Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Thumb Engineering: Artificial intelligence for managing IoT enabled houseplants

Many hobbies and household activities can be carried out with little effort, and without deeper understanding of the phenomenon at hand. Managing houseplants and herbs is such a hobby; one can simply provide water for plants and hope they would prosper. However, some people seem to have a better luck in maintaining their plants, while many fail and claim they do not possess a “green thumb”. Fortunately, this does not require magic, but engineering. We use innovative Internet of Things and artificial intelligence technologies to monitor and analyze moisture, humidity, luminosity and temperature levels to assist end users for optimization of environmental conditions for their houseplants, simultaneously increasing their understanding of plant life. For plant health monitoring, we construct a system yielding the normalized difference vegetation index, supported by visual validation by users. We run the system for a selected plant, basil, in varying environmental conditions to cater for typical home conditions in pursue of bootstrapping our artificial intelligence with the acquired data. For end users, we implement a web based user interface which provides both instructions and explanations.
The relation between the moisture-release curve and the structure of soil

Summary The limit to the precision with which moisture-release data may be related to soil structure is examined. Thin sections in two orthogonal planes were prepared from cores for which moisture release data and saturated conductivity measurements were also collected. Structural parameters are inferred indirectly through estimation of the mass fractal dimension from the moisture-release curve, and directly through measurement of the scaling properties of the pore space and solid matrix from the thin sections using image analysis. Theoretical results are presented which show that, in the absence of additional information, the interpretation of the moisture-release curve is ambiguous for several reasons. A power-law exponent is a consequence of either a fractal pore volume; a fractal solid volume; a fractal pore wall; or a non-fractal, self-similar pore wall, and one cannot infer from the measurement which is the case. Experimental results are presented which confirm that direct measurement of the fractal dimension of the solid matrix is a good predictor of the Brooks–Corey exponent for the soils studied here. Therefore, although a specific structural parameter characterizes the moisture-release curve, the latter cannot be used as a detailed measure of soil structure.
Energy-optimal Braking Velocity Planning of Connected Electric Vehicle

To improve the regeneration energy of electric vehicle, an energy-optimal braking strategy is developed. First, the vehicle braking intention is accessed by using vehicle-to-everything communication, i.e., braking distance and terminal velocity. Then, an optimal control problem with consideration of braking intention is formulated for maximizing regeneration energy. The control problem is solved by distance-based dynamic programming algorithm to plan the energy-optimal braking velocity. Finally, the effectiveness of proposed strategy is evaluated by simulation. The results show the regeneration energy efficiency of proposed strategy achieves improvement is over 10% compared with the constant speed strategy. Furtherly, the energy-optimal braking suggestions is investigated based on several traffic scenarios, i.e., a larger braking force in a high-velocity range can reduce vehicle resistance and make full use of motor generation power; the braking force was adjusted in moderated-velocity range for reducing friction braking, and a larger braking force should be used for parking quickly.
Multi-task Control for a Quadruped Robot with Changeable Leg Configuration

This paper proposes a multi-task control strategy for a quadruped robot named THU-QUAD II. The mechanical design of the robot ensures a wide range of motion for all joints, which allows it to stand and walk like a mammal as well as sprawl to the ground and crawl like a reptile. Five basic leg configurations are defined for the robot, including four mammal-type configurations with bidirectional knees and one sprawling-type configuration. A multi-task control framework is developed by combining configuration selection and gait planning. According to the locomotion environments, the robot can nimbly switch between different configurations, which gives it more flexibility when facing different tasks. For the mammal-type configuration, a parametric climbing gait is designed to traverse structural terrain. For the sprawling-type configuration, a crawling gait is designed to achieve robust locomotion on uneven terrain. Simulations and experiments show that the robot is capable to move on multiple challenging terrains, including doorsills, stairs, slopes, sand and stones. This paper demonstrates that even some challenging locomotion tasks can be achieved in a rather simple way without using complicated control algorithms, which suggests us to rethink about the leg configurations in designing quadruped robots.
A Compact, Cable-driven, Activatable Soft Wrist with Six Degrees of Freedom for Assembly Tasks

Physical softness has been proposed to absorb impacts when establishing contact with a robot or its workpiece, to relax control requirements and improve performance in assembly and insertion tasks. Previous work has focused on special end effector solutions for isolated tasks, such as the peg-in-hole task. However, as many robot tasks require the precision of rigid robots, and their performance would degrade when simply adding compliance, it has been difficult to take advantage of physical softness in real applications. A wrist that could switch between soft and rigid modes could solve this problem, but actuators with sufficient strength for this state transition would increase the size and weight of the module and decrease the payload of the robot. To solve this problem, we propose a novel design of a soft module consisting of a cable-driven mechanism, which allows the robot end effector to change between soft and rigid mode while being very compact and light. The module effectively combines the advantages of soft and rigid robots, and can be retrofitted to existing robots and grippers while preserving the characteristics of the robotic system. We evaluate the effectiveness of our proposed design through experiments modeling assembly tasks, and investigate design parameters quantitatively.
A Semi-Supervised Learning Scheme to Detect Unknown DGA Domain Names based on Graph Analysis

A large amount of malware families use the domain generation algorithms (DGA) to randomly generate a large amount of domain names. It is a good way to bypass conventional blacklists of domain names, because we cannot predict which of the randomly generated domain names are selected for command and control (C&C) communications. An effective approach for detecting known DGA families is to investigate the malware with reverse engineering to find the adopted generation algorithms. As reverse engineering cannot handle the variants of DGA families, some researches leverage supervised learning to find new variants. However, the explainability of supervised learning is low and cannot find previously unseen DGA families. In this paper, we propose a graph-based semi-supervised learning scheme to track the evolution of known DGA families and find previously unseen DGA families. With a domain relation graph, we can clearly figure out how new variants relate to known DGA domain names, which induces better explainability. We deployed the proposed scheme on real network scenarios and show that the proposed scheme can not only comprehensively and precisely find known DGA families, but also can find new DGA families which have not seen before.
Water Based Magnification of Capacitive Proximity Sensors: Water Containers as Passive Human Detectors

Sensors that detect human presence received an increasing attention due to the recent advances in smart homes, collaborative fabrication cells, and human robot interaction. These sensors can be used in collaborative robot cells and mobile robots, in order to increase the robot awareness about the presence of humans, in order to increase safety during their operation. Among proximity detection systems, capacitive sensors are interesting, since they are low cost and simple human proximity detectors, however their detection range is limited. In this article, we show that the proximity detection range of a capacitive sensor can be enhanced, when the sensor is placed near a water container. In addition, the signal can pass trough several adjacent water containers, even if they are separated by a few centimeters. This phenomenon has an important implication in establishing low cost sensor networks. For instance, a limited number of active capacitive sensor nodes can be linked with several simple passive nodes, i.e. water containers, to detect human or animal proximity in a large area such as a farm, a factory or home. Analysis on the change of the maximum proximity range with sensor dimension, container size and liquid filler was performed in order to study this effect. Examples of application are also demonstrated.
X-MAGIC: Enhancing PIM Using Input Overwriting Capabilities

Processing-in-memory (PIM) using memristive technologies is an attractive solution for the memory wall problem. PIM can improve the performance and energy efficiency of computing systems by reducing the data transfer between the memory and the processor. Memristor Aided loGIC (MAGIC) is a popular memristive PIM technique that can perform any combinational logic as a sequence of atomic NOR/NOT operations. These NOR/NOT operations rely on initializing their output cell prior to computation. In this paper, we explore input overwriting: the use of the MAGIC gate output cell as an additional input without initializing it. We extend MAGIC and introduce X-MAGIC (eXtended MAGIC) which uses input overwriting, and demonstrate it by two gates, $A\cdot(\overline{B+C})$ and $A. \overline{B}$, where $A$ is an overwritten input. We show that input overwriting improves functionality, performance, and effective lifetime of the system. Due to algorithmic difficulties, available PIM synthesis tools do not support input overwriting. We address these difficulties by modifying an existing synthesis tool for MAGIC (SIMPLER), and presenting several general principles and methods for supporting input overwriting. We examine two configurations of the modified synthesis tool using X-MAGIC gates, differing in their performance/area trade-off. Both configurations achieve a geomean improvement of over 16.5% in performance, and over 20% in effective lifetime compared to standard MAGIC. Due to algorithmic difficulties, available PIM synthesis tools do not support input overwriting. We address these difficulties by modifying an existing synthesis tool for MAGIC (SIMPLER), and presenting several general principles and methods for supporting input overwriting. We examine two configurations of the modified synthesis tool using X-MAGIC gates, differing in their performance/area trade-off. Both configurations achieve a geomean improvement of over 16.5% in performance, and over 20% in effective lifetime compared to standard MAGIC.
Identifying Cross-Version Function Similarity Using Contextual Features

The identification of similar functions in malware assists analysis by supporting the exclusion of functions that have been previously analysed, allows the identification of new variants, supports authorship attribution, and the analysis of malware phylogeny. A function's context is a set comprising the function itself and all the program functions that may be executed when this function is called. Contextual features consist of data that is extracted from the functions contained in the function context. This paper presents a novel technique called Cross Version Contextual Function Similarity (CVCFS) to identify function pairs in two programs using features based on both individual functions and function context. The CVCFS technique uses Support Vector Machine (SVM) machine learning of function similarity features to pre-filter function pairs and then applies an edit distance technique using function semantics to reduce false positives. A case study is provided where individual and contextual features are extracted from three versions of Zeus malware. The SVM pre-filtering, followed by the use of an edit distance technique to filter false positives, gives a function pair identification accuracy of 85 percent.
Study for Influence of Harmonic Magnetic Fields on Vibration Properties of Core of Anode Saturable Reactor in HVDC Converter Valve System

The cores of anode saturated reactor (ASR) are the main vibration source of the HVDC converter valve system. The ASR is excited with an impulse voltage, which contains many harmonic components, and the magnetostriction of cores is usually the main reason of the ASR vibration. Therefore it is significant to study the influence of the harmonic on the vibration of core of ASR. In this paper, the magneto-mechanical coupling model is established based on the magnetostriction theory and the expression of vibration acceleration in the presence of third harmonic is derived. The vibration test platform is built to test the core’s vibration acceleration. The magnetic and magnetostrictive properties of silicon steel sheet under different working conditions are measured. The three-dimensional simulation model of the core is established, and the vibration acceleration under different magnetic flux density is calculated. The comparison of the experiment and simulation results shows the rationality of the magneto-mechanical coupling model. The influence of third harmonic magnetic flux density on vibration is studied by changing the phase of the third harmonic magnetic flux density and the ratio of the third harmonic magnetic flux density to the fundamental magnetic flux density. Both the calculated and measured results show that the third harmonic magnetic flux density affects the vibration acceleration according to the double-frequency components of the third harmonic, as well the sum-frequency and difference-frequency components of the third harmonic and fundamental frequency.
DMD067785 275..282

Since its development, tariquidar (TQR; XR9576; N-[2-[[4-[2-(6,7Dimethoxy-3,4-dihydro-1H-isoquinolin-2-yl)ethyl]phenyl]carbamoyl]4,5-dimethoxyphenyl]quinoline-3-carboxamide) has been widely regarded as one of the more potent inhibitors of P-glycoprotein (P-gp), an efflux transporter of the ATP-binding cassette (ABC) transporter family. A third-generation inhibitor, TQR exhibits high affinity for P-gp, although it is also a substrate of another ABC transporter, breast cancer resistance protein (BCRP). Recently, several studies have questioned the mechanism by which TQR interfaces with P-gp, suggesting that TQR is a substrate for P-gp instead of a noncompetitive inhibitor. We investigated TQR and its interaction with human and mouse P-gp to determine if TQR is a substrate of P-gp in vitro. To address these questions, we used multiple in vitro transporter assays, including cytotoxicity, flow cytometry, accumulation, ATPase, and transwell assays. A newly generated BCRP cell line was used as a positive control that demonstrates TQR-mediated transport. Based on our results, we conclude that TQR is a potent inhibitor of both human and mouse P-gp and shows no signs of being a substrate at the concentrations tested. These in vitro data further support our position that the in vivo uptake of [C]TQR into the brain can be explained by its highaffinity binding to P-gp and by it being a substrate of BCRP, followed by amplification of the brain signal by ionic trapping in
Two-Section Broadband Couplers With Wide-Range Phase Differences and Power-Dividing Ratios

A two-section broadband coupler with wide-range phase differences and power-dividing ratios is proposed. Closed-form design equations for the proposed couplers are derived. For verification, two couplers I and II operating at 2 GHz with phase difference of 45° and power-dividing ratio of 1, and phase difference of 60° and power-dividing ratio of 2, respectively, are fabricated and measured. For the coupler I (II), the measured 15-dB return loss and isolation bandwidth is 38.0% (51.5%). The measured bandwidths of the amplitude imbalance < 1 dB and the phase imbalance < 5° of the coupler I (II) are 29.5% (35.5%) and 29.0% (43.0%) with 15-dB return loss and isolation, respectively.
A PHP and JSP Web Shell Detection System With Text Processing Based On Machine Learning

Web shell is one of the most common network attack methods, and traditional detection methods may not detect complex and flexible variants of web shell attacks. In this paper, we present a comprehensive detection system that can detect both PHP and JSP web shells. After file classification, we use different feature extraction methods, i.e. AST for PHP files and bytecode for JSP files. We present a detection model based on text processing methods including TF-IDF and Word2vec algorithms. We combine different kinds of machine learning algorithms and perform a comprehensively controlled experiment. After the experiment and evaluation, we choose the detection machine learning model of the best performance, which can achieve a high detection accuracy above 98%.
Online Localization with Imprecise Floor Space Maps using Stochastic Gradient Descent

Many indoor spaces have constantly changing layouts and may not be mapped by an autonomous vehicle, yet maps such as floor plans or evacuation maps of these places are common. We propose a method for an autonomous robot to localize itself on such maps with inconsistent scale using Stochastic Gradient Descent (SGD) with scan matching using a 2D LiDAR. We also introduce a new scale state in 2D localization to manage the possible inconsistent scale of the input map. Experiments are conducted in an indoor corridor using three different input maps - a point cloud, a floor plan, and a hand-drawn map. The SGD localization algorithm is bench-marked to Adaptive Monte Carlo Localization (AMCL). In a point cloud mapped environment, our algorithm achieves 0.264m and 5.26° average position and heading error respectively. On the hand-drawn map, our SGD localization algorithm remains robust while AMCL fails. The role of the scale state in our SGD localization algorithm is demonstrated in poorly scaled maps.
Wind Generator Impedance Matching in Small-Scale Passive Wind Energy Systems

In this paper, the improved turbine power matching of passive wind energy systems for dc-connected battery storage applications with an impedance matching method is investigated. The passive system uses a direct-drive permanent magnet synchronous generator and is directly connected via a diode rectifier to the dc fixed-voltage battery storage. To improve power matching, an external inductance is added to the passive system between the generator and the diode rectifier. A static finite element based solution method is proposed to accurately calculate the necessary external inductance to achieve near maximum power point matching. It is shown that the proposed finite element based calculation method is computationally efficient and excellently suited for generator design optimization, which is critical for this application. It is also shown that by rewinding existing machines for the correct cut-in speed and adding the external inductance for improved power matching, existing machines can be effectively recycled for passive wind energy systems. The proposed static finite element solution method’s accuracy and improved power matching are confirmed with measured results on a sub 5 kW power level.
Trusted Network Slicing among Multiple Mobile Network Operators

5G mobile networks are expected to be much bigger in size, faster in speeds and better in scalability, providing varied services to different users and businesses in contrast to previous networks. 5G will also help enabling new business models and use cases. “Network Slicing” is a driving architectural concept for multi-tenancy. Network Slicing enables Mobile Network Operators (MNOs) to deploy different services over shared physical infrastructure, increasing inter-operator resource sharing. As 5G is still in its nascent, inter operator cooperation is an area that requires immediate attention of research. Traditional inter operator trust relationship models cannot fully comprehend the needs of 5G networks. In this paper, we propose an Intel SGX based multi-MNO cooperation scheme for trusted, dynamic and efficient network slice sharing in order to support inter-operator trustworthy collaboration. Furthermore, we developed a Proof of Concept of our proposed scheme using Intel SGX, flask framework and Docker containers. The obtained results indicate the applicability of the proposed scheme with little effect on performance.
A Low Complexity Multi-Mode Radio Vortex Receiver for OFDM-OAM Networks

Using the array signal reception theory, we analyzed the mathematical model of the proposed time-modulated rectangle array (TMRA). For practical applications, the uniform circular arrays (UCAs) used in orbital angular momentum (OAM) based multiple-input-multiple-output (MIMO) systems are replaced by the general uniform rectangle arrays (URAs). The employment of radio frequency (RF) switches at the bandlimited receiver significantly reduce the hardware overhead and avoid the unlimited spectrum expansion caused by switches. The detailed derivation of an example of 4 × 4 TMRA time-modulated sequence (TMS) is given in this letter. Our numerical results demonstrate that the TMRA receiver obtains better reception gain, bandwidth, and sidelobe control than a single UCA based OAM receiver.
A Bayesian-Based Controller for Snake Robot Locomotion in Unstructured Environments

This paper presents a novel Bayesian-based controller for snake robots in cluttered environment. It extends the conventional shape-based compliant control into statistical field providing an explicit mathematical formulation with Bayesian network. A sequential density propagation rule is derived by introducing several probability densities in a unified framework. Specifically, two input influence densities are proposed to model the cumulative effect of various external forces that the snake robot undergoes. Moreover, the measurement likelihood model is exploited to give a more robust closed-loop feedback. Overall, the proposed approach provides an innovative way to handle challenging tasks of snake robot control in complicated environment. Experimental results have been demonstrated for both simulation and real-world data.
Semantic redirection obfuscation: A control flow obfuscation based on Android Runtime

Reverse engineering, repackaging and misuse of Android apps are becoming more and more widespread. Many obfuscation schemes are not enough against sustainably evolving reverse engineering techniques, while others have performance problems or limitations. In this paper, we propose a novel scheme that redirects method invocation dynamically to hide the actual control flow of the program. Firstly, some pivotal methods are replaced by others unrelated methods, so that the actual semantics of the program no longer appear. Then by modifying the entry address of the methods in the obfuscated code, the methods execution automatically jumps to the corresponding target methods without any code modification. In order to accurately restore the control flow, the encrypted mapping is decrypted using dynamic passwords from the server in the standalone Java interpreter. Since the obfuscated program semantics and program execution results can not be significantly different, it is almost impossible to reveal the real logic of the program through static analysis. The scheme can also effectively increase the difficulty of dynamic analysis. The evaluation results show that the semantic redirection obfuscation scheme can well hide the actual control flow of the program and both the time and space consumption introduced are acceptable.