dataesr/scientific-paragraphs-categorization huggingface dataset:
Other parameters used: • Advertising Interval set to 100 ms In theory, the longer the connection interval, the higher the Energy consumed, but the connection interval not only has an impact on the overall energy consumption but will also impact the throughput, hence the need to choose a CI according to the application correctly. In Scenario I, for example, there is data transmission after every sample acquisition, requiring a CI of less than 20 ms to ensure no data loss. In scenarios II and III, on the other hand, data transmission occurs only once every 2s and 4s, respectively, which means that longer connection intervals can be used. Scenario IV, on the contrary, will use a fast CI as we only connect to BLE once, send data as fast as possible, and immediately stop the BLE radio. To further assess the impact of Bluetooth connection interval, we implemented Scenario I and measured the mean Current consumed by the Arduino board to transmit data over BLE using different connection intervals.
Energy consumption in configuration The BLE radio and accelerometer sensor configuration consume current, Figs. 8(a ) and 8(b) show the overall consumption of the two parts distinctively, and Fig. 9  shows the profile of the Current consumed when the configuration of both the accelerometer and BLE is done simultaneously (running in series). In this setting, the voltage supply of the development board is 3. 3 V, as recommended. We bypassed the voltage regulator to minimize energy consumption and switch off unused features. We use the 'Keysight Cx3324 A device current waveform analyzer' to perform all measurements and power consumption analysis  [34]. No notable changes in the current consumption during the configuration phase have been noticed when the AI model is ported to the board, as the model is compiled along with the code itself and stored directly on its flash memory.
Graph databases are enjoying enormous popularity, through both their RDF and Property Graphs (PG) incarnations, in a variety of applications. To query graphs, query languages provide structured, as well as unstructured primitives. While structured queries allow expressing precise information needs, they are unsuited for exploring unfamiliar datasets, as they require prior knowledge of the schema and structure of the dataset. Prior research on keyword search in graph databases do not suffer from this limitation. However, keyword queries do not allow expressing precise search criteria when users do know some. This tutorial (1. 5 hours) builds a continuum between structured graph querying through languages such as SPARQL and GPML, a recently proposed standard for PG querying, on one hand, and graph keyword search, on the other hand.
INTRODUCTION Graph databases Graph-structured data has many applications, e. g. , social network analysis, fraud detection, biological networks, etc. [40]. One particularly notable application is that of investigative journalism (IJ), where complex journalistic investigations are backed by digital data. Such data is oftentimes heterogeneous; a database consists of different datasets, possibly of different data models. In an enterprise setting, data could be kept as such and exploited through dedicated data lake tools. However, for journalists, a data lake solution is unfeasible due to lack of IT expertise, time, and resources. Instead, heterogeneous data can be automatically This work is licensed under the Creative Commons BY-NC-ND 4. 0 International License. Visit https://creativecommons.
org/licenses/by-nc-nd/4. 0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing info@vldb. org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 16, No. 12 ISSN 2150-8097. doi:10. 14778/3611540. 3611577 transformed into graphs, which journalists then exploit. This has been pioneered by the International Consortium of Investigative Journalism (ICIJ) in their initial Panama Papers investigation; the ConnectionLens  [4, 9, 14]  system goes towards the same goal. Other well-known graph database applications related to IJ are financial crime investigation and fraud detection, used by graph database providers such as Oracle and Neo4J to advertise their products.
Graph querying To query graphs, the W3C's standard SPARQL  [42]  query language and Cypher  [39]  are among the best known query standards. Additionally, the International Organization for Standardization (ISO) and International Electrotechnical Commission (IEC) are developing GQL as a Property Graph query standard, with the graph pattern matching sub-language (GPML)  [17]  at its core. Structured queries require users to be familiar with the schema of the graphs; this is typically not the case with journalists, or users discovering datasets. This problem is partially tackled by a useful feature of modern graph query languages, namely property paths or reachability querying. This allows finding if (and how) two sets of nodes in the graph are reachable to each other. SPARQL 1. 1 queries can check, for example, if a French entrepreneur "Alice" has a path to an American entrepreneur "Bob" in the sample graph shown in Figure  1.
However, SPARQL 1. 1 does not allow returning the matching paths to users. In contrast, a GPML query may also return the paths between two given sets of nodes. This is useful, for example, in the fight against money laundering, by enabling finding routes in which money is laundered across countries, enterprises, etc. None of the graph query languages, however, natively support a general connectivity search between three or more sets of nodes. For instance, in Figure  1 , one cannot express the query seeking connections between American entrepreneurs, French entrepreneurs and French politicians. The ability to express such a query is useful for investigative journalists as money laundering usually involves multiple players. Keyword search in (graph) databases A different paradigm for searching for information in graphs is keyword search.
This has been historically studied for structured and semistructured databases, viewed as graphs, e. g. ,  [3, 13, 18, 24, 30]. Users specify 𝑚 keywords, and request trees (or subgraphs) connecting nodes from the graphs, in which at least one node label matches each keyword. For example, asking "Alice Bob Falcon" leads to returning connecting trees, each containing one node whose label matches each keyword. Keyword search can thus be seen as at one end of a continuum on an axis of information search in graph, with structured queries being at the other end. Computationally speaking, keyword search in graphs is closely related to the Group Steiner Tree Problem (GSTP), which, given 𝑚 node sets, asks for the top-score, e. g. , fewest-edges, tree connecting  one node from each set; the problem is NP-hard  [27].
TARGET AUDIENCE AND DURATION This lecture-style tutorial of 1. 5 hours is aimed for researchers, graduate students and industry practitioners, who are interested in graph-structured data and its applications. Researchers and graduate students will benefit from an introduction to graphs and ways to query it; they will also find open problems and challenges to be addressed as future work directions. Industry practitioners will get an overview of existing works in graph querying, their scope and limitations along with introduction to recent research prototypes having applicability in various industrial softwares. In general, the tutorial will also enable new users of graph databases to learn about techniques to explore their datasets with ease. No prior knowledge of graphs or databases is expected.
NOVELTY AND POSITIONING The tutorial  [11]  introduced a unified graph data model, the grammar for a structured query language involving regular expression based path querying, and its various evaluation paradigms including approximations. [34]  discussed the query processing, primarily the join algorithms, of many prominent graph databases. The tutorial  [38]  discussed graph exploration using exemplar queries. Our tutorial is the first to cover a continuum of various means of querying graphs -the extended queries support the space starting from purely structured queries for precise information needs to that of unstructured keyword queries for graph exploration. It has not been presented previously.
SPEAKERS Ioana Manolescu (https://pages. saclay. inria. fr/ioana. manolescu/) is a senior researcher at Inria Saclay and a part-time professor at Ecole Polytechnique, France. She is the lead of the CEDAR Inria team, focusing on rich data analytics at cloud scale. She is also the scientific director of LabIA, a program ran by the French government to introduce AI solutions in the public national and local administration. She has been a member of the PVLDB Endowment Board of Trustees, an Associate PVLDB Editor, president of the ACM SIGMOD PhD Award Committee, chair of the IEEE ICDE conference, and a program chair of EDBT, SSDBM, ICWE among others. She has co-authored more than 150 articles in international journals and conferences and co-authored books on "Web Data Management" and on "Cloud-based RDF Data Management".
These methods do not require LoS. However, passive RF detection faces challenges when the signals emitted by the drone coexist with numerous other signals, such as Wi-Fi or Bluetooth, which share the same frequency band. This presents a challenge in attributing each signal to its respective emitter, especially in complex urban environments. Consequently, it necessitates the collection of RF communication data from each RF source and the construction of a comprehensive database encompassing various scenarios and diverse environments to enhance detection capabilities for more general cases. To address these challenges, recent studies have made notable contributions. In 2019, Al-Sa'd et al. [34]  introduced an open-source drone database for RF-based detection and identification, demonstrating the effectiveness of deep neural networks in achieving high accuracy rates.
In 2020, Feng et al. [35]  proposed an efficient two-step method for detecting drone hijacking using a combination of genetic algorithm-extreme gradient boosting (GA-XGBoost) with GPS and inertial measurement unit (IMU) data, achieving high prediction correctness and time savings. In 2021, the study conducted by Basak et al. [31]  introduced a two-stage approach. The detection stage employed goodness-of-fit (GoF) sensing, while the classification stage utilized the deep recurrent neural network (DRNN) framework. They developed a customized you only look once (YOLO)-lite framework from scratch to achieve integrated drone RF signal detection, spectrum localization, and classification. The performance of both techniques was evaluated using a newly created drone dataset, demonstrating favorable results in terms of detection and classification.
Type Previous methods used for drone detection and classification exhibit suboptimal performance. First, to integrate new drones into the full database, it is necessary to study and record all potential communication scenarios and take into account different channel and multipath models  [32, 34, 39]. However, this approach can introduce limitations in terms of flexibility and operational efficiency. Moreover, they rely heavily on AI algorithms that operate on large data sets, resulting in a procedure that requires significant training and consumes considerable memory resources. Furthermore, the previous techniques concentrate only on the processing of the raw modulated signals without the possibility of analyzing the data encoded in the communication protocol. Therefore, they cannot extract crucial information relevant to the defense industry, such as the device manufacturer, the location of the drone, and the purpose and mission of the flight.
In this perspective of analyzing the data link layer and the network layer, some recent research has contributed to the development of this idea. Christof  [40]  reverse-engineered the Wi-Fi protocol of DJI drones. Using deductive and bit-perfect analysis, he was able to determine the structure of the protocol and extract information using specially developed open-source software. This information can be crucial for detecting and locating drones in real time. In addition, Bender  [41]  and the Department 13 article  [42]  demonstrated that DJI Drone IDs are not encrypted. This discovery is essential for drone detection, as it allows DJI drones to be spotted and identified using dedicated software-defined radio (SDR). The author proposed a real-time DJI OcuSync Drone ID detection system, using low-cost SDRs with robust packet analysis.
The manufacturer then assigns a unique value for the last 3 bytes to ensure that each MAC address is one-of-a-kind worldwide. Therefore, the first three bytes of the MAC address serves as an identifying characteristic of the supplier, which allows us to detect the drone's presence. To sum up, this technique requires a Wi-Fi drone MAC address database  [54]  for detection. This method offers several benefits. Firstly, it is independent of the size and the drone material. Secondly, it does not require a LoS between the drone and the sensor. However, this means maintaining an up-to-date database of all MAC addresses for new UAVs.
Decoding Telemetry Packets This section describes the method for decoding Wi-Fi RDID and enhanced Wi-Fi DJI Drone ID packets. To begin with, we put the Panda and the Intel wireless cards into monitor mode. This is achieved using the "aircrack-ng" tool with the following Linux command: "sudo airmon-ng start $<name_wireless_card>". Once in monitor mode, the cards capture all traffic. To focus on analyzing specific packets, a series of filters is applied.
These packets are alternately sent down the Wi-Fi link every 200 ms. Thanks to the OUI tag and the knowledge of the subcommands, Kismet can detect a DJI Drone ID packet data and classify it as "uav. device". Kismet allows tracking using a list of drone MAC addresses by retrieving a UAV's telemetry history. For more details, the DJI enhanced Wi-Fi Drone ID packet structure with the decoding process is explicitly detailed in references  [41, 42]. Knowing that a DJI enhanced Wi-Fi Drone ID occupies a 5 MHz bandwidth, Wi-Fi channels can be scanned with Kismet from 1 to 177 in the 2. 4 GHz and 5. 8 GHz frequency bands using the following bash command  [41]  shown in Figure  8. By using the Kismet_Rest application programming interface (API)  [58]  on Python, it is possible to retrieve decoded DJI Drone ID packet data from the Kismet server in the form of a JavaScript object notation (JSON) file as shown in Figure  9.
This packet contains information about a DJI drone, including its serial number, model, and frequency histogram usage. Additionally, telemetry data are included such as the drone's pitch, yaw, and roll, as well as its speed and altitude. It also includes a timestamp indicating when the telemetry data were recorded. This information can be used to track and analyze drone movements and behavior. The Python code connects to a Kismet server using the kismet_rest API to retrieve information about a wireless detected device with a specific MAC address. As shown in the flowchart (Figure  10 ), the code performs several steps. First, the code imports the required kismet_rest module. Then, it creates a KismetConnector instance and establishes a connection to the Kismet server. Next, the code defines a list of MAC address masks, stored in the mac_list variable, for the devices that need to be detected.
The system was also tested in a hypothetical scenario involving securing an area with a 200 m radius. The remaining reaction times for different drones were computed, providing useful data for applications aiming at the interception of unauthorized drones. This research presents a system that integrates detection, classification, and localization functionalities in a single module. This study demonstrated the system's capability to differentiate drone signals and track their movements. While the study showcased the system's potential in managing drone activities and its potential contribution to public safety and security, some areas need improvement. To enhance our solution, we plan future expansions. These include integrating realtime decoding of OcuSync DJI Drone IDs for other DJI drone identification and classification, incorporating AI models for detecting other drones that are not equipped with Drone IDs, improving the RF receiver to extend system coverage, supporting different frequency bands to utilize the system indifferent geographic region, and the possibility to connect the system with a jamming device  [60, 61]  for drone interception and man-machine interface (MMI).
significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.
, 2022a (Singh et al. , , 2022b)). In parallel to the ongoing development of AI algorithms, the literature has seen an increased level of chatbot related studies  (Lokman and Ameedeen, 2018). Chatbots traditionally used NLP to respond to queries raised by the user, while mapping it to the best possible response sets available in the system. In order to provide real time feedback to customers, chatbots have adopted language models along with deep learning while addressing NLP problems  (Bellegarda, 2004; Melis et al. , 2017; Kushwaha and Kar, 2021). The recent launch of OpenAI's ChatGPT significantly extends the capabilities of chatbots via the integration of deep learning and language models based on the Generative Pre-training Transformer (GPT) architecture  (Radford et al. , 2018). Language models attempt to predict the likelihood of a sequence of words a typical human interaction is likely to create through generative and discriminative algorithms, typically through the application of deep learning and transformer architectures of neural networks  (Bengio et al.
, 2000; Bellegarda, 2004; Vaswani et al. , 2017). ChatGPT uses a combination of unsupervised pre-training and supervised fine-tuning to generate human-like responses to queries and provide responses to topics that resemble that of a human expert. The GPT-3 model is the latest extension built on a language model with 175 billion parameters, trained on a diverse dataset of naturally used text obtained from different internet sources such as web pages, books, research articles and social chatter. While current language models generally utilise deep learning with a focus on supervised learning, future evolutionary models may be built more on reinforcement learning  (Uc-Cetina et al. , 2022). The recent widespread global adoption of ChatGPT has demonstrated the tremendous range of use cases for the technology including software development and testing, poetry, essays, business letters, and contracts  (Metz, 2022; Reed, 2022; Tung, 2023).
However, it has also raised a number of concerns related to the difficulty in differentiating human versus AI authorship within academic and education communities, and renewed debate on the role of traditional human endeavours  (Else, 2023; Stokel-Walker, 2023). These challenges arise because ChatGPT can be extensively used for NLP tasks such as text generation, language translation, and generating answers to a plethora of questions, engendering both positive and adverse impacts. With this background, in this article we seek to answer the two following research questions: RQ1) What are the opportunities, challenges, and implications related to generative AI technologies such as ChatGPT in the context of education, business, and society? RQ2) What are the most important research questions to investigate related to generative AI technologies such as ChatGPT in the context of education, business, and society? The remainder of this article is structured as follows.
Indeed, one of the most striking features of these generative AI platforms is that they have been adopted massively in a short period of time: launched on November 30, 2022, ChatGPT has been used by 1 million users in the first 5 days after its launch. The distinctive feature of ChatGPT is precisely its capability to generate textual content. In just 3 months after its release, ChatGPT has been deployed by many software developers, creative writers, scholars/ teachers, and songwriters to generate computer software and apps, text, academic essays, song lyrics. Platform users have expressed mixed feelings. I tried ChatGPT myself in early December asking the platform to develop an essay on the relationships between "big data" and "innovation management". The result was rather disappointing as the text seemed rather weak in terms of logical flow, inaccurate in terms of factuality and truth, not critical in terms of elaboration of data, and not novel.
As AI platforms and the underlying technology will evolve, future research will need to investigate if and to what extent the role played by generative AI will be increasingly relevant in triggering innovation outcomes. 2. 1. 3. Contribution 3 2. 1. 3. 1. Human and generative AI collaboration 2. 1. 3. 1. 1. Lessons from utilitarianism -Lemuria Carter and Soumyadeb Chowdhury. ChatGPT is a cutting-edge AI language model that leverages generative AI techniques to provide algorithm generated conversational responses to question prompts  (van Dis et al. , 2023). The outputs from generative AI models are almost indistinguishable from human-generated content, as they are trained using nearly everything available on the web (for e. g. , around 45 terabytes of text data in the case of ChatGPT). The model can be trained to perform specific tasks, such as preparing slides in a specific style, writing marketing campaigns for a specific demographic, online gaming commentary and generating high resolution images  (Chui et al.
2. 1. 5. 2. ChatGPT as a partner. ChatGPT is based on GPT-3 (Generative Pre-trained Transformer 3), which was introduced in June 2020. GPT-3 has 175 billion parameters 3 and is one of the most powerful language models available to date. Whereas ChatGPT uses the same dataset and parameters as GPT-3, its conversational focus and ease of use (or readiness-to-use) made it a huge success (which even surprised many 2 Thanks to Markus Luczak-Roesch, Shahper Richter, Kai Riemer, Gerhard Schwabe as well as David Wagner for comments that helped me to improve this contribution and to the anonymous student who shared their insightful experience with ChatGPT with me and allowed me to use it in this article. 3 The more parameters (or tunable values) a model has, the more complex patterns it can learn and the better it can perform on a variety of tasks.