Miracle-dz/UniFAR-FTUs huggingface dataset:
Achieving Ultra Energy-efficient and Collision-free Data Collection in Wake-up Radio Enabled mIoT

To achieve ultra-low energy consumption and decade-long battery lifetime for Internet of things (IoT) networks, wake-up radio (WuR) appears as an eminent solution. While keeping devices in deep sleep for most of the time, a WuR enabled IoT device can be woken up for data transmission at any time by a wake-up call (WuC). However, collisions happen among WuCs for transmitter-initiated data reporting and among data packets for receiver-initiated data collection. In this paper, we propose two novel hashing-based schemes in order to achieve collisionfree data transmissions for receiver-initiated data collection. We consider first a simple scenario in which all devices in a region of interest are covered by a data collector and propose a scheme which facilitates a scheduled time for data uploading of each device. Then we extend our scheme to cover a more realistic scenario where IoT devices are distributed across a larger region that cannot be covered by a single data collector. In this case, we propose a partitioning algorithm for data collection across multiple partitions. Both analysis and simulations are performed to demonstrate the effectiveness of the proposed schemes.
Targeting the purine salvage pathway in in vitro models of cerebral ischemia

An interruption of the blood supply to the brain, as occurs during ischemic stroke, results in a rapid decline of ATP levels and a subsequent loss of neuronal function and viability. Under physiological conditions the brain reuses ATP degradation metabolites, such as hypoxanthine, via the purine salvage pathway, to restore its ATP pool. However, the massive degradation of ATP during ischemia results in the accumulation and loss of diffusible purine metabolites and thereby leads to a reduction in the post-ischemic ATP pool size, leaving the brain more vulnerable to secondary ischemic insults (recurrent strokes) and less able to deploy reparative mechanisms. The aim of this study was to improve the recovery of post-ischemic ATP levels by enhancing the purine salvage pathway, with substances that are already known to be tolerated in humans. Using acute hippocampal rat brain slices, I found that 1 mM Ribose (Rib) and 50 μM Adenine (Ade), two main metabolites of the purine salvage pathway, significantly increased the tissue ATP levels under basal conditions. Rib/Ade pre-treatment results in accelerated decline of synaptic transmission after onset of oxygen/glucose deprivation (OGD), due to increased adenosine release. However, this intervention does not delay the onset of anoxic depolarisation, or improve the recovery of synaptic transmission after prolonged ischemic periods. Pre-treatment of brain slices with 1 mM creatine, which increases phosphocreatine levels and thereby buffers the rapid decline of ATP levels upon energy shortage, significantly delays the onset of AD and helps to improve the recovery of synaptic transmission. By using cultured cerebellar granule cells, for more protracted studies on cell viability after OGD, I show that addition of Rib/Ade after ischemia helps to improves cell viability. Therefore my results suggest that both, delaying the decline of ATP upon onset of OGD (pre-treatment with creatine), or enhancing the post-ischemic recovery of ATP (post-treatment with Rib/Ade) are useful strategies to improve cell survival and function after in vitro ischemia.
Design and Implementation of System Features for the MC68000

The MC68000 combines state-of-the-art technology, advanced circuit design techniques, and computer science to achieve an architecturally advanced 16-bit microprocessor. The processor is implemented by a microprogrammed control of an execution unit. The processor incorporates advanced system features, including multi-level vectored interrupts, privilege states, illegal instruction policing, and bus cycle abort. This paper discusses the implementation of the system features and the influence of the implementation method on the processor design.
Machine Learning Application to Priority Scheduling in Smart Microgrids

The need to integrate flexible and intelligent mechanisms for energy management becomes a necessity. In this paper, we are considering a microgrid with infrastructures having production capacities and consumption needs. Several data and constraints related to the microgrid consumption have been collected, in addition to data concerning the production of renewable energy from Photovoltaic panels (PV). Data history is used as input to a neural network to predict one day ahead of consumption and production. Then, a prioritized scheduling family of algorithms is presented. First, we introduce a mathematical formulation to our problem. Then, we propose various scenarios that go from an exact solution to heuristic-based use cases, including scheduling of several energy classes with a maximum scheduling time lapse. Results show that prioritized scheduling, including time lapse based on predictions, can give more reliable results than scheduling based on bin packing.
Reward Tuning for self-adaptive Policy in MDP based Distributed Decision-Making to ensure a Safe Mission Planning

Markov Decision Process (MDP) becomes a standard model for sequential decision making under uncertainty. This planning gives the appropriate sequence of actions to perform the goal of the mission in an efficient way. Often a single agent makes decisions and performs a single action. However, in several fields such as robotics several actions can be executed simultaneously. Moreover, with the increase of the complexity of missions, the decomposition of an MDP into several sub-MDPs becomes necessary. The decomposition involves parallel decisions between different agents, but the execution of concurrent actions can lead to conflicts. In addition, problems due to the system and to sensor failures may appear during the mission; these can lead to negative consequences (e.g. crash of a UAV caused by the drop in battery charge). In this article, we present a new method to prevent behavior conflicts that can appear within distributed decision-making and to emphasize the action selection if needed to ensure the safety and the various requirements of the system. This method takes into consideration the different constraints due to antagonist actions and wile additionally considering some thresholds on transition functions to promote specific actions that guarantee the safety of the system. Then it automatically computes the rewards of the different MDPs related to the mission in order to establish a safe planning. We validate this method on a case study of UAV mission such as a tracking mission. From the list of the constraints identified for the mission, the rewards of the MDPs are recomputed in order to avoid all potential conflicts and violation of constraints related to the safety of the system, thereby ensuring a safe specification of the mission.
Application of metaheuristic for optimization of iris Image segmentation by using evaluation Hough Transform and methods Daugman

The biometric domain is very important for multinational corporations and economic companies all over the field of research and innovation and the protection of personal confidential information. Thus, governments invest millions of dollars in the protection and the identification of citizens’ personal data and use different types of biometric data modalities (iris, fingerprints, faces, etc.) for the recognition of the individual. The iris modality requires special and adequate treatment so that the biometric recognition system can recognize the person and this by going through several stages: image acquisition, segmentation, normalization, feature extraction and finally matching. The segmentation is a delicate step for the iris localization. The process detects the iris by two concentric circles, the first circle for the pupil region and the second circle for the iris region. The two most widely used methods in the literature are the John Daugman method (Intergro-Differential Operator) [7] and the Hough Transform circular [8]. The method of John Daugman detects the iris region and the pupil as well as the arches of the upper and lower eyelids. The Hough circular transform makes it possible to obtain the coordinates of the radius, the center of the pupil and the iris. The main objective is to obtain the essential information, which is the size of the pupil radius and the iris radius. And that makes it possible to crop the radius rl of the pupil and the radius r2 of the iris to have a good segmentation. Experiments were made on the following iris data: Casia-iris-interval v4 [21], Ubiris vl [23], MMU 1, MMU 2 [22] and the SDULMA-HCM iris database [24].
Human Navigation Using Phantom Tactile Sensation Based Vibrotactile Feedback

In recent years, multiple navigation systems using vibrotactile feedback have been studied, due to their ability to convey information while keeping free the visual and auditory channels, besides eliciting rapid responses from users. In the current stage, most navigation systems with vibrotactile feedback in the literature focus on guiding users around space using a fixed number of vibrotactile cues, which are limited by the number of vibrators. Achieving more precise guidance with a limited number of conveyable directions is difficult, as users cannot be directly guided to the desired position. In this letter, we present an approach to guide people around space using multidirectional vibrotactile feedback (MVF). The MVF can produce vibratory cues on the user's left lower leg with an average directional resolution of $\mathbf {15.35}^{\circ }$ for cases when the user is not moving, using only six vibration motors, by exploiting a vibrotactile illusion called Phantom Tactile Sensation (PTS). In a preliminary test of dynamic direction recognition experiment, users reported they tend to become less sensitive to vibration under long-time continuous vibration. As a result, besides offering users a continuous vibration to indicate directions, we also considered producing the cues during either the swing or stance phase to users in this experiment. The result of a direction recognition experiment while walking shows that the average recognition error for the cues when produced in the swing or stance phases are lower than the recognition error when the cues are continuously produced. We carried out a navigation experiment to test the feasibility of using the proposed direction display to guide people around an open area in real-time. In this experiment, users were able to reach the goal within the time limit guided only by the proposed feedback around 90% of the times for both gait phases.
Density Functions for Guaranteed Safety on Robotic Systems

The recent study on density functions as the dual of value functions for optimal control gives a new method for synthesizing safe controllers. A density function describes the state distribution in the state space, and its evolution follows the Liouville Partial Differential Equation (PDE). The duality between the density function and the value function in optimal control can be utilized to solve constrained optimal control problems with a primal-dual algorithm. This paper focuses on the application of the method on robotic systems and proposes an implementation of the primal-dual algorithm that is less computationally demanding than the method used in the literature. To be specific, we use kernel density estimation to estimate the density function, which scales better than the ODE approach in the literature and only requires a simulator instead of a dynamic model. The Hamilton Jacobi Bellman (HJB) PDE is solved with the finite element method in an implicit form, which accelerates the value iteration process. We show an application of the safe control synthesis with density functions on a segway control problem demonstrated experimentally.
Information Dissemination for the Adaptive Replica Selection algorithm in Key-Value Stores

In distributed key-value stores, multiple replica servers are always available for each key-value access operation when the eventually consistency model is employed. Accordingly, the replica selection algorithm is crucial to the tail latency of the key-value access operations generated by end-user requests, especially under the environment of heterogeneous replica servers. The main challenge of making replica selection decision is to know the status of replica serves with acceptable overhead for each lightweight key-value access operation. Recently, aware of the time-varying performance and load of replica servers, the adaptive replica selection algorithm C3 suggests piggybacking the information of replica server via the returned value” to guide the replica selection. Although the good performance of C3 has been verified by experiments and simulations, the poor timeliness of feedback information makes a large performance gap between C3 and the ideal replica selection algorithm. To narrow this gap, we propose the information dissemination mechanism for C3, which lets both “client” and “server” store the records about the status of replica servers, and both key” and value” piggyback multiple records. In this way, the information dissemination bottleneck at “slow” server would be removed with the help of multiple clients and servers. As confirmed by simulation results, the information dissemination mechanism can significantly improve the timeliness of feedback information with acceptable overhead and accordingly helps C3 to greatly reduce the tail latency (by about 35% under the default scenario).
Cervical Cancer Prediction through Different Screening Methods using Data Mining

Cervical cancer remains an important reason of deaths worldwide because effective access to cervical screening methods is a big challenge. Data mining techniques including decision tree algorithms are used in biomedical research for predictive analysis. The imbalanced dataset was obtained from the dataset archive belongs to the University of California, Irvine. Synthetic Minority Oversampling Technique (SMOTE) has been used to balance the dataset in which the number of instances has increased. The dataset consists of patient age, number of pregnancies, contraceptives usage, smoking patterns and chronological records of sexually transmitted diseases (STDs). Microsoft azure machine learning tool was used for simulation of results. This paper mainly focuses on cervical cancer prediction through different screening methods using data mining techniques like Boosted decision tree, decision forest and decision jungle algorithms as well performance evaluation has done on the basis of AUROC (Area under Receiver operating characteristic) curve, accuracy, specificity and sensitivity. 10-fold cross-validation method was utilized to authenticate the results and Boosted decision tree has given the best results. Boosted decision tree provided very high prediction with 0.978 on AUROC curve while Hinslemann screening method has used. The results obtained by other classifiers were significantly worse than boosted decision tree.
Wi-Fi Roaming as a Location-based Service

Time- and jitter-sensitive traffic, such as VoIP and live streaming, have made fast Wi-Fi roaming an important topic in mobile communications. Conventional Wi-Fi roaming is generally initiated by clients, which have limited understanding of the network topology and the surrounding environments. In this paper, we couple localization and roaming together. We present an on-demand localization technique at the infrastructure side that uses a standard 802.11v protocol to collect information necessary for localization. The location information is then used in a roaming decision. Our approach does not require any client-side modification because we exploited the capabilities of Wi-Fi standards and the existing control logic in most current Linux/Android-based systems. To demonstrate the effectiveness of our approach, we provide experimental evidence of implementation in an enterprise-level wireless network.
LAMCO: A Layered Approach to Mobile Application Computation Offloading

Mobile computational offloading offers a solution to extend the computational power of a mobile device allowing it to run heavyweight activities/tasks. This turns low-end mobile devices into a capable machine of running heavyweight applications that require special processing power. Code offloading is achieved through migration. Migrating code from a local machine (i.e. mobile device) to the cloud where the code is actually executed is challenging. Questions such as when and how migration takes place are critical. Quite a few offloading frameworks are proposed in the literature. They are, mostly, mostly not efficient due to the lack of a standard offloading architecture, some did not propose an architecture at all. The abundance of Quality of Service (QoS) component is another concern. This paper introduces a layered approach to mobile computation offloading called LAMCO. LAMCO is implemented in the Android operating system and the run-time performance of the framework is evaluated in this paper. One advantage of LAMCO is that it does not require any modifications in the underlying operating system as the framework is dynamically installed and updated while the operating system is intact.
Feature-Level Attentive Neural Model for Session-Based Recommendation

The main goal of session-based recommendation is to predict a user’s next click based on historical anonymous session data. One important aspect is capturing the interest drift that occurs in a user’s click sequences. Recent studies have mainly exploited the attention mechanism to extract the main intentions of users and reduce the influence of unintentional actions on the performance of recommendation systems. However, the previous works attempt to extract a user’s interests at the item level; they ignore the inherent correlations between a user’s interest drift. Therefore, we propose a novel Feature-level Attentive Neural Model (FANM) as a solution capable of capturing a user’s current interests by considering interest drift at the feature level. Our model exploits a gated recurrent unit (GRU) and a multihead attention mechanism to extract a user’s current interests at the feature level from click sequences; then, it integrates the user’s long-term and short-term interests with their last actions (e.g., clicks) to predict the next action. Our proposed model effectively captures a user’s interest drift by performing sufficient modeling of a user’s sequence data, resulting in increased recommendation accuracy. The experiments on the two real-world datasets show that FANM performs significantly better than baseline methods.
Indoor Coexistence Analysis Among 5G New Radio, LTE-A and NB-IoT in the 700 MHz Band

This work reports a common indoor experimental coexistence performance analysis among 5G New Radio (5G NR), 4G Long Term Evolution Advanced Pro (LTE-A Pro) and Narrowband Internet of Things (NB-IoT), using co-channel and adjacent channels in the 700 MHz Band. The required frequency offset for the 4G and 5G generations simultaneous operation has been evaluated, as a function of the modulation scheme in order to minimize the adjacent channel interference. A suitable management of the numerology and allocating time-frequency resources, configuring CORESET, enables a peaceful coexistence, as well as high performance for all evaluated systems. Experimental results demonstrate an efficient spectrum sharing of 10 MHz-bandwidth for a 5G NR, an LTE-A Pro and three NB-IoT downlink carriers, aiming to take advantage of 700 MHz propagation aspects for the current 4G and future 5G. Constellation and error vector magnitude (EVMRMS) in accordance to the 3GPP requirements reinforce the successful 4G and 5G spectrum refarming implementation.
Inheritance of Resistance in Tomato to Race T3 of the Bacterial Spot Pathogen

Hawaii 7981 tomato (Lycopersicon esculentum Mill.), resistant to race T3 of the bacterial spot pathogen (Xanthomonas campestris pv. vesicatoria (Doidge) Dye), was crossed to the susceptible tomato inbred, Fla. 7060, and subsequently F2 and backcross seed were obtained. These generations were planted in the field, inoculated with the race T3 pathogen and evaluated for disease severity over two summer seasons. Data were tested for goodness-of-fit to a model based on control by the incompletely dominant gene Xv3 that confers hypersensitivity. The F1 was intermediate in disease severity to the parents for both seasons. When data were combined over both seasons, the backcrosses fit the expected 1:1 ratios although each deviated from the expected ratio in one of the 2 years tested. The F2 did not fit the expected 1:2:1 ratio in either year or when data from the two years were combined due to a deficiency of resistant plants. Thirty-three F2 plants representing an array of disease severities and hypersensitivity reactions were selected in the second season and their F3 progeny were inoculated and evaluated for disease severity. Hawaii 7981 was significantly more resistant than the 12 most resistant F3 selections even though all expressed hypersensitivity. A hypersensitive F3 with intermediate field resistance was crossed to Hawaii 7981 and subsequently, F2 and backcross generations were obtained. These generations were field inoculated with the race T3 pathogen and evaluated for disease severity. Hawaii 7981 was significantly more resistant than the F3 parent as in the previous year. The data did not fit an additive-dominance model and epistatic interactions were significant. Thus, it appears that field resistance to race T3 of bacterial spot found in Hawaii 7981 is conferred quantitatively by Xv3 and other resistance genes. Breeding implications are discussed. plants were resistant in the field, but ≈60% were hypersensitive. Wang (1992) reported correlation coefficients of only 0.39 to 0.41 between hypersensitivity and field resistance in two field F2 populations. Later, Somodi et al. (1996) found correlation coef- ficients of 0.31 to 0.52 between hypersensitivity and field resis- tance in two F2 populations. Hypersensitive resistance to race T3 was discovered in Hawaii 7981 and L. pimpinellifolium L. accessions PI 126932 and PI 128216 (Jones et al., 1995). The highest level of resistance in the field was in Hawaii 7981 (Scott et al., 1995). In a growth chamber experiment, the hypersensitive response in Hawaii 7981 was determined to be controlled by an incompletely dominant gene designated Xcv-3 (Scott et al., 1996) although Xv3 is now the preferred gene designation and will be used herein. The objective of this study was to characterize inheritance of race T3 bacterial spot resistance in the field. This information will be useful in developing breeding strategies to incorporate T3 resistance into commercial cultivars.
Resource Allocation for Mobile Edge Computing System Based on PDT Network

With the application of new technologies in public security affairs, such as face recognition, speech recognition etc. the data processing ability of police terminals is required to be higher and higher. But at the same time, the acceleration of execution speed will also accelerate the consumption of energy. In this paper, Mobile Edge Computing is used to offload the computing tasks to the mobile edge server by using the Police Digital Trunking network, which not only ensures the real-time performance of the task, but also saves the energy consumption of the battery. We set up a system model about the energy consumption and optimize it according to the access method. We propose a time slot resource allocation strategy for each user to ensure that the total energy consumption of the terminal in the system is the lowest under the maximum delay tolerance.