Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Value Biases: How LLMs Deviate Towards the Ideal

Large-Language-Models (LLMs) are deployed in a wide range of applications, and their response has an increasing social impact. Understanding the non-deliberate(ive) mechanism of LLMs in giving responses is essential in explaining their performance and discerning their biases in real-world applications. This is analogous to human studies, where such inadvertent responses are referred to as sampling. We study this sampling of LLMs in light of value bias and show that the sampling of LLMs tends to favour high-value options. Value bias corresponds to this shift of response from the most likely towards an ideal value represented in the LLM. In fact, this effect can be reproduced even with new entities learnt via in-context prompting. We show that this bias manifests in unexpected places and has implications on relevant application scenarios, like choosing exemplars. The results show that value bias is strong in LLMs across different categories, similar to the results found in human studies.
TriSampler: A Better Negative Sampling Principle for Dense Retrieval

Negative sampling stands as a pivotal technique in dense retrieval, essential for training effective retrieval models and significantly impacting retrieval performance. While existing negative sampling methods have made commendable progress by leveraging hard negatives, a comprehensive guiding principle for constructing negative candidates and designing negative sampling distributions is still lacking. To bridge this gap, we embark on a theoretical analysis of negative sampling in dense retrieval. This exploration culminates in the unveiling of the quasi-triangular principle, a novel framework that elucidates the triangular-like interplay between query, positive document, and negative document. Fueled by this guiding principle, we introduce TriSampler, a straightforward yet highly effective negative sampling method. The keypoint of TriSampler lies in its ability to selectively sample more informative negatives within a prescribed constrained region. Experimental evaluation show that TriSampler consistently attains superior retrieval performance across a diverse of representative retrieval models.
Exploring Barriers in the Engineering Workplace: Hostile, Unsupportive, and Otherwise Chilly Conditions

Previous studies of the engineering workplace often emphasize understanding why and how women consider leaving a job and in some cases, exit engineering altogether. A 'chilly' climate has often been implicated as a primary or contributing reason for these exit decisions and has been classified in the literature under such descriptors as a hostile or macho work culture, mysterious pathways to career advancement, and extreme work pressures. This study expands on these previous studies by (a) emphasizing the engineering workplace experiences of millennials in order to understand whether these chilly climate conditions have evolved over time or are tending to persist into the next generation; and (b) studying men as well as women to gain deeper insight into which negative working conditions tend to occur across gender and which may be gender specific. We include results of interviews with 45 individuals who graduated with an engineering or computer science bachelor’s degree between the years 1998 and 2015 from five different institutions including those that are research and teaching focused, big, mid-sized, and small, and geographically disparate (from the Midwest, Southeast, Northeast, and Northwest areas of the United States). All of those interviewed for this study can be considered millennials (graduating around the year 2000 or later) and 64% of those interviewed are women. Interviews were coded using existing classifications of chilly workplace conditions and expanded to include new codes as needed. Qualitative analysis of these results showed that of the five existing classifications of chilly climate, hostile culture was predominantly expressed by women. However, extreme work pressure, mysterious career pathways, and isolation were reported by both men and women; diving catch situations (where risk averse individuals are penalized in the promotion and advancement structure) emerged only once, and seven new classifications of negative workplace conditions emerged related to type of work (boring, inconsistent, underutilized); nature of the work environment (job insecurity, oppressive physical environment, poor management); and work/life conflicts.
Evaluation of ChatGPT's Smart Contract Auditing Capabilities Based on Chain of Thought

Smart contracts, as a key component of blockchain technology, play a crucial role in ensuring the automation of transactions and adherence to protocol rules. However, smart contracts are susceptible to security vulnerabilities, which, if exploited, can lead to significant asset losses. This study explores the potential of enhancing smart contract security audits using the GPT-4 model. We utilized a dataset of 35 smart contracts from the SolidiFI-benchmark vulnerability library, containing 732 vulnerabilities, and compared it with five other vulnerability detection tools to evaluate GPT-4's ability to identify seven common types of vulnerabilities. Moreover, we assessed GPT-4's performance in code parsing and vulnerability capture by simulating a professional auditor's auditing process using CoT(Chain of Thought) prompts based on the audit reports of eight groups of smart contracts. We also evaluated GPT-4's ability to write Solidity Proof of Concepts (PoCs). Through experimentation, we found that GPT-4 performed poorly in detecting smart contract vulnerabilities, with a high Precision of 96.6%, but a low Recall of 37.8%, and an F1-score of 41.1%, indicating a tendency to miss vulnerabilities during detection. Meanwhile, it demonstrated good contract code parsing capabilities, with an average comprehensive score of 6.5, capable of identifying the background information and functional relationships of smart contracts; in 60% of the cases, it could write usable PoCs, suggesting GPT-4 has significant potential application in PoC writing. These experimental results indicate that GPT-4 lacks the ability to detect smart contract vulnerabilities effectively, but its performance in contract code parsing and PoC writing demonstrates its significant potential as an auxiliary tool in enhancing the efficiency and effectiveness of smart contract security audits.
Empirical Study on Updating Key-Value Memories in Transformer Feed-forward Layers

The feed-forward networks (FFNs) in transformers are recognized as a group of key-value neural memories to restore abstract high-level knowledge. In this work, we conduct an empirical ablation study on updating keys (the 1st layer in the FFNs layer) or values (the 2nd layer in the FFNs layer). We compare those two methods in various knowledge editing and fine-tuning tasks of large language models to draw insights to understand FFNs further. Code is available at $\href{https://github.com/qiuzh20/Tuning-keys-v.s. -values}{this\,repo}$.
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives

Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios. To address this gap, we introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives. Specifically, we designed hierarchical relationship categories and manually extracted and annotated role-oriented relationships from the perspectives of various characters, incorporating both public relationships known to most characters and secret ones known to only a few. Our experiments with advanced Large Language Models (LLMs) like GPT-3.5, GPT-4, and Llama2 reveal their limitations in inferencing complex relationships and handling longer narratives. The combination of the Conan dataset and our pipeline strategy is geared towards understanding the ability of LLMs to comprehend nuanced relational dynamics in narrative contexts.
First year progress report

Outreach. Staff created a webpage for the Regional Food Policy Council and prepared an article on Council activities that was published in the Spring/Summer 2011 issue of APA Regional and Intergovernmental Planning. Staff also represented the Council at various external meetings, including the Food Policy: From Neighborhood to Nation Conference. At that conference, staff organized and participated in panel session on food policy councils operating at different geographic scales.
Treewidth versus clique number. IV. Tree-independence number of graphs excluding an induced star

Many recent works address the question of characterizing induced obstructions to bounded treewidth. In 2022, Lozin and Razgon completely answered this question for graph classes defined by finitely many forbidden induced subgraphs. Their result also implies a characterization of graph classes defined by finitely many forbidden induced subgraphs that are $(tw,\omega)$-bounded, that is, treewidth can only be large due to the presence of a large clique. This condition is known to be satisfied for any graph class with bounded tree-independence number, a graph parameter introduced independently by Yolov in 2018 and by Dallard, Milani\v{c}, and \v{S}torgel in 2024. Dallard et al. conjectured that $(tw,\omega)$-boundedness is actually equivalent to bounded tree-independence number. We address this conjecture in the context of graph classes defined by finitely many forbidden induced subgraphs and prove it for the case of graph classes excluding an induced star. We also prove it for subclasses of the class of line graphs, determine the exact values of the tree-independence numbers of line graphs of complete graphs and line graphs of complete bipartite graphs, and characterize the tree-independence number of $P_4$-free graphs, which implies a linear-time algorithm for its computation. Applying the algorithmic framework provided in a previous paper of the series leads to polynomial-time algorithms for the Maximum Weight Independent Set problem in an infinite family of graph classes.
Diffuse Sound Field Synthesis

Can uncorrelated surrounding sound sources be used to generate extended diffuse sound fields? By definition, targets are a constant sound pressure level, a vanishing average sound intensity, uncorrelated sound waves arriving isotropically from all directions. Does this require specific sources and geometries for surrounding 2D and 3D source layouts? As methods, we employ numeric simulations and undertake a series of calculations with uncorrelated circular/spherical source layouts, or such with infinite excess dimensions, and we point out relations to potential theory. Using a radial decay 1/r^b modified by the exponent b, the representation of the resulting fields with hypergeometric functions, Gegenbauer polynomials, and circular as well as spherical harmonics yields fruitful insights. In circular layouts, waves decaying by the exponent b=1/2 synthesize ideally extended, diffuse sound fields; spherical layouts do so with b=1. None of the layouts synthesizes a perfectly constant expected sound pressure level but its flatness is acceptable. Spherical t-designs describe optimal source layouts with well-described area of high diffuseness, and non-spherical, convex layouts can be improved by restoring isotropy or by mode matching for a maximally diffuse synthesis. Theory and simulation offer a basis for loudspeaker-based synthesis of diffuse sound fields and contribute physical reasons to recent psychoacoustic findings in spatial audio.
Wireless Distributed Matrix-Vector Multiplication using Over-the-Air Computation and Analog Coding

In this paper, we propose an over-the-air (OTA)-based approach for distributed matrix-vector multiplications in the context of distributed machine learning (DML). Thanks to OTA computation, the column-wise partitioning of a large matrix enables efficient workload distribution among workers (i.e., local computing nodes) based on their computing capabilities. In addition, without requiring additional bandwidth, it allows the system to remain scalable even as the number of workers increases to mitigate the impact of slow workers, known as stragglers. However, despite the improvements, there are still instances where some workers experience deep fading and become stragglers, preventing them from transmitting their results. By analyzing the mean squared error (MSE), we demonstrate that incorporating more workers in the OTA-based approach leads to MSE reduction without the need for additional radio resources. Furthermore, we introduce an analog coding scheme to further enhance the performance and compare it with conventional coded multiplication (CM) schemes. Through simulations, it is shown that the OTA-based approach achieves comparable performance to CM schemes while potentially requiring fewer radio resources.
From Information Literacy to the Learner Journey: Aligning Academics and Librarians through Pedagogic Research

Abstract The learner journey is here defined as the study, information, and research skills that a student brings to university with them and develops throughout the course of their degree program. Research on academics’ perceptions, expectations, and assumptions regarding the learner journey was conducted via semistructured interviews, in order to underpin a refreshed library teaching “menu” at the University of Worcester. Consistencies and differences in approaches to levels of study and a number of common themes, including student independence, transition, and technology were revealed. From the evidence base, the project yielded both tangible outputs (the teaching menu, a self-audit tool, a PGCert session) and less quantifiable ones, such as positioning Library Services as pedagogic partners and researchers.
Advanced tracking through efﬁcient image processing and visual–inertial sensor fusion $

This article presents a new visual–inertial tracking device for augmented and virtual reality applications and addresses two fundamental issues of such systems. The ﬁrst one concerns the deﬁnition and modelling of the sensor fusion problem. Much work has been conducted in this area and several models for exploiting gyroscopes and linear accelerometers have been proposed. However, the respective advantages of each model and in particular the beneﬁts of the integration of the accelerometer data in the ﬁlter are still unclear. A comparison of different models with special investigation of the effects of using accelerometers on the tracking performance is therefore provided. The second contribution is the development of an image processing approach that does not require special landmarks but uses natural features. The solution relies on a 3D model of the scene that is used to predict the appearances of the features by rendering the model based on data from the sensor fusion algorithm. The feature localisation is robust and accurate mainly because local lighting is also estimated. The ﬁnal system is evaluated with help of ground-truth and real data. High stability and accuracy are demonstrated also for large environments.
The KeyTrap Denial-of-Service Algorithmic Complexity Attacks on DNS

—Availability is a major concern in the design of DNSSEC. To ensure availability, DNSSEC follows Postel’s Law [RFC1122]: ”Be liberal in what you accept, and conservative in what you send. ” Hence, nameservers should send not just one matching key for a record set, but all the relevant cryptographic material, e.g., all the keys for all the ciphers that they support and all the corresponding signatures. This ensures that validation succeeds, and hence availability, even if some of the DNSSEC keys are misconfigured, incorrect or correspond to unsupported ciphers. We show that this design of DNSSEC is flawed. Exploiting vulnerable recommendations in the DNSSEC standards, we develop a new class of DNSSEC-based algorithmic complexity attacks on DNS, we dub KeyTrap attacks. All popular DNS implementations and services are vulnerable. With just a single DNS packet, the KeyTrap attacks lead to a 2.000. 000x spike in CPU instruction count in vulnerable DNS resolvers, stalling some for as long as 16 hours. This devastating effect prompted major DNS vendors to refer to KeyTrap as “ the worst attack on DNS ever discovered ”. Exploiting KeyTrap, an attacker could effectively disable Internet access in any system utilizing a DNSSEC-validating resolver. We disclosed KeyTrap to vendors and operators on November 2, 2023, confidentially reporting the vulnerabilities to a closed group of DNS experts, operators and developers from the industry. Since then we have been working with all major vendors to mitigate KeyTrap, repeatedly discovering and assisting in closing weaknesses in proposed patches. Following our disclosure, an umbrella CVE has already been assigned.
COMPETING FOR SKILLS: US IMMIGRATION POLICY SINCE 1990

March 2004 by Susan Martin* and B. Lindsay Lowell Institute for the Study of International Migration (ISIM) Georgetown University 3307 M Street, NW, Suite 302 Washington, DC 20007 Tel: (202) 687-2258 Fax: (202) 687-2541 martinsf@georgetown.edu lowellbl@georgetown.edu * Paper prepared for presentation at North American Integration: Migration, Trade and Security, Fairmont Château Laurier, Ottawa April 1 -2, 2004.
STUDI EMPIRIS TENTANG PENANAMAN MODAL ASING LANGSUNG DI INDONESIA

Sebagai Negara berkembang, Indonesia memiliki masalah uitama dalam hal permodalan. Modal dibagi menjadi dua, yaitu modal dai dalam negeri dan modal dari luar negeri yang biasa disebut dengan utang. Dalam perkembangannnya, Indonesia lebih memilih untuk menggunakan cara instan dan cepat untuk membiayai perekonomian dengan menggunakan utang luar negeri. Penggunaan utang luar negeri ini dalam jangka pendek maupun panjang akan mempengaruhi perekonomian. Indikator lain yang juga terkait dengan permodalan adalah Penanaman Modal Asing (PMA). Tujuan penelitian ini adalah untuk mengetahui bagaimana pengaruh variabel Produk Domestik Regional Bruto, Inflasi, Ekspor, Upah dan Infrastruktur terhadap Penanaman Modal Asing di Indonesia dari tahun 2004-2014. Analisis data pada penelitian ini menggunakan Metode Regresi Data Panel. Uji Asumsi Klasik dengan Multikolinearitas dan Heterokesdastisitas. Data-data yang digunakan dalam penelitian ini adalah data Produk Domestik Regional Bruto, Inflasi, Ekspor, Upah Minimum Provinsi, Infrastruktur Jalan, Infrastruktur Listrik, dan Infrastruktur PelabuhanTahun 2004-2014. Hasil penelitian menunjukkan bahwa variabel Produk Domestik Regional Bruto, Inflasi, Ekspor, Infrastruktur Listrik dan Pelabuhan memiliki pengaruh yang signifikan terhadap Penanaman Modal Asing (PMA). Variabel Upah Minimum Provinsi dan Infrastruktur Jalan memiliki pengaruh yang tidak signifikan terhadap Penanaman Modal Asing (PMA). Kata Kunci : Penanaman Modal Asing, Produk Domestik Regional Bruto, Inflasi, Ekspor, Upah Minimum Provinsi, Infrastruktur Jalan, Infrastruktur Listrik, Infrastruktur Pelabuhan
A Study on Strategies to Hold Customers by Airtel Telecom in the Context of Digitalization Trends

: In the contemporary telecommunications industry, the surge of digitalization trends has reshaped traditional paradigms, necessitating innovative strategies for customer retention. This conceptual study aims to assimilate theoretical frameworks to elucidate the strategies employed by Airtel Telecom in retaining its customer base amidst the digitalization wave. Drawing upon theories of customer relationship management (CRM), digital marketing, and technology adoption, this study synthesizes existing literature to develop a comprehensive understanding of effective customer retention strategies in the digital era. Theoretical constructs such as customer value creation, relationship marketing, and digital engagement are examined in the context of Airtel Telecom's approach to holding customers amid digitalization trends. The theoretical assimilation underscores the importance of leveraging digital technologies to enhance customer engagement, personalize offerings, and deliver superior value propositions. Airtel Telecom's strategic initiatives, including data-driven analytics, AI-driven insights, and omni-channel communication, are analyzed through the lens of CRM theories, highlighting their role in fostering long-term customer relationships. Moreover, the study explores the interplay between digital marketing strategies and customer retention, emphasizing the significance of targeted campaigns, social media engagement, and mobile applications in driving customer loyalty. By aligning theoretical frameworks with empirical observations, this conceptual study provides insights into the synergy between digitalization trends and customer retention strategies employed by Airtel Telecom. The findings contribute to theoretical advancements by elucidating the dynamics of customer retention in the digital age and offer practical implications for telecom industry practitioners. The theoretical assimilation serves as a foundation for future empirical research aimed at validating the proposed conceptual framework and exploring its applicability across diverse telecom contexts. In conclusion, this study bridges the gap between theoretical insights and practical applications, offering a nuanced understanding of how Airtel Telecom navigates digitalization trends to hold its customer base. Theoretical assimilation facilitates a holistic perspective on customer retention strategies, paving the way for enhanced customer-centric approaches in the evolving landscape of telecommunications.
Decoding viewer behavior: a multi-faceted exploration of marketing mix, brand equity, e-WOM, and perceived value in Indonesia's film market

In light of the challenges faced by Indonesia's film industry, such as limited screens, the dominance of major production houses, and low purchase intentions among local film viewers, this research aims to address the critical gaps in understanding viewer behaviour. By exploring the intricate relationships between purchase intention, e-WOM, perceived value, marketing mix, and brand equity, the study offers valuable insights and effective marketing strategies essential for the sustainable growth of the Indonesian film market. This research used quantitative research methods. A total of 255 respondents were collected using random sampling. Data were analyzed using Structural Equation Modelling through SmartPLS. The main findings of the study demonstrate that (i) marketing mix does not influence purchase intention, (ii) marketing mix influence on purchase intention is mediated by perceived value, (iii) brand equity influences purchase intention, (iv) brand equity influences on purchase intention is mediated by perceived value, (v) perceived value influences purchase intention and finally (vi) perceived value influences on purchase intention are moderated by e-WOM.