Reviewerly/exHarmony huggingface dataset:
Optimization tools for distance-preserving flag fault-tolerant error correction

Lookup table decoding is fast and distance-preserving, making it attractive for near-term quantum computer architectures with small-distance quantum error-correcting codes. In this work, we develop several optimization tools that can potentially reduce the space and time overhead required for flag fault-tolerant quantum error correction (FTQEC) with lookup table decoding on Calderbank-Shor-Steane (CSS) codes. Our techniques include the compact lookup table construction, the Meet-in-the-Middle technique, the adaptive time decoding for flag FTQEC, the classical processing technique for flag information, and the separated $X$ and $Z$ counting technique. We evaluate the performance of our tools using numerical simulation of hexagonal color codes of distances 3, 5, 7, and 9 under circuit-level noise. Combining all tools can result in more than an order of magnitude increase in pseudothreshold for the hexagonal color code of distance 9, from $(1.34 \pm 0.01) \times 10^{-4}$ to $(1.42 \pm 0.12) \times 10^{-3}$.
HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention

Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity of social platforms. Though existing graph-based Twitter bot detection methods achieved state-of-the-art performance, they are all based on the homophily assumption, which assumes users with the same label are more likely to be connected, making it easy for Twitter bots to disguise themselves by following a large number of genuine users. To address this issue, we proposed HOFA, a novel graph-based Twitter bot detection framework that combats the heterophilous disguise challenge with a homophily-oriented graph augmentation module (Homo-Aug) and a frequency adaptive attention module (FaAt). Specifically, the Homo-Aug extracts user representations and computes a k-NN graph using an MLP and improves Twitter's homophily by injecting the k-NN graph. For the FaAt, we propose an attention mechanism that adaptively serves as a low-pass filter along a homophilic edge and a high-pass filter along a heterophilic edge, preventing user features from being over-smoothed by their neighborhood. We also introduce a weight guidance loss to guide the frequency adaptive attention module. Our experiments demonstrate that HOFA achieves state-of-the-art performance on three widely-acknowledged Twitter bot detection benchmarks, which significantly outperforms vanilla graph-based bot detection techniques and strong heterophilic baselines. Furthermore, extensive studies confirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's ability to demystify the heterophilous disguise challenge.
Integrating Reading and Writing Instruction for Cultivating Students' Critical Thinking

This study aimed to cultivate students’ knowledge and skills imperative for the development of critical thinking. This research employed qualitative method to explore students’ critical thinking through an integrated instruction of reading and writing tomeasure the extent to which the students understood and afford to learn English in reading and writing skills. In this research, researchers collected the data based on three factors, namely treatment, person, and outcome factor. Treatment factor is a technique of collecting data by observing students through the process of reading and writing instruction in classroom. Person factor, on the other hand, is a technique of collecting data by observing students observed directly by the other researchers in the process of English learning in classroom.Last, outcome factor is a technique of collecting data by using an open-ended question in a questionnaire based on students’ achievement in learning English. The data were analyzed and interpreted to answer the research questions. Based on the result of the study, we found out that the integration of reading and writing enabled the instruction to construct students’ prior knowledge and critical thinking by organizing the appropriate task-based activities. The study also developed the teaching strategies to be more effective in language teaching.
Implicit spoken language diarization

Spoken language diarization (LD) and related tasks are mostly explored using the phonotactic approach. Phonotactic approaches mostly use explicit way of language modeling, hence requiring intermediate phoneme modeling and transcribed data. Alternatively, the ability of deep learning approaches to model temporal dynamics may help for the implicit modeling of language information through deep embedding vectors. Hence this work initially explores the available speaker diarization frameworks that capture speaker information implicitly to perform LD tasks. The performance of the LD system on synthetic code-switch data using the end-to-end x-vector approach is 6.78% and 7.06%, and for practical data is 22.50% and 60.38%, in terms of diarization error rate and Jaccard error rate (JER), respectively. The performance degradation is due to the data imbalance and resolved to some extent by using pre-trained wave2vec embeddings that provide a relative improvement of 30.74% in terms of JER.
xSIM++: An Improved Proxy to Bitext Mining Performance for Low-Resource Languages

We introduce a new proxy score for evaluating bitext mining based on similarity in a multilingual embedding space: xSIM++. In comparison to xSIM, this improved proxy leverages rule-based approaches to extend English sentences in any evaluation set with synthetic, hard-to-distinguish examples which more closely mirror the scenarios we encounter during large-scale mining. We validate this proxy by running a significant number of bitext mining experiments for a set of low-resource languages, and subsequently train NMT systems on the mined data. In comparison to xSIM, we show that xSIM++ is better correlated with the downstream BLEU scores of translation systems trained on mined bitexts, providing a reliable proxy of bitext mining performance without needing to run expensive bitext mining pipelines. xSIM++ also reports performance for different error types, offering more fine-grained feedback for model development.
Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Transformer models have been widely adopted in various domains over the last years, and especially large language models have advanced the field of AI significantly. Due to their size, the capability of these networks has increased tremendously, but this has come at the cost of a significant increase in necessary compute. Quantization is one of the most effective ways to reduce the computational time and memory consumption of neural networks. Many studies have shown, however, that modern transformer models tend to learn strong outliers in their activations, making them difficult to quantize. To retain acceptable performance, the existence of these outliers requires activations to be in higher bitwidth or the use of different numeric formats, extra fine-tuning, or other workarounds. We show that strong outliers are related to very specific behavior of attention heads that try to learn a "no-op" or just a partial update of the residual. To achieve the exact zeros needed in the attention matrix for a no-update, the input to the softmax is pushed to be larger and larger during training, causing outliers in other parts of the network. Based on these observations, we propose two simple (independent) modifications to the attention mechanism - clipped softmax and gated attention. We empirically show that models pre-trained using our methods learn significantly smaller outliers while maintaining and sometimes even improving the floating-point task performance. This enables us to quantize transformers to full INT8 quantization of the activations without any additional effort. We demonstrate the effectiveness of our methods on both language models (BERT, OPT) and vision transformers.
Perancangan Front End Pemeriksaan dan Perawatan Wesel dengan Metode User Centered Design

In the era of globalization like now the website becomes very important to spread information or to store data. The internet is one of the tools to spread information that is easy and fast because it can be done anywhere and anytime. With the existence of the internet and websites can be used to store data inspection and note maintenance. At present the Indonesian railroad company is still reporting the results of the inspection and maintenance of the letter of notes manually, making it difficult to find inspection and maintenance information when the supervisor of the railroad company needs to conduct an inspection. The purpose of this research is to build an information system or website inspection and maintenance website (sell maintenance) so that it makes it easier for railroad company employees to store inspection and note maintenance data so that the data becomes more structured. Making a system or website is done by collecting data through Wawan Ways, Observation and Literature Review. Creating the Sell Maintenance website uses the User Centered Design method by applying the PHP programming language and using the Bootstrap framework to make the display more attractive. The result of this study is a website that suits the needs and preferences of users so that it is easier to use and intuitive because in this study conduct direct observations and interactions with users to understand how users use these products or services. In this study after testing the railroad company employees it was found that the presentation font in the reporting status display was changed to make it greater, the notes photo in the asset data display was changed to be smaller so that it was not too much scroll down, the header and footer on the printed display were removed, The font size on the print display is reduced, the size of the photo pixel and the layout are repaired so that they are not too many pages
Vision Based Anomalous Human Behaviour Detection using CNN and Transfer Learning

Abstract: With the advent of the Internet of Things (IoT), there have been significant advancements in the area of human activity recognition (HAR) in recent years. HAR is applicable to wider application such as elderly care, anomalous behavior detection and surveillance system. Several machine learningalgorithms have been employed to predict the activities performed by the human in an environment. However, traditional machine learning approaches have been outperformed by feature engineering methods which can select an optimal set of features. Onthe contrary, it is known that deep learning models such as Convolutional Neural Networks (CNN) can extract features and reduce the computational cost automatically. In this paper, we use CNN model to detect human activities from Image Dataset model. Specifically, we employ transfer learning to get deep image features and trained machine learning classifiers. Our experimental results showed the accuracy of 96.95% using VGG16. Our experimental results also confirmed the high performance of VGG-16 as compared to rest of the applied CNN models.
THE INFLUENCE OF IMAGINE, ELABORATE, PREDICT, AND CONFIRM (IEPC) STRATEGY ON STUDENTS’ READING SKILL

This research aimed to know whether the IEPC strategy influences the students’ reading skills. The writer used the quasi-experimental method as a research design. The population of this research was the eight grade students of State Junior High School 20 Bandar Lampung. In collecting the data, the writers used a multiple choice test. To analyze the data, the writers used the t-test formula. The result of the t-test was 6.25 which is higher than the t-table. Therefore, there was an influence of the IEPC strategy on the students’ reading skills.
Text Processing Using Support Vector Machine for Scientific Research Paper Content Classification

Research related to text processing is carried out to analyze information in text data which can then be used in strategies for the development of the field of science and technology, one of which is the text processing of scientific research paper.Classification of scientific research paper is carried out to obtain information related to the field of research based on the substance of the scientific articles, then to map them into categories to identify areas of excellence and to determine development strategies related to publications and research.In this research, text processing of scientific research paper is carried out by implementing the supervised machine learning model, Support vector machine (SVM), for classification algorithms which divide it's into several groups based on the substance of scientific research paper in each field or subject area.Feature extraction on the substance of scientific research paper becomes input for modeling with SVM.Furthermore, the concept of kernel functions on the support vector machine forms the classifier model and classifies scientific research paper text data which results in high accuracy.
Learning When to Trust Which Teacher for Weakly Supervised ASR

Automatic speech recognition (ASR) training can utilize multiple experts as teacher models, each trained on a specific domain or accent. Teacher models may be opaque in nature since their architecture may be not be known or their training cadence is different from that of the student ASR model. Still, the student models are updated incrementally using the pseudo-labels generated independently by the expert teachers. In this paper, we exploit supervision from multiple domain experts in training student ASR models. This training strategy is especially useful in scenarios where few or no human transcriptions are available. To that end, we propose a Smart-Weighter mechanism that selects an appropriate expert based on the input audio, and then trains the student model in an unsupervised setting. We show the efficacy of our approach using LibriSpeech and LibriLight benchmarks and find an improvement of 4 to 25\% over baselines that uniformly weight all the experts, use a single expert model, or combine experts using ROVER.
Exploring Leadership Styles Adopted in Organizational Performance of ADNOC Company

Leadership style is critical for improving organisational performance, and inappropriate leadership styles harm both employees and the overall performance of any organisation. Poor leadership is likely to be the cause of unethical practises, a high turnover rate, and employee disengagement. Furthermore, they may use an ineffective leadership style when dealing with project subordinates. Subordinate performance that is effective is always desired, but it does not always occur. As a result, the current study investigatedthe four leadership styles and the characteristics that influence organisational performance. The analysis of 398 questionnaire forms collected from ADNOC Company participants revealed that allthe attributes of the four leadership styles used in the UAE are reliable. Transformational leadership isfound to be the most influential leadership style, while democratic leadership is the least influential in influencing the organisational performance of the ADNOC Company in the UAE. In transformational leadership, leaders focus on employees' beliefs about their jobs as the most influential attribute. Leaders in our organisation are reported to have good communication with other employeesas one of the most influential attributes in Transactional leadership style. Similarly, the most influential attributes in Charismatic Leadership style are leaders in our organisation are effective in managing our organisation. The most influential attribute of Democratic leadership is that leaders in our organisation work hard with other workers.Results of this study will able to assist the company in dealing its leadership style.
Analysis of the Academic Performance of Mechanical-Electrical Engineering Students during the COVID-19 Pandemic: A Case Study in a Private University in Peru

The COVID-19 pandemic, brought with a number of problems in different sectors, one of the most affected was the education sector, especially in engineering programs, since these have a practical nature and need laboratories to consolidate meaningful learning. In many cases, the virtualization generated some anomalous behavior in the academic indicators that could be used for the analysis of the successes and errors that could lay the foundations for a modern education in engineering careers. The main objective of this research is to analyze the average academic performance per course of a mechanical-electrical engineering program, for which some data was taken from 2018 to 2021. To explain the behavior of the curves, a qualitative survey was applied to 357 students. Three academic indicators are used in the study: grades average, “not pass” rate, desertion rate. The results of the analysis showed that in the first period of the year 2020 (starting period of confinement) they presented very different parameters from those already known in previous years, which could be based on three main causes. The first was due to the full-time dedication of the students to carry out their academic activities, the second is based on the proper technology, resources and strategies adopted by the university and finally, the economic aid from the Peruvian government.
Virtual Learning Environments: University faculty experiences during and after the COVID 19 pandemic

To improve and innovate the teaching and learning process, it is necessary to develop or use technological means that allow effective communication between teachers and students. Likewise, the use of virtual learning environments, with the appearance of COVID 19, have become a priority in teaching practice, revolutionizing many fields of engineering and the educational system in general. A mixed approach research with descriptive and correlational design was carried out in a sample of 59 teachers, whose objective is to share the experiences of professors of a higher education institution, during and after the pandemic, on the use of virtual environments, such as Moodle and Google Classroom. Also, the benefits and difficulties encountered in virtual classes and currently, in face-to-face classes. Some of the results are 67.2% of the professors already knew about the use of learning environments, but even so, 98.2% concluded that they did develop new skills and aptitudes, most frequently 71.4%, in the creation of courses in virtual platforms and 66.1% in basic competencies in the use of virtual learning platforms, as others to promote quality and educational innovation in the institution.
Harris Hawks Approach for Distinct Economic Dispatch Problems

In this paper, a nature-inspired optimization paradigm is suggested, which is called Harris Hawks Approach (HHA). The main inspiration of HHA is the cooperative behavior and chasing style of Harris’ hawks in nature called surprise pounce. In this intelligent strategy, diverse hawks cooperatively pounce victim from distinct directions in an attempt to surprise it. Harris hawks can detect a variety of chasing patterns based on the dynamic nature of scenarios and escaping styles of the victim. HHA is developed for solving the Economic Dispatch (ED) process. Since the ED is a non-linear, complex and constrained optimization process, its main objective is to reduce the total generation cost while matching the equality and inequality constraints of the system. To prove the capabilities of the developed HHA, several test systems including 6, 13, 40 and 10 units have been implemented and tested for solving various ED problems. The simulation results illustrate the strength of the HHA compared with other algorithms recently reported in literature.
University Auto Reply FAQ Chatbot Using NLP and Neural Networks

When new students enter college, they often have similar questions - ”Where to study for this subject?”, ”How to prepare Data Struc- tures and Algorithms?”, ”How to connect with seniors?” and so on. The use of chatbots can help them get answers to their questions quickly and eﬃciently. This study proposes a Deep Learning (DL) chatbot for addressing common doubts of university students, pro- viding eﬃcient and accurate responses to college-speciﬁc questions. A self-curated dataset is used for the purpose of building the chat- bot and natural language processing techniques (NLP) are utilized for the pre-processing of raw data gathered. The study compares two deep learning models - a bidirectional Long Short Term Memory (LSTM) network and a simple feed-forward neural network model.
Secure Data Analysis and Data Privacy

A secure data system is an essential tool for the performance and stability of any enterprise. Data loss, theft, or contamination might have severe consequences on an entire enterprise’s activities. Enterprises utilize secure data analytic tools to prevent, tackle, and recover from data breaches prevent such occurrences. The nature of the breach or the analysts’ objectives determines the methods through which the tools are deployed. Different organizations implement different strategies to secure their data. The strategies provide long-term solutions and must be carefully constructed. This chapter includes dissecting the concepts of data, data analysis, data security, and data privacy and how they interact. The information is developed in a systematic format where one concept leads to the next. This chapter discusses the leading data analysis strategies, types, methods, and tools currently utilized in data analysis and how they fit with contemporary computing technologies. This chapter summarizes effective data security types, strategies, and methods, showing situations in which, each can be utilized. Also, we project the relationship between data security processes and contemporary issues within the organizations and on advancing global patterns. The patterns include advancements in AI, cloud technology, and quantum adoption. This chapter includes a discussion on data privacy and how it compares with data security. The content information aims to inform the essence of data protection and guide on ways to deal with oncoming threats to the data.