Reviewerly/exHarmony huggingface dataset:
Adaptive Vague Preference Policy Learning for Multi-round Conversational Recommendation

Conversational recommendation systems (CRS) aim to elicit user preferences and provide satisfying recommendations through natural language interactions. Existing CRS methods often assume that users have clear and consistent preferences, which may not reflect the reality of user decision-making processes. In this paper, we introduce a novel scenario called Vague Preference Multi-round Conversational Recommendation (VPMCR), which considers users' vague and dynamic preferences in CRS. In the VPMCR setting, we propose a solution called Adaptive Vague Preference Policy Learning (AVPPL), which consists of two components: Ambiguity-aware Soft Estimation (ASE) and Dynamism-aware Policy Learning (DPL). ASE estimates the vagueness of user feedback and captures their dynamic preferences using a choice-based preference extraction module and a time-aware decaying strategy. DPL leverages the preference distribution estimated by ASE to guide the conversation and adapt to changes in user preferences using a graph-based conversation modeling module and a vague preference policy learning module. We conduct extensive experiments on four real-world datasets and demonstrate the effectiveness of our method in the VPMCR scenario, setting a new benchmark for future research in CRS.
ICON$^2$: Reliably Benchmarking Predictive Inequity in Object Detection

As computer vision systems are being increasingly deployed at scale in high-stakes applications like autonomous driving, concerns about social bias in these systems are rising. Analysis of fairness in real-world vision systems, such as object detection in driving scenes, has been limited to observing predictive inequity across attributes such as pedestrian skin tone, and lacks a consistent methodology to disentangle the role of confounding variables e.g. does my model perform worse for a certain skin tone, or are such scenes in my dataset more challenging due to occlusion and crowds? In this work, we introduce ICON$^2$, a framework for robustly answering this question. ICON$^2$ leverages prior knowledge on the deficiencies of object detection systems to identify performance discrepancies across sub-populations, compute correlations between these potential confounders and a given sensitive attribute, and control for the most likely confounders to obtain a more reliable estimate of model bias. Using our approach, we conduct an in-depth study on the performance of object detection with respect to income from the BDD100K driving dataset, revealing useful insights.
ANALISIS KEGIATAN LITERASI DALAM MENUMBUHKAN SEMANGAT PESERTA DIDIK

&#x0D; Literacy culture is not just an individual's hobby or preference in terms of reading, but rather an effort to meet the needs and challenges of the times. Literacy culture is important in creating an intelligent society, so that it will form a superior and quality nation. Through literacy activities, a person has the provision to face the challenges that exist today and the challenges that will come in the future. This is because literacy activities will affect one's mindset in reading existing situations and opportunities, so that a person will be more careful in making future decisions. Literacy culture needs to be carried out wherever and whenever. Schools in fostering students' enthusiasm for learning can be seen from how the literacy culture is carried out. This research was conducted at SD Sampang 02 using a descriptive qualitative approach with teachers and students as subjects. Data collection techniques used in this study were interviews, observation, and documentation. Techniques to test the validity of the data using triangulation techniques. SDN Sampangan 02 implements a literacy culture with new innovations, literacy activities at SDN Sampangan 02 are called Acikamen which means I Read You Listen. Acikamen activities are carried out in the school yard by all students. This activity not only guides students to read books, but also channel their interests and talents. Through this Acikamen literacy activity it is hoped that students will be more enthusiastic about learning.
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal

An accurate and substantial dataset is essential for training a reliable and well-performing model. However, even manually annotated datasets contain label errors, not to mention automatically labeled ones. Previous methods for label denoising have primarily focused on detecting outliers and their permanent removal - a process that is likely to over- or underfilter the dataset. In this work, we propose AGRA: a new method for learning with noisy labels by using Adaptive GRAdient-based outlier removal. Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process. By comparing the aggregated gradient of a batch of samples and an individual example gradient, our method dynamically decides whether a corresponding example is helpful for the model at this point or is counter-productive and should be left out for the current update. Extensive evaluation on several datasets demonstrates AGRA's effectiveness, while a comprehensive results analysis supports our initial hypothesis: permanent hard outlier removal is not always what model benefits the most from.
Enhancing In-Context Learning with Answer Feedback for Multi-Span Question Answering

Whereas the recent emergence of large language models (LLMs) like ChatGPT has exhibited impressive general performance, it still has a large gap with fully-supervised models on specific tasks such as multi-span question answering. Previous researches found that in-context learning is an effective approach to exploiting LLM, by using a few task-related labeled data as demonstration examples to construct a few-shot prompt for answering new questions. A popular implementation is to concatenate a few questions and their correct answers through simple templates, informing LLM of the desired output. In this paper, we propose a novel way of employing labeled data such that it also informs LLM of some undesired output, by extending demonstration examples with feedback about answers predicted by an off-the-shelf model, e.g., correct, incorrect, or incomplete. Experiments on three multi-span question answering datasets as well as a keyphrase extraction dataset show that our new prompting strategy consistently improves LLM's in-context learning performance.
Development of Tour Guide Application Providing Location Based Information

Abstract: As technology is growing immensely travel is one of the top sectors are growing disrupted by technology. Travelling apps are making center stage in the mobile app development. In recent market, there are different apps with various functions. As an example, Google maps for locations, another is trivago for hotels bookings and so on. We will be using an android studio to implement our project. Android studio is the official integrated development environment (IDE) for android applications development. Android studio provides more features to make various functionalities in application that enhance our productivity while building android apps. It supports Java programming languages to build the app. In this project we will build a tour guide application. This app will provide the information about tourist places and nearby place recommendations such as hotels, tourist places, helpline numbers and also give brief information about the places. We will also sort the places category wise like hill historical, cultural, parks etc. We are implementing all the various functions of different applications gather in one app. Thus the name tour guide app
StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code

Code LLMs are being rapidly deployed and there is evidence that they can make professional programmers more productive. Current benchmarks for code generation measure whether models generate correct programs given an expert prompt. In this paper, we present a new benchmark containing multiple prompts per problem, written by a specific population of non-expert prompters: beginning programmers. StudentEval contains 1,749 prompts for 48 problems, written by 80 students who have only completed one semester of Python programming. Our students wrote these prompts while working interactively with a Code LLM, and we observed very mixed success rates. We use StudentEval to evaluate 5 Code LLMs and find that StudentEval is a better discriminator of model performance than existing benchmarks. We analyze the prompts and find significant variation in students' prompting techniques. We also find that nondeterministic LLM sampling could mislead students into thinking that their prompts are more (or less) effective than they actually are, which has implications for how to teach with Code LLMs.
Embracing Background Knowledge in the Analysis of Actual Causality: An Answer Set Programming Approach

This paper presents a rich knowledge representation language aimed at formalizing causal knowledge. This language is used for accurately and directly formalizing common benchmark examples from the literature of actual causality. A definition of cause is presented and used to analyze the actual causes of changes with respect to sequences of actions representing those examples.
Correction of Errors in Preference Ratings from Automated Metrics for Text Generation

A major challenge in the field of Text Generation is evaluation: Human evaluations are cost-intensive, and automated metrics often display considerable disagreement with human judgments. In this paper, we propose a statistical model of Text Generation evaluation that accounts for the error-proneness of automated metrics when used to generate preference rankings between system outputs. We show that existing automated metrics are generally over-confident in assigning significant differences between systems in this setting. However, our model enables an efficient combination of human and automated ratings to remedy the error-proneness of the automated metrics. We show that using this combination, we only require about 50% of the human annotations typically used in evaluations to arrive at robust and statistically significant results while yielding the same evaluation outcome as the pure human evaluation in 95% of cases. We showcase the benefits of approach for three text generation tasks: dialogue systems, machine translation, and text summarization.
Putting Humans in the Image Captioning Loop

Image Captioning (IC) models can highly benefit from human feedback in the training process, especially in cases where data is limited. We present work-in-progress on adapting an IC system to integrate human feedback, with the goal to make it easily adaptable to user-specific data. Our approach builds on a base IC model pre-trained on the MS COCO dataset, which generates captions for unseen images. The user will then be able to offer feedback on the image and the generated/predicted caption, which will be augmented to create additional training instances for the adaptation of the model. The additional instances are integrated into the model using step-wise updates, and a sparse memory replay component is used to avoid catastrophic forgetting. We hope that this approach, while leading to improved results, will also result in customizable IC models.
Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps

Digital contents have grown dramatically in recent years, leading to increased attention to copyright. Image watermarking has been considered one of the most popular methods for copyright protection. With the recent advancements in applying deep neural networks in image processing, these networks have also been used in image watermarking. Robustness and imperceptibility are two challenging features of watermarking methods that the trade-off between them should be satisfied. In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image content. Dynamic embedding helps the network to have the lowest effect on the visual quality of the watermarked image. Different image processing attacks are simulated as a network layer to improve the robustness of the model. Our method is a blind watermarking approach that replicates the watermark string to create a matrix of the same size as the input image. Instead of diffusing the watermark data into the input image, we inject the data into the feature space and force the network to do this in regions that increase the robustness against various attacks. Experimental results show the superiority of the proposed method in terms of imperceptibility and robustness compared to the state-of-the-art algorithms.
Revisiting Runtime Dynamic Optimization for Join Queries in Big Data Management Systems

Query Optimization remains an open problem for Big Data Management Systems. Traditional optimizers are cost-based and use statistical estimates of intermediate result cardinalities to assign costs and pick the best plan. However, such estimates tend to become less accurate because of filtering conditions caused either from undetected correlations between multiple predicates local to a single dataset, predicates with query parameters, or predicates involving user-defined functions (UDFs). Consequently, traditional query optimizers tend to ignore or miscalculate those settings, thus leading to suboptimal execution plans. Given the volume of today's data, a suboptimal plan can quickly become very inefficient. In this work, we revisit the old idea of runtime dynamic optimization and adapt it to a shared-nothing distributed database system, AsterixDB. The optimization runs in stages (re-optimization points), starting by first executing all predicates local to a single dataset. The intermediate result created from each stage is used to re-optimize the remaining query. This re-optimization approach avoids inaccurate intermediate result cardinality estimations, thus leading to much better execution plans. While it introduces the overhead for materializing these intermediate results, our experiments show that this overhead is relatively small and it is an acceptable price to pay given the optimization benefits. In fact, our experimental evaluation shows that runtime dynamic optimization leads to much better execution plans as compared to the current default AsterixDB plans as well as to plans produced by static cost-based optimization (i.e. based on the initial dataset statistics) and other state-of-the-art approaches.
Personal data in biomedical research

The fundamental element of biomedical research is analysing the personal data of millions of people across the world. Recent rapid developments in digital health and other technical advances have facilitated the collection of all forms of data. It includes the data registered by health care and allied institutions, data recorded by people revealing their lifestyle and behavioural patterns, catalogued by social media and accessories such as smartwatches. These advances also assist in storing and sharing such data and its analyses. However, in the last few years, some serious concerns about the protection of patient privacy and the secondary use of personal data have been raised. Several legal initiatives specifically related to data protection have been brought into force to safeguard the privacy of the participant in biomedical research. On the other hand, these legal measures and concerns are deemed a potential hurdle for research by some health researchers. Therefore, in biomedical research, dealing with personal data along with adequate privacy protection and maintaining the researcher’s scientific freedom is a double-edged sword. In this editorial, we have discussed several pertinent issues related to personal data, data protection and laws governing data sharing in biomedical research.
Role of Parents In Learning Spirit Early Childhood

The research entitled "The Role of Parents in Early Childhood Learning Enthusiasm" aims to discover how important parents' role is in seeing their children and educating their children to be passionate about learning. This type of research is qualitative research. The subjects of this study were parents and students of AWWABIEN Kindergarten in Jambi City for the 2022/2023 academic year, totalling 34 students, 18 boys and 16 girls. The data collection technique used was interviews with teachers, students and parents. The data analysis technique used is to analyze the instruments that have been given. The results of this study are parents who have the most role in the enthusiasm for learning of their students. The closest example for their children is their parents; they imitate what they see. Therefore parents must always show a positive attitude in front of their children. Thus learning also requires guidance from parents so that mature attitudes and learning responsibilities grow in children. Parents who are more concerned with their work means that children do not get supervision/guidance from parents, but have an impact on children, so they experience learning difficulties.
Kinerja penginputan transaksi akuntansi berbasis akrual secara manual pada penyusunan laporan keuangan

Performance of manually inputting accrual based accounting transactions in the preparation of financial reports. Advances in technology today greatly affects the company to move forward. Not a few applications that can help the preparation and transactions in accounting to be accurate. However, there are still many who use the manual preparation or recording method because of the high cost of the application as well as the difficulty in using the application as well as the difficulty in using the application and the factor of companies that do not have sophisticated systems. The purpose of this research is to find out how the performance in inputting manual based accounting transactions. The performance of inputting accrual based accounting transactions in the preparation of financial reports provide more comprehensive information, not only on the budget, the data produced is also more efficient data. Manually inputting accounting transactions minimize the risk of errors or confusion by financial workers related to company credibility. Manual accounting is more trusted among companies because sometimes accounting using applications or computerization can be changed by anyone without being noticed. Entering accrual based transactions manually can also help companies minimize costs, so that a small organization or small company does not have to pay to buy computerized systems and software that are quite expensive. Entering transactions manually has an important role for the company because it is a medium of communication in financial reporting
Electronic Component Identification using Voice Recognition

Abstract: Any fundamental discrete device or physical object in an electronic system that affects electrons or the fields around them is referred to as an electronic component. Electrical elements, which are conceptual, should not be confused with electronic components, the majority of which are industrial goods that are only available in a single form. Abstractions used to represent idealised electronic parts and components. There are numerous electrical terminals or leads on electronic components. To build an electronic circuit with a specific purpose (such as an amplifier, radio receiver, or oscillator), these leads link to other electrical components, frequently by wire. Basic electronic components can be integrated inside of discrete packages like semiconductor integrated circuits, hybrid integrated circuits, or thick film devices, as well as arrays or networks of similar components. The list of electrical components that follows concentrates on their discrete form and treats such bundles as independent components
Behavioral Mapping, Using NLP to Predict Individual Behavior : Focusing on Towards/Away Behavior

“What candidates or team members will do in specific circumstances” has always been an important piece of information for most employees or team leaders to consider when making a decision. Different companies take a significant amount of time to determine who is the best candidate for a particular job position. Companies are looking for the most efficient method for making this decision. Most of the time, personality assessments are used to identify an individual’s character traits. Regardless of personality type, individuals will behave differently in a positive atmosphere than in a stressful one. Hence, characteristics alone can not predict behavior. Thus, text analysis and the identification of candidate behaviors (behaviorism) now enable companies to understand how people think, feel, and act in a given situation and then choose from a vast pool of candidates the best candidate for the job. By leveraging the existing intellectual property data associated with behavioral mapping in AccuMatch Behavior Intelligence as well as expert data and using tools such as Amazon comprehend service (ACS), IBM Watson Natural Language Understanding (NLU), and Machine Learning (ML) techniques, five different methods have been provided and analyzed to predict how an individual in a team gets motivated. Therefore, this paper presents multiple proposed methods to predict Towards/Away behavior and discusses the rationale behind the selection of these methods along with the results obtained.
Natural Text-Driven, Multi-Attribute Editing of Facial Images with Robustness in Sparse Latent Space

This study presents a novel text-driven method for the editing of portraits with robustness in a sparse latent space. Due to the development of GAN and the proposal of many excellent models like StyleGAN, text-driven image editing and image generation have made great progress in recent years, but the task of generating facial images under the guidance of text is still lacking in some special situations. Our model combines and makes good use of two pre-training models, CLIP2Latent and StyleGAN2, to conduct a preliminary exploration of the above task. The latent code of the input portrait is driven to be edited and manipulated in the StyleGAN latent space via a CLIP-based text-driven generation module. Finally, some promising results have been obtained, especially in the sparse region of the generator latent space and when simultaneously changing numerous attributes.
Chatbots: A Game Changer in mHealth

&lt;p&gt;This is a review of chatbots in the field of mHealth&lt;/p&gt;
ON THE ACCOMMODATION OF TAUTOLOGIES IN THE ABSENCE OF BACKGROUND KNOWLEDGE

In this experimental study, we aim to determine whether tautological utterances with proper names require any mutually shared background knowledge to be fully understood. We argue that such tautologies can be sufficient when used independently and thus can be accommodated even if one does not have any mutually shared assumptions with their interlocutor. The results of our survey show that the ability to accommodate a tautology depends on the existence of its referent's behavioral manifestation clues explicitly stated in a preceding piece of discourse.
Internet of things in medicine

The Internet of things (IoT) has greatly impacted many industries including the medical. The application of IoT is increasing daily in every aspect of the medical industry. In fact, IoT has been identified as a technological solution to some medical challenges and a game changer for the healthcare services. This paper provides a brief introduction to the use of IoT in medicine.