A multipacking in an undirected graph $G=(V,E)$ is a set $M\subseteq V$ such that for every vertex $v\in V$ and for every integer $r\geq 1$, the ball of radius $r$ around $v$ contains at most $r$ vertices of $M$, that is, there are at most $r$ vertices in $M$ at a distance at most $r$ from $v$ in $G$. The Multipacking problem asks whether a graph contains a multipacking of size at least $k$. For more than a decade, it remained an open question whether the Multipacking problem is NP-complete or solvable in polynomial time. Whereas the problem is known to be polynomial-time solvable for certain graph classes (e.g., strongly chordal graphs, grids, etc). Foucaud, Gras, Perez, and Sikora [Algorithmica 2021] made a step towards solving the open question by showing that the Multipacking problem is NP-complete for directed graphs and it is W[1]-hard when parameterized by the solution size. In this paper, we prove that the Multipacking problem is NP-complete for undirected graphs, which answers the open question. Moreover, the problem is W[2]-hard for undirected graphs when parameterized by the solution size. Furthermore, we have shown that the problem is NP-complete and W[2]-hard (when parameterized by the solution size) even for various subclasses: chordal, bipartite, and claw-free graphs. Whereas, it is NP-complete for regular, and CONV graphs (intersection graphs of convex sets in the plane). Additionally, the problem is NP-complete and W[2]-hard (when parameterized by the solution size) for chordal $\cap$ $\frac{1}{2}$-hyperbolic graphs, which is a superclass of strongly chordal graphs where the problem is polynomial-time solvable. On the positive side, we present an exact exponential-time algorithm for the Multipacking problem on $n$-vertex general graphs, which breaks the $2^n$ barrier by achieving a running time of $O^*(1.58^n)$.
This study aims to analyze the effect of customer experience and trust on repurchase interest in the Shopee marketplace. In February 2023, there was a decline, indicating that e-commerce faces challenges in maintaining customer loyalty and that consumers are starting to be more selective in choosing shopping platforms. This research is a quantitative study with a survey method. Data collection was carried out using a questionnaire instrument; the number of samples used was 404 respondents. The analysis technique used is Structural Equation Modeling - Partial Least Squares (SEM-PLS) with SmartPLS 3.0 software. The results of the study show that customer experience has a significant influence on repurchase interest with a path coefficient of 0.211. Trust also has a positive and significant effect on repurchase interest with a path coefficient of 0.447. Simultaneously with an F-count value of 83.09, which is greater than the F-table of 3.02, the customer experience and trust variables have a positive and significant effect on repurchase interest.
This investigation is a rigorous theoretical study of the Single Differential Cross Section (SDCS) for the ionization of hydrogen in the 3s state by electron impact computed by means of the First-Born Approximation. The transition matrix has been found by means of the integral process of Bethe-Lewis. The effect of the Coulomb attractive force and the continuum of outgoing radiation have been taken into account in conjunction with the hypergeometric function, which has been used to denote the states of collision. It has hence been possible to deduce the SDCS. for a considerable range of respective incoming electron energies (100 eV to 250 eV). The results show a very distinct marked peak in the rate of ionization taking place for these energies (about 200 eV) with a gradual fall-off after with an increase in energy. The diffuse structure of the wave function in the 3s state serves to a certain extent to make variations in the rate of ionization with that of the incoming electron increased. The final results have been arrived at by means of numerical integrations done so by means of a MATLAB computer, which has yielded very accurate numbers for the cross sections. The results show up very fatally, with experimental results existing at the present date and with theoretical deductions validating the procedure of the FBA. in giving the results of the ionization of excited hydrogen atoms within the field of ionizing processes of electron-atom impact systems. The work gives a most complete basis for the further study of those systems where the processes of ionization and the complexities of the scattering process take place in excited states.
Entanglement generation in long-distance quantum networks is a difficult process due to resource limitations and the probabilistic nature of entanglement swapping. To maximize success probability, existing quantum routing algorithms employ computationally expensive solutions (e.g., linear programming) to determine which links to entangle and use for end-to-end entanglement generation. Such optimization methods, however, cannot meet the delay requirements of real-world quantum networks, necessitating swift yet efficient real-time optimization models. In this paper, we propose reinforcement learning (RL)-based models to determine which links to entangle and proactively swap to meet connection requests. We show that the proposed RL-based approach is 20x faster compared to linear programming. Moreover, we show that one can take advantage of the longevity of entanglements to (i) cache entangled links for future use and (ii) proactively swap entanglement on high-demand path segments, thereby increasing the likelihood of request success. Through comprehensive simulations, we demonstrate that caching unused entanglements leads to a 10-15% improvement in the performance of state-of-the-art quantum routing algorithms. Complementing caching with proactive entanglement swapping further enhances the request success rate by up to 52.55%.
This research was conducted to determine the effect of product quality and service quality on customer loyalty through customer satisfaction for Honda Vario motorbike users in Sidoarjo. The sampling technique used in this research was purposive sampling with a total of 100 respondents. The method used in this research is Partial Least Square (PLS) analysis using SmartPLS 4.0 software. It is known that the results of this research show that 1) product quality has a positive and significant influence on customer satisfaction. 2) Service quality has a positive and significant influence on customer loyalty. 3) product quality does not have a positive and insignificant influence on customer loyalty. 4) Service quality has a positive and significant influence on customer satisfaction. 5) Customer satisfaction has a positive and significant influence on customer loyalty. 6) Product quality has a positive and significant influence through customer satisfaction on customer loyalty. 7) Service quality has a positive and significant influence through customer satisfaction on customer loyalty.
Dinin temelini oluşturan ayet ve hadislerin gerçekleştirmek istedikleri hedefler, “makasıdu’ş-şeria” kavramıyla ifade edilmiştir. Fert ve toplumun maslahatı için önemli değerleri barındıran bu kavram, tarihten günümüze birçok araştırmaya konu olmuştur. Bu çalışma, din eğitiminin hedefleriyle yakın ilişkisi bulunan “makasıdu’ş-şeria” nın ortaya koyduğu değerlerin din eğitiminin hedefleri olarak kabul edilme imkanının araştırılması amacıyla yapılmıştır. Din eğitiminin hedefleri incelendiği zaman; dinden gelen mesajların içselleştirilerek bu mesajların içerdiği değerlerin kişisel ve sosyal hayatta davranışa dönüştürülmesi olarak görülmektedir. Ayet ve hadislerin nihai hedeflerinin toplum ve bireylerin dini ve dünyevi maslahatlarını gerçekleştirmek olduğunu savunan “Maksıdu’ş-Şeria” ile dinden gelen mesajları davranışa dönüştürme amacında olan din eğitiminin, hedefleri arasında bir yakınlık olduğu görülmektedir. Makasıdu’ş-şeria ile ifade edilen değerler, din eğitimi yoluyla kazandırılmak istenilen hedefler midir, “makasıdu’ş-şeria” nın din eğitiminin hedefleri olabilme imkânı var mıdır, soruları çalışmanın problemini oluşturmaktadır. Makasıdu’ş-şeria kavramı tarih boyunca eğitime konu olmasına rağmen daha çok “Usul-ü Fıkıh ve Kelam” disiplini çerçevesinde incelenmiştir. Din eğitimi alanıyla da alakalı olduğu düşünülen konu üzerinde, din eğitimi perspektifiyle yeterli çalışma yapılmamış olması, bir eksiklik olarak görülmektedir. Konuyla alakalı yapılacak araştırmanın önemli bir boşluğu dolduracağı ve disiplinler arası bir çalışma olacağı öngörülmektedir. Temel insan haklarıyla da yakından alakalı olan konu, çalışmayı özgün ve önemli kılmaktadır. Araştırmamızda İslam’ın temel kaynakları ve konuyla alakalı eserler incelenerek yorumlanmıştır. Çalışmamız nitel bir araştırma olup doküman analizi yöntemi kullanılmıştır. Konu, din eğitiminin “içe dönük” ve “ötekine dönük” hedefleri bağlamında ele alınmıştır. Çalışmamızın sonucunda “makasıdu’ş-şeria” nın alt başlığı olan “Zarurat-ı hamse”nin, insanların bireysel ve toplumsal mutlulukları için dini değer ve hedefler olarak öğretilme potansiyeline sahip olduğu; din eğitiminin hedefleri olarak öğretilmesi durumunda din eğitimini daha kapsayıcı ve evrensel kılacağı kanaatine varılmıştır.
Md Mirajul Islam, Md Nahiyan Uddin, Maoyejatun Hasana
et al.
People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly different. For instance, consider one person's reaction to an invitation to smoke versus another person's response to a query about their sleep quality. The identification of these individual traits through the observation of common physical parameters opens the door to a wide range of applications, including psychological analysis, criminology, disease prediction, addiction control, and more. While there has been previous research in the fields of psychometrics, inertial sensors, computer vision, and audio analysis, this paper introduces a novel technique for identifying human traits in real time using brainwave data. To achieve this, we begin with an extensive study of brainwave data collected from 80 participants using a portable EEG headset. We also conduct a statistical analysis of the collected data utilizing box plots. Our analysis uncovers several new insights, leading us to a groundbreaking unified approach for identifying diverse human traits by leveraging machine learning techniques on EEG data. Our analysis demonstrates that this proposed solution achieves high accuracy. Moreover, we explore two deep-learning models to compare the performance of our solution. Consequently, we have developed an integrated, real-time trait identification solution using EEG data, based on the insights from our analysis. To validate our approach, we conducted a rigorous user evaluation with an additional 20 participants. The outcomes of this evaluation illustrate both high accuracy and favorable user ratings, emphasizing the robust potential of our proposed method to serve as a versatile solution for human trait identification.
Large Language Models (LLMs) are capable of generating persuasive Natural Language Explanations (NLEs) to justify their answers. However, the faithfulness of these explanations should not be readily trusted at face value. Recent studies have proposed various methods to measure the faithfulness of NLEs, typically by inserting perturbations at the explanation or feature level. We argue that these approaches are neither comprehensive nor correctly designed according to the established definition of faithfulness. Moreover, we highlight the risks of grounding faithfulness findings on out-of-distribution samples. In this work, we leverage a causal mediation technique called activation patching, to measure the faithfulness of an explanation towards supporting the explained answer. Our proposed metric, Causal Faithfulness quantifies the consistency of causal attributions between explanations and the corresponding model outputs as the indicator of faithfulness. We experimented across models varying from 2B to 27B parameters and found that models that underwent alignment tuning tend to produce more faithful and plausible explanations. We find that Causal Faithfulness is a promising improvement over existing faithfulness tests by taking into account the model's internal computations and avoiding out of distribution concerns that could otherwise undermine the validity of faithfulness assessments. We release the code in \url{https://github.com/wj210/Causal-Faithfulness}
The field of time series forecasting has garnered significant attention in recent years, prompting the development of advanced models like TimeSieve, which demonstrates impressive performance. However, an analysis reveals certain unfaithfulness issues, including high sensitivity to random seeds and minute input noise perturbations. Recognizing these challenges, we embark on a quest to define the concept of \textbf{\underline{F}aithful \underline{T}ime\underline{S}ieve \underline{(FTS)}}, a model that consistently delivers reliable and robust predictions. To address these issues, we propose a novel framework aimed at identifying and rectifying unfaithfulness in TimeSieve. Our framework is designed to enhance the model's stability and resilience, ensuring that its outputs are less susceptible to the aforementioned factors. Experimentation validates the effectiveness of our proposed framework, demonstrating improved faithfulness in the model's behavior. Looking forward, we plan to expand our experimental scope to further validate and optimize our algorithm, ensuring comprehensive faithfulness across a wide range of scenarios. Ultimately, we aspire to make this framework can be applied to enhance the faithfulness of not just TimeSieve but also other state-of-the-art temporal methods, thereby contributing to the reliability and robustness of temporal modeling as a whole.
We present a Chain-of-Action (CoA) framework for multimodal and retrieval-augmented Question-Answering (QA). Compared to the literature, CoA overcomes two major challenges of current QA applications: (i) unfaithful hallucination that is inconsistent with real-time or domain facts and (ii) weak reasoning performance over compositional information. Our key contribution is a novel reasoning-retrieval mechanism that decomposes a complex question into a reasoning chain via systematic prompting and pre-designed actions. Methodologically, we propose three types of domain-adaptable `Plug-and-Play' actions for retrieving real-time information from heterogeneous sources. We also propose a multi-reference faith score (MRFS) to verify and resolve conflicts in the answers. Empirically, we exploit both public benchmarks and a Web3 case study to demonstrate the capability of CoA over other methods.
Large Language Models (LLMs) are deployed as powerful tools for several natural language processing (NLP) applications. Recent works show that modern LLMs can generate self-explanations (SEs), which elicit their intermediate reasoning steps for explaining their behavior. Self-explanations have seen widespread adoption owing to their conversational and plausible nature. However, there is little to no understanding of their faithfulness. In this work, we discuss the dichotomy between faithfulness and plausibility in SEs generated by LLMs. We argue that while LLMs are adept at generating plausible explanations -- seemingly logical and coherent to human users -- these explanations do not necessarily align with the reasoning processes of the LLMs, raising concerns about their faithfulness. We highlight that the current trend towards increasing the plausibility of explanations, primarily driven by the demand for user-friendly interfaces, may come at the cost of diminishing their faithfulness. We assert that the faithfulness of explanations is critical in LLMs employed for high-stakes decision-making. Moreover, we emphasize the need for a systematic characterization of faithfulness-plausibility requirements of different real-world applications and ensure explanations meet those needs. While there are several approaches to improving plausibility, improving faithfulness is an open challenge. We call upon the community to develop novel methods to enhance the faithfulness of self explanations thereby enabling transparent deployment of LLMs in diverse high-stakes settings.
Lisa Oktiviani Tanaga, Eni Maryani, Evi Rosfiantika
Sweet Home is a South Korean Netflix film series featuring predominantly masculine female characters. In South Korea, films or series with openly feminist issues receive backlash from parties who strongly oppose feminism. However, this film series gained success, and its masculine female characters received various praises. This study aims to identify the representation of female masculinity in the film. This study applied qualitative research using John Fiske’s semiotic analysis. It found that there are codes of masculinity in the female characters at the level of reality. The female characters are strong, athletic, active individuals, leaders, technicians, and adventurers. At the level of representation, the female characters are identified as masculinity as they are subjects who can determine attitudes and make decisions. Then, if the women in the film are allowed to speak, act, and behave like men, the women are superior to men. The female characters represent a belief that men are the opposite of women. The women need to appear to defeat or to be more significant, which can be categorized as radical feminism at the ideological level.
The Palm Oil Industry is one of the important industries that support the Indonesian economy because it brings in a lot of foreign exchange and absorbs a lot of work. One of the parties involved in the supply chain of the palm oil industry is a supplier of equipment and spare parts for palm oil processing mills. The purpose of this paper is to analyze the importance of equipment and spare parts supplier companies to add to using the appropriate marketing mix to develop their company's business. The method used in processing interview data and collecting questionnaire data. The results obtained are inputs in the form of business strategy adjustments for the company.
Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.
A system-ancilla bipartite state capable of containing the complete information of an unknown quantum channel acting on the system is called faithful. The equivalence between faithfulness of state and invertibility of the corresponding Jamiolkowski map proved by D'Ariano and Presti has been a useful characterization for ancilla-assisted process tomography albeit the proof was incomplete as they assumed trace nonincreasing quantum operations, not quantum channels. We complete the proof of the equivalence and introduce the generalization of faithfulness to various classes of quantum channels. We also explore a more general notion we call sensitivity, the property of quantum state being altered by any nontrivial action of quantum channel. We study their relationship by characterizing both properties for important classes of quantum channels such as unital channels, random unitary operations and unitary operations. Unexpected (non-)equivalence results among them shed light on the structure of quantum channels by showing that we need only two classes of quantum states for characterizing quantum states faithful or sensitive to various subclasses of quantum channels. For example, it reveals the relation between quantum process tomography and quantum correlation as it turns out that only bipartite states that has no local classical observable at all can be used to sense the effect of unital channels.
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing specific policies. Our goal in this paper is to take a first step towards understanding these highly decentralized settings. We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook and analyze how political campaigns use some kind of demographic targeting by location, gender, or age. Furthermore, we analyze the temporal dynamics of the political ads on election polls.
In this study, the Multivariate Empirical Mode Decomposition (MEMD) is applied to multichannel EEG to obtain scale-aligned intrinsic mode functions (IMFs) as input features for emotion detection. The IMFs capture local signal variation related to emotion changes. Among the extracted IMFs, the high oscillatory ones are found to be significant for the intended task. The Marginal Hilbert spectrum (MHS) is computed from the selected IMFs. A 3D convolutional neural network (CNN) is adopted to perform emotion detection with spatial-temporal-spectral feature representations that are constructed by stacking the multi-channel MHS over consecutive signal segments. The proposed approach is evaluated on the publicly available DEAP database. On binary classification of valence and arousal level (high versus low), the attained accuracies are 89.25% and 86.23% respectively, which significantly outperform previously reported systems with 2D CNN and/or conventional temporal and spectral features.
Surgical captioning plays an important role in surgical instruction prediction and report generation. However, the majority of captioning models still rely on the heavy computational object detector or feature extractor to extract regional features. In addition, the detection model requires additional bounding box annotation which is costly and needs skilled annotators. These lead to inference delay and limit the captioning model to deploy in real-time robotic surgery. For this purpose, we design an end-to-end detector and feature extractor-free captioning model by utilizing the patch-based shifted window technique. We propose Shifted Window-Based Multi-Layer Perceptrons Transformer Captioning model (SwinMLP-TranCAP) with faster inference speed and less computation. SwinMLP-TranCAP replaces the multi-head attention module with window-based multi-head MLP. Such deployments primarily focus on image understanding tasks, but very few works investigate the caption generation task. SwinMLP-TranCAP is also extended into a video version for video captioning tasks using 3D patches and windows. Compared with previous detector-based or feature extractor-based models, our models greatly simplify the architecture design while maintaining performance on two surgical datasets. The code is publicly available at https://github.com/XuMengyaAmy/SwinMLP_TranCAP.