Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Network for the Iterative Estimations of Students’ Cognitive Skills

The effective educational systems aimed at improving Cognitive Skills (CS) both in and out of institutions. Such system relies on timely prediction of CS during students’ activities in intuitions. Meanwhile, literature is saturated with the number of approaches which have used study schedules, biological and environmental factors to predict CS. However, the loopholes in prior studies have become the main source of inspiration for the current attempt. In this study, we propose a Bayesian Neural Network which predicts CS by iterative manipulations of CS under the profound influence of Student’s Basic Attributes. Initially, the study classifies the Basic Attributes into three factors (1.age group, 2. gender, and 3. parent’s cohabitation status) which have multiple layers. Furthermore, the technique splits the range of CS into 20 periodic outcome variables (with a period of 0.5). Eventually, the network iteratively estimates each outcome of CS by feed-forward process through Basic Attributes layers. We have reviewed the performance of the proposed network by using a students’ score dataset. The results have illustrated that the current technique obtained significant prediction accuracy in terms of accuracy measures.
P4STA: High Performance Packet Timestamping with Programmable Packet Processors

QoS requirements of current network control and management applications require the ability to conduct precise measurements of network elements, including switches, routers and Virtual Network Functions (VNFs). State-of-the-art network switches have a forwarding delay of 1µs and below and offer high bandwidths of hundreds Gigabits per second. This imposes high time accuracy and loss-detection requirements on measurement equipment that are not met by existing, software-based measurement tools. The use of specialized tools, meeting these requirements, is restricted by limited flexibility and high cost.In this work, we introduce P4STA, an open source frame-work that combines the flexibility of software-based traffic load generation with the accuracy of hardware packet timestamping. Our evaluation results, obtained using an off-the-shelf P4-programmable switch, show that a time resolution up to 1ns can be achieved on these programmable data plane platforms. Moreover we show how to combine the traffic load of multiple software-based load generators to achieve a measurement load of up to 100Gbit/s per port. Experiments on further programmable platforms, specifically on P4-SmartNICs and FPGAs, show similar results. With this work, we make P4STA available for the research community to advance high performance experiment measurements at nanosecond accuracy.
Allocating and Scheduling Measurement Applications

Improvements in containerization and orchestration have facilitated the management of distributed measurement platforms, helping rationalize networks and services monitoring, a vital but demanding activity. Nonetheless, orchestration in this domain, still requires optimization, which we address in this paper. More precisely, we invest our efforts in the optimization of allocating and scheduling network and service measurement applications, deployed in a pool of physical machines in the network. First, we formulate the problem into an Integer Linear Programming model and prove it to be NP-Hard. Then, we propose an approximate resolution based on the Ant Colony System (ACS) metaheuristic. Finally, we introduce an open-source distributed measurement platform implementing our algorithm. Results show that the ACS algorithm provides efficient orchestration solutions that outperform those provided by a Branch and Bound algorithm and a First Fit Decreasing algorithm.
NOTES ON THE BIOLOGY OF TELENOMUS COELODASIDIS (HYMENOPTERA: SCELIONIDAE) AND ITS RELATIONSHIP TO THE SADDLED PROMINENT, HETEROCAMPA GUTTIVITTA (LEPIDOPTERA: NOTODONTIDAE)

Abstract A biological study of Telenomus coelodasidis Ashm. revealed a close temporal synchronization between this egg parasitoid and its primary host, Heterocampa guttivitta (Wlkr.). The wasp reproduced parthenogenetically in the laboratory and completed development in unfertilized host eggs. Wasp activity was greatest at relatively low temperatures, and longevity was significantly greater when individuals were provided with food. The second and third stage larvae and the pupa are described, as well as the sequence of visible changes occurring in parasitized host eggs. The egg stage of H. guttivitia should be sampled about 5–7 days after peak oviposition by the host, and eggs held for 4–6 days to obtain an accurate estimate of apparent mortality.
Research on Recognition of Fly Species Based on Improved RetinaNet and CBAM

Flies carry pathogens that endanger the health of humans and animals. The color and shape of the fly species are very similar, which is difficult to recognize. This paper proposes a fly species recognition method based on improved RetinaNet and convolutional block attention module (CBAM). Firstly, the proposed method used ResNeXt101 as a feature extraction network, and the improved CBAM called Stochastic-CBAM was added. Then, we built a multi-scale feature pyramid through an improved feature pyramid network (FPN) and integrated multi-level feature information. Finally, the small full convolutional network (FCN) was used as the classification subnet and the bounding box regression subnet. The Kullback-Leibler (KL) loss replaced smooth L1 loss as a bounding box regression loss function for learning bounding box regression and positioning uncertainty at the same time. We experimentally compared the proposed method with other the state-of-the-art methods on the established dataset. Experimental results showed that the mean Average Precision (mAP) of this method reached 90.38%, which was better than the state-of-the-art methods. The average time to recognize a single image was 0.131s. This method has a good detection effect on the fly species recognition.
Poster Abstract: Multi-Drone Assisted Internet of Things Testbed Based on Bluetooth 5 Communications

In this paper, a multi-hop airborne system is built based on Bluetooth 5 connected autonomous drones to relay real-time data of Internet of Things (IoT). A new lightweight Onboard Bluetooth Transceiver (OBT) is developed for reliable drone-to-drone and drone-to-ground communications. A graphical user interface is presented to monitor real-time flight trajectory of the drones and end-to-end data delivery. Outdoor experiments are conducted in real world to test autonomous flight control of the drones and received signal strength of the OBT communications.
Potential candidates for new SBAS signals

Satellite-Based Augmentation Systems (SBAS) provide single-frequency services for aeronautical users by broadcasting corrections and integrity information on L1 frequency. Future dual-frequency services for aeronautical users will be broadcast on L5-I/E5a-I frequency. New services that could be potentially provided by SBAS to non-aeronautical community are currently being investigated. These services may require higher data rates that challenge current SBAS signal definitions. Considering there is, at this time, no standardized SBAS signal neither on L5/E5a-Q component nor on E5b frequency band, an opportunity to evaluate new signal candidates is identified. In this paper, several candidates are proposed and compared with a baseline candidate. The baseline candidate is the current signal structure of SBAS signals, L1-I and L5-I/E5a-I. The comparison is made in terms of data rate, latency, data demodulation performance and receiver complexity as number of real additions and multiplications. It is observed that it is possible to increase the data rate and to reduce the latency without degrading the data demodulation threshold and with a reasonable complexity increase. Moreover, a compatibility analysis has been conducted which shows an impact smaller than 0.1dB of C/No degradation in any signal processing operation of any L5/E5a frequency band signal.
Multi-Orthogonal High-Order Mode Converter Based on Acoustically Induced Fiber Gratings

We experimentally investigate the generation of high-order modes (HOMs) with flexible switchable output. Cascaded acoustically induced fiber gratings (AIFGs) act as mode converters. A periodic micro-bending in the AIFG is produced by travelling flexural acoustic wave, and two modes with adjacent azimuthal numbers can be coupled to each other. Different HOMs (LP21a/b and LP11a/b) could be obtained just by tuning the frequency of the radio frequency (RF) driving signal. The dependence of RF frequencies on the two acousto-optic (AO) coupling stages is theoretically analyzed and experimentally verified. Based on cascaded AIFGs mode converter, a multiple-level intensity modulator and an all-fiber Yb-doped laser with flexible mode switching output of HOMs (LP21a/b and LP11a/b) are demonstrated. Moreover, by optimizing the outer diameter of the second part of the AO coupling regions, the mode conversion between LP01 and LP21 can be realized by applying a single frequency on the piezoelectric transducer (PZT).
Present and future applications of lipases

Proprietes des lipases: structure, activite catalytique, specificite. clivage des matieres grasses. Modification des huiles et matieres grasses (reaction d'interesterification). Synthese de composes organiques autres que triglycerides. Utilisation pour la fabrication de detergents. Applications a la biochimie analytique
A Low Complexity MMSE Hybrid Precoding Algorithm

In order to solve the problem of difficult design of hybrid precoding scheme for millimeter-wave massive multi-input and multi-output (Massive MIMO) systems, which is not only difficult in hardware implementation, but also with high algorithm complexity, a SIC hybrid precoding scheme based on partially connected structure is proposed. The hybrid precoding research in this paper is based on a simple fixed sub-connection structure, which converts the non-convex problem that maximizes the system reachability into the sum of the antenna sub-array rates, avoiding the problem of large-scale matrix-matrix inversion and singular value decomposition, thus greatly reducing the algorithm complexity. Through algorithm complexity analysis and system capacity performance simulation, the performance of the algorithm can be close to the optimal unconstrained algorithm, and the performance is stable and the algorithm complexity is based on 10% of sparse scattering precoding. Experimental results show that the SIC hybrid precoding algorithm not only has low algorithm complexity, but also meets the system performance requirements.
Survey on Privacy Preservation Technique

Data mining in privacy preservation is the process of hiding and protecting sensitive statistics of individuals. Data mining in privacy preservation foresee the risk of executing mining algorithms on secured data which should not be revealed to party running algorithm. The enormous quantity of broad data is regularly gathered and analyzed by some of the application which uses mining concept(i.e. sharing data that will be useful to end users). PPDM has become a notable issue while sharing secured data. This paper presents the PPDM techniques in a brief manner and also other privacy preservation methods in order to prevent and safeguard the data from the unauthorized parties.
Software Development for Automated System of Recording the Range of Motion of the Shoulder Joint During Rehabilitation

The article considers the shortcomings of the goniometric method used today to test the range of motion of the shoulder (glenohumeral) joint during rehabilitation. This report provides an overview of the technical means for the anthropometric measurements of a glenohumeral joint. The method of human motion recognition with one Microsoft Kinect controller is analyzed. The analysis of the workplace layout is based on previously conducted experiments. The article presents in detail the developed contactless method, patients’ examination algorithm, system interface, LabVIEW program, and report generation, based on rapid test results. The article presents the results of experiments involving real patients to compare the accuracy of measurements of the range of motion of the glenohumeral joint using an automated system and a goniometer. The technical requirements for the automated system are given. Based on the developed system, there were outlined specific tasks that must eventually be achieved in order to implement this method at the premises of the Privolzhsky Research Medical University.
Animal phylogeny in the light of the trochaea theory

Ultrastructural similarities unite Choanoflagellata and Metazoa as the Kingdom Animalia. Mctazoa (Porifera + Placozoa + Gastraeozoa) are characterized by the presence of collagen, septate/tight junctions and spermatozoa. Porifera and Placozoa lack basal lamina, nerve cells and synapses, which characterize Gastraeozoa (Cnidaria + Trochaeozoa). Gnidaria have cnidoblasts and lack the multiciliate cells found in almost all Trochaeozoa (Gastroneuralia + Protornaeozoa). Gastroneuralia (Spiralia + Aschelminthes) have an apical brain and a pair of ventral nerves, a blastopore which becomes mouth and anus, a mouth surrounded by a downstream collecting system of compound cilia, and a mesoderm formed from the blastopore lips. Spiralia (Articulata + Parenchymia + Bryozoa) have spiral cleavage and 4d-cell mesoderm, whereas these characters are lacking in Aschelminthes, which all lack primary larvae. Protornaeozoa (Ctenophora + Notoneuralia) have mesoderm from vegetal cells. Ctenophores have colloblasts. Notoneuralia have a dorsal nervous system behind the apical area and form a new mouth surrounded by an upstream collecting system of single cilia on monociliate cells; the blastopore becomes the anus surrounded by a ring of compound cilia. These features fit the trochaea theory, which proposes that Gastroneuralia and Notoneuralia evolved independently from the trochaea, a blastaea with the blastopore surrounded by a ring of compound cilia, which were both locomotory and particle collecting.
Royal jelly inhibited N-acetylation and metabolism of 2-aminofluorene in human liver tumor cells (J5)

The present study was carried out to evaluate the question of whether or not royal jelly affects N-acetylation and metabolism of 2-aminofluorene (2-AF) in the human liver tumor cell line (J 5). N-acetylation and metabolism of 2-AF in intact J5 cells was determined by using high performance liquid chromatography for the amounts of acetylated and nonacetylated 2-AF and profile of 2-AF metabolism. The results indicated that royal jelly displayed a dose-dependent inhibition of N-acetylation of 2-AF in J5 cells. Royal jelly also decreased the profile of 2-AF metabolites in J5 cells. This report is the first demonstration which showed that royal jelly affects N-acetylation of 2-AF in human liver tumor cells (J5).
How Higher Harmonics Affect the Electrical Facilities in Mining Power Systems

This paper presents the research into how higher harmonics of current and voltage can affect the electric drives used in the power systems at mining sites. As an object of study, the skip hoisting installation of the mine and its electric drive system were adopted. The installation is a single-drum machine of a compact design, which includes a CH4 skip, a DSf-500-M154-6 induction motor, a ProCrane-X2-MPR-690-PECe frequency converter. It proves the higher harmonics to negatively affect variable-frequency drives of skip hoists used in mines. The paper further dwells upon the mathematical processing of higher harmonics measured in the power system of a mining company. It proposes ways to reduce the above-mentioned effects.
A Survey on Test Point Insertion (TPI) Schemes

With advances in semiconductor technology, device integration density increases resulting in more complex designs with more testing challenges. Limited speed, memory and I/O channels of Automatic Test Equipment (ATE) is a testing challenge. Test power is also a major concern for the reliability of the device. The requirement of fewer test patterns for achieving high test quality and minimizing test time is a challenge in scan-based testing, therefore test point insertion scheme is used to solve these issues at a cost of area and performance overhead. This paper is a study of various test point insertion schemes and analyzing how these test point insertion algorithms have an effect on patterns generation, area, power, test time and fault coverage of a circuit.
Unsymmetrical Fault Analysis of PV for Different Transformer Configurations

Modular nature, less geographical constraints, low maintenance requirement and the ease of implementation make Photovoltaic (PV) based electricity generation more favorable than other solar power based generation, especially for a load-side generation. Hence, in recent times the PV based microgrids both DC and AC has turned up as a new way to meet the load demand. However, the increased generation from the PV sources has also raised some protection issues due to its limited reactive power capability and low fault current contribution. Furthermore, the transformer configuration used after the PV inverter also affects the fault current behaviour of it during the fault such as the voltage level of the healthy phases and the propagation of the effect of fault will depend upon the transformer configuration during unsymmetrical fault. In this paper, we have thoroughly performed the unsymmetrical fault analysis of the PV on a standard IEEE 10-bus system on DigSilent/Powerfactory and has also studied the effect of different transformer configuration on it.
Spatial–Spectral–Temporal Framework for Emotion Recognition

An emotion recognition method based on multispectral imaging technology and tissue oxygen saturation (StO2) is proposed in this study. This method is called spatial–spectral–temporal adjustment convolutional neural network (SACNN). First, we use the algorithm to extract the StO2 content of an emotionally sensitive nose area through real-time multispectral imaging technology. Compared with facial expression data, StO2 data are more objective and cannot be controlled and changed artificially. Second, we construct a clustering algorithm based on the emotional state by extracting the spectral, StO2, and spatial features of the nose image to obtain accurate signals of emotionally sensitive areas. To utilize the correlation between spectral and spatial signals, we propose an adjustment-based CNN module, which reorganizes the relationship between all previous layers of the feature map, thereby making the relationship among layers close and highly quantitative. The features extracted through this method are consistent with spatial–spectral features. Third, we incorporate the extracted temporal feature signal into the long short-term memory module and finally complete the correlation between the spatial–spectral–temporal features. Experimental results show that the accuracy of the SACNN algorithm in emotional recognition reaches 90%, and the proposed method is more competitive than state-of-the-art approaches. To the best of our knowledge, this study is the first to use time-series StO2 signals for emotion recognition.
Carbohydrate accumulation and utilization in soybean plants in response to altered phosphorus nutrition

We conducted a study to determine whether increased starch accumulation is the cause or the result of decreased growth in phosphorus-deficient soybean plants (Glycine max [L.] Merr. cv. Ransom). Nitrate-dependent plants were provided with nutrient solution containing either 0.05 or 1.00 mM phosphorus. On day 22 after transplanting the phosphorus concentration in solutions supplied to one-half of the plants that had received 0.05 mM phosphorus was increased to 1.0 mM phosphorus and the phosphorus concentration in solutions supplied to plants that had received 1.0 mM phosphorus was decreased to 0.05 mM P. The other half of plants in each treatment remained at the same external phosphorus concentration to serve as phosphorus-deficient or phosphorus-sufficient controls. Onset of phosphorus deficiency decreased (1) photosynthetic capacity through negative effects on whole plant leaf area and photosynthetic rate per unit leaf area and (2) relative growth rate and leaf elongation rate. However, continuous increases in starch concentration in all organs during onset of phosphorus deficiency indicated that decreased nonstructural carbohydrate availability was not the primary cause of decreased growth. During onset of phosphorus deficiency, significant decreases in relative growth rate (3–8-day interval) and in day and night leaf elongation rate (8 days) occurred before or at the same time as significant increases in stem, leaf and root starch concentrations. These results support the conclusion that disruption of metabolic functions associated with growth impaired utilization of available nonstructural carbohydrate in plants adjusting to phosphorus deficiency. After phosphorus-deficient plants were transferred to the sufficient external phosphorus concentration, starch concentrations in leaves and stems decreased to those of phosphorus-sufficient control plants within 3 days. These decreases in starch concentration in leaves and stems preceded measurable changes in relative growth rate (3–8-day interval), day and night leaf elongation rate (8 days) or CO2 exchange rate (8 days). Therefore, when soybean plants recover from phosphorus deficiency, starch stored in leaves and stems is ready to be utilized in the synthesis of structural biomass during the time required for activation and/or development of additional photosynthetic capacity.
Secured Architecture for Internet of Things (IoT) Based Smart Healthcare

Internet of Things (IoT) plays a vital role in making human lives smarter. Variety of IoT applications and services are deployed day by day with the tremendous efforts of Information Technology today. As the IoT applications and services bring forth numerous benefits, there also potential threats such as security while implementing these IoT based smart applications. Health Care is a major domain in which IoT can be used to deploy smart health care. This article puts forward a secured architecture for IoT based smart health care. The architecture proposed enables a secured transaction of health care data from the patients to IoT Health Seva Kendra that processes or aggregates the data to facilitate the healthcare practitioners with the necessary information.
Towards a Dynamic Policy Enhanced Integrated Security Architecture for SDN Infrastructure

Enterprise networks are increasingly moving towards Software Defined Networking, which is becoming a major trend in the networking arena. With the increased popularity of SDN, there is a greater need for security measures for protecting the enterprise networks. This paper focuses on the design and implementation of an integrated security architecture for SDN based enterprise networks. The integrated security architecture uses a policy-based approach to coordinate different security mechanisms to detect and counteract a range of security attacks in the SDN. A distinguishing characteristic of the proposed architecture is its ability to deal with dynamic changes in the security attacks as well as changes in trust associated with the network devices in the infrastructure. The adaptability of the proposed architecture to dynamic changes is achieved by having feedback between the various security components/mechanisms in the architecture and managing them using a dynamic policy framework. The paper describes the prototype implementation of the proposed architecture and presents security and performance analysis for different attack scenarios. We believe that the proposed integrated security architecture provides a significant step towards achieving a secure SDN for enterprises.