Miracle-dz/UniFAR-FTUs huggingface dataset:
The Gut Microbiome on a Periodized Low-Protein Diet Is Associated With Improved Metabolic Health

A periodized (14 days on/14 days off) 5% low protein-high carbohydrate (pLPHC) diet protects against weight gain, improves glucose tolerance in mice and interacts with concurrent voluntary activity wheel training on several parameters including weight maintenance and liver FGF21 secretion. The gut microbiome (GM) responds to both diet and exercise and may influence host metabolism. This study compared the cecal GM after a 13.5-week intervention study in mice on a variety of dietary interventions ± concurrent voluntary exercise training in activity wheels. The diets included chronic chow diet, LPHC diet, 40 E% high protein-low carbohydrate (HPLC) diet, an obesigenic chronic high-fat diet (HFD) and the pLPHC diet. Our hypothesis was that the GM changes with pLPHC diet would generally reflect the improved metabolic health of the host and interact with concurrent exercise training. The GM analyses revealed greater abundance phylum Bacteroidetes and the genus Akkermansia on chronic and periodized LPHC and higher abundance of Oscillospira and Oscillibacter on HFD. The differences in diet-induced GM correlated strongly with the differences in a range of host metabolic health-measures. In contrast, no significant effect of concurrent exercise training was observed. In conclusion, pLPHC diet elicits substantial changes in the GM. In contrast, only subtle and non-significant effects of concurrent activity wheel exercise were observed. The pLPHC-associated microbiome may contribute to the healthier host phenotype observed in these mice.
Hybrid Control Application Using Mobile Visual Servoing for Flexible Manufacturing Mechatronics Line

The aim of this paper is to present a real time control application to ensure the reusing/reprocessing capability of the non-compliant parts in an Assembly/Disassembly Mechatronics Line (A/DML). The novelty of this paper consists in the use of a Mobile Visual Servoing Application in order to guarantee the correct handling of the reused/reprocessed parts. These parts are the result of the disassembly station and there are taken by a mobile robot equipped with a 7DOF manipulator without having any apriori information concerning the position and orientation of the parts. In order to perform this task, the Wheeled Mobile Robot (WMR) is synchronized with the A/DML. The manufacturing lines served by mobile robots have hybrid characteristics, consisting of continuous dynamic behaviors (mobile robots) and discrete event behaviors (A/DML). Real-time control application is designed and implemented allowing automated processing and reprocessing of a production volume. The solution is validated using the real time implementation of the designed architecture for the hybrid system consisting of the Assembly/Disassembly Mechatronics Line (A/DML), with fixed number of workstations, HERA Horstmann completed with a disassembly workstation, served by Wheeled Mobile Robot (WMR), People Bot equipped with a 7DOF Robotic Manipulator (RM) with mobile visual servoing system.
Forecasting without context problem

This work presents an analysis of four regression systems. Two of them are statistical: the widely used Auto-regressive Integrated Moving Average (ARIMA) and the state-of-the-art Facebook Prophet. From the deep learning school, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is also evaluated. We finish our quartet with a fine-tuned Nearest Neighbor model. The study is carried out over seventeen benchmarks; fifteen coming from M4-Competition and two more power systems time series, i.e., electricity demand and hydropower generation. For all the models, the regression systems are fitted and optimized to minimize user intervention. The results show that deep learning models obtained the best performance; nonetheless, the performance difference is not statistically significant with the rest of the systems tested.
Designing Behaviour Change Support Systems for Behaviour Transformation

Behaviour Change Support Systems (BCSSs) are not utilising conscious psychological behavioural models and instead focusing on non-conscious behaviour change. A well established model of behaviour change is the Transtheoretical Model (TTM) which combined with Motivational Interviewing (MI) can help transform a person’s behaviour. In this paper we outline how we can integrate the TTM and MI techniques within BCSSs with the aim of enacting sustained behaviour change.
HyperFuzzing for SoC Security Validation

Automated validation of security properties in modern systems-on-chip (SoC) designs is challenging due to three reasons: (i) specification of security in the presence of adversarial behavior, (ii) co-validation of hardware (HW) and firmware (FW) as security bugs may span the HW/FW interface, and (iii) scaling verification to the analysis of large systems-on-chip designs. In this paper, we address the above concerns via the development of a unified co-validation framework for SoC security property specification. On the specification side, we introduce a new logic, HyperPLTL (Hyper Past-time Linear Temporal Logic), that enables intuitive specification of SoC security concerns. On the validation side, we introduce a framework for mutational coverage-guided fuzzing of hyperproperties. The three novel aspects of our validation framework are a novel methodology for incorporating adversarial behavior through tamper functions, novel coverage-metrics that guide the fuzzer toward generating useful stimuli, and algorithms for efficient evaluation of hyperproperty satisfaction. Experiments on a small but realistic SoC show promising results.
Machine Learning for Smart Farming: A Focus on Desert Agriculture

Globally, farming is the great support of all economic growth. Three main developments such as demographics, scarcity of natural resources, climate changes are placing pressure on agriculture to meet the demands of the future. Smart farming achieves lucrative and supportable agriculture due to the advancement of the Internet of Things (IoT). Applications of machine learning techniques in the development of smart farming applications help the farmers to have access to farming information, financial and marketing services. This paper aims to address the supports provided by IoT and machine learning techniques in agriculture data collection, processing, and decision-making services for smart farming. Also, this paper addressed the general trends for applying smart farming techniques, its challenges and recommendations to cultivate differently in the desert areas of Saudi Arabia.
Nature Inspired Approach Integration in Cloud Environment for Performance Elevation with Internet of Things

The cloud computing based environment is now days moving towards the integration of enormous applications including Smart Water, Snow Level Monitoring, Soil Quality Measurement, Supply Chain Control, Telemedicine, Temperature Monitoring, Traffic Congestion and many others. In this research manuscript, the integration of Nature Inspired Approach (NIA) is done to integrate the higher degree of security and integrity in the cloud based environment with the simulation patterns for the effectual outcomes. The work is presenting the secured environment with the elevated factors towards the security and integrity in the cloud and Internet of Things (IoT) based implementations. All these needs higher phases of performance and the usage of nature inspired approaches with the soft computing provides the escalated levels of effectiveness.
A Regulated Pulse Current Driver with Spread Spectrum Technique

This paper proposes a regulated pulse current driver with spread spectrum technique to lower electromagnetic interference (EMI) effect. A spread spectrum clock generator (SSCG) is used and implemented with triangular wave bias. The results show a 7dBm reduction of the peak power level with a frequency deviation of 10%, demonstrating that the driver effectively reduces peak power and EMI.
NEST: DIMM based Near-Data-Processing Accelerator for K-mer Counting

With the ability to help wildlife conservation, precise medical care, and disease understanding, genomics analysis is becoming more and moe important. Recently, with the development and wide adoption of the Next-Generation Sequencing (NGS) technology, bio-data grows exponentially, putting forward great challenges for k-mer counting - a widely used application in genomics analysis. Many hardware approaches have been explored to accelerate k-mer counting. Most of those approaches are compute-centric, i.e., based on CPU/GPU/FPGA. However, the space for performance improvement is limited for compute-centric accelerators, because k-mer counting is a memory-bound application. By integrating memory and computation close together and embracing higher memory bandwidth, Near-Data-Processing (NDP) is a good candidate to accelerate k-mer counting. Unfortunately, due to challenges of communication, bandwidth utilization, workload balance, and redundant memory accesses, previous NDP accelerators for k-mer counting cannot fully unleash the power of NDP. To build a practical, scalable, high-performance, and energy-efficient NDP accelerator for k-mer counting, we perform hardware/software co-design and propose the DIMM based Near-Data-Processing Accelerator for k-mer counting (NEST). To fully unleash the potential of NEST architecture, we modify the k-mer counting algorithm and propose a dedicated workflow to support efficient parallelism. Moreover, the proposed algorithm and workflow are able to reduce unnecessary inter-DIMM communication. To improve memory bandwidth utilization, we propose a novel address mapping scheme. The challenge of workload balance is addressed with the proposed task scheduling technique. In addition, scattered memory access and task switching are proposed to eliminate redundant memory access. Experimental results show that NEST provides 677.33x/27.24x/6.02x performance improvement and 1076.14x/62.26x/4.30x energy reduction, compared with a 48-thread CPU, a CPU/GPU hybrid approach, and a state-of-the-art NDP accelerator, respectively.
Convolutional-Neural-Network-Based Partial Discharge Diagnosis for Power Transformer Using UHF Sensor

Given the enormous capital value of power transformers and their integral role in the electricity network, increasing attention has been given to diagnostic and monitoring tools as a safety precaution measure to evaluate the internal condition of transformers. This study overcomes the fault diagnosis problem of power transformers using an ultra high frequency drain valve sensor. A convolutional neural network (CNN) is proposed to classify six types of discharge defects in power transformers. The proposed model utilizes the phase–amplitude response from a phase-resolved partial discharge (PRPD) signal to reduce the input size. The performance of the proposed method is verified through PRPD experiments using artificial cells. The experimental results indicate that the classification performance of the proposed method is significantly better than those of conventional algorithms, such as linear and nonlinear support vector machines and feedforward neural networks, at 18.78%, 10.95%, and 8.76%, respectively. In addition, a comparison with the different representations of the data leads to the observation that the proposed CNN using a PA response provides a higher accuracy than that using sequence data at 1.46%.
Multi-view Vehicle Re-Identification Method Based on Siamese Convolutional Neural Network Structure

Vehicle re-identification is a popular issue in intelligent traffic research. A lot of proposed method achieve vehicle re-identification by recognizing their license plate, because of the uniqueness. However, license plate can be stolen and pinned to different vehicles by criminals to hide their identities. In addition, the license plate number might be covered by dirt or stain, even hided from different viewpoints, makes the character recognition result might be wrong or unrecognized. To get more robust re-identification result, not only the license plate should be considered, but also the appearance. In this paper, we adopt Siamese Convolutional Neural Network structure, take license plate and vehicle appearance as input, come up with a neural network for vehicle re-identification task. We validate our proposed method on VeRi-776 dataset, and proof that it can deal with vehicle re-identification task well, even under variant viewpoints scenarios.
Concurrent Weight Encoding-based Detection for Bit-Flip Attack on Neural Network Accelerators

The recent revealed Bit-Flip Attack (BFA) against deep neural networks (DNNs) is highly concerning, as it can completely mislead the inference of quantized DNNs by only flipping a few weight bits in hardware memories through manners like DRAM rowhammer. A key question before applying any BFA mitigation solutions, such as retraining or model reloading, is how to quickly and accurately detect such an attack without impacting the normal inference. In this paper, we propose a weight encoding-based framework to concurrently detect BFA by leveraging the spatial locality of bit flipping in BFA and a fast encoding of sensitive weights only. Extensive experimental results show that our method can accurately differentiate the malicious fault models under BFA and the random bit flipping that could also occur in weight memories but does not impact accuracy as that of BFA, with very low overhead across various DNNs on both CIFAR-10 and ImageNet datasets. To the best of our knowledge, this is the first real-time detection framework for BFA attack against quantized DNNs that are widely deployed in hardware accelerators.
Extremely Stable Dual Gate Coplanar Amorphous InGaZnO Thin Film Transistor With Split Active Layer by N2O Annealing

We report the effect of N<sub>2</sub>O post-fabrication annealing (PFA) on the electrical stability of dual gate, active split amorphous indium gallium zinc oxide (a-IGZO) thin-film transistor (TFT). Optimized N<sub>2</sub>O PFA at 360 °C for 3 h results in threshold voltage (V<sub>TH</sub>) close to zero and an increase in the on-current almost 2 times than as-fabricated TFT. N<sub>2</sub>O annealed TFT exhibited robust V<sub>TH</sub> stability under positive bias temperature stress at 60 °C and negative bias illumination stress, exhibiting <inline-formula> <tex-math notation="LaTeX">$\Delta \text{V}_{\text {TH}}$ </tex-math></inline-formula> of 0.1 V, and- 0.1 V, respectively, whereas as-fabricated TFT showed large <inline-formula> <tex-math notation="LaTeX">$\Delta \text{V}_{\text {TH}}$ </tex-math></inline-formula> of ~ 10 V and −3 V. The X-ray photoelectron spectroscopy depth profile reveals the origin of such improvement as the increasing metal-oxide bonds and the reduction in oxygen vacancy / OH related defects.
Short-Term Orchestral Music Training Modulates Hyperactivity and Inhibitory Control in School-Age Children: A Longitudinal Behavioural Study

Survey studies have shown that participating in music groups produces several benefits, such as discipline, cooperation and responsibility. Accordingly, recent longitudinal studies showed that orchestral music training has a positive impact on inhibitory control in school-age children. However, most of these studies examined long periods of training not always feasible for all families and institutions and focused on children’s measures ignoring the viewpoint of the teachers. Considering the crucial role of inhibitory control on hyperactivity, inattention and impulsivity, we wanted to explore if short orchestral music training would promote a reduction of these impulsive behaviors in children. This study involved 113 Italian children from 8 to 10 years of age. 55 of them attended 3 months of orchestral music training. The training included a 2-hour lesson per week at school and a final concert. The 58 children in the control group did not have any orchestral music training. All children were administered tests and questionnaires measuring inhibitory control and hyperactivity near the beginning and end of the 3-month training period. We also collected information regarding the levels of hyperactivity of the children as perceived by the teachers at both time points. Children in the music group showed a significant improvement in inhibitory control. Moreover, in the second measurement the control group showed an increase in self-reported hyperactivity that was not found in the group undergoing the music training program. This change was not noticed by the teachers, implying a discrepancy between self-reported and observed behavior at school. Our results suggest that even an intense and brief period of orchestral music training is sufficient to facilitate the development of inhibitory control by modulating the levels of self-reported hyperactivity. This research has implications for music pedagogy and education especially in children with high hyperactivity. Future investigations will test whether the findings can be extended to children diagnosed with ADHD.
The Impact of Chronic Heat Stress on the Growth, Survival, Feeding, and Differential Gene Expression in the Sea Urchin Strongylocentrotus intermedius

To explore the impact of chronic heat stress on commercial echinoderms, the present study assessed the effects of chronic high temperature on the growth, survival, feeding, and differential gene expression in the sea urchin Strongylocentrotus intermedius cultured in northern Yellow Sea in China. One suitable seawater condition (20°C) and one laboratory-controlled high temperature condition (25°C) were set up. After 28 days incubation, our results showed that: (1) The specific growth, survival, and ingestion rates of S. intermedius reared under high temperature (25°C) decreased compared to those reared under optimal temperature (20°C) conditions; (2) comparative transcriptome analysis identified 2,125 differentially expressed genes (DEGs) in S. intermedius reared under high temperature (25°C) compared to those subjected to optimal temperature condition (20°C), which included 1,015 upregulated and 1,100 downregulated genes. The accuracy of the transcriptome profiles was verified by quantitative real-time PCR (qRT-PCR). Further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses revealed that these DEGs mainly enriched the functional categories of ribosome, protein processing in endoplasmic reticulum, and prion diseases. A total of 732 temperature-induced expressed genes, such as ATP5, heat shock protein 70, and heat shock protein 90, were identified as candidates that were closely correlated with heat resistance in S. intermedius. Differentially expressed transcription factors (TFs), such as AP-1, Fos, CREB, and ZNF, were also identified as potential regulators that regulate the molecular network that was associated with responses to heat stress in sea urchins. Observations in the present study provide additional information that improves our understanding of the molecular mechanism of temperate echinoid species in response to heat stress, as well as theoretical basis for the molecular-assisted breeding of heat-resistant sea urchins.
Unlocking Wordline-level Parallelism for Fast Inference on RRAM-based DNN Accelerator

In-memory computing is rapidly rising as a viable solution that can effectively accelerate neural networks by overcoming the memory wall. Resistive RAM (RRAM) crossbar array is in the spotlight as a building block for DNN inference accelerators since it can perform a massive amount of dot products in memory in an area- and power-efficient manner. However, its in-memory computation is vulnerable to errors due to the non-ideality of RRAM cells. This error-prone nature of RRAM crossbar limits its wordline-level parallelism as activating a large number of wordlines accumulates non-zero current contributions from RRAM cells in the high-resistance state as well as current deviations from individual cells, leading to a significant accuracy drop. To improve performance by increasing the maximum number of concurrently activated wordlines, we propose two techniques. First, we introduce a lightweight scheme that effectively eliminates the current contributions from high-resistance state cells. Second, based on the observation that not all layers in a neural network model have the same error rates and impact on the inference accuracy, we propose to allow different layers to activate non-uniform numbers of wordlines concurrently. We also introduce a systematic methodology to determine the number of concurrently activated wordlines for each layer with a goal of optimizing performance, while minimizing the accuracy degradation. Our proposed techniques increase the inference throughput by 3-10× with a less than 1% accuracy drop over three datasets. Our evaluation also demonstrates that this benefit comes with a small cost of only 8.2% and 5.3% increase in area and power consumption, respectively.
Improved Differential Evolution Algorithm for Multi-Target Response Inversion Detected by a Portable Transient Electromagnetic Sensor

The superimposed response of multi-target causes difficulty in locating and characterizing each target when detecting unexploded ordnance with a portable transient electromagnetic sensor, constructed with a single-layer transmitting coil and five three-component receiving coils. Differential evolution (DE) algorithm is improved here with Gram–Schmidt orthogonalization and position rearrangement for superimposed response inversion based on the multisource model, which represents the multi-target response with a set of magnetic dipoles distributed over the interrogated area. The Gram–Schmidt orthogonalization turns the coefficient matrix of each target into an orthonormal basis. Accordingly, the best magnetic polarization tensor can be directly extracted from the superimposed response without inverting large and potentially ill-conditioned matrices. The position rearrangement groups the positions of individuals in the contemporary population to maximize the likelihood that the positions in the same group belong to the same target. The convergence speed of multi-target inversion is accelerated with the crossover operation of DE algorithm performed within the groups. Simulated experiment results show that the error in estimated position and characteristic response for improved DE algorithm is only 10% of that of the conventional DE algorithm. Field experiment is also conducted, and its results show that the error in estimated position for improved DE algorithm is only 20% of that of the conventional DE algorithm. The improved DE algorithm can accurately estimate the position and characteristic response of each target from superimposed response.
Knowledge representation of passages type TOEFL

Reading comprehension in the English language is a process that has been studied from different disciplines, due to the need for certification of the English language as a second language; this process is required in the qualification processes of students enrolled in a postgraduate course. Therefore, in this paper, a new model is proposed, based on the versatility of first order logic, situation calculus and semantic relationships to model the passages of this type of certification. The model is represented by a knowledge base, which considers the cognitive model of Van Dijk and Kinstch.