Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweight Multimodal Fusion Computing Model for Emotional Streaming in Edge Platform

In the era of ultra-low latencies with 5G technology, users can experience the emotions of the other person on a smartphone call using an AI model. Although a lot of development has happened in the field of multimodal emotion analysis, however lightweight multimodal emotional streaming over device-to-device communication using AI models is a challenging task considering the synchronization of modalities such as video, audio, and text. Moreover, these AI models take a lot of time to execute, and deploying them completely on smartphone devices in a real-time application is impractical due to high end-to-end latency. To mitigate the issues, we propose a Lightweight Multimodal Fusion Computing (L-MFC) model, which can be easily deployable in smartphones and can offload the execution of the AI model to a nearby edge device to perform the computation in lesser time. The results show a trade-off between AI model accuracy (making L-MFC lightweight) and device computation capabilities; where L-MFC achieves 75.13 % model accuracy and provides 75 %, 16 %, and 41 % improvements for average prediction time, CPU utilization, and model size, respectively compared to BC-LSTM model. Further, the L-MFC model can provide end-to-end inference latency improvement of 59.72 % while performing full offload (i.e., edge execution) compared to the on-device execution. Finally, we experimented a scenario where split execution of L-MFC model is beneficial while network bandwidth is not very high to validate our proposed solutions.
The COTRE Project: How to model and verify Real Time Architecture?

Abstract COTRE (‘‘COmposant Temps Reel’’) is a two years project starting in January 2002, supported byReseau National des Technologies Logicielles (RNTL)The COTRE Project aims at providing a design methodology and its associated software environmentfor the development of embedded real−time avionic systems. It contributes at bridging the gap betweenrequirements of such systems, typically expressed in Architecture Description Languages, and formaldevelopment techniques, relying on modeling of systems and verification. This paper will summarize the final status of the project:• The graphical and textual description language used to model the architecture. We will alsodescribe the work done with the AADL (Avionics Architecture Description Language) SAEstandard sub−committee,• The specification and verification of properties of COTRE components through the definition of averification language and the use of formal proof techniques.URL: http://www.LAAS. fr/COTRE Main author Biography Patrick Farail is Airbus Methods Development Engineer for Airborne Computer Software since1989. He is the Team leader of experts on Software development activities including Specification,Design, Coding and Requirements Engineering. He was involved earlier in Ada development (since 1983), he was a member of Ada Real−time groupsARTEWG and ExTRA and then participated to the definition of the HOOD method in 1987. Sincethese first experiments, he investigates, improves and promotes the use of software engineeringmethods in Airbus airborne computer software.
Human Adipose Tissue Derived Mesenchymal Stem Cells Aggravate Chronic Cyclosporin Nephrotoxicity by the Induction of Oxidative Stress

The aim of this study was to investigate whether hATMSCs protect against cyclosporine (CsA)-induced renal injury. CsA (7.5 mg/kg) and hATMSCs (3×106/5 mL) were administered alone and together to rats for 4 weeks. The effect of hATMSCs on CsA-induced renal injury was evaluated by assessing renal function, interstitial fibrosis, infiltration of inflammatory cells, and apoptotic cell death. Four weeks of CsA-treatment produced typical chronic CsA-nephropathy. Combined treatment with CsA and hATMSCs did not prevent these effects and showed a trend toward further renal deterioration. To evaluate why hATMSCs aggravated CsA-induced renal injury, we measured oxidative stress, a major mechanism of CsA-induced renal injury. Both urine and serum 8-hydroxydeoxyguanosine(8-OHdG) levels were higher in the CsA+hATMSCs group than in the CsA group (P<0.05). An in vitro study showed similar results. Although the rate of apoptosis did not differ significantly between HK-2 cells cultured in hATMSCs-conditioned medium and those cultured in DMEM, addition of CsA resulted in greater apoptosis in HK-2 cells cultured in hATMSCs-conditioned medium. Addition of CsA increased oxidative stress in the hATMSCs-conditioned medium. The results of our study suggest that treatment with hATMSCs may aggravate CsA-induced renal injury because hATMSCs cause oxidative stress in the presence of CsA.
A Semantic Analysis Prototype for Distributed Syrian Government Data

The COVID-2019 pandemic has exposed the importance of government data analysis in responding to such emergencies, but at the same time, government data seemed unprepared to meet the requirements of the case, due to the privacy, distribution, and heterogeneous nature of government data. Through this research, we draw the government's attention to the importance of this matter, and present a proposal that adopts the best practices and standards to achieve this purpose. In the first phase (the objective of this paper) we outline the regulation rules of the data analysis life-cycle through our proposed prototype which relies on semantic web and natural language processing techniques. In the second phase, we develop the model's algorithms like semantic indexing, graph capturing and the other model units. We take into account the privacy of the Arabic language due to its complex nature, which means that English language tools will not work efficiently with the Arabic language. In addition, there is a lack of technical resources and research works.
Hierarchical Geometric Path-Following Control for UAV Slung Load Transport

This paper addresses the motion control problem of a slung load system (SLS), which consists of an unmanned aerial vehicle (UAV) carrying a pendulum. The pendulum is pivoted at the UAV's center of mass through a 2-degree-of-freedom rotational joint. The other end of the pendulum carries a payload to be transported by the SLS. The control design is based on a geometric SLS model, where UAV and pendulum rotations evolve in SO(3) and S2, respectively. Hence, the control design is geometric in nature and does not adhere to the parameterization of SO(3) and S2. The objective is to control payload position and UAV heading without using timed reference trajectories. Instead, the control design stabilizes the so-called path set to control payload motion using a hierarchical control structure. Utilizing the Reduction Theorem, the paper proves that the path set is almost globally asymptotically stable. The Reduction Theorem simplifies the stability proof as no Lyapunov function is needed and allows for modularity in individual control-loop designs. Numerical simulations are presented to validate the result and show its almost global asymptotic nature.
Collaborative scientific publishing : a new research ecosystem

This thesis introduces PubPub, a complete publishing system that is consonant with the way software and research ideas are developed. It is author-driven, continuous, collaborative, and allows for data and code to be directly integrated into the document. PubPub is optimized for collaboration and iterative document creation; taking inspiration from the software development cycle it allows for more participatory forms of review. We hypothesize that by changing the scientific review process from one of static critique to one of interactive collaboration we can increase the error-detection rate of scientific review. We present an experiment to test this hypothesis by measuring error detection rates across several interactive and non-interactive documents. This work is motivated by a growing recognition that in many fields, notably those that rely on data analysis and computing, the existing review process is not sufficiently fair, accurate, or timely. Thesis Supervisor: Andrew Lippman Title: Associate Director & Senior Research Scientist, MIT Media Lab
Robotic Peg-in-Hole Insertion with Tight Clearances: A Force-based Deep Q-Learning Approach

The automatic execution of contact-rich assembly tasks such as peg-in-hole insertion still remains a challenge in industrial manufacturing automation. Deep reinforcement learning (RL) enables agents to learn complex robotic skills, but requires extensive data collection and relatively long execution times when trained online on the physical hardware. In this paper, a robotic setup and RL implementation are presented, which can learn how to successfully perform the peg-in-hole insertion task. The state vector for our force-based learning approach only consists of force/torque (F/T) signals from the robot tooltip without using any position information. We introduce a deep Q-learning (DQN) framework adapted to the task at hand and gather a training data set with a total of 984 peg insertion attempts and 7,884 experiences on the physical setup. Based on this, we apply an offline learning process to improve efficiency by training a large number of policies with different parameter configurations in short time. Finally, the best model configurations are deployed and evaluated on the physical setup reaching a 100% success rate for the insertion task with 0.2 mm clearance.
THE EFFECT OF ANTISTRIP ADDITIVES ON THE PROPERTIES OF ASPHALT CEMENT

Antistrip additives are commonly used by many highway agencies to improve the water resistance of asphalt concrete mixtures. The effect of these additives on the properties, behavior, and performance of asphalt has received little documentation in the literature. It is often assumed that the antistrip additives are absorbed on the aggregate surface and do not interact with the asphalt. Experience in some states indicates that antistrip additives can soften the asphalt cement, and at least 1 state agency does make allowance for specification noncompliance (viscosity) when antistrip additives are used. This paper further examines and discusses the effect of antistrip additives on the properties of asphalt cement.
Evaluating the Inclusiveness of Artificial Intelligence Software in Enhancing Project Management Efficiency – A review and examples of quantitative measurement methods

The escalating integration of Artificial Intelligence (AI) in various domains, especially Project Management (PM), has brought to light the imperative need for inclusivity in AI systems. This paper investigates the role of AI software in augmenting both the inclusiveness and efficiency within the realm of PM. The research pivots around specific criteria that define and measure the inclusiveness of AI in PM, highlighting how AI, when developed with inclusiveness in mind, can significantly enhance project outcomes. However, there are inherent challenges in achieving this inclusiveness, primarily due to biases embedded in AI learning databases and the design and development processes of AI systems. The study offers a comprehensive examination of AI's potential to revolutionize PM by enabling managers to concentrate more on people-centric aspects of their work. This is achieved through AI’s ability to perform tasks such as data collection, reporting, and predictive analysis more consistently and efficiently than human counterparts. However, the incorporation of AI in PM extends beyond mere efficiency; it represents a paradigm shift in the epistemology of PM, calling for a deeper understanding of AI's role and impact on society. Despite these advantages, the adoption of AI comes with significant challenges, particularly in terms of bias and inclusiveness. Biased AI learning databases, which use shared and reusable datasets, often perpetuate initially discriminatory algorithms. Moreover, unconscious biases and stereotypes of AI designers, developers, and trainers can inadvertently influence the behavior of the AI systems they create. This necessitates a paradigmatic shift in how AI systems are developed and governed to ensure they do not replicate or exacerbate existing social inequalities. The research proposes a methodological approach involving the development of criteria for inclusion and exclusion, alongside data extraction, to evaluate the inclusiveness and efficiency of AI software in enhancing PM. This approach is crucial for understanding and addressing the challenges and limitations of AI in the context of PM. By focusing on inclusiveness, the study underscores the importance of a synergy between technological advancement and ethical consideration, demanding a comprehensive understanding and regulation to mitigate risks and maximize benefits. In conclusion, this paper presents a detailed exploration of AI’s role in PM highlighting both its potential benefits and the ethical challenges it poses. The findings and recommendations of this study contribute to the growing discourse on the need for inclusive AI systems in PM, offering insights for AI developers and Project Managers (PMs) alike.
Construction of Escherichia coli Strains for Conversion of Nitroacetophenones to ortho-Aminophenols

ABSTRACT The predominant bacterial pathway for nitrobenzene (NB) degradation uses an NB nitroreductase and hydroxylaminobenzene (HAB) mutase to form the ring-fission substrate ortho-aminophenol. We tested the hypothesis that constructed strains might accumulate the aminophenols from nitroacetophenones and other nitroaromatic compounds. We constructed a recombinant plasmid carrying NB nitroreductase (nbzA) and HAB mutase A (habA) genes, both from Pseudomonas pseudoalcaligenes JS45, and expressed the enzymes in Escherichia coli JS995. IPTG (isopropyl-β-d-thiogalactopyranoside)-induced cells of strain JS995 rapidly and stoichiometrically converted NB to 2-aminophenol, 2-nitroacetophenone (2NAP) to 2-amino-3-hydroxyacetophenone (2AHAP), and 3-nitroacetophenone (3NAP) to 3-amino-2-hydroxyacetophenone (3AHAP). We constructed another recombinant plasmid containing the nitroreductase gene (nfs1) from Enterobacter cloacae and habA from strain JS45 and expressed the enzymes in E. coli JS996. Strain JS996 converted NB to 2-aminophenol, 2-nitrotoluene to 2-amino-3-methylphenol, 3-nitrotoluene to 2-amino-4-methylphenol, 4-nitrobiphenyl ether to 4-amino-5-phenoxyphenol, and 1-nitronaphthalene to 2-amino-1-naphthol. In larger-scale biotransformations catalyzed by strain JS995, 75% of the 2NAP transformed was converted to 2AHAP, whereas 3AHAP was produced stoichiometrically from 3NAP. The final yields of the aminophenols after extraction and recovery were >64%. The biocatalytic synthesis of ortho-aminophenols from nitroacetophenones suggests that strain JS995 may be useful in the biocatalytic production of a variety of substituted ortho-aminophenols from the corresponding nitroaromatic compounds.
On Fault-Tolerant Microarchitectural Techniques for Voltage Underscaling in On-Chip Memories of CNN Accelerators

Aggressively underscaling the supply voltage $(V_{dd})$ below the safe voltage $(V_{min})$ margin is an effective solution to attain substantial energy savings. Unfortunately, operating at such low voltages is challenging due to the high number of permanent faults as a result of variations in the manufacturing process of current technology nodes. This work characterizes the impact of permanent faults on the accuracy of a Convolutional Neural Network (CNN) inference accelerator with on-chip activation memories supplied at low $V_{dd}$ below $V_{min}$. Based on these observations, this paper proposes a couple of low-cost microarchitectural techniques, referred to as flipping and patching, that ensure the accuracy of CNN applications despite the presence of permanent faults. Contrary to prior work, the proposed techniques are transparent to the programmer and do not depend on application characteristics. Experimental results show that the proposed techniques maintain the original CNN accuracy with a minimal impact on system performance (less than 0.05%), while reducing the energy consumption of activation memories by 11.2% and 46.7% compared to those of a conventional accelerator operating at safe and nominal supply voltages, respectively.
Aerosol Properties Observed in the Subtropical North Pacific Boundary Layer

The impact of anthropogenic aerosol on climate forcing remains uncertain largely due to inadequate representation of natural aerosols in climate models. The marine boundary layer (MBL) might serve as a model location to study natural aerosol processes. Yet source and sink mechanisms controlling the MBL aerosol number, size distribution, chemical composition, and hygroscopic properties remain poorly constrained. Here aerosol size distribution and water uptake measurements were made aboard the R/V Hi'ialakai from 27 June to 3 July 2016 in the subtropical North Pacific Ocean. Size distributions were predominantly bimodal with an average integrated number concentration of 197 ± 98 cm−3. Hygroscopic growth factors were measured using the tandem differential mobility analyzer technique for dry 48, 96, and 144 nm particles. Mode kappa values for these were 0.57 ± 0.12, 0.51 ± 0.09, and 0.52 ± 0.08, respectively. To better understand remote MBL aerosol sources, a new algorithm was developed which decomposes hygroscopicity distributions into three classes: carbon‐containing particles, sulfate‐like particles, and sodium‐containing particles. Results from this algorithm showed low and steady sodium‐containing particle concentrations while the sulfate‐like and carbon‐containing particle concentrations varied during the cruise. According to the classification scheme, carbon‐containing particles contributed at least 3–7%, sulfate‐like particles contributed at most 77–88% and sodium‐containing particles at least contributed 9–16% to the total aerosol number concentration. Size distribution and hygroscopicity data, in conjunction with air mass back trajectory analysis, suggested that the aerosol budget in the subtropical North Pacific MBL may be controlled by aerosol entrainment from the free troposphere.
LiDAR-Equipped Speed Bump Alert System: Pioneering Safety Innovation

Speed breakers in India are usually ambiguous, with no adequate indication or sign boards to signify them. Drivers are distracted by the frequent deployment of speed breakers on domestic routes. Furthermore, drivers frequently fail to recognize the sight of unsigned speed breakers and lose control of their vehicles, resulting in major accidents and fatalities. There are a few approaches in the literature to alert on-road drivers of oncoming speed breakers, but they are prone to error and demand time. Furthermore, none focus on tracking the information of violating speed breakers. In this research, we propose a system for automatic dynamic speed breaker detection and warning generation for drivers. The suggested method incorporates early detection of speed breakers utilizing a TF-Mini Micro LiDAR sensor to alert vehicles to the presence of speed breakers on rural and urban roadways. A LiDAR sensor is used to measure distance in the most effective way possible. The sensor detects and measures distance using light beams. Using sensors resulted in a high degree of precision in detecting road obstructions. Other unknown obstacles on the road may also frighten the driver. The appearance of speed breakers is brought to the driver's attention so that accidents can be reduced on a much larger scale. The automobile can be protected against harm by informing the driver of the speed breakers.
A Fully Integrated Hybrid Power Management Unit for Passive UHF RFID in 130-nm Process

This paper presents a fully integrated hybrid power management unit (HPMU) realized in the 130-nm complementary metal-oxide-semiconductor. The design improves the performance of RFID tags—especially those with passively powered sensors—by intelligently managing harvested power. The HPMU opportunistically takes advantage of the excess harvested power beyond the operational requirements of a tag and stores this energy externally. Through smart power routing, this stored energy is used to sustain the supply voltage of tag circuitry. This approach can reduce cold start-up time, mitigate consequences of brownouts and, effectively, extend the operational range and the responsiveness of tags—especially those with passively powered sensors. In addition, the HPMU switches off when the harvested power, indicative of a weak interrogating signal, is too low to conserve stored power. The entire HPMU has been optimized for low power consumption, which not only reduces the power overhead that HPMU introduces, but also ensures that as much power as possible is siphoned and stored rather than dissipated in the power management circuitry. In off state, the HPMU consumes only around 10 nA from the external storage element and during charge storage mode of operation, the HPMU has a peak conversion efficiency of approximately 65%.
Optical Distributions in Dual-Core Surface Plasmon Resonance Sensor with Dual Coating of Metal and Metal Oxide Suitable for Sensing Applications

In this paper, a dual core photonic crystal fiber (PCF) sensor has been designed in the near infrared region around 1.55 µm to 1.85 µm. The preliminary result of optical distributions such as core mode, plasmonic mode, resonance condition and dispersion relations of core mode and SPP mode of refractive index 1.36 is studied. Silver (Ag) is taken as the plasmonic material which is deposited outside the fiber structure to detect changes of the surrounding medium’s refractive index.
Rotating Object Detection for Aerial Images Based on Improved YOLOv5

For the problem of low accuracy and ignoring target directionality in aerial object detection, a rotating object detection algorithm for aerial images based on improved YOLOv5s is proposed. Firstly, angles are processed using CSL to transform angle regression prediction into angle classification prediction, which solves the periodicity of angular and the exchangeability of edges, and improves detection accuracy. Secondly, the ability of the model to extract important features is enhanced by fusing an attention mechanism module. Finally, the NWD loss function is introduced to optimize the CIoU, mitigating the sensitivity of the IoU to small object position deviations and improving the robustness and generalization of the model. The experiment uses the DOTA dataset released by Wuhan University, and the result show that the improved YOLOv5 network model improved the mAP from 72.57% to 76.60 compared to the original network, demonstrating better performance in aerial images object detection by the proposed algorithm.
Nearest Neighbor Based Unsupervised Deep Learning Image Recognition Method

Although deep learning are commonly employed for image recognition, usually huge amount of labeled training data is required, which may not always be readily available. This leads to a noticeable performance disparity when compared to state-of-the-art unsupervised face verification techniques. In this work, we propose a method to narrow this gap by leveraging an autoencoder to convert the face image vector into a novel representation. Notably, the autoencoder is trained to reconstruct neighboring face image vectors rather than the original input image vectors. These neighbor face image vectors are chosen through an unsupervised process based on the highest cosine scores with the training face image vectors. The proposed method achieves a relative improvement of 56% in terms of EER over the baseline system on Labeled Faces in the Wild (LFW) dataset. This has successfully narrowed down the performance gap between cosine and PLDA scoring systems.