Reviewerly/exHarmony huggingface dataset:
Analysis of Data Exit Security Review under the Background of Digital Trade: with Comments on the Chinese Measures for Data Exit Security Assessment (Draft for comments)

Data exit brings new growth and opportunities for the development of digital trade in various countries, but without regulation, it may damage national security interests. Therefore, it is necessary to study the review rules of data exit, which is of great significance to maintain national security and ensure the development of digital trade. Therefore, under the legal basis provided by the Cybersecurity Law and the Data Security Law, China actively explored and formulated specific rules for data exit assessment, and published the Measures for Data Exit Security Assessment (Draft for comments) (hereinafter “the Measures”) in October 2021. The Measures is undoubtedly a beneficial attempt to protect national security and realize the orderly exit of data. It has made a significant breakthrough in relevant rules. However, there are still some rules to be improved. In terms of such rules in other territories, the United States and the European Union have adopted data exit review mechanisms with different value orientations, which have their own advantages and characteristics, can provide references for China to improve data exit security review rules.
Creating Interactive Crowds with Reinforcement Learning

The entertainment industry, as well as the field of Computer Graphics, frequently faces the issue of creating large virtual crowds that would populate a scene. One of the ways to achieve that, particularly with modern rendering techniques, is by using simulation -- this, however, is nontrivial to design and control. The main goal of my PhD work is working towards the creation of a tool enabling the creation of virtual crowds that one can interact with, and we believe the best way to that is through Multiagent Reinforcement Learning techniques. These animated crowds can then be used both in movies and video games. Especially for the latter, it is highly desirable that both the crowd as a whole, as well as the individual characters, can react to the user's input in real time.
Code Representation Learning Using Prüfer Sequences (Student Abstract)

An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for code representation learning. In this study, we propose to use the Prufer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Results from our experiment show that prufer-sequence-based representation is indeed highly effective and efficient.
Enhancing Social and Emotional Learning (SEL) Competencies among Education Students through the “Share a Book, Wake a Thought (SABWAT)” Activity

The holistic development of every learner is one vital aim of schools. Thus, the College of Education of the University of Batangas maintains and enhances students’ intellectual, physical and socio-emotional growth and development through developmentally-appropriate activities such as the Share a Book Wake a Thought (SABWAT) project. This research intended to enhance the social and emotional learning competencies of the student-donors of the books and eventually improve the ways the SABWAT activity would be handled in the next years. It utilized the descriptive research design with a self-constructed questionnaire as the main data gathering tool based on the Collaborative for Academic, Social and Emotional Learning (CASEL) Frameworks. It also used focus group discussion to verify the information gathered. Using stratified random sampling, the researchers selected 111 out of 235 enrolled students in the College of Education. The researchers used descriptive statistics such as mean and standard deviation. The findings revealed that the Education students, after being involved in the SABWAT activity, gained social awareness, self-awareness, responsible decision making, relationship skills, and self-management skills; the manifested application of these competencies in life. Major issues and challenges were the limited number of books donated, the small number of students being involved in the actual donation day, and the lack of information about the activity. The proposed SABWAT improvement plan aims to address the issues and challenges like the aforementioned. It is also intended to improve the SEL competencies of students through the various activities relative to SABWAT.
An Investigation on English Teacher’s Strategies for Speaking Activities at SMA Muhammadiyah 6 Palembang

By incorporating an English teacher and a class of 30 students, this study aims to represent teacher practices in teaching speaking to students at SMA Muhammadiyah 6. This qualitative research is in the form of a case study. An English teacher was chosen by using purposive sampling as the participant on this study. A semi-structured interview was used to gather data and identify teaching tactics for speaking. The data were then discussed by using thematic analysis. The results demonstrated that the teacher employed a variety of strategies, including group discussion, brainstorming, and storytelling. The teacher used the strategies to encourage students to participate in the learning process. Because the strategies were implemented properly, they might be able to assist students in becoming more involved in the teaching and learning process
Visual Definition Modeling: Challenging Vision &amp; Language Models to Define Words and Objects

Architectures that model language and vision together havereceived much attention in recent years. Nonetheless, most tasks in this field focus on end-to-end applications without providing insights on whether it is the underlying semantics of visual objects or words that is captured. In this paper we draw on the established Definition Modeling paradigm and enhance it by grounding, for the first time, textual definitions to visual representations. We name this new task Visual Definition Modeling and put forward DEMETER and DIONYSUS, two benchmarks where, given an image as context, models have to generate a textual definition for a target being either i) a word that describes the image, or ii) an object patch therein. To measure the difficulty of our tasks we finetuned six different baselines and analyzed their performances, which show that a text-only encoder-decoder model is more effective than models pretrained for handling inputs of both modalities concurrently. This demonstrates the complexity of our benchmarks and encourages more research on text generation conditioned on multimodal inputs. The datasets for both benchmarks are available at https://github.com/SapienzaNLP/visual-definition-modeling as well as the code to reproduce our models.
IMPLEMENTASI METODE PROFILE MATCHING DAN E-VOTING PADA SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KETUA KARANG TARUNA BERBASIS WEBSITE

&lt;strong&gt;Abstract : &lt;/strong&gt;In the development of information technology, especially in the electoral system, it is urgently needed to manage and present information, which includes the results of the election of youth leaders. Besides that, the solution to every problem is by utilizing the internet, online voting can increase voter participation and help restore public confidence in the electoral process. This method is safer and the results are accurate. Especially in the selection system for youth leaders in Babatan hamlet which has not been touched by computer technology. To facilitate the process of making information about the selection of youth leaders, a decision support system for Profile Matching and E-Voting methods was created to assist the process of selecting candidates for the selection of youth leaders of the Babatan Youth Association in Babatan Hamlet. By using this system, the admin can enter data on the candidate for chairman and voter data so that voters can log into the application and perform E-Voting on the candidate for chairman that was inputted earlier.
Keeping it under lock and keywords: exploring new ways to open up the web archives with notebooks

Abstract The UK Government Web Archive (UKGWA) has been archiving government websites since 1996 and now holds regular snapshots of over 5000 sites. Currently, this material can be accessed through browsing or a simple keyword search interface on their website and has also been catalogued in The National Archives’ online catalogue, Discovery. However, the scale of the UKGWA exposes the limits of the current search interface, and there is no facility to understand the archive in aggregate. This article seeks to go beyond the simple keyword search by exploring the data sources available, from APIs to web crawling, for computational analysis of the UKGWA. The article is accompanied by two Python Notebooks which present examples of analysis using each data source. Notebooks lower the technical barriers for the reader to explore and interpret the UKGWA as data, while surfacing the challenges around making web material computationally accessible.
How General-Purpose Is a Language Model? Usefulness and Safety with Human Prompters in the Wild

The new generation of language models is reported to solve some extraordinary tasks the models were never trained for specifically, in few-shot or zero-shot settings. However, these reports usually cherry-pick the tasks, use the best prompts, and unwrap or extract the solutions leniently even if they are followed by nonsensical text. In sum, they are specialised results for one domain, a particular way of using the models and interpreting the results. In this paper, we present a novel theoretical evaluation framework and a distinctive experimental study assessing language models as general-purpose systems when used directly by human prompters --- in the wild. For a useful and safe interaction in these increasingly more common conditions, we need to understand when the model fails because of a lack of capability or a misunderstanding of the user's intents. Our results indicate that language models such as GPT-3 have limited understanding of the human command; far from becoming general-purpose systems in the wild.
Towards Robust Named Entity Recognition via Temporal Domain Adaptation and Entity Context Understanding

Named Entity Recognition models perform well on benchmark datasets but fail to generalize well even in the same domain. The goal of my th esis is to quantify the degree of in-domain generalization in NER, probe models for entity name vs. context learning and finally improve their robustness, focusing on the recognition of ethnically diverse entities and new entities over time when the models are deployed.
Efficient Dialog Policy Learning by Reasoning with Contextual Knowledge

Goal-oriented dialog policy learning algorithms aim to learn a dialog policy for selecting language actions based on the current dialog state. Deep reinforcement learning methods have been used for dialog policy learning. This work is motivated by the observation that, although dialog is a domain with rich contextual knowledge, reinforcement learning methods are ill-equipped to incorporate such knowledge into the dialog policy learning process. In this paper, we develop a deep reinforcement learning framework for goal-oriented dialog policy learning that learns user preferences from user goal data, while leveraging commonsense knowledge from people. The developed framework has been evaluated using a realistic dialog simulation platform. Compared with baselines from the literature and the ablations of our approach, we see significant improvements in learning efficiency and the quality of the computed action policies.
Secure Cloud Services by Integrating CASB Based Approach

In the last decades, cloud computing has attracted much attention in business, as it provides numerous computing functions (e.g., Software-as-a- Service (SaaS), Platform as-a-Service (PaaS) and Infrastructure-as-a-service (IaaS). Shadow IT refers to the use of cloud apps and services without the explicit approval of IT. Early on, the practice was one of the main drivers of Cloud Access Security Broker (CASB) adoption. There are two risks we found, they are i.) Users typically use unapproved software-as-a- service (SaaS) applications for file sharing, social media, collaboration and web conferencing. Ii.) Another growing challenge, third-party apps and scripts with OAuth permissions. OAuth-connected third-party apps access IT-approved cloud services, such as Microsoft 365 and Google G Suite. Some of these pose risks because of poor design, giving them broader than necessary data permissions. The danger of OAuth is once a token is authorized, access to enterprise data and applications continues until it’s revoked—even if the user’s password is changed. Integrating CASB solution into broader web security infrastructure can provide deeper visibility into all unapproved web apps. Keywords: cloud computing, insider threats, web security, CASB, OAuth
MITIGASI HOAX DI ERA DISRUPSI MELALUI LITERASI DIGITAL

AbstractThis study discusses efforts to ward off hoaxes in the era of disruption through digital literacy. In this sophisticated era of disruption, hoax information or fake news is circulating in people's lives. Hoax information can divide society because it is misleading, so this needs to be addressed. In this case, digital literacy can be a solution to mitigate hoaxes with the aim of creating critical power. The research method discussed is a qualitative method with a literature study approach. The conclusion is that the aspect of developing digital literacy can be done strategically through digital literacy learning for schools, families, and communities. The greater the digital literacy ability they have, the greater the critical power they have.Keywords: hoax, disruption, digital literacy, internet, information
Implementation of the Experiential Learning Courses of the College of Teacher Education and Arts and Sciences: An Assessment

The Experiential Learning Courses (ELC) are indispensable components of the New Teacher Education Curriculum per CHED Memorandum Order No. 30 s., 2004. This is pursuant to the National Competency-Based Teacher Standards (NCBTS), core of the Teacher Education and Development (TEDP) of the government. The researcher utilized descriptive research in here with the aid of an instrument on Practice Teaching used by the CTEAS Practice Teaching supervisors in evaluating the exposure of the pre-service students and the implementation of the Experiential Learning Courses (ELC). The instrument used were the accomplished evaluation rating sheets of the Practice Teachers for the first and second shifts during their deployment in the second semester from SY 2010 – 2011 to 2013 – 2014. These were accomplished by the cooperating teachers where the Practice Teachers were assigned. The instrument was composed of three parts namely; instructional competence, professional and personal characteristics, and punctuality and attendance. There were three subcomponents of instructional competence namely lesson planning, learner’s involvement, and school, home and community involvement. It was found out that the student teachers both for BEED and BSED are within the age expected of them to be completing for the tertiary level of education. Mostly were female and have a very satisfactory academic performance. Also, the performance of BEED and BSED student teachers in demonstration teaching is very satisfactory in instructional competence, professional and personal characteristics, and attendance and punctuality. And the BEED and BSED student teachers displayed difference in terms of professional and personal characteristics. Therefore, these results could be a baseline for future improvement of experiential learning courses.
Expert Knowledge Induced Logic Tensor Networks: A Bearing Fault Diagnosis Case Study

In the recent past deep learning approaches have achieved some remarkable results in the area of fault diagnostics and anomaly detection. Nevertheless, these algorithms rely on large amounts of data, which is often not available, and produce outputs, which are hard to interpret. These deficiencies make real life applications difficult. Before the broad success of deep learning machine faults were often classified using domain expert knowledge based on experience and physical models. In comparison, these approaches only require small amounts of data and produce highly interpretable results. On the downside, however, they struggle to predict unexpected patterns hidden in data. Merging these two concepts promises to increase accuracy, robustness and interpretability of models. In this paper we present a hybrid approach to combine expert knowledge with deep learning and evaluate it on rolling element bearing fault detection. First, we create a knowledge base for fault classification derived from the expected physical attributes of different faults in the envelope spectrum of vibration signals. This knowledge is used to derive a similarity function for comparing input signals to expected faulty signals. Afterwards, the similarity measure is incorporated into different neural networks using a Logic Tensor Network (LTN). This enables logical reasoning in the loss function, in which we aim to mimic the decision process of an expert analyzing the input data. Further, we extend LTNs by weight schedules for axiom groups. We show that our approach outperforms the baseline models on two bearing fault data sets with different attributes and directly gives a better understanding of whether or not fault signals are influenced by other effects or behave as expected.
Conjugated Discrete Distributions for Distributional Reinforcement Learning

In this work we continue to build upon recent advances in reinforcement learning for finite Markov processes. A common approach among previous existing algorithms, both single-actor and distributed, is to either clip rewards or to apply a transformation method on Q-functions to handle a large variety of magnitudes in real discounted returns. We theoretically show that one of the most successful methods may not yield an optimal policy if we have a non-deterministic process. As a solution, we argue that distributional reinforcement learning lends itself to remedy this situation completely. By the introduction of a conjugated distributional operator we may handle a large class of transformations for real returns with guaranteed theoretical convergence. We propose an approximating single-actor algorithm based on this operator that trains agents directly on unaltered rewards using a proper distributional metric given by the Cramér distance. To evaluate its performance in a stochastic setting we train agents on a suite of 55 Atari 2600 games using sticky-actions and obtain state-of-the-art performance compared to other well-known algorithms in the Dopamine framework.
XDC: Adversarial Adaptive Cross Domain Face Clustering (Student Abstract)

In this work we propose a scheme, called XDC, that uses adversarial learning to train an adaptive cross domain clustering model. XDC trains a classifier on a labeled dataset and assigns labels to an unlabeled dataset. We benefit from adversarial learning such that the target dataset takes part in the training. We also use an existing image classifiers in a plug-and-play fashion (i.e, it can be replaced with any other image classifier). Unlike existing works we update the parameters of the encoder and expose the target dataset to the model during training. We apply our model on two face dataset and one non-face dataset and obtain comparable results with state-of-the-art face clustering models.
Game Balancing in Dominion: An Approach to Identifying Problematic Game Elements

In the popular card game Dominion, the configuration of game elements greatly affects the experience for players. If one were redesigning Dominion, therefore, it may be useful to identify game elements that reduce the number of viable strategies in any given game configuration - i.e. elements that are unbalanced. In this paper, we propose an approach that assigns credit to the outcome of an episode to individual elements. Our approach uses statistical analysis to learn the interactions and dependencies between game elements. This learned knowledge is used to recommend elements to game designers for further consideration. Designers may then choose to modify the recommended elements with the goal of increasing the number of viable strategies.
Implementasi Algoritma Haversine Formula Pada Aplikasi Sadewa (Sistem Informasi Destinasi Wisata) Kota Salatiga Berbasis Android

Abstract - Salatiga is a city in Central Java Province which is located in the middle of two big cities, namely Semarang City which are 49 km away and Surakarta City which is 52 km away. During the colonialism, the Dutch government dubbed Salatiga as "De Schoonste Stad van Midderi-Javaborder" or the most beautiful city in Central Java. At this time the government of Salatiga City is working to make Salatiga again the most beautiful city in Central Java by making development efforts to beautify the city. To support this program, an information technology is needed to improve information about tourism. Tourists who will visit to need information on the nearest tourist destination to plan a trip to a tourist destination. The search for the nearest location can be applied using one of the algorithms, namely the Haversine Formula algorithm. This study uses Google Maps to display location routes. In terms of a utilization, it is hoped that this information system can make it easier for tourists to visit tourist destinations in Salatiga City.
Hoax and Fake News by Saracen Syndicate and the Problems for National Cyber Security

Along with the times, information technology has taken an important role in society. Information technology not only has a positive impact but also has a negative impact, this is what then gives rise to an idiom that we often hear together, that information technology is like a double-edged knife. On the one hand, with the existence of information technology, it encourages demand for products. the technology itself, such as computers, modems, facilities to build internet networks and so on, then facilitates business transactions, especially financial businesses in addition to other businesses. But on the other hand, the use of information technology also invites crime. Cybercrime is a form of virtual crime by utilizing computer media that is connected to the internet and includes everything related to criminal acts.
Undercover Boolean Matrix Factorization with MaxSAT

The k-undercover Boolean matrix factorization problem aims to approximate a m×n Boolean matrix X as the Boolean product of an m×k and a k×n matrices A◦B such that X is a cover of A◦B, i.e., no representation error is allowed on the 0’s entries of the matrix X. To infer an optimal and “block-optimal” k-undercover, we propose two exact methods based on MaxSAT encodings. From a theoretical standpoint, we prove that our method of inferring “block-optimal” k-undercover is a (1 - 1/e) ≈ 0.632 approximation for the optimal k-undercover problem. From a practical standpoint, experimental results indicate that our “block-optimal” k-undercover algorithm outperforms the state-of-the-art even when compared with algorithms for the more general k-undercover Boolean Matrix Factorization problem for which only minimizing reconstruction error is required.