Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk aversion in learning algorithms and recommendation systems

Consider online learning algorithms that simultaneously make decisions and learn from feedback. Such algorithms are widely deployed in recommendation systems for products and digital content. This article exhibits a bias of online learning algorithms towards less risky alternatives, and how it shapes demand on recommendation systems. First, we consider k -armed bandits. We prove that ε -Greedy chooses a riskless arm over a risky arm of equal expected reward with probability arbitrarily close to one. This is a consequence of undersampling of arms with bad reward estimates. Through experiments, we show that other online learning algorithms exhibit risk aversion as well. In a recommendation system environment we show that content that yields less noisy reward from users is favored by the algorithm. Combined with equilibrium forces driving strategic content creators towards content of similar expected quality, the advantage for content that is not necessarily better, just less volatile, is exaggerated.
Just-in-time Compilation in Vectorized Query Execution

Database management systems face high interpretation overhead when supporting flexible declarative query languages such as SQL. One approach to solving this problem is to on-thefly generate and compile specialized programs for evaluating each query. Another, used by the VectorWise DBMS, is to process data in bigger blocks at once, which allows to amortize interpretation overhead over many database records and enables modern CPUs to e.g. use SIMD instructions. Both of these approaches provide an order of magnitude performance improvement over traditional DBMSes. In this master thesis we ask the question whether combining these two techniques can yield yet significantly better results than using each of them alone. Using a proof of concept system we perform micro benchmarks and identify cases where compilation should be combined with block-wise query execution. We implement a JIT query execution infrastructure in VectorWise DBMS. Finally, using our implementation, we confirm our findings in benchmarks inside the VectorWise system.
CAHIER DU LAMSADE 316 Janvier 2012 Monitoring of the upstream part of a supply chain dedicated to the customized mass production with a revisited version of MRP

This article focuses on the monitoring of a supply chain dedicated to the mass production of strongly diversified products. Specifically, we are interested in the part of this chain that contributes to the production of a set of alternative modules assembled on a work station of one or several assembly lines, whose production levels are stable. The MRP approach is adopted for the monitoring of this chain. The distance between the production units leads to a mix between production to stock and production to order. In this article, we establish the relations that allow us to define, in a steady state, the quantities to produce that address the requirements of the Master Production Schedule and that are partially or completely random to limit the stockout risk to a very low predetermined level. We will distinguish two cases by accounting for, or not accounting for, problems that are related to quality.
Lake Erie, phosphorus, and microcystin: Is it really the farmer's fault?

The size of the harmful algal blooms in western Lake Erie is strongly correlated with agricultural phosphorus (P) loading from tributaries. Despite farmers' efforts to reduce sediment-bound P loadings and fertilize using current guidance, the media and public have singled them out as the culprit in Lake Erie re-eutrophication. In this paper, two farmer surveys were used to evaluate if farmers in the Lake Erie region follow P fertilizer recommendations, and we also review historic and current P management guidance provided by the scientific community and agricultural industry. The majority (56% to 80%) of farmers apply P fertilizers at or below the current fertility recommendations. Wholesale agronomic changes (e.g., no-tillage adoption, crop cultivar advances, and fertilizer application and formulation) have occurred since current fertilizer recommendations were developed. Although crop P uptake mechanisms have not changed, these agronomic changes have altered P cycling in soil and water. Based on these results, it is time that the scientific community and agricultural industry acknowledge that our current guidance may be contributing to eutrophication. We must ask whether or not we have (1) developed appropriate fertility guidance, (2) developed and recommended appropriate practices to protect water quality, (3) adequately considered “the law of unintended consequences” in conservation recommendations, and (4) focused too much on short-term economic outcomes while disregarding environmental quality. Improved understanding, reconsideration of traditional recommendations, and wider farmer adoption of the most effective practices are needed to develop a sustainable agricultural system in the Western Lake Erie Basin that produces needed commodities while preserving ecosystem integrity.
Smith-Waterman Protein Search with OpenCL on FPGA

The well-known Smith-Waterman (SW) algorithm is a high-sensitivity method for local alignments. Unfortunately, SW is expensive in terms of both execution time and memory usage, which makes it impractical in many scenarios. Previous research have shown that massive parallel architectures such as Graphic Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs) are able to mitigate the computational problems achieving impressive speedups. In this paper we have explored SW acceleration on a FPGA device by means of OpenCL. We efficiently exploit data and thread-level parallelism on an Altera Stratix V FPGA card reaching upto 39 GCUPS with less than 25 watt power requirements. Keywords—Bioinformatics, Smith-Waterman, FPGA, Altera, OpenCL.
Overcoming Exploration: Deep Reinforcement Learning for Continuous Navigation in Complex Environments from Temporal Logic Speciﬁcations

—Exploration is a fundamental challenge in Deep Reinforcement Learning (DRL) based model-free navigation control since typical exploration techniques for target-driven navigation tasks rely on noise or greedy policies, which are sensitive to the density of rewards. In practice, robots are always deployed in complex cluttered environments, containing dense obstacles and narrow passageways, raising natural spare rewards that are hard to be explored for training. Such a problem becomes even more serious when pre-deﬁned tasks are complex and have rich expressivity. In this paper, we focus on these two aspects and present a deep policy gradient algorithm for a task-guided robot with unknown dynamic systems deployed in a complex cluttered environment. Linear Temporal Logic (LTL) is applied to express a rich robotic speciﬁcation. To overcome the environmental challenge of exploration during training, we propose a novel path planning-guided reward scheme that is dense over the state space, and crucially, robust to the infeasibility of computed geometric paths due to the black-box dynamics. To facilitate LTL satisfaction, our approach decomposes the LTL mission into sub-tasks that are solved using distributed DRL, where the sub-tasks can be trained in parallel, using Deep Policy Gradient algorithms. Our framework is shown to signiﬁcantly improve performance (effectiveness, efﬁciency) and exploration of robots tasked with complex missions in large-scale complex environments. The Video demo can be found on YouTube Channel: https://youtu.be/YQRQ2-yMtIk.
in Computer

— Wireless Sensor Networks (WSNs) are always under the risk of security threats and vulnerabilities. This is because these networks are operated under less human intervention with self-organizing capabilities. Hence, there is always a thirst for novel security protocols and techniques to combat against the attacks. In this context, we propose a random keying technique based on the concepts of memetic theory as a counter measure against spoofing attacks and serves as an improvement over [1] by increasing the randomness in choosing the keys for communication. The simulation results prove that the proposed technique provides an effective way of combating against the spoofing attacks.
Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000

During the ultraprecise cutting of micro-structure surface with fast tool servo (FTS), the hysteresis of piezoelectric actuators (PEAs) are affected by dynamic exciting characteristics and real-time cutting force, which declines the servo accuracy and cutting performance. In this paper, for a multi-inputsingle-output (MISO) cutting system, a cross-coupling rate-dependent Prandtl-Ishlinskii (CRPI) model is proposed and identified for the dynamic hysteresis of PEAs under dynamic voltage excitation and external loads. A model reference adaptive control method is then presented to eliminate the positioning nonlinearity of PEAs. The hysteresis modeling accuracy is discussed and the adaptive controller is validated through experiments. INDEX TERMS Piezoelectric actuator, dynamic hysteresis model, cross-coupling, model reference adaptive control.
You Shall not Pass: the Zero-Gradient Problem in Predict and Optimize for Convex Optimization

Predict and optimize is an increasingly popular decision-making paradigm that employs machine learning to predict unknown parameters of optimization problems. Instead of minimizing the prediction error of the parameters, it trains predictive models using task performance as a loss function. In the convex optimization domain, predict and optimize has seen signiﬁcant progress due to recently developed methods for differentiating optimization problem solutions over the problem parameters. This paper identiﬁes a yet unnoticed drawback of this approach – the zero-gradient problem – and introduces a method to solve it. The suggested method is based on the mathematical properties of differential optimization and is veriﬁed using two real-world benchmarks.
Development of ER Brake and its Application to Passive Force Display

Force display systems are important in virtual reality and other applications. While conventional force displays are active systems with actuators and as such may become inherently dangerous, passive force displays are effective methods for assuring safety. In this paper, we developed a brake using electrorheological (ER) fluid, and showed a passive force display system using ER brakes with two degrees of freedom. We also discuss basic control experiments of the system.
Integral and Indexing based Feature Extraction Method for Human Detection and Tracking

The detection and tracking of the human being in Video surveillance is a vibrant research topic in computer vision. It replaces old traditional method of monitoring camera feed by the human being by automatically detect and tracking along with the understanding of human behavior. Surveillance systems must be self-directed to improve the performance and eliminate such operator errors. Ideally, an automated surveillance system should only require the objectives of an application, in which there are real-time understanding of activities. Human detection includes detecting humans in each frame of video. Either each tracking method requires an object detection mechanism in every frame or when the object first appears in the video. Human tracking is the method of detecting human over time on the camera feed. The fast and efficient camera, high-quality videos, low priced camera and increasing need for the automatic system for detection and tracking will create interest in human tracking algorithms.
A Real Time Collision Avoidance Algorithm for Mobile Robot based on Elastic Force

method for mobile robot to avoid obstacles with a real time system in an uncertain environment. The method deals with the problem of robot in driving from an initial position to a target position based on elastic force and potential field force. To avoid the obstacles, the robot has to modify the trajectory based on signal received from the sensor system in the sampling times. It was evident that with the combination of Modification Elastic strip and Pseudomedian filter to process the nonlinear data from sensor uncertainties in the data received from the sensor system can be reduced. The simulations and experiments of these methods were carried out.
Software Engineering for Machine-Learning Applications: The Road Ahead

The First Symposium on Software Engineering for Machine Learning Applications (SEMLA) aimed to create a space in which machine learning (ML) and software engineering (SE) experts could come together to discuss challenges, new insights, and practical ideas regarding the engineering of ML and AI-based systems. Key challenges discussed included the accuracy of systems built using ML and AI models, the testing of those systems, industrial applications of AI, and the rift between the ML and SE communities. This article is part of a theme issue on software engineering’s 50th anniversary.
TED: A PRETRAINED UNSUPERVISED SUMMARIZA-

Text summarization aims to extract essential information from a piece of text and transform it into a concise version. Existing unsupervised abstractive summarization models use recurrent neural networks framework and ignore abundant unlabeled corpora resources. In order to address these issues, we propose TED, a transformer-based unsupervised summarization system with pretraining on largescale data. We first leverage the lead bias in news articles to pretrain the model on large-scale corpora. Then, we finetune TED on target domains through theme modeling and a denoising autoencoder to enhance the quality of summaries. Notably, TED outperforms all unsupervised abstractive baselines on NYT, CNN/DM and English Gigaword datasets with various document styles. Further analysis shows that the summaries generated by TED are abstractive and containing even higher proportions of novel tokens than those from supervised models.
Using Paired In Situ High Frequency Nitrate Measurements to Better Understand Controls on Nitrate Concentrations and Estimate Nitrification Rates in a Wastewater‐Impacted River

We used paired continuous nitrate ( NO3– ) measurements along a tidally affected river receiving wastewater discharge rich in ammonium ( NH4+ ) to quantify rates of change in NO3– concentration ( RΔNO3 ) and estimate nitrification rates. NO3– sensors were deployed 30 km apart in the Sacramento River, California (USA), with the upstream station located immediately above the regional wastewater treatment plant (WWTP). We used a travel time model to track water transit between the stations and estimated RΔNO3 every 15 min (October 2013 to September 2014). Changes in NO3– concentration were strongly related to water temperature. In the presence of wastewater, RΔNO3 was generally positive, ranging from about 7 µM d−1 in the summer to near zero in the winter. Numerous periods when the WWTP halted discharge allowed the RΔNO3 to be estimated under no‐effluent conditions and revealed that in the absence of effluent, net gains in NO3– were substantially lower but still positive in the summer and negative (net sink) in the winter. Nitrification rates of effluent‐derived NH4 ( RNitrific_E ) were estimated from the difference between RΔNO3 measured in the presence versus absence of effluent and ranged from 1.5 to 3.4 µM d−1, which is within literature values but tenfold greater than recently reported for this region. RNitrific_E was generally lower in winter (∼2 µM d−1) than summer (∼3 µM d−1). This in situ, high frequency approach provides advantages over traditional discrete sampling, incubation, and tracer methods and allows measurements to be made over broad areas for extended periods of time. Incorporating this approach into environmental monitoring programs can facilitate our ability to protect and manage aquatic systems.
WSNs Coverage Hole Partial Recovery by Nodes' Constrained and Autonomous Movements Using Virtual alpha-chords

Abstract—In WSNs, in order to recover from coverage holesand to mitigate their indirect/direct effects on networks’ perfor-mance, different recovery strategies such as increasing proximatenodes’ transmission range and/or relocation of nodes towardscoverage holes seem to be appropriate solutions. Since the major-ity of a mobile node’s energy is consumed by movement and sincenodes’ residual energy may be affected by damage events, nodemovements should be performed sparingly. Conventional nodes’information exchange in real-time applications with security andinterference concerns are neither practical nor secure. Therefore,for the aforementioned scenarios, at the price of possible node col-lisions, disconnections, and reasonable compromises, promisingdistributed and autonomous node movement algorithms based onlimited 1-hop neighbour knowledge are proposed. Our proposedautonomous and constrained node movement model based on anode’s 1-hop perception provides a feasible and rapid recoverymechanism for large scale coverage holes in real-time and harshenvironments. Our model not only maintains moving nodes’connectivity to the rest of network to some extent, but also offersemergent cooperative recovery behaviour among autonomousmoving nodes. Our movement model based on virtual chordsformed by nodes and their real and virtual 1-hop neighbours,not only conﬁnes node movement range, but also takes the issueof moving nodes’ connectivity into account. Suitable performancemetrics for partial recovery via constrained movement are intro-duced to compare the performance and efﬁciency of our modelwith conventional Voronoi-based movement algorithms. Resultsshow that our proposed model’s performance is comparable withVoronoi-based movement algorithms.Index Terms—Coverage holes; autonomous and constrainedmovements; Wireless sensor networks; virtual chord.
S WITCHING O NE -V ERSUS - THE -R EST L OSS TO I NCREASE L OGIT M ARGINS FOR A DVERSARIAL R OBUSTNESS

Adversarial training is a promising method to improve the robustness against adversarial attacks. To enhance its performance, recent methods impose high weights on the cross-entropy loss for important data points near the decision boundary. However, these importance-aware methods are vulnerable to sophisticated attacks, e.g., Auto-Attack. In this paper, we experimentally investigate the cause of their vulnerability via margins between logits for the true label and the other labels because they should be large enough to prevent the largest logit from being ﬂipped by the attacks. Our experiments reveal that the histogram of the logit margins of na¨ıve adversarial training has two peaks. Thus, the levels of difﬁculty in increasing logit margins are roughly divided into two: difﬁcult samples (small logit margins) and easy samples (large logit margins). On the other hand, only one peak near zero appears in the histogram of importance-aware methods, i.e., they reduce the logit margins of easy samples. To increase logit margins of difﬁcult samples without reducing those of easy samples, we propose switching one-versus-the-rest loss (SOVR), which switches from cross-entropy to one-versus-the-rest loss (OVR) for difﬁcult samples. We derive trajectories of logit margins for a simple problem and prove that OVR increases logit
Mobile Device Cloud Server Mobile App Cloud Daemon Memory Object Object Memory Compressive Offloading ( Replicas )

Mobile device hardware can limit the sophistication of mobile applications. One strategy for side-stepping these constraints is to opportunistically offload computations to the cloud, where more capable hardware can do the heavy lifting. We propose a platform that accomplishes this via compressive offloading, a novel application of compressive sensing in a distributed shared memory setting. Our prototype gives up to an order-of-magnitude acceleration and 60% longer battery life to the end user of an example handwriting recognition app. We argue that offloading is beneficial to both end users and cloud providers—the former experiences a performance boost and the latter receives a steady stream of small computations to fill periods of under-utilization. Such workloads, originating from ARM-based mobile devices, are especially well-suited for offloading to emerging ARM-based data centers.
Evaluation of Different Feature Extraction Techniques for Continuous Speech Recognition

Extracting human's voice feature is the most important process in any speech recognition system. There are many feature extraction techniques which are already used such as MFCC, LPC and ZCPA; but still have some problems especially in the continuous speech. It is important to evaluate different feature extraction techniques for continuous speech by making a comparison between these techniques as a trial to find the most suitable technique for speech recognition process, and trying to enhance the result by using PCA.
Bulletin of Electrical Engineering and Informatics

Received May 1, 2022 Revised Aug 26, 2022 Accepted Sep 24, 2022 Hardware problems are the most detrimental issues to channel estimates in wireless communication systems. Because of the enormous number of antennas at the base station (BS) in cellular massive multiple-input multipleoutput (MIMO) systems and because one radio frequency (RF) chain per antenna is required, hardware impairments in such systems will be quite severe. Many research publications have used a quality-cost tradeoff to adjust for RF unit hardware issues. In this study, we have taken a different approach by reducing the error floor caused by impairments in the predicted channels. Here are two steps to remedy the problem. In phase 1, a single active user channel in a single cell was calculated statistically rather than parametrically. In phase 2, a convex optimization approach was used to regularize the estimated channel in phase 1 to reduce error and provide a robust channel estimate. The results of our proposed procedure are measured by the normalized minimum mean squared error (NMSE) versus a range from the effective signal-to-noise ratio, and it shows a significant reduction (nearly one order of magnitude) in the error floor as compared with the conventional one, especially at high signal-to-noise ratio (SNR) in the range of (20 dB-30 dB). Simulation results were extracted in MATLAB R2020a.
Saturn's Dusty Ionosphere

Measurements of electrons and ions in Saturn's ionosphere down to 1,500‐km altitudes as well as the ring crossing region above the ionosphere obtained by the Langmuir probe onboard the Cassini spacecraft are presented. Five nearly identical deep ionosphere flybys during the Grand Finale orbits and the Final plunge orbit revealed a rapid increase in the plasma densities and discrepancies between the electrons and ions densities (Ne and Ni) near the closest approach. The small Ne/Ni ratio indicates the presence of a dusty plasma, a plasma which charge carrier is dominated by negatively charged heavy particles. Comparison of the Langmuir probe obtained density with the light ion density obtained by the Ion and Neutral Mass Spectrometer confirmed the presence of heavy ions. An unexpected positive floating potential of the probe was also observed when Ne/Ni ≪ 1. This suggests that Saturn's ionosphere near the density peak is in a dusty plasma state consisting of negatively and positively charged heavy cluster ions. The electron temperature (Te) characteristics in the ionosphere are also investigated and unexpectedly high electron temperature value, up to 5000 K, has been observed below 2,500‐km altitude in a region where electron‐neutral collisions should be prominent. A well‐defined relationship between Te and Ne/Ni ratio was found, implying that the electron heating at low altitudes is related to the dusty plasma state of the ionosphere.