Reviewerly/exHarmony huggingface dataset:
Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment

The statistical regularities of natural images, referred to as natural scene statistics, play an important role in no-reference image quality assessment. However, it has been widely acknowledged that screen content images (SCIs), which are typically computer generated, do not hold such statistics. Here we make the first attempt to learn the statistics of SCIs, based upon which the quality of SCIs can be effectively determined. The underlying mechanism of the proposed approach is based upon the mild assumption that the SCIs, which are not physically acquired, still obey certain statistics that could be understood in a learning fashion. We empirically show that the statistics deviation could be effectively leveraged in quality assessment, and the proposed method is superior when evaluated in different settings. Extensive experimental results demonstrate the Deep Feature Statistics based SCI Quality Assessment (DFSS-IQA) model delivers promising performance compared with existing NR-IQA models and shows a high generalization capability in the cross-dataset settings. The implementation of our method is publicly available at https://github.com/Baoliang93/DFSS-IQA.
Simulating the Photon Statistics of Multimode Gaussian States by Automatic Differentiation of Generating Functions

Advances in photonics require photon-number resolved simulations of quantum optical experiments with Gaussian states. We demonstrate a simple and versatile method to simulate the photon statistics of general multimode Gaussian states. The derived generating functions enable simulations of the photon number distribution, cumulative probabilities, moments, and factorial moments of the photon statistics of Gaussian states as well as of multimode photon-added and photon-subtracted Gaussian states. Numerical results are obtained by automatic differentiation of these generating functions by employing the software framework PyTorch. Our approach is particularly well suited for practical simulations of the photon statistics of quantum optical experiments in realistic scenarios with low photon numbers, in which various sources of imperfections have to be taken into account. As an example, we calculate the detection probabilities for a recent multipartite time-bin coding quantum key distribution setup and compare them with the corresponding experimental values.
A Review of Challenges in Machine Learning based Automated Hate Speech Detection

The spread of hate speech on social media space is currently a serious issue. The undemanding access to the enormous amount of information being generated on these platforms has led people to post and react with toxic content that originates violence. Though efforts have been made toward detecting and restraining such content online, it is still challenging to identify it accurately. Deep learning based solutions have been at the forefront of identifying hateful content. However, the factors such as the context-dependent nature of hate speech, the intention of the user, undesired biases, etc. make this process overcritical. In this work, we deeply explore a wide range of challenges in automatic hate speech detection by presenting a hierarchical organization of these problems. We focus on challenges faced by machine learning or deep learning based solutions to hate speech identification. At the top level, we distinguish between data level, model level, and human level challenges. We further provide an exhaustive analysis of each level of the hierarchy with examples. This survey will help researchers to design their solutions more efficiently in the domain of hate speech detection.
PRE-INCUBATION FRAMEWORK OF SOFTWARE STARTUP USING GROW COACHING MODEL, BLOCK SCHEDULING AND DAILY SCRUM

Pre-incubation of software startup is a training and coaching activity to improve understanding of knowledge and skills in developing software. The problem that occurs between education in universities and business incubators is the gap, namely the lack of mastery of knowledge and skills in developing software. The solution to this problem is to develop a framework for pre-incubation activities. In this framework, the methodology is used as a combination of the GROW coaching model, block scheduling, and daily stand-up meeting (daily scrum). If before participating in the pre-incubation the tenant takes 4 months to 6 months to build a website application, then after participating in the pre-incubation the tenant can create a website application within 1 week to 4 weeks.
Graph Neural Modeling of Network Flows

Network flow problems, which involve distributing traffic such that the underlying infrastructure is used effectively, are ubiquitous in transportation and logistics. Among them, the general Multi-Commodity Network Flow (MCNF) problem concerns the distribution of multiple flows of different sizes between several sources and sinks, while achieving effective utilization of the links. Due to the appeal of data-driven optimization, these problems have increasingly been approached using graph learning methods. In this paper, we propose a novel graph learning architecture for network flow problems called Per-Edge Weights (PEW). This method builds on a Graph Attention Network and uses distinctly parametrized message functions along each link. We extensively evaluate the proposed solution through an Internet flow routing case study using $17$ Service Provider topologies and $2$ routing schemes. We show that PEW yields substantial gains over architectures whose global message function constrains the routing unnecessarily. We also find that an MLP is competitive with other standard architectures. Furthermore, we analyze the relationship between graph structure and predictive performance for data-driven routing of flows, an aspect that has not been considered by existing work in the area.
Implementation Of Revenue Sharing System On Loading Workers In Islamic Economic Perspective At The Goods Terminal In Banda Aceh

Study this aim for knowing application Revenue Sharing for workers demolish load goods in town band aceh. For knowing the data from the research process that has been done. method data collection used is type study qualitative descriptive that uses data in the form of results interview to owner truck, manager companies and workers demolish load. The application of the revenue sharing system is a system for results income clean without reduce burden costs. this system is a system of giving right job given by Mudarib to all group workers who do demolish load goods at the unloading terminal load goods in town Banda Aceh. Revenue Sharing is one of the to cooperate with contract Mudharabah. Application Principle Revenue sharing for give Justice to owner companies, managers and workers who do demolish load goods.
MENGINVESTIGASI MODEL PEMBELAJARAN MUSLIM YANG EFEKTIF ANAK USIA DINI DIMASA PANDEMIC COVID-19

The corona virus (Covid-19) that is hitting the world today is very influential on social and economic life, and even has an impact on the world of education. The Indonesian government has taken a policy which is stated in Government Regulation No. 21 of 2020. The article explains to limit school activities with BDR (Learning From Home) learning activities through the circular letter of the Minister of Education and Culture 36962/MPK.A/HK/2020 which stated that all learning must be done online to prevent the spread of the corona virus disease (Covid-19). The existence of government policies that enforce online learning ultimately requires parents to be able to guide their children to study at home and can replace teachers at school, so that parents play an important role in achieving online learning goals. This study specifically discusses the involvement of parent-teacher relationships in achieving effective learning models during the Covid-19 pandemic. This research is a qualitative research with a phenomenological method. The research population includes parents and teachers from kindergarten to elementary school grade 2 and the sample technique using Purposive Sampling includes 10 parents and 10 teachers. Qualitative data analysis consists of five stages including collecting data into a formal database (compile database), breaking down data in the database (disassemble database), reassembly (compiling), interpreting (interpreting data) and the last stage is providing conclusions (conclude). Research data processing consists of coding (coding), data classification (classify), followed by categorization or categorization, or also called in-vivo term or in-vivo coding. Government policies that enforce online learning in fact make children less able to understand the material so that learning becomes less effective. The results of the assessment found that the problem was that not all levels of education were able to support the implementation of education through the use of the internet. Some teachers in various regions also feel uneasy about the online learning model. This is inseparable from the importance of face-to-face learning which should be important for early childhood children. Learning that is applied during the pandemic shows a big change and tends to lead to a decline. This is because the educational model contradicts the educational goals that are applied and needed for early childhood who require a lot of interaction with other people.&#x0D; &#x0D; Keywords : Effectiveness of learning, online learning, phenomenology, basic education
Art Exhibition in Digital Art Space as Social Interaction Arena for Artists Towards the Metaverse Era

With the help of several websites and smartphone application platforms, the number of digital visual arts performances and exhibitions is expanding. This indicates that social relationships have migrated into the virtual realm, offering artists more communication flexibility with a larger audience. This research investigates the social interactionbetween artists and viewers during an art exhibition and performance in a digital art environment. As actual space transforms into virtual space, the creation of digital art space signals a shift in the way art actors and viewers interact with art exhibitions. Digital art spaces allow users to connect and communicate as they see fit in the virtual world. Art exhibitions in the digital art space may be defines as taking place in a 3D environment arena with sophisticated interaction between organizers, artists, and spectators using a variety of communication method.
A Syntactic Analysis of Noun Phrase through “Hansel and Gretel” Short Story To 1st Semester of Accounting Students at University of Bina Sarana Informatika

Languages all over the world have their own grammar for constructing words and their components. The aim of the research is to recognize, classify and analyze noun phrases by reading Hansel and Gretel short story. A noun phrase is a part of the syntax. The syntax is a part of Linguistics. It has meaningful elements together to form words, words together to form phrases, phrases together to form clauses, clauses together to form sentences, and sentences together to form texts. Noun phrase has some aspects; they are identifier, adjective, noun modifier, quantifier, preposition phrase, participle clause, conjunctions, and indefinite clause. Hansel and Gretel short story was used as the research instrument about noun phrases. The method used to do the research was descriptive qualitative. The participants of this research are the 1st semester of accounting students at the University of Bina Sarana Informatika. The results of this research are: there are 25 noun phrases, 16 noun phrases are about rules 1, determiner + headword, meanwhile 9 other noun phrases are about common noun phrase, found in Hansel and Gretel short story.
Discovering Rhetorical Strategies in the Islamic Discourse of Tuan Guru Bajang Muhammad Zainul Majdi

A leader is prevalently a communicator in an organization, no matter the rule. A good leader typically has good communication skills, which allow them to elicit participation from the people they oversee. In particular, Muslim scholars are also pioneers of social and religious growth. Thus, competent communication efficiency is essential for them to lead people to a better way of life by part of Allah SWT. However, it is still found vast of Muslim scholars, which is uneven Islamic teaching presentation quality. Thus, the goal of this study is to come up with a decent speech that anaesthetizes the audience. This was accomplished by examining the use of hidden rhetoric in the address of TGB. Muhammad Zainul Majdi, the best Indonesian preacher in 2021. As a result, this qualitative study uses discourse analysis to examine his two speeches as samples and data sources. The result indicated that TGB. Muhammad Zainul Majdi applied a variety of hidden rhetorical techniques. In his Safari Da'wah Nusantara and national addresses, Muhammad Zainul Majdi used similitude, reverence, expertise, intelligence, and goodwill to demonstrate credibility or ethos and persuade the audience. Love or friendship versus hatred, Fear versus Confidence, Indignation versus Pity, and Admiration versus Envy are some of the pathos he uses. In terms of logos, He deals with three issues: parables, enthymeme, and data.
An Analysis of Teacher’s Strategy in Teaching Speaking at SMA Negeri 9 Haltim

This research is aimed at describing the strategies used by the English teacher of the students of SMA Negeri 9 Haltim in teaching speaking. Based on pre-observation, the researcher found that SMA Negeri 9 Haltim applied teaching strategies in the teaching and learning process, especially in speaking and it makes the students easier to understand speaking English. This study was conducted by using a descriptive qualitative design. The subjects of this research were the English teacher and the students of SMA Negeri 9 Haltim. The data was gained by observation, interview, and documentation. The results indicated that there are five strategies in teaching speaking which applied by the English teacher. They are drilling, simulation and role play, the portrait interview, creative tasks, and group work. There were also two principles applied by the teacher to develop vocabulary mastery and fluency, the first is the activity is meaning-focused, and the second is the learners take a part in activities where all the language items are within their previous experiences. The teacher applied those strategies to help the students become active so that they can understand the lessons and also enjoy the teaching and learning process.
Benefits of Drilling Repetition in Enhancing Second Language Learners’ Speaking Ability

This paper aims to analyze the significance of drilling repetition in enhancing second language learners' speaking ability. In learning a second language, some learners may overcome problems in saying words or sentences. In some cases, they also mispronounce the words. These can occur because of some problems, such as a lack of confidence, a lack of practice, and less feedback from the teacher. Based on this issue, this paper is concerned with the discussion of how the drilling technique can be a solution to the problem and its benefits to the students' speaking performance. This paper uses qualitative research analysis and a descriptive method to discuss the finding. The equipment that was implemented for this paper is journals and books. The benefits of drilling repetition are providing learners with intensive practice in hearing and saying particular word phrases. They can help with memorization and automation of common language patterns, providing the opportunity for learners to get immediate feedback, assisting learners in noticing the correct form or pronunciation of a word or phrase, which makes the learners speaking ability accurately, and building confidence. This paper finally points out that the benefits of adopting drilling repetition in enhancing second language learners' speaking ability are helpful to building learners' confidence and fluency in speaking the second language.
An Analysis of Attitude Resources on Coverage concerning COVID-19 in New York Times

This paper adopts the attitudinal subsystem of appraisal theory to conduct a discourse analysis on news reports in the New York Times concerning COVID-19 in the initial stage. The result reveals that Appreciation resources account for the highest proportion in related coverage, while Affect and Judgement come in second and third place, respectively, with similar percentages. With regard to Affect, New York Times tends to use In/security and Dis/inclination; Judgement, capacity and Appreciation, Composition. From the perspective of the polarity of attitude, it can be seen that writers in the New York Times use both explicit and implicit attitude resources to express negative feelings toward the Chinese government’s response to this pandemic.
Authentication of Things: Authentication and Access Control for the Entire IoT Device Life-Cycle

As the number of Internet of Things (IoT) devices already grows faster than the population, the need for strong authentication and access control mechanisms is greater than ever. Legacy authentication schemes are usually computationally expensive which makes them unsuitable for resource-constrained IoT devices. On the other hand, solutions that target such devices typically base their access control mechanism solely on authentication. In a complex smart environment, however, IoT devices often offer and consume a range of resources, which demands a fine-grained access control mechanism. Besides, the IoT paradigm also beckons safe interoperability among devices that belong to different smart environments. Last, there is a lack of options for authentication and access control solutions that cover the entire IoT device life-cycle, i.e., from device manufacturing to decommissioning. In this work, we propose Authentication of Things (AoT), a holistic authentication and fine-grained access control solution for the entire IoT device life-cycle. AoT comprises a suite of protocols which relies on Identity-Based Cryptogra phy (IBC) to distribute keys and authenticate devices as well as Attribute-Based Cryptography (ABC) to cryptographically enforce a fine-grained Attribute-Based Access Control (ABAC). We evaluate an AoT prototype at different security levels implemented on a variety of platforms, representing a wide range of IoT devices, from smartphones to microcontrollers. Our results indicate that AoT performance ranges from affordable on resource-constrained devices to highly efficient on powerful devices.
A Vote Tallying System Based on Computer Vision

In this paper, we describe an ongoing project on a vote tallying system based on computer vision. The core idea consists of recording the votes into paper ballots containing markings that can be read by a software using computer vision. The software reads the markings from a video recorded during the elections. The video can be made publicly available, so that any interested party can check that the tallying is correct.
Event2Ledger: Container traceability using Docker Swarm and consortium Hyperledger blockchain

Cloud computing employing container-based virtualization enables dynamic allocation of computational resources, providing scalability and fault tolerance, etc. However, this flexibility could imply a drawback: container environment monitoring is highly challenging due to the large flow of calls and (de)allocations. In this work, we present event2ledger, a blockchain-based solution that implements a distributed log with data sent by authorized and customized collectors to a permissioned consortium blockchain, responsible for ordering and storage in a distributed and auditable manner. A proof-of-concept is implemented with a Hyperledger Fabric consortium blockchain, composed and maintained by the scenario actors (i.e., Providers, Users, and Developers), which stores signed container life cycle events.
On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator

The convergence of policy gradient algorithms in reinforcement learning hinges on the optimization landscape of the underlying optimal control problem. Theoretical insights into these algorithms can often be acquired from analyzing those of linear quadratic control. However, most of the existing literature only considers the optimization landscape for static full-state or output feedback policies (controllers). We investigate the more challenging case of dynamic output-feedback policies for linear quadratic regulation (abbreviated as dLQR), which is prevalent in practice but has a rather complicated optimization landscape. We first show how the dLQR cost varies with the coordinate transformation of the dynamic controller and then derive the optimal transformation for a given observable stabilizing controller. At the core of our results is the uniqueness of the stationary point of dLQR when it is observable, which is in a concise form of an observer-based controller with the optimal similarity transformation. These results shed light on designing efficient algorithms for general decision-making problems with partially observed information.
Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization

Nonsmooth nonconvex optimization problems broadly emerge in machine learning and business decision making, whereas two core challenges impede the development of efficient solution methods with finite-time convergence guarantee: the lack of computationally tractable optimality criterion and the lack of computationally powerful oracles. The contributions of this paper are two-fold. First, we establish the relationship between the celebrated Goldstein subdifferential~\citep{Goldstein-1977-Optimization} and uniform smoothing, thereby providing the basis and intuition for the design of gradient-free methods that guarantee the finite-time convergence to a set of Goldstein stationary points. Second, we propose the gradient-free method (GFM) and stochastic GFM for solving a class of nonsmooth nonconvex optimization problems and prove that both of them can return a $(\delta,\epsilon)$-Goldstein stationary point of a Lipschitz function $f$ at an expected convergence rate at $O(d^{3/2}\delta^{-1}\epsilon^{-4})$ where $d$ is the problem dimension. Two-phase versions of GFM and SGFM are also proposed and proven to achieve improved large-deviation results. Finally, we demonstrate the effectiveness of 2-SGFM on training ReLU neural networks with the \textsc{Minst} dataset.
CSL: A Large-scale Chinese Scientific Literature Dataset

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code are available at https://github.com/ydli-ai/CSL