Reviewerly/exHarmony huggingface dataset:
Neuron Sensitivity Guided Test Case Selection for Deep Learning Testing

Deep Neural Networks~(DNNs) have been widely deployed in software to address various tasks~(e.g., autonomous driving, medical diagnosis). However, they could also produce incorrect behaviors that result in financial losses and even threaten human safety. To reveal the incorrect behaviors in DNN and repair them, DNN developers often collect rich unlabeled datasets from the natural world and label them to test the DNN models. However, properly labeling a large number of unlabeled datasets is a highly expensive and time-consuming task. To address the above-mentioned problem, we propose NSS, Neuron Sensitivity guided test case Selection, which can reduce the labeling time by selecting valuable test cases from unlabeled datasets. NSS leverages the internal neuron's information induced by test cases to select valuable test cases, which have high confidence in causing the model to behave incorrectly. We evaluate NSS with four widely used datasets and four well-designed DNN models compared to SOTA baseline methods. The results show that NSS performs well in assessing the test cases' probability of fault triggering and model improvement capabilities. Specifically, compared with baseline approaches, NSS obtains a higher fault detection rate~(e.g., when selecting 5\% test case from the unlabeled dataset in MNIST \& LeNet1 experiment, NSS can obtain 81.8\% fault detection rate, 20\% higher than baselines).
Fraud

Fraud committed via the Internet is another type of cybercrime that has become increasingly sophisticated. It can be both national and transnational. It can involve the fraudulent use of personal data in financial transactions. Fraud crosses over with a number of other offences compared in this book, such as, the interference with a computer and data, the interception of data, and identity theft. It can have devastating impacts on individuals and entities in the digital economy. This chapter briefly highlights the impact of fraudulent activity in the financial sector.
Evaluation of Drainage Channel on Meteorological Road, Laut Dendang Village, Deli Serdang Regency

Drainage is one way of removing unwanted excess water in an area and dealing with the effects of excess water in the system. When the rainy season comes, there are almost floods or puddles on urban roads. The large population also makes the volume of drainage channels fill up quickly, while the drainage capacity tends to decrease due to rainwater (erosion) and sedimentation. Meteorological Street, Laut Dendang Village, Percut Sei Tuan sub-district, Deli Serdang Regency, is one of the villages that is frequently flooded every time it rains. On these issues, the researcher highlights the importance of evaluating the Deli Serdang Regency's drainage system along Meteorological Road to anticipate when the rainy season comes. The general exponential method can determine whether the existing drainage volume can still accommodate terrestrial run-off and domestic wastewater in the next 30 years and calculate the population growth. Based on the research results from rainfall data obtained from Meteorology Road, the rainfall intensity value for the last ten years (2010 - 2019) was 10.538 mm/day. Thus, the amount of discharge flowing in the drainage channel (Qplansning) is equal to 0.832 m3/sec, while the planned Q for the next ten years is 0.874 m3/sec, for the next 20 years is 0.953 m3/sec, and 30 years into the future. front is 1.111 m3/sec. Based on the calculation results, Qplans exceeds Qeksisting. Therefore, a new channel plan is needed.
The Effect of Metacognitive Strategy on Students' Reading Comprehension of Academic Text

Reading comprehension is an active reading process in which students can dig up information in reading sources. These skills are very important so that students are able to adapt and compete in facing future challenges. In the reading process, students are required to be active and understand how and when to implement strategies so that reading comprehension is more effective. One strategy that can be used is the metacognitive strategy. Metacognitive abilities are very important for reading achievement. The Purpose of Metacognitive is to prepare further develop readers to become more aware of their thinking alone while reading. Educators provide express preparation on the use of reading strategies. Metacognitive strategy trains students to manage themselves to get reading comprehension. This research aims to investigated whether there was significance difference on students reading comprehension of academic text by using metacognitive strategy and who are not. The research used quasi experiment, all students of six semesters were the population of the research while techniques sampling was purposive sampling. Six semester students from 6A and 6D are the sample in this research. From data analysis and finding, metacognitive give positive effect toward students reading comprehension of academic texts. Sig. (2-tailed) value was 0.015 &lt; 0.05 means that Ho was rejected and Ha was accepted. It conclude that there was significance difference on students’ reading comprehension of academic text score thought by metacognitive and conventional strategy.
Storage Retention

The retention of data is a complex subject, and is not often considered until there are data breaches. This chapter will demonstrate the varied approaches taken by nation states to regulating data retention. It will be argued that more work is needed to address the inconsistent approaches to retention requirements. The chapter will also highlight how terminology differs across states, which has resulted in a level of confusion of obligations for retention. It will be argued that the Commonwealth should undertake a specific research project on data retention and the future issues that might arise from technology advancements such as artificial intelligence (AI). This is because AI could provide further challenges in holding data for longer than is needed. When coupled with other technology such as quantum, the challengers could be further heightened. Conversely, it could provide benefits to shorten data retention periods, to a short period unless the data subject consents to the retention of their data being retained for extended periods.
A Novel Image Encryption Algorithm Based On Chaotic Map

Information security is the most important thing in this digital era. This work proposes a novel new image encryption algorithm based on chaotic map. Four different chaotic sequences are generated using logistic map. The generated sequences are used to perform confusion and diffusion operation. The position of the pixels are scrambled using confusion operation. Bit level value manipulation is performed using diffusion operation. The results shows that the encryption algorithm can resist statistical and differential attacks and hence gives a better security compared to other encryption methods.
PENINGKATAN MINAT BELAJAR SISWA DALAM PELAJARAN MATEMATIKA DENGAN MENGGUNAKAN MEDIA PEMBELAJARAN FLASH CARD PADA SISWA KELAS II SEKOLAH DASAR NEGERI 004 NONGSA

This research was carried out with the aim of improving the ability to recognize and remember flat figures using flash card learning media for second grade students at SDN 004 Nongsa in the 2018/2019 academic year. This research is a classroom action research (classroom action research). According to Suharsimi Arikunto, et al (2007: 3) classroom action research is an observation of learning activities in the form of actions that are intentionally raised and occur in a class together. The purpose of this study was to increase students' interest in learning mathematics using flash card learning media for second grade students at SDN 004 Nongsa. The results showed that in the first cycle at the first meeting the percentage of student activity was 34.29%, then at the second meeting the average percentage of student activity was 54.28%. While in the second cycle at the 1st and 2nd meetings, it increased to 74.28% and 88.57%, respectively. The results of the evaluation during the pre-cycle the average value of completeness obtained was 70.57, the first cycle the average value obtained was 78.54 and the second cycle the average value of the students increased to 84.63.
SoK: Mitigation of Front-Running in Decentralized Finance

Front-running is the malicious, and often illegal, act of both manipulating the order of pending trades and injecting additional trades to make a profit at the cost of other users. In decentralized finance (DeFi), front-running strategies exploit both public knowledge of user trades from transactions pending on the network and the miner’s ability to determine the final transaction order. Given the financial loss and increased transaction load resulting from adversarial front-running in decentralized finance, novel cryptographic protocols have been proposed to mitigate such attacks in the permission-less blockchain setting. We systematize and discuss the state-of-the-art of front-running mitigation in decentralized finance, and illustrate remaining attacks and open challenges.
EFL TEACHERS’ AND STUDENTS’ PERCEPTION ABOUT E-LEARNING IN THE ENGLISH SUBJECT: A STUDY AT ISLAMIC STATE UNIVERSITY OF ALAUDDIN MAKASSAR

This study highlighted teachers’ and students’ perception in order to investigate teachers’ and students’ perception about e-learning when English subject is concerned. The population of this study was the seventh semester of the English Education department of UIN Alauddin Makassar who have experienced in implementing e-learning in teaching and learning activities. Sixteen students and four lecturers were involved as a sample of the study. This study employed quantitative method were the data collected by using Questionnaire that is consisting 24 items based on likert scale. The data showed that there were above 62,2% of respondents gave positive responses, 28,5% stated neutral, and 9,79% disagree.
Alleviating the Effect of Data Imbalance on Adversarial Training

In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored in previous works. Compared with conventional adversarial training on balanced datasets, this process falls into the dilemma of generating uneven adversarial examples (AEs) and an unbalanced feature embedding space, causing the resulting model to exhibit low robustness and accuracy on tail data. To combat that, we theoretically analyze the lower bound of the robust risk to train a model on a long-tailed dataset to obtain the key challenges in addressing the aforementioned dilemmas. Based on it, we propose a new adversarial training framework -- Re-balancing Adversarial Training (REAT). This framework consists of two components: (1) a new training strategy inspired by the effective number to guide the model to generate more balanced and informative AEs; (2) a carefully constructed penalty function to force a satisfactory feature space. Evaluation results on different datasets and model structures prove that REAT can effectively enhance the model's robustness and preserve the model's clean accuracy. The code can be found in https://github.com/GuanlinLee/REAT.
CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy.
Causal Laws and Multi-Valued Fluents

This paper continues the line of work on representing properties of actions in nonmonotonic formalisms that stresses the distinction between being "true" and being "caused", as in the system of causal logic introduced by McCain and Turner and in the action language C proposed by Giunchiglia and Lifschitz. The only fluents directly representable in language C+ are truth-valued fluents, which is often inconvenient. We show that both causal logic and language C can be extended to allow values from arbitrary nonempty sets. Our extension of language C, called C+, also makes it possible to describe actions in terms of their attributes, which is important from the perspective of elaboration tolerance. We describe an embedding of C+ in causal theories with multi-valued constants, relate C+ to Pednault's action language ADL, and show how multi-valued constants can be eliminated in favor of Boolean constants.
Design principles of multi-map variation in biological systems

Complexity in biology is often described using a multi-map architecture, where the genotype, representing the encoded information, is mapped to the functional level, known as the phenotype, which is then connected to a latent phenotype we refer to as fitness. This underlying architecture governs the processes that drive evolution. Moreover, natural selection, along with other neutral forces, can modify these maps. At each hierarchical level, variation is observed. Here, I propose the need to establish principles that can aid in understanding the transformation of variation within this multi-map architecture. Specifically, I will introduce three, related to the presence of modulators, constraints, and the modular channeling of variation. By comprehending these design principles in various biological systems, we can gain better insights into the mechanisms underlying these maps and their evolutionary dynamics.
Zero-shot Domain-sensitive Speech Recognition with Prompt-conditioning Fine-tuning

In this work, we propose a method to create domain-sensitive speech recognition models that utilize textual domain information by conditioning its generation on a given text prompt. This is accomplished by fine-tuning a pre-trained, end-to-end model (Whisper) to learn from demonstrations with prompt examples. We show that this ability can be generalized to different domains and even various prompt contexts, with our model gaining a Word Error Rate (WER) reduction of up to 33% on unseen datasets from various domains, such as medical conversation, air traffic control communication, and financial meetings. Considering the limited availability of audio-transcript pair data, we further extend our method to text-only fine-tuning to achieve domain sensitivity as well as domain adaptation. We demonstrate that our text-only fine-tuned model can also attend to various prompt contexts, with the model reaching the most WER reduction of 29% on the medical conversation dataset.
Smooth polynomials with several prescribed coefficients

Let $\mathbb{F}_q[t]$ be the polynomial ring over the finite field $\mathbb{F}_q$ of $q$ elements. A polynomial in $\mathbb{F}_q[t]$ is called $m$-smooth (or $m$-friable) if all its irreducible factors are of degree at most $m$. In this paper, we investigate the distribution of $m$-smooth (or $m$-friable) polynomials with prescribed coefficients. Our technique is based on character sum estimates on smooth (friable) polynomials proved by the analytic approach of Panario, Gourdon, Flajolet (1998), Bourgains's argument (2015) applied for polynomials by Ha (2016) and on double character sums on smooth (friable) polynomials.
Analyzing sports commentary in order to automatically recognize events and extract insights

In this paper, we carefully investigate how we can use multiple different Natural Language Processing techniques and methods in order to automatically recognize the main actions in sports events. We aim to extract insights by analyzing live sport commentaries from different sources and by classifying these major actions into different categories. We also study if sentiment analysis could help detect these main actions.
Tapestry of Time and Actions: Modeling Human Activity Sequences using Temporal Point Process Flows

Human beings always engage in a vast range of activities and tasks that demonstrate their ability to adapt to different scenarios. Any human activity can be represented as a temporal sequence of actions performed to achieve a certain goal. Unlike the time series datasets extracted from electronics or machines, these action sequences are highly disparate in their nature -- the time to finish a sequence of actions can vary between different persons. Therefore, understanding the dynamics of these sequences is essential for many downstream tasks such as activity length prediction, goal prediction, next action recommendation, etc. Existing neural network-based approaches that learn a continuous-time activity sequence (or CTAS) are limited to the presence of only visual data or are designed specifically for a particular task, i.e., limited to next action or goal prediction. In this paper, we present ProActive, a neural marked temporal point process (MTPP) framework for modeling the continuous-time distribution of actions in an activity sequence while simultaneously addressing three high-impact problems -- next action prediction, sequence-goal prediction, and end-to-end sequence generation. Specifically, we utilize a self-attention module with temporal normalizing flows to model the influence and the inter-arrival times between actions in a sequence. In addition, we propose a novel addition over the ProActive model that can handle variations in the order of actions, i.e., different methods of achieving a given goal. We demonstrate that this variant can learn the order in which the person or actor prefers to do their actions. Extensive experiments on sequences derived from three activity recognition datasets show the significant accuracy boost of ProActive over the state-of-the-art in terms of action and goal prediction, and the first-ever application of end-to-end action sequence generation.
SecureTrack- A contact tracing IoT platform for monitoring infectious diseases

The COVID-19 pandemic has highlighted the need for innovative solutions to monitor and control the spread of infectious diseases. With the potential for future pandemics and the risk of outbreaks particularly in academic institutions, there is a pressing need for effective approaches to monitor and manage such diseases. Contact tracing using Global Positioning Systems (GPS) has been found to be the most prevalent method to detect and tackle the extent of outbreaks during the pandemic. However, these services suffer from the inherent problems of infringement of data privacy that creates hindrance in adoption of the technology. Non-cellular wireless technologies on the other hand are well-suited to provide secure contact tracing methods. Such approaches integrated with the Internet of Things (IoT) have a great potential to aid in the fight against any type of infectious diseases. In response, we present a unique approach that utilizes an IoT based generic framework to identify individuals who may have been exposed to the virus, using contact tracing methods, without compromising the privacy aspect. We develop the architecture of our platform, including both the frontend and backend components, and demonstrate its effectiveness in identifying potential COVID-19 exposures (as a test case) through a proof-of-concept implementation. We also implement and verify a prototype of the device. Our framework is easily deployable and can be scaled up as needed with the existing infrastructure.
Twits, Toxic Tweets, and Tribal Tendencies: Trends in Politically Polarized Posts on Twitter

Social media platforms are often blamed for exacerbating political polarization and worsening public dialogue. Many claim hyperpartisan users post pernicious content, slanted to their political views, inciting contentious and toxic conversations. However, what factors, actually contribute to increased online toxicity and negative interactions? In this work, we explore the role that political ideology plays in contributing to toxicity both on an individual user level and a topic level on Twitter. To do this, we train and open-source a DeBERTa-based toxicity detector with a contrastive objective that outperforms the Google Jigsaw Persective Toxicity detector on the Civil Comments test dataset. Then, after collecting 187 million tweets from 55,415 Twitter users, we determine how several account-level characteristics, including political ideology and account age, predict how often each user posts toxic content. Running a linear regression, we find that the diversity of views and the toxicity of the other accounts with which that user engages has a more marked effect on their own toxicity. Namely, toxic comments are correlated with users who engage with a wider array of political views. Performing topic analysis on the toxic content posted by these accounts using the large language model MPNet and a version of the DP-Means clustering algorithm, we find similar behavior across 6,592 individual topics, with conversations on each topic becoming more toxic as a wider diversity of users become involved.
TokenFlow: Consistent Diffusion Features for Consistent Video Editing

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io/
DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation

The generation of synthetic tabular data that preserves differential privacy is a problem of growing importance. While traditional marginal-based methods have achieved impressive results, recent work has shown that deep learning-based approaches tend to lag behind. In this work, we present Differentially-Private TaBular AutoRegressive Transformer (DP-TBART), a transformer-based autoregressive model that maintains differential privacy and achieves performance competitive with marginal-based methods on a wide variety of datasets, capable of even outperforming state-of-the-art methods in certain settings. We also provide a theoretical framework for understanding the limitations of marginal-based approaches and where deep learning-based approaches stand to contribute most. These results suggest that deep learning-based techniques should be considered as a viable alternative to marginal-based methods in the generation of differentially private synthetic tabular data.