Reviewerly/exHarmony huggingface dataset:
Making Social Media More Social

This chapter explores how academic libraries have used social media for broadcasting information, responsive communication, and engagement. Many libraries focus on the marketing aspect of social media, since it is a successful method of promoting events, services, and resources. However, exclusively using social media as a marketing tool ignores the best part of social media: the connections it fosters between people. The online community is just an extension of the in-person community that the academic library serves. This chapter examines the state of the literature on libraries' use of social media through the lens of increasing engagement and connections with the community as the key to successful social media.
Optimalization Jaro Winkler Algorithm Using Fuzzy Logic to Evaluate Essay Questions in E-Learning System Based Microserver

Microserver is a server device that has limited computing power. However, this technology is widely developed due to the use of very minimal power so that it can be turned on using battery power. One of the uses of this microserver is as an E-Learning server. E-learning is a system that helps in the distance learning process where with the E-Learning system teachers and students can interact in learning. One of them is teaching, giving assignments, to exams. One of the features in E-Learning is the Computer Based Test System feature. Computer Based Test system offers the convenience of administering an exam. The exam itself sometimes takes a long time in terms of correcting answers. However, there are several weaknesses in the Computer Based Test system system which does not have a feature to correct answers in the form of essays. Therefore, in this study, a method for correcting an essay answer will be offered using the Jaro Winkler method combined with a fuzzy logic algorithm. This method offers a high level of accuracy and less computing processed compared to other methods.
Multi-granularity Chinese Text Matching Model Combined with Bidirectional Attention

Text matching is an important task in natural language processing field, and it is widely used in intelligent question answering, information retrieval and other fields. With the rise of deep learning, text matching has gradually shifted from the traditional word similarity method to the neural network research. Currently, text matching methods based on deep learning are mainly divided into representation-based text matching models and interaction-based text matching models. Among them, the representation-based text matching model encodes two sentences separately, which easily loses the semantic focus and makes it difficult to measure the importance of context between sentences. The interaction-based text matching model ignores global information, and thus affects the global matching effect. Based on this, this paper proposes a multi-granularity text matching model combined with bidirectional attention (MGBA). The model enables two text tensors to interact in advance through the bidirectional attention mechanism, and then extracts the multi-granularity features of text tensors through LSTM and CNN, so that the model can focus on different levels of text information, thus solving the problem that traditional deep models tend to lose semantic focus and ignore global information in the process of text matching.
Scikit-Weak: A Python Library for Weakly Supervised Machine Learning

In this article we introduce and describe scikit-weak, a Python library inspired by scikit-learn and developed to provide an easy-to-use framework for dealing with weakly supervised and imprecise data learning problems, which, despite their importance in real-world settings, cannot be easily managed by existing libraries. We provide a rationale for the development of such a library, then we discuss its design and the currently implemented methods and classes, which encompass several state-of-the-art algorithms.
Two-Round MPC Without Round Collapsing Revisited – Towards Efficient Malicious Protocols

Recent works have made exciting progress on the construction of round optimal, two-round, Multi-Party Computation (MPC) protocols. However, most proposals so far are still complex and inefficient. In this work, we improve the simplicity and efficiency of two-round MPC in the setting with dishonest majority and malicious security. Our protocols make use of the Random Oracle ( $${\textsf{RO}}$$ ) and a generalization of the Oblivious Linear Evaluation ( $$\textsf{OLE}$$ ) correlated randomness, called tensor $$\textsf{OLE}$$ , over a finite field $$\mathbb {F}$$ , and achieve the following: Both protocols achieve security levels inverse proportional to the size of the field $$|\mathbb {F}|$$ . Our construction is built upon the simple two-round MPC protocols of [Lin-Liu-Wee TCC’20], which are only semi-honest secure. Our main technical contribution lies in ensuring malicious security using simple and lightweight checks, which incur only a constant overhead over the complexity of the protocols by Lin, Liu, and Wee. In particular, in the case of computing Boolean circuits, our malicious MPC protocols have the same complexity (up to a constant overhead) as (insecurely) computing Yao’s garbled circuits in a distributed fashion. Finally, as an additional contribution, we show how to efficiently generate tensor $$\textsf{OLE}$$ correlation in fields of characteristic two using OT.
Moving Target Detection Algorithm Based on SIFT Feature Matching

After the computer technology is mature, the sequence images obtained from the camera are processed by the computer, and the images converted into digital signals are processed by the computer, so the new discipline of computer vision is born. The reason why we should pay attention to computer vision is to use computers to replace the human brain and human eyes to extract, identify, track and other series of understanding and analysis of moving targets in specific scenes. The purpose of this paper is to research the moving target detection algorithm based on SIFT feature matching. According to the current video surveillance requirements for moving target detection, reduce the number of candidate samples in the input detection module in the TLD algorithm, so as to optimize the TLD algorithm and make it meet the real-time requirements. A tracking module that uses SIFT algorithm to optimize the TLD algorithm is proposed, and the TLD algorithm based on SIFT feature matching is tested. The experimental results show that the tracking accuracy of the algorithm reaches more than 90%, and the algorithm has better robustness to the rotation of moving objects.
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination

Learning robotic tasks in the real world is still highly challenging and effective practical solutions remain to be found. Traditional methods used in this area are imitation learning and reinforcement learning, but they both have limitations when applied to real robots. Combining reinforcement learning with pre-collected demonstrations is a promising approach that can help in learning control policies to solve robotic tasks. In this paper, we propose an algorithm that uses novel techniques to leverage offline expert data using offline and online training to obtain faster convergence and improved performance. The proposed algorithm (AWET) weights the critic losses with a novel agent advantage weight to improve over the expert data. In addition, AWET makes use of an automatic early termination technique to stop and discard policy rollouts that are not similar to expert trajectories-to prevent drifting far from the expert data. In an ablation study, AWET showed improved and promising performance when compared to state-of-the-art baselines on four standard robotic tasks.
Neural network-based prediction of the stopping moment for text recognition in a video stream

This paper considers the problem of finding the stopping point for the process of text recognition in a video stream. An overview of existing approaches is presented, which are based on modelling and thresholding of the expected recognition error. As an alternative, this paper proposes a neural network-based method. The method uses a fully-connected network that takes a sequence of normalized Levenshtein distances between the consecutive combined recognition results of several previous frames and predicts the pseudo-probability of stopping on the current frame. A process of data preparation and training protocols, as well as the parameters of the model, are presented.Experimental evaluation of the proposed method shows that the learning-based approach allows to reduce the mean recognition error when compared to the base stopping method in every tier of the mean number of frames, with a maximal normalized Levenshtein distance improvement of 12.7% with not more than 10 processed frames on average on MIDV-2020 dataset and 15.4% with not more than 17 processed frames on average on the test subset of RoadText-1K dataset.
Unsupervised Learning of Domain-Independent User Attributes

Learning user attributes is essential for providing users with a service. In particular, for e-commerce portals which deal in variety of goods ranging from clothes to foods to home electronics, it is especially important to learn “domain-independent” attributes such as age, gender, and personality that affect people’s behavior across various domains of daily life (e.g., clothing, eating and housing) because these attributes can be used for personalization in diverse domains their service covers. Thus far, researchers have proposed approaches to learn user representation (UR) from user-item interactions, trying to embed rich information about user attributes in UR. However, very few can learn URs that are domain-independent without confounding them with domain-specific attributes (e.g., food preferences). This could consequently undermine the former’s utility for personalizing services in other domains from which the URs are not learned. To address this, we propose an approach to learn URs that exclusively reflect domain-independent attributes. Our approach introduces a novel multi-layer RNN with two types of layers: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Domain Specific Layers (DSLs)</i> for modeling behavior in individual domains and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Domain Independent Layer (DIL)</i> for modeling attributes that affect behavior across multiple domains. By exchanging hidden states between these layers, the RNNs implement the process of domain-independent attributes affecting domain-specific behavior and makes the DIL learn URs that capture domain-independence. Our evaluation results confirmed that the URs learned by our approach have greater utility in predicting behavior in the other domains from which these URs were not learned thereby demonstrating adaptability to various domains.
Understanding the Relationship Between Humour and Context in Visual Information Design

As an effective element of design, humour can help users better perceive and understand visual information such as signage in a specific context. However, the application of humour in visual information design is purely dependent on the designer’s intuition. In addition, there has been little research on how humour can be used in visual information design and how the perception of humour is influenced by context. Therefore, the study explored how humour is dependent on the context in visual information design. From the literature study, three types of humour and four types of contexts were derived. A designer workshop and focus group interview were conducted in the study. In the workshop, signages representing three types of humour respectively were designed. The signages were evaluated in the focus group interview to figure out how they were perceived. The results indicate that humour is dependent on the characteristics of context in visual information design. If this finding is well considered in design practice, it could help the user better understand visual information and have positive experience.
Utility-Oriented Quality Assessment of Sonar Image Super-Resolution

Sonar sensors are widely used for underwater tasks because of their advantages of working in turbid environment and having a long working range. However, there is a trade-off between working range and image resolution in practical applications. For improving the quality of the acquired acoustic images, image super-resolution (SR) has recently drawn considerable attention. Due to the inconsistent characteristics between natural images and sonar images, the existing image quality assessment (IQA) methods for natural scene images do not well provide constructive suggestions for super-resolution of sonar images. To solve this problem, we first established a super-resolution sonar image (SRSI) database, where the database was built around its usefulness of image for marine tasks. Then, a no-reference IQA specifically designed for SRSIs based on a brain-inspired target recognition mechanism was proposed, which is accomplished in a hierarchically organized ventral visual pathway. Experimental results show that our database has certain reliability and that the proposed metric outperforms the state-of-the-art IQA methods.
A Complexity Aware Scheduler With Dynamic Slot Allocation for Cloud Video Transcoding

For a multimedia cloud computing platform, it needs to perform video transcoding to provide bandwidth-compatible bit-streams for users. In configuring a MapReduce system, it allocates slots to workers with the assumption of homogeneous worker power and performs task scheduling by assuming equal task time complexity. However, the computing power of a practical cluster of workers and the time complexity of tasks is time-varying in any case. The task-scheduling algorithm has to well manipulate these heterogeneous cloud resources and task complexity. We first find a good partition size for a video transcoding job for efficient processing. We proposed a Complexity-Aware Scheduling (CAS) algorithm that reorders task assignment priorities according to task complexity to maintain load-balancing operations. By utilizing a Neural Network model to refine the task complexity estimation for the CAS(CASNN), the scheduling and transcoding speedup performances can further be improved. Based on the CASNN, we proposed a Dynamically Adjusting Slot number allocation (DAS) method, DASCASNN, to adjust the slots according to resource utilization status to improve the processing performance. Experimental results show the proposed DASCASNN can help reduce 30% of the transcoding time on average as compared to available schedulers and increase the resource utilization rates from 82.7% to 98%.
Epidata, Clues, Threads, Webs

We propose Epidata as a term for all the sorts of clues to finding, encountering, and understanding/making use of information that exist outside the ordinarily available tools of data and metadata. Our retelling of the Ariadne and Theseus tale presents the utility of a single clue. Our example of intellectual connections presented in Chap. 2 demonstrate some of the complexities that clues offer in leading to information discovery. Now we sketch some scenarios using multiple clues, forming webs of proximity.
Secure Asynchronous Federated Learning for Edge Computing Devices

Federated learning (FL) solves the problem of isolated data islands and improves machine learning models by training on large amounts of data collected from multiple parties. However, the traditional synchronous FL training method will lead to the problem of laggards due to the insufficient computing power of some edge computing devices, which in turn affects the accuracy of training and increases communication overhead. Asynchronous federated learning solves the training problem of traditional FL very well. However, traditional privacy-preserving methods for FL are not suitable for asynchronous federated learning, existing asynchronous training schemes cannot guarantee the privacy of data. Faced with this challenge, this paper proposes a weighted secure asynchronous federated learning scheme, WASecAgg, which can ensure training accuracy and data privacy on the premise of guaranteeing the training efficiency of federated learning. The final experiments show that WASecAgg performs better in terms of training efficiency, training accuracy and privacy-preserving of federated learning.
To Overfit, or Not to Overfit: Improving the Performance of Deep Learning-Based SCA

Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary’s knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for profiling attacks, deep neural networks can learn strong models without feature selection with the drawback of expensive hyperparameter tuning. Unfortunately, due to very large search spaces, one usually finds very different model behaviors, and a widespread situation is to face overfitting with typically poor generalization capacity. Usually, overfitting or poor generalization would be mitigated by adding more measurements to the profiling phase to reduce estimation errors. This paper provides a detailed analysis of different deep learning model behaviors and shows that adding more profiling traces as a single solution does not necessarily help improve generalization. We recognize the main problem to be the sub-optimal selection of hyperparameters, which is then difficult to resolve by simply adding more measurements. Instead, we propose to use small hyperparameter tweaks or regularization as techniques to resolve the problem.
Using Voice User Interface to Assist Designers in the Early Stage of Product Design

In the early stage of product design, the designer's work first needs to find available domain information from their own cognitive memory or external resources, and then internalize this information into visual images for communication. At this stage, designers need to spend most of their time searching through a lot of references and thinking twice. They also face the dilemma of thoughts and associations that they cannot follow. The design conception stage can be said to dominate the subsequent design trends. Therefore, its process is extremely important for product designers. This study proposes the introduction of a voice user interface to improve the product design ideation process. Through expert interviews with design experts in product design-related fields, and according to 3E indicators, a semi-automatic design tool that combines voice technology to search for information is designed. Evaluate using the Think Aloud method to improve user convenience through voice search keywords. The result is the construction of a voice user interface system that helps designers search for information during the ideation phase while also incorporating digital age voice technology. The experiment found that the voice system does help designers to imagine, which greatly saves the time of searching for information, thus improving the convenience of experience.
Sentimental Analysis with Emojis by Using Machine Learning

In the current situation, it is very common for people to share their reviews or feedback on the products they buy or the services they use or share their ideas on any event. A person's emotional impact plays an important role in their daily lives. Sensor analysis is the default process by which text information is assessed and comments are commented on as bad, good or neutral. An existing system involved in dynamic energy construction of Dictionaries and classification techniques, but the advent of electronic learning processes in him included when the models work well because of their automatic reading ability. It's a way of a subset of artificial intelligence and is also involved in the scientific study of algorithms and mathematical models used by computer programs to perform a specific task without explicit user commands, relying on patterns and problems instead. Involved in emotion analysis or an emotional or customer document in which emotions can be represented by emoji. Various forms of human emotions can be positive or negative.
Krathu-500: Post-Comments Thai Corpus

The Krathu-500 contains 574 post titles and a post body with all comments on each post on Pantip.com. The corpus contains a total of 63,293 comments in Thai language that is used in real-life situations, with various contexts and types, in a conversational form. The corpus serves as a good way to improve the capabilities of machine learning techniques that deal with the Thai language. A smaller sentiment-labeled corpus of comments is also provided with 6,306 records. The labeled corpus is a human-annotated dataset with three labels for negative, neutral, and positive comments. Three baseline models were developed for labeled corpus using simple LSTM, CNN, and BERT, respectively. The project also consists of an open-source repository that allows anyone who is interested to modify and built on top of the current source code and dataset.
A fast mixed distributed fusion for systems with random parameter matrices and correlated noises

A fast mixed distributed fusion algorithm for the system with random parameter matrices and correlated noises is proposed in this paper. First, It has fewer estimators because every local estimator can handle more than one sensor measurement data and also has robustness and reliability. Then, to reduce the computational complexity significantly, a sequential fast covariance intersection(SFCI) approach is applied to fuse the local filter estimations. It avoids the calculation of the cross-covariance matrices and the optimization of the cost function. Finally, a numerical example is given to verify the usefulness of the proposed filter algorithm.
Towards Anonymous yet Accountable Authentication for Public Wi-Fi Hotspot Access with Permissionless Blockchains

Anonymous yet accountable authentication can protect users' privacy and security and prevent users from misbehaving when they access public Wi-Fi hotspots. However, most existing privacy-enhanced authentication schemes either do not meet the accountability requirements in public Wi-Fi hotspot access or they are inherently dependent on trusted third parties, and therefore are undeployable in practical settings. In this paper, we design and implement an access authentication scheme to simultaneously and efficiently provide anonymity and accountability without relying on any trusted third party by utilizing a permissionless blockchain (e.g., Bitcoin or Ethereum) and Intel SGX. Inspired by the recent progress on Bitcoin techniques such as Colored Coins, we utilize the unmodified Bitcoin blockchain as the powerful platform to manage access credentials without introducing any trusted third party. We leverage SGX-based mixer to allow users to anonymously exchange their access credentials and design the verification path of access credentials to support blacklisting misbehaving access credentials without compromising users' anonymity. By integrating with the anti-double-spending property of the Bitcoin blockchain, our scheme can simultaneously provide users' accountability and anonymity without involving any trusted third party. Finally, we demonstrate that our proposed scheme is compatible with the current Bitcoin system or other permissionless blockchains, and is highly effective and practical for public Wi-Fi hotspot access control systems.