Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning with approximate computing: an energy eﬃcient approach

Faster and energy-eﬃcient deep learning implementations will certainly beneﬁt various application domains, especially those deployed in systems with energy and payload limitations, such as aerial and space devices. Under this premise, low-precision formats have proven to be an eﬃcient way to reduce not only the memory footprint but also the hardware resources and power consumption of deep learning computations. For this purpose, the posit numerical format stands out as a highly viable substitute for the IEEE ﬂoating-point, but its application to neural networks training still requires further research. Some preliminary results have shown that 8-bit (and even smaller) posits may be used for inference and 16-bit for training while maintaining the model accuracy. The presented work aims to evaluate the feasibility to train deep convolutional neural networks using posits, focusing on precisions less than or equal to 16 bits. For such purpose, a software framework was developed to use simulated posits and quires in end-to-end deep learning training and inference. This implementation allowed to train and test deep learning models using any bit size, conﬁguration, and even mixed-precision, suitable for diﬀerent precision requirements in various stages. Additionally, a variation of the posit format able to underﬂow was also evaluated for low-precision posits. The obtained results suggest that 8-bit posits can replace 32-bit ﬂoats in a mixed low-precision posit conﬁguration for the training phase, with no negative impact on the resulting accuracy. Moreover, enabling posits to underﬂow increased their testing accuracy for very low precisions.
Clinical Dashboard for Hospital Quality Indicator Assessing

This work presents the development of a dashboard for the generation of health quality indicators related to a discharge note. The dashboard’s intuitive interface displays the different dimensions related to the mandatory Quality Criteria presented in the Lettre de Liaison (discharge letter), as required by the Haute Autorité de Santé (HAS), the French national authority for the assessment of health quality indicators. The Centre Hospitalier Universitaire Grenoble Alpes (CHUGA), where the dashboard was developed, is a healthcare institution in France that aims to improve and automate the calculation of the percentages of the mandatory Quality Criteria in the discharge letter, related to internal medicine, surgery and obstetrics activities (Médecine Chirurgie Obstétrique (MCO) activities). The developed dashboard provides the general panorama on the quality of the discharge note. An evaluation of the usability showed good results, with CHUGA demonstrating an interest in integrating it in its information quality system. Keywords[Health Quality Indicators, Information Dashboards, Data Analytic, Clinical Engineering]
Asymmetric New Cryptography Algorithm Based on ASCII Code

: Encryption is a process of generating secret text from the input text using a secret key and a encryption algorithm. Input text is referred to as plain text and the secret text generated is known as cipher text. Encryption algorithms are mainly divided into two categories which are symmetric key encryption algorithm and asymmetric key encryption algorithm. In Symmetric key encryption algorithm the same key is used by both sender and receiver but in asymmetric key algorithms use different keys for encryption and decryption. The keys are: Private Key and Public Key. The encryption key is public, decryption key is secret. Anyone can encrypt a message but only the one who knows the corresponding private key can decrypt it. The decryption key cannot be derived from the encryption key. Asymmetric key algorithms used for transmitting encryption keys or other data securely even when the parties have no opportunity to agree on a secret key in private. In this paper, we present a technique based on asymmetric key encryption algorithm which uses ASCII vales of input text to encrypt the data. Text data encryption techniques are very useful in data communication where one user want to send some secret messages to another users.
Voltage-clamped forward quasi-resonant converter with soft switching and reduced switch stress

An auxiliary voltage-clamped reset winding is introduced into a conventional forward quasi-resonant converter (QRC) for improving the common defect of huge voltages across resonant switches. In this circuit topology, the parasitic capacitance of the switch is used with the magnetising inductance of the transformer for the resonant action. Moreover, the auxiliary winding in this circuit is utilised for resetting the magnetic flux of the transformer and clamping the peak resonant voltage of the switch. In addition, experimental results taken from a laboratory prototype with input voltage of 90 V and output voltage of 50 V are presented to demonstrate the effectiveness of the proposed voltage-clamped forward QRC in comparison with a conventional forward QRC.
Number of Sunspot Groups and Individual Sunspots Recorded by Tevel for the Period 1816–1836 in the Dalton Minimum

Cornelis Tevel made sunspot observations during the period 1816–1836, including the Dalton Minimum. In this work, the first revision of these observations since Wolf incorporated them into his database is presented. On the one hand, the number of individual sunspots from Tevel’s drawings was counted. This is of special interest for the sunspot number reconstruction because this kind of information is not as common in historical sunspot records as the number of groups. Thus, Tevel could be considered for the future reconstruction of the sunspot number index. On the other hand, the number of groups counted according to modern sunspot group classifications finding significant misinterpretations with the number of groups assigned to Tevel in the existing databases. Tevel was a relevant sunspot observer in the Dalton Minimum. In fact, he was the observer with the highest number of groups observed in Solar Cycles 6 and 7 according to the existing sunspot group number databases. According to the raw group number recount in this work, the maximum amplitudes for Solar Cycles 6 and 7 are, respectively, 27% and 7% lower than those previously determined. Moreover, Solar Cycle 6 is the weakest solar cycle since the Maunder Minimum after applying these new counts. Group counts from Tevel’s observations were compared with those from relevant contemporary astronomers, demonstrating that Schwabe and Tevel systematically recorded a higher number of groups than Flaugergues and Derfflinger. In addition, sunspot areas and positions recorded by Tevel should be used with caution for scientific purposes.
MTMSN: Multi-Task and Multi-Modal Sequence Network for Facial Action Unit and Expression Recognition

Facial action unit (AU) and basic expression recognition are two basic tasks in the area of human affective behavior analysis. Most of the existing methods are developed in restricted scenarios which are not practical for in-the-wild settings. The Affective Behavior Analysis in-the-wild (ABAW) 2021 Contest provides a benchmark for this in-the-wild problem.In this paper, we propose a multi-task and multi-modal sequence network (MTMSN) to mine the relationships between the above two different tasks and effectively utilize both visual and audio information of the video. We use both AU and expression annotations to train the model and apply a sequence model to further extract associations between video frames. We achieve an AU score of 0.7508 and an expression score of 0.7574 on the validation set.
Appendix: Visual Distant Supervision for Scene Graph Generation

Require: Λ: commonsense knowledge base Require: DS : distantly labeled image data Require: f(·; θ): any scene graph model, with parameter θ Optional: Φ: external semantic signal 1: Randomly initialize the model parameter θ 2: // Initial E step: estimate the probabilistic distant labels 3: Obtain distant labels d = Ψ(s, o,Λ) 4: if external signal Φ available then 5: Initialize r = e 6: else 7: Initialize r = d 8: end if 9: // Initial M step: optimize model parameter 10: Optimize θ = arg maxθ L(D S ; θ) 11: while not done do 12: // E step: estimate the probabilistic distant labels 13: if external signal Φ available then 14: Estimate ri = ωfi(s, o; θ t−1) + (1− ω)ei 15: else 16: Estimate ri = fi(s, o; θ t−1) 17: end if 18: Eliminate noisy object pairs 19: // M step: optimize model parameter 20: Optimize θ = arg maxθ Lp(D S ; θt−1) 21: end while
Clinical Nutrition of CCl 4 Induced Hepatointoxicated Male Albino Rats by Curry Leaves , Turmeric and Their Mix Hayfa

This investigation aimed to evaluate the effect of turmeric, curry leaves and mix diets on hepatointoxicated rats injected with CCl4. Thirty (30) adult male Sprague Dawley rats were divided into five groups. Group (1): Normal rats fed on basal diet as control negative (C-), Group (2): Control positive (C+) (untreated group). Group (3): Hepatointoxicated rats injected with CCl4 fed on basal diet and turmeric (4%). Group (4) Hepatointoxicated rats injected with CCl4 fed on basal diet and curry leaves (4%). Group (5): Hepatointoxicated rats injected with CCl4 fed mix diets (4%). At the end of experiment, after 28 days of feeding, all serum samples were analyzed for biochemical parameters. Injection with CCl4 caused significant decreases in BWG, FI, FER, TP, Alb, Glb, HDL & antioxidant enzymes with significant increases recorded also in organs weight, TC, TG, VLDL, LDL, U.A, creatinine, urea, GOT, GPT, ALP, glucose, total bilirubin, direct bilirubin (D.B) and indirect bilirubin (I.B). Rats treated with various (turmeric, curry) diets, showed the improvement in all previous parameters.
Detection of Malicious URLs Using Classification Algorithms

: Malicious URL, a.k. a. malicious website, is a common and serious threat to cyber security. Malicious URLs host unsolicited content (spam, phishing, drive-by downloads, etc.) and lure unsuspecting users to become victims of scams (monetary loss, theft of private information, and malware installation), and cause losses of billions of dollars every year. It is imperative to detect and act on such threats in a timely manner. Traditionally, this detection is done mostly through the usage of blacklists. However, blacklists cannot be exhaustive, and lack the ability to detect newly generated malicious URLs. To improve the generality of malicious URL detectors, machine learning techniques have been explored with increasing attention in recent years. This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning. We present the formal formulation of Malicious URL Detection as a machine learning task, and categorize and review the contributions of literature studies that addresses different dimensions of this problem (feature representation, algorithm design, etc.). Further, this article provides a timely and comprehensive survey for a range of different audiences, not only for machine learning researchers and engineers in academia, but also for professionals and practitioners in cyber security industry, to help them understand the state of the art and facilitate their own research and practical applications. We also discuss practical issues in system design, open research challenges, and point out important directions for future research.
Common and Disorders-Specific Cortical Thickness Alterations in Internalizing, Externalizing and Thought Disorders over a 2-year Period in the Preadolescents of the ABCD Study

Common and Disorders-Specific Cortical Thickness Alterations in Internalizing, Externalizing and Thought Disorders over a 2-year Period in the Preadolescents of the ABCD Study Gechang Yu, Xinran Wu, Zhaowen Liu, Benjamin Becker, Kai Zhang, Nanyu Kuang, Jujiao Kang, Guiying Dong, Xing-Ming Zhao, Gunter Schumann, Jianfeng Feng, Barbara J. Sahakian, Trevor W. Robbins, Lena Palaniyappan, Jie Zhang Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, PR China Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence, Fudan University, Ministry of Education, PR China Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine,
Insights on the Molecular Behavior of Polypropylene in the Process of Ultrasonic Injection Molding

Product miniaturization is a constant trend in industries that demand ever-smaller products that can be mass produced while maintaining high precision dimensions in the final pieces. Ultrasonic micro injection molding (UMIM) technology has emerged as a polymer processing technique capable of achieving the mass production of polymeric parts with micro-features, while still assuring replicability, repeatability, and high precision, contrary to the capabilities of conventional processing technologies of polymers. In this study, it is shown that the variation of parameters during the UMIM process, such as the amplitude of the ultrasound waves and the processing time, lead to significant modification on the molecular structure of the polymer. The variation of both the amplitude and processing time contribute to chain scission; however, the processing time is a more relevant factor for this effect as it is capable of achieving a greater chain scission in different areas of the same specimen. Further, the presence of polymorphism within the samples produced by UMIM is demonstrated. Similarly to conventional processes, the UMIM technique leads to some degree of chain orientation, despite the fact that it is carried out in a relatively small time and space. The results presented here aim to contribute to the optimization of the use of the UMIM process for the manufacture of polymeric micro parts.
Sheep-Dips as a Source of Pollution of Freshwaters – A Study in Grampian Region

The results of organochlorine insecticide analysis of samples taken during 1984–85 from the River Ugie and its tributaries in Grampian Region showed, by the timing and the nature of the compounds found, that sheep-dips were likely sources of pollution of freshwaters in the Region. Subsequent investigation of freshwaters and treated waters for organophosphorus insecticides typical of sheep-dips supports this hypothesis. However, there is no evidence of serious contamination of freshwaters in the Region by phenolic compounds from sheep-dips, even though these are present at high concentrations in sheep-dip preparations.
Multi-Task Ofﬂine Reinforcement Learning with Conservative Data Sharing

Many recent ofﬂine RL algorithms attain both good empirical performance and enjoy theoretical guarantees, but their applicability is limited to settings where data is collected for only solving a single task. However, a natural use case of such methods is in settings where we can pool large amounts of data collected in a number of different scenarios for solving various tasks, and utilize all this data to learn strategies for all the tasks more effectively rather than training each one in isolation. To this end, we study the ofﬂine multi-task RL problem, with the goal of devising data-sharing strategies for effectively learning behaviors across all of the tasks. While it is possible to share all data across all tasks which is expected to improved performance due to better handling of sampling error, we ﬁnd that this simple strategy can actually exacerbate the distributional shift be-tween the learned policy and the dataset, which in turn can lead to very poor performance. We characterize the tradeoff and devise a simple technique for data-sharing in multi-task ofﬂine RL, conservative data sharing (CDS) that we theoretically analyze. Empirically, CDS attains outperforms prior methods on challenging problems including locomotion, maze navigation and real-world robotic manipulation domains.
MRGAN: Multi-Rooted 3D Shape Representation Learning with Unsupervised Part Disentanglement

We introduce MRGAN, multi-rooted GAN, the first generative adversarial network to learn a part-disentangled 3D shape representation without any part supervision. The network fuses multiple branches of tree-structured graph convolution layers which produce point clouds in a controllable manner. Specifically, each branch learns to grow a different shape part, offering control over the shape generation at the part level. Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind. Of these, a novel convexity loss incentivizes the generation of parts that are more convex, as semantic parts tend to be. In addition, a root-dropping loss further ensures that each root seeds a single part, preventing the degeneration or over-growth of the point-producing branches. We evaluate the performance of our network on a number of 3D shape classes, and offer qualitative and quantitative comparisons to previous works and baseline approaches. We demonstrate the controllability offered by our part-disentangled representation through two applications for shape modeling: part mixing and individual part variation, without receiving segmented shapes as input.
Controlling the PVTOL Aircraft System with an Inverted Pendular Load by means of Nested Saturation Functions and GPI Controller

This paper presents a control scheme that allows height position regulation and stabilization for an unmanned planar vertical takeoff and landing aircraft system with an inverted pendular load. The proposed controller consists of nested saturations and a generalized proportional integral (GPI). The GPI controls the aircraft height and the roll attitude; the latter is used as the fictitious input control. Next, the system is reduced through linear transformations, expressing it as an integrator chain with a nonlinear perturbation. Finally, the nested saturation function-based controller stabilizes the aircraft’s horizontal position and the pendulum’s angle. Obtaining the control approach was a challenging task due to the underactuated nature of the aircraft, particularly ensuring the pendulum’s upright position. The stability analysis was based on the second method of Lyapunov using a simple candidate function. The numerical simulation confirmed the control strategy’s effectiveness and performance. Additionally, the numerical simulation included a comparison against a PD controller, where its corresponding performance indexes were estimated, revealing that our controller had a better response in the presence of unknown disturbances.
Requirements for integrated flight and traffic management during final approach

It is pointed out that significant runway throughput is lost due to ineffective control of aircraft spacing during final approach. The solution is to integrate the terminal air traffic management (ATM) automation process with cockpit avionics via data link. Components of the ultimate integrated TM-cockpit design might include an ATM spacing algorithm that accounts for wake vortex and runway occupancy spacing constraints; downlink of desired final approach speed profile; uplink of desired spacing and threshold crossing times; a heads-up traffic display to provide pilot spacing guidance; a flight management system to drive the aircraft to meet timing requirements; and an ATM display to allow close monitoring of the landing process. The research required to develop this integrated ATM-cockpit concept is presented. <<ETX>>
Efficient Implementation of Attention with Relative Positional Encoding

for their constructive comments. This work was funded in part by a research contract from Galen Robotics and in part by Johns Hopkins University internal funds. Disclosure. Under a license agreement between Galen Robotics, Inc. and the Johns Hopkins University, Dr. Russell H. Taylor and the University are entitled to royalty distributions on technology related to technology described in the study discussed in this publication. Dr. Russell H. Taylor also is a paid consultant to and owns equity in Galen Robotics, Inc. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict-of-interest policies.
Licensed under Creative Commons Attribution Cc by the Survey Paper on Asp2p: an Advanced Botnet Based on Social Networks over Hybrid P2p

Botnets, regarded as the worms with fundamental command and control (C&C) channel, are becoming the typically frustrating threats to the Internet infrastructure. In order to defeat the botnets effectively, we should not only focus on known botnets, but also need to conduct some researches on the potential advanced botnets that could be developed by the adversary in the future. By investigating the vital C&C mechanism deeply, we present an advanced social peer-to-peer (ASP2P) botnet practically that combines the advantages of social networks and hybrid P2P structure in the way that bots receive encrypted commands from the social networks via HTTP and the C&C topology is hybrid peer-to-peer structure. ASP2P botnet is feasible and showed to be more covert and robust that it can be hardly detected and shut down compared with other P2P botnets.
The surface/bulk micromachining (SBM) process: a new method for fabricating released MEMS in single crystal silicon

This paper presents the surface/bulk micromachining (SBM) process to allow fabricating released microelectromechanical systems using bulk silicon. The process starts with a [111]-oriented silicon wafer. The structural patterns are defined using the reactive ion etching technique used in surface micromachining. Then the patterns, as well as sidewalls, are passivated with an oxide film, and bare silicon is exposed at desired areas. The exposed bare silicon is further reactive ion etched, which defines sacrificial gap dimensions. The final release is accomplished by undercutting the exposed bulk silicon sidewalls in aqueous alkaline etchants. Because {111} planes are used as etch stops, very clean structural surfaces can be obtained. Using the SBM process, 5-, 10-, and 100-/spl mu/m-thick arbitrarily-shaped single crystal silicon structures, including comb-drive resonators, at 5-, 30-, and 100-/spl mu/m sacrificial gaps, respectively, are fabricated. An electrostatic actuation method using p-n junction isolation is also developed in this paper, and it is applied to actuate comb-drive resonators. The leakage current and junction capacitance of the reversed-biased p-n junction diodes are also found to be sufficiently small for sensor applications. The developed SBM process is a plausible alternative to the existing micromachining methods in fabricating microsensors and microactuators, with the advantage of using single crystal silicon.
Embedded wireless strain sensors based on printed RFID tag

Purpose – The purpose of this paper is to develop a wireless strain sensor for measuring large strains. The sensor is based on passive ultra high‐frequency radio frequency identification (RFID) technology and it can be embedded into a variety of structures.Design/methodology/approach – Silver ink conductors and RFID tags were printed by the screen printing method on stretchable polyvinyl chloride and fabric substrates. The development of the strain‐sensitive RFID tag was based on the behavior of the selected antenna and substrate materials. Performance of the tags and the effect of mechanical strain on tag functioning were examined.Findings – The results showed that large displacements can be successfully measured wirelessly using a stretchable RFID tag as a strain‐sensitive structure. The behavior of the tag can be modified by selection of the material.Research limitations/implications – New tag designs, which are more sensitive to small levels of strain and which have a linear response will be the subje.