Utilization of Real Time Behavior and Geographical Attraction for Location Recommendation

Personalized location recommendation is an increasingly active topic in recent years, which recommends appropriate locations to users based on their temporal and geospatial visiting patterns. Current location recommendation methods usually estimate the users’ visiting preference probabilities from the historical check-ins in batch. However, in practice, when users’ behaviors are updated in real-time, it is often cost-inhibitive to re-estimate and updates users’ visiting preference using the same batch methods due to the number of check-ins. Moreover, an important nature of users’ movement patterns is that users are more attracted to an area where have dense locations with same categories for conducting specific behaviors. In this paper, we propose a location recommendation method called GeoRTGA by utilizing the real time user behaviors and geographical attractions to tackle the problems. GeoRTGA contains two sub-models: real time behavior recommendation model and attraction-based spatial model. The real time behavior recommendation model aims to recommend real-time possible behaviors which users prefer to visit, and the attraction-based spatial model is built to discover the category-based spatial and individualized spatial patterns based on the geographical information of locations and corresponding location categories and check-in numbers. Experiments are conducted on four public real-world check-in datasets, which show that the proposed GeoRTGA outperforms the five existing location recommendation methods.

DeepRoute+: Modeling Couriers’ Spatial-temporal Behaviors and Decision Preferences for Package Pick-up Route Prediction

Over 10 billion packages are picked up every day in China. A fundamental task raised in the emerging intelligent logistics systems is the couriers’ package pick-up route prediction, which is beneficial for package dispatching, arrival-time estimation and overdue-risk evaluation, by leveraging the predicted routes to improve those downstream tasks. In the package pick-up scene, the decision-making of a courier is affected by strict spatial-temporal constraints (e.g., package location, promised pick-up time, current time, and courier’s current location). Furthermore, couriers have different decision preferences on various factors (e.g., time factor, distance factor, and balance of both), based on their own perception of the environments and work experience. In this article, we propose a novel model, named DeepRoute+, to predict couriers’ future package pick-up routes according to the couriers’ decision experience and preference learned from the historical behaviors. Specifically, DeepRoute+ consists of three layers: (1) The representation layer produces experience- and preference-aware representations for the unpicked-up packages, in which a decision preference module can dynamically adjust the importance of factors that affects the courier’s decision under the current situation. (2) The transformer encoder layer encodes the representations of packages while considering the spatial-temporal correlations among them. (3) The attention-based decoder layer uses the attention mechanism to generate the whole pick-up route recurrently. Experiments on a real-world logistics dataset demonstrate the state-of-the-art performance of our model.

Internet of Things Concept in the Context of the COVID-19 Pandemic: A Multi-Sensor Application Design

In this paper, we present the design, development and implementation of an integrated system for the management of COVID-19 patient, using the LoRaWAN communication infrastructure. Our system offers certain advantages when compared to other similar solutions, allowing remote symptom and health monitoring that can be applied to isolated or quarantined people, without any external interaction with the patient. The IoT wearable device can monitor parameters of health condition like pulse, blood oxygen saturation, and body temperature, as well as the current location. To test the performance of the proposed system, two persons under quarantine were monitored, for a complete 14-day standard quarantine time interval. Based on the data transmitted to the monitoring center, the medical staff decided, after several days of monitoring, when the measured values were outside of the normal parameters, to do an RT-PCR test for one of the two persons, confirming the SARS-CoV2 virus infection. We have to emphasize the high degree of scalability of the proposed solution that can oversee a large number of patients at the same time, thanks to the LoRaWAN communication protocol used. This solution can be successfully implemented by local authorities to increase monitoring capabilities, also saving lives.

Health Monitoring and Tracking System for Soldiers Using Internet of Things (IoT)

The paper reports an Internet of Thing (IoT) based health monitoring and tracking system for soldiers. The proposed system can be mounted on the soldier’s body to track their health status and current location using GPS. These information will be transmitted to the control room through IoT. The proposed system comprise of tiny wearable physiological equipment’s, sensors, transmission modules. Hence, with the use of the proposed equipment, it is possible to implement a low cost mechanism to protect the valuable human life on the battlefield

Many-Objective Quantum-Inspired Particle Swarm Optimization Algorithm for Placement of Virtual Machines in Smart Computing Cloud

Particle swarm optimization algorithm (PSO) is an effective metaheuristic that can determine Pareto-optimal solutions. We propose an extended PSO by introducing quantum gates in order to ensure the diversity of particle populations that are looking for efficient alternatives. The quality of solutions was verified in the issue of assignment of resources in the computing cloud to improve the live migration of virtual machines. We consider the multi-criteria optimization problem of deep learning-based models embedded into virtual machines. Computing clouds with deep learning agents can support several areas of education, smart city or economy. Because deep learning agents require lots of computer resources, seven criteria are studied such as electric power of hosts, reliability of cloud, CPU workload of the bottleneck host, communication capacity of the critical node, a free RAM capacity of the most loaded memory, a free disc memory capacity of the most busy storage, and overall computer costs. Quantum gates modify an accepted position for the current location of a particle. To verify the above concept, various simulations have been carried out on the laboratory cloud based on the OpenStack platform. Numerical experiments have confirmed that multi-objective quantum-inspired particle swarm optimization algorithm provides better solutions than the other metaheuristics.

Two Massive Jupiters in Eccentric Orbits from the TESS Full-frame Images

We report the discovery of two short-period massive giant planets from NASA’s Transiting Exoplanet Survey Satellite (TESS). Both systems, TOI-558 (TIC 207110080) and TOI-559 (TIC 209459275), were identified from the 30 minute cadence full-frame images and confirmed using ground-based photometric and spectroscopic follow-up observations from TESS’s follow-up observing program working group. We find that TOI-558 b, which transits an F-dwarf (M * = 1.349 − 0.065 + 0.064 M ⊙, R * = 1.496 − 0.040 + 0.042 R ⊙, T eff = 6466 − 93 + 95 K, age 1.79 − 0.73 + 0.91 Gyr) with an orbital period of 14.574 days, has a mass of 3.61 ± 0.15 M J, a radius of 1.086 − 0.038 + 0.041 R J, and an eccentric (e = 0.300 − 0.020 + 0.022 ) orbit. TOI-559 b transits a G dwarf (M * = 1.026 ± 0.057 M ⊙, R * = 1.233 − 0.026 + 0.028 R ⊙, T eff = 5925 − 76 + 85 K, age 6.8 − 2.0 + 2.5 Gyr) in an eccentric (e = 0.151 ± 0.011) 6.984 days orbit with a mass of 6.01 − 0.23 + 0.24 M J and a radius of 1.091 − 0.025 + 0.028 R J. Our spectroscopic follow up also reveals a long-term radial velocity trend for TOI-559, indicating a long-period companion. The statistically significant orbital eccentricity measured for each system suggests that these planets migrated to their current location through dynamical interactions. Interestingly, both planets are also massive (>3 M J), adding to the population of massive giant planets identified by TESS. Prompted by these new detections of high-mass planets, we analyzed the known mass distribution of hot and warm Jupiters but find no significant evidence for multiple populations. TESS should provide a near magnitude-limited sample of transiting hot Jupiters, allowing for future detailed population studies.

Social Engineering Attacks on Facebook – A Case Study

Background/Purpose: Facebook is an American business that offer online social networking services. Facebook was founded in 2004 by Harvard University freshmen Mark Zuckerberg, Eduardo Saverin, Dustin Moskovitz, and Chris Hughes. Free access to Facebook enables new users to create profiles, upload photos to existing groups, and start new ones. Every user's profile page has a Timeline area where they can upload material and their social network connections may reply with messages and Status updates informing them of their current location or condition. Additionally, Facebook includes a function called News Feed that notifies users of updates to their friends' profiles and statuses. Users can communicate with one another and exchange private messages using Facebook Messenger. Additionally, Facebook users may express their approval of a type of content by clicking the "Like" button. Every day, more than a billion people use Facebook, making it the most common social network on the planet. Menlo Park, California, is where the company's headquarters are located. Objective: To analyse how Facebook is misused and turned into an attack platform, in order to get sensitive information that can be used to create an attack profile against an individual. Design/Methodology/Approach: SWOT framework is being used to analyse and display information gathered from scholarly publications, web articles, and other sources. Findings/Results: Social Engineering Attacks using Facebook help the attackers to steal sensitive private information from unaware users. Using a false profile is one of the most frequent techniques to execute a large-scale data harvesting attack. Cyber Criminals use Facebook as the main target for social engineering attacks because of its high number of users and popularity. Originality/Value: This paper study gives a brief overview of Social Engineering Attacks on Facebook based on a variety of data collected. Paper Type: Case study-based Research Analysis

Fingerprinting/indoor positioning using complex planar splines

The rapid development of various LBS-based applications and services that operate on the basis of the user’s current location, both global GPS and local LBS, today require the development of new and improved methods. This concerns, first of all, methods for determining the local location of LPS users in premises, if there is a high concentration of users and the presence of difficulties in the propagation of radio signals. the use of local methods of location determination based on the fingerprinting method is considered. It is shown that to improve the user positioning accuracy, it is expedient to use a combination of several methods. to determine the local location of the user, a method based on the finite element method and linear complex planar splines is proposed. the construction of linear complex planar splines is considered, their coefficients are found. finding the error in determining the coordinates of the user’s UE location is shown. The use of the proposed method will improve the accuracy of determining the coordinates of the user’s location and will ensure the provision of LBS services and applications to users in the premises under various conditions of their provision.

SISTEM INFORMASI POTENSI WISATA DI KABUPATEN KOLAKA TIMUR BERBASIS WEB

Kabupaten Kolaka Timur merupakan salah satu Kabupaten di Provinsi Sulawesi Tenggara yang memiliki cukup banyak daerah potensi wisata. Namun masih banyak wisatawan yang tidak mengenal wisata yang terdapat di Kabupaten Kolaka Timur. Kurangnya pengetahuan tentang wisata tersebut disebabkan oleh informasi tentang tempat wisata yang masih minim dan belum dipublikasikan dengan baik. Dalam kasus tersebut, untuk membantu pihak terkait dan juga wisatawan dalam mengenal wisata yang terdapat di Kabupaten Kolaka Timur, maka dibangun sebuah website yang dapat memberikan informasi yang detail tentang wisata dan rute menuju tempat wisata yang terdapat di daerah Kabupaten Kolaka Timur. Untuk memberikan informasi rute, menggunakan Google Maps yang akan mengakses current location dari user menuju arah tempat wisata yang ingin dikunjungi. Dengan adanya website ini diharapkan dapat membantu wisatawan dalam mengenal potensi wisata yang ada di daerah Kabupaten Kolaka Timur dan juga membantu pengelola tempat wisata dalam mengenalkan daerah wisata yang ada di Kabupaten Kolaka Timur