Miracle-dz/UniFAR-FTUs huggingface dataset:
An intrusion detection method based on log sequence clustering of honeypot for Modbus TCP protocol

Modbus TCP protocol is a famous and widely used industrial communication protocol for many supervisory control and data acquisition (SCADA) systems. However, with an increasing number of attacks on industrial control systems, honeypot systems must be deployed to collect attacking patterns and understand them clearly. Therefore, identifying potential attacking patterns from honeypots and providing a clear graphical user interface to express attacks are essential. In this paper, we propose an intrusion detection method that uses a honeypot for the Modbus TCP protocol. The proposed method uses an unsupervised clustering method based on log sequence similarity to cluster attacking behaviors, followed by a flow-based graph to express extracted representative attacks. Our method was implemented using the open-source honeypot system Conpot to collect attacking behaviors on Modbus TCP. The experimental results revealed that the proposed method has 90% accuracy for identifying attacking patterns, and can detect unexpected ones at 92% true positive and 0% false positive rates.
A high-voltage half-bridge gate drive circuit for GaN devices with high-speed low-power and high-noise-immunity level shifter

A high-voltage half-bridge gate drive circuit for E-mode GaN device with high-speed low-power and high-noise-immunity level shifter is proposed in this paper. It adopts digital-level detection concept by combining fast-slewing and output-maintain circuits in high noise environments to achieve both small response time and high dVsw/dt noise immunity. The proposed gate driver is fabricated in 0. 5μm 80V HV CMOS process where the active area is 1699×1522μm2. Simulation and experimental results of the level shifter demonstrate that propagation delay is only about 1.618ns and dVsw/dt noise immunity is up to 50V/ns with a low FOM equal to 0.04ns/μm·V.
Design and Implementation of Robot Assisted Surgery Based on Internet of Things (IoT)

This paper presents a controllable robotic arm via the use of the Internet of Things (IoT). A nurse could control the robotic arm in a hospital to assist a doctor during a surgery and take care of the patient with the picking and placing tasks using the robotic arm. In this system, a smart phone will be used to control the robotic arm. An accelerometer and a gyroscope are used to capture the gestures and postures of the smart phone. The signals of the accelerometer and the gyroscope will be captured by an Android application and sent to a Raspberry Pi to control the robotic arm. By integrating the Internet of Things (IoT), a worldwide controllable robotic arm is achieved. The Android application is developed by using Android Studio. Python script is employed in the Raspberry Pi to develop a program that will be able to control the robotic arm and to receive the commands from the smart phone. A system with a kinematic model will be used in the Raspberry Pi to control the robotic arm. Besides, a video streaming from computer is implemented to monitor the robotic arm. The performance of the robot is investigated in term of latency of the system and IoT platform.
Incidence of ill-health related job loss and related social and occupational factors. The “unfit for the job” study: a one-year follow-up study of 51,132 workers

Objective The analysis of ill-health related job loss may be a relevant indicator for the prioritization of actions in the workplace or in the field of public health, as well as a target for health promotion. The aim of this study was to analyze the medical causes, the incidence, and the characteristics of employees medically unfit to do their job. Methods This one-year prospective study included all workers followed by occupational physicians in an occupational health service in the south of France. The incidence of unfitness for work have been grouped according to the main medical causes and analyzed. We performed a multivariate analysis in order to adjust the observed risk of job loss based on the age groups, sex, occupation and the activity sectors. Results A total of 17 occupational physicians followed up 51,132 workers. The all-cause incidence of being unfit to return to one’s job was 7.8‰ (n = 398). The two main causes of being unfit for one’s job were musculoskeletal disorders (47.2%, n = 188) and mental ill-health (38.4%, n = 153). Being over 50 years old (Odds ratio (OR) 2.63, confidence interval 95% CI [2.13–3.25]) and being a woman (OR 1.52, 95% CI [1.21–1.91]) were associated with the all-cause unfitness, independent of occupation and activity sector. Conclusions Identification of occupational and demographic determinants independently associated with ill-health related job loss may provide significant and cost-effective arguments for health promotion and job loss prevention.
Collaborative access control of cloud storage systems

Recently, cloud storage systems have become a popular means of data storage and sharing in a wide spectrum of application scenarios, especially in cloud computing systems. Although the present implementations of cloud storage systems such as Google Drive or HackMD offer the functionality to let multiple users edit the same documents at the same time, some issues on access controls might emerge. First, the existing access control policies allow the reads or writes of a file or directory if the corresponding access privileges are granted, in an all-or-nothing basis, and finer-grained controls are not realized. Then, the privileges to read or write a file are not completely orthogonal, thus the access controls for reads and writes should be asymmetric. In particular, a user that are granted to write a file cannot be always guaranteed to be able to read the same file, and vice versa. Last but not least, to avoid any intended or careless sabotage of any single user on critical files, multiple users must be able to collaboratively control the accesses to a file. Unfortunately, while the collaborative access control of files on cloud storage systems has been extensively studied, more sophisticated access control policies based on sets of users — referred to as manager groups in this work — still remain an unanswered problem, which motivates this work. In this work, we propose a collaborative access control strategy based on manager groups, which allow multiple groups to be connected in series or in parallel to further enhance the flexibility of access control of files on the cloud. In addition, we also considered several key implementation issues such as the performance of management, as well as the overheads for file encryption.
Meta‐analytic findings reveal lower means but higher variances in visuospatial ability in dyslexia

Conflicting empirical and theoretical accounts suggest that dyslexia is associated with either average, enhanced, or impoverished high-level visuospatial processing relative to controls. Such heterogeneous results could be due to the presence of wider variability in dyslexic samples, which is unlikely to be identified at the single study level, due to lack of power. To address this, this study reports a meta-analysis of means and variances in high-level visuospatial ability in 909 non-dyslexic and 956 dyslexic individuals. The findings suggest that dyslexia is associated not only with a lower mean performance on visuospatial tasks but also with greater variability in performance. Through novel meta-analytic techniques, we demonstrate a negative effect size for mean differences (-.457), but a positive effect size for SD differences (+.118; SD ratio = 1.107). In doing so, this is the first study to demonstrate impoverished visuospatial processing of the majority of individuals with dyslexia in addition to greater variance in performance in this group. The findings advocate for further consideration of both the presence of, and reasons for, increased variance in perception, attention, and memory across neurodevelopmental disorders.
Achievable Rates for Full-Duplex Massive MIMO Systems Over Rician Fading Channels

We study the uplink and downlink achievable rate of full-duplex large-scale multi-input multi-output (MIMO) systems with a base station (BS) and users over Rician fading channels, based on maximum ratio combining/maximum ratio transmission and zero-forcing reception/zero-forcing transmission processing. Contrary to previous related works over Rayleigh fading channels, this paper assumes that the fast fading MIMO channel matrix follows the Rice distribution which will be more common in future 5G wireless communication systems. We derive approximate expressions of the uplink and downlink achievable rate for perfect channel state information when the number of BS antennas grows large. Based on the theoretical analysis, it is found that when the antennas of the BS are large enough and the power scaling law is applied properly, the impact of multi-user interference, loop interference, inter-user interference, and noise can be suppressed. In addition, the simulation results show that the uplink and downlink achievable rates increase with the number of BS antennas and they will converge to fixed values with the increasing Rician $K$ -factor.
Document recommendation with implicit feedback based on matrix factorization and topic model

Recommender systems have been applied in many domains to solve the information-overload problem, and most of them make recommendations based on explicit data which expressed ratings in different scores. However, there are a lot of implicit data in the real world, such as users' purchase history, click history, browsing activity and so on, and it is difficult to find users' preferences based on this kind of data. In this work, we proposed a novel recommendation method, which incorporates topic model and matrix factorization. The content of documents and similar users' preferences are used to predict the negative and positive examples. The proposed approach achieves better performance than other recommender systems with implicit feedback.
Exploring Critical Aspects of CNN-based Keyword Spotting. A PHOCNet Study

Deep convolutional neural networks are today the new baseline for a wide range of machine vision tasks. The problem of keyword spotting is no exception to this rule. Many successful network architectures and learning strategies have been adapted from other vision tasks to create successful keyword spotting systems. In this paper, we argue that various details concerning this adaptation could be re-examined, to the end of building stronger spotting models. In particular, we examine the usefulness of a pyramidal spatial pooling layer versus a simpler approach, and show that a zoning strategy combined with fixed-size inputs can be just as effective while less computationally expensive. We also examine the usefulness of augmentation, class balancing and ensemble learning strategies and propose an improved network. Our hypotheses are tested with numerical experiments on the IAM document collection, where the proposed network outperforms all other existing models.
GNSS Spoofing Detection Based on Unsynchronized Double-Antenna Measurements

Global navigation satellite systems are now threatened by spoofing attacks. The double-antenna countermeasure is one of the most robust spoofing detection techniques. However, the existing double-antenna countermeasure is based on the assumption that the measurements are synchronized, which may result in failure if the assumption is not valid. In the paper, we propose an unsynchronized model for the pseudorange and carrier phase measurements and discuss why these measurements are not suitable for spoofing detection when the measurements are not synchronized. In addition, a new detection method based on double-antenna power measurements is proposed. This method can be used in the unsynchronized case, extending the application of the double-antenna-based spoofing countermeasure.
Genetic Architecture of Nitrogen-Deficiency Tolerance in Wheat Seedlings Based on a Nested Association Mapping (NAM) Population

Genetic divergence for nitrogen utilization in germplasms is important in wheat breeding programs, especially for low nitrogen input management. In this study, a nested association mapping (NAM) population, derived from “Yanzhan 1” (a Chinese domesticated cultivar) crossed with “Hussar” (a British domesticated cultivar) and another three semi-wild wheat varieties, namely, “Cayazheda 29” (Triticum aestivum ssp. tibetanum Shao), “Yunnan” (T. aestivum ssp. yunnanense King), and “Yutian” (T. aestivum petropavloski Udats et Migusch), was used to detect quantitative trait loci (QTLs) for nitrogen utilization at the seedling stage. An integrated genetic map was constructed using 2,059 single nucleotide polymorphism (SNP) markers from a 90 K SNP chip, with a total coverage of 2,355.75 cM and an average marker spacing of 1.13 cM. A total of 67 QTLs for RDW (root dry weight), SDW (shoot dry weight), TDW (total dry weight), and RSDW (root to shoot ratio) were identified under normal nitrogen conditions (N+) and nitrogen deficient conditions (N−). Twenty-three of these QTLs were only detected under N− conditions. Moreover, 23 favorable QTLs were identified in the domesticated cultivar Yanzhan 1, 15 of which were detected under N+ conditions, while only four were detected under N− conditions. In contrast, the semi-wild cultivars contributed more favorable N−−specific QTLs (eight from Cayazheda 29; nine from Yunnan), which could be further explored for breeding cultivars adapted to nitrogen-deficient conditions. In particular, QRSDW-5A.1 from YN should be further evaluated using high-resolution mapping.
Zerumbone inhibits epithelial‐mesenchymal transition and cancer stem cells properties by inhibiting the β‐catenin pathway through miR‐200c

Colorectal cancer (CRC) is one of the most lethal and rampant human malignancies in the world. Zerumbone, a sesquiterpene isolated from subtropical ginger, has been found to exhibit an antitumor effect in various cancer types. However, the effect of Zerumbone on the biological properties of CRC, including epithelial‐mesenchymal transition (EMT) and cancer stem cells (CSCs) has not been fully elucidated. Here, we investigated the inhibitory action of Zerumbone on the EMT process, CSC markers, and the β‐catenin signaling pathway in the presence or absence of miR‐200c. The effect of Zerumbone on HCT‐116 and SW‐48 cells viability was examined by 3‐(4, 5‐dimethylthiazol‐2‐yl)‐2, 5‐diphenyltetrazolium bromide assay. The effects of Zerumbone on EMT‐related genes, CSCs markers, cell migration, invasion, sphere‐forming, and β‐catenin signaling pathway were explored. To evaluate the role of miR‐200c in anticancer effects by Zerumbone, miR‐200c was downregulated by LNA‐anti‐miR‐200c. Zerumbone significantly inhibited cell viability, migration, invasion, and sphere‐forming potential in HCT‐116 and SW‐48 cell lines. Zerumbone significantly suppressed the EMT and CSC properties as well as downregulated the β‐catenin. Silencing of miR200c reduced the inhibitory effects of Zerumbone on EMT and CSCs in CRC cells. These data indicated that Zerumbone may be a promising candidate for reducing the risk of CRC progression by suppressing the β‐catenin pathway via miR‐200c.
Prototyping non-developable paper models without gluing tabs

The recently revived interest in thin shell architecture demands an efficient method to quickly prototype non-developable surface models for design evaluation. This paper focus on improving the traditional papercraft technique of approximating smooth surfaces with discretised planar meshes. The necessary step to unfold strips of planar faces often creates many separate pieces. These pieces require tedious manual work in gluing and assembling them together. The addition of gluing tabs also results in an aesthetically unpleasing underside of the surface. This paper proposes a double-shell construction to overcome the challenges of edge jointing and eliminate the need for gluing tabs. A double-shell is created using two sets of unfolded planar strips that were split at different seams. When the two sides are folded and glued back together, the folded connections bridge the seams on another side and vice versa. Spray-on contact adhesive can be globally applied to the underside of each set, thereby speeding up the assembly process. Models of trimmed and untrimmed NURBS surfaces and triangular mesh surfaces are constructed using this method as demonstration. Quantitative and qualitative effect of model size and paper thickness are explored to provide baseline application parameters. As a result, this method allows anyone with a laser or drag knife cutter to rapidly create mesh models that are easy to assemble and aesthetically clean on both sides.
Community care for diabetic retinopathy and glaucoma in India: A panel discussion

Diabetic retinopathy (DR) and glaucoma are emerging causes of blindness and visual impairment in India and the world. Both diseases do not have any early warning symptoms, and once the symptoms appear, the diseases are reasonably advanced. Because of the long-standing nature of the diseases, one cannot adopt the cataract detection and treatment model so successfully developed in India. It requires an altogether different approach for screening and related infrastructure including human capital development. The solutions developed to reduce the burden of DR/glaucoma should be customized to urban, semi-urban, and rural areas. Greater advocacy, improving the health-seeking behavior, development of infrastructure and skilled personnel appropriate for the points of care, and an emphasis in comprehensive eye care are some of the solutions.
Automatically Conceptualizing Social Media Analytics Data via Personas

Social media analytics is insightful but can also be difficult to use within organizations. To address this, we present Automatic Persona Generation (APG), a system and methodology for quantitatively generating personas using large amounts of online social media data. The APG system is operational, deployed in a pilot version with several organizations in multiple industry verticals. APG uses a robust web and stable back-end database framework to process tens of millions of user interactions with thousands of online digital products on multiple social media platforms, including Facebook and YouTube. APG identifies both distinct and impactful audience segments for an organization to create persona profiles by enhancing the social media analytics data with pertinent features, such as names, photos, interests, etc. We demonstrate the architecture development, and main system features. APG provides value for organizations distributing content via online platforms and is unique in its approach to leveraging social media data for audience understanding. APG is online at https://persona.qcri. org.
Cow’s milk neutralizes the cytotoxicity of acrolein, a putative carcinogen in cigarette smoke

Cigarette smoke is a strong and independent risk factor for esophageal cancer, while the consumption of cow’s milk has been proposed as a protective factor. The mechanistic role of milk in preventing cancer, however, has not been clarified. We focused our study on acrolein, an abundant unsaturated aldehyde present in cigarette smoke. Acrolein is a highly toxic compound and a putative carcinogen. Using a cell culture system, we found that (1) acrolein caused necrosis in Ramos Burkitt’s lymphoma cells, (2) the necrosis was inhibited by preincubation of acrolein with milk, and (3) acrolein formed adducts with milk proteins. These results indicated the protective effects of cow’s milk against acrolein-induced cytotoxicity via protein-acrolein adduct formation.
A Silica‐Aerogel‐Reinforced Composite Polymer Electrolyte with High Ionic Conductivity and High Modulus

High‐energy all‐solid‐state lithium (Li) batteries have great potential as next‐generation energy‐storage devices. Among all choices of electrolytes, polymer‐based systems have attracted widespread attention due to their low density, low cost, and excellent processability. However, they are generally mechanically too weak to effectively suppress Li dendrites and have lower ionic conductivity for reasonable kinetics at ambient temperature. Herein, an ultrastrong reinforced composite polymer electrolyte (CPE) is successfully designed and fabricated by introducing a stiff mesoporous SiO2 aerogel as the backbone for a polymer‐based electrolyte. The interconnected SiO2 aerogel not only performs as a strong backbone strengthening the whole composite, but also offers large and continuous surfaces for strong anion adsorption, which produces a highly conductive pathway across the composite. As a consequence, a high modulus of ≈0.43 GPa and high ionic conductivity of ≈0.6 mS cm−1 at 30 °C are simultaneously achieved. Furthermore, LiFePO4–Li full cells with good cyclability and rate capability at ambient temperature are obtained. Full cells with cathode capacity up to 2.1 mAh cm−2 are also demonstrated. The aerogel‐reinforced CPE represents a new design principle for solid‐state electrolytes and offers opportunities for future all‐solid‐state Li batteries.
Lunar Crater Detection Based on Terrain Analysis and Mathematical Morphology Methods Using Digital Elevation Models

Lunar impact craters are the most typical geomorphic feature on the moon and are of great importance in studies of lunar terrain features. This paper presents a crater detection algorithm (CDA) that is based on terrain analysis and mathematical morphology methods. The proposed CDA is applied to digital elevation models (DEMs) to identify the boundaries of impact craters. The topographic and morphological characteristics of impact craters are discussed, and detailed steps are presented to detect different types of craters, such as dispersal craters, connective craters, and con-craters. The DEM from the Lunar Reconnaissance Orbiter, which has a resolution of 100 m, is used to verify the proposed CDA. The results show that the boundaries of impact craters can be detected. The results enable increased understanding of surface processes through the characterization of crater morphometry and the use of crater size–frequency distributions to estimate the ages of planetary surfaces.
Structural outlier detection: A tourist walk approach

Outlier detection is a fundamental task for knowledge discovery in data mining. Its aim is to detect data patterns that deviate from normal behavior. In this paper, we present a new outlier detection technique using tourist walks starting from each data sample and varying the memory size. Specifically, a data sample gets a higher outlier score if it participates in few tourist walk attractors, while it gets a low score if it participates in a large number of attractors. Experimental results on artificial and real data sets show good performance of the proposed method. In comparison to classical methods, the proposed one shows the following salient features: 1) It finds out outliers by identifying the structure of the input data set instead of considering only physical features, such as distance, similarity or density. 2) It can detect not only external outliers as classical methods do, but also internal outliers staying among various normal data groups. 3) By varying the memory size, the tourist walks can characterize both local and global structures of the data set. 4) The proposed method is a deterministic technique. Therefore, only one run is sufficient, in contrast to stochastic techniques, which require many runs.