia is a medium that has a composition of materials that are not sequential.Using integrated text, graphics, video, and audio elements in computer software is known as hypermedia, which can make it simpler for users to switch between different types of information.Because of this, institutions will use multimedia, particularly with students who are aspiring teachers, to emphasize the importance of having strong skills before entering the sector.The Covid-19 pandemic issue has also forced the field of education, particularly in learning, to adjust to the current circumstances.Where internet learning is still practiced.Implementing a new idea that facilitates the management of the educational process is therefore crucial.This study was done to construct learning hypermedia using computer assisted instruction (CAI), which is appropriate for use in learning, in order to alleviate these issues.