Accelerated Computer Vision Inference with AI on the Edge
استنتاج چشم انداز رایانه ای سریع با هوش مصنوعی در لبه-2020
Computer vision is not just about breaking down images or videos into constituent pixels, but also about making sense of those pixels and comprehending what they represent. Researchers have developed some brilliant neural networks and algorithms for modern computer vision. Tremendous developments have been observed in deep learning as computational power is getting cheaper. But data-driven deep learning and cloud computing based systems face some serious limitations at edge devices in real-world scenarios. Since we cannot bring edge devices to the data-centers, so we bring AI to the edge devices with AI on the Edge. OpenVINO toolkit is a powerful tool that facilitates deployment of high-performance computer vision applications to the edge devices. It converts existing applications into hardwarefriendly and inference-optimized deployable runtime packages that operate seamlessly at the edge. The goals of this paper are to describe an in-depth survey of problems faced in existing computer vision applications and to present AI on the Edge along with OpenVINO toolkit as the solution to those problems. We redefine the workflow for deploying computer vision systems and provide an efficient approach for development and deployment of edge applications. Furthermore, we summarize the possible works and applications of AI on the Edge in future in regard to security and privacy.
Index Terms: Artificial Intelligence | Deep Learning | Neural Networks | Computer Vision | AI on the Edge | OpenVINO
Interactive Transport Enquiry with AI Chatbot
استعلام حمل و نقل تعاملی با هوش مصنوعی Chatbot-2020
Public transportation is used efficiently by millions of people all over the world. People tend to travel to different places and at certain times they may feel completely lost in a new place. Our chatbot comes to rescue at this time. A Chatbot is often described as one of the most promising tools for communication between humans and machines using artificial intelligence. It is a software application that is used to conduct an online chat conversation via text by using natural language processing (NLP) and deep learning techniques. It provides direct contact with a live human agent in the form of GUI. This AI chatbot confirms the current location and the final destination of the user by asking a few questions. It examines the user’s query and extracts the appropriate entries from the database. The deep learning techniques that are used in this chatbot are responsible for understanding the user intents accurately to avoid any misconceptions. Once the user’s intention has been recognized, the chatbot provides the most relevant response for the user’s query request. Then the user gets all the information about the bus names along with their numbers so that the person can travel safely to the desired location. Our chatbot is implemented in pythons Keras library and used Tkinter for GUI.
Keywords: artificial intelligence | chatbot | natural language processing | deep learning | Keras | GUI | Tkinter
ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial
غربالگری هدایت شده با هوش مصنوعی ECG برای کسر کم دفع (EAGLE): منطق و طراحی یک آزمایش تصادفی خوشه عملی-2020
Background A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment. Objectives To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices. Design The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize N100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report. Summary This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms. (Am Heart J 2020;219:31-6.)
Predicting academic performance with Artificial Intelligence (AI), a new tool for teachers and students
پیش بینی عملکرد تحصیلی با هوش مصنوعی ، ابزاری جدید برای معلمان و دانش آموزان-2020
Abstract—Learning Analytics (LA) is data science applied to the educational field. It enables the measurement, collection, and analysis of learners’ data and their context. In this research we utilized two algorithms from the field of artificial intelligence (AI): KNearest Neighbor and Random Forest. These algorithms trained a predictive model for the academic performance of students pursuing an engineering degree. This research found that a general picture of the performance of the group is enough to improve, despite the forecast for each student not being accurate. This allowed the instructor to adapt their teaching technique to get better results. Finally, most students agree to take advantage of LA and they think that knowing their predictive results at the beginning of the course will help them do better in class.
Keywords: Artificial intelligence | Educational innovation | Learning analytics | Higher education
AI-enabled recruiting: What is it and how should a manager use it?
استخدام با هوش مصنوعی: چه چیزیست و یک مدیر چگونه باید از آن استفاده کند؟-2020
AI-enabled recruiting systems have evolved from nice to talk about to necessary to utilize. In this article, we outline the reasons underlying this development. First, as competitive advantages have shifted from tangible to intangible assets, human capital has transitioned from supporting cast to a starring role. Second, as digitalization has redesigned both the business and social landscapes, digital recruiting of human capital has moved from the periphery to center stage. Third, recent and near-future advances in AI-enabled recruiting have improved recruiting efficiency to the point that managers ignore them or procrastinate their utilization at their own peril. In addition to explaining the forces that have pushed AI-enabled recruiting systems from nice to necessary, we outline the key strategic steps managers need to take in order to capture its main benefits.
KEYWORDS : AI-enabled recruiting | Artificial intelligence | Digital recruiting | technology | Human resources
Problems of Poison: New Paradigms and "Agreed" Competition in the Era of AI-Enabled Cyber Operations
مسئله سم: پارادایم های جدید و رقابت "توافق شده" در عصر عملیات سایبری با هوش مصنوعی-2020
Few developments seem as poised to alter the characteristics of security in the digital age as the advent of artificial intelligence (AI) technologies. For national defense establishments, the emergence of AI techniques is particularly worrisome, not least because prototype applications already exist. Cyber attacks augmented by AI portend the tailored manipulation of human vectors within the attack surface of important societal systems at great scale, as well as opportunities for calamity resulting from the secondment of technical skill from the hacker to the algorithm. Arguably most important, however, is the fact that AI-enabled cyber campaigns contain great potential for operational obfuscation and strategic misdirection. At the operational level, techniques for piggybacking onto routine activities and for adaptive evasion of security protocols add uncertainty, complicating the defensive mission particularly where adversarial learning tools are employed in offense. Strategically, AI-enabled cyber operations offer distinct attempts to persistently shape the spectrum of cyber contention may be able to pursue conflict outcomes beyond the expected scope of adversary operation. On the other, AI-augmented cyber defenses incorporated into national defense postures are likely to be vulnerable to “poisoning” attacks that predict, manipulate and subvert the functionality of defensive algorithms. This article takes on two primary tasks. First, it considers and categorizes the primary ways in which AI technologies are likely to augment offensive cyber operations, including the shape of cyber activities designed to target AI systems. Then, it frames a discussion of implications for deterrence in cyberspace by referring to the policy of persistent engagement, agreed competition and forward defense promulgated in 2018 by the United States. Here, it is argued that the centrality of cyberspace to the deployment and operation of soon-to-be-ubiquitous AI systems implies new motivations for operation within the domain, complicating numerous assumptions that underlie current approaches. In particular, AI cyber operations pose unique measurement issues for the policy regime.
Keywords: deterrence | persistent engagement | cyber | AI | machine learning
Comparison of the effects of two shortened timed-AI protocols on pregnancy per AI in beef cattle
مقایسه اثرات دو پروتکل کوتاه شده با هوش مصنوعی به هنگام روی بارداری در هوش مصنوعی در گاوهای گوشتی-2020
The objective was to compare pregnancy per AI (P/AI) between two shortened timed-AI (TAI) protocols in beef cattle. This study also determined whether administration of eCG in heifers and timing of AI in cows would affect P/AI. Cattle were submitted at random to either a modified 5-d Co-synch protocol (Day 0 ¼ progesterone releasing device (CIDR); Day 5 ¼ CIDR removal and 500 mg of cloprostenol (PGF); Day 8 ¼ 100 mg GnRH concurrent with AI) or J-synch protocol (Day 0 ¼ CIDR insertion and 2mg of estradiol benzoate i.m.; Day 6 ¼ CIDR removal and 500 mg PGF; Day 9 ¼100 mg GnRH concurrent with AI). In Experiment 1, 1135 heifers (13e15 mo of age) received an estrus detection patch (Estrotect™) on Day 5 and 579 were selected at random to receive 300 IU of equine chorionic gonadotropin (eCG) at the time of CIDR removal. Patches were scored from 0 to 3 based on color change between initial application and AI; 0 ¼ unchanged, 1¼ 50% change, 2 ¼ > 50% change, 3 ¼ missing. Estrus was defined to have occurred when the patch was scored 2 or 3. In Experiment 2, 399 cyclic, non-lactating beef cows from 1 location were submitted to either the modified 5-d Co-synch or J-synch protocol and within each protocol cows were TAI at either 66 ±1 (n¼ 199) or 72 ±1 h (n ¼ 200) following CIDR removal. Transrectal ultrasonography was used in both experiments to determine presence of a corpus luteum (CL) on Day 0, and to diagnose pregnancy 35 d after TAI. In Experiment 1, eCG increased estrus rate only in heifers without a CL on day 0 that were submitted to the modified 5-d Co-synch protocol (41.9 vs. 69.6%). Heifers submitted to the J-synch protocol had greater (P ¼ 0.03) P/AI compared with those in the modified 5-d Co-synch (48.7 vs. 41.1%) and heifers that expressed estrus before AI had increased (P < 0.0001) P/AI compared to those that did not (53.6 vs. 36.5%). Administration of eCG and presence of a CL tended to affect P/AI (P ¼ 0.13). In Experiment 2, cows submitted to the J-synch protocol tended (P ¼ 0.07) to have greater P/AI compared to those in the modified 5-d Co-synch (74.1 vs. 66.5%). There was no association between P/AI and timing of AI. In summary, the J-synch protocol resulted in greater P/AI than the modified 5-day Co-synch protocol in heifers and cows. Administration of eCG increased estrus rate in heifers without a CL at the start of the protocol and tended to improve P/AI in all heifers. Timing of AI (66 vs. 72 h) had no effect on P/AI in cows subjected to either TAI protocol.
Keywords: 5-d Co-Synch | J-synch | Pregnancy per AI | Cyclicity | Estrus detection | eCG
Geo-semantic-parsing: AI-powered geoparsing by traversing semantic knowledge graphs
تجزیه جغرافیایی-معنایی: تجزیه و تحلیل ژئوپارسی با هوش مصنوعی با عبور از نمودارهای دانش معنایی-2020
Online social networks convey rich information about geospatial facets of reality. However in most cases, geographic information is not explicit and structured, thus preventing its exploitation in real-time applications. We address this limitation by introducing a novel geoparsing and geotagging technique called Geo-Semantic- Parsing (GSP). GSP identifies location references in free text and extracts the corresponding geographic coordinates. To reach this goal, we employ a semantic annotator to identify relevant portions of the input text and to link them to the corresponding entity in a knowledge graph. Then, we devise and experiment with several efficient strategies for traversing the knowledge graph, thus expanding the available set of information for the geoparsing task. Finally, we exploit all available information for learning a regression model that selects the best entity with which to geotag the input text. We evaluate GSP on a well-known reference dataset including almost 10 k event-related tweets, achieving F1=0.66. We extensively compare our results with those of 2 baselines and 3 state-of-the-art geoparsing techniques, achieving the best performance. On the same dataset, competitors obtain F1 ≤ 0.55. We conclude by providing in-depth analyses of our results, showing that the overall superior performance of GSP is mainly due to a large improvement in recall, with respect to existing techniques.
Keywords: Geoparsing | Geotagging | Artificial intelligence | Knowledge graphs | Twitter
Analyzing patient health information based on IoT sensor with AI for improving patient assistance in the future direction
تجزیه و تحلیل اطلاعات سلامت بیمار مبتنی بر حسگر اینترنت اشیا با هوش مصنوعی برای بهبود کمک به بیمار در مسیر آینده-2020
Internet of Things (IoT) and Artificial Intelligence (AI) play a vital role in the upcoming years to improve the assistance systems. The IoT devices utilize several sensor devices that able to collect a large volume of data in different domains which is processed by AI techniques to make the decision about the assistance problems. Among several applications, in this work, IoT with AI is used to examine the healthcare sectors to improve patient assistance and patient care in the future direction. Traditional health care assistance system fails to predict the exact patient health information and needs which reduces the accuracy of patient assistance process. For these issues, an IoT sensor with AI is used to predict the exact patient details such as fitness tracker, medical reports, health activity, body mass, temperature, and other health care information which helps to choose the right assistance process. Healthcare mobile application is used to achieve this goal and collect the patient’s information. This information is shared in the cloud environment, which is accessed and processed by applying the optimized machine learning techniques. The gathered patient details are processed according to the iterative golden section optimized deep belief neural network (IGDBN). The introduced network examines the patient’s details from the previous health information which helps to predict the exact patient health condition in the future direction. The efficiency of IoT sensor with an AI-based health assistance prediction process is developed using MATLAB tool. Excellence is determined in terms of precision (99.87), loss error (0.045), simple matching coefficient (99.71%), Matthews correlation coefficient (99.10%) and accuracy (99.86%).
Keywords: IoT | Sensor | AI | Patient health condition | Mobile application | MATLAB
AI-Powered Blockchain : A Decentralized Secure Multiparty Computation Protocol for IoV
بلاکچین با هوش مصنوعی: یک پروتکل محاسباتی محرمانه چند جانبه امن برای IoV-2020
The rapid advancements in autonomous technologies have paved way for vehicular networks. In particular, Vehicular Ad-hoc Network (VANET) forms the basis of the future of Intelligent Transportation System (ITS). ITS represents the communication among vehicles by acquiring and sharing the data. Though congestion control is enhanced by Internet of Vehicles (IoV), there are various security criteria where entire communication can lead to many security and privacy challenges. A blockchain can be deployed to provide the IoV devices with the necessary authentication and security feature for the transfer of data. Blockchain based IoV mechanism eliminates the single source of failure and remains secure at base despite having strong security, the higher level layers and applications are susceptible to attacks. Artificial Intelligence (AI) has the potential to overcome several vulnerabilities of current blockchain technology. In this paper, we propose an AI-Powered Blockchain which provides auto coding feature for the smart contracts making it an intelligent contract. Moreover, it speeds up the transaction verification and optimises energy consumption. The results show that intelligent contracts provide higher security compared to smart contracts considering range of different scenarios.
Index Terms: Blockchain | Artificial Intelligence | Smart contract | Internet of Vehicles | Vehicular Network