Reviewerly/exHarmony huggingface dataset:
LOGICDEF: An Interpretable Defense Framework against Adversarial Examples via Inductive Scene Graph Reasoning

Deep vision models have provided new capability across a spectrum of applications in transportation, manufacturing, agriculture, commerce, and security. However, recent studies have demonstrated that these models are vulnerable to adversarial attack, exposing a risk-of-use in critical applications where untrusted parties have access to the data environment or even directly to the sensor inputs. Existing adversarial defense methods are either limited to specific types of attacks or are too complex to be applied to practical vision models. More importantly, these methods rely on techniques that are not interpretable to humans. In this work, we argue that an effective defense should produce an explanation as to why the system is attacked, and by using a representation that is easily readable by a human user, e.g. a logic formalism. To this end, we propose logic adversarial defense (LogicDef), a defense framework that utilizes the scene graph of the image to provide a contextual structure for detecting and explaining object classification. Our framework first mines inductive logic rules from the extracted scene graph, and then uses these rules to construct a defense model that alerts the user when the vision model violates the consistency rules. The defense model is interpretable and its robustness is further enhanced by incorporating existing relational commonsense knowledge from projects such as ConceptNet. In order to handle the hierarchical nature of such relational reasoning, we use a curriculum learning approach based on object taxonomy, yielding additional improvements to training and performance.
Go-Gallon App With A Star (A*) Algorithm Implementation Using Android Kotlin

The need for drinking water now is not to boil water, but to buy it in the form of gallon bottles, which are available wherever it is sold, either at the refill of water or in the supermarket. In a housing estate with many heads of families, it is also difficult to find and obtain water in gallon bottles under certain conditions, and there is also the problem that there is only one brand of gallon bottles, which also makes it difficult to obtain. For this reason, the author provides a solution by creating a go-gallon application with the application of the A-Star (A*) algorithm to be able to detect the service takers of the nearest people, which is basically intended for residential environments, where with this application others can ask to find and purchase gallons and then deliver them to their homes. The A-Star algorithm is one of the distance search algorithms that has an optimal and complete ability to solve problems related to finding or determining a route with the least distance. The hope is that with this application, especially housewives, there is no need to worry when they run out of gallons of drinking water at home, enough with this application, these problems can be overcome.
Pose-Invariant Face Recognition via Adaptive Angular Distillation

Pose-invariant face recognition is a practically useful but challenging task. This paper introduces a novel method to learn pose-invariant feature representation without normalizing profile faces to frontal ones or learning disentangled features. We first design a novel strategy to learn pose-invariant feature embeddings by distilling the angular knowledge of frontal faces extracted by teacher network to student network, which enables the handling of faces with large pose variations. In this way, the features of faces across variant poses can cluster compactly for the same person to create a pose-invariant face representation. Secondly, we propose a Pose-Adaptive Angular Distillation loss to mitigate the negative effect of uneven distribution of face poses in the training dataset to pay more attention to the samples with large pose variations. Extensive experiments on two challenging benchmarks (IJB-A and CFP-FP) show that our approach consistently outperforms the existing methods.
FPAdaMetric: False-Positive-Aware Adaptive Metric Learning for Session-Based Recommendation

Modern recommendation systems are mostly based on implicit feedback data which can be quite noisy due to false positives (FPs) caused by many reasons, such as misclicks or quick curiosity. Numerous recommendation algorithms based on collaborative filtering have leveraged post-click user behavior (e.g., skip) to identify false positives. They effectively involved these false positives in the model supervision as negative-like signals. Yet, false positives had not been considered in existing session-based recommendation systems (SBRs) although they provide just as deleterious effects. To resolve false positives in SBRs, we first introduce FP-Metric model which reformulates the objective of the session-based recommendation with FP constraints into metric learning regularization. In addition, we propose FP-AdaMetric that enhances the metric-learning regularization terms with an adaptive module that elaborately calculates the impact of FPs inside sequential patterns. We verify that FP-AdaMetric improves several session-based recommendation models' performances in terms of Hit Rate (HR), MRR, and NDCG on datasets from different domains including music, movie, and game. Furthermore, we show that the adaptive module plays a much more crucial role in FP-AdaMetric model than in other baselines.
Cross-speaker Emotion Transfer Based On Prosody Compensation for End-to-End Speech Synthesis

Cross-speaker emotion transfer speech synthesis aims to synthesize emotional speech for a target speaker by transferring the emotion from reference speech recorded by another (source) speaker. In this task, extracting speaker-independent emotion embedding from reference speech plays an important role. However, the emotional information conveyed by such emotion embedding tends to be weakened in the process to squeeze out the source speaker's timbre information. In response to this problem, a prosody compensation module (PCM) is proposed in this paper to compensate for the emotional information loss. Specifically, the PCM tries to obtain speaker-independent emotional information from the intermediate feature of a pre-trained ASR model. To this end, a prosody compensation encoder with global context (GC) blocks is introduced to obtain global emotional information from the ASR model's intermediate feature. Experiments demonstrate that the proposed PCM can effectively compensate the emotion embedding for the emotional information loss, and meanwhile maintain the timbre of the target speaker. Comparisons with state-of-the-art models show that our proposed method presents obvious superiority on the cross-speaker emotion transfer task.
Disentangled Action Recognition with Knowledge Bases

Action in video usually involves the interaction of human with objects.Action labels are typically composed of various combinations of verbs and nouns, but we may not have training data for all possible combinations.In this paper, we aim to improve the generalization ability of the compositional action recognition model to novel verbs or novel nouns that are unseen during training time, by leveraging the power of knowledge graphs.Previous work utilizes verb-noun compositional action nodes in the knowledge graph, making it inefficient to scale since the number of compositional action nodes grows quadratically with respect to the number of verbs and nouns.To address this issue, we propose our approach: Disentangled Action Recognition with Knowledge-bases (DARK), which leverages the inherent compositionality of actions.DARK trains a factorized model by first extracting disentangled feature representations for verbs and nouns, and then predicting classification weights using relations in external knowledge graphs.The type constraint between verb and noun is extracted from external knowledge bases and finally applied when composing actions.DARK has better scalability in the number of objects and verbs, and achieves state-of-the-art performance on the Charades dataset.We further propose a new benchmark split based on the Epic-kitchen dataset which is an order of magnitude bigger in the numbers of classes and samples, and benchmark various models on this benchmark.
What Makes the Story Forward?

Prediction over event sequences is critical for many real-world applications in Information Retrieval and Natural Language Processing. Future Event Generation (FEG) is a challenging task in event sequence prediction because it requires not only fluent text generation but also commonsense reasoning to maintain the logical coherence of the entire event story. In this paper, we propose a novel explainable FEG framework, Coep. It highlights and integrates two types of event knowledge, sequential knowledge of direct event-event relations and inferential knowledge that reflects the intermediate character psychology between events, such as intents, causes, reactions, which intrinsically pushes the story forward. To alleviate the knowledge forgetting issue, we design two modules, IM and GM, for each type of knowledge, which are combined via prompt tuning. First, IM focuses on understanding inferential knowledge to generate commonsense explanations and provide a soft prompt vector for GM. We also design a contrastive discriminator for better generalization ability. Second, GM generates future events by modeling direct sequential knowledge with the guidance of IM. Automatic and human evaluation demonstrate that our approach can generate more coherent, specific, and logical future events.
Contextual Conversational Agent to Address Vaccine Hesitancy: Protocol for a Design-Based Research Study

Background Since the beginning of the COVID-19 pandemic, people have been exposed to misinformation, leading to many myths about SARS-CoV-2 and the vaccines against it. As this situation does not seem to end soon, many authorities and health organizations, including the World Health Organization (WHO), are utilizing conversational agents (CAs) in their fight against it. Although the impact and usage of these novel digital strategies are noticeable, the design of the CAs remains key to their success. Objective This study describes the use of design-based research (DBR) for contextual CA design to address vaccine hesitancy. In addition, this protocol will examine the impact of DBR on CA design to understand how this iterative process can enhance accuracy and performance. Methods A DBR methodology will be used for this study. Each phase of analysis, design, and evaluation of each design cycle inform the next one via its outcomes. An anticipated generic strategy will be formed after completing the first iteration. Using multiple research studies, frameworks and theoretical approaches are tested and evaluated through the different design cycles. User perception of the CA will be analyzed or collected by implementing a usability assessment during every evaluation phase using the System Usability Scale. The PARADISE (PARAdigm for Dialogue System Evaluation) method will be adopted to calculate the performance of this text-based CA. Results Two phases of the first design cycle (design and evaluation) were completed at the time of this writing (April 2022). The research team is currently reviewing the natural-language understanding model as part of the conversation-driven development (CDD) process in preparation for the first pilot intervention, which will conclude the CA’s first design cycle. In addition, conversational data will be analyzed quantitatively and qualitatively as part of the reflection and revision process to inform the subsequent design cycles. This project plans for three rounds of design cycles, resulting in various studies spreading outcomes and conclusions. The results of the first study describing the entire first design cycle are expected to be submitted for publication before the end of 2022. Conclusions CAs constitute an innovative way of delivering health communication information. However, they are primarily used to contribute to behavioral change or educate people about health issues. Therefore, health chatbots’ impact should be carefully designed to meet outcomes. DBR can help shape a holistic understanding of the process of CA conception. This protocol describes the design of VWise, a contextual CA that aims to address vaccine hesitancy using the DBR methodology. The results of this study will help identify the strengths and flaws of DBR’s application to such innovative projects.
Design Flow of Blowfish Symmetric-Key Block Cipher on FPGA

Cryptography is an essential topic for modern digital systems. In the past two decades, FPGA-based encryptions has proved to be effective. Through this project, we explore a cryptographic application of FPGAs by implementing the Blowfish symmetric-key block cipher on a Xilinx ZedBoard. The motivation behind this is to explore how FPGAs can be used to accelerate the performance of a symmetric-key block cipher. Our goal is to compare the performance of a hardware implementation of Blowfish to a software implementation. Our Blowfish block cipher is implemented in VHDL and is functionally verified against a Python reference model. The design as a whole supports user input of various keys for key initialization along with full encryption and decryption functionality. Furthermore, the design was successfully synthesized and implemented on the ZedBoard FPGA. A performance evaluation was conducted between the hardware implementation and a Blowfish library running in Python by comparing the time it takes to perform the key initialization and encrypt/decrypt operation.
Pseudogapping in English: a direct interpretation approach

Abstract Gapping elides a finite verb in the non-initial conjunct of a coordinate structure while VP ellipsis deletes a whole VP after an auxiliary. Unlike these two, pseudogapping elides most of the VP except one remnant. Pseudogapping additionally differs from gapping and VP ellipsis, in that it involves ellipsis of part of a non-finite VP. In this paper we provide a Construction Grammar account of pseudogapping that captures its similarities with as well as differences from other related elliptical constructions like VP ellipsis. Our construction-based analysis, which capitalizes on the inheritance network of constructions to capture broad similarities and unique differences among these constructions, allows us to account for the full range of extant data.
Scenarios, Virtualization and Applications for Blockchain Technology in Smart Grids

The evolution of smart grids (SGs) has brought about various changes in the structure of existing power grids. In this context, blockchain technology is offered an encouraging solution to overcome the issues in modern power systems. In this paper, the impacts, virtualization and applications of blockchain technology on energy systems are investigated and analyzed. The major contributions of this paper are to address the main concepts of blockchain technology related to power systems. Blockchain-based the SG management scenarios such as peer to peer (P2P) energy trading, industrial virtual power plants (VPPs) and demand response (DR) loads have been addressed and discussed. Besides, all potential challenges and opportunities of blockchain applications in the SGs are determined and discussed. As far as the authors’ knowledge, this paper is one of the few studies that deeply investigate and examine the popular applications of blockchain technology in power systems.
Putting the Con in Context: Identifying Deceptive Actors in the Game of Mafia

While neural networks demonstrate a remarkable ability to model linguistic content, capturing contextual information related to a speaker's conversational role is an open area of research.In this work, we analyze the effect of speaker role on language use through the game of Mafia, in which participants are assigned either an honest or a deceptive role.In addition to building a framework to collect a dataset of Mafia game records, we demonstrate that there are differences in the language produced by players with different roles.We confirm that classification models are able to rank deceptive players as more suspicious than honest ones based only on their use of language.Furthermore, we show that training models on two auxiliary tasks outperforms a standard BERT-based text classification approach.We also present methods for using our trained models to identify features that distinguish between player roles, which could be used to assist players during the Mafia game.
Gender Biases and Where to Find Them: Exploring Gender Bias in Pre-Trained Transformer-based Language Models Using Movement Pruning

Language model debiasing has emerged as an important field of study in the NLP community.Numerous debiasing techniques were proposed, but bias ablation remains an unaddressed issue.We demonstrate a novel framework for inspecting bias in pre-trained transformer-based language models via movement pruning.Given a model and a debiasing objective, our framework finds a subset of the model containing less bias than the original model.We implement our framework by pruning the model while finetuning it on the debiasing objective.Optimized are only the pruning scores -parameters coupled with the model's weights that act as gates.We experiment with pruning attention heads, an important building block of transformers: we prune square blocks, as well as establish a new way of pruning the entire heads.Lastly, we demonstrate the usage of our framework using gender bias, and based on our findings, we propose an improvement to an existing debiasing method.Additionally, we re-discover a biasperformance trade-off: the better the model performs, the more bias it contains.
A New Data Model for the Privacy Protection of Medical Images

Benefiting from the intelligent Medical Internet of Things (IoMT), the medical industry has dramatically improved its quality and productivity. The transmission of biomedical data in an open and untrusted network poses a new challenge to the privacy protection of patient information. The low processing power of IoMT limited the application of traditional encryption to protect sensitive data. In this paper, we developed a new data protection model for medical images. The model uses visual cryptography (VC) to store biomedical data in a separate database, which can transfer the sensitive data of patients simply and securely. To alleviate the degradation of biomedical recognition performance caused by VC-based noise, we further use transfer learning to train an optimized neural network. The experimental results show that this proposed method provides privacy in the IoMT environment and maintains the high accuracy of biomedical recognition.
Natural Language Processing for Mapping Exam Questions to the Cognitive Process Dimension

Exam questions as a test instrument to measure educational success must have good quality. This quality can be measured by the level of cognition expected of students. The level of cognition reflects the mastery of learning materials as a form of evaluation of the teaching and learning process outlined in the curriculum. For this reason, the exam questions need to be mapped into the cognitive process dimensions, namely based on the categories in the Revised Bloom's Taxonomy (RBT). The mapping method used is Natural Language Processing (NLP) as one of the fields of Artificial Intelligence development. The stages in this mapping are pre-processing, including tokenization, stemming, stop-word removal, and feature extraction using POS Tagging. The output of this mapping process is in the form of categories of test items into RBT: remembering (C1), understanding (C2), applying (C3), analyzing (C4), evaluating (C5), and creating (C6). The classification results obtained information that the exam questions prepared were still dominated at the C2 cognitive level, which was indicated by the use of operational verbs in the understanding category. The results of testing the method used produce an accuracy of 82.22%. Thus, the NLP method can classify test items into Revised Bloom's Taxonomy to determine the dimensions of students' cognitive processes.
A compendium and evaluation of taxonomy quality attributes

Abstract Introduction Taxonomies capture knowledge about a particular domain in a succinct manner and establish a common understanding among peers. Researchers use taxonomies to convey information about a particular knowledge area or to support automation tasks, and practitioners use them to enable communication beyond organizational boundaries. Aims Even though this important role of taxonomies in software engineering, their quality is seldom evaluated. Our aim is to identify and define taxonomy quality attributes that provide practical measurements, helping researchers and practitioners to compare taxonomies and choose the one most adequate for the task at hand. Methods We reviewed 324 publications from software engineering and information systems research and synthesized, when provided, the definitions of quality attributes and measurements. We evaluated the usefulness of the measurements on six taxonomies from three domains. Results We propose the definition of seven quality attributes and suggest internal and external measurements that can be used to assess a taxonomy's quality. For two measurements we provide implementations in Python. We found the measurements useful for deciding which taxonomy is best suited for a particular purpose. Conclusion While there exist several guidelines for creating taxonomies, there is a lack of actionable criteria to compare taxonomies. In this article, we fill this gap by synthesizing from a wealth of literature seven, non‐overlapping taxonomy quality attributes and corresponding measurements. Future work encompasses their further evaluation of usefulness and empirical validation.
Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Prior

Image processing neural networks, natural and artificial, have a long history with orientation-selectivity, often described mathematically as Gabor filters. Gabor-like filters have been observed in the early layers of CNN classifiers and even throughout low-level image processing networks. In this work, we take this observation to the extreme and explicitly constrain the filters of a natural-image denoising CNN to be learned 2D real Gabor filters. Surprisingly, we find that the proposed network (GDLNet) can achieve near state-of-the-art denoising performance amongst popular fully convolutional neural networks, with only a fraction of the learned parameters. We further verify that this parameterization maintains the noiselevel generalization (training vs. inference mismatch) characteristics of the base network, and investigate the contribution of individual Gabor filter parameters to the performance of the denoiser. We present positive findings for the interpretation of dictionary learning networks as performing accelerated sparse-coding via the importance of untied learned scale parameters between network layers. Our network’s success suggests that representations used by low-level image processing CNNs can be as simple and interpretable as Gabor filterbanks.