Reviewerly/exHarmony huggingface dataset:
Auditing Elon Musk’s Impact on Hate Speech and Bots

On October 27th, 2022, Elon Musk purchased Twitter, becoming its new CEO and firing many top executives in the process. Musk listed fewer restrictions on content moderation and removal of spam bots among his goals for the platform. Given findings of prior research on moderation and hate speech in online communities, the promise of less strict content moderation poses the concern that hate will rise on Twitter. We examine the levels of hate speech and prevalence of bots before and after Musk's acquisition of the platform. We find that hate speech rose dramatically upon Musk purchasing Twitter and the prevalence of most types of bots increased, while the prevalence of astroturf bots decreased.
From TPACK to DPACK: The “Digitality-Related Pedagogical and Content Knowledge”-Model in STEM-Education

Digitalization is a keyword in the discourse of educational science, but it is often linked to technological challenges, although digital changes occur throughout society. Therefore, STEM teachers are required to cope with technological changes in the subject, the increasing and diverse education and training technologies, and the ever-changing paths of information and communication of adolescents in their role as members of a changing society and culture. The TPACK-model focuses educators’ professional knowledge based on teachers’ expertise concerning technological knowledge per se and the pedagogy and content of their subjects. In contrast, knowledge relevant to daily life and social and cultural interaction beyond this is not clearly included in the TPACK-model at present. This article proposes supplementing the TPACK-model with the knowledge components of digital cultural transformations (digitality) and, therefore, extending the TPACK-model to a DPACK-model, where D stands for digitality. Therefore, digital transformation in STEM teaching requires additional professional knowledge considering the transformation of communication, mediatization and society. Through this expansion, the focus should also be directed on the necessity that children and young people in the digitally shaped world must also be able to critically reflect on the processes of change and shape them in an ethically responsible manner. For this reason, teachers require professional knowledge to reflect, analyze, use and shape the digital transformation, which is regularly demanded of them by national and international educational standards. As a foundation of STEM teachers’ education and training, an integrated model combining these facets of knowledge and skills is provided for discussion, and, as a result, quickly found its way into the educational policy guidelines and educational science discourses in Germany. In order to integrate the sociocultural consequences of digitalization into TPACK, this paper proposes a new hemisphere, sociocultural knowledge, which extends the existing TPACK components.
FreeNeRF: Improving Few-Shot Neural Rendering with Free Frequency Regularization

Novel view synthesis with sparse inputs is a challenging problem for neural radiance fields (NeRF). Recent efforts alleviate this challenge by introducing external supervision, such as pre-trained models and extra depth signals, or by using non-trivial patch-based rendering. In this paper, we present Frequency regularized NeRF (FreeNeRF), a surprisingly simple baseline that outperforms previous methods with minimal modifications to plain NeRF. We analyze the key challenges in few-shot neural rendering and find that frequency plays an important role in NeRF's training. Based on this analysis, we propose two regularization terms: one to regularize the frequency range of NeRF's inputs, and the other to penalize the near-camera density fields. Both techniques are “free lunches” that come at no additional computational cost. We demonstrate that even with just one line of code change, the original NeRF can achieve similar performance to other complicated methods in the few-shot setting. FreeNeRF achieves state-of-the-art performance across diverse datasets, including Blender, DTU, and LLFF. We hope that this simple baseline will motivate a rethinking of the fundamental role of frequency in NeRF's training, under both the low-data regime and beyond. This project is released at FreeNeRF.
A Hybrid Architecture for Fuzzy Connectionist Expert System

This chapter describes a hybrid architecture for classification expert systems that combines semantic networks and neural networks for representing knowledge. A semantic network is used to describe the objects of the problem domain and their relations at the intensional and extensional levels (classes of objects and instances). The relation Influences is the most important in this semantic network and signals where evidential reasoning (defined here as the possibilistic evaluation of objects representing hypotheses, based on the possibility values of objects representing evidences) may be performed. The possibilistic evaluation is performed in fact by a fuzzy neural network based on the authors’ combinatorial neural model and genetic algorithms. This hybrid scheme allows the construction of fuzzy connectionist expert systems able to inherit desirable properties from both the fields of sub-symbolic neural networks and symbolic expert systems, such as: expert knowledge representation, integration of multiple expert knowledge sources, multiple problem domain views, heuristic learning from examples, incremental learning, feature selection, treatment of vague, imprecise and partial input data, cost conscious inquiry, and reasoning explanation.
Financial development and total factors productivity channel: Evidence from Africa

We discuss the link between financial development and economic growth through Total Factor Productivity (TFP) canal in African economies. First, we use a composite index to hierarchize financial development in 40 African countries. Then, we study this relationship by using the methodology of panel data based on the Breusch-Pagan LM Test and Hausman Test, to determine the nature of the specific effect, in a panel of 22 economies. The main results of our study show that the development of the financial sector does not promote total factors productivity in low-income and upper-middle-income countries. For the lower middle-income countries, the Finance-TFP relation- ship is significantly positive. The reforms of African financial systems should be designed and directed to increase the adequacy of financial services with the needs of each economy.
Adaptive Resource Management for Analyzing Video Streams from Globally Distributed Network Cameras

There has been tremendous growth in the amount of visual data available on the Internet in recent years. One type of visual data of particular interest is produced by network cameras providing real-time views. Millions of network cameras around the world continuously stream data to viewers connected to the Internet. This data may be used by a wide variety of applications such as enhancing public safety, urban planning, emergency response, and traffic management which are computationally intensive. Analyzing this data requires significant amounts of computational resources. Cloud computing can be a preferred solution for meeting the resource requirements for analyzing these data. There are many options when selecting cloud instances (amounts of memory, number of cores, locations, etc.). Inefficient provisioning of cloud resources may become costly in pay-per-use cloud computing. This paper presents a method to select cloud instances in order to meet the performance requirements for visual data analysis at a lower cost. We measure the frame rates when analyzing the data using different computer vision methods and model the relationships between frame rates and resource utilizations. We formulate the problem of managing cloud resources as a Variable Size Bin Packing Problem and use a heuristic solution. Experiments using Amazon EC2 validate the model and demonstrate that the proposed solution can reduce the cost up to 62 percent while meeting the performance requirements.
Effective Gradient Descent-Based Chroma Subsampling Method for Bayer CFA Images in HEVC

The most widely used color filter array (CFA) pattern in commercial digital color cameras is the Bayer pattern, and the captured image is called the Bayer CFA image, in which each pixel contains only one color value and each image consists of 25% red, 50% green and 25% blue color values. The chroma 4:2:2 or 4:2:0 subsampling of Bayer CFA images is a necessary process prior to compression. According to the block-distortion minimization principle, in this paper, we propose an effective gradient descent-based chroma subsampling (GDCS) method for Bayer CFA images. Based on the test Bayer CFA images collected from the Kodak and IMAX datasets, experimental results demonstrated that in high efficiency video coding, our GDCS method has better quality and quality-bitrate tradeoff performance of the reconstructed images when compared with the existing chroma subsampling methods.
Medial axis extraction algorithm specializing in porous media

Thinning is an iterative process which erodes an object layer by layer until only a ‘skeleton’ or ‘medial axis’ of the object remains. A medial axis with high quality lays the foundation for quantifying a porous structure under a pore scale, which has a major effect on many engineering processes and applications. In this study, an algorithm was developed to compute the medial axis and radius of a pore in porous medium simultaneously from 3D images by conducting a labelling process. This ensures that every point possesses a number denoting its distance to the nearest solid. We used 231 computer-generated models with typically ordered porous structures and 3D images from an active carbon system acquired using X-ray computed tomography to validate the algorithm. By comparing the calculated porous volume with the actual porous volume of 30 computer-generated single pore models, we determined that the relative error of the porous volume was <6%. In addition, the radius of a pore in the reconstructed model of the active carbon system, which was measured manually using ImageJ, an image processing software, and calculated automatically by the proposed algorithm, shared the same value. The results indicate that the proposed algorithm can successfully compute the medial axis and radius of the void space for any porous system, irrespective of the shape of solid particles or the geometry of the pores.
A new method of Thangka image inpainting quality assessment

In order to solve the problem of Thangka image inpainting quality assessment (IIQA) and existing quality evaluation methods are not suitable for inpainting Thangka image, this paper proposes a new non-reference quality evaluation method which can effectively solve this problem. Firstly, due to lack of original Thangka image, the proposed method using symmetry of Thangka images to predicted an undamaged image. Secondly, we extract harries corner in Thangka inpainting images to show the structural feature of total graph. Thirdly, demonstrate subjective evaluation score of inpainting Thangka image by caparison difference of structural feature between inpainting and predicted original area. Finally, in order to compensate the lack of Thangka images in existing database, we add Generative Adversarial Nets (GANs) to generate large number of available image. Experiments show that our proposed method generates a state-of-the-art index for Thangka image inpainting quality which correlated with human vision.
Computation-Performance Optimization of Convolutional Neural Networks With Redundant Filter Removal

Convolutional neural networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many filters to extract the knowledge behind it. However, while the depth of convolutional layers gets deeper and deeper in recent years, the enormous computational complexity makes it difficult to be deployed on embedded systems with limited hardware resources. In this paper, inspired by rate-distortion optimization in image and video coding, we propose a computation-performance optimization (CPO) method to remove the redundant convolution filters in a CNN with performance constraints. To prove the effectiveness of the proposed method, CPO is applied to the networks for image super-resolution and image classification. Under almost the same PSNR drop and accuracy drop for performance evaluation in these two tasks, we can achieve the best parameter and computation reduction when compared with previous works.
Motif Shape Primitives on Fibonacci Weighted Neighborhood Pattern for Age Classification

Age classification from facial images is increasingly receiving attention in age-based computer vision applications. To address this classification problem, the present paper proposes a new method of age grouping with motif shape primitives on the fibonacci weighted neighborhood pattern. FWNP on the image is computed, and motif shape primitives are evaluated on this FWNP image. These shape primitives are used for age variation of different persons. This method is investigated on facial image datasets of FG-NET database. The experimental study has shown the good performance of our proposed method against the other existing methods.
A Strain Distribution Sensing System for Bone-Implant Interfaces Based on Digital Speckle Pattern Interferometry

This paper aims to provide an effective measurement method for the distribution of deformations and strains focusing on the response to external loading of bone-implant interfaces. To achieve this target, a novel speckle interference imaging method is proposed by introducing phosphate buffer saline medium, in which the samples were completely placed into a phosphate buffer saline solution medium to stable the water molecules. The stability of interferometry imaging is analyzed by using the concepts of co-occurrence matrix and moment of inertia. A series of experiments to measure load-driven deformation and strain in the bone-implant interface was carried out, and the experiments results were analyzed and discussed. It shows that the proposed method is feasible and effective for the no-contact strain measurements of biomaterials in a physiological condition. The proposed strain distribution sensing system will contribute to evaluating computational simulations and improving selection of implant designs and materials.
Leveraging Product Characteristics for Online Collusive Detection in Big Data Transactions

Online fraud transaction has been a big concern for e-business platform. As the development of big data technology, e-commerce users always evaluate the sellers according to the reputation scores supplied by the platform. The reason why the sellers prefer chasing high reputation scores is that high reputations always bring high profit to sellers. By collusion, fraudsters can acquire high reputation scores and it will attract more potential buyers. It has been a crucial task for the e-commerce website to recognizing the fake reputation information. E-commerce platforms try to solve this continued and growing problem by adopting data mining techniques. With the high development of the Internet of Things, big data plays a crucial role in economic society. Big data brings economic growth in different domains. It supplies support to the management and decision-making ability in e-business through analyzing operational data. In online commerce, the big data technology also helps in providing users with a fair and healthy reputation system, which improves the shopping experience. This paper aims to put forward a conceptual framework to extract the characteristics of fraud transaction, including individual- and transaction-related indicators. It also contains two product features: product type and product nature. The two features obviously enhance the accuracy of fraud detection. A real-world dataset is used to verify the effectiveness of the indicators in the detection model, which is put forward to recognize the fraud transactions from the legitimate ones.
Jobseeker-industry matching system using automated keyword selection and visualization approach

Learning opportunities are available with the accessibility of new learning technologies, discovery of untraditional learning pathways and awareness of the importance of connecting current knowledge with new learning. Such situation allows the expansion in the number of courses, programs and professional certifications offered to the students resulting to the increment of the number of graduates annually. The graduates then employed by the industry for executing the job. However, there is a growing concern about the increment of unemployed graduates in the job market. One of the reasons of the mismatch between graduates’ skills and employers’ needs is that the jobseekers tend to choose wrong job because they are overwhelmed by the choices and typically they just randomly send the application because it is time consuming to filter relevant advert. Such action may have repercussion to the industry because the employers need to select relevant candidates to fill up the post from the unfiltered pile of applications making the selection process lengthy and time consuming. In this paper we proposed an automated approach to match the graduates’ and employers’ needs using a hybrid of text mining and visualization approach to facilitate jobseekers’ task of relevant job application. The important keywords are automatically extracted based on the frequency of the word used in the adverts. Then, the graduates’ skills are matched from their personalized profile. Relevant visualization approaches are incorporated to facilitate the selection. It is practical and feasible for the proposed approach to be incorporated in job searching websites that can optimize jobseekers and employers time and effort for a suitable match.
Towards dependable and trustworthy outsourced computing: A comprehensive survey and tutorial

Cloud computing provides the clients with diversified services in a flexible manner. Recently, the cloud platforms have been the basic underling-support for The IoT and mobile computing. The client outsources data storage or computation overhead to the cloud, which accordingly yields a new computing paradigm – the outsourced computing. In such a scenario, the clients hope to acquire the service quality, and wish to not leak their sensitive data to the cloud service provider. Thus, the trustworthy (verifiability) and privacy has been a hot issue for outsourced computing. In this survey, we systematically present the cryptographic methods for ensuring verifiability and privacy. We first describe the research advancement in verifiable computing and the cryptographic primitives for privacy-preserving techniques; and then, taking outsourcing cryptographic operations as an example, to show how them primitives can be integrated to achieve a dependable and privacy-preserving outsourced computing scheme. This paper bridges the gap between the theoretical cryptographic research and engineering secure cloud computing systems.
CRAFT: Complementary Recommendation by Adversarial Feature Transform

We propose a framework that harnesses visual cues in an unsupervised manner to learn the co-occurrence distribution of items in real-world images for complementary recommendation. Our model learns a non-linear transformation between the two manifolds of source and target item categories (e.g., tops and bottoms in outfits). Given a large dataset of images containing instances of co-occurring items, we train a generative transformer network directly on the feature representation by casting it as an adversarial optimization problem. Such a conditional generative model can produce multiple novel samples of complementary items (in the feature space) for a given query item. We demonstrate our framework for the task of recommending complementary top apparel for a given bottom clothing item. The recommendations made by our system are diverse, and are favored by human experts over the baseline approaches.
FaaStest - Machine Learning Based Cost and Performance FaaS Optimization

With the emergence of Function-as-a-Service (FaaS) in the cloud, pay-per-use pricing models became available along with the traditional fixed price model for VMs and increased the complexity of selecting the optimal platform for a given service. We present FaaStest - an autonomous solution for cost and performance optimization of FaaS services by taking a hybrid approach - learning the behavioral patterns of the service and dynamically selecting the optimal platform. Moreover, we combine a prediction based solution for reducing cold starts of FaaS services. Experiments present a reduction of over 50% in cost and over 90% in response time for FaaS calls.
State-of-the-Art and Future Trends of Blockchain Based on DAG Structure

Blockchain is a decentralized storage technology rose rapidly recent years, it has the advantages of decentralization, untamperability and unforgeability, high safety and reliability, and has attracted vast attentions from governments, financial institutions, technology enterprises and capital markets. Blockchain has numerous application scenarios and huge development potential, Bitcoin is the most representative one of it. Primarily, the first generation blockchain and the second generation blockchain and their respective representative crypto-currencies: Bitcoin and Ether are introduced briefly. Next, a new type of blockchain based on DAG (Directed Acyclic Graph) structure is illustrated, it is considered to be the possible evolution route of the next generation blockchain. Furthermore, two influential applications of DAG blockchain systems: IOTA and Byteball are introduced. Finally, Conclusion and forecast of blockchain are discussed.
3D Bounding Boxes for Road Vehicles: A One-Stage, Localization Prioritized Approach Using Single Monocular Images

Understanding 3D semantics of the surrounding objects is critically important and a challenging requirement from the safety perspective of autonomous driving. We present a localization prioritized approach for effectively localizing the position of the object in the 3D world and fit a complete 3D box around it. Our method requires a single image and performs both 2D and 3D detection in an end to end fashion. Estimating depth of an object from a monocular image is not as generalizable as pose and dimensions. Hence, we approach this problem by effectively localizing the projection of the center of bottom face of 3D bounding box (CBF) to the image. Later in our post processing stage, we use a look up table based approach to reproject the CBF in the 3D world. This stage is a single time setup and simple enough to be deployed in fixed map communities where we can store complete knowledge about the ground plane. The object’s dimension and pose are predicted in multitask fashion using a shared set of features. Experiments show that our method is able to produce smooth tracks for surround objects and outperforms existing image based approaches in 3D localization.
Extended Codebook with Multispectral Sequences for Background Subtraction

: The Codebook model is one of the popular real-time models for background subtraction. In this paper, we first extend it from traditional Red-Green-Blue (RGB) color model to multispectral sequences. A self-adaptive mechanism is then designed based on the statistical information extracted from the data themselves, with which the performance has been improved, in addition to saving time and effort to search for the appropriate parameters. Furthermore, the Spectral Information Divergence is introduced to evaluate the spectral distance between the current and reference vectors, together with the Brightness and Spectral Distortion. Experiments on five multispectral sequences with different challenges have shown that the multispectral self-adaptive Codebook model is more capable of detecting moving objects than the corresponding RGB sequences. The proposed research framework opens a door for future works for applying multispectral sequences in moving object detection.