Reviewerly/exHarmony huggingface dataset:
Expert System of Land Suitability for Fruit Cultivation Using Case-Based Reasoning Method

Agriculture is one of the economic sectors in Indonesia that has the potential to be developed to support the achievement of the nation's food independence. Horticultural farming has great opportunities to be developed. However, there are problems in the productivity and management of existing gardens. An example is the empty plot of land because often the planted plants can not survive, especially when the dry season arrives. Lack of information about land suitability and the limited ability of garden extension about the condition of land characteristics is the cause of the problem that occurs. This research aims to design and implement an expert system of land suitability selection for fruit crops. The method used for problem-solving using case-based reasoning is to compare new cases with old cases and calculate case similarity values. The result of this study is an expert system of determining the suitability of land for the cultivation of fruit crops that can provide recommendations on what plants are suitable for the land analyzed. The proposed case-based reasoning method can analyze the condition of the land by taking into account the conditions with their respective weights. Accuracy testing using the cyclomatic complexity method obtained expert system accuracy obtained results of 80% of 30 test cases.
Goal Modelling for Deep Reinforcement Learning Agents

Goals provide a high-level abstraction of an agent's objectives and guide its behavior in complex environments. As agents become more intelligent, it is necessary to ensure that the agent's goals are aligned with the goals of the agent designers to avoid unexpected or unwanted agent behavior. In this work, we propose using Goal Net, a goal-oriented agent modelling methodology, as a way for agent designers to incorporate their prior knowledge regarding the subgoals an agent needs to achieve in order to accomplish an overall goal. This knowledge is used to guide the agent's learning process to train it to achieve goals in dynamic environments where its goal may change between episodes. We propose a model that integrates a Goal Net model and hierarchical reinforcement learning. A high-level goal selection policy selects goals according to a given Goal Net model and a low-level action selection policy selects actions based on the selected goal, both of which use deep neural networks to enable learning in complex, high-dimensional environments. The experiments demonstrate that our method is more sample efficient and can obtain higher average rewards than other related methods that incorporate prior human knowledge in similar ways.
Secure Data Transmission in Smart Meters Using Q-Learning in Fog Computing Environment

With the advancement in technology and connectivity, the concept of smart grids with smart meters has significantly increased the interest of both electric consumers and power suppliers. Nowadays the smart cities are using the technology of smart meters in their household, which can transfer the electrical data in a bi-directional way to consumers and smart grids. It consists of various Internet-connected sensors, software applications, to connect different devices. This makes the smart grid network more complex which is further vulnerable to the cyber-attack. Moreover, it gives rise to various malicious attack such as false data injection (FDI) in smart meters which leads to incorrect decisions due to a large number of open access points in a network; which is accessible to outside intruders. In smart meters, Home Area Network (HAN) and smart meter collectors are more vulnerable for cyber-attacks as they are directly involved in bi-directional communication between the devices such as smart grid, data connectors, and smart meters. Since smart meters work in a distributed environment where fog computing (FC) placed at the edge of the networks can play a major role by integrating with blockchain for secure electric data transmission and to avoid the FDI in smart meters. Here FC nodes will act as miners to makes the decisions on the data transmission while monitoring the states of the system. Hence, we propose a novel solution for the above mention issue which consists of 3-tier FC-based blockchain architecture and Smart Meter-based Q-Learning Encryption (SMQE) algorithm to verify the system in FC environment. The algorithm performs the identification of FDI, and malicious attacks conducted on smart meters during data transmission. It opens a blockchain-based channel for secure data transmission in smart meters. The simulation tools used for this proposed work are iFogSim, SimBlock, and Python (Anaconda) with Geth version 1.9.25.
Online Teaching Mode of College Sports Dance Course under the Background of Internet Plus

The development of Internet technology has brought opportunities and challenges to dance teaching in colleges and universities. In the era of "Internet plus", traditional dance teaching mode in universities has been unable to meet the needs of students. Effective reform measures must be taken to strengthen the reform of dance teaching in colleges and universities. This paper takes the dance teaching in colleges as the research breakthrough point, and makes a detailed research and analysis of the dance teaching mode in the era of "Internet plus".
Resource Efficient Mountainous Skyline Extraction using Shallow Learning

Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented reality applications. We present a novel mountainous skyline detection approach where we adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions. Unlike earlier approaches, which either rely on extraction of explicit feature descriptors and their classification, or fine-tuning general scene parsing deep networks for sky segmentation, our approach learns linear filters based on local structure analysis. At test time, for every candidate edge pixel, a single filter is chosen from the set of learned filters based on pixel's structure tensor, and then applied to the patch around it. We then employ dynamic programming to solve the shortest path problem for the resultant multistage graph to get the sky-mountain boundary. The proposed approach is computationally faster than earlier methods while providing comparable performance and is more suitable for resource constrained platforms e.g., mobile devices, planetary rovers and UAVs. We compare our proposed approach against earlier skyline detection methods using four different data sets. Our code is available at https://github.com/TouqeerAhmad/skyline_detection.
Non-monotonic Explanation Functions

Explaining black-box classification models is a hot topic in AI, it has the overall goal of improving trust in decisions made by such models. Several works have been done and diverse explanation functions have been proposed. The most prominent ones, like Anchor and LIME, return abductive explanations which highlight key factors that cause predictions. Despite their popularity, the two functions may return inaccurate and sometimes incorrect explanations.In this paper, we study abductive explanations and identify the origin of this shortcoming. We start by defining two kinds of explanations: absolute explanations that are generated from the whole feature space, and plausible explanations (like those provided by Anchors and LIME) that are constructed from a proper subset of the feature space. We show that the former are coherent in that two compatible sets of features cannot explain distinct classes while the latter may however be incoherent, leading thus to incorrect explanations. Then, we show that explanations are provided by non-monotonic functions. Indeed, an explanation may no longer be valid if new instances are received. Finally, we provide a novel function that is based on argumentation and that returns plausible explanations. We show that the function is non monotonic and its explanations are coherent.
An Empirical Analysis of Measure-Valued Derivatives for Policy Gradients

Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate (low bias) gradient estimator is crucial to face increasingly complex tasks. Traditional policy gradient algorithms use the likelihood-ratio trick, which is known to produce unbiased but high variance estimates. More modern approaches exploit the reparametrization trick, which gives lower variance gradient estimates but requires differentiable value function approximators. In this work, we study a different type of stochastic gradient estimator: the Measure-Valued Derivative. This estimator is unbiased, has low variance, and can be used with differentiable and non-differentiable function approximators. We empirically evaluate this estimator in the actor-critic policy gradient setting and show that it can reach comparable performance with methods based on the likelihood-ratio or reparametrization tricks, both in low and high-dimensional action spaces.
Bayesian AirComp with Sign-Alignment Precoding for Wireless Federated Learning

In this paper, we consider the problem of wireless federated learning based on sign stochastic gradient descent (signSGD) algorithm via a multiple access channel. When sending locally computed gradient&#x0027;s sign information, each mobile device requires to apply precoding to circumvent wireless fading effects. In practice, however, acquiring perfect knowledge of channel state information (CSI) at all mobile devices is infeasible. In this paper, we present a simple yet effective precoding method with limited channel knowledge, called sign-alignment precoding. The idea of sign-alignment precoding is to protect sign-flipping errors from wireless fadings. Under the Gaussian prior assumption on the local gradients, we also derive the mean squared error (MSE)-optimal aggregation function called Bayesian over-the-air computation (BayAirComp). Our key finding is that one-bit precoding with BayAirComp aggregation can provide a better learning performance than the existing precoding method even using perfect CSI with AirComp aggregation.
Voting Theory in the Lean Theorem Prover

There is a long tradition of fruitful interaction between logic and social choice theory. In recent years, much of this interaction has focused on computer-aided methods such as SAT solving and interactive theorem proving. In this paper, we report on the development of a framework for formalizing voting theory in the Lean theorem prover, which we have applied to verify properties of a recently studied voting method. While previous applications of interactive theorem proving to social choice (using Isabelle/HOL and Mizar) have focused on the verification of impossibility theorems, we aim to cover a variety of results ranging from impossibility theorems to the verification of properties of specific voting methods (e.g., Condorcet consistency, independence of clones, etc.). In order to formalize voting theoretic axioms concerning adding or removing candidates and voters, we work in a variable-election setting whose formalization makes use of dependent types in Lean.
Batch Part-mask Network for person re-identification

The combination of global feature and local feature is an important method to improve the recognition performance of person re- identification (re- ID). Exiting component-based methods mainly learn local representations by locating regions with specific semantics, which increases the difficulty of learning and lacks effectiveness and robustness for scenarios with large differences. In this paper, we propose Batch Part-mask Network, which takes ResNet-50 which add a self-attention mechanism to effectively improved pixel-level ground truth data as a baseline of the global branch and Batch Part-mask Branch. The global branch encodes the global feature representation, and the Batch Part-mask Branch composed of part 1 Branch and part 2 Branch learns the local detail feature. The network then links features from the two branches and provides a more comprehensive representation of spatial distribution features. Our method performs well in person reID. On the Duke dataset, its Rank-1 accuracy is 88.4%, which is improved by 1 %-2%. The mAP accuracy on the Market-1501 dataset was improved by 2%-3%, reaching 86.3%.
To See Facial Expressions Through Occlusions via Adversarial Disentangled Features Learning with 3D Supervision

Facial expression recognition (FER) is still a challenging problem if face images are contaminated by occlusions, which lead to not only noisy features but also loss of discriminative features. To address the issue, this paper proposes a novel adversarial disentangled features learning (ADFL) method for recognizing expressions on occluded face images. Unlike previous methods, our method defines an explicit noise component in addition to the identity and expression components to isolate the occlusion-caused noise features. Besides, we learn shape features with joint supervision of 3D shape reconstruction and facial expression recognition to compensate for the occlusion-caused loss of features. Evaluation on both in-the-lab and in-the-wild face images demonstrates that our proposed method effectively improves FER accuracy for occluded images, and can even deal with noise beyond occlusions.
Classification of Domains in Computer Science Using Random Forest Algorithm for YouTube Dataset

The user generated contents such as blogs, comments, videos, tweets, forums, podcasts, and discussions have seen a rapid growth due to the easier availability of Internet and mobile devices. People find it difficult to search appropriate contents from the ocean of information. Henceforth, it is highly required to develop proper filters for such voluminous contents along with an appropriate classifier. Since, YouTube videos are the most popular user generated content, this paper has performed a case study on YouTube videos to show the importance of classifiers. With the advancement in technologies, the user generated content can be streamlined and it can be used to perform trend prediction, trend analysis, forecasting, classification, mining and more. In this way, the proposed research work has used a supervised machine learning algorithm and random forest on VTT (Video Text Tracks) file of the computer science videos downloaded from YouTube. A classifier has been designed to classify the files into different computer science domains. The authors have considered the timeline of 5 years (2015–2019) for the downloaded videos. Here, the classifier has been trained by using a statistical model to calculate the weight of key phrases available in the VTT files. Based on this weight, the statistical analysis on the outcome of the algorithm is shown. On the basis of which a trend analysis on different computer science videos uploaded on YouTube has been done for the period 2015–2019.
A Study and Review on Image Steganography

Steganography is the science that involves encrypting data in a suitable multimedia carrier, such as image, audio, and video files. The main purpose of image steganography is to hide the data in images. This means that it encrypts the text in the form of an icon. Steganography is done when there is communication takes place between sender and receiver. In a day of data transfer over the network, security is paramount. Before the development of stenography, data security is a major research concern for researchers. Steganography is gaining importance due to the rapid development of users on the Internet and secret communication. In this paper, we discuss about various type of existing image steganography techniques and analyze the advantages and disadvantages of different types of image steganography techniques.
A Multi-Task Learning Approach for Recommendation based on Knowledge Graph

Sparsity and cold start problem are two classic problems of collaborative filtering. To alleviate these issues, researchers usually add side information to the recommendation models to boost the performance. In this paper, we propose a multi-task learning approach for recommendation based on knowledge graph (KGeRec), which takes recommendation as the main task and the knowledge graph as an auxiliary task to provide side information for recommendation. To fully capture the correlation information between these two tasks, a feature interaction layer (FlU) based on cross networks is designed to share features between them. Besides, a side information embedding layer (SIE) is also designed in the recommendation task to exploit more feature information. We apply KGeRec to three public datasets about movie, book, and music. Experimental results show that the proposed KGeRec outperforms the state-of-the-art approaches (+2.2% in AUC, +2.6% in Accuracy, +2.5% and in F1-score, compared to the maximum value in Type I models; +1.3% in AUC, +0.8% in Accuracy, and +2% in F1-score, compared to the maximum value in Type II models) and it performs well in sparse datasets. We also validate the effectiveness of knowledge graphs in improving recommendation performance.
Evaluating AI Chatbot Platforms by a Fuzzy AHP Approach

In the era of Industrial Revolution 4.0, the needs to enhance customers shopping experiences have also raised. Many innovative intelligence services are invented to support the business processes. Applying AI enabled chatbots in many industries has appeared as a promising option. Designing and implementing chatbots used to be a sophisticated tasks. However, there are many platforms which support the AI chatbots implementing process significantly. Furthermore, evaluating suitable AI chatbot platforms becomes more complicated since there are many criteria and platforms to be considered. This paper proposed a novel approach based on fuzzy AHP approach to assess the criteria of AI chatbot platforms. The results of this paper can be applied for companies in their selecting chatbots implementation solutions to enhance their business performance.
XMRec: Workshop on Cross-Market Recommendation

Online markets are spreading quickly across the globe, supporting a huge network of product sales to billions of customers with various cultures, lifestyles, economic interests, and languages. These global markets introduce many novel opportunities—as well as challenges. Our workshop, called XMRec, concerns the problem of recommending relevant products to users in a target market (e.g., a resource-scarce market) by leveraging data from similar high-resource markets, e.g. using data from the U.S. market to improve recommendations in a target market. We hypothesize that data from one market can be used to improve recommendation in another. We aim to create a dynamic and interactive atmosphere where researchers of diverse backgrounds and interests can discuss their ideas on cross-market recommendation and how it can be further pursued in the community. To this end, XMRec features a series of seed talks both from industry and academia, discussing the future of cross-market recommendation and its potentials as a new line of research. The seed talks will be followed by a panel discussion where a diverse set of researchers discuss their ideas and opinion about the topic. Finally, we will invite the participants and the panelists to take part in interactive brainstorming breakout sessions to further discuss their ideas. We aim to motivate a range of studies (like analyzing market-specific biases, conversational recommendation, and predicting early adopters) beyond the cross-domain recommendation by extending markets and content languages.
From Degree to Chief Information Security Officer (CISO): A Framework for Consideration

Introduction to Research, CS120 -Introduction to Computing in Aviation
Face Replacement and Image Animation System in Cultural Experience

Abstract As a unique representative of Chinese culture and civilization, Chinese traditional portrait painting has a certain cultural influence all over the world. However, what kind of human-computer interaction system can attract more people’s interest and attention, is still a field worthy of exploration. This paper focuses on the instantiation expression of Chinese traditional portrait, improves the regional feature point matching based on the research of Photo to Chinese Portrait method (P-CP), compares the effects of different color correction approaches on the experimental results, and introduces the form of real-time image driving animation. We hope that we can provide new inspiration for the dissemination of traditional culture and the application of modern technology.
Multi-Grounding Navigator for Self-Supervised Vision-and-Language Navigation

Vision-and-language navigation (VLN) based on human-robot interaction has become increasingly attractive in recent years for flexible robot navigation. As key metrics, the success rate and path length are equally important in VLN, where the former represents the accuracy of navigation and the latter indicates the efficiency. However, the most current approaches aim to improve the accuracy of navigation while rarely consider the path efficiency. For this situation, we propose a progress predictor module, where the progress predictor is used to predict the future navigation progress to be attained by taking action. Using the progress predictor, we design a multi-grounding navigator (MGN) to generate the distribution of actions by adding the predicted future progress for improving the accuracy and efficiency simultaneously. After action distribution is given, a path-shortening (PS) method is presented to further improve the path efficiency. On the other hand, by considering an extra progress monitor, we design a self-supervised (SS) module to further improve the navigation performance. Experiments using Room-to-Room (R2R) dataset are conducted to demonstrate the effectiveness of our MGN with SS and/or PS. Experiment results show that our scheme enhances accuracy and efficiency than baselines, especially in unseen environments.
A Concept for the Use of Chatbots to Provide the Public with Vital Information in Crisis Situations

In times of crisis, at which many people experience insecurity, fear, and uncertainty, the government plays a critical role when it comes to civil protection and the distribution of vital information. However, in many cases, misleading or contradictory information are spread by various sources, exacerbating the desperation of the local population and leading to wrong decisions, which might worsen the situation. To counteract this problem, a single system that is highly trustworthy and supports the citizens in gathering tailored high-quality information to overcome the crisis appears to be sensible. In this paper, a conceptual approach is presented that attempts to provide this single point of interaction by combining chatbots, robotic process automation, and data analytics for the tailored provisioning of important information, sourced from pre-selected institutions. This assures the highest possible degree of information quality and therefore acceptance in the potential user base, while also allowing for a fast dissemination of new findings and statistics as well as the ability to monitor the users' behaviour to gain additional insights.