Reviewerly/exHarmony huggingface dataset:
The Analysis of Students’ Difficulties in Using Computer Terms

Computer is used for many purposes especially for improving and supporting in education system. Students can operate computer as well as possible, but most of them don’t know the meaning of some terms in computer science. The aims of this study are to investigate the Students’ difficulties in Using Computer Science Terms at STMIK Pelita Nusantara, then to explore the students’ difficulties in Using Computer Science Terms at STMIK Pelita Nusantara. This is a qualitative research. The participants of this study were students of second semester of STMIK Pelita Nusantara. The total of students were 35 students that consist of 17 males and 18 females. The result showed that computer terminology is the one problem that the students face. The lack of vocabulary become the main reason why the students cannot use the terms well. Students’ obstacles were not only in mastering vocabulary but also in mastering the translation in using computer terms. Sometimes the students know the usage of computer terms but they don’t know the meaning of them.
Mendeley Software Training in Facilitating Reference Management in Thesis Writing for ITB Postgraduate Program Ahmad Dahlan Lamongan

The purpose of holding the Mendeley software training program is to make it easier for postgraduate students to write automatic bibliography as well as find references for reputable international journals and accredited national journals in particular and write citations. The method used in training activities is carried out by the team in the form of lectures, practice and question and answer. This training was conducted at the ITB Ahmad Dahlan Lamongan campus on Saturday 17 December 2022, involving lecturers from ITB Ahmad Dahlan Lamongan. The result of the training is to increase students' knowledge of using delay software and making bibliography using reference management. The implication of the training activities is that these activities can be scheduled for each batch of postgraduate students in the future
Community Behavior in Artificial Intelligence-Based Waste Management

The community is not yet aware that proper and correct waste management is the main capital in waste management. It is hoped that the community will improve their behavior and be used as a guide by the local government regarding what steps to take in the future to be more synergized with the community in managing waste. The people who have been tested are seen from recent education, discipline, and environmental knowledge, with a total sample of 100 people. The study used questionnaires and interviews as data collection tools. The data that has been obtained is analyzed by testing the criteria used is the Rough set. The results of the extracted criteria produce 9 varied general rules, which can produce solutions and steps forward as supporting success in creating a clean environment from waste
Increasing English Vocabulary with Snakes and Ladders Media

English is one of the subjects that must be mastered by students from elementary and secondary levels. However, students often experience difficulty communicating due to a lack of mastery of English vocabulary. This is also supported by learning in schools that use more textbooks than using other more interesting learning methods so that community service activities need to be carried out at SDN 105308 Namo Bintang. The Community Service will be held on February 8, 2023. The research method used is descriptive qualitative. The purpose of this community service activity is to train elementary school students to communicate in English to develop knowledge and vocabulary skills by using snakes and ladders media. The method of implementing the activity is carried out through training for children who live at SDN 105308 Namo Bintang. With the use of snakes and ladders media, students at SDN 105308 Namo Bintang are increasingly trained to speak English and master a lot of English vocabulary around their environment
Application of Graph Coloring in Compilation of Work Schedules for Dr. General Hospital Nurses Ferdinand Lumbantobing Sibolga

Preparation of a schedule is needed to regulate the course of work activities, but errors often occur such as schedules that are not according to the rules and schedules that clash, for that we need other alternatives that can help the process of preparing the schedule. An alternative that can be used is Graph Coloring. Graph coloring is the process of giving color to graph nodes so that no neighboring vertices have the same color, the vertices represent the nurses and the edges represent the relationships between nurses. Then coloring the graph nodes with the Welch-Powell algorithm, where each neighboring (related) vertex is not colored with the same color so that nurses with the same criteria have different groups. So that each working group of nurses formed has nurses with each criterion according to predetermined rules and there are no conflicting schedules.
DEEP LEARNING FOR MODELING HUMAN OBJECT RECOGNITION AND FACIAL PERCEPTION

Deep learning has revolutionized the field of artificial intelligence, enabling machines to learn from large datasets and perform complex tasks such as object recognition and facial perception. In recent years, there has been significant progress in developing deep learning models for these tasks. This paper discusses the potential of deep learning in object recognition and facial perception, two critical areas of computer vision. The first part of the paper focuses on deep learning for object recognition, discussing the development of models that can detect and classify objects in images and videos. The second part of the paper focuses on deep learning for facial perception, discussing the development of models that can detect and analyze facial expressions, emotions, and features. The paper also discusses the challenges and limitations of deep learning models, including their dependence on large datasets and the need for interpretability. Finally, we explore the potential applications of deep learning in fields such as healthcare, security, and entertainment.
Detecting and Correcting Contextual Mistakes in Sentences Using Part of Speech Tags

A grammar checker is a tool to check each sentence in a text to see whether it conforms to the grammar. In case it finds a structure that conflicts with the conformity to the grammar, it would give suggestions for alternatives. The grammar checkers for European languages and some Indic languages are well developed. However, perhaps, owing to Tamil being a morphologically rich and agglutinative language this has been a challenging task. An approach to detecting and correcting grammatical mistakes due to subject and finite-verb disagreement with regard to person, number and/or gender and due to disagreement in tense aspects in Tamil sentences is proposed in this paper. A method has been proposed that uses hierarchical part-of-speech tags of words to detect the grammatical mistakes in subject and finite-verb agreement and mistakes in tense aspects in Tamil sentences. Two sets of Tamil grammar rules are used to generate suggestions for the grammatical mistakes. Test results show that the proposed grammatical mistake detection and correction system performs well.
Chatbot for Diagnosis of Pregnancy Disorders using Artificial Intelligence Markup Language (AIML)

Artificial Intelligence has evolved in sophistication and widespread use. This study aims to create a chatbot application in the health sector regarding the early diagnosis of pregnancy disorders. Based on basic health research, only 44 percent of pregnant women know the danger signs of pregnancy. The chatbot application developed is expected to facilitate and increase knowledge for pregnant women about the danger signs of pregnancy, especially early diagnosis of pregnancy disorders. The chatbot application was developed with artificial intelligence technology based on Artificial Intelligence Markup Language with the question-answer concept using the Pandorabots framework. The test is carried out in two stages: functional and pattern matching. The functional testing uses the black-box testing method, and the pattern-matching test on the chatbot uses the sentence similarity and bigram methods based on user input and keywords similarity in the bot's knowledge base. The functional testing results show that the chatbot application runs well, with the eligibility criteria reaching 81.4% and the results of the keyword similarity test (pattern matching) are zero to one, in the sense that the value of one has the same similarity between user input and pattern. Meanwhile, the zero value has no similarities, so the bot will respond to it as free input. So it can be concluded that the bot can respond to user questions when the pattern and input have the same level of similarity.
K-Means Algorithm Analysis for Election Cluster Prediction

The general election is a democratic process that is carried out in every country whose system of government is presidential, including Indonesia, which conducts it every five years. In fact, some people abstain, leading to budget wasting and missing target. Thus, it is very important to identify clusters of general election districts and map the number of voters to map the budget for the upcoming election. This process needs prediction to help reduce budgeting risk as an early warning. Based on the latest election data taken from Margokaton, Yogyakarta, Indonesia, many people voted in 2021, but the number of abstainers is high. In this case, cluster prediction is important to identify the election participants in each area. The K-Means algorithm could also predict abstainer areas in election activities to facilitate early mitigation in drafting election budgeting. Therefore, this study aimed to identify the pattern of voters in the election using the K-means algorithm. The data parameters comprised the list of voters, Unused ballot papers, and the sum of abstainers. This study is important because it contributes to reducing the election budget of each area. The data obtained from the Indonesia Ministry of Internal Affairs official website in 2021 were processed using the RapidMiner tool. The results showed more than 11% of the non-voters in cluster 1, 16% in Cluster 2, and 8% in cluster 3. The evaluation of clusters value is 2.04, indicating that the clustering using K-means is suitable, as shown by the DBI value close to 0. The results indicate that testing the cluster optimization of the K-Means algorithm using DBI is highly recommended. Based on this prediction result, the government needs special attention to clusters with many abstainers to decrease the number of abstainers and prevent overbudgeting. These results indicate the need to review the election participant data in 2024. Furthermore, there is a need for continuous socialization and education about election activities to reduce the number of abstainers and prevent overbudgeting.
What Is Synthetic Data? The Good, The Bad, and The Ugly

Sharing data can often enable compelling applications and analytics. However, more often than not, valuable datasets contain information of a sensitive nature, and thus, sharing them can endanger the privacy of users and organizations. A possible alternative gaining momentum in both the research community and industry is to share synthetic data instead. The idea is to release artificially generated datasets that resemble the actual data -- more precisely, having similar statistical properties. In this article, we provide a gentle introduction to synthetic data and discuss its use cases, the privacy challenges that are still unaddressed, and its inherent limitations as an effective privacy-enhancing technology.
Domain-aware Triplet loss in Domain Generalization

Despite much progress being made in the field of object recognition with the advances of deep learning, there are still several factors negatively affecting the performance of deep learning models. Domain shift is one of these factors and is caused by discrepancies in the distributions of the testing and training data. In this paper, we focus on the problem of compact feature clustering in domain generalization to help optimize the embedding space from multi-domain data. We design a domainaware triplet loss for domain generalization to help the model to not only cluster similar semantic features, but also to disperse features arising from the domain. Unlike previous methods focusing on distribution alignment, our algorithm is designed to disperse domain information in the embedding space. The basic idea is motivated based on the assumption that embedding features can be clustered based on domain information, which is mathematically and empirically supported in this paper. In addition, during our exploration of feature clustering in domain generalization, we note that factors affecting the convergence of metric learning loss in domain generalization are more important than the pre-defined domains. To solve this issue, we utilize two methods to normalize the embedding space, reducing the internal covariate shift of the embedding features. The ablation study demonstrates the effectiveness of our algorithm. Moreover, the experiments on the benchmark datasets, including PACS, VLCS and Office-Home, show that our method outperforms related methods focusing on domain discrepancy. In particular, our results on RegnetY-16 are significantly better than state-of-the-art methods on the benchmark datasets. Our code will be released at https://github.com/workerbcd/DCT
MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering

Recently, finetuning pretrained Vision-Language Models (VLMs) has been a prevailing paradigm for achieving state-of-the-art performance in Visual Question Answering (VQA). However, as VLMs scale, finetuning full model parameters for a given task in low-resource settings becomes computationally expensive, storage inefficient, and prone to overfitting. Current parameter-efficient tuning methods dramatically reduce the number of tunable parameters, but there still exists a significant performance gap with full finetuning. In this paper, we propose MixPHM, a redundancy-aware parameter-efficient tuning method that outperforms full finetuning in low-resource VQA. Specifically, MixPHM is a lightweight module implemented by multiple PHM-experts in a mixture-of-experts manner. To reduce parameter redundancy, MixPHM reparameterizes expert weights in a low-rank subspace and shares part of the weights inside and across experts. Moreover, based on a quantitative redundancy analysis for adapters, we propose Redundancy Regularization to reduce task-irrelevant redundancy while promoting task-relevant correlation in MixPHM representations. Experiments conducted on VQA v2, GQA, and OK-VQA demonstrate that MixPHM outperforms state-of-the-art parameter-efficient methods and is the only one consistently surpassing full finetuning.
Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning

Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challenging due to ambiguous human-object associations, large search space of detecting HOIs and highly noisy training signal. A promising strategy to address those challenges is to exploit knowledge from large-scale pretrained models (e.g., CLIP), but a direct knowledge distillation strategy~\citep{liao2022gen} does not perform well on the weakly-supervised setting. In contrast, we develop a CLIP-guided HOI representation capable of incorporating the prior knowledge at both image level and HOI instance level, and adopt a self-taught mechanism to prune incorrect human-object associations. Experimental results on HICO-DET and V-COCO show that our method outperforms the previous works by a sizable margin, showing the efficacy of our HOI representation.
Non asymptotic analysis of Adaptive stochastic gradient algorithms and applications

In stochastic optimization, a common tool to deal sequentially with large sample is to consider the well-known stochastic gradient algorithm. Nevertheless, since the stepsequence is the same for each direction, this can lead to bad results in practice in case of ill-conditionned problem. To overcome this, adaptive gradient algorithms such that Adagrad or Stochastic Newton algorithms should be prefered. This paper is devoted to the non asymptotic analyis of these adaptive gradient algorithms for strongly convex objective. All the theoretical results will be adapted to linear regression and regularized generalized linear model for both Adagrad and Stochastic Newton algorithms.
A Vision for Semantically Enriched Data Science

The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are areas where we have seen little automation. Data Scientists have long leveraged common sense reasoning and domain knowledge to understand and enrich data for building predictive models. In this paper we discuss important shortcomings of current data science and machine learning solutions. We then envision how leveraging "semantic" understanding and reasoning on data in combination with novel tools for data science automation can help with consistent and explainable data augmentation and transformation. Additionally, we discuss how semantics can assist data scientists in a new manner by helping with challenges related to trust, bias, and explainability in machine learning. Semantic annotation can also help better explore and organize large data sources.
The Ladder in Chaos: A Simple and Effective Improvement to General DRL Algorithms by Policy Path Trimming and Boosting

Knowing the learning dynamics of policy is significant to unveiling the mysteries of Reinforcement Learning (RL). It is especially crucial yet challenging to Deep RL, from which the remedies to notorious issues like sample inefficiency and learning instability could be obtained. In this paper, we study how the policy networks of typical DRL agents evolve during the learning process by empirically investigating several kinds of temporal change for each policy parameter. On typical MuJoCo and DeepMind Control Suite (DMC) benchmarks, we find common phenomena for TD3 and RAD agents: 1) the activity of policy network parameters is highly asymmetric and policy networks advance monotonically along very few major parameter directions; 2) severe detours occur in parameter update and harmonic-like changes are observed for all minor parameter directions. By performing a novel temporal SVD along policy learning path, the major and minor parameter directions are identified as the columns of right unitary matrix associated with dominant and insignificant singular values respectively. Driven by the discoveries above, we propose a simple and effective method, called Policy Path Trimming and Boosting (PPTB), as a general plug-in improvement to DRL algorithms. The key idea of PPTB is to periodically trim the policy learning path by canceling the policy updates in minor parameter directions, while boost the learning path by encouraging the advance in major directions. In experiments, we demonstrate the general and significant performance improvements brought by PPTB, when combined with TD3 and RAD in MuJoCo and DMC environments respectively.
MLANet: Multi-Level Attention Network with Sub-instruction for Continuous Vision-and-Language Navigation

Vision-and-Language Navigation (VLN) aims to develop intelligent agents to navigate in unseen environments only through language and vision supervision. In the recently proposed continuous settings (continuous VLN), the agent must act in a free 3D space and faces tougher challenges like real-time execution, complex instruction understanding, and long action sequence prediction. For a better performance in continuous VLN, we design a multi-level instruction understanding procedure and propose a novel model, Multi-Level Attention Network (MLANet). The first step of MLANet is to generate sub-instructions efficiently. We design a Fast Sub-instruction Algorithm (FSA) to segment the raw instruction into sub-instructions and generate a new sub-instruction dataset named ``FSASub". FSA is annotation-free and faster than the current method by 70 times, thus fitting the real-time requirement in continuous VLN. To solve the complex instruction understanding problem, MLANet needs a global perception of the instruction and observations. We propose a Multi-Level Attention (MLA) module to fuse vision, low-level semantics, and high-level semantics, which produce features containing a dynamic and global comprehension of the task. MLA also mitigates the adverse effects of noise words, thus ensuring a robust understanding of the instruction. To correctly predict actions in long trajectories, MLANet needs to focus on what sub-instruction is being executed every step. We propose a Peak Attention Loss (PAL) to improve the flexible and adaptive selection of the current sub-instruction. PAL benefits the navigation agent by concentrating its attention on the local information, thus helping the agent predict the most appropriate actions. We train and test MLANet in the standard benchmark. Experiment results show MLANet outperforms baselines by a significant margin.
WiCE: Real-World Entailment for Claims in Wikipedia

Textual entailment models are increasingly applied in settings like fact-checking, presupposition verification in question answering, or summary evaluation. However, these represent a significant domain shift from existing entailment datasets, and models underperform as a result. We propose WiCE, a new fine-grained textual entailment dataset built on natural claim and evidence pairs extracted from Wikipedia. In addition to standard claim-level entailment, WiCE provides entailment judgments over sub-sentence units of the claim, and a minimal subset of evidence sentences that support each subclaim. To support this, we propose an automatic claim decomposition strategy using GPT-3.5 which we show is also effective at improving entailment models' performance on multiple datasets at test time. Finally, we show that real claims in our dataset involve challenging verification and retrieval problems that existing models fail to address.
Suppression of coherent errors in Cross-Resonance gates via recursive DRAG

The high-precision control of quantum logical operations is a prerequisite to increasing circuit depths in quantum processors, implementing useful quantum algorithms, and reaching fault-tolerant scalable architectures. A ubiquitous approach used for entangling gates has been all-microwave control of superconducting qubits, primarily using the Cross-Resonance two-qubit gate; however, fidelities are still limited by control imperfections. Here, we derive an analytical method that significantly improves fidelities in Cross-Resonance gates, suppressing both the three off-resonant transitions on the control qubit and unwanted two-qubit rotation operators. This reduces the total coherent errors by one to three orders of magnitude across all parameter regimes studied. Our approach uses a simple recursive composition of DRAG pulses derived for each spurious coupling in the corresponding two-level subspace and a correction tone applied resonantly on the target qubit, requiring no additional control hardware overhead.