Miracle-dz/UniFAR-FTUs huggingface dataset:
AIC-Based Selection of Growth Models: The Case of Piglets from Organic Farming

The selection and comparison of different growth models for describing weight gain of piglets raised in organic farming is investigated by using the Akaike’s Information Criterion (AIC). In total, 49,699 data points of 5188 piglets recorded between 2007 and 2013 were considered. From the day of birth, up to 40 days (i.e. until weaning) the model of von Bertalanffy was favored by the AIC. This model is with 60.32% more likely to truly reflect reality than any other of the analyzed models. Up to 105 days, the two-linear model was favored by the AIC (probability 99.75%). The intersection point of the two-linear model was calculated by 53.8 days, which fitted well to the actual change in the food situations.
Framing Image Description as a Ranking Task: Data, Models and Evaluation Metrics (Extended Abstract)

In [Hodosh et al., 2013], we establish a ranking-based framework for sentence-based image description and retrieval. We introduce a new dataset of images paired with multiple descriptive captions that was specifically designed for these tasks. We also present strong KCCA-based baseline systems for description and search, and perform an in-depth study of evaluation metrics for these two tasks. Our results indicate that automatic evaluation metrics for our ranking-based tasks are more accurate and robust than those proposed for generation-based image description.
Understanding and Detecting Abused Image Hosting Modules as Malicious Services

As a new type of underground ecosystem, the exploitation of Abused IHMs as MalIcious sErvices (AIMIEs) is becoming increasingly prevalent among miscreants to host illegal images and propagate harmful content. However, there has been little effort to understand this new menace, in terms of its magnitude, impact, and techniques, not to mention any serious effort to detect vulnerable image hosting modules on a large scale. To fulfill this gap, this paper presents the first measurement study of AIMIEs. By collecting and analyzing 89 open-sourced AIMIEs, we reveal the landscape of AIMIEs, report the evolution and evasiveness of abused image hosting APIs from reputable companies such as Alibaba, Tencent, and Bytedance, and identify real-world abused images uploaded through those AIMIEs. In addition, we propose a tool, called Viola, to detect vulnerable image hosting modules (IHMs) in the wild. We find 477 vulnerable IHM upload APIs associated with 338 web services, which integrated vulnerable IHMs, and 207 victim FQDNs. The highest-ranked domain with vulnerable web service is baidu.com, followed by bilibili.com and 163.com. We have reported abused and vulnerable IHM upload APIs and received acknowledgments from 69 of them by the time of paper submission.
SyzDirect: Directed Greybox Fuzzing for Linux Kernel

Bug reports and patch commits are dramatically increasing for OS kernels, incentivizing a critical need for kernel-level bug reproduction and patch testing. Directed greybox fuzzing (DGF), aiming to stress-test a specific part of code, is a promising approach for bug reproduction and patch testing. However, the existing DGF methods exclusively target user-space applications, presenting intrinsic limitations in handling OS kernels. In particular, these methods cannot pinpoint the appropriate system calls and the needed syscall parameter values to reach the target location,resulting in low efficiency and waste of resources. In this paper, we present SyzDirect, a DGF solution for the Linux kernel. With a novel, scalable static analysis of the Linux kernel, SyzDirect identifies valuable information such as correct system calls and conditions on their arguments to reach the target location. During fuzzing, SyzDirect utilizes the static analysis results to guide the generation and mutation of test cases, followed by leveraging distance-based feedback for seed prioritization and power scheduling. We evaluated SyzDirect on upstream Linux kernels for bug reproduction and patch testing. The results show that SyzDirect can reproduce 320% more bugs and reach 25.6% more target patches than generic kernel fuzzers. It also improves the speed of bug reproduction and patch reaching by a factor of 154.3 and 680.9, respectively.
Executing Dynamic Data-Graph Computations Deterministically Using Chromatic Scheduling

A data-graph computation—popularized by such programming systems as Galois, Pregel, GraphLab, PowerGraph, and GraphChi—is an algorithm that performs local updates on the vertices of a graph. During each round of a data-graph computation, an update function atomically modifies the data associated with a vertex as a function of the vertex’s prior data and that of adjacent vertices. A dynamic data-graph computation updates only an active subset of the vertices during a round, and those updates determine the set of active vertices for the next round. This article introduces Prism, a chromatic-scheduling algorithm for executing dynamic data-graph computations. Prism uses a vertex coloring of the graph to coordinate updates performed in a round, precluding the need for mutual-exclusion locks or other nondeterministic data synchronization. A multibag data structure is used by Prism to maintain a dynamic set of active vertices as an unordered set partitioned by color. We analyze Prism using work-span analysis. Let G = (V, E) be a degree-Δ graph colored with χ colors, and suppose that Q⊆V is the set of active vertices in a round. Define size(Q)= |Q| + ∑v∈ Q deg(v), which is proportional to the space required to store the vertices of Q using a sparse-graph layout. We show that a P-processor execution of Prism performs updates in Q using O(χ (lg ( Q/χ ) + lg Δ ) + lg P span and Θ(size(Q) + P) work. These theoretical guarantees are matched by good empirical performance. To isolate the effect of the scheduling algorithm on performance, we modified GraphLab to incorporate Prism and studied seven application benchmarks on a 12-core multicore machine. Prism executes the benchmarks 1.2 to 2.1 times faster than GraphLab’s nondeterministic lock-based scheduler while providing deterministic behavior. This article also presents Prism-R, a variation of Prism that executes dynamic data-graph computations deterministically even when updates modify global variables with associative operations. Prism-R satisfies the same theoretical bounds as Prism, but its implementation is more involved, incorporating a multivector data structure to maintain a deterministically ordered set of vertices partitioned by color. Despite its additional complexity, Prism-R is only marginally slower than Prism. On the seven application benchmarks studied, Prism-R incurs a 7% geometric mean overhead relative to Prism.
Convergence Analysis of Distributed Subgradient Methods over Random Networks

— We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.
Poster: Vulcan -- Repurposing Accessibility Features for Behavior-based Intrusion Detection Dataset Generation

The generation of datasets is one of the most promising approaches to collecting the necessary behavior data to train machine learning models for host-based intrusion detection. While various dataset generation methods have been proposed, they are often limited and either only generate network traffic or are restricted to a narrow subset of applications. We present Vulcan, a preliminary framework that uses accessibility features to generate datasets by simulating user interactions for an extendable set of applications. It uses behavior profiles that define realistic user behavior and facilitate dataset updates upon changes in software versions, thus reducing the effort required to keep a dataset relevant. Preliminary results show that using accessibility features presents a promising approach to improving the quality of datasets in the HIDS domain.
Poster: Computing the Persistent Homology of Encrypted Data

Topological Data Analysis (TDA) offers a suite of computational tools that provide quantified shape features of high dimensional data that can be used by modern statistical and predictive machine learning (ML) models. Persistent homology (PH) transforms data (e.g., point clouds, images, time series) into persistence diagrams (PDs)--compact representations of its latent topological structures. Because PDs enjoy inherent noise tolerance, are interpretable, provide a solid basis for data analysis, and can be made compatible with the expansive set of well-established ML model architectures, PH has been widely adopted for model development including on sensitive data. Thus, TDA should be incorporated into secure end-to-end data analysis pipelines. This paper introduces a version of the fundamental algorithm to compute PH on encrypted data using homomorphic encryption (HE).
AntiFake: Using Adversarial Audio to Prevent Unauthorized Speech Synthesis

The rapid development of deep neural networks and generative AI has catalyzed growth in realistic speech synthesis. While this technology has great potential to improve lives, it also leads to the emergence of ''DeepFake'' where synthesized speech can be misused to deceive humans and machines for nefarious purposes. In response to this evolving threat, there has been a significant amount of interest in mitigating this threat by DeepFake detection. Complementary to the existing work, we propose to take the preventative approach and introduce AntiFake, a defense mechanism that relies on adversarial examples to prevent unauthorized speech synthesis. To ensure the transferability to attackers' unknown synthesis models, an ensemble learning approach is adopted to improve the generalizability of the optimization process. To validate the efficacy of the proposed system, we evaluated AntiFake against five state-of-the-art synthesizers using real-world DeepFake speech samples. The experiments indicated that AntiFake achieved over 95% protection rate even to unknown black-box models. We have also conducted usability tests involving 24 human participants to ensure the solution is accessible to diverse populations.
Designing Floating Codes for Expected Performance

Floating codes are codes designed to store multiple values in a Write Asymmetric Memory, with applications to flash memory. In this model, a memory consists of a block of n cells, with each cell in one of q states {0,1,. ,q -1}. The cells are used to represent k variable values from an ¿-ary alphabet. Cells can move from lower values to higher values easily, but moving any cell from a higher value to a lower value requires first resetting the entire block to an all 0 state. Reset operations are to be avoided; generally a block can only experience a large but finite number of resets before wearing out entirely. A code here corresponds to a mapping from cell states to variable values, and a transition function that gives how to rewrite cell states when a variable is changed. Previous work has focused on developing codes that maximize the worst-case number of variable changes, or equivalently cell rewrites, that can be experienced before resetting. In this paper, we introduce the problem of maximizing the expected number of variable changes before resetting, given an underlying Markov chain that models variable changes. We demonstrate that codes designed for expected performance can differ substantially from optimal worst-case codes, and suggest constructions for some simple cases. We then study the related question of the performance of random codes, again focusing on the issue of expected behavior.
NestFuzz: Enhancing Fuzzing with Comprehensive Understanding of Input Processing Logic

Fuzzing is one of the most popular and practical techniques for security analysis. In this work, we aim to address the critical problem of high-quality input generation with a novel input-aware fuzzing approach called NestFuzz. NestFuzz can universally and automatically model input format specifications and generate valid input. The key observation behind NestFuzz is that the code semantics of the target program always highly imply the required input formats. Hence, NestFuzz applies fine-grained program analysis to understand the input processing logic, especially the dependencies across different input fields and substructures. To this end, we design a novel data structure, namely Input Processing Tree, and a new cascading dependency-aware mutation strategy to drive the fuzzing. Our evaluation of 20 intensively-tested popular programs shows that NestFuzz is effective and practical. In comparison with the state-of-the-art fuzzers (AFL, AFLFast, AFL++, MOpt, AFLSmart, WEIZZ, ProFuzzer, and TIFF), NestFuzz achieves outperformance in terms of both code coverage and security vulnerability detection. NestFuzz finds 46 vulnerabilities that are both unique and serious. Until the moment this paper is written, 39 have been confirmed and 37 have been assigned with CVE-ids.
Dynamic perfect hashing: upper and lower bounds

A randomized algorithm is given for the dictionary problem with O(1) worst-case time for lookup and O(1) amortized expected time for insertion and deletion. An Omega (log n) lower bound is proved for the amortized worst-case time complexity of any deterministic algorithm in a class of algorithms encompassing realistic hashing-based schemes. If the worst-case lookup time is restricted to k, then the lower bound for insertion becomes Omega (kn/sup 1/k/). <<ETX>>
Formal Analysis of Access Control Mechanism of 5G Core Network

We present 5GCVerif, a model-based testing framework designed to formally analyze the access control framework of the 5G Core. With its modular design, 5GCVerif employs various abstraction techniques to craft an abstract model that captures the intricate details of the 5G Core's access control mechanism. This approach offers customizability and extensibility in constructing the abstract model and addresses the state explosion problem in model checking. 5GCVerif also sidesteps the challenge of exhaustively generating models for all possible core network configurations by restricting the model checker to explore policy violations only within the valid network configurations. Using 5GCVerif, we evaluated 55 security properties, leading to the discovery of five new vulnerabilities in 5G Core's access control mechanism. The uncovered vulnerabilities can result in multiple attacks including unauthorized entry to sensitive information, illegitimate access to services, and denial-of-services.
Affective Analysis of Explainable Artiﬁcial Intelligence in the Development of Trust in AI Systems

The rise of Explainable Artiﬁcial Intelligence (XAI) has been a game changer for the growth of Artiﬁcial Intelligence (AI) and the systems it powers. By providing human-level explanations, it systematically solves the most signiﬁcant issue that AI faces: the black-box paradox realized from the complex hidden layers of its algorithm (i.e., machine and deep learning). Fundamentally, it allows users to learn how the AI operates and comes to decisions, thus enabling cognitive calibration of trust and subsequent reliance on the system. However, as human-computer interaction and social science studies suggest, relying on cognitive calibration might be limited as the affective processing component, which is also established from the interaction, was not yet considered. Considering the limited information regarding the affective route, this study aims to examine the effects of emotions associated with the interaction with XAI in adoption thru trust and reliance. One hundred and forty-three participants partake in the online experiment. The premise was that they were hired to classify different species of animals and plants, with an XAI-equipped image classiﬁcation AI available to give them recommendations. Three key ﬁndings can be drawn from the results. First, if a person felt interestingly surprised emotions due to the XAI (e.g., interested, excited, surprised, pleased, and amazed), they would increase their trust in the AI and rely on its functionality. Second, only trust would improve for those who felt trusting emotions (e.g., happy, conﬁdent, secure, proud, and trusting). Third, fearfully dismayed (e.g., dismayed, afraid, fear, angry, and sad) or anxiously suspicious (e.g., suspicious, concerned, confused, nervous, and anxious) emotions do not translate to a signiﬁcant change in trust and reliance on the AI.
Investigating the Impact of User Experience in Turkey's Online Bookstore: Application of Flow Theory

This dissertation explores the relationship between trust, satisfaction, and intention to repurchase in the digital retail sector, specifically focusing on online book shopping. The study conducted a survey with customers of www.idefix. com, a prominent e-commerce website in Turkey. Applying flow theory, the research identified the drivers of online shopping satisfaction and repurchase intention. The analysis, using partial least squares structural equation modeling (PLS-SEM), revealed that both affective and cognitive experiential states have a direct positive impact on online shopping satisfaction and repurchase intention. The findings contribute to understanding consumers' experiences in e-commerce and provide insights for website managers to enhance customer experiences and website performance. The study also explores theoretical implications for the retail sector in the context of digitalization and e-commerce trends.
MECHANISMS TO FORM HYPOXIA IN THE NORTHERN REGION OF KAGOSHIMA BAY

In order to make clear the mechanism to form the oxygen-depleted water at the bottom layer of the northern region of Kagoshima Bay, we carried out field observations at two representative positions. Then, we evaluated the processes to form the density stratification and vertical DO profiles by the numerical simulations based on the observational results. As a result, the following findings were obtained: 1) The seasonal change in DO concentration at the bottom layer of the observational position was controlled mainly by the seasonal change in the degree of density stratification. 2) Due to the relatively large depth, the stable stratification does not disappear until December and the most anoxic water is formed in winter in Kagoshima Bay.
A Case Report On Diagnosis of Malignancy with Serum-Ascites Cholesterol Gradient

: Ascites are the accumulation of fluid in the Abdomen. It happens due to clinical complications from various diseases that cause signs and symptoms. Cirrhosis, malignant ascites, and abdominal tuberculosis are the most common causes of ascites. The multiple etiologies of ascites make determining the cause difficult. Several techniques can aid in the differential diagnosis of ascites. However, the techniques are more expensive, invasive, and require more time. As a result, a less expensive and faster method of diagnosis, at least as a screening test, would greatly benefit clinicians. Albumin and Cholesterol in serum and ascitic fluid are parameters that would help screen and diagnose cancer. The serum-ascites albumin gradient or gap (SAAG) is a medical calculation that helps determine the cause of ascites. In addition, the serum ascites cholesterol gradient (SACG) aids in the differential diagnosis.The case study aims to evaluate the significance of serum-ascites cholesterol gradient (SACG) in diagnosing ovarian carcinoma. This case report includes a 53-year-old woman who presented to the surgical department with anorexia, indigestion, heartburn, pain or discomfort in the abdominal and pelvic regions, nausea, early satiety, bloating, and weight loss for 1 month. She was diagnosed with ovarian carcinoma. The gradients of serum-ascites albumin and serum-ascites Cholesterol were calculated. Later, the results were compared to Ca-125, an ovarian cancer marker. SAAG showed low sensitivity but high specificity and poor diagnostic performance. Her diagnosis of ovarian carcinoma was confirmed and she was advised to follow up with the oncologist. The current case report will help understand the contribution of the ascitic fluid and serum parameters in a case of ovarian cancer with ascites of unknown etiology.
Temporally stable surface mass balance asymmetry across an ice rise derived from radar internal reflection horizons through inverse modeling

Ice rises are locally grounded parts of Antarctic ice shelves that play an important role in regulating ice flow from the continent towards the ocean. Because they protrude out of the otherwise horizontal ice shelves, ice rises induce an orographic uplift of the atmospheric flow, resulting in an asymmetric distribution of the surface mass balance (SMB). Here, we combine younger and older internal reflection horizons (IRHs) from radar to quantify this distribution in time and space across Derwael Ice Rise (DIR), Dronning Maud Land, Antarctica. We employ two methods depending on the age of the IRHs, i.e. the shallow layer approximation for the younger IRHs near the surface and an optimization technique based on an ice flow model for the older IRHs. We identify an SMB ratio of 2.5 between the flanks and the ice divide with the SMB ranging between 300 and 750 kg m −2 a −1 . The SMB maximum is located on the upwind side, ~4 km offset to today's topographic divide. The large-scale asymmetry is consistently observed in time until 1966. The SMB from older IRHs is less-well constrained, but the asymmetry has likely persisted for >ka, indicating that DIR has been a stable features over long time spans.