Reviewerly/exHarmony huggingface dataset:
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning

Although distributional reinforcement learning (DRL) has been widely examined in the past few years, there are two open questions people are still trying to address. One is how to ensure the validity of the learned quantile function, the other is how to efficiently utilize the distribution information. This paper attempts to provide some new perspectives to encourage the future in-depth studies in these two fields. We first propose a non-decreasing quantile function network (NDQFN) to guarantee the monotonicity of the obtained quantile estimates and then design a general exploration framework called distributional prediction error (DPE) for DRL which utilizes the entire distribution of the quantile function. In this paper, we not only discuss the theoretical necessity of our method but also show the performance gain it achieves in practice by comparing with some competitors on Atari 2600 Games especially in some hard-explored games.
Distance Learning and Globalization Processes in the Postmodern World

The article analyzes distance learning and educational processes, such as education, pedagogical technologies, methods and upbringing in the context of globalization trends in the postmodern world. The new technologies, which have prompted and eventually led to globalization processes, indicate the need to master these new technologies since teaching today requires one to apply one’s professional results efficiently. The trend of education is globalization, which seems inevitable under a dramatically changing educational reality. Global education is defined as one of the areas in the development of current pedagogical theory and practice. It aims to prepare learners for professional activities in a changing and increasingly interdependent world. Indeed, globalization lies in implementing and using internet technologies in education. Recognizing the multiplicity of meanings and views, the postmodern society is entering a state of crisis. It is because the Covid-19 pandemic and, consequently, new living conditions associated with it, require a new worldview. In this regard, education reforms are becoming a priority, given that the 21st century is the age of information and nanotechnologies. Information technology is a prerequisite for teacher training today. At the same time, informatization of education is impossible without creating, using and developing informational and educational environments, which in turn should enable the realization of an intellectually developed individual embodying the necessary professional qualities.
PROTOTYPING REMOTELY TOGETHER WITH 2D, 3D AND IMMERSIVE VIRTUAL REALITY DESIGN TOOLS

The 2020 pandemic demanded changes to design education from in-person to distant and hybrid learning and accelerated the use of new technologies in both design education and professional working methods.In this new paradigm, how do we re-engage studio-like interactions, collaborative making and co-design strategies?This preliminary study, with 25 midwestern US industrial design students, adds to previous research on designing with 2D and 3D digital desktop tools versus immersive virtual reality when codesigning prototypes.The results showed a measurable difference in the quantity of prototypes favouring digital desktop design tools, but a negligible difference in the quality of prototypes even with little experience with immersive virtual reality tools.This study suggests a need to integrate immersive virtual reality in earlier design education as more experience may lead to further advantages and design results.
Algorithmization of the System for Collecting and Processing Information on the Geometric Description of Three-Dimensional Object

The paper describes an algorithmAlgorithm for restoring the three-dimensional (3D) geometric structureGeometric structure of an object in the form of its digital imageDigital image based on the study of a set of two-dimensional images, as well as the algorithmization of the operation system for collecting information on a three-dimensional object.
Design of a modular locomotion system for autonomous mobile robots

Autonomous mobile robots are becoming more popular today and are successfully integrated into many civil and industrial projects. In the industrial sectors flexibility and modularity have an important role, so the production can be diversified and achieved with low costs. This paper presents the conceptual and functional design of a modular locomotion system for an autonomous mobile platform that can be configured according to the needs of the industrial environment it serves. The architecture of the locomotion system is based on a spring suspension system and three types of wheels: conventional wheels, omnidirectional wheels and mechanum wheels. The calculations for the chosen suspension system are presented, which ensure the contact with the ground for each type of wheel and the resignation calculations for the engine that will drive the wheel. Finally, some variants of configurations are presented that can be made on an autonomous mobile platform with these modules.
Identifying Norms from Observation Using MCMC Sampling

To promote efficient interactions in dynamic and multi-agent systems, there is much interest in techniques that allow agents to represent and reason about social norms that govern agent interactions. Much of this work assumes that norms are provided to agents, but some work has investigated how agents can identify the norms present in a society through observation and experience. However, the norm-identification techniques proposed in the literature often depend on a very specific and domain-specific representation of norms, or require that the possible norms can be enumerated in advance. This paper investigates the problem of identifying norm candidates from a normative language expressed as a probabilistic context-free grammar, using Markov Chain Monte Carlo (MCMC) search. We apply our technique to a simulated robot manipulator task and show that it allows effective identification of norms from observation.
Beyond facts – a survey and conceptualisation of claims in online discourse analysis

Analyzing statements of facts and claims in online discourse is subject of a multitude of research areas. Methods from natural language processing and computational linguistics help investigate issues such as the spread of biased narratives and falsehoods on the Web. Related tasks include fact-checking, stance detection and argumentation mining. Knowledge-based approaches, in particular works in knowledge base construction and augmentation, are concerned with mining, verifying and representing factual knowledge. While all these fields are concerned with strongly related notions, such as claims, facts and evidence, terminology and conceptualisations used across and within communities vary heavily, making it hard to assess commonalities and relations of related works and how research in one field may contribute to address problems in another. We survey the state-of-the-art from a range of fields in this interdisciplinary area across a range of research tasks. We assess varying definitions and propose a conceptual model – Open Claims – for claims and related notions that takes into consideration their inherent complexity, distinguishing between their meaning, linguistic representation and context. We also introduce an implementation of this model by using established vocabularies and discuss applications across various tasks related to online discourse analysis.
OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning

We introduce the task of open-vocabulary visual instance search (OVIS). Given an arbitrary textual search query, Open-vocabulary Visual Instance Search (OVIS) aims to return a ranked list of visual instances, i.e., image patches, that satisfies the search intent from an image database. The term ``open vocabulary'' means that there are neither restrictions to the visual instance to be searched nor restrictions to the word that can be used to compose the textual search query. We propose to address such a search challenge via visual-semantic aligned representation learning (ViSA). ViSA leverages massive image-caption pairs as weak image-level (not instance-level) supervision to learn a rich cross-modal semantic space where the representations of visual instances (not images) and those of textual queries are aligned, thus allowing us to measure the similarities between any visual instance and an arbitrary textual query. To evaluate the performance of ViSA, we build two datasets named OVIS40 and OVIS1600 and also introduce a pipeline for error analysis. Through extensive experiments on the two datasets, we demonstrate ViSA's ability to search for visual instances in images not available during training given a wide range of textual queries including those composed of uncommon words. Experimental results show that ViSA achieves an mAP@50 of 27.8% on OVIS40 and achieves a recall@30 of 21.3% on OVIS1400 dataset under the most challenging settings.
Enrich Awareness of Users to Detect Phishing Websites

Phishing attack is used for identity theft with the help of social engineering and some sophisticated attacks. To attract the user by clicking a URL and is trapped to a phishing Web page. Security for user’s credentials is one of most important factor for organizations nowadays. It can be achieved through several ways like education and training. Through training and education the level of awareness will be increased also it helps to mitigate phishing. Approach with several steps is introduced in this paper, where a user must take a look or take these precautionary measures if the user is browsing any Web browser. We found it possible to detect Phishing Web pages without anti Phishing solutions. This approach contains several steps to examine whether the Web page is a real Web page or a fake Webpage. All these steps will check the phishing features exist in that Web page or not. For evaluation of our approach we analyzed the data set of Phish Tank, this data set is full of Phishing Web Pages. The purpose of evaluation is to check the features discussed in our approach to aware the user. From the following result it is resulted that the user can detect without using any Anti Phishing solution just by taking some steps to check the Web page for certain features.
Learning Temporal Plan Preferences from Examples: An Empirical Study

Temporal plan preferences are natural and important in a variety of applications. Yet users often find it difficult to formalize their preferences. Here we explore the possibility to learn preferences from example plans. Focusing on one preference at a time, the user is asked to annotate examples as good/bad. We leverage prior work on LTL formula learning to extract a preference from these examples. We conduct an empirical study of this approach in an oversubscription planning context, using hidden target formulas to emulate the user preferences. We explore four different methods for generating example plans, and evaluate performance as a function of domain and formula size. Overall, we find that reasonable-size target formulas can often be learned effectively.
SIGN LANGUAGE RECOGNITION

For people who are deaf or hard of hearing, sign language is their main form of communication.To improve interaction among hearing and non-hearing people, computer programs that are able to recognize sign language must be developed.Accurate systems for sign language recognition have been made available in recent years because of developments in deep learning techniques.The purpose of this project is to create a deep learning-based sign language detecting system.After preprocessing the frames from the stream of video as input, the system will identify the sign language motion.To learn and categorize the sign gestures, we'll employ a convolutional neural network's architecture.A large collection of sign language films, including a variety of hand motions and forms, will be used to train CNN.The proposed system will also feature real-time tracking of hand gestures and movements in order to recognize signs efficiently.The system's performance will be compared to that of existing recognition sign language systems on a separate test dataset, and its accuracy will be evaluated.The project's long-term goal is to develop a sign language recognition system that can accurately and instantly recognize a wide range of sign gestures.
Combining (Second-Order) Graph-Based and Headed-Span-Based Projective Dependency Parsing

Graph-based methods, which decompose the score of a dependency tree into scores of dependency arcs, are popular in dependency parsing for decades.Recently, Yang and Tu (2022) propose a headed-span-based method that decomposes the score of a dependency tree into scores of headed spans.They show improvement over first-order graph-based methods.However, their method does not score dependency arcs at all, and dependency arcs are implicitly induced by their cubic-time algorithm, which is possibly sub-optimal since modeling dependency arcs is intuitively useful.In this work, we aim to combine graphbased and headed-span-based methods, incorporating both arc scores and headed span scores into our model.First, we show a direct way to combine with O(n 4 ) parsing complexity.To decrease complexity, inspired by the classical head-splitting trick, we show two O(n 3 ) dynamic programming algorithms to combine first-and second-order graph-based and headed-span-based methods.Our experiments on PTB, CTB, and UD show that combining first-order graph-based and headedspan-based methods is effective.We also confirm the effectiveness of second-order graphbased parsing in the deep learning age, however, we observe marginal or no improvement when combining second-order graphbased and headed-span-based methods 1 .
Combining Word Embeddings and Deep Neural Networks for Job Offers and Resumes Classification in IT Recruitment Domain

Now-a-days, the use of web portals known as job boards for publishing job offers by recruiters has grown considerably.The candidates in their turn, apply to the job positions via the job boards.Since the opportunities are available on a wide range and the job application process is fast and straightforward, the data flow is transformed to large-volume data sets which are hard to handle.Most companies tend to automate the candidate selection process that aims to match the job offers with suitable resumes.In this paper, we propose a supervised learning approach to classify the job offers and CVs shared in the recruitment sites in order to enhance automatic recruitment process.We used natural language processing techniques for job offers and CV preprocessing.Next, we used word embeddings and deep neural networks to train two models, the first one categorizes recruitment documents based on job skills, and the second one predicts the expertise degree class.The experiment results show that our proposal is very efficient.
Hierarchical Attention Graph Embedding Networks for Binary Code Similarity against Compilation Diversity

Binary code similarity comparison is the technique that determines if two functions are similar by only considering their compiled form, which has many applications, including clone detection, malware classification, and vulnerability discovery. However, it is challenging to design a robust code similarity comparison engine since different compilation settings that make logically similar assembly functions appear to be very different. Moreover, existing approaches suffer from high-performance overheads, lower robustness, or poor scalability. In this paper, a novel solution HBinSim is proposed by employing the multiview features of the function to address these challenges. It first extracts the syntactic and semantic features of each basic block by static analysis. HBinSim further analyzes the function and constructs a syntactic attribute control flow graph and a semantic attribute control flow graph for each function. Then, a hierarchical attention graph embedding network is designed for graph-structured data processing. The network model has a hierarchical structure that mirrors the hierarchical structure of the function. It has three levels of attention mechanisms applied at the instruction, basic block, and function level, enabling it to attend differentially to more and less critical content when constructing the function representation. We conduct extensive experiments to evaluate its effectiveness and efficiency. The results show that our tool outperforms the state-of-the-art binary code similarity comparison tools by a large margin against compilation diversity clone searching. A real-world vulnerabilities search case further demonstrates the usefulness of our system.
Uji Aktivitas Karbon Aktif Tongkol Jagung Terimpregnasi Fe2O3 sebagai Biosorben Limbah Warna Metilen Blue

Methylene blue is one of dying waste that causes harmful effect both enviromental and public health risks. A method to overcome the problem is to adsorb it with the activated carbon. Apart from that, corn cob is potential biomass with can convert to activated carbon. The aim of this research is to syntethic and to modify corn cob activated carbon with iron oxide. It is expected to increase the afinity of methylene blue to the activated carbon surface. The modification is done by wet impregnation and the effectiveness of the impregnated corn cob activated carbon is studied by analyse the persen removal of methylene blue in every interval time. The result show that in the concentration range of Fe load impregnation between 1%-3% which has been used, the corn cob activated carbon with concentration of Fe load 2% has the most optimum ability in methylene blue adsorption with the adsorption capacity 44,9%. Moreover, the effect pH shows that in acidic condition the adsorption of methylen blue is better than in netral condition
Joint Watermarking for Image Reliability Control in Encrypted and Compressed Domains

Sharing medical images can enormously help doctor in their day by day practice by permitting them to arrive at a symptomatic all the more rapidly. However, such images are sensitive and should be ensured. In this paper, propose the principal joint watermarking-encryption pressure conspire for the insurance of medical images. Its inventiveness is twofold. In a first time, it permits the admittance to watermarking-based security administrations from the scrambled and the compacted bitstreams without parsing them even part of the way. It gets conceivable to follow images and control their dependability from both the encoded and compacted spaces. In a second, it remains on the blend of spot replacement watermarking, JPEG-LS and the AES block figure in its CBC mode to make our plan. Tests directed on various medical images modalities (radiographic and retina pictures) show the ability of our framework to safely make accessible a message in both scrambled and compacted areas while limiting the contortion of the images.
On the Efficacy of Small Self-Supervised Contrastive Models without Distillation Signals

It is a consensus that small models perform quite poorly under the paradigm of self-supervised contrastive learning. Existing methods usually adopt a large off-the-shelf model to transfer knowledge to the small one via distillation. Despite their effectiveness, distillation-based methods may not be suitable for some resource-restricted scenarios due to the huge computational expenses of deploying a large model. In this paper, we study the issue of training self-supervised small models without distillation signals. We first evaluate the representation spaces of the small models and make two non-negligible observations: (i) the small models can complete the pretext task without overfitting despite their limited capacity and (ii) they universally suffer the problem of over clustering. Then we verify multiple assumptions that are considered to alleviate the over-clustering phenomenon. Finally, we combine the validated techniques and improve the baseline performances of five small architectures with considerable margins, which indicates that training small self-supervised contrastive models is feasible even without distillation signals. The code is available at https://github.com/WOWNICE/ssl-small.
Towards Unsupervised Deformable-Instances Image-to-Image Translation

Replacing objects in images is a practical functionality of Photoshop, e.g., clothes changing. This task is defined as Unsupervised Deformable-Instances Image-to-Image Translation (UDIT), which maps multiple foreground instances of a source domain to a target domain, involving significant changes in shape. In this paper, we propose an effective pipeline named Mask-Guided Deformable-instances GAN (MGD-GAN) which first generates target masks in batch and then utilizes them to synthesize corresponding instances on the background image, with all instances efficiently translated and background well preserved. To promote the quality of synthesized images and stabilize the training, we design an elegant training procedure which transforms the unsupervised mask-to-instance process into a supervised way by creating paired examples. To objectively evaluate the performance of UDIT task, we design new evaluation metrics which are based on the object detection. Extensive experiments on four datasets demonstrate the significant advantages of our MGD-GAN over existing methods both quantitatively and qualitatively. Furthermore, our training time consumption is hugely reduced compared to the state-of-the-art. The code could be available at https://github.com/sitongsu/MGD_GAN.
ANALYSIS OF BITCOIN'S IMPACT ON USERS AND THEIR IMPACT ON THE VALUE OF THE RUPIAH AND THE ROLE OF THE GOVERNMENT IN THE PRESENCE OF BITCOIN

Bitcoin has a peer to peer system that is in contrast to financial system by eliminating third parties in transactions. Countries in the world have different positions on Bitcoin, there are countries that are accept, reject or not both, so that understanding is needed more in depth to the factors that determine the position of the country above Bitcoin. This study aims to look for the influence of the development of Bitcoin, the performance of fiat money and the governance systems of countries in the world in determining its position on the legality of Bitcoin. Theories and concepts used inside this research is the international financial system and the state management system; with a quantitative approach as well as multinomial logistic regression analysis supported with secondary data. The results of the analysis in this study revealed that of the nine factors affecting the country's top position Bitcoin; it's just that the significance is different. Influencing Factors significant in determining the country's position on Bitcoin are factors political and economic factors have no significant effect. So it can be said that state acceptance of Bitcoin tends to be caused political reasons compared to the economy, even though Bitcoin itself is located on economic aspects. If Bitcoin acceptance is legalized, then the countries in the world still must accompany the role of government because Bitcoin is judged not to have clear rules and potentially detrimental to others.