Reviewerly/exHarmony huggingface dataset:
A Bipedal detection and tracking algorithm using 2D LiDAR

Human tracking is one of the most widely used applications today, with a diverse range of uses. It is a wide area for research and development. Many applications in various fields have been created based on human tracking technology. People approaching restricted areas, following a person who engages in unusual or suspicious behaviour, and, most notably, in the security department of a bank or other official location, this human tracking aids in the quick identification of suspects. Indoor localisation and monitoring of human beings is a trendy topic to explore in human tracking. A profusion of algorithms and approaches for outdoor person tracking have been created, and they have a broad range of applications. However, indoor person tracking is more challenging and distinct from outdoor human monitoring. Indoor localization is tough since it requires delocalizing all stationary items and localising humans. We have separate approaches for indoor localisation that leverage wifi or bluetooth technology and several sorts of on-body sensors. The use of a LiDAR sensor to circumvent this form of on-body sensor is proposed.
On the Complexity of Heuristic Synthesis for Satisficing Classical Planning: Potential Heuristics and Beyond

Potential functions are a general class of heuristics for classical planning. For satisficing planning, previous work suggested the use of descending and dead-end avoiding (DDA) potential heuristics, which solve planning tasks by backtrack-free search. In this work we study the complexity of devising DDA potential heuristics for classical planning tasks. We show that verifying or synthesizing DDA potential heuristics is PSPACE-complete, but suitable modifications of the DDA properties reduce the complexity of these problems to the first and second level of the polynomial hierarchy. We also discuss the implications of our results for other forms of heuristic synthesis in classical planning.
Small band rooms for large bands: Balancing loudness and musicality

When band rooms are smaller than ideal for the ensembles they support, there are safety concerns due to loudness. In the case study presented, students have experienced excessive loudness and difficult ensemble conditions due to inadequate ceiling height, absorption, and volume. This paper will discuss the strategies explored to increase acoustic volume to the extent possible within floorplate boundaries while finishing the space with a combination of absorption and diffusion. The goal of these interventions is to preserve ensemble and a sense of musicality to assure students and faculty they are working in a space designed for music rather than simply the control of loudness.
A Cybertwin-Driven Intelligent Offloading Method for IoV Applications Using DRL in Smart Cities

Internet of Vehicle (IoV) is considered as the enabling platform to improve the traffic condition in cities. Meanwhile, a variety of promising applications such as self-driving and route planning are realized, which generates various latency-critic tasks. These latency-critic tasks present high demands on computing capacity, while resource-restricted vehicles could not satisfy those requirements. Mobile edge computing (MEC) is emerging as a potential solution to mitigate resource-limited vehicles from latency-critic tasks, which enables vehicles to offload tasks to nearby road-side units. Besides, cybertwin can abstract IoV system into a digital entity to facilitate obtaining information and management. However, it is still challenging to leverage cybertwin technology to optimize offloading policy. To this end, we design a deep reinforcement learning method to train the optimal decision-making agent by a cybertwin-driven IoV system, which aims to minimize the system cost under latency constraints. The experimental results illustrate that the agent trained by our proposed method outperforms the other methods.
The optimal attack-defense scheme for secret sharing

The attack-defense issue of secret sharing has been a pressing cryptography research need. For solving this issue, this study proposes an optimal secret-share attack-defense model with game theory and information theory. This study constructs an attack channel for attackers and a defense channel for defenders. Then, we propose a game model that utilizes mutual information to measure the attack and defensive capabilities of attackers and defenders. Next, this study proves that there is a Nash equilibrium in this game model by analyzing the strategies of each attacker/defender. Finally, we analyze the change of k value and attack and defensive capabilities. Moreover, we also prove the best attack and defensive strategies.
ESSM: an extractive summarization model with enhanced spatial-temporal information and span mask encoding

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To verify the effectiveness of the model, experiments on datasets SQuAD1.1 and SQuAD2.0 are conducted. Our model achieved an EM of 86.31% and a F1 score of 92.49% on SQuAD1.1 and the numbers are 80.54% and 83.27% for SQuAD2.0. It was proved that the model is effective for extractive QA task.
Comparative Analysis and Design of Different Approaches for Twitter Sentiment Analysis and classification using SVM

Companies and organizations have always found that the views and feedback of the community are their most important and valuable resource. With everyone using social media more and more, it makes it possible to analyze and evaluate things in ways that have never been done before. Before, organizations had to use methods that were unusual, time-consuming, and prone to mistakes. This way of analyzing fits right into the field of "sentiment analysis." Sentiment analysis is a broad field that deals with putting user-generated text into well-defined groups. There are a number of tools and algorithms that can be used to detect and analyze sentiment. For example, supervised machine learning algorithms can be trained with training data and then used to classify the target corpus. Lexical techniques, which use a dictionary-based annotated corpus to do classification, and hybrid tools, which are a mix of machine learning and lexicon-based algorithms, are also used. In this paper, we used Weka's Support Vector Machine (SVM) to analyze how people feel about something. SVM is a popular supervised machine learning algorithm used to find the polarity of text. The main objective is to analyze the emotions expressed in tweets using various simulations of artificial intelligence that classify tweets as positive or negative. If a tweet has both positive and negative components, the more prevalent component should be chosen as the closing statement. Emojis, usernames, and hashtags in tweets should be controlled and transformed into a standard development. Sincerely, these events' planners have started looking into these inconspicuous web blogs (online diaries) to acquire a feel for their niche. On other discreet sites, they routinely monitor and respond to customer feedback. Better means of seeing and combining a broad assessment are one challenge. A few people, including Facebook, Twitter, and Instagram, were really introduced to social affiliation stages a year ago. The vast majority of individuals utilize internet entertainment to express their thoughts about objects, places, or people. Systems Twitter, a less common platform for publishing material to blogs, is a huge repository of well-known reviews for various persons, services, associations, and products, among other things. Assessment examinations are reviews of the public assessment structures. What is said on Twitter has a substantial context thanks to a mixture of opinions. The widespread accessibility of online tests and virtual entertainment posts in the media provides connection with crucial examination to undermine expert judgments and direct their boosting strategies to relaxing and client conclusions. In this way, virtual distraction anticipates playing a significant role in influencing the general exposure of the companies or objects selected. This study highlights the many approaches used for item depiction analyses. Check topics on Twitter to see if the general public is acting in a favorable, negative, or neutral manner.
Software Controller using Hand Gestures

New technologies emerge in response to the passage of time. Robotic hand gesture control is one of them. Collaboration frameworks based on gestures are becoming increasingly popular in the business and at home. The approach we suggested can greatly reduce the utilization of hardware components such as a keyboard and mouse. The goal of this research is to create a system that can recognize hand gestures and use them as an input command to connect with a computer or laptop. According to a recent study, the use of CNN technology is still lacking in Hand Gesture Recognition. Our research aims to leverage CNN technology to recognize gestures in both static and dynamic modes, and then deploy the trained model in real-time applications.
Updates on Software Usability

<i>Updates on Software Usability</i> is a collection of high-quality contributions for developers and non-developers alike. Beyond the preliminaries, the book is organized into two other parts: “Designing for Usability” and “Testing for Usability”. The chapters in the second section, “Designing for Usability”, offer valuable insights and practical guidance to take into account during the early stages of product conception and development. On the other hand, the chapters in the third section, “Testing for Usability”, reflect and formalize software usability’s evaluation and validation processes. These two complementary views on the subject make this book a balanced and comprehensive volume, which the reader will undoubtedly find both interesting and useful.
A Preliminary Analysis of the Impact of Artificial Intelligence on World Peace

As inevitable historical phenomena at different stages of social politics, war and peace are eternal themes in international politics.The development of Artificial Intelligence (AI) promotes world peace and development but also brings challenges to the world.With the advancement of AI, the manpower-and-material-intensive wars have basically changed to be technology-intensive.Meanwhile, the battlefield of modern warfare has grown increasingly AI-featured, so that modern combat systems are not only a powerfully defensive system, but also a precisely targeting one.Combined with terrorism, AI might pose the biggest potential risk, seriously endangering world peace.
Source Free Domain Adaptation via Combined Discriminative GAN Model for Image Classification

The unsupervised domain adaptive classification task can learn domain-invariant features between the unlabeled target domain data and the labeled source domain data, thereby improving the classification performance of the classifier in the target domain. However, privacy protection and memory-constrained often make it difficult to obtain labeled source domain samples, which will bring bottlenecks to the existing domain adaptation tasks. To this end, we propose a novel source free domain adaptive classification model, that is, without any source domain data, a classifier with good performance in the target domain can be obtained only by using the source domain pre-trained classifier and the target domain data. The method first proposes a novel conditional information generative adversarial module based on combined discriminators. Through the confrontation between combined discriminators and the generator, the middle domain with pseudo-labels is generated to solve the problem of missing source domain. Then when training the new classifier in the domain adaptation module, we add a distillation loss mechanism to deal with the lack of source domain data supervision, thereby minimizing the difference between the old classifier response and the new classifier response to ensure that the network output retains the source domain information. We conducted experiments on three groups of 10 data sets, which proved that our method can effectively solve the problem of source free domain adaptive classification and effectively improve the classification accuracy of the model in each domain.
Robust 3D rotation invariant local binary pattern for volumetric texture classification

3D local binary pattern (LBP) shows significant performance in many domains such as solid textures analysis, face recognition and tumor detection. In recent years, rotation invariant 3D LBP texture descriptors have received increasing attention and several variants have been proposed. However, they are sensitive to the noise present in the image. In this paper, we propose an efficient rotation invariant texture descriptor known as robust extended 3D LBP (RELBP) for volumetric texture classification. Unlike the current 3D LBP framework, our descriptor uses the information of neighboring voxels to reduce noise. First, the 3D weighted average filter is employed to process each voxel in the image, in which the center voxel is replaced by the average local gray level based on weights. Besides, equidistant points on a sphere are sampled to construct a set of rotation invariant features. Our experiments demonstrate that the RELBP proposed here shows superior classification performance in texture classification tasks and our method is highly robust to image noise on benchmark datasets.
A comprehensive transfer news headline generation method based on semantic prototype transduction

Most current deep learning-based news headline generation models only target domain-specific news data. When a new news domain appears, it is usually costly to obtain a large amount of data with reference truth on the new domain for model training, so text generation models trained by traditional supervised approaches often do not generalize well on the new domain-inspired by the idea of transfer learning, this paper designs a cross-domain transfer text generation method based on domain data distribution alignment, intermediate domain redistribution, and zero-shot learning semantic prototype transduction, focusing on the data problem with no reference truth in the target domain. Eventually, the model can be guided by the most relevant source domain data to generate headlines from the target domain news text through the semantic correlation between source and target domain data during the training process of generating headlines for the target domain news, even without any reference truth of the news headlines in the target domain, which improves the usability of the text generation model in real scenarios. The experimental results show that the proposed transfer text generation method has a good domain transfer effect and outperforms other existing transfer text generation methods in various text generation evaluation indexes, proving the proposed method's effectiveness in this paper.
Modified-residual network for maize stalk rots diseases classification

In this article, image classification of maize stalk rots diseases was carried out. The experiment used primary data taken from maize plantations in Bangkalan, Madura. The data consists of three classes: healthy, anthracnose, and gibberella. For deep learning experiments, we augmented the primary data. The total data was 2,211 images. An investigation is composed of three sections. First, we used five different Convolutional Neural Network (CNN) architectures, and second, the best CNN will be modified. Finally, it performed varied layer types from the second section. The parameters used were epoch 10, learning rate 3.10e-4, and minibatch-size 64. The distribution of training, validation, and testing data were 40:40:20. The result shows the best performance for the first section is ResNet18. Next step, we modify ResNet18 into six different architectures. From the second section, the best results were ResNet18 and modified-ResNet, but modified-ResNet has less number of parameters. The third section’s results showed accuracy, precision, and recall were 99.55%, 99.53%, and 99.73%, respectively. The modified-ResNet architecture is suitable for classifying maize stalk rots diseases.
Eddo Rigotti and Sara Greco: Inference in Argumentation. A Topics-Based Approach to Argument Schemes

The volume under review contains a study of what in the scholarship is known as the Argumentum Model of Topics (AMT for short). The AMT, which builds on the pragma-dialectical approach to argumentation that was developed by van Eemeren and Grootendorst (see esp. van Eemeren 2018; van Eemeren and Grootendorst 2004), is a model for the study of argument schemes.
Extensions of the Diffie-Hellman Key Agreement Protocol Based on Exponential and Logarithmic Functions

We propose a method of constructing cryptographic key exchange protocols of the Diffie-Hellman type based on the exponential and logarithmic functions over the multiplicative group of integers modulo n. The security of the proposed protocols is based on the computational difficulty of solving a set of congruence equations containing a discrete logarithm. For the multiplicative group of integers modulo n we define the non-commutative group of their automorphisms. On the defined group we construct non-commutative key exchange protocol similar to the Anshel-Anshel-Goldfeld key exchange scheme.
Detecting Unsolvability Based on Separating Functions

While the unsolvability IPC sparked a multitude of planners proficient in detecting unsolvable planning tasks, there are gaps where concise unsolvability arguments are known but no existing planner can capture them without prohibitive computational effort. One such example is the sliding tiles puzzle, where solvability can be decided in polynomial time with a parity argument. We introduce separating functions, which can prove that one state is unreachable from another, and show under what conditions a potential function over any nonzero ring is a separating function. We prove that we can compactly encode these conditions for potential functions over features that are pairs, and show in which cases we can efficiently synthesize functions satisfying these conditions. We experimentally evaluate a domain-independent algorithm that successfully synthesizes such separating functions from PDDL representations of the sliding tiles puzzle, the Lights Out puzzle, and Peg Solitaire.
A hybrid model to detect phishing-websites

Phishing and fraud sites has been widespread on the internet in recent times, in which that it is becoming a source of great concern and a serious cyber security problem, as internet fraudsters target sensitive data and personal information of users, especially the username and password. Numerous approaches has been proposed and used in order to prevent and reduce these phishing websites and attacks, and protect users and their privacy. In this paper, we proposed an anti-phishing system based on machine learning algorithms and classifiers, combined with white-list and black-list techniques. We used the C4.5 classifier that generate a decision tree that provides highly accurate results in deciding whether a site is a fraudulent website or a genuine website based on its URL. Our predictive model achieved a high accuracy rate of 83%. We have modelled our system in the form of a desktop application so that the user can add the URL of the website needed to be checked, and the results appear to the user. The system combination accuracy can exceed 95%, and the execution results prove that.
Extracted rule-based technique for anomaly detection in a global network

Phishing attacks deceive internet users into revealing sensitive information over the internet to the cybercriminals. The disguise as a result of phishing involves the creation of fake websites that are look-alikes of reputable websites. In this paper, a rule-based method to detect phishing attacks in a global network is presented. Four machine learning models were trained on a dataset consisting of 14 features. The machine learning algorithms used are K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and naïve Bayes (NB). With the random forest model, a true positive of 100% and an accuracy of 98.35% were achieved. Rules were then extracted from the random forest model and embedded in a web browser extension called PhishAlert for easy application. Evaluation of the rules shows PhishAlert as an efficient tool for phishing detection. With this technique, the internet users can be easily guided and protected against cybercriminals.
Basalts of the Bystraya zone from sources of sub-continental lithospheric mantle: the Tunka valley of the Baikal Rift System

In terms of petrochemical and trace-element data, basalts from the axial rift valley are suggested to originate from sources of the sub-continental lithospheric mantle, inhomogeneity of which was due to extracting upper and lower crustal components. Trace-element modeling indicated 3-8 %partial melting of apatite-bearing sources with varying garnet and clinopyroxene proportions.
Accuracy Analysis for Image Classification and Identification of Nutritional Values Using Convolutional Neural Networks in Comparison with Logistic Regression Model

Aim:The goal is to raise public awareness about nutritional issues by using food images to predict nutritional analysis using a novel image classification technique.Methods and Materials: The proposed research will be conducted at our university, and a total of two groups have been formed.There are two types of neural networks: a convolutional neural network and a logistic regression network.The framework uses 10 samples per group to evaluate accuracy.Gpower of 80% was used to calculate the sample size.Results: Convolutional Neural Network algorithm has predicted the nutritional analysis with the accuracy of (83.84%) which is more compared with the Logistic algorithm (72.3%) in identifying the fruit, Calorie count, amount of protein content, total fat, and subsequently carbohydrates measurement and so on.There is no statistically significant difference with (P = 0.092, >.05) among the classification algorithms.Conclusion: The analysis shows that the Convolutional Neural Network is significantly better for the whole Nutrition Analysis process compared to the Logistic regression.
Governance of Blockchain Application

The governance of blockchain is crucial for the stability and correctness of the blockchain system. With the continuous development of blockchain technology, the governance on blockchain extends to online and offline transaction contracts under the environment of smart contracts. Especially in recent years, the rapid development of blockchain applications in China and the increasing cross-chain transactions of information and assets between different blockchains also make cross-chain governance be put on the agenda. Therefore, this chapter will introduce the governance of blockchain from the perspectives of the off-chain and on-chain design. In particular, we focus on the economic incentive governance and IT technology governance for the permissioned blockchain systems, as well as the cross-chain design for the blockchain eco-environmental governance.