Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering

We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction between the modalities is paramount to retrieve information and must be captured with complex fusion models. As these models require a lot of training data, we design this pre-training task from existing work in textual Question Answering. It consists in considering a sentence as a pseudo-question and its context as a pseudo-relevant passage and is extended by considering images near texts in multimodal documents. Our method is applicable to different neural network architectures and leads to a 9% relative-MRR and 15% relative-F1 gain for retrieval and reading comprehension, respectively, over a no-pre-training baseline.
Coupled Fredkin and Motzkin chains from quantum six- and nineteen-vertex models

We generalize the area-law violating models of Fredkin and Motzkin spin chains into two dimensions by building quantum six- and nineteen-vertex models with correlated interactions. The Hamiltonian is frustration free, and its projectors generate ergodic dynamics within the subspace of height configuration that are non negative. The ground state is a volume- and color-weighted superposition of classical bi-color vertex configurations with non-negative heights in the bulk and zero height on the boundary. The entanglement entropy between subsystems has a phase transition as the qq-deformation parameter is tuned, which is shown to be robust in the presence of an external field acting on the color degree of freedom. The ground state undergoes a quantum phase transition between area- and volume-law entanglement phases with a critical point where entanglement entropy scales as a function L\log LLlogL of the linear system size LL. Intermediate power law scalings between L\log LLlogL and L^2L2 can be achieved with an inhomogeneous deformation parameter that approaches 1 at different rates in the thermodynamic limit. For the q>1q>1 phase, we construct a variational wave function that establishes an upper bound on the spectral gap that scales as q^{-L^3/8}q−L3/8.
Sorting and Grading

We propose a framework to assess how to optimally sort and grade students of heterogenous ability. Potential employers face uncertainty regarding an individual's productive value. Knowing which school an individual went to is useful for two reasons: firstly, average student ability may differ across schools; secondly, different schools may use different grading rules and thus provide varying incentives to exert effort. An optimal school system exhibits coarse stratification with respect to ability, and more lenient grading at the top-tier schools than at the bottom-tier schools. Our paper contributes to the ongoing policy debate on tracking in secondary schools.
A Thread-Oriented Memory Resource Management Framework for Mobile Edge Computing

Increasing the number of cores is one of the most effective methods to enhance performance. However, an extensive experimental study on mobile edge computing (e.g., Android devices) indicates that the memory management system has gradually become a key performance bottleneck. Studies on improving memory management mainly focus on exploring the trade-off between avoiding fragmentation and improving allocation efficiency. From our previous research, we know that the fragmentation is no longer a crucial bottleneck; instead, inter- and intra-thread behavior should be focused on, and thus, we introduce memory management based on thread behaviors (MMBTB). Unfortunately, it lacks a unified optimization program interface and good architecture. Consequently, in this paper, we propose a memory resource management at operating system (OS) layer of mobile edge computing, called the thread-oriented memory management layer (TOMML) to address this problem, which follows the microkernel architecture pattern and can meet the user’s requirements for selecting plug-ins to achieve different optimization goals. This paper is divided into several sections as follows. First, we demonstrate the efficiency of TOMML through theoretical simulation and experimentation. The experimental result is that TOMML can improve memory allocation efficiency by 12%–20%. Furthermore, we introduce a plug-in to save power, which can further promote 6%–25% bank free compared with previous excellent research.
Implementation and characterization of phase-resolved Doppler optical coherence tomography method for flow velocity measurement

In this work, the system implementation and characterization of a Phase-Resolved Doppler Optical Coherence Tomography (PR-DOCT) is presented. The phase-resolved Doppler technique was implemented on a custom built Frequency Domain OCT (FD-OCT) that was recently developed at Suranaree University of Technology. Utilizing Doppler phase changed relation in a complex interference signal caused by moving samples, PR-DOCT can produce visualization and characterization of flow activity such as blood flow in biological samples. Here we report the performance of the implemented PR-DOCT system in term of the Velocity Dynamic Range (VDR), which is defined by the range from the minimum to the maximum detectable axial velocity. The minimum detectable velocity was quantified from a histogram distribution of phase difference between consecutive depth-scan signals when performing Doppler imaging of a stationary mirror. By applying a Gaussian curve fitting to the histogram, the Full Width at Half Maximum (FWHM) of the fitted curve was measured to represent the detectable minimum flow velocity of the system. The maximum detectable velocity was limited by the phase wrapping of the Doppler signal, which is governed by the acquisition speed of the system. We demonstrate the 3D Doppler imaging and velocity measurement of feed flow phantom using 100% milk pumped through a microfluidic chip by using a syringe pump system.
Root Identification Tool for Arabic Verbs

Numerous Arabic morphology systems have been devoted toward morphed requirements of words that are required by other text analyzers. Term rooting is an essential requirement in those systems, yet rooting module in the state-of-the-art morphology systems insufficiently meet that requirement, especially verb term. Consequently, due to termination in stemming term rather than a rooting term. Since the stem of the verb is not the root of the verb, it is not feasible to generate or inference verb’s derivations and whole it’s surface forms (patterns) such tense, number, mood, person, aspect, and others of verb irregular patterns. Therefore, we propose a new model for identifying the verb’s root produced in a tool (RootIT) in order to overcome verb root extraction without disambiguation out of traditional methods, applied in current morphology systems. A major design goal of this system is that it can be used as a standalone tool and can be integrated, in a good manner, with other linguistic analyzers. The adopted approach is a mapping surface verb with full-scale derivative verbs discharged previously in the relational database. Moreover, the proposed system is tested on the adopted dataset from PATB verbs extracted from CoreNLP system. The extracted dataset, containing more than (7950) distinguishes verbs belonging to (1938) different roots. The results obtained outstrip the best-compared system by (2.74%) of high accuracy.
Comparative analysis of machine learning algorithms for predicting standard time in a manufacturing environment

Abstract Determining accurate standard time using direct measurement techniques is especially challenging in companies that do not have a proper environment for time measurement studies or that manufacture items requiring complex production schedules. New and specific time measurement techniques are required for such companies. This research developed a novel time estimation approach based on several machine learning methods. The set of collected inputs in the manufacturing environment, including a number of products, the number of welding operations, product's surface area factor, difficulty/working environment factors, and the number of metal forming processes. The data were collected from one of the largest bus manufacturing companies in Turkey. Experimental results demonstrate that when model accuracy was measured using performance measures, k-nearest neighbors outperformed other machine learning techniques in terms of prediction accuracy. “The number of welding operations” and “the number of pieces” were found to be the most effective parameters. The findings show that machine learning algorithms can estimate standard time, and the findings can be used for several purposes, including lowering production costs, increasing productivity, and ensuring efficiency in the execution of their operating processes by other companies that manufacture similar products.
Understanding the Mechanism of Through-Wall Wireless Sensing

During the last few years, there is a growing interest on the usage of Wi-Fi signals for human activity detection. A large number of Wi-Fi based sensing systems have been developed, including respiration detection, gesture classification, identity recognition, etc. However, the usability and robustness of such systems are still limited, due to the complexity of practical environments. Various pioneering approaches have been proposed to solve this problem, among which the model-based approach is attracting more and more attention, due to the advantage that it does not require a huge dataset for model training. Existing models are usually developed for Line-of-Sight (LoS) scenarios, and can not be applied to facilitating the design of wireless sensing systems in Non-Line-of-Sight (NLoS) scenarios (e.g., through-wall sensing). To fill this gap, we propose a through-wall wireless sensing model, aiming to characterize the propagation laws and sensing mechanisms of Wi-Fi signals in through-wall scenarios. Specifically, based on the insight that Wi-Fi signals will be refracted while there is a wall between the transceivers, we develop a refraction-aware Fresnel model, and prove theoretically that the original Fresnel model can be seen as a special case of the proposed model. We find that the presence of a wall will change the distribution of Fresnel zones, which we called the "squeeze effect" of Fresnel zones. Moreover, our theoretical analysis indicates that the "squeeze effect" can help improve the sensing capability (i.e., spatial resolution) of Wi-Fi signals. To validate the proposed model, we implement a through-wall respiration sensing system with a pair of transceivers. Extensive experiments in typical through-wall environments show that the respiration detection error is lower than 0.5 bpm, while the subject's vertical distance to the connection line of the transceivers is less than 200 cm. To the best of our knowledge, this is the first theoretical model that reveals the Wi-Fi based wireless sensing mechanism in through-wall scenarios.
Optimal dimensional synthesis of the trigger mechanism of a high-voltage circuit breaker

The trigger mechanism used in high-voltage circuit breakers is a nonconventional multi-body system with topological change due to body collision. This article presents an efficient method for synthesizing the optimal dimensions of the mechanism to minimize both the trigger hook response time and the maximum impact stress. An effective strategy is introduced to solve this multi-objective optimization problem by creating a relation curve of one objective against the other for making a favorable decision. In addition, a developed bond graph model is used for simulating the dynamic response of the system, which greatly reduces the computational complexities of the sensitivity analysis of the design variables and the optimization procedure. The effectiveness and accuracy of the proposed method is verified by performance test of a prototype mechanism fabricated based on the optimized dimensions.
UQRCom

While communication in the air has been a norm with the pervasiveness of WiFi and LTE infrastructure, underwater communication still faces a lot of challenges. Even nowadays, the main communication method for divers in underwater environment is hand gesture. There are multiple issues associated with gesture-based communication including limited amount of information and ambiguity. On the other hand, traditional RF-based wireless communication technologies which have achieved great success in the air can hardly work in underwater environment due to the extremely severe attenuation. In this paper, we propose UQRCom, an underwater wireless communication system designed for divers. We design a UQR code which stems from QR code and address the unique challenges in underwater environment such as color cast, contrast reduction and light interfere. With both real-world experiments and simulation, we show that the proposed system can achieve robust real-time communication in underwater environment. For UQR codes with a size of 19.8 cm x 19.8 cm, the communication distance can be 11.2 m and the achieved data rate (6.9 kbps ~ 13.6 kbps) is high enough for voice communication between divers.
GPS-assisted Indoor Pedestrian Dead Reckoning

Indoor pedestrian dead reckoning (PDR) using embedded inertial sensors in smartphones has been actively studied in the ubicomp community. However, PDR relying only on inertial sensors suffers from the accumulation of errors from the sensors. Researchers have employed various indoor landmarks detectable by smartphone sensors such as magnetic fingerprints caused by elevators and Bluetooth signals from beacons with known coordinates to compensate for the errors. This study proposes a new type of indoor landmark that does not require additional device installation, e.g., beacons, and training data collection in a target environment, e.g., magnetic fingerprints, unlike existing landmarks. This study proposes the use of GPS signals received by a smartphone to correct the accumulated errors of the PDR. While it is impossible to locate the smartphone indoors using GPS satellites, the smartphone can receive signals at a window-side area through windows from satellites aligned with the orientation of the window normal. Based on this idea, we design a machine-learning-based module for detecting the proximity of a user to a window and the orientation of the window, which enables us to roughly determine the absolute coordinates of the smartphone and to correct the accumulated errors by referring to positions of window-side areas found in the floor plan of the environment. A key technical contribution of this study is designing the module, such that it can be trained based on data from environments other than the target environment yet work in any environment by extracting GPS-related information independent of wall orientation. We evaluated the effectiveness of the proposed method using sensor data collected in real environments.
Detection of Moving Object Using Superpixel Fusion Network

Moving object detection is still a challenging task in complex scenes. The existing methods based on deep learning mainly use U-Nets and have achieved amazing results. However, they ignore the local continuity between pixels. In order to solve this problem, a method based on a superpixel fusion network (SF-Net) is proposed in this article. First, the median filter is used to extract the candidate foreground (called pixel features) and the image sequence is segmented by superpixel. Then, the histogram features (called superpixel features) of the candidate foreground superpixels are extracted. Next, the pixel features and the superpixel features are the inputs of SF-Net, respectively. Experiments show the effectiveness of SF-Net on 34 image sequences and the average F-measure reaches 0.84. SF-Net can remove more background noise and has stronger expression ability than a network with the same depth.
Compiling Higher-Order Specifications to SMT Solvers: How to Deal with Rejection Constructively

Modern verification tools frequently rely on compiling high-level specifications to SMT queries. However, the high-level specification language is usually more expressive than the available solvers and therefore some syntactically valid specifications must be rejected by the tool. In such cases, the challenge is to provide a comprehensible error message to the user that relates the original syntactic form of the specification to the semantic reason it has been rejected. In this paper we demonstrate how this analysis may be performed by combining a standard unification-based type-checker with type classes and automatic generalisation. Concretely, type-checking is used as a constructive procedure for under-approximating whether a given specification lies in the subset of problems supported by the solver. Any resulting proof of rejection can be transformed into a detailed explanation to the user. The approach is compositional and does not require the user to add extra typing annotations to their program. We subsequently describe how the type system may be leveraged to provide a sound and complete compilation procedure from suitably typed expressions to SMT queries, which we have verified in Agda.
Three Metrics for Musical Chord Label Evaluation

Harmony constitutes an essential aspect of a broad range of styles in Western music, and chords usually play a key role therein. Consequently, the generation or detection of chords is central to a wide range of computational models, for instance in chord estimation, chord sequence prediction, and harmonic structure detection. Such models are typically evaluated by comparing their outputs to ground-truth chord labels using a binary metric (“correct” or “incorrect”). As chord vocabularies continue to grow, binary metrics capture less information about the correctness of a given label, thus equating all labeling errors regardless of their severity. In this work, we present the chord-eval toolkit, which proposes three different metrics drawn, adapted, and generalized from previous work, addressing acoustic, perceptual, music-theoretical, and mechanical aspects of evaluation. We discuss use cases for which the metrics vary in appropriateness, depending on properties of the underlying music and the task at hand, and present an example of such differences.
P4Cub: A Little Language for Big Routers

P4Cub is a new intermediate representation (IR) for the P4 programming language. It has been designed with the goal of facilitating development of certified tools. To achieve this, P4Cub is organized around a small set of core constructs and avoids side effects in expressions, which avoids mutual recursion between the semantics of expressions and statements. Still, it retains the essential domain-specific features of P4 itself. P4Cub has a front-end based on Petr4, and has been fully mechanized in Coq including big-step and small-step semantics and a type system. As case studies, we have engineered several certified tools with P4Cub including proofs of type soundness, a verified compilation pass, and an automated verification tool.
Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. Over the last several years, the track on Information Retrieval from Microblogs during Disasters (IRMiDis), organized as part of the FIRE conference series, has provided annotated datasets for developing ML/NLP techniques for utilizing microblogs for various practical tasks that would help authorities better deal with disaster situations. In particular, the FIRE 2022 IRMiDis track focused on two important tasks – (i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets.
Breaking Bad: Quantifying the Addiction of Web Elements to JavaScript

While JavaScript established itself as a cornerstone of the modern web, it also constitutes a major tracking and security vector, thus raising critical privacy and security concerns. In this context, some browser extensions propose to systematically block scripts reported by crowdsourced trackers lists. However, this solution heavily depends on the quality of these built-in lists, which may be deprecated or incomplete, thus exposing the visitor to unknown trackers. In this article, we explore a different strategy by investigating the benefits of disabling JavaScript in the browser. More specifically, by adopting such a strict policy, we aim to quantify the JavaScript addiction of web elements composing a web page through the observation of web breakages. As there is no standard mechanism for detecting such breakages, we introduce a framework to inspect several page features when blocking JavaScript, that we deploy to analyze 6,384 pages, including landing and internal web pages. We discover that 43% of web pages are not strictly dependent on JavaScript and that more than 67% of pages are likely to be usable as long as the visitor only requires the content from the main section of the page, for which the user most likely reached the page, while reducing the number of tracking requests by 85% on average. Finally, we discuss the viability of currently browsing the web without JavaScript and detail multiple incentives for websites to be kept usable without JavaScript.
DimSum: A Decentralized Approach to Multi-language Semantics and Verification

Prior work on multi-language program verification has achieved impressive results, including the compositional verification of complex compilers. But the existing approaches to this problem impose a variety of restrictions on the overall structure of multi-language programs (e.g. fixing the source language, fixing the set of involved languages, fixing the memory model, or fixing the semantics of interoperation). In this paper, we explore the problem of how to avoid such global restrictions. Concretely, we present DimSum: a new, decentralized approach to multi-language semantics and verification, which we have implemented in the Coq proof assistant. Decentralization means that we can define and reason about languages independently from each other (as independent modules communicating via events), but also combine and translate between them when necessary (via a library of combinators). We apply DimSum to a high-level imperative language Rec (with an abstract memory model and function calls), a low-level assembly language Asm (with a concrete memory model, arbitrary jumps, and syscalls), and a mathematical specification language Spec. We evaluate DimSum on two case studies: an Asm library extending Rec with support for pointer comparison, and a coroutine library for Rec written in Asm. In both cases, we show how DimSum allows the Asm libraries to be abstracted to Rec-level specifications, despite the behavior of the Asm libraries not being syntactically expressible in Rec itself. We also verify an optimizing multi-pass compiler from Rec to Asm, showing that it is compatible with these Asm libraries.