Reviewerly/exHarmony huggingface dataset:
Design Thinking Approach for User Interface Design and User Experience on Campus Academic Information Systems

Abstractâ€”Currently, an academic system with structured data is needed for all lecture institutions, especially universities in Indonesia, Siliwangi University, with its academic system, namely the Campus Academic Information System (SIMAK). Over time, complaints from the visual aspect and user experience that did not keep up with the times became a new problem for SIMAK with student access rights. Therefore, the UI/UX aspect in developing an application is vital in accessing the available features. In this study, the method applied is Design Thinking to develop SIMAK WEB and SIMAK MOBILE application designs according to the data and input obtained from users. The research stages include Empathize, Define, Ideate, Prototype, and Test. The final result is user testing from expert users with ten examiners, each producing a success rate percentage of 100% for SIMAK WEB and a percentage of 90% for SIMAK MOBILE. In addition, the User Experience Questionnaire (UEQ) assessment from the same expert user plus end-users of 39 respondents and 33 respondents for web and mobile respectively increased 6 UEQ scales, namely Attractiveness, Clarity, Efficiency, Accuracy, Stimulation and lastly especially Novelty which has an increase of 5.286 and 5.264 from the initial value of -0.880. The Novelty scale is the only scale with a negative impression initially and was successfully evaluated in this study with a good score. The implication for further research is that an in-depth study and application of unique methods regarding the conversion of designs into prototype form is necessary so that coding can run smoothly.Â Keywordsâ€” Design Thinking, Campus Academic System, User Experience, User Experience Questionnaire, User Interface
Fast and Optimal Trajectory Planning for Multiple Vehicles in a Nonconvex and Cluttered Environment: Benchmarks, Methodology, and Experiments

This paper is focused on the cooperative trajectory planning problem for multiple car-like robots in a cluttered and unstructured environment narrowed by static obstacles. The concerned multi-vehicle trajectory planning (MVTP) problem is challenging because i) the scenario is nonconvex and tiny; ii) the vehicle kinematics is nonconvex; and iii) a feasible homotopy class is unavailable a priori. We propose a two-stage MVTP method: Stage 1 identifies a feasible homotopy class, and Stage 2 quickly finds a local optimum based on the identified homotopy class. Numerical optimal control, adaptive scaling, grouping, and trust region construction strategies are integrated into the proposed planner. Our planner is extensively compared in 100 benchmark cases with the state-of-the-art MVTP methods such as incremental sequential convex programming, numerical optimal control, conflict-based search, priority-based trajectory optimizer, and optimal reciprocal collision avoidance. The simulation results demonstrate our method's superiority in runtime and optimality. Experiments with three car-like robots demonstrate the efficiency of our proposed planner. Source codes are in https://github.com/libai1943/MVTP_benchmark.
Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences

Example sentences for targeted words in a dictionary play an important role to help readers understand the usage of words.Traditionally, example sentences in a dictionary are usually created by linguistics experts, which are labor-intensive and knowledge-intensive.In this paper, we introduce the problem of dictionary example sentence generation, aiming to automatically generate dictionary example sentences for targeted words according to the corresponding definitions.This task is challenging especially for polysemous words, because the generated sentences need to reflect different usages and meanings of these targeted words.Targeted readers may also have different backgrounds and educational levels.It is essential to generate example sentences that can be understandable for different backgrounds and levels of audiences.To solve these problems, we propose a controllable target-word-aware model for this task.Our proposed model can generate reasonable examples for targeted words, even for polysemous words.In addition, our model allows users to provide explicit control over attributes related to readability, such as length and lexical complexity, thus generating suitable examples for targeted audiences.Automatic and human evaluations on the Oxford dictionary dataset show that our model can generate suitable examples for targeted words with specific definitions while meeting the desired readability.
Smooth Perturbations for Time Series Adversarial Attacks

Adversarial attacks represent a threat to every deep neural network. They are particularly effective if they can perturb a given model while remaining undetectable. They have been initially introduced for image classifiers, and are well studied for this task. For time series, few attacks have yet been proposed. Most that have are adaptations of attacks previously proposed for image classifiers. Although these attacks are effective, they generate perturbations containing clearly discernible patterns such as sawtooth and spikes. Adversarial patterns are not perceptible on images, but the attacks proposed to date are readily perceptible in the case of time series. In order to generate stealthier adversarial attacks for time series, we propose a new attack that produces smoother perturbations. We find that smooth perturbations are harder to detect by the naked eye. We also show how adversarial training can improve model robustness against this attack, thus making models less vulnerable.
Unsupervised Extractive Opinion Summarization Using Sparse Coding

Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews.We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner.SemAE uses dictionary learning to implicitly capture semantic information from the review and learns a latent representation of each sentence over semantic units.A semantic unit is supposed to capture an abstract semantic concept.Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews.Se-mAE is also able to perform controllable summarization to generate aspect-specific summaries.We report strong performance on SPACE and AMAZON datasets, and perform experiments to investigate the functioning of our model.
Implementation of Data Mining on a Secure Cloud Computing over a Web API using Supervised Machine Learning Algorithm

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model.
DLRG@TamilNLP-ACL2022: Offensive Span Identification in Tamil usingBiLSTM-CRF approach

Identifying offensive speech is an exciting and essential area of research, with ample traction in recent times.This paper presents our system submission to the subtask 1, focusing on using supervised approaches for extracting Offensive spans from code-mixed Tamil-English comments.To identify offensive spans, we developed the Bidirectional Long Short-Term Memory (BiLSTM) model with Glove Embedding.With this method, the developed system achieved an overall F1 of 0.1728.Additionally, for comments with less than 30 characters, the developed system shows an F1 of 0.3890, competitive with other submissions.
The Future of Marketing: Artificial Intelligence, Virtual Reality, and Neuromarketing

The emergence of new technologies is influencing marketing research and business decisions. Specifically, artificial intelligence (AI), virtual reality (VR), and neuropsychological tools are changing the way we collect, store, and analyse marketing data. To realise the benefits of these technologies, which have implications for marketing communications as they facilitate the in-depth understanding of consumer experience, strategic and proactive orientations are required. To this end, this chapter reviews extant literature on AI, VR, and neuromarketing, providing guidance on how future marketing decisions will be made through leveraging these new technology-based tools. With its increasing computer power, AI will support broad marketing applications, such as analysis and targeting of customers for effective decision-making. VR can enhance a customer's experience through the purchase journey, thus providing ample opportunities for marketers to utilise innovative marketing campaigns. Neuromarketing can capture tacit cognitive and emotional responses to marketing stimuli and enable informed forecasting of consumers' purchase decisions.
Reports of personal experiences and stories in argumentation: datasets and analysis

Reports of personal experiences or stories can play a crucial role in argumentation, as they represent an immediate and (often) relatable way to back up one's position with respect to a given topic.They are easy to understand and increase empathy: this makes them powerful in argumentation.The impact of personal reports and stories in argumentation has been studied in the Social Sciences, but it is still largely underexplored in NLP.Our work is the first step towards filling this gap: our goal is to develop robust classifiers to identify documents containing personal experiences and reports.The main challenge is the scarcity of annotated data: our solution is to leverage existing annotations to be able to scale-up the analysis.Our contribution is two-fold.First, we conduct a set of in-domain and cross-domain experiments involving three datasets (two from Argument Mining, one from the Social Sciences), modeling architectures, training setups and fine-tuning options tailored to the involved domains.We show that despite the differences among datasets and annotations, robust crossdomain classification is possible.Second, we employ linear regression for performance mining, identifying performance trends both for overall classification performance and individual classifier predictions.
A Hybrid RNN based Deep Learning Approach for Text Classification

Despite the fact that text classification has grown in relevance over the last decade, there are a plethora of approaches that have been created to meet the difficulties related with text classification.To handle the complexities involved in the text classification process, the focus has shifted away from traditional machine learning methods and toward neural networks.In this work the traditional RNN model is embedded with different layers to test the accuracy of the text classification.The work involves the implementation of RNN+LSTM+GRU model.This model is compared with RCNN+LSTM and RNN+GRU.The model is trained by using the GloVe dataset.The accuracy and recall are obtained from the models is assessed.The F1 score is used to compare the performance of both models.The hybrid RNN model has three LSTM layers and two GRU layers, whereas the RCNN model contains four convolution layers and four LSTM levels, and the RNN model contains four GRU layers.The weighted average for the hybrid RNN model is found to be 0.74, RCNN+LSTM is 0.69 and RNN+GRU is 0.77.RNN+LSTM+GRU model shows moderate accuracy in the initial epochs but slowly the accuracy increases as and when the epochs are increased.
Model Distillation for Faithful Explanations of Medical Code Predictions

Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making.In clinical or other high-risk settings, domain experts may be unwilling to trust model predictions without explanations.Work in explainable AI must balance competing objectives along two different axes: 1) Models should ideally be both accurate and simple.2) Explanations must balance faithfulness to the model's decisionmaking with their plausibility to a domain expert.We propose to use knowledge distillation, or training a student model that mimics the behavior of a trained teacher model, as a technique to generate faithful and plausible explanations.We evaluate our approach on the task of assigning ICD codes to clinical notes to demonstrate that the student model is faithful to the teacher model's behavior and produces quality natural language explanations.
TRAttack”:" Text Rewriting Attack Against Text Retrieval

Text retrieval has been widely-used in many online applications to help users find relevant information from a text collection. In this paper, we study a new attack scenario against text retrieval to evaluate its robustness to adversarial attacks under the black-box setting, in which attackers want their own texts to always get high relevance scores with different users’ input queries and thus be retrieved frequently and can receive large amounts of impressions for profits. Considering that most current attack methods only simply follow certain fixed optimization rules, we propose a novel text rewriting attack (TRAttack) method with learning ability from the multi-armed bandit mechanism. Extensive experiments conducted on simulated victim environments demonstrate that TRAttack can yield texts that have higher relevance scores with different given users’ queries than those generated by current state-of-the-art attack methods. We also evaluate TRAttack on Tencent Cloud’s and Baidu Cloud’s commercially-available text retrieval APIs, and the rewritten adversarial texts successfully get high relevance scores with different user queries, which shows the practical potential of our method and the risk of text retrieval systems.
High-Speed Multi-Layer Convolutional Neural Network Based on Free-Space Optics

Convolutional neural networks (CNNs) are at the heart of several machine learning applications, while they suffer from computational complexity due to their large number of parameters and operations.Recently, all-optical implementation of the CNNs has achieved many attentions, however, the recently proposed optical architectures for CNNs cannot fully utilize the tremendous capabilities of optical processing, due to the required electro-optical conversions in-between successive layers.To implement an all-optical multi-layer CNN, it is essential to optically implement all required operations, namely convolution, summation of channels' output for each convolutional kernel feeding the nonlinear unit, nonlinear activation function, and finally, pooling operations.Considering the lack of multi-layer photonic CNN implementation, in this paper, we explore a fully-optical design for implementing successive convolutional layers in an optical CNN.As a proof of concept, and without loss of generality, we considered two successive optical layers in the proposed network, named as 2L-OPCNN, for comparative studies against electrical counterpart and single optical layer CNN.Our simulation results confirm nearly the same accuracies for classifying images of Kaggle Cats and Dogs challenge, CIFAR-10, and MNIST datasets, compared to the electrical counterpart, as well as improved accuracies compared to single optical layer CNN.
BERT-Based Sequence Labelling Approach for Dependency Parsing in Tamil

Dependency parsing is a method for doing surface-level syntactic analysis on natural language texts.The scarcity of any viable tools for doing these tasks in Dravidian Languages has introduced a new line of research into these topics.This paper focuses on a novel approach that uses word-to-word dependency tagging using BERT models to improve the malt parser performance.We used Tamil, a morphologically rich and free word order language.The individual words are tokenized using BERT models and the dependency relations are recognized using Machine Learning Algorithms.Oversampling algorithms such as SMOTE (Chawla et al., 2002) and ADASYN (He et al., 2008) are used to tackle data imbalance and consequently improve parsing results.The results obtained after oversampling (label accuracy of 69.94% IndicBERT-SVM) are used in the malt parser and this can be accustomed to further highlight that featurebased approaches can be used for such tasks.
Low-Light Image Enhancement Using the Cell Vibration Model

Low light very likely leads to the degradation of an image’s quality and even causes visual task failures. Existing image enhancement technologies are prone to overenhancement, color distortion or time consumption, and their adaptability is fairly limited. Therefore, we propose a new single low-light image lightness enhancement method. First, an energy model is presented based on the analysis of membrane vibrations induced by photon stimulations. Then, based on the unique mathematical properties of the energy model and combined with the gamma correction model, a new global lightness enhancement model is proposed. Furthermore, a special relationship between image lightness and gamma intensity is found. Finally, a local fusion strategy, including segmentation, filtering and fusion, is proposed to optimize the local details of the global lightness enhancement images. Experimental results show that the proposed algorithm is superior to nine state-of-the-art methods in avoiding color distortion, restoring the textures of dark areas, reproducing natural colors and reducing time cost.
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?

Identifying argument components from unstructured texts and predicting the relationships expressed among them are two primary steps of argument mining.The intrinsic complexity of these tasks demands powerful learning models.While pretrained Transformerbased Language Models (LM) have been shown to provide state-of-the-art results over different NLP tasks, the scarcity of manually annotated data and the highly domaindependent nature of argumentation restrict the capabilities of such models.In this work, we propose a novel transfer learning strategy to overcome these challenges.We utilize argumentation-rich social discussions from the ChangeMyView subreddit as a source of unsupervised, argumentative discourse-aware knowledge by finetuning pretrained LMs on a selectively masked language modeling task.Furthermore, we introduce a novel promptbased strategy for inter-component relation prediction that compliments our proposed finetuning method while leveraging on the discourse context.Exhaustive experiments show the generalization capability of our method on these two tasks over within-domain as well as out-of-domain datasets, outperforming several existing and employed strong baselines.
Structured Covariance Matrix Estimation for Noise-Type Radars

Standard noise radars, as well as noise-type radars such as quantum two-mode squeezing radar, are characterized by a covariance matrix with a very specific structure. This matrix has four independent parameters: the amplitude of the received signal, the amplitude of the internal signal used for matched filtering, the correlation between the two signals, and the relative phase between them. In this paper, we derive estimators for these four parameters using two techniques. The first is based on minimizing the Frobenius norm between the structured covariance matrix and the sample covariance matrix; the second is maximum likelihood parameter estimation. The two techniques yield the same estimators. We then give probability density functions (PDFs) for all four estimators. Because some of these PDFs are quite complicated, we also provide approximate PDFs. Finally, we apply our results to the problem of target detection and derive expressions for the receiver operating characteristic curves of two different noise radar detectors. In summary, our work gives a broad overview of the basic statistical behavior of noise-type radars.
Antenna Additively Manufactured Engineered Fingerprinting for Physical-Layer Security Enhancement for Wireless Communications

Physical layer security is increasingly being exploited as a technique to enhance the security of wireless communications. Well-known hardware security techniques leverage unintended manufacturing process variations or fixed unique hardware structures in the semiconductors for identification of different copies of an RF system. The fundamentally different concept of engineering a unique fingerprint for each antenna produced, by leveraging additive manufacturing, is presented in this work. To the best of the author&#x2019;s knowledge, this is the first application of the concept of radio frequency (RF) fingerprint engineering using additively manufactured (AM) antennas for hardware-based security mechanisms. AM engineered fingerprints (AMEF) are based on intentional features added to antenna&#x2019;s geometries using 3D printing, enabling more accurate signal source identification and classification. Such unique features per unit produced would be prohibitively costly by using traditional photolithographic processes. The AMEF concept is validated using AM right hand circularly polarized (RHCP) truncated corner probe fed (TCPF) patch antennas and a testbed setup with software-defined radios and MATLAB implementations to create, transmit, receive, and prepare a convolutional neural network (CNN) to classify the transmitted raw I/Q data of Wi-Fi signals (IEEE 802.11). The AMEF technique greatly improves the physical layer security performance limitations in large-scale applications where the features of the devices can overlap, at a low-cost and low production time impact, and it allows the use of the antenna features for both individual and type classification and identification of RF sources.
Finding Structural Knowledge in Multimodal-BERT

In this work, we investigate the knowledge learned in the embeddings of multimodal-BERT models.More specifically, we probe their capabilities of storing the grammatical structure of linguistic data and the structure learned over objects in visual data.To reach that goal, we first make the inherent structure of language and visuals explicit by a dependency parse of the sentences that describe the image and by the dependencies between the object regions in the image, respectively.We call this explicit visual structure the scene tree, that is based on the dependency tree of the language description.Extensive probing experiments show that the multimodal-BERT models do not encode these scene trees.Code available at https://github.com/VSJMilewski/multimodal-probes.