Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging Virtual Prototypes and Metamorphic Testing for Verification of Embedded Graphics Libraries *

In this extended abstract, we summarize our work from [6], where we proposed a novel approach focusing on integration testing of embedded graphics libraries. We leveraged Virtual Prototypes (VPs) and integrated them with Metamorphic Testing (MT). Additionally, we eliminated the need for physical hardware by virtualizing the displays within the virtual environment. On an extended RISC-V VP for the GD32V platform we found 15 distinct bugs for the widely used TFT_eSPI embedded graphics library
Propagation of uncertainty by Monte Carlo simulations in case of basic geodetic computations

: The determination of the accuracy of functions of measured or adjusted values may be a problem in geodetic computations. The general law of covariance propagation or in case of the uncorrelated observations the propagation of variance (or the Gaussian formula) are commonly used for that purpose. That approach is theoretically justi ﬁ ed for the linear functions. In case of the non-linear functions, the ﬁ rst-order Taylor series expansion is usually used but that solution is affected by the expansion error. The aim of the study is to determine the applicability of the general variance propagation law in case of the non-linear functions used in basic geodetic computations. The paper presents errors which are a result of negligence of the higher-order expressions and it determines the range of such simpli ﬁ cation. The basis of that analysis is the comparison of the results obtained by the law of propagation of variance and the probabilistic approach, namely Monte Carlo simulations. Both methods are used to determine the accuracy of the following geodetic computations: the Cartesian coordinates of unknown point in the three-point resection problem, azimuths and distances of the Cartesian coordinates, height differences in the trigonometric and the geometric levelling. These simulations and the analysis of the results con ﬁ rm the possibility of applying the general law of variance propagation in basic geodetic computations even if the functions are non-linear. The only condition is the accuracy of observations, which cannot be too low. Generally, this is not a problem with using present geodetic instruments.
Training Feed-Forward Artificial Neural Network using Enhancement Bat Algorithm for classification of fingerprint image

The Artificial Neural Network (ANN) contributes to important advantages in biometrics such as fingerprints, facial recognition, and others. Fingerprint recognition is one of the most reliable and popular biometric methods used for personal identification. It plays a very important role in forensic and civilian applications. ANN plays an important role through the application of neural networks, such as pattern recognition, classification, and others. However, much effort nowadays is focused on the development of neural networks. In this study, a new variant of the BAT algorithm is proposed, called the Enhancement Bat Algorithm (EBat). The proposed approach modifies the BAT standard by enhancing exploitation capabilities and avoiding escapes from local minimums. The ANN training process is a complex continuous improvement task. This paper aims to demonstrate a detailed comparative performance analysis of training the Forward Neural Network (FFNN) from the proposed technique (EBANN) over other decent gradient algorithms and population-based approaches on the fingerprint datasets taken from the FVC 2002 database. As our approach was done in three stages, the next step is to improve the fingerprint images using a preprocessing process using HOQ. The next stage is to extract features. The extraction of invariant features is the core of recognition systems. This work proposes a novel feature extractor-fusion scheme using two powerful feature descriptors known as Gabor Filters (GFs) and Histogram of Oriented Gradient (HOG). which do not need much preprocessing and multiple stages of improvement and can be combined with other metrics easily: After extracting features from both, the two filters are dimensioned reduced by the LDA algorithm, and then the features between the two filters are combined by concatenation. The combination of the two methods leads to a significant increase in the recognition of fingerprint images, better than the use of filters separately. The last step is to use FFANN with a better bat algorithm to classify the data.
Harnessing the Power of General-Purpose LLMs in Hardware Trojan Design

Large language models (LLMs) are becoming a powerful transformative force of automation in the areas of software engineering and cybersecurity. In software-centric security research, the LLMs have undertaken a prime role in the identification and repair of security vulnerabilities and bugs. However, in hardware related fields such as logic design and hardware security, use of LLMs has only recently started to get traction. In this work we aim to explore the potential of LLMs in the offensive hardware security domain. More specificaly, we explore the level of assistance that LLMs can provide to attackers for the insertion of vulnerabilities, known as hardware trojans (HTs), in complex hardware designs ( e.g., CPUs). Having in mind high-level attack outlines, we test the ability of a general-purpose LLM to act as a “filter” that correlates system level concepts of security interest with specific module abstractions of hardware designs. By doing so, we tackle the challenges posed by the context length limit of LLMs, that become prevalent during LLM-based analyses of large code bases. Next, we initiate an LLM analysis of the reduced code base, that includes only the register transfer level code of the identified modules and test the LLM’s ability to locate the parts that implement the queried security related features. In this way, we reduce the complexity of the overall analysis performed by the LLM. Lastly, we instruct the LLM to insert suitable trojan functionalities by modifying the identified code parts accordingly. To showcase the potential of our automated LLM-based hardware trojan insertion flow, we craft a realistic HT for a modern RISC-V micro-architecture. We test the functionality of the LLM-generated HT on an FPGA board, by attacking the integrity and the availability of the RISC-V CPU. Hence, we demonstrate how general-purpose LLMs can navigate attackers through complex hardware designs and assist them in the implementation of realistic HT attacks.
A compact multiband wearable fractal antenna for medical and wireless applications

- A multiband microstrip patch wearable antenna based on fractal geometry is introduced for ISM band and other wireless applications. The proposed antenna is compact size antenna with 0.56mm textile substrate thickness. The proposed fractal wearable antenna resonates at 2.27 GHz, 4.3 GHz, 6.8 GHz, 8.16 GHz and 9.9 GHz frequencies. It attains high gain of 4.64 dB, 5.54 dB, 5.34 dB and 2.43 dB at 2.45 GHz, 4.3 GHz, 6.8 GHz and 9.9 GHz resonance frequencies respectively. The antenna has a stable radiation pattern at the resonance frequencies. The antenna provides percentage impedance bandwidth of 21.09 % (2.08 GHz to 2.67 GHz), 10.6 % (4.07 GHz to 4.53 GHz), 21.5 % (6.51 GHz to 7.25 GHz), 11.04% (8.6 GHz to 7.7 GHz)and 21.7 % ( 8.72 GHz to 7.55 GHz ). The proposed wearable antenna acts as multiband antenna applicable for ISM band, WLAN, WiMAX, WiFi, INSAT and Radio altimeters, C-band and Satellite TV applications.
Ternary Simulation as Abstract Interpretation (Work in Progress)

We introduce a formalization of ternary simulation as abstract interpretation along with a widening operator to speed up convergence. With the same goal, we present a subsumption algorithm that can determine termination earlier than the usual approach using hash sets. Additionally, we introduce a narrowing operator that utilizes recent advances in backbone extraction, allowing to increase the overapproximation precision in simulation at any time. The experiments evaluate the presented techniques in the context of hardware model checking.
Variations in phytoplankton assemblage in relation to environmental and climatic variation in a boreal lake

Phytop1ankton species differ in to1erance and sensitivity to light conditions, water co1wnn stability and nutrient deficiency (REYNOLDS 1990). These variab1es are strongly affected both directly and via the catchrnent by shortand long-terrn variations in the local weather conditions and the regional climate (STRAILE et al. 2003). Accordingly, year-to-year variation in the phytoplankton biomass and species composition have been related to meteorological forcing. Recent studies have shown, for example, that the timing o f the spring bloom and the annual variations o f diatoms, cryptophytes, chlorophytes and cyanobacteria depend on the timing of ice break-up, water temperature, the onset o f therrnal stratification and mixing events, which all are generally closely related to the prevailing and/or antecedent weather conditions and the North Atlantic Oscillation (ADRIAN et al. 1999, WEYHENMEYER 2001, ARVOLA et al. 2002). This investigation studied the annual variation in the phytoplankton assemblage in relation to variations in the weather and to physical properties in a boreal lake in southern Finland. The analysis was mainly done at the phytoplankton class level, but the functional groups approach (REYNOLDS et al. 2002) was also applied.
LERAY-HOPF WEAK SOLUTIONS TO THE NAVIER-STOKES EQUATIONS

The Navier-Stokes equations describe the motion of an idealized fluid. In this paper, we introduce the Helmholtz-Weyl decomposition and present a weak formulation of the Navier-Stokes equations along with its properties. We then build up to a proof of the existence of global-in-time Leray-Hopf weak solutions. We focus on the case of a torus, where solutions are periodic in space.
A Novel Pulse Coupled Genetic Particle Swarm Optimization Algorithm with Neural Network classifier for Heart Disease Prediction

- Heart disease is still a major public health concern around the world. Restricting human knowledge and ability in manual diagnosis results in faulty diagnoses in the health-care system, as well as information about various illnesses collected through various forms of medical technology that is either insufficient or inaccurate. Intelligent resources assist doctors in making better decisions and initiating therapy early since proper identification of a person's condition is critical. Data mining is used as effective support systems in predicting the disease where large volume of data involved. More amounts of attributes helps to predict the disease accurately, but takes more time. It is critical to find the suitable attributes that provide best prediction accuracy. In this paper, we propose a novel hybrid algorithm namely Pulse Coupled Genetic Particle Swarm Optimization Neural Network (PCGPSONN) for feature selection. The proposed algorithm combines Genetic algorithm, Pulse Coupled Neural Network and Particle Swarm optimization algorithm together. A heart disease prediction model is developed using the proposed algorithm and SVM classifier. The prediction model is validated with Real time Data sets, Hungarian data set and UCI machine learning repository using various performance metrics such as accuracy, precision, and recall rate. It is found that our proposed algorithm performs well in feature selection and we achieved a maximum classification accuracy of 96.29% for heart disease prediction.
A RISC-V "V" VP: Unlocking Vector Processing for Evaluation at the System Level

—In this paper we introduce the first free-and open-source SystemC TLM based RISC-V Virtual Prototype (VP) with support for the RISC-V "V" Vector Extension (RVV) Version 1.0. After an introduction to RVV, we present the integration of RVV and its 600+ instructions into an existing VP leveraging code generation for over 20k Lines of Code (LoC). Moreover, we describe the verification of the resulting VP using the Instruction Sequence Generator (ISG) FORCE-RISCV and the Instruction Set Simulator (ISS) riscvOVPsim . Our case studies demonstrate the benefits of the RVV enhanced VP for system-level evaluation. We present non-vectorized and vectorized variants of two common algorithms which are executed on the VP with varying parameters. We show that by comparing the number of simulated execution cycles, we can derive valuable assessments for the design of RVV micro-architectures.
Viral Marketing and the Diﬀusion of Trends on Social Networks

We survey the recent literature on theoretical models of diﬀusion in social networks and the application of these models to viral marketing. To put this work in context, we begin with a review of the most common models that have been examined in the economics and sociology literature, including local interaction games, threshold models, and cascade models, in addition to a family of models based on Markov random ﬁelds. We then discuss a series of recent algorithmic and analytical results that have emerged from the computer science community. The ﬁrst set of results addresses the problem of inﬂuence maximization, in which the goal is to determine the optimal group of individuals in a social network to target with an advertising campaign in order to cause a new product or technology to spread throughout the network. We then discuss an analysis of the properties of graphs that allow or prohibit the widespread propagation of trends.
Effective Bug Detection with Unused Definitions

Unused definitions are values assigned to variables but not used. Since unused definitions are usually considered redundant code causing no severe consequences except for wasting CPU cycles, system developers usually treat them as mild warnings and simply remove them. In this paper, we reevaluate the effect of unused definitions and discover that some unused definitions could indicate non-trivial bugs like security issues or data corruption, which calls for more attention from developers. Although there are existing techniques to detect unused definitions, it is still challenging to detect critical bugs from unused definitions because only a small proportion of un-used definitions are real bugs. In this paper, we present a static analysis framework ValueCheck to address the challenges of detecting bugs from unused definitions. First, we make a unique observation that the unused definitions on the boundary of developers’ interactions are prone to be bugs. Second, we summarize syntactic and semantic patterns where unused definitions are intentionally written, which should not be considered bugs. Third, to distill bugs from unused definitions, we adopt the code familiarity metrics from the software engineering field to rank the detected bugs, which enables developers to prioritize their focus. We evaluate ValueCheck with large system software and libraries including Linux, MySQL, OpenSSL, and NFS-ganesha. ValueCheck helps detect 210 unknown bugs from these applications. 154 bugs are confirmed by developers. Compared to state-of-the-art tools, ValueCheck demonstrates to effectively detect bugs with low false positives.
An antimony bead from Jerablus Tahtani

This paper presents an analysis of a bead from third millennium BC levels of Jerablus Tahtani, near Carchemish in Syria. The bead was found to consist of almost pure metallic antimony, with small amounts of lead, arsenic, copper and tin. Antimony objects are very rare in the archaeological record and very few analyses have been carried out. Until this paper, no description of the microstructure of such beads has been published. The paper goes on to discuss the probable sources of the antimony metal and the possible provenance of the bead itself.
Psychology and Religion

This study considered representations of divine and human others in the self-understanding of monotheists from three religions. Self-understanding was conceptualized on the basis of semantic and episodic knowledge in narrative response data. Given the importance of social context in the formation of cognitive schemas, the project emphasized self-understanding in a comparative religious design. The sample included sixty nominated religious exemplars who responded to a structured interview. Schemas were subsequently mapped for Jews, Muslims, and Christians by comparison of self and other representations in a computational model known as latent semantic analysis (LSA). Findings indicated that representation of the divine is far removed from parents in cognitive schemas for all participants. Unlike Jews and Christians, Muslims appear to represent human others on the basis of self-understanding which principally references the divine. When considered in a computational semantic space, exemplars generally represent the self in a manner corresponding with divine and peer figures.
SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset

Sarcasm poses a challenge in linguistic analysis due to its implicit nature, involving an intended meaning that contradicts the literal expression. The advent of social networks has propelled the utilization of multimodal data to enhance sarcasm detection performance. In prior multimodal sarcasm detection datasets, a single label is assigned to a multimodal instance. Subsequent experiments often highlight the superiority of multimodal models by demonstrating their improvements compared to unimodal models based on these unified labels across multiple modalities. However, our investigation revealed that numerous instances of sarcasm cannot be identified using a single modality. Humans employ the conflict between a statement and factual information as a cue to detect sarcasm, and these cues can stem from different modalities. Then, a unified label for a multimodal instance may be not suitable for the associated text or image. In this work, we introduce SarcNet, a multilingual and multimodal sarcasm detection dataset in English and Chinese, consisting of 3,335 image-text pair samples. We provide annotations for sarcasm in visual, textual, and multimodal data, respectively, resulting in over 10,000 labeled instances. The separated annotation schema for unimodal and multimodal data facilitates a more accurate and reasonable assessment of unimodal and multimodal models.
Improving Safety Communication in Jewellery and Metalsmithing Workshops using Chasing and Repousse Techniques

Adhering to Safety Practices in Ghana and around the world has always been through safety personnel and safety symbols; but when it comes to the local Jewellery and Metalsmithing Studios, from observation made, the impact of safety symbols is low as compared to the big industries. The local jewellery and metalsmithing studios in their haste to catch up with the modern technology through improvised means, endanger themselves in so many hazards and these are done out of complacency. Due to this attitude, apprentices also graduate and open their own shops with less attention to safety. To maximize and ensure the impact of safety symbols on jewellers and metalsmiths, this research seeks to promote the use of local jewellery and metalsmithing techniques in creating safety symbols that are positioned strategically to inform safety rules daily. With the use of qualitative research interviews and studio based research, the researchers have explored repousse and chasing as a metal forming technique and have used the end result in composing three different safety alerting devices based on conceptual art. The study was studio-based oriented with the focus on workshops in Kumasi and its environs including KNUST with global implcations. The interviews conducted were one-on-one and focus group types. The respondents who participated were fifty (50) and were made up of active men and women in the field or workshop. The criteria used for assessment were; positioning and visibility of symbols, materials used in production and routine checks done by safety experts. A number of literatures; both books and on-line contents also helped in giving a clear guide as to what is legal and even how to build an art piece suitable within the safety context. The end product of this conceptual art lies between the use of sonorous and visual means of promoting or craving attention towards safety, daily. The research recommends further study into how to strategically inform workers of safety at low cost on daily basis in order to maximize the number of safety-conscious workers in the local jewellery and metalsmithing industries in Ghana.
Segmenting Lung Fields in Serial Chest Radiographs Using Both Population-Based and Patient-Specific Shape Statistics

This paper presents a new deformable model using both population-based and patient-specific shape statistics to segment lung fields from serial chest radiographs. There are two novelties in the proposed deformable model. First, a modified scale invariant feature transform (SIFT) local descriptor, which is more distinctive than the general intensity and gradient features, is used to characterize the image features in the vicinity of each pixel. Second, the deformable contour is constrained by both population-based and patient-specific shape statistics, and it yields more robust and accurate segmentation of lung fields for serial chest radiographs. In particular, for segmenting the initial time-point images, the population-based shape statistics is used to constrain the deformable contour; as more subsequent images of the same patient are acquired, the patient-specific shape statistics online collected from the previous segmentation results gradually takes more roles. Thus, this patient-specific shape statistics is updated each time when a new segmentation result is obtained, and it is further used to refine the segmentation results of all the available time-point images. Experimental results show that the proposed method is more robust and accurate than other active shape models in segmenting the lung fields from serial chest radiographs.
Cardiothoracic Ratio Measurement Using Arti�cial Intelligence: Observer and Method Validation Studies

Arti�cial Intelligence (AI) technique for cardiothoracic ratio (CTR) measurement is a promising tool that has been technically validated but not clinically evaluated on a large dataset. This study observes and validates AI and manual methods for CTR measurement on a large dataset and investigates the clinical utility of the AI method. Five thousand normal chest x-rays and 2,517 images with cardiomegaly and CTR values, were analyzed using manual, AI-assisted, and AI only methods. AI methods obtained CTR values from a VGG-16 U-Net model. An in-house software was used to aid the study and to record measurement time. Intra and inter-observer experiments were performed on manual and AI-assisted methods and the average of each method was employed in a method variation study. AI outcomes were graded in the AI-assisted method as excellent (accepted by both users independently), good (required adjustment), and poor (failed outcome). Bland-Altman plot with coe�cient of variation (CV), and coe�cient of determination (R-squared) were employed to evaluate agreement and correlation between measurements. Finally, the performance of a cardiomegaly classi�cation test was evaluated using a CTR cutoff at the standard (0.5), optimum, and maximum sensitivity. Manual CTR measurements on cardiomegaly data were comparable to the previous radiologist reports (CV of 2.13% vs 2.04%). The observer and method variations from the AI method were about three times higher than from the manual method (CV of 5.78% vs 2.13%). AI assistance resulted in 40% excellent, 56% good, and 4% poor grading. AI assistance signi�cantly improved agreement on inter-observer measurement compared to manual methods (CV; bias: 1.72%; -0.61% vs 2.13%; -1.62%) and was faster to perform (2.2 ± 2.4 secs vs 10.6 ± 1.5 secs). R-squared and classi�cation-test were not reliable indicators to verify that the AI method could replace manual operation.