Reviewerly/exHarmony huggingface dataset:
The Inexplicable Efficacy of Language Models

column The Inexplicable Efficacy of Language Models Authors: Rachith Aiyappa Indiana University Indiana UniversitySearch about this author , Zoher Kachwala Indiana University Indiana UniversitySearch about this author Authors Info & Claims XRDS: Crossroads, The ACM Magazine for StudentsVolume 29Issue 3Spring 2023 pp 60–62https://doi.org/10.1145/3589654Published:12 April 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Making Speech Recognition Work for Children: An Interview with Amelia Kelly

What does it take to build new AI technologies for education? Dr. Amelia Kelly, chief technology officer at SoapBox Labs, shares her experience with us in this interview.
Structural Optimization of Interuniversity Campus with System Analysis of the Territorial Dispersion of Residential and Educational Facilities

This paper presents an international program that aims to create an innovative educational environment in Voronezh, Russia by integrating four universities in the city to form an interuniversity campus. The program focuses on developing an educational cluster and integrating researchers using green and energy-efficient technologies. To achieve this, the study formed distance and time matrices for pedestrian and car routes between university buildings and dormitories, and conducted a cluster analysis of university facilities. The analysis identified possible trajectories for developing an inter-university campus that optimizes energy consumption and promotes further development in this area. The main findings suggest that integrating universities into a campus can improve energy efficiency and foster collaboration among researchers, ultimately creating a more sustainable and productive educational environment. The proposed program could attract additional investments and contribute to economic growth and population influx. Overall, this study highlights the potential benefits of integrating universities to create an innovative educational environment.
Building a Knowledge Base of Bridge Maintenance Using Knowledge Graph

To effectively manage the heterogeneous and discrete knowledge of the bridge maintenance domain, this study adopts knowledge graph technology to build a knowledge base of bridge maintenance, called the bridge maintenance knowledge graph (BMKG). The BMKG uses an ontology as the knowledge organization and representation framework and a graph database as the knowledge storage tool. To facilitate the construction of the BMKG, a hybrid method combining a top-down approach and a bottom-up approach is proposed. Firstly, a bridge maintenance domain ontology (BMDO) is coded with Protégé and represented in Web Ontology Language. Secondly, rule reasoning and ontology reasoning are implemented on the BMDO in Protégé in order to automatically complete missing relations or attribute values. Thirdly, ontology reasoning is adopted to perform consistency check on the BMDO. Lastly, the BMDO model is stored in the Neo4j graph database through data format conversion, thus completing the construction of the BMKG. The BMKG is applied in a typical scenario of bridge maintenance to demonstrate its application value. Results show that the proposed hybrid method can create a knowledge graph that can realize the transformation from discrete data into interconnected knowledge. Knowledge graph offers a novel idea to create a knowledge base in the bridge maintenance domain.
Path Planning and Tracking Control of Car-like Robot Based on Improved NSGA-III and Fuzzy Sliding Mode Control

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In recent years, research on car-like robots has received more attention due to the rapid development of artificial intelligence from diverse disciplines. As essential parts, path planning and lateral path tracking control are the basis for car-like robots to complete automation tasks. Based on the two-degree-of-freedom vehicle dynamic model, this study profoundly analyzes the car-like robots’ path planning and lateral path tracking control. Three objectives: path length, path smoothness, and path safety, are defined and used to construct a multi-objective path planning model. By introducing an adaptive factor, redefining the selection of reference points, and using the cubic spline interpolation for path determination, an improved NGSA-III is proposed, which is mostly adapted in solving the multi-objective path planning problem. Furthermore, the chattering problem of sliding mode control is eliminated by introducing fuzzy control, and a sliding mode controller with fuzzy control is also applied for the lateral path tracking control of car-like robots. To verify the effectiveness of the proposed methods, simulation experiments are carried out for the planning and control layers, respectively. Comparing the improved NSGA-III with NSGA-III, HV decreased by 12.47%, SP increased by 1.17%, and the number of iterations decreased by 20.59% on average. The results show that the improved NSGA-III sacrifices part of the population diversity but has a more significant improvement in the convergence and accurate path predictions. Furthermore, the lateral path tracking controller effectively solved the chattering problem and reduced the lateral deviation by 29.35% and 52.53%, under the standard double-line-change working condition with road adhesion coefficients of 0.8 and 0.2. In addition, the proposed planning and control methods in this study can cooperate under medium and low speed conditions which is suitable for most application scenarios of car-like robots.&lt;/div&gt;&lt;/div&gt;
Methods of managing the evolution of ontologies and their alignments

Abstract Nowadays, none can expect that knowledge about some part of reality will not change. Consequently, a representation of such evolving knowledge (for example, ontologies) also changes. Such changes entail that applications incorporating such knowledge may become compromised and yield wrong results. An example of such an application is ontology alignment which can be informally described as a set of connections between two ontologies. Those connections mark elements from two ontologies that relate to the same parts of reality. In changing one of the corresponding ontologies, such connections may become invalid. One may designate the ontology alignment once again from scratch for altered ontologies. However, such an approach is time and resource-consuming. The paper comprehensively presents our ontology evolution and alignment maintenance framework. It can be used to preserve the validity of ontology alignment using only the analysis of changes introduced to maintained ontologies. The precise definition of ontologies is provided, along with a definition of the ontology change log. A set of algorithms that allow revalidating ontology alignments have been built based on such elements.
Solving large-scale multi-agent tasks via transfer learning with dynamic state representation

Many research results have emerged in the past decade regarding multi-agent reinforcement learning. These include the successful application of asynchronous advantage actor-critic, double deep Q-network and other algorithms in multi-agent environments, and the more representative multi-agent training method based on the classical centralized training distributed execution algorithm QMIX. However, in a large-scale multi-agent environment, training becomes a major challenge due to the exponential growth of the state-action space. In this article, we design a training scheme from small-scale multi-agent training to large-scale multi-agent training. We use the transfer learning method to enable the training of large-scale agents to use the knowledge accumulated by training small-scale agents. We achieve policy transfer between tasks with different numbers of agents by designing a new dynamic state representation network, which uses a self-attention mechanism to capture and represent the local observations of agents. The dynamic state representation network makes it possible to expand the policy model from a few agents (4 agents, 10 agents) task to large-scale agents (16 agents, 50 agents) task. Furthermore, we conducted experiments in the famous real-time strategy game Starcraft II and the multi-agent research platform MAgent. And also set unmanned aerial vehicles trajectory planning simulations. Experimental results show that our approach not only reduces the time consumption of a large number of agent training tasks but also improves the final training performance.
Anomaly Detection Using Convolutional Neural Network and Generative Adversarial Network

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;In the automotive embedded system domain, the measurements from vehicle and Hardware-In-Loop are currently evaluated against the testcases, either manually or via automation scripts. These evaluations are localized; they evaluate a limited number of signals for a particular measurement without considering system-level behavior. This results in defect leakage. This study aims to develop a tool that can notify anomalies at the signal level in a new measurement without referring to the testcases, considering a more significant number of system-level signals, thereby significantly reducing the defect leakage. The tool learns important features and patterns of each maneuver from many historical measurements using deep learning techniques. We tried two CNN (convolution neural network) models. The first one is a specially designed CNN that does this maneuver classification and class-specific feature extraction. The second model we tried is the FCN (Fully Convolutional Network) Classification model. CNN-based architecture can be trained faster than the recurrent neural network (RNN) model because it utilizes features extracted from the input data. A Generative Adversarial Network (GAN) model is used in series with the CNN model to clone each of these maneuvers for predicting the anomalies. During the testing phase, the CNN model maps the test measurement to the most similar maneuver from the list of already learned maneuvers, followed by the GAN model outputting the anomalies, if any. To validate the tool, 12 measurements, each of 3 different maneuvers, were selected from an old and matured function in the brake system. The class-specific feature-based classification model resulted in 33% accuracy. However, with the Fully Convolutional Network Classification model, we got 100% accuracy. We injected anomalies in one CSV file for testing purposes. The anomaly detection module predicted all the anomalies correctly. Our future goal is to implement this model at the deployment level.&lt;/div&gt;&lt;/div&gt;
Exploiting switching properties of non-volatile memory chips for data security applications

This paper presents a technique of utilizing Commercial-Off-The-Self (COTS) Non-Volatile Memory (NVM) chips for data security applications. In particular, True Random Numbers (TRNs) are generated by harnessing the latency variability observed in NVM chips. Subsequent series of mathematical operations are implemented as post-processing techniques to increase the randomness of the TRNs. The generated TRNs are then utilized as a source of random keys for One-Time Pad (OTP) cryptosystem. The proposed methodology of TRNs extraction is experimentally validated on three different types of NVM technologies. TRNG throughput in a range of 0.09 Kb/s to 0.67 Kb/s is observed for the investigated technologies. Generated TRNs pass all the tests of NIST SP 800-22 statistical test suite with significant P–values. Metrics like MSE, CC, SSIM, NPCR, UACI, PSNR, and key space are also analyzed for the OTP cryptosystem.
An Enhanced Phrase Matching Method Based on Cross-Attention

Text matching is a core problem in the field of natural language understanding. It aims to analyze and judge the semantic relevance or similarity between two texts. In the past, much work on text matching focused on long text and little on phrase optimization. However, phrase matching also has essential application scenarios. Compared with long texts, phrases have difficulties covering less semantics and polysemy, and because of the weak expression ability of phrases, it is hard to match phrases accurately. On the Kaggle patent phrase matching dataset, due to the few words in the phrase and the repeated occurrences under different patent classification numbers, it is difficult to match each other accurately. In the data-processing stage, this work proposes aggregating related targets for data fusion, expanding the background semantic information, and enhancing the expression ability of phrases. In the training stage, this work considers adding a cross-attention network to the model to make the additional related targets better used and learned. The added cross-attention network makes the model pay more attention to the most related information. The proposed method has experimented on pretrained language models such as BERT-for-patent, DeBERTa, and RoBERTa. The results show that the proposed method in this work improves by 2–4 points more than the general method without any information or additional network layers in its evaluation metric called the Pearson correlation, which enhances the performance in short-text matching.
NEO: Neuron State Dependent Mechanisms for Efficient Continual Learning

Continual learning (sequential learning of tasks) is challenging for deep neural networks, mainly because of catastrophic forgetting, the tendency for accuracy on previously trained tasks to drop when new tasks are learned. Although several biologically-inspired techniques have been proposed for mitigating catastrophic forgetting, they typically require additional memory and/or computational overhead. Here, we propose a novel regularization approach that combines neuronal activation-based importance measurement with neuron state-dependent learning mechanisms to alleviate catastrophic forgetting in both task-aware and task-agnostic scenarios. We introduce a neuronal state-dependent mechanism driven by neuronal activity traces and selective learning rules, with storage requirements for regularization parameters that grow slower with network size - compared to schemes that calculate weight importance, whose storage grows quadratically. The proposed model, NEO, is able to achieve performance comparable to other state-of-the-art regularization based approaches to catastrophic forgetting, while operating with a reduced memory overhead.
Change detection in moving-camera videos with limited samples using twin-CNN features and learnable morphological operations

This paper presents a new system to detect changes between reference and target videos suitable for small-scale datasets. Twin pre-trained ResNet-50 features are processed using a learning-based pipeline that has a limited number of adjustable parameters, allowing end-to-end training even on relatively small databases. This is achieved with two innovative modules in tandem: a low-complexity dissimilarity module and a post-processing step using learnable morphological operations. Both can be smoothly incorporated in optimization procedures that employ gradient-based algorithms. The pipeline ends with temporal consistency and change classification modules, and it is evaluated on the VDAO dataset, a challenging database of videos recorded with moving cameras in a cluttered industrial environment. Ablation studies show how each proposed module contributes to the final system performance, with a prominence role for the newly proposed ones. Results indicate that the proposed system achieves a detection performance that is about 18% superior to the one of current state-of-the-art methods. Software, results, and a pre-trained architecture of the proposed framework are available at https://github.com/rafaelpadilla/TCF-LMO.
Streaming linked data: A survey on life cycle compliance

Data streams are becoming omnipresent on the Web. The Stream Reasoning (SR) paradigm, which combines Stream Processing with Semantic Web techniques, has been successful in processing these data streams. The progress in SR research has led to several applications in domains such as the Internet of Things, social media analysis, Smart Cities, and many others. Each of these applications produces and consumes data streams, however, there are no fixed guidelines on how to manage data streams on the Web, as there are for their static counterparts. More specifically, there is no fixed life cycle for Streaming Linked Data (SLD) yet. Tommasini et al. (2020) introduced an initial proposal for a SLD life cycle, however, it has not been verified if the proposed life cycle captures existing applications and no guidelines were given for each step. In this paper, we survey existing SR applications and identify if the life cycle proposed by Tommasini et al. fully captures the surveyed applications. Based on our analysis, we found that some of the steps needed reordering or being split up. This paper proposes an update of the life cycle and surveys the existing literature for each life cycle step while proposing a number of guidelines and best practices. Compared to the initial proposal by Tommasini et al., we drill down into the details of the processing step which was previously neglected. The updated life cycle and guidelines serves as a blueprint for future SR applications. A life cycle for SLD that allows to efficiently manage data streams on the web, brings us a step closer to the realization of the SR vision.
Perspective Chapter: History and Classification of Gold Mineralization in Egypt

Gold deposits and occurrences are spread over a large number of sites which covered by Precambrian the basement rocks in the Eastern Desert of Egypt, and the gold exploration and mining activities had been districted since ancient times. The ancient Egyptians extracted gold from quartz veins of various dimensions in open pits and underground workings. Consequently, gold production in Egypt seems to have started as early as the predynastic times (about 6000 BCE) from Pre-and Early Dynastic times to Early Arab Period. In the modern days, Egyptian mineral resource authority announced many big international bid-rounds for Gold Exploration and associated minerals was launched to open the minerals sector to foreign investment in the mining industry. Many studies have been achieved to classify the gold deposits in Egypt based on various characteristics such as the nature, occurrences, type of host rock, tectonic setting, metal association, fluid inclusion, and the tectonic environment of mineralization. Gold deposit in Egypt still need more geological and mining studies to understand the gold system types, additionally, to get answers regarding to source, transportation, traps, and host rocks, as well as the whole challenges in mining sectors.
Farmer’s Assistant using AI Voice Bot

The agricultural sector contributed approximately 19% of country’s total Gross Domestic Product and employ’s in an around 62% of the India’s population in 2020, making it an essential part of the nation's economic development. This figure was previously around 15.41% until 2018, but the introduction of cutting-edge technologies such as IoT, AI, and ML has helped to increase this number. A system is created to assist farmers through a voicechat bot. The voicechat bot will address farmer queries in various languages, the voicechat bot has been developed using Google translate and its dataset. This application aims to help farmers enhance their farming practices and thus growth in their crop production. By providing guidance on crop selection and pesticide usage, this technology is expected to lead to an overall growth in the agriculture sector.
Statistical evidence and surprise unified under possibility theory

Abstract Sander Greenland argues that reported results of hypothesis tests should include the surprisal , the base‐2 logarithm of the reciprocal of a p ‐value. The surprisal measures how many bits of evidence in the data warrant rejecting the null hypothesis. A generalization of surprisal also can measure how much the evidence justifies rejecting a composite hypothesis such as the complement of a confidence interval. That extended surprisal, called surprise , quantifies how many bits of astonishment an agent believing a hypothesis would experience upon observing the data. While surprisal is a function of a point in hypothesis space, surprise is a function of a subset of hypothesis space. Satisfying the conditions of conditional min‐plus probability, surprise inherits a wealth of tools from possibility theory. The equivalent compatibility function has been recently applied to the replication crisis, to adjusting p ‐values for prior information, and to comparing scientific theories.
Quantum loss sensing with two-mode squeezed vacuum state under noisy and lossy environment

We investigate quantum advantages in loss sensing when the two-mode squeezed vacuum state is used as a probe. Following an experimental demonstration in PRX 4, 011049, we consider a quantum scheme in which the signal mode is passed through the target and a thermal noise is introduced to the idler mode before they are measured. We consider two detection strategies of practical relevance: coincidence-counting and intensity-difference measurement, which are widely used in quantum sensing and imaging experiments. By computing the signal-to-noise ratio, we verify that quantum advantages persist even under strong thermal background noise, in comparison with the classical scheme which uses a single-mode coherent state that directly suffers from the thermal noise. Such robustness comes from the fact that the signal mode suffers from the thermal noise in the classical scheme, while in the quantum scheme, the idler mode does. For a fairer comparison, we further investigate a different setup in which the thermal noise is introduced to the signal mode in the quantum schemes. In this new setup, we show that the quantum advantages are significantly reduced. Remarkably, however, under an optimum measurement scheme associated with the quantum Fisher information, we show that the two-mode squeezed vacuum state does exhibit a quantum advantage over the entire range of the environmental noise and loss. We expect this work to serve as a guide for experimental demonstrations of quantum advantages in loss parameter sensing, which is subject to lossy and noisy environment.
Impact of Blockchain Technology for Business and Information Systems: Automation of Inter-Company Debt Compensation Case Study

Debt compensation is a process via which various entities (companies, people, etc.) propose the payment of their mutual debts by compensating them according to the credits they may have in their favor. The aim is to establish the financial information of a company so that its debts can somehow be satisfied by its credits, breaking the vicious cycle of debts that cannot be paid. To this end, this financial information is retrieved from the business information system of a company, encompassing how much is owed and to which companies. From the retrieved information, this paper describes the implementation of an automatic system that allows calculating not only the debt cycles involving different companies, but also how these cycles can be effectively solved using blockchain technology thanks to the use of smart contracts. Blockchain technology can have a great impact for companies in this kind of application, guaranteeing the security and traceability of transactions among companies, which are two well-appreciated features from a financial auditing point of view.