Reviewerly/exHarmony huggingface dataset:
A Residual Diffusion Model for High Perceptual Quality Codec Augmentation

Diffusion probabilistic models have recently achieved remarkable success in generating high quality image and video data. In this work, we build on this class of generative models and introduce a method for lossy compression of high resolution images. The resulting codec, which we call DIffuson-based Residual Augmentation Codec (DIRAC), is the first neural codec to allow smooth traversal of the rate-distortion-perception tradeoff at test time, while obtaining competitive performance with GAN-based methods in perceptual quality. Furthermore, while sampling from diffusion probabilistic models is notoriously expensive, we show that in the compression setting the number of steps can be drastically reduced.
On the feasibility of attacking Thai LPR systems with adversarial examples

Recent advances in deep neural networks (DNNs) have significantly enhanced the capabilities of optical character recognition (OCR) technology, enabling its adoption to a wide range of real-world applications. Despite this success, DNN-based OCR is shown to be vulnerable to adversarial attacks, in which the adversary can influence the DNN model's prediction by carefully manipulating input to the model. Prior work has demonstrated the security impacts of adversarial attacks on various OCR languages. However, to date, no studies have been conducted and evaluated on an OCR system tailored specifically for the Thai language. To bridge this gap, this work presents a feasibility study of performing adversarial attacks on a specific Thai OCR application -- Thai License Plate Recognition (LPR). Moreover, we propose a new type of adversarial attack based on the \emph{semi-targeted} scenario and show that this scenario is highly realistic in LPR applications. Our experimental results show the feasibility of our attacks as they can be performed on a commodity computer desktop with over 90% attack success rate.
On the explainability of quantum neural networks based on variational quantum circuits

Ridge functions are used to describe and study the lower bound of the approximation done by the neural networks which can be written as a linear combination of activation functions. If the activation functions are also ridge functions, these networks are called explainable neural networks. In this paper, we first show that quantum neural networks which are based on variational quantum circuits can be written as a linear combination of ridge functions. Consequently, we show that the interpretability and explainability of such quantum neural networks can be directly considered and studied as an approximation with the linear combination of ridge functions.
PMFault: Faulting and Bricking Server CPUs through Management Interfaces

Apart from the actual CPU, modern server motherboards contain other auxiliary components, for example voltage regulators for power management. Those are connected to the CPU and the separate Baseboard Management Controller (BMC) via the I2C-based PMBus. In this paper, using the case study of the widely used Supermicro X11SSL motherboard, we show how remotely exploitable software weaknesses in the BMC (or other processors with PMBus access) can be used to access the PMBus and then perform hardware-based fault injection attacks on the main CPU. The underlying weaknesses include insecure firmware encryption and signing mechanisms, a lack of authentication for the firmware upgrade process and the IPMI KCS control interface, as well as the motherboard design (with the PMBus connected to the BMC and SMBus by default). First, we show that undervolting through the PMBus allows breaking the integrity guarantees of SGX enclaves, bypassing Intel's countermeasures against previous undervolting attacks like Plundervolt/V0ltPwn. Second, we experimentally show that overvolting outside the specified range has the potential of permanently damaging Intel Xeon CPUs, rendering the server inoperable. We assess the impact of our findings on other server motherboards made by Supermicro and ASRock. Our attacks, dubbed PMFault, can be carried out by a privileged software adversary and do not require physical access to the server motherboard or knowledge of the BMC login credentials. We responsibly disclosed the issues reported in this paper to Supermicro and discuss possible countermeasures at different levels. To the best of our knowledge, the 12th generation of Supermicro motherboards, which was designed before we reported PMFault to Supermicro, is not vulnerable.
Time-Myopic Go-Explore: Learning A State Representation for the Go-Explore Paradigm

Very large state spaces with a sparse reward signal are difficult to explore. The lack of a sophisticated guidance results in a poor performance for numerous reinforcement learning algorithms. In these cases, the commonly used random exploration is often not helpful. The literature shows that this kind of environments require enormous efforts to systematically explore large chunks of the state space. Learned state representations can help here to improve the search by providing semantic context and build a structure on top of the raw observations. In this work we introduce a novel time-myopic state representation that clusters temporal close states together while providing a time prediction capability between them. By adapting this model to the Go-Explore paradigm (Ecoffet et al., 2021b), we demonstrate the first learned state representation that reliably estimates novelty instead of using the hand-crafted representation heuristic. Our method shows an improved solution for the detachment problem which still remains an issue at the Go-Explore Exploration Phase. We provide evidence that our proposed method covers the entire state space with respect to all possible time trajectories without causing disadvantageous conflict-overlaps in the cell archive. Analogous to native Go-Explore, our approach is evaluated on the hard exploration environments MontezumaRevenge, Gravitar and Frostbite (Atari) in order to validate its capabilities on difficult tasks. Our experiments show that time-myopic Go-Explore is an effective alternative for the domain-engineered heuristic while also being more general. The source code of the method is available on GitHub.
A Comprehensive Survey of Dataset Distillation

Deep learning technology has developed unprecedentedly in the last decade and has become the primary choice in many application domains. This progress is mainly attributed to a systematic collaboration in which rapidly growing computing resources encourage advanced algorithms to deal with massive data. However, it has gradually become challenging to handle the unlimited growth of data with limited computing power. To this end, diverse approaches are proposed to improve data processing efficiency. Dataset distillation, a dataset reduction method, addresses this problem by synthesizing a small typical dataset from substantial data and has attracted much attention from the deep learning community. Existing dataset distillation methods can be taxonomized into meta-learning and data matching frameworks according to whether they explicitly mimic the performance of target data. Although dataset distillation has shown surprising performance in compressing datasets, there are still several limitations such as distilling high-resolution data or data with complex label spaces. This paper provides a holistic understanding of dataset distillation from multiple aspects, including distillation frameworks and algorithms, factorized dataset distillation, performance comparison, and applications. Finally, we discuss challenges and promising directions to further promote future studies on dataset distillation.
Risk Sensitive Dead-end Identification in Safety-Critical Offline Reinforcement Learning

In safety-critical decision-making scenarios being able to identify worst-case outcomes, or dead-ends is crucial in order to develop safe and reliable policies in practice. These situations are typically rife with uncertainty due to unknown or stochastic characteristics of the environment as well as limited offline training data. As a result, the value of a decision at any time point should be based on the distribution of its anticipated effects. We propose a framework to identify worst-case decision points, by explicitly estimating distributions of the expected return of a decision. These estimates enable earlier indication of dead-ends in a manner that is tunable based on the risk tolerance of the designed task. We demonstrate the utility of Distributional Dead-end Discovery (DistDeD) in a toy domain as well as when assessing the risk of severely ill patients in the intensive care unit reaching a point where death is unavoidable. We find that DistDeD significantly improves over prior discovery approaches, providing indications of the risk 10 hours earlier on average as well as increasing detection by 20%.
Evaluating the Managerial Excellence Characteristics of students' Activities Departments Managers at Iraqi Universities from the Sport Employees' Point of View

The current research aims at :-Build a scale of the managerial excellence characteristics for the managers of students' activities departments in the Iraqi Universities from the sport employees' point of view.-Identifying the level of the managerial excellence characteristics of the student' activities departments managers in the Iraqi Universities from the sport employees' point of view.The researchers used the descriptive method by surveying.The samples of building and final application included ( 249) and ( 166) individuals respectively, who are staff working at the department of students' activities at the Iraqi government universities.In order to obtain the data, a scale was constructed for the managerial excellence characteristics, represented by (innovative vision, self-confidence, & confidence in others, change, the courage to demonstrate the opinions and suggestions and the managerial independence).The two researchers applied the scale after verifying its scientific requirements.Data was processed statistically using some statistical tools like: the arithmatic average, percentage, simple correlation and T test for two independent samples in addition to Alpha Kronbach coefficient. The researchers concluded the following:-Obtaining the scale of managerial excellence characteristics for the managers of students' activities departments at the Iraqi government Universities and this scale is effective in terms of measurement.-Managers of students' activities departments at the Iraqi government universities enjoy a high level of most of the managerial excellence characteristics from the sport employees' point of view who work in these departments except for the change characteristic that was of moderate level.
The current study aims at setting efficiency

The current study aims at setting efficiency of round house strategy on developing life skills for third year students / department of sciences /college of basic education / university of Mosul to reach such an aim, semi experimental was adopted on sample of 46 students from physics branch /third year /department of sciences for academic year 2017/2018 tools and materials of study (life skills) , content analysis survey, teachers guide ,curriculum book of astronomy was chosen according to round house strategy, test of life skills .Statistical analysis was found using means and t test for independent samples.Researcher concluded that there is a statistical difference at level of 0,05 between means of experimental and control groups in post test of life skills.Mean of control group was 9,74 while experimental, s was 22,17 with an increase of 12,43 skills of experimental group was arranged (solving problems and taking decisions 4,61) good communication with others (4,43) creative and critical thinking (4,30) lastly citizenship and self awareness was (4,39) given these findings researcher recommends the use of round house strategy in teaching theoretical and practical materials due to its efficiency in in developing life skills of students
Vaksinasi Terhadap Motivasi Belajar Anak Usia Sekolah

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Background&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;:&lt;/em&gt; &lt;em&gt;One of the government's efforts to reduce learning loss (low learning motivation) in students due to the Covid-19 pandemic is to conduct limited face-to-face learning (PTMT), with the condition that all school equipment has been vaccinated. Especially when the spread of the Covid-19 virus is increasingly massive and easy to attack children. Children's rights must still be given, but still prioritize the health and safety of children. Therefore, the provision of vaccines is not only applied to teaching staff, but must also be given to students, especially school-age children. The purpose of this study was to determine the effect of vaccination on student motivation in one of the SD Negeri Bukittinggi. &lt;strong&gt;Methods: &lt;/strong&gt;This study uses a quasi-experimental method with a one group pre-test-post-test only approach. This study was conducted on 53 elementary school students who participated in vaccination using a questionnaire adapted from Natun (2019). &lt;strong&gt;Results: &lt;/strong&gt;The results showed that there was a significant effect of vaccination on the learning motivation of elementary school students (p.value 0.006). &lt;strong&gt;Conclusion: &lt;/strong&gt;It is hoped that teachers and parents can jointly motivate children to take vaccinations. And through this research, it is expected to add concrete data in the morning for health workers and related communities that vaccination does not reduce the learning motivation of school-age students.&lt;/em&gt;&lt;strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
Problematika Pembelajaran Online Mahasiswa Program Studi Pendidikan Bahasa Jerman

Before the Covid 19 pandemic, online learning was not the first choice for lecturers and teachers in presenting their teaching materials. Therefore, its use during a pandemic represents a sudden change that can cause problems. The purpose of this study is to describe the problems faced by students in the German as a Foreign Language program, FKIP UNPATTI, related to the use of online learning during the Covid 19 pandemic. This was a descriptive research method involving 25 active students. The results of the analysis showed that 21 (84%) of total 25 students participated in online lectures by using the Internet on campus, but were not satisfied because of the weak Internet network, so they had to prepare their own data packages, while 20 (80%) of the total students admitted that online learning was a new experience for them, even though 23 (92%) wanted normal lectures as they had done before. Thus, there are two major issues identified in this study, namely the availability of support facilities for online learning and the mental readiness of students to face the changes in learning culture
SISTEM PENDUKUNG KEPUTUSAN DALAM MENENTUKAN TINGKAT KEPUASAN PELANGGAN TERHADAP PELAYANAN PADA CAFE ALGO MAKASSAR

Decision Support Systems can be defined as information systems and analytical models designed to determine decision makers to get better information. By utilizing this SPK, it is believed that it will really help dynamics in overcoming problems to determine the level of customer satisfaction with Cafe Algo services. The SPK method in this study uses SAW, this method is used to determine the weight of the criteria for attributes, in the positioning system. it will create the best option with absolutely precise calculations. The alternative is customers assessing service quality based on predetermined standards. The final result of this calculation concludes that in general, the average customer is satisfied with the service quality of Cafe Algo.
The Development of Carrom Learning Media to Improve the Ability of Mild Mentally Retarded Children to Read Words in SLB Negeri 1 Makassar

This study aims to produce carrom media to improve the ability to read words of mild mentally retarded children. The study implemented the research and development design based on the research stages of Borg &amp; Gall. Data collection techniques used in this study were interviews, observations, and questionnaires. The data were analyzed through qualitative and quantitative analysis techniques. The object of this research is the modified Carrom learning media, with the research stages covering; 1) the information gathering stage; 2) the planning stage; 3) the product development stage, and 4) the design validation and revision stages. The results of this study are; needs analysis of grade 3 mentally retarded students at SLB Negeri 1 Makassar showed that teachers and students needed modified carrom as learning media, carrom media design/prototypes were modified starting fromplanning, product development, expert validation, and validity testing stages and the validation from the material experts showed that the media was considered valid and the validity level was in the very good category so that the modified carrom media deserved to be tested and resulted in a very good category.
The Influence of Village Demographics on the Prediction of Community Participation in Elections Using the Naïve Bayes Algorithm (Case Study: Pacitan City)

Voters' demographic factors have an influence in regional head elections or regional elections. The study will classify based on general election data obtained from villages in sub-districts in Pacitan Regency using data mining techniques. The variables that will be used in classifying villages are TPS, DPT, Attendance and Golput. The method that will be used is the Naïve Bayes method which is one of the classification techniques in data mining. Based on the research conducted, it is concluded that the information system created can classify villages into 2 types, namely low and high by Naïve Bayes method.
The Effect of the Johari Window Self-Awareness Training on Teenagers’ Self-Confidence at Aisyiyah Orphanage in Sidoarjo

This research is aimed to increase the self-confidence of teenagers living at Aisyiyah Orphanage in Sidoarjo by using the Johari Window self-awareness training. This study implemented a quasi-experimental treatment group with pre-treatment and post-treatment assessments. To test the effectiveness, this study used Paired Sample T-Test and the results showed that there was a difference in self-confidence scale between pre-test and post-test results. The hypothesis in this study was there are differences in the level of self-confidence among teenagers before and after the Johari Window self-awareness training. To test the hypothesis, an analysis of paired T-Test data using the JASP application was carried out. Based on the results of the analysis, teenagers generally experienced an increase in self-confidence by 2,200 (SE: 0,530) after the Johari Window self-awareness training. The paired sample t-test showed a significant increase in self-confidence (t (30) = -4.147, p &lt; .001) and the Cohen's d value showed an effect of 0.757. Based on these results, the hypothesis in this study was accepted since it illustrates that experimental training using the Johari Window training method can increase self-confidence of the teenagers at Aisyiyah Orphanage in Sidoarjo.
Measuring Students’ Aptitude in Writing Makassar Lontara’ Script Using Card Letters Media

The purpose of this study is to compare the capacity of students to write Makassar Lontara script before and after utilizing the letter card medium. This investigation used three steps: pre-test, therapy, and post-test. This study included fifty public Yunior High School students in the Makassar city, South Sulawesi, Indonesia. Participatory observation, testing, and interviews were employed to obtain data for this study. Data description, average similarity test, and analysis requirement test are among the data analysis approaches employed. The findings revealed that learning through the medium of letter cards was more successful. The post-test analysis demonstrates the usefulness of using the letter card medium. Using Makassar Lontara's letter card medium, the level of achievement is higher than the pre-test result. Furthermore, when employing letter card media in the learning process, pupils are more active than when not using the media. Teachers of local content subjects are encouraged to use learning methods and enjoyable media for students to achieve more effective learning. The Makassar Lontara' letter card media is one of the methods employed. Because the media can contribute significantly to both the learning process and the desired outcomes, it is intended that by using the Makassar Lontara' letter card learning media, students will be more interested in and motivated to study local content themes that have previously been intimidating to them. Further research is needed to increase the quality of learning, and it is hoped that future researchers will address any flaws found in this study.
Implementation of the Honeypot Method in Form Input Security Against Sql-Injection Attacks

At this time the internet is something that can not be separated in the life of modern society. For this reason, fast, safe and reliable internet is very important. To achieve this on the internet there are various kinds of technology that make it very influential in our lives. In the scope of security for safe and smooth internet web surfing, we need security, especially in the excess part of the website. One way to do this is to commit website crimes, namely Sql Injection. Sql injection is a hacking action that is carried out in the client application by modifying the SQL commands that are in the client application memory. SQL Injection is a technique for exploiting web applications, commands can be entered as desired by the user even though the user is not authorized to do so. The damage done by the user that can lead to fatal consequences can be done by using SQL injection. One of the methods used to prevent website security attacks is Honeypot. Honeypot is a system or computer that is deliberately used as bait to become a target for attacks from attackers.
Research on the Information Security Investment Strategies Considering Budget Constraints and the Attacker's Preferences

Information security investment is the basis to ensure the stable operation of information systems. By adopting expected utility theory, the paper studies the influences of budget constraints, attacker preferences, attack types and other factors on firm information security investment strategies. The results show, under a certain budget constraint, when opportunistic attackers prefer to select attack targets from the system vulnerability and targeted attackers prefer to select attack targets from the value of information assets, the optimal information security investment of a firm has a minimum value and the minimum value increases with the security investment efficiency of defending against targeted attacks. When the network exposure is small, investment on defending against targeted attack decreases with the network exposure. When the network exposure is relatively large, security investment on defending against targeted attack increases with the network exposure, and security investment on defending against opportunistic attack decreases with the network exposure.