Reviewerly/exHarmony huggingface dataset:
Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors

Interactive recommender systems (RSs) allow users to express intent, preferences and contexts in a rich fashion, often using natural language. One challenge in using such feedback is inferring a user’s semantic intent from the open-ended terms used to describe an item, and using it to refine recommendation results. Leveraging concept activation vectors (CAVs) [21], we develop a framework to learn a representation that captures the semantics of such attributes and connects them to user preferences and behaviors in RSs. A novel feature of our approach is its ability to distinguish objective and subjective attributes and associate different senses with different users. Using synthetic and real-world datasets, we show that our CAV representation accurately interprets users’ subjective semantics, and can improve recommendations via interactive critiquing.
Context-Enriched Learning Models for Aligning Biomedical Vocabularies at Scale in the UMLS Metathesaurus

The Unified Medical Language System (UMLS) Metathesaurus construction process mainly relies on lexical algorithms and manual expert curation for integrating over 200 biomedical vocabularies. A lexical-based learning model (LexLM) was developed to predict synonymy among Metathesaurus terms and largely outperforms a rule-based approach (RBA) that approximates the current construction process. However, the LexLM has the potential for being improved further because it only uses lexical information from the source vocabularies, while the RBA also takes advantage of contextual information. We investigate the role of multiple types of contextual information available to the UMLS editors, namely source synonymy (SS), source semantic group (SG), and source hierarchical relations (HR), for the UMLS vocabulary alignment (UVA) problem. In this paper, we develop multiple variants of context-enriched learning models (ConLMs) by adding to the LexLM the types of contextual information listed above. We represent these context types in context-enriched knowledge graphs (ConKGs) with four variants ConSS, ConSG, ConHR, and ConAll. We train these ConKG embeddings using seven KG embedding techniques. We create the ConLMs by concatenating the ConKG embedding vectors with the word embedding vectors from the LexLM. We evaluate the performance of the ConLMs using the UVA generalization test datasets with hundreds of millions of pairs. Our extensive experiments show a significant performance improvement from the ConLMs over the LexLM, namely +5.0% in precision (93.75%), +0.69% in recall (93.23%), +2.88% in F1 (93.49%) for the best ConLM. Our experiments also show that the ConAll variant including the three context types takes more time, but does not always perform better than other variants with a single context type. Finally, our experiments show that the pairs of terms with high lexical similarity benefit most from adding contextual information, namely +6.56% in precision (94.97%), +2.13% in recall (93.23%), +4.35% in F1 (94.09%) for the best ConLM. The pairs with lower degrees of lexical similarity also show performance improvement with +0.85% in F1 (96%) for low similarity and +1.31% in F1 (96.34%) for no similarity. These results demonstrate the importance of using contextual information in the UVA problem.
Unsupervised Coherent Video Cartoonization with Perceptual Motion Consistency

In recent years, creative content generations like style transfer and neural photo editing have attracted more and more attention. Among these, cartoonization of real-world scenes has promising applications in entertainment and industry. Different from image translations focusing on improving the style effect of generated images, video cartoonization has additional requirements on the temporal consistency. In this paper, we propose a spatially-adaptive semantic alignment framework with perceptual motion consistency for coherent video cartoonization in an unsupervised manner. The semantic alignment module is designed to restore deformation of semantic structure caused by spatial information lost in the encoder-decoder architecture. Furthermore, we introduce the spatio-temporal correlative map as a style-independent, global-aware regularization on perceptual motion consistency. Deriving from similarity measurement of high-level features in photo and cartoon frames, it captures global semantic information beyond raw pixel-value of optical flow. Besides, the similarity measurement disentangles temporal relationship from domain-specific style properties, which helps regularize the temporal consistency without hurting style effects of cartoon images. Qualitative and quantitative experiments demonstrate our method is able to generate highly stylistic and temporal consistent cartoon videos.
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension

Multilingual pre-trained models are able to zero-shot transfer knowledge from richresource to low-resource languages in machine reading comprehension (MRC).However, inherent linguistic discrepancies in different languages could make answer spans predicted by zero-shot transfer violate syntactic constraints of the target language.In this paper, we propose a novel multilingual MRC framework equipped with a Siamese Semantic Disentanglement Model (S 2 DM) to disassociate semantics from syntax in representations learned by multilingual pre-trained models.To explicitly transfer only semantic knowledge to the target language, we propose two groups of losses tailored for semantic and syntactic encoding and disentanglement.Experimental results on three multilingual MRC datasets (i.e., XQuAD, MLQA, and TyDi QA) demonstrate the effectiveness of our proposed approach over models based on mBERT and XLM-100.
Enhancing Class Understanding Via Prompt-Tuning For Zero-Shot Text Classification

Zero-shot text classification (ZSTC) poses a big challenge due to the lack of labeled data for unseen classes during training. Most studies focus on transferring knowledge from seen classes to unseen classes, which have achieved good performance in most cases. Whereas, it is difficult to transfer knowledge when the classes have semantic gaps or low similarities. In this paper, we propose a prompt-based method, which enhances semantic understanding for each class and learns the matching between texts and classes for better ZSTC. Specifically, we first generate discriminative words for class description with prompt inserting (PIN). Then, a prompt matching (POM) model is learned to determine whether the text can well match the class description. Experiments on three benchmark datasets show the great advantages of our proposed method. In particular, we achieve the state-of-the-art performance on the unseen classes, while maintaining comparable strength with the existing ZSTC approaches regarding to the seen classes.
Which Discriminator for Cooperative Text Generation?

Language models generate texts by successively predicting probability distributions for next tokens given past ones. A growing field of interest tries to leverage external information in the decoding process so that the generated texts have desired properties, such as being more natural, non toxic, faithful, or having a specific writing style. A solution is to use a classifier at each generation step, resulting in a cooperative environment where the classifier guides the decoding of the language model distribution towards relevant texts for the task at hand. In this paper, we examine three families of (transformer-based) discriminators for this specific task of cooperative decoding: bidirectional, left-to-right and generative ones. We evaluate the pros and cons of these different types of discriminators for cooperative generation, exploring respective accuracy on classification tasks along with their impact on the resulting sample quality and computational performances. We also provide the code of a batched implementation of the powerful cooperative decoding strategy used for our experiments, the Monte Carlo Tree Search, working with each discriminator for Natural Language Generation.
MOVING OBJECTS DETECTION, CLASSIFICATION AND TRACKING OF VIDEO STREAMING BY IMPROVED FEATURE EXTRACTION APPROACH USING K-SVM

The computer vision plays a vital role in variety of applications such as traffic surveillance, robotics, human interaction devices, etc. The video surveillance system has designed to detect, track and classify the moving objects. The moving object detection, classification and tracking of video streaming has various challenges, which utilizes various novel approaches. The existing work uses spatiotemporal feature analysis using sample consistency algorithm for moving object detection and classification. It is not performed well with the complex scene on the video. The binary masking representation of moving object is the challenging task for the researchers. These video streams are partitioned based on the frames, shots, and scenes; here the proposed research work utilizes kernel-Support Vector Machine learning technique for moving object detection and tracking. In this approach, the MIO-TCD DATASET is used for moving object detection. Here the feature extraction is the major part of foreground and background analysis in the video streaming, which utilizes the vehicle features based video data. The SURF (Speeded-Up Robust Feature) feature is used to recognize/register the object and it also used for classification of moving objects. Here the optical flow method is to quantify the relative motion of object in the video streams. Based on the differences on the partitioned frames, the optical flow features hold the object for measuring the pixel of the moving objects. The feature extraction process is improved by combining feature class with intensity level of optical flow result, which makes the gradient analysis of first order derivative function. The proposed method achieves the result of recall, precision, and f-measures than the existing work. The proposed method is done with the help of MATLAB 2018a. Keywords: Computer Vision and Pattern Recognition; Kernel-SVM; SURF features; Optical Flow; Texture feature; Moving object detection, tracking and classification;
Deep-ADCA: Development and Validation of Deep Learning Model for Automated Diagnosis Code Assignment Using Clinical Notes in Electronic Medical Records

Currently, the International Classification of Diseases (ICD) codes are being used to improve clinical, financial, and administrative performance. Inaccurate ICD coding can lower the quality of care, and delay or prevent reimbursement. However, selecting the appropriate ICD code from a patient's clinical history is time-consuming and requires expert knowledge. The rapid spread of electronic medical records (EMRs) has generated a large amount of clinical data and provides an opportunity to predict ICD codes using deep learning models. The main objective of this study was to use a deep learning-based natural language processing (NLP) model to accurately predict ICD-10 codes, which could help providers to make better clinical decisions and improve their level of service. We retrospectively collected clinical notes from five outpatient departments (OPD) from one university teaching hospital between January 2016 and December 2016. We applied NLP techniques, including global vectors, word to vectors, and embedding techniques to process the data. The dataset was split into two independent training and testing datasets consisting of 90% and 10% of the entire dataset, respectively. A convolutional neural network (CNN) model was developed, and the performance was measured using the precision, recall, and F-score. A total of 21,953 medical records were collected from 5016 patients. The performance of the CNN model for the five different departments was clinically satisfactory (Precision: 0.50~0.69 and recall: 0.78~0.91). However, the CNN model achieved the best performance for the cardiology department, with a precision of 69%, a recall of 89% and an F-score of 78%. The CNN model for predicting ICD-10 codes provides an opportunity to improve the quality of care. Implementing this model in real-world clinical settings could reduce the manual coding workload, enhance the efficiency of clinical coding, and support physicians in making better clinical decisions.
Securing the Confidentiality and Integrity of Cloud Computing Data

Cloud Service Providers (CSPs) are becoming increasingly popular as the amount of available digital data continues to grow at an exponential rate. A new set of benefits and economies made possible by cloud technologies in terms of bandwidth, computing, storage, and transmission costs, which will all reduce the overall costs for data storage for the company, which provide customers with convenient and efficient storage services. For the protection of the Cloud Client (CC) data in the cloud, several different security models are available. In order to verify that CSPs and the CC are in compliance with these safety criteria, the Third-Party Auditor (TPA) has a responsibility to thoroughly assess evidence of compliance between the CC and the CSP. Cloud user data are vulnerable to loss of confidentiality and integrity as data are stored in off premises and maintained by CSP. As a result, in order to ensure the confidentiality and integrity of stored data, the concept of auditing has been introduced into the Cloud Computing Environment. Cloud users can seek the assistance of a TPA who has extensive experience in conducting cloud audits in order to assess the risk associated with their services.
Tangible Interaction for Supporting Well-being

Our workshop aims to bring together researchers and practitioners across disciplines in HCI who share an interest in promoting well-being through tangible interaction. The workshop forms an impassioned response to the worldwide push towards more digital and remote interaction in nearly all domains of our lives in the context of the COVID-19 pandemic. One question we raise is: to what extent will measures like remote interaction remain in place post-pandemic, and to what extent these changes may influence future agendas for the design of interactive products and services to support living well? We aim to ensure that the workshop serves as a space for diverse participants to share ideas and engage in cooperative discussions through hands-on activities resulting in the co-creation of a Manifesto to demonstrate the importance of embodied and sensory interaction for supporting well-being in a post-pandemic context. All the workshop materials will be published online on the workshop website and disseminated through ongoing collaboration.
SIG on Data as Human-Centered Design Material

Designers and HCI researchers from industry and academia have been exploring the opportunities that emerge from incorporating behavioral data into the design process. For this, designers employ and combine data from multiple sources, multiple scales, and types to obtain valuable insights that inform and support design decisions. This combination unfolds through interdisciplinary collaborations, enabled by various methods and approaches, including participatory data analysis, sense-making interviews, co-design workshops, and data storytelling. However, due to the personal nature of behavioral data and the open-ended, iterative approach of Human-Centered Design, data-centric design activities clash with current HCI and data science practices. As both industry and academia increasingly use data-centric design processes, we recognize a need to share both examples and experiences to reinforce that most practices (and failed experiences) do not yet emerge solely from the literature. In this Special Interest Group, we aim to provide a space for design, data, and HCI researchers and practitioners to connect, reflect on the current practices, and explore potential approaches to further integrating behavioral data into design activities.
Linear types for large-scale systems verification

Reasoning about memory aliasing and mutation in software verification is a hard problem. This is especially true for systems using SMT-based automated theorem provers. Memory reasoning in SMT verification typically requires a nontrivial amount of manual effort to specify heap invariants, as well as extensive alias reasoning from the SMT solver. In this paper, we present a hybrid approach that combines linear types with SMT-based verification for memory reasoning. We integrate linear types into Dafny, a verification language with an SMT backend, and show that the two approaches complement each other. By separating memory reasoning from verification conditions, linear types reduce the SMT solving time. At the same time, the expressiveness of SMT queries extends the flexibility of the linear type system. In particular, it allows our linear type system to easily and correctly mix linear and nonlinear data in novel ways, encapsulating linear data inside nonlinear data and vice-versa. We formalize the core of our extensions, prove soundness, and provide algorithms for linear type checking. We evaluate our approach by converting the implementation of a verified storage system (about 24K lines of code and proof) written in Dafny, to use our extended Dafny. The resulting system uses linear types for 91% of the code and SMT-based heap reasoning for the remaining 9%. We show that the converted system has 28% fewer lines of proofs and 30% shorter verification time overall. We discuss the development overhead in the original system due to SMT-based heap reasoning and highlight the improved developer experience when using linear types.
An Efficient AES Algorithm for Cryptography Using VLSI

In today's data exchange framework, information security is the most important consideration. Encryption is a key component of Information security and is used extensively. Encryption techniques are used by all sorts of organisations to protect sensitive data. An technique known as Data Encryption Standard (DES), which uses the same key for both encryption and decryption at the same time, was the first to be employed. That algorithm's key drawback is that it is readily hacked and vulnerable to attacks. Compared to DES and Triple DES, the calculations require enormous memory space and cannot be performed on the hardware stage. There are numerous ways in which AES, the Advanced Encryption Standard algorithm, has supplanted DES. An effective cryptographic method, AES uses multiple key lengths and surpassed DES when compared to other algorithms, such as SHA-1. It is possible to implement equipment stage circumstances inferable from the reconfigurability, cheap cost, and publicizing space with the use of field programmable entryway clusters (FPGAs). To scramble and decode complex data, the RIJNDAEL algorithmic guideline uses a square figure. It also supports cryptographic keys with lengths of 28,192 and 256 bits. The round keys, which are used throughout specific emphases of encryption, are developed in simultaneously with the Encryption strategy, which is a remarkable normal for the proposed pipelined design. Dual-stage architecture is the key to the expansion. It is possible to run at higher clock frequencies with this method, resulting in increased throughput and lower power use.
Speech Recognition for Power Customer Service Based on DNN and CNN Models

This paper gives a comprehensive research on the speech recognition technology based on deep learning, combined with the speech data of the electric power professional to build a speech database. Through the study of the speech recognition technology based on deep learning, the acoustic and language models are constructed, and different type speech recognition models are compared especially on the practical effect of the speech recognition model in the electric customer service scene. Specifically, the main works of this paper are as follows: (1) Construct a variety of DNN network-based speech recognition architectures, and analyze the recognition results of various optimization methods; (2) Construction of a text corpus and a speech grid business scenario corpus GS-Data based on meetings and reports; (3) Study the acoustic model and language model construction technology based on deep full-sequence convolutional neural network speech recognition, construct the acoustic model and language model of speech recognition technology; (4) Compare the accuracy of different speech recognition schemes.
Hate Speech Detection using OpenAI and GPT-3

The toxicity of speech being spewed via cyber media is a critical issue of concern in the cyberverse these days. This problem is associated with using offensive, violent or abusive phrases countering any person, any group or some minority community. Amongst the latest advanced and highlevel language models for natural language processing (NLP) is the generative pre-trained transformer model from OpenAI, code named GPT-3 which could both produce and predict hatred-based text that bullies the specific community or group. With this capability, the concern is anyhow the large language models could be used for analysis and identification of hated speech and also the classification as positive or negative. GPT-3 classifies text as hate or non-hate speech with different learning models including zero to a few shot versions. Amongst these, the zero- and one-shot based learning models achieve an accuracy between 45 to 72 percent, while using few shots learning models, accuracy could be achieved up to 80 percent. The results of this study indicate that in hate and toxic speech detection, large language models play a vital role that also need development so as to counter the toxic content resulting in hatred and can help with safe cyberspace. Keywords— Cybersafety, GPT-3, Language models, NLP, Toxicity in speech
Distilling Inter-Class Distance for Semantic Segmentation

Knowledge distillation is widely adopted in semantic segmentation to reduce the computation cost. The previous knowledge distillation methods for semantic segmentation focus on pixel-wise feature alignment and intra-class feature variation distillation, neglecting to transfer the knowledge of the inter-class distance in the feature space, which is important for semantic segmentation such a pixel-wise classification task. To address this issue, we propose an Inter-class Distance Distillation (IDD) method to transfer the inter-class distance in the feature space from the teacher network to the student network. Furthermore, semantic segmentation is a position-dependent task, thus we exploit a position information distillation module to help the student network encode more position information. Extensive experiments on three popular datasets: Cityscapes, Pascal VOC and ADE20K show that our method is helpful to improve the accuracy of semantic segmentation models and achieves the state-of-the-art performance. E.g. it boosts the benchmark model (``PSPNet+ResNet18") by 7.50% in accuracy on the Cityscapes dataset.