Reviewerly/exHarmony huggingface dataset:
Compare and Contrast LiDAR and Non-LiDAR Technology in an Autonomous Vehicle: Developing a Safety Framework

Ensuring safety has always been of utmost importance in vehicular operations, where traditionally, human drivers have been solely responsible for driving. However, with the emergence of advanced technologies, we are now on the brink of a new era with the introduction of Autonomous Vehicles (AVs), in which control over the vehicle gradually shifts to Artificial Intelligence (AI). The safety of these vehicles has raised concerns among the public. In terms of safety, human drivers heavily rely on visual perception. Key elements that contribute to safe driving include situational awareness, vehicle control, reaction capabilities, and anticipation of potential hazards. With the introduction of AVs, these fundamental factors remain unchanged. AVs rely on two primary technologies, namely LiDAR and Non-LiDAR, to perceive their surroundings. This research focuses on three primary aspects. Firstly, it involves the development of an image classification model to assess the safety of AVs. This model determines whether the images captured by LiDAR and Non-LiDAR technologies can be accurately predicted using supervised learning. An algorithm is employed to identify the input images obtained from both LiDAR and Non-LiDAR technologies. The results demonstrate that the model achieved a high accuracy rate of 94.63% in predicting the images. Secondly, a Safety Framework is established to facilitate the subsequent proposal for Experimental Research, which is the third aspect. The Safety Framework incorporates the application of LiDAR and Non-LiDAR technologies mounted on a vehicle that is operated in diverse weather conditions by both a human driver and an autonomous system. The Scenario tables, which are currently in a blank state, will be populated upon completion of the Experimental Research. The Experimental Research aims to compare and contrast the performance of LiDAR and Non-LiDAR technologies on a vehicle driven by a human operator versus an Autonomous Vehicle. The findings of this research will be documented in the Scenario table outlined within this study, ultimately shedding light on the safety implications of implementing LiDAR and Non-LiDAR technologies within an AV context.
Evaluation of Feature Descriptors for Scene Classification

The current article discusses the performance of local and global descriptors, as well as convolutional neural networks (CNNs), in tasks involving image recognition in interior spaces. The purpose of the test is to identify several realistic situations that closely resemble the typical working conditions for mobile robots. A robot interacting with its environment may be able to see portions of scenes in which objects are seen from various angles or changes in the lighting in various settings. The purpose is to investigate how well the different descriptors perform in identifying situations that meet the above criteria. In order to evaluate the effectiveness of visual descriptors and convolutional neural networks in the classification of images taken from the perspective of mobile robots in indoor environments, a proprietary database was implemented and subjected to several controlled transformations. These modifications made it possible to analyze the performance of Bag-of-Visual-Words (BoVW), Fisher Vectors (Fisher), Vector of Locally Aggregated Descriptors (VLAD), Global Image Descriptors (GIST), and CNN descriptors in visual categorization tasks according to the situational perception of mobile robots.The findings highlight the advantages of descriptors for the various test scenarios and highlight the need for hybrid models that employ both descriptors and CNNs for scene identification tasks in interior areas where mobile robots operate.
A Brief Review of Explainable Artificial Intelligence (XAI) Techniques

The increasing use of artificial intelligence (AI) in various domains has led to a growing need for AI systems to provide interpretable and understandable results. This need has given rise to the field of explainable artificial intelligence (XAI). XAI refers to the development of AI systems that can provide a clear and interpretable explanation of their decision-making processes to the end-users. In this paper, we provide a comprehensive review of the state-of-the-art techniques in XAI. We start by proposing a classification of the different XAI techniques, from the moment of application to the extent of the explanation and other specific properties of the methods. We then review multiple XAI methods, including ad-hoc techniques, local and global explanations, and other subgroups. Not only the theory behind them is explained, but also their practical application, so as to show the different outputs that can be obtained with different python implementations. Finally, we conclude the paper by highlighting the future lines of research in XAI and its potential impact on society.
SISTEM INFORMASI MONITORING EKSTRA KURIKULER SMP NEGERI 7 KOTA JAMBI BERBASIS WEB

The extracurricular system at SMP Negeri 7 Jambi City which is currently running is still manual, that is, by means of every activity announcement, it is still notified or delivered in the school wall or directly on the school field and notification via WhatsApp group. The management of extracurricular activities makes the academic section of students experience internal problems which will make the process of managing extracurricular activities take a long time. The method used in making this system by means of data collection, system planning, system analysis, system design and database. The system built on this website uses the PHP programming language and MySQL database. The input needs on the Extracurricular Website of SMP Negeri 7 Jambi City are extracurricular data, student data, and registration data. Process requirements needed are: Login Process, Registration Process, and input, edit, and delete processes. The output data produced is in the form of information that will be attached, including: registration activity reports. The purpose of making this Job Training is the creation of an extracurricular website for SMP Negeri 7 Jambi City. With the existence of a new system that has been designed and created by researchers, it can help facilitate the parties involved in developing extracurricular activities effectively.
PENINGKATAN KEMAMPUAN GURU DALAM MENDESAIN EDUGAMES PADA PEMBELAJARAN MATEMATIKA SD

With the development of technology in the 21st century it has been able to provide pro-active learning, easy to understand and comprehensive as a learning environment, so it needs to be created IT-based learning media, namely edugames to help the learning process in elementary schools. The problem encountered in this study is that a small portion of teachers do not understand how to design edugames learning. The purpose of this study is to improve the ability of teachers to design edugames learning in mathematics learning. The type of research used is development using the Plomp model. The subjects in this study were teachers of Bukittingi City 02 Pilot School. The instrument in this study was edugames learning media. This research produces edugames media in the learning process created by the teacher
The identity of information science

Purpose This paper offers a definition of the core of information science, which encompasses most research in the field. The definition provides a unique identity for information science and positions it in the disciplinary universe. Design/methodology/approach After motivating the objective, a definition of the core and an explanation of its key aspects are provided. The definition is related to other definitions of information science before controversial discourse aspects are briefly addressed: discipline vs. field, science vs. humanities, library vs. information science and application vs. theory. Interdisciplinarity as an often-assumed foundation of information science is challenged. Findings Information science is concerned with how information is manifested across space and time. Information is manifested to facilitate and support the representation, access, documentation and preservation of ideas, activities, or practices, and to enable different types of interactions. Research and professional practice encompass the infrastructures – institutions and technology –and phenomena and practices around manifested information across space and time as its core contribution to the scholarly landscape. Information science collaborates with other disciplines to work on complex information problems that need multi- and interdisciplinary approaches to address them. Originality/value The paper argues that new information problems may change the core of the field, but throughout its existence, the discipline has remained quite stable in its central focus, yet proved to be highly adaptive to the tremendous changes in the forms, practices, institutions and technologies around and for manifested information.
Quantum-inspired Computing: Entanglement-enhanced Technique for Short Portfolio in Global Markets

Portfolio optimization is an essential aspect of the development of quantum computing. The quantum-inspired op-timization (QIO) algorithm facilitates an efficient search for the optimal solution by simulating quantum mechanics on a classical computer, thus bridging quantum theory and the actual implementation of quantum computing. This study proposes an entanglement-based QIO to optimize the short-selling portfolio in a group of seven (G7) industrialized nations, which are the world's largest markets and significantly impact global economies. To diversify investment options in response to the ever-changing markets, short-selling is a worthy topic for dis-cussion. The innovative trend ratio can precisely determine the performance of a short-selling portfolio during a stable downward trend. Implementing the short-selling trend ratio model in the significant G7 markets broadens its applicability.
Modified gate activation functions of Bi-LSTM-based SC-FDMA channel equalization

Abstract In recent years, artificial neural networks (ANNs) have grown a lot and helped solve numerous problems in wireless communication systems. We have evaluated the performance of the Bidirectional-Long-Short-Term-Memory (Bi-LSTM) recurrent neural networks (RNNs) for joint blind channel equalization and symbol detection using a variety of activation functions (Afs) for the gate units (sigmoid) of Bi-LSTMs without requiring any prior knowledge of channel state information (CSI). The performance of Bi-LSTM networks with different AFs found in the literature is compared. This comparison was carried out with the assistance of three different learning algorithms, namely Adam, rmsprop, and SGdm. The research findings clearly show that performance, as measured by equalization accuracy, can be improved. Furthermore, demonstrate that the sigmoid gate activation function (GAF), which is commonly used in Bi-LSTMs, does not significantly contribute to optimal network behavior. In contrast, there are a great many less well-known AFs that are capable of outperforming the ones that are most frequently utilized.
Blockchain Technology Approach on Securing Smart Water Metering Networks Toward Anomaly Free: An Overview and Future Research Directions

Innovation within the water sector has seen the adoption of innovative technological solutions (i.e. artificial intelligence, Internet of Things (IoT), and blockchain) in water utilities that allow the integration of information and Communication Technologies (ICT) into traditional water distribution systems, making them Smart Water Metering Networks (SWMNs). However, the birth of SWMNs has not only enabled a convenient, reliable, error-free transmission of water consumption readings to the control center, but it has exposed these networks to security hazards that can only be solved by security mechanisms that can foster wider adoption of the SWMNs. A lot of researchers have been researching applications of machine learning to detect and locate anomalies in SWMNs, and just like smart grids, there is little work done on protecting these networks from security attacks. Due to the heterogeneous nature and limited computed resources of SWMNs, they hinder the construction of a trusted network. Although there has been little work done on using blockchain technology to secure SWMNs, research has proven that blockchain technology has the potential to strengthen and secure these networks. Moreover, blockchain sort of guarantees the trustworthiness and robustness of the network, which makes it resistant to cyberattacks. This work provides a survey on evidence-based scoping, exhausting the available literature on blockchain technology approaches to securing SWMNs, and identifies potential research for future investigations. Finally, to provide answers and fill research gaps in SWMNs using a limited set of blockchain technologies.
Demo: First Demonstration of Real-Time Photonic-Electronic DNN Acceleration on SmartNICs

We demonstrate Lightning, a reconfigurable photonic-electronic deep learning smartNIC that serves real-time inference requests at 4.055 GHz compute frequency. To do so, Lightning uses a novel datapath to feed traffic from the NIC into its photonic computing cores without incurring digital data movement bottlenecks. Lightning achieves this by employing a reconfigurable count-action abstraction, which decouples the compute control plane from the data plane. The count-action abstraction counts the number of operations for each computation task in the Directed Acyclic Graph (DAG). It then triggers the execution of the next task(s) as soon as the previous task is finished without interrupting the dataflow. Our prototype shows that Lightning achieves 99.25% photonic MAC accuracy. When serving real-time inference requests, Lightning accelerates the end-to-end inference latency of the LeNet DNN by 9.4× and 6.6× compared to Nvidia P4 and A100 GPUs, respectively.
Boolean matrix factorization for symmetric binary variables

Binary variables classify into two types: asymmetric variables, where one state (1 or 0) is significantly more valuable than the other, and symmetric variables, where both states are equally valuable. Boolean matrix factorization (BMF), a popular methodology of preprocessing and analyzing tabular binary data, handles its input as asymmetric variables. In the paper, we develop an alternative that handles Boolean matrices as symmetric variables. Our method differs from traditional BMF in that the factors are linearly ordered by priority, and factors can contradict each other, meaning that one factor can assign a value of 1 while the other assigns a value of 0. In such a case, the factor with higher priority is the relevant one. Through experiments, we demonstrate that our approach provides a more compact data description than a straightforward application of the traditional BMF methods. Moreover, it is even able to overcome the Schein rank.
Poster: P4-DPLL: Accelerating SAT Solving Using Switching ASICs

People have been leveraging the capabilities of programmable switches, which are programmable in the data plane and process packets at the line rate, to improve the performance of distributed systems. However, few have explored whether programmable switches can speed up problem-solving. In this demonstration, we take a first step to explore the feasibility and benefits of this line of research. Specifically, we select the SAT problem, one of the most fundamental problems in computer science, as a case study. Our intuition is that by exploiting the parallel lookup capability of programmable switches, we can substantially speed up the process of checking whether an assignment is a solution to a SAT problem. In particular, we base on the classical DPLL algorithm and design P4-DPLL [5], which consists of (1) match action tables using TCAM to quickly check assignment satisfiability, and (2) a stack data structure using register and SRAM to efficiently make variable search decisions in the data plane. We implement a prototype of P4-DPLL and evaluate its performance extensively. Results show that P4-DPLL improves the solving time by 101x speedup on 90% quantile of all test cases, compared with a CPU-based DPLL implementation.
GGFAST: Automating Generation of Flexible Network Traffic Classifiers

When employing supervised machine learning to analyze network traffic, the heart of the task often lies in developing effective features for the ML to leverage. We develop GGFAST, a unified, automated framework that can build powerful classifiers for specific network traffic analysis tasks, built on interpretable features. The framework uses only packet sizes, directionality, and sequencing, facilitating analysis in a payload-agnostic fashion that remains applicable in the presence of encryption.
Poster: PipeLLM: Pipeline LLM Inference on Heterogeneous Devices with Sequence Slicing

Large Language Models (LLMs) has fostered the creation of innovative requirements. Locally deployed LLMs for micro-enterprise mitigates potential issues such as privacy infringements and sluggish response. However, they are hampered by the limitations in computing capability and memory space of possessed devices. We introduce PipeLLM, which allocates the model across devices commensurate with their computing capabilities. It enables the parallel execution of layers with slicing input sequence along the token dimension. PipeLLM demonstrates the potential to accelerate LLM inference with heterogeneity devices, offering a solution for LLM deployment in micro-enterprise hardware environment.
Veritas: Answering Causal Queries from Video Streaming Traces

In this paper, we consider the task of answering what-if questions in the context of adaptive bit rate (ABR) video streaming without access to randomized control trials (RCTs) (e.g., no A/B testing) - i.e., given recorded data of an existing deployed system, what would be the performance impact if we changed its design. Our work makes three contributions. First, we show the problem is challenging since data may only be available for a single ABR algorithm without RCTs, and since it is necessary to deal with the cascading effects that past ABR decisions have on future decisions. Next we present Veritas, the first framework that tackles causal reasoning for video streaming without requiring data collected through RCTs. Integral to Veritas is an easy-to-interpret domain-specific ML model that relates the latent stochastic process (intrinsic bandwidth that the video session can achieve) to actual observations (download times), while exploiting counterfactual queries via abduction using the observed TCP states (e.g., congestion window) for blocking the cascading dependencies. Third, we evaluate Veritas's ability to accurately answer a wide range of what-if questions using emulation experiments, and data of real video sessions from Puffer. The results show that (i) Veritas accurately tackles a wider range of what-if questions (e.g., change of buffer size or video quality) that existing approaches cannot; (ii) Veritas without RCT training data achieves performance comparable or better than a recent parallel approach that requires RCT data; and (iii) in many scenarios Veritas achieves accuracy close to an ideal oracle.
Impact of smart classrooms combined with student-centered pedagogies on rural students’ learning outcomes: Pedagogy and duration as moderator variables

Rural students' lack of access to high-quality educational resources leads to lagging academic achievement and a deficiency in 21st-century skills. Smart classrooms integrate advanced technology to provide equitable digital learning and interactive environments for these students. However, determining optimal pedagogical approaches for smart classrooms and investigating the potential technology novelty effect remain areas of exploration. This study delved into the impact of smart classrooms combined with various student-centered pedagogies (SC-SCP)—team-based learning (TBL), problem-based learning (PBL), and project-based learning (PjBL), on rural students' learning outcomes. These outcomes encompassed academic achievement, collaboration, and problem-solving skills. Additionally, the research examined how student-centered pedagogies and intervention duration moderated the effects of SC-SCP. A meta-analysis across 24 rural schools in Taiwan was conducted, each with a comparison class and an experimental class, resulting in a total of 48 classes. While the comparison classes within traditional classrooms combined with teacher-centered pedagogies (TC-TCP), the experimental classes embraced SC-SCP in smart classrooms. The findings indicate that: 1) The experimental classes outperformed the comparison classes in enhancing students' learning outcomes; 2) when considering the moderating impact of student-centered pedagogies, PjBL exhibited the most substantial improvement in problem-solving skills, followed by PBL, and finally TBL; additionally, 3) SC-SCP demonstrated a minor improvement effect size in problem-solving skills after four weeks of intervention, which escalated to a significant effect size after eight weeks of intervention. In summary, these results suggest that a combination of smart classrooms and PjBL, implemented for a duration of over two months, leads to the most substantial enhancement in learning outcomes for rural students.
Poster: Understanding Interactions between Overload Control Core Allocation in Low-Latency Network Stacks

Modern data center applications require servers to respond to requests from thousands of clients while maintaining micro-second-scale SLOs. Efficient operation of the data center infrastructure requires assigning the exact amount of resources needed by the application, no more and no less. A burst of incoming traffic that exceeds allocated capacity can cause long queues, requiring efficient and responsive overload control schemes. A sudden drop in demand requires re-allocation of resources to other applications. Within a single host, several mechanisms have been proposed for overload control [1, 3, 8, 9] and dynamic core allocation [2, 4, 6, 7]. The state of the art in both control loops is designed to react to microsecond-level changes in load. We present a simulation-based study of the interaction between Overload Controllers and Core Allocators at microsecond timescales, examining their macroscopic implications at larger timescales.
Beyond Limits: How to Disable Validators in Secure Networks

Relying party validator is a critical component of RPKI: it fetches and validates signed authorizations mapping prefixes to their owners. Routers use this information to block bogus BGP routes.
Demo: PDNS: A Fully Privacy-Preserving DNS

The Domain Name System (DNS) is a key component of Internet-based communication and its privacy has been neglected for years. Recently, DNS over HTTPS has improved the situation by fixing the issue of in-path middleboxes. Further progress has been made with proxy-based solutions such as Oblivious DoH, which separate a user's identity from their DNS queries. However, these solutions rely on non-collusion between DNS resolvers and proxy networks. This paper instead proposes PDNS, a new DNS extension that uses Private Information Retrieval to allow DNS resolvers to operate on blind queries, thereby eliminating any privacy leaks.