Reviewerly/exHarmony huggingface dataset:
PICO

Internet of Things (IoT) platforms typically require IoT devices and users to provide fine-grained information to determine whether access to resources and services can be granted. However, this information can be sensitive for users and its disclosure can lead to severe privacy threats, forcing users to decide between receiving a service or protecting their privacy. To close this gap, this work proposes PICO, a framework for privacy-preserving access control in IoT scenarios through incomplete information. PICO allows IoT devices to evaluate the privacy risks of disclosing the information needed to access a service and determine at which level of granularity such information can be disclosed. At the same time, PICO empowers IoT platforms to evaluate access control policies even when incomplete information is provided and possibly grant access to services based on a customized service-dependent risk factor. Through simulations using data from real IoT devices, we show the existence of a trade-off between privacy and energy consumption on IoT devices running PICO, and that more privacy can be achieved for such devices only by sacrificing a consistent portion of the overall energy capacity.
Deep reinforcement learning for improving competitive cycling performance

Developing expert systems that make use of artificial intelligence (AI) to provide predictive analytics as well as targeted recommendations for decision support has been gaining momentum in recent years. Both academia and industry are looking into creating such systems to solve real-world problems and tackle specific challenges. In our work, we investigate the potential application of different machine learning approaches to solutions around competitive cycling. Specifically, we build and evaluate prediction models that are capable of accurately predicting a cyclist’s speed and heart rate using sensory information collected during bike rides. In addition, we create a recommendation module that is able to provide real-time action suggestions to cyclists regarding their posture with the goal of improving their overall performance. We achieve this using a combination of model-based reinforcement learning (RL) and deep RL. In particular, we use model-based RL to learn a “simulator” of bike rides using the prediction models and action profiles extracted from sensors placed on the cyclists’ back. We then use deep Q-learning in the simulator to extract policies that improve a cyclist’s behavior during a bike ride. Our evaluation shows that by recommending specific actions throughout the ride, cyclists can increase their overall average speed with only a minimal impact on their heart rate. The results presented in this paper constitute clear evidence that advanced AI techniques are a prime candidate for further developing intelligent solutions in competitive cycling and other similar areas.
Mining high average utility pattern using bio-inspired algorithm

High average utility pattern (itemset) Mining (HAUIM) is a necessary research problem in the field of knowledge discovery and data mining. Several algorithms have been proposed to mine high average-utility itemsets (HAUIs). Nonetheless, the large search space leads to poor performance because of excessive execution time and memory usage. To handle this limitation, particle swarm optimization (PSO) is applied to mine HAUIs. In this paper, an effective Binary PSO-based algorithm namely HAUIM-BPSO is proposed to explore HAUI efficiently. In general, HAUIM-BPSO first sets the number of discovered potential high average-utility 1-itemsets (1-PHAUIs) as the size of a particle based on average utility upper bound (AUUB) property. The sigmoid function is also used in the updating process of the individual of the proposed HAUIM-BPSO algorithm. Substantial experiments conducted on publicly available datasets show that the proposed algorithm has better results than existing state-of-the-art algorithms in terms of runtime which can significantly reduce the combinational problem, memory usage, and convergence speed.
Enriching an online suicidal dataset with active machine learning

The scarce and often small size of annotated suicide-related datasets is one of the main obstacles toward automating the process of identifying the online users of high suicide risk. In this paper, we present a framework to annotate a mental-health-related textual dataset with suicide attempts and suicide ideations in the posts and comments using active machine learning method. This approach starts from a relatively small annotated dataset, and learns from a domain expert by obtaining the expert's judgments on the most contradictory samples when the active machine learning model is not able to make the judgments. Meanwhile, the model annotates new samples without asking for the expert's input when it is confident enough about the new samples. The active machine learning models were evaluated and updated when a batch of new samples was annotated, including parameter tuning, replacing models, or changing the representations of samples. The dataset we used is from the SuicideWatch Reddit channel. We expanded the dataset from initially 200 manually annotated samples to 1000 ones.
ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Models

Nowadays, pretrained language models (PLMs) have dominated the majority of NLP tasks.While, little research has been conducted on systematically evaluating the language abilities of PLMs.In this paper, we present a large-scale empirical study on genEral language ability evaluation of PLMs (ElitePLM).In our study, we design four evaluation dimensions, i.e., memory, comprehension, reasoning, and composition, to measure ten widely-used PLMs within five categories.Our empirical results demonstrate that: (1) PLMs with varying training objectives and strategies are good at different ability tests; (2) fine-tuning PLMs in downstream tasks is usually sensitive to the data size and distribution; (3) PLMs have excellent transferability between similar tasks.Moreover, the prediction results of PLMs in our experiments are released as an open resource for more deep and detailed analysis on the language abilities of PLMs.This paper can guide the future work to select, apply, and design PLMs for specific tasks.We have made all the details of experiments publicly available at https: //github.com/RUCAIBox/ElitePLM.
Trinity: Neural Network Adaptive Distributed Parallel Training Method Based on Reinforcement Learning

Deep learning, with increasingly large datasets and complex neural networks, is widely used in computer vision and natural language processing. A resulting trend is to split and train large-scale neural network models across multiple devices in parallel, known as parallel model training. Existing parallel methods are mainly based on expert design, which is inefficient and requires specialized knowledge. Although automatically implemented parallel methods have been proposed to solve these problems, these methods only consider a single optimization aspect of run time. In this paper, we present Trinity, an adaptive distributed parallel training method based on reinforcement learning, to automate the search and tuning of parallel strategies. We build a multidimensional performance evaluation model and use proximal policy optimization to co-optimize multiple optimization aspects. Our experiment used the CIFAR10 and PTB datasets based on InceptionV3, NMT, NASNet and PNASNet models. Compared with Google’s Hierarchical method, Trinity achieves up to 5% reductions in runtime, communication, and memory overhead, and up to a 40% increase in parallel strategy search speeds.
Nudging to Improve Human-AI Symbiosis

The goal of human-AI symbiosis is a mutual benefit for humans and AI. The challenges of gaining a human-AI symbiosis are manyfold. One challenge is what is communicated to humans and when. Too frequent inquiries by an AI will cause the human to feel distracted, which can lead to negative experiences. However, if the AI takes over some or all of the task solving for humans, there is a risk that humans will become dependent on the technology and loose skills. The right type of communication between humans and AI is important. The goal of our PHD research is to develop a method to optimize human-AI symbiosis through appropriate interaction and explainability. Our approach is to use AI to encourage humans through nudging to discover solution strategies on their own. In this way, the cognitive performance of a human is aimed to be preserved as much as possible. This could also counteract common problems in modern AI systems such as parameter drift. Therefore, we will study the effect of nudging in human-AI interactions and gain insights into what would happen if the machine nudges a human in the wrong direction. For this purpose, a user study will be conducted in which the human interaction with an AI is simulated. Afterwards an AI will be developed that uses nudging mechanisms that reliably nudge the user towards the correct solution without losing the human&#x2019;s own cognitive effort. The AI should support humans unobtrusively and thus be integrated into daily working life.
Enhancing Digital Skills of Early Childhood Teachers Through Online Science, Technology, Engineering, Art, Math Training Programs in Estonia

Teacher professional development programs, including mid- and long-term Science, Technology, Engineering, Art, Math (STEAM) courses, have recently moved from in person learning at university premises to an online environment. Whether it is a temporary change in learning methods caused by the COVID-19 restrictions or whether it will become a new normal is currently under discussion in many teacher training institutions around the world. The aim of this study was to design and implement time- and money-saving synchronous online teacher training format for conducting co-design courses for early childhood teachers in the theme of STEAM integrated learning activities. Based on Tallinn University’s curriculum of in-person training courses on the same topic, with the volume of 40 contact hours, we delivered the content in two different formats: in 11-months (as it used to be in pre-COVID period) and in 4-months, adapted to participants’ needs. We used a self-assessment survey, based on DigCompEdu framework, to assess the increase of digital competences in the two formats. The long-format course had 31 participants and the short-format course had 50 participants. The assessment was based on pre- and post-test and we used structured live video presentations to let participants retrospectively describe their learning experiences. Results indicate that the participants of both courses had improved their digital competences and achieved the learning outcomes set by course content. There was no significant difference in increase of digital competences or the way the course was perceived between participants of both courses. This brings us to the cautious consideration that it is possible to achieve desired outcomes of STEAM courses even in a shorter period when conducting them online compared to the in-person courses. There is a need for further research where results from participants of in-person and online teaching courses are compared.
A Decision Support Framework based on FCM for Selecting Key Performance Indicators

One of the main issues that firms encounter in building an effective performance measurement system (PMS) is selecting the most relevant performance indicators, often known as key performance indicators (KPI). These indicators should be chosen according to the company&#x2019;s objectives, strategies, and key success factors. On the other hand, in a production system, KPIs are not independent and may have inherent interactions with each other. These interactions should be investigated and taken into account in the KPI selection process. The goal of this research is to present a decision support framework based on a Fuzzy Cognitive Map (FCM) for guiding decision-makers in the selection of KPI. The model is based on the idea that KPI may be assessed and chosen based on their impacts on the company&#x2019;s strategic objectives. In addition, the model allows for the consideration of different causal interdependence between the KPI.
Human-machine dialogue intention recognition after fusion of global positional information of sentence text intention classification

Text intent classification technology, most of them are based on training semantic word vectors to represent words to encode text. The word vectors considers semantics, grammar, etc. However, the global position information of the sentence is relatively lacking. Although the LSTM and other sequence models have short-term memory, the ability to extract global position information is still slightly insufficient. This article proposes to add global position information to the word vector to make the word vector feature it is stronger, and we have verified the text classification effect of FastText, TextCNN, TextRCNN, LSTM-Attention, GRU-Attention and other models in the medical consultation question and answer data. After adding global location information, the FastText classification effect has increased by 1.92%, and the classification effect of the TextCNN model has improved by 2.19%, the classification effect of the TextRCNN model has increased by 0.72%, the classification effect of the LSTM_Attention model has increased by 0.62%, and the classification ability of the GRU_Attention model has increased by 0.34%. From the experimental results of embedding location information into different models, we can note that the global location Compared with the traditional method, the position encoding embedding word vector method has higher classification accuracy and efficiency.
Bridging the gap between source code and high-level concepts in static code analysis

Static code analysis has proven beneficial for many domains, including security assessment, error detection, and more. As systems have grown more reliant on conceptual components without a one-to-one mapping in the source code, however, traditional static code analysis tools have become less ideal for the task. This is greatly because current static code analysis libraries focus on individual code constructs, forcing the library's user to manually combine these into high-level components before performing any meaningful analysis. To bridge this gap, we put forth a novel approach for extracting components from programs using sets of parsers called Relative Static Structure Analyzers (ReSSA). This approach builds a grammar to match structures within an abstract syntax tree, abstracting away the boilerplate of manually traversing, matching, and extracting data. This empowers the user to focus on the structure of components in their chosen language and framework, and their final output. Our approach holds potential to greatly simplify static code analysis, allowing for fast and accurate identification of important components without needing to utilize arcane parsing libraries.
Querying multidimensional big data through a chatbot system

Multidimensional big data organizes information as data cubes, characterized by dimensions and measures. The existing multidimensional approaches usually do not focus on user assistance, meaning that users who are unaware of how the database is structured cannot easily analyze the data. Conversational interfaces, such as chatbots, can minimize these issues, by promoting a comprehensive interaction and facilitating the query formulation. Also, chatbots are able to capture intentions expressed in natural language, which is more user-friendly than other typical multidimensional query formats. This paper presents a chatbot approach for querying multidimensional big data, which captures users intentions through a conversation flow, linking them to multidimensional metadata. Through this process, the bot can set the query parameters and use them to access the data. We implemented the chatbot proposal for accessing an open database containing about 2.5 billions records and over 1700 attributes, including dimensions and metrics. When performing the querying process, the bot presents concise answers to the user.
Visualization analysis of satellite intelligence based on scientific knowledge graph

The research frontiers, hotspots and development trends related to satellites are important data for the study of satellite intelligence. In this paper, the scientific knowledge graph is applied to the field of satellite intelligence analysis. Using the satellite research data published on the network, Citespace is used to draw the cooperative knowledge graph, keyword co-occurrence graph and keyword clustering graph of satellite research. The research results show that the research heat of satellites in various countries is increasing, and the scientific research cooperation among institutions is closer. The research frontiers focus on humanitarian applications, communication secrecy, broadband Ka-band terminal design and anti-satellite warfare and so on.
An Evaluation of a Semantic Thing To Service Matching Approach in Industrial IoT Environments

Industrial Internet of Things Platforms enable the use of available data to improve production and business processes. However, the data exchange and provisioning between the data sources and platform services remain a challenge as such platforms are usually vendor specific, proprietary, and associated with specific IoT hardware. Therefore, we propose a detailed description of an open-source software-based solution, Thing to Service Matching (TSMatch) which performs fine-grained semantic matching between available IoT data and services. Moreover, the paper presents the actual implementation of the proposed solution in 2 different Aerospace production environments, and a performance evaluation in a testbed environment.
Query-relevant sentiment summarization based on facet identification and sentence clustering

Multi-document sentiment analysis is an important natural language processing problem. Summaries generated by these analyzers can greatly reduce the time necessary to read a collection of topically-related documents to locate the desired information needs of a user. With the ever-increasing globalization and technology of the modern day, analysis of online user reviews on different products is an especially pertinent application of the aforementioned problem. At present there are way too many user reviews on popular products for potential buyers to spend adequate time to read and extract the most salient product details and opinions of previous buyers. In solving this problem, we propose a fully-automated summarizer to reduce the workload of online customers. The proposed system takes a user query and extracts the most relevant and essential comments made by individual reviewers. As opposed to existing multi-document summarization approaches, our summarizer compiles comprehensive reviews by extracting important facets and sentiment information based on various sentence features rather than applying complex machine learning algorithms. The design of our summarizer is easy to understand and implement, without the required massive training data and excessive training time. The conducted empirical study shows that the proposed summarization system outperforms current state-of-the-art multi-document sentiment summarization approaches.
An attention-based network for serial number recognition on banknotes

The serial number recognition (SNR) on banknotes is essential for currency circulation. The performance of the existing SNR methods is significantly influenced by character segmentation, which is challenging due to uneven illumination and complex background. In this paper, we apply deep learning techniques to SNR by proposing an attention-based network, which can be end-to-end trained to avoid the problem of character segmentation. The proposed framework contains two parts: rectification and recognition. First, the rectification network, which can be trained in a weakly supervised manner without additional manual annotations, is built to automatically rectify the tilted and loosely-bounded images and reduce the difficulty of recognition. Then, the recognition network, an attention-based sequence model, recognizes serial number characters from the rectified image holistically, rather than segmenting and recognizing individual characters. To address the problem of complex textures on banknotes, we integrate the deformable convolution into the recognition network, which adaptively focuses on the character regions by using flexible receptive fields to accurately extract optimal character features, while ignoring redundant background information. Extensive experiments conducted on CNY, KRW, EUR and JPY banknotes, demonstrate that the proposed method achieves higher accuracy than the existing methods.
Accuracy Enhancement for Breast Cancer Detection Using Classification and Feature Selection

Chronic disease like kidney failure, heart disease, cancer etc. is the major cause of deaths now days worldwide. Especially for the females the most dangerous type of disease from which the women of every age group are suffering especially the middle age group women’s is the breast cancer. To detect this type of disease at an early stage is a challenging task. In order to predict the breast cancer at an early stage classification algorithm of high accuracy and less error rate are desirable. In this research work we have used 4 classification algorithms K-NN, J48, Logistic regression and Bayes Net for building the predictive model, also the wrapper method of feature selection is used to enhance the accuracy rate and reduce the error rate of the used classifiers. To carry out this research we have used Wisconsin Diagnostic Breast Cancer dataset which contains 569 instances along with 32 attributes and a class attribute which will predict the type of cancer i.e. Benign or Malignant.
Lightweight Code Assurance Proof for Wireless Software

Software-defined radio (SDR) and the softwarization of the wireless and mobile systems enable intelligent processing and control in wireless networking. We design and build a lightweight code assurance proof scheme for wireless system software implementations. More specifically, our scheme assures that a wireless user/prover holds the correct software codes, e.g., the correct version, for its wireless networking implementations. In contrast to the previous research for code attestation in trusted computing, our scheme forgoes hardware-based security and real-time networking, thus substantially increasing the application feasibility. We further design our scheme to be efficient in computing by using a Merkle tree for the efficiency of the verification of the assurance proof. We implement our scheme for proof-of-concept on srsRAN (a popular open-source software for cellular technology) and conduct preliminary measurements to demonstrate the lightweight design. We envision our scheme to be orthogonal and supplementary to the previous trustworthy code attestation because it provides different properties (assurance vs. attestation) and because the lightweight aspect yields greater applicability and lower overheads in hardware and networking. Our scheme will therefore be appropriate for the wireless/mobile environment which uses broadcasting (where receiving/verifications occur more frequently than transmitting/generations) and whose devices are resource-constrained.