Reviewerly/exHarmony huggingface dataset:
Relational Abstractions for Generalized Reinforcement Learning on Symbolic Problems

Reinforcement learning in problems with symbolic state spaces is challenging due to the need for reasoning over long horizons. This paper presents a new approach that utilizes relational abstractions in conjunction with deep learning to learn a generalizable Q-function for such problems. The learned Q-function can be efficiently transferred to related problems that have different object names and object quantities, and thus, entirely different state spaces. We show that the learned, generalized Q-function can be utilized for zero-shot transfer to related problems without an explicit, hand-coded curriculum. Empirical evaluations on a range of problems show that our method facilitates efficient zero-shot transfer of learned knowledge to much larger problem instances containing many objects.
3E-Solver: An Effortless, Easy-to-Update, and End-to-End Solver with Semi-Supervised Learning for Breaking Text-Based Captchas

Text-based captchas are the most widely used security mechanism currently. Due to the limitations and specificity of the segmentation algorithm, the early segmentation-based attack method has been unable to deal with the current captchas with newly introduced security features (e.g., occluding lines and overlapping). Recently, some works have designed captcha solvers based on deep learning methods with powerful feature extraction capabilities, which have greater generality and higher accuracy. However, these works still suffer from two main intrinsic limitations: (1) many labor costs are required to label the training data, and (2) the solver cannot be updated with unlabeled data to recognize captchas more accurately. In this paper, we present a novel solver using improved FixMatch for semi-supervised captcha recognition to tackle these problems. Specifically, we first build an end-to-end baseline model to effectively break text-based captchas by leveraging encoder-decoder architecture and attention mechanism. Then we construct our solver with a few labeled samples and many unlabeled samples by improved FixMatch, which introduces teacher forcing, adaptive batch normalization, and consistency loss to achieve more effective training. Experiment results show that our solver outperforms state-of-the-arts by a large margin on current captcha schemes. We hope that our work can help security experts to revisit the design and usability of text-based captchas. The source code of this work is available at https://github.com/SJTU-dxw/3E-Solver-CAPTCHA.
Environmental investment is a powerful "support" for the progressive and sustainable growth of the post-pandemic economy of Russia

The objective need for environmental investment to overcome the reproduction of anti-sustainable environmental trends in the modern Russian economy and its post-pandemic recovery is substantiated. Environmental investments that meet the criteria of the global ESG agenda are positioned as responsible in their essence and transformative in their functional role. Original scientific judgments are presented on the impact of such investments on the dynamics and intensity of "green" innovations (new technologies, production processes, supply chains capable of solving issues of waste processing and industrial reproduction of raw materials from waste resources, as well as the use of alternative energy sources, etc.), which are able to generate long-term and sustainable growth of aggregate factor productivity (TFP) and ensure the radical transformations of the economic system associated with the formation of a "green" circular economy. Regressive models (growth curves) have been constructed for the current domestic economy, taking into account the volume of environmental investment, confirming the hypothesis of the "weakness" of this process in the Russian Federation, including due to the lack of significant changes in the state environmental policy. The minimum necessary economic tools of the state policy in the field of stimulating environmental investment in Russia have been formed in order to post-pandemic recovery of its economy and ensure long-term sustainable growth of TFP. The conducted research makes a certain contribution to the development of the theory of endogenous economic growth by taking into account the impact of environmental investment, which is initially focused on the effective use of natural capital, on the maximum involvement of waste resources in economic turnover, the replacement of traditional production technologies with environmentally friendly or low-carbon, the improvement of ecosystems, the production potential of the economy, the quality of the environment and social changes.
Formation of a student’s subjective position at a university

Modernization of education and its acquisition of a value-oriented foundation of the state policy of Russia assumes that the basis of pedagogical activity at the university is the creation of conditions for the self-development of a young person, his understanding of the role and personal participation in the development of the economy of the state. Currently, there is a problem of inclusion and involvement of the student in learning, which directly affects the formation of his active position in life and development prospects. One of the solutions may be the formation of a students subjective position in the process of learning and upbringing. In the article, the author defines subjectivity as an integrative property of personality, the system-forming components of which (motivational, cognitive and activity) reflect the orientation of the individual to understand the personal educational result. Based on the proposed structure, the stages of the formation of the students subjective position are considered. Each stage contributes to the formation of a certain type of universal educational actions, as a result of which there is a gradual increment of subjectivity. As a result, the student has an understanding of his own learning result, which contributes to the formation of specified competencies within the framework of external educational goals.
Multifunctional and Low Cost Autonomous Mobile Robot

With the development of micro controllers and single-board computer technologies, robotic systems can be designed at a lower cost.In this context, a low-cost autonomous mobile robot has been developed in presented study.By adding a variety of systems to the autonomous mobile robot, remotely controlled and steered robot was designed.In this study, Arduino and Raspberry cards were used for the autonomous mobile robot, which has a four-wheel and tracked structure.Using the Raspberry card and the Pi camera in the robot, the activities around the robot were transferred to the another computer via internet.A Wi-Fi (wireless fidelity) module was used to connect the internet.The Bluetooth module allows the camera to move right-to-left or up-to-down.The GPS (global positioning system) module has been utilized for the robot to act autonomously.With the ultrasonic distance sensor, the autonomous mobile robot can find a new route by overcoming obstacles.The battery can be charged with wireless charging without the need to be charged with a cable in case the battery weakens during movement.This robot can be used in cargo handling, patient assistance, food and beverage service, a relief vehicle in disaster situations.Designed low-cost autonomous mobile robot has distinguished features and presented development phases of robot can assist researchers.
Effective Approaches to Neural Query Language Identification

Abstract Query language identification (Q-LID) plays a crucial role in a cross-lingual search engine. There exist two main challenges in Q-LID: (1) insufficient contextual information in queries for disambiguation; and (2) the lack of query-style training examples for low-resource languages. In this article, we propose a neural Q-LID model by alleviating the above problems from both model architecture and data augmentation perspectives. Concretely, we build our model upon the advanced Transformer model. In order to enhance the discrimination of queries, a variety of external features (e.g., character, word, as well as script) are fed into the model and fused by a multi-scale attention mechanism. Moreover, to remedy the low resource challenge in this task, a novel machine translation–based strategy is proposed to automatically generate synthetic query-style data for low-resource languages. We contribute the first Q-LID test set called QID-21, which consists of search queries in 21 languages. Experimental results reveal that our model yields better classification accuracy than strong baselines and existing LID systems on both query and traditional LID tasks.1
How Do UK University Students Achieve Sustainable Learning in Digital Era

The competence and expertise to learn in the setting of a digital society are pivotal for university students. Therefore, this article aims to explore what factors hinder UK university students from achieving sustainable learning in the digitalized realm. It first construed the meaning and characteristics of sustainable learning and digitalization. Then the paper identified the obstacle that hinders learning. Some UK university students suffer from nervousness, tension, and other psychological burdens, which have a substantial influence on the self. Secondly, many international students face barriers to digital learning in the UK, such as culture shock and language difficulties, and they worried that digital learning would interfere with their experience of campus culture. The third factor is the capacity to adjust to a changing learning environment. Many freshmen without the essential experience to acclimate to a minority culture, and the phase shift will affect students' learning results. Simultaneously, growing tuition costs add to their financial burden. The article also provides several suggestions, Firstly, the support of a personal mentor and the assistance of the university's professional information system are then recommended as viable answers to these challenges. Students can also benefit from using the Internet to improve their overall communication and interaction abilities, as well as universities' ability to reply responsibly and academic institutions' engagement techniques. Ultimately, government actions and bank flexibility of the student loan system will alleviate financial hardship.
Collaborative Speculations on Future Themes for Participatory Design in Germany

Abstract Participatory Design means recognizing that those who will be affected by a future technology should have an active say in its creation. Yet, despite continuous interest in involving people as future users and consumers into designing novel and innovative future technology, participatory approaches in technology design remain relatively underdeveloped in the German HCI community. This article brings together the diversity of voices, domains, perspectives, approaches, and methods that collectively shape Participatory Design in Germany. In the following, we (1) outline our understanding of participatory practice and how it is different from mere user involvement; (2) reflect current issues of participatory and fair technology design within the German Participatory Design community; and (3) discuss tensions relevant to the field, that we expect to arise in the future, and which we derived from our 2021 workshop through a speculative method. We contribute an introduction and an overview of current themes and a speculative outlook on future issues of Participatory Design in Germany. It is meant to inform, provoke, inspire and, ultimately, invite participation within the wider Computer Science community.
Design of the College Students’ Music Big Data Management System Based on Computer Assistance

With the rapid development of science and technology, the college music big data management system also needs a computer-aided data model for optimization. In order to improve the efficiency of computer-aided teaching of the music big data management system in colleges and universities, this paper analyzes and processes the music database of music software based on the computer-aided Bayesian algorithm and establishes a Bayesian model. Firstly, this paper introduces the principle and function theorem of the Bayesian algorithm. The data is divided into original music and new music, and a college music data management system with search recommendations as the core is established. We iteratively calculate the optimal hidden semantic matrix and we finally select ReLU function as the activation function of this experiment. After the model of the music management system is established, it is applied to a university. The experimental results show that the experiment has reached the expected standard. When the hidden factor dimension is 11, the system model has the best representation of music features. Students’ use of the music search and the recommendation system has greatly improved their music literacy and music coverage.
Probing Genuine Multipartite Einstein–Podolsky–Rosen Steering and Entanglement Under an Open Tripartite System

Einstein–Podolsky–Rosen steering is a peculiar quantum nonlocal correlation and has unique physical characteristics and a wide application prospect. Even more importantly, multipartite steerable states have more vital applications in the future quantum information field. Thus, in this work, we explored the dynamics characteristics of both genuine multipartite steering (GMS) and genuine multipartite entanglement (GME) and the relations of both under an open tripartite system. Specifically, the tripartite decoherence system may be modeled by the three parties of a tripartite state that undergo the noisy channels. The conditions for genuine entangled and steerable states can be acquired for the initial tripartite state. The results showed that decoherence noises can degrade the genuine multipartite entanglement and genuine multipartite steering and even induce its death. Explicitly, GME and GMS disappear with the increase in the decoherence strength under the phase damping channel. However, GME and GMS rapidly decay to death with the increase in the channel-noise factor and then come back to life soon after in the bit flip channel. Additionally, the results indicate that GMS is born of GME, but GME does not imply GMS, which means that the set of genuine multipartite steerable states is a strict subset of the set of genuine multipartite entangled states. These conclusions may be useful for discussing the relationship of quantum nonlocal correlations (GME and GMS) in the decoherence systems.
Modelling the Turtle Python library in CSP

Software verification is an important tool in establishing the reliability of critical systems.One potential area of application is in the field of robotics, as robots take on more tasks in both day-to-day areas and highly specialised domains.Robots are usually given a plan to follow, if there are errors in this plan the robot will not perform reliably.The capability to check plans for errors in advance could prevent this.Python is a popular programming language in the robotics domain, through the use of the Robot Operating System (ROS) and various other libraries.Python's Turtle package provides a mobile agent, which we formally model here using Communicating Sequential Processes (CSP).Our interactive toolchain CSP2Turtle with CSP model and Python components, enables Turtle plans to be verified in CSP before being executed in Python.This means that certain classes of errors can be avoided, and provides a starting point for more detailed verification of Turtle programs and more complex robotic systems.We illustrate our approach with examples of robot navigation and obstacle avoidance in a 2D grid-world.
Neural QBAFs: Explaining Neural Networks Under LRP-Based Argumentation Frameworks

In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.
Performance Analysis of Hybrid Cryptography System for High Security and Cloud-Based Storage

Now a day, the security of the data is playing a major part in communication systems due to further bushwhackers between channels media. The security position depends on cache crucial and as per literature, advanced the bit size of the key, advanced the security and also larger data size comes major challenge task for the further process. Thus, the generation of crucial with the further size is a major grueling task and at present, the Advanced Encryption Standard (AES) is a better cryptography system where the encryption and decryption can perform with a fixed key. The literature says the holomorphic function is well-suitable for data size reduction. To address this issue, new Holomorphic grounded encryption and decryption and AES are combined to increase the security position. The alternate novelty is that variable crucial generation using Elliptic Wind Cryptography (ECC) due to its enlarged proportion of consideration in assiduity and experimenters. The ECC uses point addition and point doubling to induce 256 values and addition operations can be avoided. After the generation of the matrix, each matrix value is translated and decrypted using a Holomorphic algorithm. The proposed work has been designed using MATLAB 2017a, dissembled, and validated with different datasets in real decors. Cloud computing is expected to be considered one of the primary computing platforms in the field of storage and security as it possesses many advantages such as profitability, efficiency as well as lower implementation overheads. Contemporary cloud computing security algorithms are enhanced extensions of cryptography. Data privacy, as well as data protection, are the major areas of concern in Cloud computing. The cryptographic with holomorphic based data encryption and interchange of information is exchanged and then accumulated in the cloud through holomorphic encryption which uses point addition and doubling operation to ensure data confidentiality of owners as well as users. Proposed work novel hybrid algorithm based on the context of encryption and decryption and thus integrates cryptography hybrid techniques include modified 126-bit AES and ElGamal based ECC through splitting algorithm. The advantage of splitting the larger data in size into binary form and then processing for encryption and decryption leads to optimization of latency, increase throughput, and security. The proposed hybrid approach has better security towards information sharing as well as cloud storage intrusions. Based on obtained results in MATLAB 2017a software tool, the obtained results show that 43% improvement in throughput and 12% reduction in latency, and a 21% improvement in security level.
Feature-Level Camera Style Transfer for Person Re-Identification

The person re-identification (re-ID) problem has attracted growing interest in the computer vision community. Most public re-ID datasets are captured by multiple non-overlapping cameras, and the same person may appear dissimilar in different camera views due to variances of illuminations, viewpoints and postures. These differences, collectively referred to as camera style variance, make person re-ID still a challenging problem. Recently, researchers have attempted to solve this problem using generative models. The generative adversarial network (GAN) is widely used for the pose transfer or data augmentation to bridge the camera style gap. However, these methods, mostly based on image-level GAN, require huge computational power during the training of generative models. Furthermore, the training process of GAN is separated from the re-ID model, which makes it hard to achieve a global optimal for both models simultaneously. In this paper, the authors propose to alleviate camera style variance in the re-ID problem by adopting a feature-level Camera Style Transfer (CST) model, which can serve as an intra-class augmentation method and enhance the model robustness against camera style variance. Specifically, the proposed CST method transfers the camera style-related information of input features while preserving the corresponding identity information. Moreover, the training process can be embedded into the re-ID model in an end-to-end manner, which means the proposed approach can be deployed with much less time and memory cost. The proposed approach is verified on several different person re-ID baselines. Extensive experiments show the validity of the proposed CST model and its benefits for re-ID performance on the Market-1501 dataset.
Social Media in Politics: Interrogating Electorate-Driven Hate Speech in Nigeria's 2019 Presidential Campaigns

Social media has become an indispensable and dominant means of communication and dissemination of information worldwide. This paper focuses on the use of Facebook by political supporters and electorates to canvass for support for their preferred presidential candidates in the 2019 general elections and the underlying hate speech that emanated therefrom. In this context, this paper seeks to critically evaluate how political supporters and electorates used the instrumentality of Facebook to share hate messages during the 2019 presidential election and its impact on Nigeria’s political space. The results of this paper indicate widespread dissemination of hate comments by political supporters and electorates in the furtherance of their support for their preferred presidential candidates. The paper advocates responsible use of Facebook in electioneering and the imperative of regulation to guard against the circulation of hate electoral comments that could heat up the political arena and trigger electoral violence.
ZoomP3: Privacy-Preserving Publishing of Online Video Conference Recordings

The COVID-19 epidemic has made online video conferencing extremely popular throughout the world, with many schools, companies and government sectors using video conferencing applications (e.g., Zoom, Google Meet) in a daily basis. These applications also provide local or cloud recording services, which allow the replay or sharing of video conference recordings (VCRs) in a later time. Such convenience, however, can easily cause infringement of privacy as meeting participants’ personally identifiable information (e.g., face, name, voice) may be exposed to the public without their awareness or consent. While privacy regulation and training can help relieve the situation, efficient and effective tools are also highly desired to protect the privacysensitive users in the VCRs before their public releases. In this work, we propose the first Privacy-Preserving Publishing system (ZoomP3 ) that automatically processes video and audio information in VCRs for privacy protection. Besides leveraging and integrating multiple state-of-the-art computer vision and audio processing tools seamlessly into our system, a number of optimization algorithms are proposed to improve the scalability of the system, enabling it to protect the privacy of long video conferences. We have conducted various tests with short and long videos, and the results (with online demos) verified that ZoomP3 system is suitable for largescale use. It may be applied as an online service, e.g., by Zoom, or by large organizations such as universities, research institutes and government sectors.