Reviewerly/exHarmony huggingface dataset:
Assaults on Freedom of Speech: Why Social Studies Must Defend the First Amendment

Freedom of expression is the core political ideal undergirding American democracy and recent attacks on freedom of speech are a direct threat to the liberties and rights guaranteed in the United States Constitution. Freedom of expression is essential for participatory democracy, scientific progress, individualism, and civic education in K-12 schools and universities. Citizens must have access to all opinions, empirical evidence, historical information, and competing narratives to make informed decisions regarding political candidates, policies, and issues. This necessitates the right to express offensive, controversial, repugnant, and radical opinions and values that that are shocking to government or specific groups. Indeed, this is the primary purpose of the First Amendment’s protection of freedom of speech and the press. However, this freedom is experiencing a withering assault from special interest groups, Big Tech social media entities, and most ironically, universities, ostensibly the ultimate guardians of freedom of expression. “Hate” speech codes and other attempts at censorship are anathema to democracy. Attacks on freedom of expression are an existential threat to social studies education. Thus, it is incumbent upon all social studies professors and K-12 social studies educators to combat this dangerous attack on the First Amendment.
Digital Data Identification for Deduplication Process using Cryptographic Hashing Techniques

The cloud storage system is a very big boon for the organizations and individuals who are all in the need of storage space to accommodate huge volume of digital data. The cloud storage space can handle various types of digital data like text, image, video and audio. Since the storage space can be shared among different users, it is possible to have duplicate copies of data in the storage space. An efficient mechanism is required to identify the digital data uniquely in order to check the duplicity. There are various ways by which the digital data can be identified. One among such technique is hash-based identification. Using cryptographic hashing algorithms, every data can be uniquely identified. The unique property of hashing algorithm helps to identify the data uniquely. In this research work, we are going to discuss the advantage of using cryptographic hashing algorithm for digital data identification and the comparison of various hashing algorithms.
IoTD: An approach to identify E-mails sent by IoT devices

As the number of low-cost Internet-of-Things (IoT) devices increases dramatically in recent years, they have become ideal targets for E-mail spammers. Some network cameras are shipped to the market with default passwords. Operating systems of many IoT devices are often outdated or not well-configured. Those practices make IoT devices easy to be compromised. Some of these compromised IoT devices may be used for E-mail spamming. Hence, how to handle undesired connections from client IoT devices becomes an important issue for mail server administrators. Even though the whitelist or blacklist are adopted by a mail server only allowing to receive E-mails relayed from few trusted SMTP servers, such list-based approach apparently cannot be applied for global SMTP clients when considering to the flexibility and cost of list maintenance. For most mail servers providing SMTP for desktop, laptop, or mobile clients rather than IoT devices, this paper proposes a server-side approach, called IoT detector (IoTD), to detect E-mails which are sent from IoT devices. Because the majority of IoT devices are not used by human users to send E-mail, administrators of mail servers may consider the E-mail sent by an IoT device as spam directly if IoT clients are not expected. Experimental results show that IoTD can accurately detect E-mails sent by IoT devices. The accuracy evaluation among five IoT devices and two non-IoT devices of this study shows that all tests for these five IoT devices are true positives, and all tests for these two non-IoT devices are true negatives as well.
Biosensor for Stress Detection Using Machine Learning

Biosensor analytics is a crucial tool for monitoring health conditions for patients and individuals with availability of wearable sensors and other devices. Biosensor studies can depict the detection of stress with precision. However, in this study, a review of feasible machine learning algorithms is reviewed with their comparative analysis as per healthcare data analytics. To design and implement a biosensor, suitable machine learning algorithm should be selected in order to detect anxiety and stress levels. Using machine learning algorithms, the analysis can be fault-tolerant and stress detection could be effective in terms of convenience.
Clustering Sensitivity Analysis for Gaussian Process Regression Based Solar Output Forecast

Power system operations are becoming more challenging with the increasing penetration of renewable-based re- sources such as photovoltaic (PV) generation. In this regard, obtaining accurate solar power output forecasts allows a deepening penetration of renewable-based resources in a secure and reliable way. In this paper, we propose a probabilistic framework to predict short-term PV output taking into account the uncertainty of weather data as well as the variability of PV output over time. To this end, we use datasets comprising of meteorological weather data such as temperature, irradiance, zenith, and azimuth and solar power output. We cluster these data in categories and train a Matérn 5/2 Gaussian Process Regression model for each cluster. More specifically, we cluster the data into one to eight different partitions by making use of the k-means algorithm. In order to identify the optimal number of clusters we use the Elbow and Gap methods. We compare the results obtained for the different number of clusters with the (i) 5-fold cross-validation; and (ii) holding out 30 representative days as test data. The results showed that the optimal number of clusters is four, since in comparison to higher number of clusters the increase in the forecast error was marginal.
Motion Estimation with L<sub>0</sub> Norm Regularization

In this paper, we proposed a novel variational optical flow model with L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm regularization, which uses a sparse flow gradient counting scheme, and can globally control how many non-zero flow gradients are preserved to recover important motion structures in a sparsity-control manner. It is particularly effective for enhancing major flow edges while eliminating a manageable degree of low-amplitude motion structures to control smoothing and reduce oversegmentation artifacts. Different from other edge-preserving smoothing regularizers, it does not depend on local motion features, but locates important flow edges globally. It will not cause edge blurriness due to avoiding local filtering or average operation, and can preserve salient motion structures, even small-scale motion structures with high contrast can be preserved remarkably well. Benefited from the advantages of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm regularization, the proposed optical flow method shows outstanding performance in sharpening major flow edges, flattening insignificant motion details to control smoothing and reduce oversegmentation artifacts while preserving fine-scale motion structures with high contrast. It also achieves good performance on the Middlebury dataset.
Fitness Caching - From a Minor Mechanism to Major Consequences in Modern Evolutionary Computation

In the field of Evolutionary Computation, the main objective is to find a high-quality solution to the considered problem. However, the other important issue is to find a solution efficiently. Therefore, evolutionary methods use various techniques to adjust to the problem and reduce the amount of resources consumed during an optimization process. One of the well-known and relatively simple techniques is storing the already evaluated genotypes and their ratings. Whenever an evolutionary method is to evaluate the fitness for a genotype that was already rated, instead of re-evaluating it, a method may use the value that was stored in the repository. Surprisingly, despite its simplicity, such a fitness caching technique was shown to cause many important phenomena. When an evolutionary method is stuck, fitness caching may cause such significant fitness function evaluation (FFE) reduction that FFE will not be a reliable resource consumption measure anymore. Moreover, fitness caching may help in detecting the drop in the number of new solutions investigated by a method. Thus, it may help in dynamic population-size management. Such a consequence is far more sophisticated than a simple FFE reduction. Therefore, in this paper, we investigate fitness caching in more detail. We analyze its influence on chosen state-of-the-art methods employed to solve well-known theoretical problems.
Gradual Security Types and Gradual Guarantees

Information flow type systems enforce the security property of noninterference by detecting unauthorized data flows at compile-time. However, they require precise type annotations, making them difficult to use in practice as much of the legacy infrastructure is written in untyped or dynamically-typed languages. Gradual typing seamlessly integrates static and dynamic typing, providing the best of both approaches, and has been applied to information flow control, where information flow monitors are derived from gradual security types. Prior work on gradual information flow typing uncovered tensions between noninterference and the dynamic gradual guarantee- the property that less precise security type annotations in a program should not cause more runtime errors.This paper re-examines the connection between gradual information flow types and information flow monitors to identify the root cause of the tension between the gradual guarantees and noninterference. We develop runtime semantics for a simple imperative language with gradual information flow types that provides both noninterference and gradual guarantees. We leverage a proof technique developed for FlowML and reduce noninterference proofs to preservation proofs.
Modelling General Properties of Nouns by Selectively Averaging Contextualised Embeddings

While the success of pre-trained language models has largely eliminated the need for high-quality static word vectors in many NLP applications, static word vectors continue to play an important role in tasks where word meaning needs to be modelled in the absence of linguistic context. In this paper, we explore how the contextualised embeddings predicted by BERT can be used to produce high-quality word vectors for such domains, in particular related to knowledge base completion, where our focus is on capturing the semantic properties of nouns. We find that a simple strategy of averaging the contextualised embeddings of masked word mentions leads to vectors that outperform the static word vectors learned by BERT, as well as those from standard word embedding models, in property induction tasks. We notice in particular that masking target words is critical to achieve this strong performance, as the resulting vectors focus less on idiosyncratic properties and more on general semantic properties. Inspired by this view, we propose a filtering strategy which is aimed at removing the most idiosyncratic mention vectors, allowing us to obtain further performance gains in property induction.
Mimicry

The authors have collaborated on a machine learning multiscreen video installation powered by computer algorithms and inspired by mimicry in the natural world. The artwork explores a pseudo-environment loop system in nature and artificial mechanical organisms combining living flowers with projectors, webcams, and computer monitors. Technically, the software adopts a genetic algorithm to simulate the process of mimicry; conceptually, this real-time art installation is in conversation with Nam June Paik's piece TV Garden. The project explores the possibilities of integrating artificial intelligence and nature in the landscape of the future.
Comparison of Machine Learning and Neural Network Models on Fraud Detection

Fraud detection is to determine the fraud transaction from massive transactions to prevent economic loss. The method operated from learning the previous fraud and nonfraud transactions to create a model that can identify fraud transactions in daily transaction data. In this paper, four methods are used to achieve fraud transaction detection for the credit card. They are Deep neural network (DNN) based on pytorch and tensorflow, random forest and XGBoost. The random forest and XGBoost method use a logistic regression model to analyze and determine the feature of the dataset. For the deep neural network, pytorch and tensorflow are used to analyze features. The data of fraud transactions comes from transaction logs of an online platform. The dataset contains features of the basic transaction including value, platform, card type and much detailed information to specify each payment. The result shows that fixing the parameter according to the data features will increase the AUC-ROC score for a different method. What's more, the results show that pytorch has a higher score than all other methods for fraud detection.
Compressing convolutional neural networks via intermediate features

Deep convolutional neural network (CNN) is difficult to deploy to mobile and portable devices due to its large number of parameters and floating-point operations (FLOPs). To tackle this problem, we propose a novel channel pruning method. We use the modified squeeze-and-excitation blocks (MSEB) to measure the importance of the channels in the convolutional layers. The unimportant channels, including convolutional kernels related to them, are pruned directly, which greatly reduces the storage cost and the number of calculations. For ResNet with basic blocks, we propose an approach to consistently prune all residual blocks in the same stage to ensure that the compact network structure is dimensionally correct. After pruning we retrain the compact network from scratch to restore the accuracy. Finally, we verify our method on CIFAR-10, CIFAR-100 and ILSVRC-2012. The results indicate that the performance of the compact network is better than the original network when the pruning rate is small. Even when the pruning amplitude is large, the accuracy can also be maintained or decreased slightly. On the CIFAR-100, when reducing the parameters and FLOPs up to 82% and 62% respectively, the accuracy of VGG-19 even improve by 0.54% after retraining. The source code is available at https://github.com/JingfeiChang/UCP.
Bitrate Reduction for Omnidirectional Video Streaming: Comparing Variable Quantization Parameter and Variable Resolution Approaches

Immersive and Extended Reality applications are getting increasingly popular due to the recent improvements and wide availability of Head Mounted Displays in the mass market. Among the many new immersive applications, Omnidirectional Video Streaming (OVS) - or 360°Video Streaming - is attracting the attention of both the industry and the research community. The increased immersivity of 360° videos comes at the cost of larger bandwidth requirements compared to classic 2D videos. To tackle the issue of reducing bandwidth requirements, tiling is a viable technique that allows to encode the portions of the 360° video most likely to fall outside of the users' viewport at lower quality using a higher quantization parameter. Tiling requires new encoders to be used which however do not have available hardware decoders in mobile devices yet. The variable resolution approach instead shrinks areas not falling in the region of interest to decrease the overall resolution and thus allowing bitrate reduction with any codec. This paper quantitatively compares the two approaches to find the trade-offs between achievable bitrate reduction and visual quality measured using the VMAF visual quality metric.
An Exploration-only Exploitation-only Hybrid for Large Scale Global Optimization

Two factors affect the effectiveness of exploration, the bias introduced by selection and the concurrence of exploration and exploitation. The Leaders and Followers metaheuristic was designed to reduce the bias from selection by using a two-population scheme. Minimum Population Search was designed to limit the concurrence of exploration and exploitation through the use of Thresheld Convergence in its sampling strategy. This paper presents Unbiased Exploratory Search, which combines both approaches and simultaneously addresses the effects of these two factors. An exploration-only exploitation-only hybrid is then presented using Unbiased Exploratory Search for the exploration-only phase of the hybrid. The hybrid is tested on the CEC large scale optimization benchmark.
Ordered fuzzy rules generation based on incremental dataset

This paper proposes a novel approach for building transparent knowledge-based systems by generating interpretable fuzzy rules that allow for present dependences between quantitative variables by accounting for uncertainty and the dynamics of their values. In the approach, IF-THEN rules are used to show the conditional relationship between the ordered fuzzy numbers, which contain additional information about the tendencies of variables' value changes. This paper elaborates an approach of mining ordered fuzzy rules from numerical data included in an incremental database. This approach develops the ability to record uncertainty and its change in the context of rapidly changing data. In addition, it is the basis for the development of research on the inference method with ordered fuzzy rules, which may become an indispensable tool for decision-making in an uncertain environment.
No Free Lunch: Overcoming Reward Gaming in AI Safety Gridworlds

We present two heuristics for tackling the problem of reward gaming by self-modification in Reinforcement Learning agents. Reward gaming occurs when the agent's reward function is mis-specified and the agent can achieve a high reward by altering or fooling, in some way, its sensors rather than by performing the desired actions. Our first heuristic tracks the rewards encountered in the environment and converts high rewards that fall outside the normal distribution into penalities. Our second heuristic relies on the existence of some validation action that an agent can take to check the reward. In this heuristic, on encountering an abnormally high reward, the agent performs a validation step before either accepting the reward as it is, or converting it into a penalty. We evaluate the performance of these heuristics on variants of the tomato watering problem from the AI Safety Gridworlds suite.
MobileStyleGAN.pytorch: PyTorch-based toolkit to compress StyleGAN2 model

In recent years, the use of Generative Adversarial Networks (GANs) has become very popular in generative image modeling. While style-based GAN architectures yield state-of-the-art results in high-fidelity image synthesis, computationally, they are highly complex. In our work, we focus on the performance optimization of style-based generative models. We introduce an open-source toolkit called MobileStyleGAN.pytorch to compress the StyleGAN2 model.
Memory Encryption Support for an FPGA-based RISC-V Implementation

Security is an important driver for the evolution of the RISC-V architecture. Several initiatives aim at exploiting the privileged architecture and the Physical Memory Protection mechanisms foreseen by the RISC-V specification as a foundation for robust trusted execution environments. This short paper introduces a memory encryption unit fitting the organization of the RISC-V privileged architecture. The unit is suitable for very resource-constrained systems and is mainly targeted at FPGA devices. The design relies on a flexible and efficient stream cipher, the ChaCha algorithm. The work presents an overview of the system architecture and the detail of the FPGA-based implementation of the memory encryption unit, along with some experimental evaluation and comparisons with state-of-the-art contributions.
Analysis on the Aesthetic Spirit in Computer Music based on Wavelet Analysis and Signal Fusion

Analysis on the aesthetic spirit in computer music based on wavelet analysis and signal fusion is conducted in this manuscript. In the continuous exploration of various high-quality technologies, wavelet transform technology can be applied in the audio compression processing process, which can improve the audio quality very well, achieve a larger dynamic range, and greatly improve the coding efficiency. To achieve the better performance, this paper used the wavelet analysis and the fusion of the data to construct the efficient model. Through the test on the real collected database, the estimation accuracy is proved.
Computer-based learning: 3D visualization and animation as content development for digital learning materials for traditional Indonesian cloth (Songket Palembang)

Abstract The animation is currently developing very rapidly, especially in 3D animation. Animated 3D visualization not only develops in the field of film, but also develops in digital-based learning such as interactive applications, presentation slides, e-books, and games, as well as supporting content for immersive technology, such as Augmented Reality, Virtual Reality, Mix Reality, and others. The development of 3D animation nowadays, but not accompanied by the use of animation as the delivery of information with local cultural content. The young generation’s lack of interest in local cultural arts is accompanied by a lack of digital learning materials and the delivery of interesting information for the younger generation. Therefore, the researcher aims to create 3D animated visualization assets that can motivate students to learn Indonesian cultural arts. Researchers applying the method Subdivision Surface, UV-mapping, 3d Painting, and walk cycle animation in the 3D visualization animation production process. The final result of this research is 3D Visualization and Animation as Content Development for Digital Learning Material for Indonesian Traditional Fabrics: Songket Palembang based on cloud for elementary school students.
Subject search in modern library information retrieval systems

The author reviews in brief the library catalogs as the origins of subject search in the modern search systems. He examines the concept of “subject search”, offers its definition (lacking in the state standards – GOSTs), specifies the types (subject search, search by keywords, systematic search) and characterizes them. The libraries use intensively all three types of subject search while the Internet-based search engines offer just one type, i.e. search by keywords. The universal search systems, like Google and Yandex, do not entail cataloguing, however the possibility of subject search by keywords is designed within html-code and tagging system. The author investigates into the application of systematic search on the Internet. He concludes that this search type emphasizes is intensively applied to the resources related to libraries or science and research (library e-catalogs, abstract databases, etc.). He suggests that in time tagging, the Internet popular subject search system, will tend to classifying and systematic search.