Reviewerly/exHarmony huggingface dataset:
Research on Intelligent Dialogue Systems

Intelligent dialogue systems, as a subfield of artificial intelligence, have very important research significance and application value. Todays AI dialogue systems are still in a relatively early stage, but they are developing very rapidly. In recent years, intelligent dialogue systems have been applied in many fields, such as intelligent customer service in online transactions, intelligent voice assistants in smartphones, and virtual chatbots. This paper introduces the background of intelligent dialogue systems and the current research status of key technologies and discusses some challenges in this field and some recent research to improve the system. Most of the current intelligent dialogue systems can perform effective human-computer interaction and respond accordingly. But for the next generation of intelligent dialogue system, more human characteristics are needed so that it can better understand and express human language, have its own personality, and maintain the consistency and logic of dialogue.
Pursuit-evasion games on latin square graphs

We investigate various pursuit-evasion parameters on latin square graphs, including the cop number, metric dimension, and localization number. The cop number of latin square graphs is studied, and for $k$-MOLS$(n),$ bounds for the cop number are given. If $n>(k+1)^2,$ then the cop number is shown to be $k+2.$ Lower and upper bounds are provided for the metric dimension and localization number of latin square graphs. The metric dimension of back-circulant latin squares shows that the lower bound is close to tight. Recent results on covers and partial transversals of latin squares provide the upper bound of $n+O\left(\frac{\log{n}}{\log{\log{n}}}\right)$ on the localization number of a latin square graph of order $n.$
Evaluation of Net Metering for Solar Rooftop Systems

Abstract: A bidirectional metre that can record both import and export energy values is utilized by net metering mechanisms. Using self-produced renewable energy and selling any surplus to the grid are the main goals of net metering. There is a decrease in electricity costs as a result. In net metering, solar photovoltaic systems are heavily utilised. The cleanest, most abundant, most environmentally friendly source of renewable energy is solar energy. In the interest of deciding whether net metering is feasible in India, the notion of net metering using a solar system and data analysis are studied in this research. The study also examines the annual savings in electricity and the viability of solar rooftop photovoltaic systems from an economic perspective. This paper's study examines various solar tracking system tracking mechanisms in detail. The two main categories of solar tracking systems are single axis trackers and dual axis trackers. A single axis tracker is an area near the equator where there is little variation in the sun's precise position. Dual axis trackers are designed for locations where the sun's movement is during the day from east to west, and during the seasons from east to north or south
Android-Based Mobile Application for Operations and Inventory Management

Abstract: In this era, many small-scale industries have been facing problems regarding the management of their supply chains resulting in delays in their deliveries. This happens due to a lack of commitment from the suppliers and other components of the supply chain. This affects the manufacturer’s reputation which leads to losses in the long term. In this paper, we are introducing a mobile application called “Invento”, which aims to help manufacturers and suppliers to manage their inventories and operations effectively and hence increase their productivity. It uses a reward-based system to encourage suppliers to deliver the required material to the manufacturer on time.
Fraud Through Online Store

Now times have progressed and technology is also growing.All activities can be completed with technology.Including economic activity.Now the community does not only carry out economic activities directly, but by using technology the community can carry out buying and selling activities online.Consumers no longer have to go somewhere, but can make purchases only from home.Only by capitalizing on the internet network, buying and selling technology activities can already be carried out.Thus one can streamline time wherever and whenever without having to meet face to face.This activity is called E-Commerce.The presence and development of ecommerce transactions certainly cannot be separated from a problem, in e-commerce transactions, there is the potential for fraud from the products offered, so many types of fraud can occur in transactions at these online stores.Therefore this study aims to analyze problems regarding fraud that occurs in online store transactions.It will be found from the results of the research that the form of criminal responsibility for perpetrators of online fraud crimes can only be sentenced using Article 28 paragraph (1) Juncto Article 45 paragraph (2) of Law No. 8 of 2011 concerning Information and Electronic Transactions.It will be found from the results of the research that the form of criminal responsibility for perpetrators of online fraud crimes can only be sentenced using Article 28 paragraph (1) Juncto Article 45 paragraph (2) of Law No. 8 of 2011 concerning Information and Electronic Transactions.It will be found from the results of the research that the form of criminal responsibility for perpetrators of online fraud crimes can only be sentenced using Article 28 paragraph (1) Juncto Article 45 paragraph (2) of Law No. 8 of 2011 concerning Information and Electronic Transactions.
Build a Recommendation System for Movies or Books

Abstract: These days everyone will change their life style to search the movies on the internet. They will provide the information of their preferences which the y like to watch. There are many recommended and popular system is applied to search their favorite things like books, articles music videos, movies etc. these paper we are proposed a movie recommendation system. It will be working on the various filter or collaborate filter method that will collect and gave the information to the user and it will also analyzes the user and gave them the best movies to there users at that time. We sorted the movies according to the user recommendation of the previous users preferences so for these purpose we use k-means algorithm. Movie recommendation system can also help the user to find the choices of there movies are based on there previous experiences and manners without wasting its useless time on the browser to search their best movies. It will give us various type of previous recommendation using customized the database. then the user can browse it easily and choice his best movie
Adaptive Weed Detection and Removal Using Cutter and Pesticide Spraying AGBOT

Abstract: Agriculture is one amongst the world's oldest sources of human nourishment. Weeds are a problem as they compete with desirable crops and use up water, nutrients, and space. Development of a successful weed removal system involves correct identification of the unwanted vegetation. The working of the robot is done on the basis of Arduino Uno, where every motion is controlled by the program which is fed into the Arduino. The main purpose of the robot is to identify the weed and remove the weed using cutter and spray the pesticide, using pesticide sprayer only where weeds were present.
Adaptive Human Matting for Dynamic Videos

The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with highly diverse and unstructured videos. We address this limitation by introducing Adaptive Matting for Dynamic Videos, termed AdaM, which is a framework designed for simultaneously differentiating foregrounds from backgrounds and capturing alpha matte details of human subjects in the foreground. Two interconnected network designs are employed to achieve this goal: (1) an encoder-decoder network that produces alpha mattes and intermediate masks which are used to guide the transformer in adaptively decoding foregrounds and backgrounds, and (2) a transformer network in which long- and short-term attention combine to retain spatial and temporal contexts, facilitating the decoding of foreground details. We benchmark and study our methods on recently introduced datasets, showing that our model notably improves matting realism and temporal coherence in complex real-world videos and achieves new best-in-class generalizability. Further details and examples are available at https://github.com/microsoft/AdaM.
Localizing Model Behavior with Path Patching

Localizing behaviors of neural networks to a subset of the network's components or a subset of interactions between components is a natural first step towards analyzing network mechanisms and possible failure modes. Existing work is often qualitative and ad-hoc, and there is no consensus on the appropriate way to evaluate localization claims. We introduce path patching, a technique for expressing and quantitatively testing a natural class of hypotheses expressing that behaviors are localized to a set of paths. We refine an explanation of induction heads, characterize a behavior of GPT-2, and open source a framework for efficiently running similar experiments.
Selecting Robust Features for Machine Learning Applications using Multidata Causal Discovery

Robust feature selection is vital for creating reliable and interpretable Machine Learning (ML) models. When designing statistical prediction models in cases where domain knowledge is limited and underlying interactions are unknown, choosing the optimal set of features is often difficult. To mitigate this issue, we introduce a Multidata (M) causal feature selection approach that simultaneously processes an ensemble of time series datasets and produces a single set of causal drivers. This approach uses the causal discovery algorithms PC1 or PCMCI that are implemented in the Tigramite Python package. These algorithms utilize conditional independence tests to infer parts of the causal graph. Our causal feature selection approach filters out causally-spurious links before passing the remaining causal features as inputs to ML models (Multiple linear regression, Random Forest) that predict the targets. We apply our framework to the statistical intensity prediction of Western Pacific Tropical Cyclones (TC), for which it is often difficult to accurately choose drivers and their dimensionality reduction (time lags, vertical levels, and area-averaging). Using more stringent significance thresholds in the conditional independence tests helps eliminate spurious causal relationships, thus helping the ML model generalize better to unseen TC cases. M-PC1 with a reduced number of features outperforms M-PCMCI, non-causal ML, and other feature selection methods (lagged correlation, random), even slightly outperforming feature selection based on eXplainable Artificial Intelligence. The optimal causal drivers obtained from our causal feature selection help improve our understanding of underlying relationships and suggest new potential drivers of TC intensification.
Toward Cohort Intelligence: A Universal Cohort Representation Learning Framework for Electronic Health Record Analysis

Electronic Health Records (EHR) are generated from clinical routine care recording valuable information of broad patient populations, which provide plentiful opportunities for improving patient management and intervention strategies in clinical practice. To exploit the enormous potential of EHR data, a popular EHR data analysis paradigm in machine learning is EHR representation learning, which first leverages the individual patient's EHR data to learn informative representations by a backbone, and supports diverse health-care downstream tasks grounded on the representations. Unfortunately, such a paradigm fails to access the in-depth analysis of patients' relevance, which is generally known as cohort studies in clinical practice. Specifically, patients in the same cohort tend to share similar characteristics, implying their resemblance in medical conditions such as symptoms or diseases. In this paper, we propose a universal COhort Representation lEarning (CORE) framework to augment EHR utilization by leveraging the fine-grained cohort information among patients. In particular, CORE first develops an explicit patient modeling task based on the prior knowledge of patients' diagnosis codes, which measures the latent relevance among patients to adaptively divide the cohorts for each patient. Based on the constructed cohorts, CORE recodes the pre-extracted EHR data representation from intra- and inter-cohort perspectives, yielding augmented EHR data representation learning. CORE is readily applicable to diverse backbone models, serving as a universal plug-in framework to infuse cohort information into healthcare methods for boosted performance. We conduct an extensive experimental evaluation on two real-world datasets, and the experimental results demonstrate the effectiveness and generalizability of CORE.
Anticipating Unintended Consequences of Technology Using Insights from Creativity Support Tools

Our society has been increasingly witnessing a number of negative, unintended consequences of digital technologies. While post-hoc policy regulation is crucial in addressing these issues, reasonably anticipating the consequences before deploying technology can help mitigate potential harm to society in the first place. Yet, the quest to anticipate potential harms can be difficult without seeing digital technologies deployed in the real world. In this position paper, we argue that anticipating unintended consequences of technology can be facilitated through creativity-enhancing interventions, such as by building on existing knowledge and insights from diverse stakeholders. Using lessons learned from prior work on creativity-support tools, the HCI community is uniquely equipped to design novel systems that aid in anticipating negative unintended consequences of technology on society.
Block particle filters for state estimation of stochastic reaction-diffusion systems

In this work, we consider a differential description of the evolution of the state of a reaction-diffusion system under environmental fluctuations. We are interested in estimating the state of the system when only partial observations are available. To describe how observations and states are related, we combine multiplicative noise-driven dynamics with an observation model. More specifically, we ensure that the observations are subjected to error in the form of additive noise. We focus on the state estimation of a Belousov-Zhabotinskii chemical reaction. We simulate a reaction conducted in a quasi-two-dimensional physical domain, such as on the surface of a Petri dish. We aim at reconstructing the emerging chemical patterns based on noisy spectral observations. For this task, we consider a finite difference representation on the spatial domain, where nodes are chosen according to observation sites. We approximate the solution to this state estimation problem with the Block particle filter, a sequential Monte Carlo method capable of addressing the associated high-dimensionality of this state-space representation.
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization

Combinatorial optimization (CO) aims to efficiently find the best solution to NP-hard problems ranging from statistical physics to social media marketing. A wide range of CO applications can benefit from local search methods because they allow reversible action over greedy policies. Deep Q-learning (DQN) using message-passing neural networks (MPNN) has shown promise in replicating the local search behavior and obtaining comparable results to the local search algorithms. However, the over-smoothing and the information loss during the iterations of message passing limit its robustness across applications, and the large message vectors result in memory inefficiency. Our paper introduces RELS-DQN, a lightweight DQN framework that exhibits the local search behavior while providing practical scalability. Using the RELS-DQN model trained on one application, it can generalize to various applications by providing solution values higher than or equal to both the local search algorithms and the existing DQN models while remaining efficient in runtime and memory.
SePEnTra: A secure and privacy-preserving energy trading mechanisms in transactive energy market

In this paper, we design and present a novel model called SePEnTra to ensure the security and privacy of energy data while sharing with other entities during energy trading to determine optimal price signals. Furthermore, the market operator can use this data to detect malicious activities of users in the later stage without violating privacy (e.g., deviation of actual energy generation/consumption from forecast beyond a threshold). We use two cryptographic primitives, additive secret sharing and Pedersen commitment, in SePEnTra. The performance of our model is evaluated theoretically and numerically. We compare the performance of SePEnTra with the same Transactive energy market (TEM) framework without security mechanisms. The result shows that even though using advanced cryptographic primitives in a large market framework, SePEnTra has very low computational complexity and communication overhead. Moreover, it is storage efficient for all parties.
LeafAI: query generator for clinical cohort discovery rivaling a human programmer

Objective: Identifying study-eligible patients within clinical databases is a critical step in clinical research. However, accurate query design typically requires extensive technical and biomedical expertise. We sought to create a system capable of generating data model-agnostic queries while also providing novel logical reasoning capabilities for complex clinical trial eligibility criteria. Materials and Methods: The task of query creation from eligibility criteria requires solving several text-processing problems, including named entity recognition and relation extraction, sequence-to-sequence transformation, normalization, and reasoning. We incorporated hybrid deep learning and rule-based modules for these, as well as a knowledge base of the Unified Medical Language System (UMLS) and linked ontologies. To enable data-model agnostic query creation, we introduce a novel method for tagging database schema elements using UMLS concepts. To evaluate our system, called LeafAI, we compared the capability of LeafAI to a human database programmer to identify patients who had been enrolled in 8 clinical trials conducted at our institution. We measured performance by the number of actual enrolled patients matched by generated queries. Results: LeafAI matched a mean 43% of enrolled patients with 27,225 eligible across 8 clinical trials, compared to 27% matched and 14,587 eligible in queries by a human database programmer. The human programmer spent 26 total hours crafting queries compared to several minutes by LeafAI. Conclusions: Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base. We demonstrate that LeafAI can rival an experienced human programmer in finding patients eligible for clinical trials.
Explanation of Face Recognition via Saliency Maps

Despite the significant progress in face recognition in the past years, they are often treated as "black boxes" and have been criticized for lacking explainability. It becomes increasingly important to understand the characteristics and decisions of deep face recognition systems to make them more acceptable to the public. Explainable face recognition (XFR) refers to the problem of interpreting why the recognition model matches a probe face with one identity over others. Recent studies have explored use of visual saliency maps as an explanation, but they often lack a deeper analysis in the context of face recognition. This paper starts by proposing a rigorous definition of explainable face recognition (XFR) which focuses on the decision-making process of the deep recognition model. Following the new definition, a similarity-based RISE algorithm (S-RISE) is then introduced to produce high-quality visual saliency maps. Furthermore, an evaluation approach is proposed to systematically validate the reliability and accuracy of general visual saliency-based XFR methods.
Boosting Video Object Segmentation via Space-time Correspondence Learning

Current top-leading solutions for video object segmentation (VOS) typically follow a matching-based regime: for each query frame, the segmentation mask is inferred according to its correspondence to previously processed and the first annotated frames. They simply exploit the supervisory signals from the groundtruth masks for learning mask prediction only, without posing any constraint on the space-time correspondence matching, which, however, is the fundamental building block of such regime. To alleviate this crucial yet commonly ignored issue, we devise a correspondence-aware training framework, which boosts matching-based VOS solutions by explicitly encouraging robust correspondence matching during network learning. Through comprehensively exploring the intrinsic coherence in videos on pixel and object levels, our algorithm reinforces the standard, fully supervised training of mask segmentation with label-free, contrastive correspondence learning. Without neither requiring extra annotation cost during training, nor causing speed delay during deployment, nor incurring architectural modification, our algorithm provides solid performance gains on four widely used benchmarks, i.e., DAVIS2016&2017, and YouTube-VOS2018&2019, on the top of famous matching-based VOS solutions.
A Comprehensive Survey on the Implementations, Attacks, and Countermeasures of the Current NIST Lightweight Cryptography Standard

This survey is the first work on the current standard for lightweight cryptography, standardized in 2023. Lightweight cryptography plays a vital role in securing resource-constrained embedded systems such as deeply-embedded systems (implantable and wearable medical devices, smart fabrics, smart homes, and the like), radio frequency identification (RFID) tags, sensor networks, and privacy-constrained usage models. National Institute of Standards and Technology (NIST) initiated a standardization process for lightweight cryptography and after a relatively-long multi-year effort, eventually, in Feb. 2023, the competition ended with ASCON as the winner. This lightweight cryptographic standard will be used in deeply-embedded architectures to provide security through confidentiality and integrity/authentication (the dual of the legacy AES-GCM block cipher which is the NIST standard for symmetric key cryptography). ASCON's lightweight design utilizes a 320-bit permutation which is bit-sliced into five 64-bit register words, providing 128-bit level security. This work summarizes the different implementations of ASCON on field-programmable gate array (FPGA) and ASIC hardware platforms on the basis of area, power, throughput, energy, and efficiency overheads. The presented work also reviews various differential and side-channel analysis attacks (SCAs) performed across variants of ASCON cipher suite in terms of algebraic, cube/cube-like, forgery, fault injection, and power analysis attacks as well as the countermeasures for these attacks. We also provide our insights and visions throughout this survey to provide new future directions in different domains. This survey is the first one in its kind and a step forward towards scrutinizing the advantages and future directions of the NIST lightweight cryptography standard introduced in 2023.